The test is based on DELFI, short for DNA Evaluation of Fragments for Early Interception. Rather than searching for a single mutation or protein, the platform examines millions of DNA fragments released into the bloodstream when cells die. These fragments, known as cell-free DNA, carry information about the tissues from which they originated and about the processes affecting those tissues. Cancer changes the way DNA is packaged and fragmented, producing genome-wide patterns, or “fragmentomes,” that can be detected computationally.

The new study evaluated a previously developed liver cancer classifier in blood samples from 377 people in Guatemala and Romania. Participants included individuals with and without hepatocellular carcinoma, the most common form of primary liver cancer. The two groups represented sharply different disease backgrounds. In Romania, liver cancer was frequently associated with viral hepatitis or alcohol-related liver disease. In Guatemala, many participants had metabolic liver disease, obesity and diabetes, and a substantial number had been exposed to aflatoxin, a naturally occurring toxin produced by certain fungi and strongly linked to liver cancer.

Despite these differences, the classifier consistently identified liver cancer in both populations. The researchers reported that combining fragmentome analysis with alpha-fetoprotein, or AFP, and basic clinical variables such as age and sex improved detection of both early- and late-stage disease compared with blood testing based on AFP alone. AFP is widely used in liver cancer surveillance, but it can remain normal in many patients with early tumors and can also be elevated in people who do not have cancer.

The findings are important because liver cancer is often diagnosed after curative treatment options have narrowed. Its global burden continues to grow as metabolic dysfunction-associated liver disease becomes more common, alongside persistent risks from viral hepatitis, alcohol exposure and environmental carcinogens. Ultrasound and AFP remain central to surveillance, but their performance can vary with patient characteristics, disease stage and the quality of imaging. A blood test capable of detecting biological changes before a tumor becomes readily visible could help expand screening, although prospective studies will be needed before the approach can be incorporated into routine care.

To understand why the test works, the investigators used a new analytical method called MethID. The method traces DNA fragments to their likely tissues of origin by examining methylation patterns, chemical marks that regulate gene activity and differ among cell types. This analysis showed that the DELFI signal is not generated solely by tumor DNA. Fragments from liver cells, blood-vessel cells and immune cells also contributed to the pattern, reflecting the surrounding tissue response to cancer.

That broader signal may help explain why fragmentome analysis can detect disease even when tumors shed relatively little DNA into the circulation. As liver cancer develops, malignant cells interact with the liver environment, alter blood-vessel behavior and recruit immune cells. Cell death and tissue remodeling then release DNA fragments with distinctive distribution, size and methylation characteristics. By integrating these signals across the genome, the algorithm can recognize a biological state associated with liver cancer rather than relying on one tumor-specific alteration.

The researchers also found molecular differences between the two populations. Participants from Guatemala showed a characteristic mutation pattern associated with aflatoxin exposure, providing a genomic indication of the environmental pathway that contributed to some cancers. Yet this regional signature did not prevent the overall fragmentome classifier from performing across both groups. The result suggests that the test captures fundamental features of liver cancer shared across populations while retaining the capacity to reveal causes and exposures that vary by region.

The work extends earlier research from the Johns Hopkins team showing that genome-wide cell-free DNA fragmentation patterns could identify liver fibrosis and cirrhosis, conditions that frequently precede liver cancer. Together, the studies point toward a potential continuum of blood-based monitoring, in which molecular signals could help identify chronic liver injury, advanced scarring and malignant transformation. The same technological framework is also being investigated for other diseases, including lung cancer, through the FirstLook Lung test developed with DELFI Diagnostics.

The researchers say the next steps include prospective clinical validation and refinement of multimodal testing that combines fragmentome signals, protein biomarkers and clinical risk factors. Such studies must establish how the test performs in routine surveillance, how often it produces false-positive results, and whether earlier detection ultimately improves survival. The investigators also disclosed financial and intellectual-property relationships involving DELFI Diagnostics and Artemyx, including company ownership, consulting roles and patents licensed from Johns Hopkins University. These relationships have been reviewed under the university’s conflict-of-interest policies.

Subject of Research: AI-powered liquid biopsy for early detection of hepatocellular carcinoma using cell-free DNA fragmentomics.

News Publication Date: July 31

Web References: Johns Hopkins Kimmel Cancer Center: https://www.hopkinsmedicine.org/kimmel-cancer-center; Johns Hopkins Bloomberg School of Public Health: https://publichealth.jhu.edu/; Johns Hopkins University School of Medicine: https://www.hopkinsmedicine.org/som; Cell Press: https://www.cell.com/cell-press-blue/home

Keywords: liver cancer, hepatocellular carcinoma, artificial intelligence, liquid biopsy, cell-free DNA, DELFI, fragmentomics, MethID, AFP, liver disease, cirrhosis, fibrosis, aflatoxin, viral hepatitis, early cancer detection

The significance of FAM83H-AS1 lies in its classification as a long noncoding RNA (lncRNA). These molecules, which do not encode proteins, have been garnering attention for their regulatory roles in various biological processes. It is well-established now that these lncRNAs can influence gene expression, cellular processes, and play pivotal roles in cancer biology. The use of lncRNAs in a clinical setting, particularly as accessible and noninvasive biomarkers, marks a shift in how we approach the diagnostic landscape for cancer.

In their study published in the Journal of Ovarian Research, the authors delineate how FAM83H-AS1 is significantly overexpressed in the serum of ovarian cancer patients compared to healthy controls. This finding positions FAM83H-AS1 as a compelling target for further exploration in cancer diagnostics. Such a noninvasive marker holds the potential for earlier detection of ovarian cancer, which drastically improves treatment options and patient outcomes.

The methodology employed by the researchers included a robust analysis of serum samples obtained from both ovarian cancer patients and healthy individuals. Utilizing techniques such as quantitative real-time polymerase chain reaction (qRT-PCR) allowed the team to precisely measure the levels of FAM83H-AS1, thereby establishing its association with ovarian cancer. The rigorous approach taken underscores the scientific merit of the research and its implications for clinical practice.

Moreover, the study details critical statistical analyses that support the reliability of FAM83H-AS1 levels as a marker for disease presence. The sensitivity and specificity data showcased in the results speak volumes about the potential this noncoding RNA has for real-world application in diagnostic settings. Diagnostic tools that can accurately differentiate between healthy individuals and those with ovarian cancer are urgently needed, given the complexities and variations of the disease.

Importantly, the exploration of lncRNA biomarkers like FAM83H-AS1 aligns well with a broader trend in personalized medicine. As treatment options for cancer become increasingly tailored to individual patient profiles, the identification of specific biomarkers will be essential in guiding therapeutic decisions. This trend prioritizes patient-centric approaches and raises the potential for enhanced efficacy and minimized side effects in treatment regimens.

Nonetheless, while the study illuminates FAM83H-AS1’s diagnostic capabilities, it is paramount to consider the next steps in this research journey. Future investigations are needed to validate these findings in larger, more diverse cohorts to ensure the robustness of these biomarkers across different populations. Additionally, understanding the biological mechanisms through which FAM83H-AS1 influences cancer progression could pave the way for new therapeutic strategies.

Adopting this lncRNA as a diagnostic tool would also require the development of standardized protocols for its measurement in clinical laboratories, ensuring widespread adoption in oncology practices. The integration of FAM83H-AS1 into current diagnostic paradigms could represent a significant advancement in the fight against ovarian cancer. This progress will inevitably lead to improved survival rates for patients if implemented effectively.

The implications of FAM83H-AS1 reach beyond ovarian cancer, as research into other cancers might reveal similar lncRNA roles in tumor biology and diagnosis. Thus, the study stands as a testament to the advancements in our understanding of cancer-related lncRNAs and their potential applications in medical diagnostics. As we further explore the landscape of lncRNAs, they may very well unlock new strategies not only in understanding cancer but also in developing innovative treatment modalities.

In the quest for early detection methods, the role of noninvasive biomarkers such as FAM83H-AS1 cannot be overstated. By circumventing invasive procedures typically associated with cancer diagnosis, such as biopsies, this innovation could significantly enhance patient comfort, reduce healthcare costs, and improve access to screening for ovarian cancer. As awareness of ovarian cancer grows, it is essential for researchers and clinicians to advocate for the incorporation of such advances into routine practice.

As the research community continues to embrace multidisciplinary approaches to cancer biology and therapeutics, the work by Tian et al. serves as a beacon of the promising future that lies ahead. With the potential for lncRNAs to be used in other diagnostic contexts, there is a need for continued collaborations across scientific disciplines to unravel the complexities of cancer.

Ultimately, the study of FAM83H-AS1 serves as an exciting entry point in the exploration of lncRNAs and their contributions to ovarian cancer diagnostics. It is hoped that this research will spur further exploration into the clinical applications of noncoding RNAs, heralding a new era in cancer diagnostics. The path forward is bright, and with concerted efforts within the scientific community, we can anticipate transformative shifts in how we detect, diagnose, and ultimately treat ovarian cancer.

In conclusion, the emergence of FAM83H-AS1 as a potential noninvasive biomarker for ovarian cancer reflects the vibrant research landscape and the ongoing pursuit of innovative approaches in oncology. As we delve deeper into the uncharted territories of molecular biology, the intersections of diagnostics, therapeutics, and personalized medicine will continue to pave the way for advancements in cancer care.

Subject of Research: Long noncoding RNA FAM83H-AS1 as a potential noninvasive diagnostic biomarker for ovarian cancer.

Article Title: Serum long noncoding RNA FAM83H-AS1 serves as a potential noninvasive diagnostic biomarker for ovarian cancer.

Article References:

Tian, C., Sun, H., Li, R. et al. Serum long noncoding RNA FAM83H-AS1 serves as a potential noninvasive diagnostic biomarker for ovarian cancer. J Ovarian Res (2026). https://doi.org/10.1186/s13048-026-01995-1

Image Credits: AI Generated

DOI: 10.1186/s13048-026-01995-1

Keywords: ovarian cancer, long noncoding RNA, FAM83H-AS1, biomarkers, noninvasive diagnostics

This multicenter, retrospective study compiled data from 206 HCC patients with pathologically confirmed diagnoses, sourced from three distinct hospitals, ensuring a diverse and robust dataset for model training and validation. The investigative team focused sharply on the hepatobiliary phase images (HBP) of gadoxetic acid-enhanced MRI, a specialized imaging sequence known for its superior liver lesion characterization. To harness the full potential of this imaging modality, three variations of deep learning architectures were meticulously developed and assessed: two-dimensional (2D), three-dimensional (3D), and a novel 2.5-dimensional (2.5D) deep multi-instance learning (MIL) model.

Among these approaches, the 2.5D MIL technique emerged as the most potent, ingeniously integrating information from all axial slices encompassing the tumor and surrounding peritumoral regions. This comprehensive slice selection allowed the model to capture both intratumoral heterogeneity and critical peritumoral microenvironmental features, which are hypothesized to play pivotal roles in MVI development. Remarkably, this approach outperformed conventional models with area under the curve (AUC) values reaching 0.802 in internal validation and 0.759 in the external test cohort, highlighting its strong generalizability across patient populations.

Building on these promising findings, the researchers extended their methodology by incorporating additional MRI sequences—T1-weighted fat-suppressed (T1WI-FS) and T2-weighted fat-suppressed (T2WI-FS) images—into a multimodal prediction framework. The synergistic use of these complementary sequences aimed to further refine the predictive algorithm by capturing distinct tissue contrasts and pathological signatures of MVI. This multimodal deep learning model demonstrated exceptional predictive capacity, with AUC values soaring as high as 0.954 in the training set, and sustaining robust performance metrics with AUCs of 0.857 and 0.788 in independent validation and test sets respectively.

These advancements are not merely incremental; they represent a paradigm shift in medical imaging diagnostics for liver cancer. The exploitation of 2.5D MIL in this context signifies a novel computational strategy that leverages the spatial and contextual richness of MRI datasets more effectively than traditional machine learning or purely 2D/3D frameworks. By embracing the heterogeneous nature of tumor microenvironments across consecutive axial slices, this approach unlocks a more nuanced understanding of tumor biology, which is crucial for preoperative risk stratification.

Importantly, the ability to noninvasively predict MVI preoperatively holds profound clinical implications. Surgeons and oncologists can now potentially tailor treatment regimens with increased precision—deciding between surgical resection, transplantation, or adjuvant therapies based on individualized risk profiles. Moreover, early detection of MVI propensity may guide surveillance intensity post-operation, improving patient outcomes through timely interventions.

The retrospective design of this study, coupled with its multicenter data acquisition, lends credibility to the model’s robustness and applicability across different clinical settings. However, the authors underscore the necessity for prospective trials to validate and eventually integrate these computational tools into routine clinical workflows. Additionally, further research into integrating radiogenomics could uncover deeper mechanistic links between image features and genetic underpinnings of microvascular invasion.

On the technical front, the study exemplifies an exemplary application of advanced artificial intelligence methodologies in oncological imaging. The use of deep learning architectures capable of handling multi-instance learning tasks shows how algorithmic innovation can extract actionable insights from complex and high-dimensional medical images. Fine-tuning such models with diverse MRI sequences reinforces the importance of multimodality in capturing the multifaceted nature of cancer pathology.

This research also emphasizes the crucial role of peritumoral tissue analysis, an often-overlooked area in conventional imaging assessments. The findings suggest that changes in the tumor microenvironment surrounding hepatocellular carcinoma lesions carry significant predictive information, urging the clinical community to expand diagnostic focus beyond tumor margins. Such insights will likely spur new investigations into tumor-stroma interactions and their implications in cancer progression.

Future directions proposed by the research team involve the development of automated MRI preprocessing pipelines and user-friendly software interfaces to democratize the use of their predictive models in community hospitals and cancer centers worldwide. Integrating these outputs with electronic health records and decision-support systems could democratize access to personalized oncology care, aligning with global efforts toward precision medicine.

In conclusion, the creation and validation of a deep multi-instance learning model based on gadoxetic acid-enhanced MRI heralds a new age in the preoperative management of hepatocellular carcinoma. By harnessing the power of 2.5D imaging data and multimodal sequences, this approach offers an unprecedented window into tumor invasiveness, enabling clinicians to make better-informed decisions and ultimately improving patient prognoses. As artificial intelligence continues to evolve, such innovations underscore the transformative potential of combining computational prowess with clinical expertise in battling complex diseases like HCC.

Subject of Research: Microvascular invasion prediction in hepatocellular carcinoma using advanced deep learning models applied to gadoxetic acid-enhanced MRI.

Article Title: Deep multi-instance learning model based on gadoxetic acid-enhanced MRI for predicting microvascular invasion of hepatocellular carcinoma: a multicenter, retrospective study.

Article References: Luo, Y., Zhang, G., Zhong, S. et al. Deep multi-instance learning model based on gadoxetic acid-enhanced MRI for predicting microvascular invasion of hepatocellular carcinoma: a multicenter, retrospective study. BMC Cancer 25, 1626 (2025). https://doi.org/10.1186/s12885-025-14971-7

Image Credits: Scienmag.com

DOI: https://doi.org/10.1186/s12885-025-14971-7

Historically, the detection of prostate cancer has heavily depended on blood tests measuring PSA, a protein produced by both cancerous and noncancerous prostate tissues. Although PSA testing has been an essential tool in screening, its specificity is limited. Elevated PSA levels above 4.0 nanograms per milliliter often prompt urologists to recommend prostate biopsies, which involve extracting multiple tissue samples via needles. However, these biopsies can be negative or result in overtreatment of low-grade prostate cancers unlikely to progress aggressively. This gap in diagnostic precision has driven the need for more accurate, minimally invasive methods.

The team spearheaded by Dr. Ranjan Perera, director of the Center for RNA Biology at Johns Hopkins All Children’s Hospital, employed advanced molecular profiling techniques to analyze urine samples from prostate cancer patients before and after prostatectomy, as well as from healthy individuals. By meticulously isolating prostate cells shed in urine and conducting RNA sequencing alongside real-time quantitative polymerase chain reaction (qPCR), researchers narrowed down 815 prostate-specific genes to a critical trio: TTC3, H4C5, and EPCAM. These markers were robustly linked to the presence of prostate cancer, showing significant expression in pre-surgery urine samples and near absence following surgical removal of the prostate.

TTC3, or tetratricopeptide repeat domain 3, is particularly notable for its role in asymmetric cell division in cancerous cells, a process vital to tumor heterogeneity and progression. H4C5 refers to an H4 clustered histone variant, a protein influential in chromatin remodeling, which impacts gene expression regulation and genome stability within malignant cells. EPCAM, the epithelial cell adhesion molecule, is a surface glycoprotein commonly overexpressed in epithelial-derived cancers. The synergistic detection of these three biomarkers in urine offers a molecular fingerprint that is both highly sensitive and specific to prostate malignancies.

In comprehensive validation studies, the three-marker panel demonstrated an impressive area under the curve (AUC) of 0.92, indicating near-perfect diagnostic performance. The test accurately identified prostate cancer in 91% of cases and effectively ruled out non-cancerous individuals 84% of the time. Remarkably, it also distinguished prostate cancer patients from those with benign prostatic hyperplasia (BPH), a benign enlargement of the prostate that often confounds clinical diagnoses. This specificity extends even to patients whose PSA levels remain within normal ranges, addressing a critical diagnostic blind spot where current PSA tests falter.

The researchers did not stop at typical PSA-positive cases but intentionally investigated the panel’s effectiveness in PSA-negative prostate cancers. Even within this challenging subset, the test retained high diagnostic accuracy, correctly identifying malignancies in 78.6% of cases during development and 85.7% during validation. Such sensitivity could transform early detection protocols for men who otherwise might be overlooked by PSA screening. Furthermore, this assay showed the ability to differentiate prostate cancer from prostatitis, an inflammatory prostate disease that can also obscure clinical assessments.

The methodology entailed extensive sample collection from multiple centers, capturing a diverse cross-section of patients and controls. In total, the study evaluated over 1,300 urine specimens across both development and validation phases, ensuring statistical robustness. The high-throughput analyses coupled with immunohistochemical studies on tissue biopsies correlated biomarker expression in urine with that observed directly in malignant prostate tissues. This multi-platform validation confirms that these biomarkers derive specifically from prostate cancer cells, reinforcing the biological relevance of the test.

Current prostate cancer diagnostic standards are burdened by the limitations of PSA screening – namely its lack of specificity and the invasive nature of follow-up biopsies. As Dr. Perera emphasizes, these biopsies carry risks such as infection and bleeding, and negative results occur frequently, leading to patient anxiety and increased healthcare costs. By introducing a sensitive, urine-based assay, patients could potentially avoid these invasive procedures altogether unless clearly indicated, optimizing both patient well-being and resource allocation.

Co-author Dr. Christian Pavlovich, a distinguished professor of Urologic Oncology, highlights the clinical practicality of urine as a diagnostic medium. Given that urine collection is noninvasive, inexpensive, and easy to implement in outpatient settings, the adoption of such a test could be swift and widespread, enhancing prostate cancer screening while reducing dependence on blood-based PSA measurements. The test’s ability to act as an adjunct or standalone diagnostic tool heralds a new era in precision urology.

Looking ahead, investigators are contemplating integrating the three-biomarker panel with PSA testing to create a “super PSA” assay, combining the strengths of both approaches to maximize diagnostic accuracy. Clinical trials at independent institutions are planned to further validate the assay’s performance, with the ultimate goal of transitioning this discovery from research laboratories into clinical practice. Efforts are also underway to patent the technology and explore commercial development opportunities through technology transfer and startup formation.

The research, supported by several funding agencies including the National Institutes of Health, the Bankhead-Coley Cancer Research Program, and the International Prostate Cancer Foundation, signifies a major leap forward in biomarkers for urologic oncology. As this panel advances through clinical validation and regulatory review, it promises to redefine prostate cancer diagnosis, improving patient outcomes through earlier intervention and reducing the emotional and physical toll of unnecessary procedures.

In conclusion, this innovative urine test targeting TTC3, H4C5, and EPCAM biomarkers marks a transformative step towards precision medicine in prostate cancer. With its superior sensitivity, specificity, and noninvasive nature, it addresses critical limitations in current screening paradigms and paves the way for personalized diagnostic strategies. This scientific milestone reflects a multidisciplinary triumph, blending molecular biology, clinical oncology, and cutting-edge technology to confront one of the most prevalent malignancies affecting men worldwide.

Subject of Research: Prostate cancer diagnosis using urine biomarkers
Article Title: Novel Urine Biomarker Panel Demonstrates High Accuracy for Noninvasive Prostate Cancer Detection
News Publication Date: September 2, 2025
Web References: Johns Hopkins Kimmel Cancer Center (https://www.hopkinsmedicine.org/kimmel_cancer_center/), Johns Hopkins All Children’s Hospital (https://www.hopkinsmedicine.org/all-childrens-hospital)
References: Published in EBioMedicine on September 2, 2025
Image Credits: Johns Hopkins All Children’s Hospital
Keywords: Prostate cancer, biomarkers, TTC3, H4C5, EPCAM, urine test, noninvasive diagnostics, PSA, biopsy alternative