To establish the test’s patient-specific signature, researchers compared DNA from a surgical pathology sample with DNA from a buffy-coat sample. Both sets underwent whole-genome sequencing (WGS) to generate a mutational pattern associated with the patient’s tumor. At follow-up visits, investigators extracted cell-free DNA from plasma, performed WGS and analyzed it using the patient-derived signature together with an artificial-intelligence pattern-recognition algorithm. The reported result was that positive ctDNA findings were highly sensitive markers of minimal residual disease, while negative results were strongly correlated with sustained remission in the study population.

The work was led by Matija Snuderl, MD, of NYU Langone Health and the Laura and Isaac Perlmutter Cancer Center, with co-lead investigator Harvey I. Pass, MD, of NYU Langone Health. Asaf Zviran, PhD, of C2i Genomics developed the WGS liquid-biopsy technology. The investigators said that serial monitoring across treatment stages could provide information about relapse before standard imaging detected a physical tumor. The release describes the work as an experimental study and identifies the journal article as “Predicting Recurrence of Stage IA Lung Adenocarcinoma Using Circulating Tumor Cell-Free DNA Whole-Genome Mutational Signatures.”

Patients with stage I–II non–small-cell lung cancer are usually treated with surgery, and the source reports that recurrence rates in stage I disease range from 5% to 19%. Imaging remains the standard form of follow-up after surgery or initial therapy. The study’s finding is that a blood-based molecular signal was associated with recurrence in the earliest clinical stage, at a time when the disease was not yet apparent on scans. The authors present this earlier signal as a potential basis for risk-adapted surveillance and personalized post-treatment care, but the reported study does not demonstrate that changing treatment on the basis of test results improves survival or prevents recurrence.

The source does not provide the number of participants, the timing distribution of blood collections, or numerical sensitivity, specificity or lead-time estimates. Its findings therefore support the conclusion that, in the experimental cohort, serial WGS analysis of ctDNA patterns was associated with relapse before imaging showed tumors and that negative results were associated with sustained remission. The publication’s focus on stage IA lung adenocarcinoma also limits the direct scope of the reported finding: the release describes monitoring across treatment stages, but does not establish the same performance for all stages or all forms of lung cancer.

Subject of Research: Experimental ctDNA whole-genome liquid biopsy for recurrence prediction in stage IA lung adenocarcinoma

Article Title: Novel blood test detects lung cancer traces long before scans

Article References: Novel blood test detects lung cancer traces long before scans. Original publication (Bibliographic details unavailable.)

Image Credits: AI Generated

DOI: Not provided

Keywords: lung adenocarcinoma, liquid biopsy, circulating tumor DNA, cancer recurrence, whole-genome sequencing, minimal residual disease, molecular monitoring, early-stage lung cancer, artificial intelligence, Novel, blood, test

The biological signal is striking. GSTP1 encodes the pi-class glutathione S-transferase, which participates in glutathione conjugation and protection against oxidative and genotoxic stress. The gene is strongly expressed in normal basal prostate cells, can be induced in proliferative inflammatory atrophy, and is frequently silenced in high-grade prostatic intraepithelial neoplasia and prostate adenocarcinoma. Experiments in prostate cancer cells showed that reversing promoter methylation restored both GSTP1 transcription and protein expression, while methylation of promoter constructs reduced promoter activity. This supports a direct relationship between the epigenetic alteration and transcriptional silencing, although restoring GSTP1 did not reverse the malignant growth of already transformed cells.

More recent tissue analysis indicates that methylation accumulates as prostate lesions evolve. In a study of normal epithelium, proliferative inflammatory atrophy, high-grade prostatic intraepithelial neoplasia and cancer from 32 prostatectomy specimens, 0.52% and 1.3% of evaluated CpG sites were methylated in normal and atrophic epithelium, compared with 24% in high-grade intraepithelial neoplasia and 51% in invasive carcinoma. Methylation clustered near the transcription start site and binding regions for AP1 and SP1 transcription factors. Separate studies have detected GSTP1 methylation in 91.1% of malignant tissues versus 5.4% of adjacent healthy tissue and in 97% of radical-prostatectomy tumors, establishing a powerful malignant–benign contrast in specimens rich in tumor DNA.

That strength comes with important qualifications. GSTP1 methylation appears early in carcinogenesis and is highly prevalent, meaning it may identify malignant prostate epithelium without reliably distinguishing consequential from indolent tumors. In a 2024 analysis, higher tumor methylation was associated with biochemical recurrence and metastatic relapse in univariable models, but the association did not remain independent after adjustment for established clinicopathological factors. The marker also exists in heterogeneous forms: partially methylated promoter alleles and different patterns in separate tumor foci can coexist. Which CpG sites an assay interrogates may therefore change not only whether a sample appears positive, but also the amount of signal measured. The review treats GSTP1 principally as a marker of malignant tissue, not a stand-alone measure of tumor aggressiveness.

Measurement conditions are equally consequential. Tissue contains the greatest proportion of tumor-derived DNA and consequently gives the most reproducible signal. In urine, prostate cells and nucleic acids can be recovered non-invasively, but collection method, prostate manipulation, preservatives, processing delays, temperature, pH, storage and DNA extraction all affect recovery. Urinary cell-free DNA is especially vulnerable to degradation and loss of short fragments. Clinical findings in 2000 already illustrated the gap between tissue and urine: one study detected methylated GSTP1 in 94% of tumors but only 36% of urine samples, with higher detection after prostate massage. Urine is therefore promising, but “urinary GSTP1 test” does not describe a single standardized measurement.

A recent 120-patient study suggests that a carefully controlled workflow can improve urinary performance. Among biopsy-referred patients, including 59 with cancer and 61 with benign prostatic hyperplasia, standardized post-examination first-void urine analysis reported 81.4% sensitivity, 83.6% specificity, 82.5% accuracy and an area under the receiver operating characteristic curve of 0.87. In the PSA grey zone of 4–10 nanograms per milliliter, the corresponding values were 87.5%, 86.2%, 86.7% and 0.90. These findings were encouraging but came from a retrospective, single-center cohort already selected for biopsy, and the endpoint was any biopsy-confirmed cancer rather than clinically significant disease defined as Grade Group 2 or higher. Comparison with PSA alone also did not establish that GSTP1 adds useful information beyond PSA density, risk calculators, multiparametric MRI and other biomarkers.

Blood-based results are more limited because circulating tumor DNA may account for about 1% or less of total cell-free DNA in localized disease. A 2023 meta-analysis of cell-free-DNA studies pooled 37% sensitivity and 97% specificity, with substantial heterogeneity, indicating that a positive result may be informative while a negative result cannot reliably exclude a small localized tumor. A smaller 2024 study illustrates the variation: GSTP1 methylation was detected in only 3 of 13 cancers, producing 23% sensitivity and 32% specificity among 39 biopsy-received men and 15 healthy controls. Adding RASSF1 improved both measures, whereas requiring GSTP1, RASSF1 and RASSF2 raised specificity to 83% but reduced sensitivity to 8%. Multigene testing therefore does not automatically produce simultaneous gains in sensitivity and specificity.

Assay technology has advanced from qualitative methylation-specific polymerase chain reaction to quantitative, digital and sequencing approaches, but the review cautions that greater molecular resolution has not yet yielded a uniformly validated stand-alone test. Quantitative assays can be affected by unmethylated CpG sites located within primer and probe sequences, especially in urine containing partially methylated epialleles. Bisulfite conversion remains practical for PCR assays but can damage scarce DNA; enzymatic conversion and third-generation sequencing can preserve molecular integrity while introducing greater cost and bioinformatics demands. A small single-molecule plasma study found that combinations of methylation patterns at GSTP1 and two other loci could improve discrimination, but its 20 cancer cases, 17 patients with benign prostatic hyperplasia and 18 healthy donors raised concerns about model overfitting. Prespecified targets, thresholds and independent validation are therefore essential.

The clearest clinical niche is the repeat-biopsy setting, where ConfirmMDx measures GSTP1, APC and RASSF1 methylation in histologically negative biopsy tissue to identify an epigenetic field effect associated with potentially unsampled cancer. The American Urological Association and Society of Urologic Oncology lists the assay among biomarkers available for this setting. Population screening, pre-MRI triage, assessment after equivocal imaging and risk refinement in the PSA grey zone remain proposed research contexts rather than established indications. For pre-biopsy studies, Grade Group 2 or higher cancer should be the principal endpoint, and prospective multicentre testing should compare GSTP1-assisted care with contemporary pathways that already include risk models, established biomarkers and multiparametric MRI. The decisive measure would be whether the assay reduces unnecessary biopsies or scans without increasing missed clinically significant cancers, rather than whether it merely improves stand-alone sensitivity, specificity or area under the curve.

Subject of Research: Review of GSTP1 promoter methylation as a prostate cancer detection biomarker and its clinical translation

Article Title: GSTP1 promoter hypermethylation in prostate cancer detection: current evidence, clinical positioning, and translational gaps

Article References: Joo, J.-L., Wong, R. S.-Y., Goh, B. H., & Ng, N. C.-S. (2026). GSTP1 promoter hypermethylation in prostate cancer detection: current evidence, clinical positioning, and translational gaps. Molecular Biology Reports, 53(1), Article 1777. https://doi.org/10.1007/s11033-026-13011-2

Image Credits: AI Generated

DOI: 10.1007/s11033-026-13011-2

Keywords: GSTP1 methylation, prostate cancer, DNA methylation, liquid biopsy, urine biomarkers, cell-free DNA, ConfirmMDx, prostate cancer screening, multiparametric MRI, clinical validation, GSTP1, promoter

Metastasis accounts for approximately 90% of cancer-related deaths, according to the perspective, making the distribution of secondary tumors a central problem in oncology. Physicians currently combine CT, MRI, PET, pathological examination, serum markers, and blood-based analyses to assess metastatic disease. Imaging can map lesions anatomically, while liquid biopsies can track tumor burden, molecular changes, treatment response, and resistance. The proposed Spatial Liquid Biopsy approach would seek a missing layer of information: the probable location of disease inferred from a peripheral blood sample, rather than from an anatomical image.

Extracellular vesicles, or EVs, are membrane-bounded particles released by cells that carry proteins, lipids, and nucleic acids. Their molecular composition often reflects that of the cells that produced them, and they can participate in communication between cancer cells and their surroundings. Tumor-associated proteins on EV membranes can be used to enrich a population of vesicles from blood. Candidate capture markers discussed in the article include EpCAM, MUC1, and EGFR for lung cancer; EpCAM, HER2, and MUC1 for breast cancer; and EpCAM and CEA for colorectal cancer. Such markers are not exclusive to cancer, however, because normal epithelial cells can also express them, complicating the separation of tumor-associated EVs from the much larger background of vesicles released by healthy tissues.

Spatial Liquid Biopsy is based on a two-stage analytical principle. First, tumor-associated EVs would be captured with a selected surface marker or marker combination. Those vesicles would then be examined for extracellular proteins associated with particular organs. Candidate organ-associated proteins include brevican and neurocan for the brain, leukocyte cell-derived chemotaxin-2 and secreted phosphoprotein 2 for the liver, alkaline phosphatase and bone sialoprotein for bone, and surfactant-associated proteins A1 and C for lung. The goal would be a double-positive population bearing both tumor-associated and organ-associated signals, which the framework proposes could be more spatially specific than either dimension alone.

The biological hypothesis is that cancer cells colonizing another organ interact with local stromal, immune, endothelial, extracellular-matrix, and tissue-resident cells. In the liver, those interactions involve cell types such as Kupffer cells and hepatic stellate cells; in the brain, astrocytes and microglia; and in bone, osteoblasts, osteoclasts, and marrow stromal cells. These exchanges may influence both the metastatic tumor and the vesicles it releases. Organ-associated extracellular proteins might become associated with the surface of locally generated tumor EVs, allowing the vesicles to carry a composite signature of malignancy and location. This mechanism has not yet been directly demonstrated for the proposed strategy, making it a central question for future experiments.

Existing studies provide indirect support. Experimental work has shown that tumor-derived exosomes from models with different metastatic organ preferences carried distinct surface integrin profiles: α6β4 and α6β1 were associated with lung tropism, while αvβ5 was associated with liver tropism. Those integrins are tumor-cell proteins that help determine where cancer cells travel, rather than proteins derived from the destination organ, so they do not prove the Spatial Liquid Biopsy mechanism. Nevertheless, they demonstrate that the surface composition of tumor EVs can contain information linked to organ-specific metastatic behavior.

Human evidence comes from a study comparing plasma EV proteomes among patients with lung cancer and either solitary liver metastasis, solitary brain metastasis, or locally advanced disease without distant metastasis. The investigators found EV-associated protein profiles that differed by metastatic site and subsequently validated candidate proteins associated with brain or liver metastases in a larger cohort. Because the analysis examined bulk plasma EVs rather than EVs specifically enriched for tumor origin, the cellular source of the detected proteins could not be assigned to cancer cells, metastatic organs, or other host cells. The results nevertheless suggest that circulating EV protein profiles can carry information associated with the anatomical site of established disease.

Separate cell-culture experiments found that breast cancer cell lines with different organotropic metastatic phenotypes released EVs with distinct proteomic profiles. That supports an association between metastatic phenotype and EV composition, but the vesicles were collected from cultured tumor cells rather than from animals or patients with established metastases. Taken together, the evidence supports three observations: tumor EVs can carry proteins linked to metastatic tropism, bulk circulating EV profiles can differ according to metastatic site in patients, and tumor cells with different organotropic phenotypes can release compositionally distinct EVs. It does not yet establish that blood-borne, tumor-enriched EVs display organ-derived surface proteins in the manner required by the new framework.

If validated, multiplex profiling could reveal several metastatic locations in one blood sample. A patient with liver and bone metastases might have separate EV subpopulations associated with each organ, rather than individual vesicles carrying markers from both sites. The absolute abundance of a spatial signal should not automatically be read as a direct measure of metastatic burden. EV levels can also be influenced by tumor volume, release rates, vascular access, clearance, treatment effects, and the biology of individual metastatic microenvironments. Determining whether a signal tracks lesion number or volume would therefore require comparison with independently established disease burden, while repeated measurements within a patient could test whether changes parallel progression or regression at particular sites.

Development would require staged validation. An initial proof-of-concept study could test the proposed marker panel in archived plasma from patients with anatomically confirmed metastases, using single-dominant-site cohorts to ask whether the signatures distinguish brain, bone, liver, or other locations. Lung cancer could serve as an initial model because it commonly spreads to several anatomically distinct organs. Retrospective validation would need to precede studies involving multiorgan disease, and prospective longitudinal studies would have to determine whether emerging signals precede radiographic detection and whether changes correspond to pathology, treatment response, and clinical outcomes. Improvements in EV enrichment, antibody engineering, multiplex protein detection, ultrasensitive assays, single-EV analysis, and computational interpretation may eventually improve performance, but the proposed starting markers must first be tested directly.

The perspective positions Spatial Liquid Biopsy as a possible complement to imaging rather than a replacement. Its ultimate value would depend on whether prospective studies show that blood-based organ signals can reliably identify metastatic involvement, reveal changes earlier than current surveillance methods, or support more targeted use of diagnostic imaging. The framework is scientifically testable, but its strongest claims remain conditional: it has not yet been shown that organ-associated proteins bind to circulating tumor EVs, that such double-positive vesicles accurately locate metastases, or that their abundance reflects tumor burden. If those links are established, the approach could turn extracellular vesicles into minimally invasive molecular maps of where cancer has spread; without them, it would remain a promising but unvalidated strategy for expanding liquid biopsy beyond answering whether cancer is present to addressing where metastatic disease may be located.

Subject of Research: Conceptual use of extracellular-vesicle biomarkers to infer the anatomical location of cancer metastases from blood

Article Title: From liquid biopsy to spatial liquid biopsy: extracellular vesicles as molecular maps of cancer metastasis

Article References: Mitsuhashi, M. (2026). From liquid biopsy to spatial liquid biopsy: extracellular vesicles as molecular maps of cancer metastasis. Frontiers in Oncology, 16, Article 1940169. https://doi.org/10.3389/fonc.2026.1940169

Image Credits: AI Generated

DOI: 10.3389/fonc.2026.1940169

Keywords: liquid biopsy, cancer metastasis, extracellular vesicles, spatial biomarkers, blood biomarkers, tumor microenvironment, precision oncology, organ-specific metastasis, cancer surveillance, liquid, biopsy, spatial

Diabetic retinopathy is a progressive microvascular complication whose prognosis and management change substantially as it advances from non-proliferative disease to pathological neovascularization in proliferative diabetic retinopathy. Fundus photography and optical coherence tomography effectively identify established structural abnormalities, but they offer limited information about early molecular changes or the likelihood of an imminent stage transition. Existing blood markers, including vascular endothelial growth factor, inflammatory mediators, oxidative-stress indicators and metabolic measures, often reflect systemic disease, vary substantially among individuals and do not reliably distinguish retinal stages. The authors therefore propose a framework in which molecular testing would complement, rather than replace, established imaging.

MicroRNAs are short ncRNAs, generally about 18–25 nucleotides long, that regulate messenger RNA stability and translation. Long non-coding RNAs, usually longer than 200 nucleotides, influence chromatin, transcription and post-transcriptional regulation, while circular RNAs have covalently closed structures that make them comparatively resistant to degradation. All three classes have been linked in experimental and clinical literature to endothelial dysfunction, inflammation, oxidative stress and angiogenesis, processes that are central to diabetic retinopathy. MicroRNAs are the most extensively investigated circulating class and currently provide the most reproducible stage-associated signals, although evidence for most individual candidates remains limited by small cohorts, cross-sectional designs and inconsistent methods.

Early-stage evidence is particularly sparse and requires careful qualification. MicroRNA-3976 was reported as increased in a serum extracellular-vesicle fraction in a discovery and internal validation study comparing 18 people with early NPDR and diabetes but no retinopathy. MicroRNA-23a-3p was lower in serum in a single cohort of 45 people, while microRNA-126-5p was lower in plasma in an RNA-sequencing discovery, quantitative reverse-transcription PCR validation and clinical cohort involving mild NPDR. MicroRNA-26a-5p was associated with retinal nerve fibre layer thinning in an early-disease correlation study. These candidates are “anchored” to explicit early-stage comparisons or retinal structural changes, but the review’s qualitative grading assigned them low evidence because of limited replication and validation.

More established signals appear at more advanced stages. MicroRNA-21, which rises across the non-proliferative-to-proliferative severity continuum, has produced some of the strongest reported discrimination results. One study reported an area under the receiver operating characteristic curve of 0.89, with 95% confidence interval 0.86–0.96, sensitivity of 82–86% and specificity of 84–95% for distinguishing PDR from NPDR, and the review rated its evidence as moderate because it involved three independent cohorts and multistage validation. A three-microRNA serum panel comprising microRNA-21, microRNA-181c and microRNA-1179 also achieved an AUC of 0.89 for PDR versus NPDR across two validation sets. These are cross-sectional diagnostic results, not evidence that baseline microRNA levels predict subsequent progression.

Severe NPDR is the pivotal clinical transition because it precedes overt neovascularization and is associated with substantial near-term risk. Natural-history evidence cited in the review indicates approximately a 50% one-year risk of PDR conversion for severe NPDR and more than 75% for very severe NPDR. MicroRNA-21 reportedly increases progressively from normal fundus through advancing NPDR to PDR, whereas microRNA-34a declines, a pattern the authors interpret as complementary evidence of increasing inflammatory and angiogenic signaling alongside depletion of cytoprotective reserves. Yet these gradients are largely derived from cross-sectional studies. Direct comparisons of mild versus moderate NPDR, moderate versus severe NPDR, and severe NPDR versus early PDR are uncommon, and few studies preselected these adjacent-stage contrasts as primary endpoints.

Long non-coding RNAs and circular RNAs occupy a different evidentiary position. The EV-associated PPT2-EGFL8 transcript was lower in PDR than in non-PDR and diabetes-without-retinopathy groups, while NR2F1-AS1 increased across healthy, diabetes, NPDR and PDR categories. MIAT, HOTTIP and SNHG16 were higher in PDR than NPDR, and the circular RNA hsa_circ_0001953 was elevated in PDR compared with NPDR and healthy controls. Serum EV-associated circMKLN1 also rose across control, diabetes, NPDR and PDR groups. Each finding came from a single small, cross-sectional study, however, with no longitudinal prediction or independent replication. The review accordingly treats these molecules as advanced-stage-associated or context-defining candidates rather than standalone diagnostic markers.

Extracellular vesicles may help stabilize circulating ncRNAs because their lipid membranes protect cargo from enzymatic degradation, and they can selectively package molecules involved in intercellular communication. This has led some researchers to describe vesicle-associated ncRNAs as potentially enriched for disease-relevant signals. The review warns, however, that vesicle association does not establish retinal origin. Blood–retinal barrier disruption is more pronounced in advanced disease and may partly explain why circulating signals are easier to detect in PDR. Isolation methods such as ultracentrifugation, polymer precipitation and size-exclusion chromatography also differ in yield, purity and enrichment, while particle size or a common marker alone cannot prove that a preparation consists of endosome-derived exosomes. Serum, plasma, total circulating RNA and EV-enriched fractions therefore cannot be treated as interchangeable.

The proposed solution is a hierarchy of purpose-built panels rather than a longer catalogue of isolated markers. MicroRNAs would provide core effector readouts, while lncRNAs and circRNAs could add regulatory context and vesicle compartments could improve analytical stability. A competing-endogenous-RNA framework offers a biological rationale for combining them: lncRNAs and circRNAs can buffer shared microRNAs, meaning that a transcript’s diagnostic behavior may depend on its network and disease context rather than its abundance alone. Such interactions are often inferred from correlations or computational predictions, so direct binding, perturbation and rescue evidence are needed before a predicted relationship can be treated as established molecular competition.

Clinical translation would depend on cohorts designed around the difficult transitions rather than broad DR-versus-no-DR comparisons. The review calls for longitudinal, severity-stratified and imaging-integrated studies with prespecified endpoints, independent multicenter validation, adjustment for age, renal function, glycemic control, systemic inflammation, diabetes duration, medication use and relevant comorbidities, and transparent controls for hemolysis, vesicle characterization and RNA normalization. Machine learning and multi-omics may help refine biologically grounded panels, but they cannot replace mechanistic or prospective validation. Until adjacent-stage discrimination and incremental value beyond imaging are demonstrated, circulating ncRNA panels remain promising but unproven decision-support tools rather than clinically established tests for diabetic retinopathy.

Subject of Research: Stage-specific circulating and extracellular vesicle-associated non-coding RNAs as potential biomarkers for diabetic retinopathy

Article Title: Circulating and extracellular vesicle-associated non-coding RNAs in diabetic retinopathy: a stage-aware framework for network-informed biomarker panels

Article References: Yu, J.-W., Choi, M.-J., Lee, D. C., & Park, J.-H. (2026). Circulating and extracellular vesicle-associated non-coding RNAs in diabetic retinopathy: a stage-aware framework for network-informed biomarker panels. Frontiers in Endocrinology, 17, Article 1970838. https://doi.org/10.3389/fendo.2026.1970838

Image Credits: AI Generated

DOI: 10.3389/fendo.2026.1970838

Keywords: diabetic retinopathy, non-coding RNAs, microRNAs, long non-coding RNAs, circular RNAs, extracellular vesicles, biomarker panels, risk stratification, liquid biopsy, Circulating, extracellular, vesicle-associated

The study drew from GEO series GSE59856, GSE106817, GSE112264, GSE113486 and GSE124158, which together contained pancreatic cancer and healthy-control serum profiles measured with 3D Gene human miRNA microarray platforms. The sample distribution was not uniform across studies: GSE59856 contained 150 controls and 100 pancreatic cancer samples, GSE106817 115 controls and 200 cancer samples, GSE112264 50 controls and 41 cancer samples, GSE113486 40 controls and 100 cancer samples, and GSE124158 30 controls and 275 cancer samples. Such variation motivated the researchers to harmonize identifier conventions, impute missing values, align features across platforms and apply ComBat batch correction, an empirical-Bayes procedure designed to reduce nonbiological differences among datasets. The analysis started with normalized expression matrices already processed by the original studies rather than raw fluorescence measurements.

After log transformation, the researchers used the limma statistical package to compare cancer and control samples. The researchers used a limma differential expression workflow, with feature selection based on a false discovery rate threshold below 0.05 and an absolute log2 fold-change threshold of at least 2. The researchers treated the 148 resulting differentially expressed microRNAs as the input features for predictive modeling. Principal component analysis showed separation between the two groups, with the first two components accounting for 17.5% and 14.6% of variance, respectively. The researchers presented this as evidence of discriminatory information in the integrated expression profiles, but the retrospective design, harmonization of archival data and lack of uniformly available clinical variables limit how directly that separation can be interpreted in an unselected patient population.

For classification, the researchers partitioned the combined data into an 80% training set and a 20% test set, retaining proportional representation of cancer and control samples. The training set was further divided into stack-training and validation subsets. Five base algorithms—Extreme Gradient Boosting, Random Forest, Support Vector Classifier, Naïve Bayes and k-nearest neighbors—generated probabilities for the cancer class. The researchers combined those outputs through a Bayesian stacking ensemble, estimating non-negative weights by minimizing binary cross-entropy with the Sequential Least Squares Programming algorithm and constraining the weights to form a convex combination. Synthetic Minority Over-Sampling Technique was applied only within the training partitions to address the moderate class imbalance, while the test set was not oversampled. The researchers also introduced controlled label noise into 3% of training labels as an auxiliary robustness exercise; they emphasized that this was not an estimate of the real-world diagnostic misclassification rate.

On the held-out set, the ensemble’s receiver operating characteristic curve rose steeply at low false-positive rates, consistent with the reported AUC of 0.97. Its sensitivity of 94% indicates that the model identified 94% of the cancer samples in that test set, while its specificity of 99% reflects the proportion of controls correctly classified. The reported F1 score was 96%. These figures were obtained from samples drawn from the same five archived studies used to build the broader dataset, however, rather than from a strictly independent external cohort. The researchers explicitly noted a potential source of information leakage because differential expression analysis was performed before the train-test split. Although the authors characterized this as a biological feature-discovery step rather than predictive selection, they said future work should use nested cross-validation or fold-wise feature selection to test robustness more rigorously.

To make the model more transparent, the researchers applied SHAP, or Shapley Additive Explanations, to the XGBoost component of the ensemble. SHAP decomposes individual predictions into feature contributions and can rank variables by their average absolute influence across samples. The analysis used 200 samples from the SMOTE-balanced training distribution as a reference background and calculated values for all features in the test set. The resulting ranking highlighted 20 microRNAs, including hsa-miR-125a-3p, hsa-miR-6839-5p, hsa-miR-1290, hsa-miR-614, hsa-miR-4436a and hsa-miR-497-5p. hsa-miR-125a-3p ranked first and was downregulated in cancer samples, with a log2 fold change of −2.944 and an adjusted p value below 0.001. Low expression of this microRNA was associated with a stronger contribution toward a cancer classification.

Several other leading candidates were upregulated in the integrated data. hsa-miR-1290 had a log2 fold change of 2.774, hsa-miR-614 had 2.250 and hsa-miR-128-2-5p had 2.522, each with an adjusted p value below 0.001. hsa-miR-497-5p was among the downregulated features, with a log2 fold change of −2.665 and an adjusted p value below 0.001. The overlap with previously reported pancreatic-cancer markers was interpreted as supporting biological plausibility: earlier work had associated serum miR-1290 with early pancreatic cancer and miR-125a-3p with pancreatic and biliary cancer. The researchers also emphasized the limits of that comparison. A high SHAP value describes how strongly a feature influences this particular model; it does not demonstrate that the microRNA causes cancer, acts upstream in tumor development or would independently improve patient outcomes.

The team next examined predicted or reported targets for the 20 SHAP-ranked microRNAs using DIANA-miRPath v4.0, miRDB, miRTarBase 2022, miRBase v22.1 and KEGG resources. Twelve microRNAs had DIANA-associated targets connected to pathways relevant to pancreatic cancer, including genes involved in cell-cycle control, MAPK signaling, apoptosis, DNA-damage responses, angiogenesis and inflammation. The highlighted targets included CDK4, CDK6, CCND1, CDKN1A, MAPK1, RAF1, MAP2K1, TP53, BCL2L1, E2F3, VEGFA and RELA. The researchers reported that hsa-miR-497-5p targeted several components of the MAPK pathway, while hsa-miR-125a-3p was linked to CDK4 and VEGFA. Six candidates not represented in DIANA were examined through miRDB, where their predicted targets were associated with cytoskeletal remodeling, ubiquitination, transcriptional regulation and metabolic adaptation. These database relationships generate mechanistic hypotheses but do not experimentally verify the proposed interactions.

Pancreatic cancer remains difficult to detect early because it can progress without obvious symptoms, while benign pancreatic disorders can resemble it clinically and radiologically, and definitive diagnosis may involve invasive tissue sampling. That context makes minimally invasive blood markers attractive, but the new results do not establish a ready screening test. The study used a case-control design comparing pancreatic cancer samples with healthy controls rather than a prospective screening population, and it did not evaluate performance against benign pancreatitis, other cancers or patients with other pancreatic diseases. Disease stage, tumor subtype, demographic characteristics, treatment history and other clinical variables were not consistently available across the five public datasets, so the resulting signature cannot be interpreted as specific to a stage, subtype or treatment group. Its reported diagnostic performance may therefore be optimistic in routine care, where disease prevalence, spectrum and sample handling differ from the research setting.

Translation would also require a reproducible laboratory assay, clinically meaningful decision thresholds and prospective testing in cohorts that represent the intended screening population. The 20-microRNA panel may need to be reduced to a smaller set to limit cost and complexity, and its performance should be compared with, or combined with, established markers such as CA19-9 rather than assumed to replace existing testing. Orthogonal methods, including RT-qPCR or digital PCR, would be needed to confirm that the prioritized microRNAs can be measured reliably in serum. Independent cohorts, standardized collection and processing, assays against benign pancreatic disease and functional experiments would then test whether the candidates retain their predictive value and whether their proposed target relationships have biological meaning. The study therefore offers a detailed and reproducible computational shortlist rather than a validated pancreatic-cancer diagnostic: its strongest contribution is the combination of multi-cohort data, transparent model attribution and pathway-based context, while its clinical promise remains to be tested.

Subject of Research: Computational identification of circulating microRNA candidates for pancreatic cancer detection

Article Title: Explainable artificial intelligence identifies circulating microRNA candidate biomarkers for liquid biopsy-based pancreatic cancer detection

Article References: Mandal, S., Ansari, M. A., & Chanu, W. P. (2026). Explainable artificial intelligence identifies circulating microRNA candidate biomarkers for liquid biopsy-based pancreatic cancer detection. Frontiers in Artificial Intelligence, 9, Article 1810551. https://doi.org/10.3389/frai.2026.1810551

Image Credits: AI Generated

DOI: 10.3389/frai.2026.1810551

Keywords: pancreatic cancer, liquid biopsy, circulating microRNAs, explainable artificial intelligence, machine learning, SHAP, biomarker discovery, serum diagnostics, Explainable, artificial, intelligence, identifies

Coronary atherosclerosis develops through the interplay of disturbed lipid metabolism and chronic inflammation within arterial walls. In the PROSPECT cohort of patients after acute coronary syndrome, nearly half of subsequent composite cardiovascular events were attributed to previously untreated nonculprit lesions that appeared mild on baseline angiography. Most of those events were rehospitalizations for angina rather than myocardial infarction or death.

Coronary angiography is the conventional diagnostic reference standard, but it is invasive and primarily measures luminal narrowing rather than plaque biology or coronary microcirculatory injury. Traditional risk factors, including low-density lipoprotein cholesterol, hypertension and diabetes, likewise provide broad estimates of disease probability rather than precise, individualized screening. The review therefore presents multi-omics as a possible route toward earlier molecular assessment, not as a replacement for imaging or an established clinical test.

Each omics layer interrogates a different biological level and has a distinct time scale. Genome-wide association studies have identified hundreds of single-nucleotide polymorphisms associated with coronary atherosclerosis in pathways involving lipid metabolism, inflammation and cell proliferation. Because DNA is stable throughout life, these variants can indicate inherited susceptibility, but they do not report real-time plaque progression, and the functions of many noncoding variants remain difficult to interpret.

Whole-exome sequencing complements common-variant studies by detecting rare, high-effect variants, including pathogenic genes in families with familial hypercholesterolemia, although it is less able to explain complex disease driven by many small polygenic effects. Epigenomics, including DNA methylation and histone modifications, captures changes in gene regulation without altering DNA sequence. These layers can nominate mechanisms, but evidence from cell lines, mouse models, peripheral blood or noncoronary vascular tissue cannot automatically be treated as evidence of a human coronary plaque mechanism.

Transcriptomic analysis provides a dynamic view of gene-expression states in endothelial cells, macrophages and vascular smooth-muscle cells. It has implicated molecular events ranging from altered messenger RNA stability in endothelial dysfunction to a multipotent intermediate state through which vascular smooth-muscle cells pass during atherogenesis before adopting different downstream fates. Proteomics adds the functional layer by measuring disease-associated proteins, including apolipoproteins, inflammatory proteins and calcification-related proteins.

A nested analysis of the PACMAN-AMI trial found plasma apolipoprotein and lipid changes after PCSK9-antibody treatment on a background of high-intensity statins, illustrating how molecular responses can be measured during treatment. These findings support treatment-response monitoring as a research strategy, but they do not provide a validated multi-omics rule for identifying nonresponders, switching therapy or preventing adverse effects. Metabolomics and lipidomics capture relatively rapid physiological changes that may be measured repeatedly in blood.

In a cohort of patients undergoing elective coronary angiography, higher circulating trimethylamine N-oxide was associated with subsequent major adverse cardiovascular events. In another angiography cohort, imidazole propionate was associated with prevalent coronary artery disease; cell and mouse experiments linked the metabolite to endothelial dysfunction and increased atherosclerosis. Panels containing cholesterol sulfate and other lipid-related compounds have shown diagnostic or prognostic promise in research cohorts.

A score combining selected ceramides and phosphatidylcholines predicted residual event risk across independent cohorts among patients with established coronary artery disease. Such observations generate risk hypotheses and refine mechanistic understanding, but they do not show that assigning lipid-lowering treatment according to a lipidomic profile reduces clinical events. Metabolite levels can also vary with diet, medication, activity and sampling conditions.

Gut metagenomics adds a host-microbe dimension to these profiles. Gut microbes convert dietary choline into trimethylamine, which host enzymes oxidize to trimethylamine N-oxide. Higher circulating concentrations of the latter were associated with later cardiovascular events in an angiography cohort, while experimental work has linked it to vascular inflammation and platelet hyperreactivity.

Combining microbial genes with measured metabolites can help test gut-heart pathways, including those involving trimethylamine N-oxide, imidazole propionate and 3-hydroxybutyrate. An observational acute coronary syndrome study associated prior statin use with differences in gut microbiota, circulating metabolites and adverse cardiovascular events. However, these findings do not establish that manipulating the microbiome prevents human coronary events, and geography, diet and medication can affect observed microbial associations.

Integrated multi-omics can reveal cross-layer relationships that are difficult to detect in isolation. Metabolome-transcriptome analysis in a coronary artery disease cohort associated an LPCAT1 haplotype and several glycerophospholipid metabolites with disease status, nominating lipid remodeling as a pathway without establishing a complete causal chain from the genetic variant to endothelial injury. Proteome-transcriptome integration can identify discordance between transcription and protein production.

One study linked inflammatory activation of mast cells to increased CALB2 transcription and secretion, followed by changes in macrophage calcium dynamics, foam-cell formation and erosion of the plaque fibrous cap. A separate proteome-metabolome study in Apoe-deficient mice found that a high-protein diet increased amino acids, activated macrophage mTORC1, suppressed mitophagy and increased macrophage apoptosis and necrotic-core formation. That experiment provides mechanistic evidence in a mouse aortic atherosclerosis model; it does not establish the same dietary response in human coronary arteries.

Cross-omics associations therefore remain hypotheses until supported by perturbation, replication and appropriately designed causal analyses. Single-cell and spatial methods are beginning to resolve the heterogeneity hidden by bulk measurements. Spatial transcriptomic analysis of autopsy-derived human coronary arteries from 10 individuals with histologically classified stable or unstable plaques identified region- and cell-specific transcriptional programs while retaining plaque architecture.

Unstable plaques were enriched for inflammatory and prothrombotic signaling. Other studies combined regional profiling with single-cell spatial transcriptomics to map immune-cell distributions, microvascular endothelial activity and smooth-muscle-cell transitions toward fibromyocyte-like states across human arterial specimens. A study of 27 human coronary arteries localized FAP-positive modulated smooth-muscle cells within macrophage-rich neointimal niches.

These atlases can connect molecular cell states with plaque microarchitecture, but some studies include carotid or femoral plaques, while others focus on mouse aortic roots. A state found in another vascular bed or species requires confirmation in human coronary tissue before it can support coronary-specific diagnostic or therapeutic claims. Artificial intelligence can combine molecular measurements, clinical information and coronary imaging.

In a histology-referenced ex vivo study of 10 cadaveric human hearts, deep learning on hybrid intravascular ultrasound and optical coherence tomography images improved plaque-component classification compared with single-modality models and human readers. Separate pilots have combined whole-blood transcriptomics with clinical data to predict coronary calcification and integrated proteomic, metabolomic and clinical variables for research-stage risk prediction. Such systems are vulnerable to overfitting when features outnumber participants, as well as to information leakage, batch effects, missing modalities and dataset shift.

Preprocessing, imputation, feature selection and hyperparameter tuning must therefore remain within training folds, with repeated samples from one participant kept together. External institutional or temporal validation, calibration, uncertainty estimates, performance at prespecified thresholds, incremental value and net benefit remain necessary before a model can guide treatment. Liquid biopsy offers a less invasive route for measuring circulating proteomic, metabolomic, lipidomic and transcriptomic signals, while extracellular vesicles are being investigated as carriers of tissue-derived markers.

Research models have addressed different clinical questions, so their performance figures cannot be compared directly. A three-protein model combined with age, sex and prior percutaneous coronary intervention reported areas under the curve of 0.85 internally and 0.86 in an external emergency-department cohort for angiographically defined coronary disease, although the endpoints and thresholds differed between datasets. A separate metagenomic model in 42 participants reported an AUC of 0.89 when bacterial features and predicted metabolites were combined, compared with 0.79 and 0.78 for the individual feature sets, but the small study lacked external validation.

Other models have addressed coronary calcification, angiographic severity, ischemia, plaque classification and incident cardiovascular outcomes. No cited study has yet prospectively demonstrated population screening or subtype-guided treatment selection using a liquid-biopsy multi-omics signature. The review’s central conclusion is that multi-omics could support a closed loop spanning risk-factor discovery, mechanistic investigation, plaque subtyping, prognosis and therapeutic-response monitoring.

Mendelian randomization may help evaluate specific causal questions when its instrument-validity, independence and exclusion-restriction assumptions are met, but even concordant estimates do not prove that a molecule drives disease or is safe to target. Clinical implementation requires an evidence chain beyond association or discrimination. Specimen type, fasting, collection time, medication exposure, processing delay, hemolysis, storage duration and freeze-thaw cycles must be standardized.

Assays must be locked, analytically reproducible and supported by quality controls. Models must then show incremental value and net benefit over established biomarkers, imaging and clinical care, while multicenter prospective cohorts test whether using their results changes decisions or improves outcomes. The review’s message is therefore both promising and cautionary: integrated molecular profiling is reshaping the understanding of coronary atherosclerosis, but standardized, independently validated and clinically tested systems are still required before precision medicine becomes routine.

Subject of Research: Integrated multi-omics research for mechanisms, diagnosis and prognosis in coronary atherosclerosis.

Article Title: Multi-omics in coronary atherosclerosis: mechanistic insights and advances in precision diagnosis and treatment

Article References: Chen, J., Duan, F., Zhu, G., Liu, H., Wang, X., Cui, Y., Luo, Y., & Zhang, H. (2026). Multi-omics in coronary atherosclerosis: mechanistic insights and advances in precision diagnosis and treatment. Frontiers in Endocrinology, 17, Article 1974206. https://doi.org/10.3389/fendo.2026.1974206

Image Credits: AI Generated

DOI: 10.3389/fendo.2026.1974206

Keywords: coronary atherosclerosis, multi-omics, plaque biology, liquid biopsy, spatial transcriptomics, gut-heart axis, risk prediction, precision medicine, causal inference, clinical validation, coronary, atherosclerosis

Ovarian cancer accounted for 324,398 new cases and 206,839 deaths globally in GLOBOCAN 2022, a mortality burden disproportionately high relative to incidence. Outcomes are more favorable when disease is detected early, but large screening efforts combining CA125 and transvaginal ultrasonography have not demonstrated sufficient survival benefit to support population screening. CA125 also has limited sensitivity in early-stage disease and can be elevated in benign and non-ovarian malignant conditions. Modelling cited by the review indicates that an early-stage screening test would need sensitivity above 75% and specificity of at least 99.6% to achieve a positive predictive value of 10% or greater.

DNA methylation offers a possible signal because aberrant methylation may arise early in tumourigenesis and can be measured in circulating cell-free DNA. However, plasma cell-free DNA contains material from many tissues, and only a fraction may be tumour-derived. Delays in blood processing can increase contamination with high-molecular-weight DNA, while methylation conversion, extraction and threshold selection can further affect measured signal. These issues are particularly consequential when early tumours release relatively few tumour-derived molecules.

Single-gene studies produced variable results. RASSF1A sensitivities of 25.4%, 37.7% and 85.7% were reported across three blood-based studies. OPCML showed the strongest overall performance among commonly assessed single-gene markers, with reported sensitivities of approximately 80.0% to 97.8%, but it has not been reproduced in large independent prospective cohorts. In a North Indian case-control study, serum HOXA9 and HIC1 methylation had sensitivities of 62.2% and 71.1%, respectively, at 100% specificity, although the stage I–II subset contained only 10 patients and controls were healthy rather than women with benign pelvic disease.

Multi-gene panels often improved discrimination within their source cohorts. A HOXA9–HIC1 combination tested in 45 epithelial ovarian cancer cases and 25 healthy controls achieved 88.9% sensitivity, 100% specificity and an area under the curve of 0.95, and was positive in all 10 stage I–II serum cases. Separately, an 11-marker random-forest model achieved 79% sensitivity at 96% specificity in 91 ovarian cancer cases and 91 controls and identified all five stage I–II high-grade serous cancers included in its plasma cohort. These small early-stage denominators and retrospective case-control designs do not establish screening performance.

Pre-diagnostic testing exposed a substantial gap between diagnostic discrimination and early detection. The three-region WID-cfOC score achieved 97.6% specificity and 66.7% sensitivity across 27 cancer cases in a diagnostic cohort that included women with benign pelvic pathology. Applied to prediagnostic samples from 29 women who subsequently developed ovarian cancer and 29 risk-matched controls, however, sensitivity was 27.3% for high-risk cancers with relatively lower genomic-DNA contamination and 33.3% in samples collected within one year of diagnosis, despite 100% specificity. Neither methylation nor CA125 detected stage I cancers in the diagnostic cohort, and combining the tests did not improve sensitivity beyond CA125 alone in the pre-diagnostic subset.

Machine-learning classifiers produced encouraging but threshold-dependent results. A transformer-based MethylBERT model evaluated 493 cfDNA methylation markers in 754 ovarian cancer cases, including 205 early-stage cases, and 1,118 healthy women. In its held-out internal validation set, the model achieved 89.24% sensitivity at 94.39% specificity and 79.45% sensitivity among 73 early-stage cases. Raising specificity from about 95% to 99.2% reduced early-stage sensitivity to 63.01%. Combining the classifier with CA125 increased early-stage sensitivity from 82.51% to 89.62% but reduced specificity from 97.36% to 93.55%, illustrating the trade-off between sensitivity and false-positive classification.

Validation quality remains a central limitation. A tissue-based high-grade serous classifier developed with more than 850,000 probes achieved an area under the curve of 0.84 (95% CI 0.76–0.93) in external validation after reduction to nine probes, compared with 0.98 for a much larger model. Its tissue samples, predominantly advanced-stage disease and a largely homogeneous population limit extrapolation to blood-based early detection. Across the wider literature, retrospective case-control designs, small FIGO stage I–II subsets, predominance of serous disease and healthy-control groups limit generalisability. Histotype-specific MethylBERT estimates also rested on only 43 serous, 11 endometrioid, eight mucinous and six clear-cell early-stage cases.

Assay chemistry and pre-analytical procedures add further uncertainty. Targeted PCR methods are comparatively practical for predefined loci, but bisulfite conversion can cause DNA fragmentation and loss, with reported recovery rates ranging from approximately 22% to 66% across conversion kits. Enzymatic and enrichment methods can reduce some conversion losses and work with low DNA input, but they have not established ovarian cancer screening performance. In 29 reviewed blood-based studies, only six reported the interval between collection and processing, only four used double centrifugation, and only 11 used extraction kits designed specifically for cell-free DNA. The review’s bounded conclusion is that methylation assays remain promising research components rather than clinically validated population-screening tests.

Subject of Research: Evaluation of DNA methylation biomarkers for early detection of ovarian cancer

Article Title: DNA methylation biomarkers for early detection of ovarian cancer

Article References: Leow, J. Y.-S., Ng, N. C.-S., Wong, R. S.-Y., Ming, L. C., Yap, T. L., & Goh, B. H. (2026). DNA methylation biomarkers for early detection of ovarian cancer. Molecular Biology Reports, 53(1), Article 1562. https://doi.org/10.1007/s11033-026-12750-6

Image Credits: AI Generated

DOI: 10.1007/s11033-026-12750-6

Keywords: ovarian cancer, DNA methylation, cell-free DNA, early detection, liquid biopsy, biomarkers, machine learning, CA125, diagnostic accuracy, methylation, early, detection

Several studies focused on transcriptomic risk models designed to estimate outcomes. In breast cancer, one investigation combined weighted gene co-expression network analysis, protein–protein interaction networks and LASSO-Cox regression to define a six-gene signature related to NETosis and inflammation. The genes were ZMYND10, IL12B, CXCL13, TFF1, LTB and SPIB, and the resulting risk score was reported to be independent of age and other clinical features. Five genes were confirmed by RT-qPCR; LTB was excluded because of limited tissue availability. A separate breast-cancer analysis derived and validated a seven-gene metastasis-associated signature in the TCGA-BRCA, AURORA US, SCAN-B and GEO cohorts. Tumors classified as low risk were described as immune-hot, with activated CD8+ T cells, whereas high-risk tumors showed T-cell exclusion and enrichment of kinase and metabolic pathways. RTN1 and TLR10 were proposed as candidate drivers, but their functional roles were not established.

In clear-cell renal cell carcinoma, investigators used single-cell and bulk transcriptomic data to construct a nucleotide-metabolism prognostic signature that was reported to outperform 122 previously published models. IFI30 was validated in laboratory experiments both in vitro and in vivo. That work did not establish that the signature can guide treatment decisions or improve outcomes in patients. Another breast-cancer study moved from prognosis toward prediction of response to neoadjuvant chemotherapy. A pretreatment 15-gene score achieved an area under the curve of 0.809 in one external cohort and 0.876 in another when predicting non-pathological complete response. The authors traced part of the signal to a pyruvate–CCND1–YAP1–HIF1A/ARNT–MET axis, found that serum pyruvate was elevated in tumors without a complete pathological response, and reported that capmatinib, a MET inhibitor, attenuated pyruvate-driven phenotypes. These findings nominate a pathway and compound for further testing, but they do not demonstrate clinical benefit from capmatinib in this setting.

In pancreatic ductal adenocarcinoma, investigators combined genes related to lactylation and programmed cell death to identify HMGA1 and KIF2C as core prognostic drivers. They supported the model with drug-sensitivity analyses and clinical specimens, connecting a molecular signature with candidate therapeutic compounds. The blood-cancer studies used complementary approaches spanning molecular prognosis, imaging prediction and routine clinical risk assessment. In DLBCL, SYTL4 was reported to be upregulated and independently associated with adverse outcome in a tissue-microarray subgroup, with a multivariable hazard ratio of 25.5, a 95% confidence interval of 1.8 to 367.3 and P = 0.017. The unusually wide confidence interval reflects the subgroup’s small size of 48 cases. SYTL4-high tumors were enriched for extracellular-matrix remodeling, whereas mitochondrial translation and oxidative phosphorylation were negatively enriched.

Another DLBCL study trained deep-learning models on features extracted from 18F-FDG PET/CT images to predict cancer-related cognitive impairment in patients without central nervous system involvement. This was a single-center study with a modest sample of 112 patients, making external validation a prerequisite before the approach could support clinical use. In chronic-phase CML, investigators compared three comorbidity indices—CCI, ACE-27 and CIRS-G—among 559 patients receiving flumatinib. CIRS-G contributed the largest incremental value for predicting 12-month major molecular response, with a change in area under the curve of 0.078. SHAP analysis exposed a nonlinear threshold at CIRS-G ≥ 8, above which predicted response declined, although the authors framed the finding as exploratory. The analysis therefore identifies a pattern in the model rather than demonstrating that comorbidity scoring can prospectively improve treatment selection.

Liquid biopsy contributed one candidate diagnostic panel to the collection. A study of publicly available microRNA-sequencing data identified a circulating three-miRNA panel—hsa-miR-29b-3p, hsa-miR-19b-3p and hsa-miR-30e-5p—for early-stage ovarian cancer. A random-forest classifier reached 91.7% accuracy and an area under the curve of 0.991, while pathway analysis implicated TP53 and VEGFA signaling. These retrospective performance figures do not establish accuracy in population screening or among symptomatic patients, both of which require confirmation. The editorial also notes that platelets and extracellular vesicles can carry tumor-derived material, including in cholangiocarcinoma. That observation raises the same unresolved question for these sample types: whether analytically detectable signals can be converted into reliable diagnostic measurements.

Two single-cell studies examined interactions among tumor, immune and stromal cell populations at a resolution that homogenized tissue samples can obscure. One profiled 70,760 cells from 11 melanoma samples, resolved nine subpopulations and used CellChat to infer signaling relationships. Prognostically favorable subpopulations engaged MIF–CD74/CD44/CXCR4 and MHC-I signaling, with CD8+ T cells as the predominant receivers. A derived 15-gene signature reached a mean concordance index of 0.675, and high EIF5A expression was associated with poor outcome. The other study combined single-cell transcriptomics with Mendelian randomization in lung adenocarcinoma, identifying seven prognostic genes related to endoplasmic-reticulum stress and butyrate metabolism: VDAC1, TXNRD1, GDF15, TRIB3, LPL, KCNQ1 and PKP2. The genes were validated by RT-qPCR, and the analysis placed macrophages centrally in progression. Both studies provide biological candidates that could be diluted when cells of different types and states are averaged together.

Across the collection, the most conspicuous limitation is the absence of prospective validation. Most analyses rely on public repositories, retrospective cohorts or data from a single institution, and the editorial states that none of the signatures is ready for inclusion in a clinical guideline. Prospective, multicenter validation with harmonized preprocessing will be needed to determine whether the reported associations persist in independent patients and translate into useful clinical decisions. Reporting standards also need to tighten as models become more complex. The authors recommend routine use of TRIPOD+AI for prediction-model studies and CLEAR for radiomics, with bias, calibration and failure modes assessed explicitly before deployment. Methods used to interpret model behavior, including SHAP attribution, attention weights and pathway mapping, should appear in the main report rather than only in supplementary material.

The authors identify spatial multi-omics as a natural next step for connecting single-cell inference with tissue architecture. Signatures that account for regional heterogeneity and distinguish immune-excluded from inflamed niches may generalize better than those derived from homogenized bulk tissue, while combining spatial context with liquid-biopsy dynamics could permit non-invasive tracking of spatial evolution. Spatial profiling was an explicit aim of the research call, however, and was absent from the collection. Multimodal frameworks that encode genomics, imaging, clinical text and longitudinal outcomes in one model are also attractive, but their bias, calibration and failure modes require explicit evaluation before clinical deployment. Eight of the 11 studies paired a computational hypothesis with orthogonal validation, providing useful examples of how bioinformatics findings can be checked experimentally. Taken together, the evidence supports a cautious conclusion: computational integration is generating cancer-biomarker candidates and illuminating biological pathways for investigation, while prospective testing, standardized reporting and clinical validation remain the decisive steps toward patient use.

Subject of Research: Computational cancer-biomarker discovery across solid tumors and blood malignancies

Article Title: Editorial: Bioinformatics and big data driving the discovery of clinical biomarkers for solid tumors and blood malignancies

Article References: Chen, M., Zhang, J., & Jiang, X. (2026). Editorial: Bioinformatics and big data driving the discovery of clinical biomarkers for solid tumors and blood malignancies. Frontiers in Genetics, 17, Article 1999998. https://doi.org/10.3389/fgene.2026.1999998

Image Credits: AI Generated

DOI: 10.3389/fgene.2026.1999998

Keywords: cancer biomarkers, bioinformatics, machine learning, single-cell analysis, liquid biopsy, multi-omics, PET/CT, prognostic signatures, prospective validation, solid tumors, hematologic malignancies, Editorial

The central biological argument is that cancer detectability depends on more than tumor volume. The TRACERx study cited by the authors found that the extent to which lung tumors shed circulating tumor DNA is an intrinsic feature of tumor biology, with greater shedding associated with lymphovascular invasion and a high Ki67 proliferation index, both indicators of aggressive disease. Galleri does not search only for individual mutated DNA fragments. Instead, it analyzes genome-wide methylation patterns at thousands of informative CpG sites, which the authors say can produce a detectable signal even when mutant allele fractions are extremely low. They also cite previous work suggesting that circulating tumor allele fraction is a stronger predictor of classifier performance than absolute ctDNA concentration or clinical stage alone.

The authors further contend that Galleri preferentially identifies tumors with a likelihood of causing harm rather than indiscriminately detecting every small growth. In substudy 3 of the Circulating Cell-free Genome Atlas, five-year survival among participants whose tumors were detected by Galleri was comparable to survival among people whose cancers were found through conventional clinical pathways. Participants whose tumors were not detected by the test generally had significantly better five-year overall survival than those whose tumors were detected. These observations are consistent with a test that tends to find biologically consequential cancers, although the correspondence does not present them as proof that Galleri can predict whether every individual tumor will become dangerous.

Diagnostic sensitivity in the early CCGA substudy rose with stage: 16.8% for stage I cancer, 40.4% for stage II, 77.0% for stage III and 90.1% for stage IV, with the respective 95% confidence intervals reported in the article. More recent prospective results came from PATHFINDER 2, in which slightly more than half of newly diagnosed cancers were found at stage I or II and about 70% at stages I through III. Nearly three-quarters of cancers detected by Galleri in that study occurred in cancer types without an existing recommended screening program. For 12 aggressive cancers accounting for roughly two-thirds of US cancer deaths, episode sensitivity reached 70%. NHS-Galleri produced results the authors describe as similar, reinforcing the evidence that the test can supplement screening for cancers for which routine population screening is unavailable.

Those results must be considered alongside the unsuccessful primary endpoint in NHS-Galleri. The trial did not find a statistically significant reduction in combined stage III and IV diagnoses among 12 prespecified cancers. Nevertheless, the authors report several secondary findings after three screening rounds: a 16% increase in stage I and II diagnoses, a reduction of more than 20% in metastatic diagnoses after rounds two and three, a fourfold increase in screen-detected cancers and a 21% reduction in diagnoses made during emergency presentation. Galleri detected 366 stage I or II cancers, compared with 290 cancers of all stages found through standard-of-care screening alone. These are observed trial outcomes associated with adding Galleri to standard screening, but they do not establish that the test reduced cancer mortality.

The correspondence disputes the view that the absence of a demonstrated mortality benefit makes a screening test clinically ineffective. It argues that clinical utility can also include earlier diagnosis, a greater opportunity for curative treatment, fewer metastatic presentations, more efficient diagnostic evaluation and reduced reliance on emergency care. The authors note that screening technologies have often been implemented through gradual evidence generation before definitive mortality benefits were available. They cite the recently reported NordICC colonoscopy trial as an example of a screening method that had not demonstrated a mortality benefit after 13 years of follow-up, although colonoscopy remains an accepted standard in the United States. Even so, NHS-Galleri was neither designed nor statistically powered to establish mortality reduction, making a definitive answer dependent on larger samples and longer follow-up.

The authors also reject the characterization of reductions in stage IV disease as merely a migration from one advanced stage to another. They point to improved five-year survival across US cancers, now exceeding 70%, with particularly large gains among patients diagnosed at regional stage. From their perspective, the clinically important distinction is whether a cancer is identified before it becomes metastatic, rather than relying on a simple division between early and late disease. The NHS-Galleri findings are consistent with that interpretation because metastatic diagnoses fell while earlier-stage diagnoses increased. The trial results nevertheless need longer follow-up to determine whether those shifts translate into lower cancer-specific deaths.

Prospective evidence provides a separate answer to concerns about false positives and procedural harm. PATHFINDER 2 reported a false-positive rate of 0.36%, a positive predictive value of 60%, cancer signal-of-origin accuracy of 91.7% and a median time to diagnostic resolution of 47 days. Fewer than 1% of participants underwent an invasive diagnostic procedure, and serious study-related adverse events were few. NHS-Galleri reported a false-positive rate of 0.45%, a positive predictive value of 58% and signal-of-origin accuracy of 92.5%, again with few serious study-related adverse events. Anxiety measured with validated instruments rose only mildly and transiently, returning to baseline within 12 months. The authors also cite an FDA advisory committee vote of 10–0 in favor of the test’s safety profile, while the present correspondence remains an argument by the test’s developers and does not substitute for independent regulatory review.

False negatives are interpreted as a limitation of Galleri’s intended role as an addition to, rather than a replacement for, established screening. A negative “No Cancer Signal Detected” result does not rule out cancer or justify discontinaging guideline-recommended screening. The authors say available observations do not support claims that a negative result reduces adherence: PATHFINDER 2 found no reduction at 12 months in colorectal, cervical, breast or lung cancer screening adherence, while real-world evidence found no reduction in mammography use during the 24 months after a negative result. PATHFINDER 2 also detected 91 cancers through standard screening and another 85 through routine care prompted by symptoms or incidental findings. NHS-Galleri separately detected 236 cancers through standard screening within its intervention arm, illustrating how the tests are intended to operate as complementary pathways.

Health-economic findings remain model-based rather than observed savings from the trial. Analyses cited in the correspondence estimate that Galleri used alongside standard care may be cost-effective over a lifetime horizon when changes in cancer stage, treatment intensity and diagnostic pathways are included, with the most favorable results when the high costs of advanced cancer care are incorporated. A separate model estimated that annual screening of approximately 13 million eligible people in England would increase colonoscopy demand by 2.09% and CT use by 0.76%, equivalent to about 1,040 additional colonoscopies and 4,720 CT examinations per million people screened each year. The correspondence also corrects the characterization of the Nancy Gardner Sewell Medicare MCED Screening Coverage Act: rather than mandating coverage, it creates a Medicare benefit category and gives the Centers for Medicare & Medicaid Services authority to apply its standard evidence-based review process. Taken together, the authors argue that continued evaluation is more justified than categorical dismissal, while mortality outcomes remain the unresolved question.

Subject of Research: Clinical and economic evidence for the Galleri multi-cancer early detection blood test

Article Title: Rebuttal to “Early Warnings About the Galleri Test Were Disregarded”

Article References: Klein, E. A., Venn, O., & Fung, E. T. (2026). Rebuttal to “Early Warnings About the Galleri Test Were Disregarded”. Clinical Proteomics, 23(1), Article 51. https://doi.org/10.1186/s12014-026-09644-x

Image Credits: AI Generated

DOI: 10.1186/s12014-026-09644-x

Keywords: Galleri, multi-cancer early detection, cancer screening, liquid biopsy, ctDNA, NHS-Galleri, PATHFINDER 2, false-positive rate, stage shift, cancer mortality, Rebuttal, Early

The researchers analyzed peripheral blood immune cells from patients undergoing diagnostic evaluation for suspected prostate cancer and isolated extracellular vesicles from plasma liquid-biopsy samples for functional experiments. Peripheral blood monocytes were also isolated and differentiated into macrophages ex vivo. In patient-derived macrophages, elevated miR-302/367-cluster levels coincided with reduced inflammatory cytokine production and diminished antigen-presenting characteristics, while the cells displayed more M2-like features. The association between macrophage phenotype and histological grade indicates that the observed cellular changes varied with tumor grade, although the experiments do not establish that microRNA activity causes grade progression.

Functional inhibition implicated two microRNAs within the cluster. Blocking miR-302a-5p and miR-367-3p restored M1-like characteristics across all prostate-cancer grades examined while sparing macrophages derived from biopsy-negative controls. Complementary experiments showed that prostate-cancer cells released extracellular vesicles enriched in these microRNAs. Exposure to the vesicles induced an M2-like program in monocyte-derived macrophages, whereas removing the microRNAs from the vesicles promoted M1-like polarization. Vesicles isolated from plasma of prostate-cancer patients reproduced this pattern, supporting the possible clinical relevance of the tumor-derived vesicle pathway.

Together, the results support an extracellular-vesicle miR-302/367 axis as a functional regulator of macrophage reprogramming in prostate cancer. The study used patient-derived immune cells, tumor-cell vesicles, plasma vesicles and ex vivo monocyte differentiation, providing mechanistic evidence that circulating tumor material can influence macrophage state. It does not demonstrate that the pathway determines treatment response or clinical outcomes in people, nor does the supplied evidence show that liquid-biopsy measurement of miR-302/367 can independently diagnose prostate cancer. The findings are best understood as laboratory evidence of an immune-suppressive interaction between tumor-derived vesicles and myeloid cells, with additional work needed to establish its significance in patients.

Subject of Research: Tumor-derived extracellular-vesicle miR-302/367 signaling and macrophage reprogramming in prostate cancer

Article Title: Liquid biopsy identifies tumor-derived mir-302/367 cluster as a macrophage-reprogramming axis sustaining immune cold prostate cancer

Article References: Raia, T., Pecoraro, M., Matteocci, A., Ceci, V., Pediconi, N., Trocchianesi, S., Saracini, S., Gugliuzza, G., Laschena, L., Caioli, A., di Norscia, F., Conte, F., Ferretti, E., Panebianco, V., Chiurchiù, V., & Catanzaro, G. (2026). Liquid biopsy identifies tumor-derived mir-302/367 cluster as a macrophage-reprogramming axis sustaining immune cold prostate cancer. Journal of Experimental & Clinical Cancer Research. https://doi.org/10.1186/s13046-026-03856-7

Image Credits: AI Generated

DOI: 10.1186/s13046-026-03856-7

Keywords: prostate cancer, liquid biopsy, extracellular vesicles, miR-302/367, macrophages, tumor microenvironment, immune suppression, myeloid conditioning, Liquid, biopsy, identifies, tumor-derived