Meningiomas account for more than 40 percent of all primary central nervous system tumors, and their incidence rises with age, with a two- to three-fold female predominance. While most grade 1 meningiomas are cured by surgical resection, atypical grade 2 tumors carry a substantially higher risk of recurrence, and a clinically significant subset of grade 1 and 2 tumors behaves far more aggressively than their microscopic appearance predicts. The 2021 WHO CNS5 classification introduced molecular biomarkers such as TERT promoter mutations and CDKN2A/B deletions for grade 3 disease, but the biological basis of unpredictable behavior in lower grades remains incompletely understood. Known oncogenic drivers — loss of NF2 and activating mutations in AKT1, SMO, TRAF7, and KLF4 — explain much but not all meningioma biology, which has focused attention on cytokine-mediated signaling, particularly the TGF-β axis.

TGF-β is a family of three closely related signaling molecules, TGF-β1, TGF-β2, and TGF-β3, which share a common receptor system and signal through SMAD2/3 proteins as well as non-canonical MAPK and PI3K/AKT pathways to coordinate cell proliferation, differentiation, apoptosis, and immune regulation. In cancer biology, the family is famously two-faced: it can suppress tumor growth early in tumorigenesis while paradoxically promoting tumor progression in advanced disease through epithelial-to-mesenchymal transition-like processes, immunosuppression, angiogenesis, and fibrosis. The three isoforms are not functionally interchangeable in most cancers, yet until now no systematic, grade-stratified, isoform-resolved characterization of TGF-β1 through TGF-β3 had ever been carried out in meningioma across the full range of molecular layers.

The new study addressed that gap with an unusually comprehensive design. Between October 2022 and December 2024, the investigators prospectively enrolled 154 patients undergoing elective meningioma resection at two neurosurgical centers in Kraków, Poland: 124 with grade 1 meningothelial tumors and 30 with grade 2 atypical tumors. The cohort was demographically well matched across grades, with no significant differences in age or sex distribution. Frozen tissue from each specimen was subjected to a four-pronged molecular workup: quantitative reverse-transcription PCR to measure mRNA levels of all three isoforms, methylation-specific PCR to assess promoter CpG island methylation, RT-qPCR quantification of six bioinformatically prioritized targeting microRNAs, and protein measurement by enzyme-linked immunosorbent assay, Western blotting, and immunohistochemistry.

The transcriptional results were dramatic and strikingly isoform-specific. TGF-β2 mRNA was upregulated 5.68-fold in grade 2 tumors relative to grade 1, and TGF-β3 mRNA rose 5.23-fold — both roughly five-fold increases that reached statistical significance. TGF-β1, by contrast, showed a fold-change of just 0.039, indicating near-complete loss of detectable mRNA in atypical tumors. Protein-level analyses broadly corroborated the transcriptional picture for two of the three isoforms. ELISA measurements of tissue homogenates showed TGF-β2 concentrations significantly higher in grade 2 tumors (67.23 versus 56.19 pg/mL) and TGF-β3 nearly doubled in grade 2 (198.23 versus 122.17 pg/mL), while TGF-β1 was significantly lower in grade 2 (276.91 versus 345.89 pg/mL). Immunohistochemistry told a consistent story: TGF-β1 immunoreactivity was significantly reduced in grade 2, while TGF-β3 optical density rose approximately 61 percent in atypical tumors. Western blotting, however, failed to detect the increases in TGF-β2 and TGF-β3, a discrepancy the authors attribute to differences in assay sensitivity and epitope detection rather than a true biological divergence, since both assays used the same homogenates.

Perhaps the most epigenetically revealing finding came from the methylation analysis. In grade 1 tumors, the promoters of all three TGF-β genes were hypermethylated in 95 to 97 percent of specimens — a near-universal pattern the authors interpret as a lineage-associated epigenetic phenotype. In grade 2 tumors, that pattern was largely reversed, with 73 to 77 percent of promoters unmethylated. Fisher’s exact test confirmed the shift was highly significant for all three isoforms. This wholesale epigenetic reprogramming between grades echoes patterns seen in other tumor types during malignant progression and suggests that promoter methylation status could serve as a molecular adjunct to histological grading in borderline cases.

The microRNA analysis added a third regulatory layer. Using three complementary databases — miRDB, TargetScan, and the experimentally validated miRTarBase — the team prioritized a six-miRNA panel predicted to target TGF-β isoforms: hsa-miR-200a-3p and hsa-miR-141-3p targeting TGF-β2, hsa-miR-663a and hsa-miR-425-5p targeting TGF-β1, hsa-miR-29b-3p as a candidate pan-isoform regulator, and hsa-let-7a-3p targeting TGF-β3. All six microRNAs were coordinately downregulated in grade 2 tumors, with the two miR-200 family members showing the greatest reduction — miR-200a-3p fell to 21 percent of its grade 1 level. The miR-200 family is a well-established regulator of epithelial identity and epithelial-to-mesenchymal transition through its targets ZEB1 and ZEB2, and its loss has previously been linked to meningioma invasiveness via reduced E-cadherin and Wnt/beta-catenin pathway activation.

Cross-platform correlation analysis across all 154 specimens reinforced the coherence of these signals. For TGF-β2 and TGF-β3, promoter methylation correlated inversely with mRNA levels — exactly what methylation-associated repression would predict. The two miR-200 family members correlated inversely with TGF-β2 mRNA, consistent with a de-repression model in which loss of these microRNAs releases the brake on TGF-β2 production. For TGF-β1, the story inverted: methylation correlated positively with mRNA, and the miR-663a and miR-425-5p candidates correlated positively with TGF-β1 mRNA, arguing against either mechanism as the principal driver of TGF-β1 suppression.

That paradoxical TGF-β1 collapse is, in the authors’ view, the most intriguing observation. In many advanced cancers, TGF-β1 is the prototypical prometastatic isoform, yet in these atypical meningiomas its mRNA nearly vanished despite a predominantly unmethylated promoter in 76.7 percent of grade 2 cases. The authors propose candidate mechanisms that methylation analysis alone cannot capture: silencing mediated by polycomb repressive complex 2 and the H3K27me3 histone mark, suppression of SP1 and AP-1 transcription factors driven by the AKT1 E17K mutation found in a subset of meningiomas, or loss of NF2-dependent signaling. Distinguishing among these possibilities will require functional follow-up work that this observational study was not designed to perform.

The findings also intersect with prior, sometimes conflicting, literature. Earlier work by Johnson and colleagues reported TGF-β1 through TGF-β3 and their receptors across meningioma grades, with TGF-β1 restraining proliferation in some grade 1 cultures, while Ma and colleagues found TGF-β3 mRNA declining with increasing grade — the opposite of the upregulation observed here. The authors attribute such discrepancies to differences in cohort composition, platform, and normalization approaches, and emphasize that their own contribution is the concurrent, isoform-resolved characterization across four regulatory layers within the same specimens. They also note that single-cell studies have identified macrophage, fibroblast-like, and mesenchymal populations as prominent, grade-associated components of meningiomas, and that M2-macrophage-derived exosomes have been shown to promote meningioma progression through TGF-β signaling — raising the possibility that stromal and immune cells, not tumor cells alone, contribute to the tissue-level TGF-β signal captured by ELISA and immunohistochemistry.

The translational implications, while preliminary, are tangible. Elevated TGF-β2 nominates it as a candidate therapeutic target, building on clinical experience with TGF-β pathway inhibitors in glioblastoma and other solid tumors. The grade-discriminant microRNA panel — particularly the miR-200 family and miR-29b-3p — could in principle be developed as minimally invasive biomarkers detectable in cerebrospinal fluid or blood, though the authors caution this remains speculative pending dedicated validation cohorts. Locus-specific methylation testing by methylation-specific PCR might also add molecular resolution where histological grade is ambiguous.

The study has acknowledged limitations: it is observational and cross-sectional, documents associations rather than mechanistic causality, lacked non-neoplastic control meningeal tissue, enrolled only meningothelial and atypical subtypes, did not assess downstream SMAD signaling components or receptors, and lacks an independent validation cohort. The authors state that functional validation of candidate microRNA–TGF-β interactions and single-cell profiling of TGF-β’s cellular source are planned as follow-up work. Even so, by demonstrating that transcriptional, epigenetic, post-transcriptional, and protein-level signals converge on the TGF-β2/TGF-β3 axis in atypical meningioma, the study delivers one of the most complete molecular portraits yet of how a single signaling family behaves as this common brain tumor turns aggressive.

Clear cell renal cell carcinoma, abbreviated ccRCC, accounts for the majority of kidney cancer cases worldwide, and its clinical management hinges on a pathological grading system known as WHO/ISUP grading. Under this system, pathologists examine tumor tissue under a microscope and assign a grade, typically ranging from 1 to 4, based on the prominence of nucleoli and other cellular features. The grade carries real consequences for patients: higher-grade tumors behave more aggressively, are more likely to metastasize, and influence decisions about surgery, surveillance, and adjuvant therapy. The problem is that this grading traditionally requires a biopsy or a resected specimen, meaning that oncologists and urological surgeons must often make treatment plans without knowing how aggressive the tumor actually is.

The new research, led by Dingyang Lv, Xiaomei Yao, and Jinshuai Li, with corresponding authors Ying Qiao and Weibing Shuang, set out to close that information gap. Their approach rests on a simple but powerful premise: the biological aggressiveness of a tumor leaves fingerprints in the way it enhances with contrast dye across different phases of a CT scan. Rapidly dividing, poorly differentiated tumor cells tend to have different vascular architectures, different cell densities, and different patterns of contrast wash-in and wash-out compared with indolent, well-differentiated ones. While a human radiologist can perceive some of these differences qualitatively, radiomics quantifies them at a scale and sensitivity far beyond visual assessment.

To build and test their model, the researchers assembled one of the largest cohorts yet used for this task: 884 patients with confirmed ccRCC drawn from three separate hospitals in Shanxi Province, China—the First Hospital of Shanxi Medical University, Shanxi Provincial People’s Hospital, and Shanxi Bethune Hospital. The multicenter design matters. Models trained on data from a single institution often perform impressively on that institution’s patients but collapse when confronted with scans from different CT scanners, different imaging protocols, and different patient populations. By splitting the cohort into a training set of 459 patients, an internal validation set of 198, and an external validation set of 227 drawn from other centers, the team forced their model to prove that it could generalize.

The technical pipeline began with the extraction of radiomics features from two phases of contrast-enhanced CT: the arterial phase, when contrast dye floods into the renal arteries and highlights a tumor’s blood supply, and the venous phase, when the dye has distributed more broadly through the tissue. Regions of interest were delineated around each tumor, and from these volumes the researchers computed hundreds of quantitative descriptors capturing tumor shape, first-order intensity statistics, and higher-order textures describing how pixel intensities are spatially arranged. Because raw radiomics data are notoriously noisy and redundant, the team applied feature dimensionality reduction and selection procedures, including LASSO—least absolute shrinkage and selection operator regression—to distill the raw feature set down to a stable, informative core.

What followed was an unusually systematic model-building exercise. Rather than committing to a single algorithm, the researchers fed their selected arterial-phase features, venous-phase features, and combined two-phase features into six different machine learning classifiers, generating eighteen candidate radiomics models in total. The classifier roster spanned the standard arsenal of modern machine learning: logistic regression, support vector machines, random forests, extremely randomized trees (ExtraTrees), eXtreme gradient boosting (XGBoost), and LightGBM, a gradient boosting framework known for its speed and efficiency with large feature sets. Each candidate was evaluated with receiver operating characteristic analysis, and the winner emerged clearly: an ExtraTrees model built on the combined arterial and venous phase features, denoted CTA_CTV, which achieved an area under the curve of 0.691 in the external validation set. The ExtraTrees algorithm, which injects additional randomness into how decision trees split and sample the data, has a well-earned reputation for resisting overfitting—a critical property when the number of features is large relative to the number of patients.

But the team did not stop with imaging data alone. In parallel, they used univariate and multivariate logistic regression to identify clinical and radiological characteristics—variables such as patient demographics and conventional imaging findings—that independently contributed to grading prediction, and used the ExtraTrees classifier again to construct a clinical model from these features. The final and perhaps most consequential step was fusion. Using stacking ensemble learning, a technique in which a meta-learner is trained to optimally combine the outputs of multiple base models, the researchers integrated their radiomics model and clinical model into a single decision framework, with LightGBM serving as the combiner.

The results validated the strategy. The combined model achieved an AUC of 0.703 (95% confidence interval: 0.628–0.778) in the external validation cohort, outperforming both the clinical model and the radiomics model on their own. In other words, neither the patient’s clinical picture nor the tumor’s quantitative imaging texture told the whole story alone, but stacked together they carried complementary information. Beyond raw discrimination, the team evaluated clinical usefulness using decision curve analysis, a method that quantifies the net benefit of acting on a model’s predictions across a range of decision thresholds. The combined model delivered the highest net benefit, suggesting that in practical terms it would help more patients than it would mislead.

The implications for clinical practice are substantial. Preoperative knowledge of WHO/ISUP grade could reshape how urologists counsel patients about partial versus radical nephrectomy, how they select candidates for active surveillance of small renal masses, and how they stratify risk in trials of neoadjuvant and adjuvant therapies. For patients with small kidney tumors—which are increasingly detected incidentally as abdominal imaging becomes more common—a reliable non-invasive grade estimate could mean the difference between immediate surgery and careful monitoring. The model’s modest but meaningful performance in a fully external cohort is notable in a field where reported accuracies often shrink dramatically outside the institution where the model was born.

The authors are careful to frame their findings appropriately. An AUC in the low seventies, while clinically useful, is not a substitute for pathology, and the study is retrospective in design, drawing on patients already diagnosed and treated at three centers within a single Chinese province. Broader validation across international populations, diverse scanner vendors, and prospectively collected cohorts will be needed before such models can be deployed at the bedside. The written informed consent of patients was waived owing to the retrospective nature of the work, but the study complied with the Declaration of Helsinki and received approval from the ethics boards of all three participating hospitals.

Nevertheless, the study adds to a rapidly growing body of evidence that the information needed to characterize a tumor’s biology is already embedded in images clinicians acquire every day. By demonstrating that multi-phase CT radiomics, carefully selected and intelligently fused with clinical variables, can approximate pathological grading non-invasively, the researchers have taken a concrete step toward a future in which risk stratification for kidney cancer begins the moment a scan is performed—before a single cell has been examined under a microscope. The work was published open access, and the authors declare no competing interests and received no external funding for the study.

The investigation enrolled 81 patients diagnosed with resectable colorectal cancer, alongside 20 control individuals diagnosed with adenoma. Researchers meticulously quantified peripheral blood CTCs employing advanced microfluidic chip technology, both prior to and following surgical intervention. This technological approach ensures high precision and sensitivity in detecting these elusive tumor-derived cells, which circulate in the bloodstream and are believed to be instrumental in metastasis formation.

One of the most striking findings in this study was the significant reduction in median CTC count observed after surgery. Before tumor removal, patients exhibited a median count of 16 CTCs per 5 milliliters of blood; this number sharply declined to a median of 4 postoperatively. This rapid decrease underscores the immediate impact of surgical resection on tumor cell shedding into the bloodstream, highlighting the potential of CTCs as real-time biomarkers reflecting tumor burden and surgical efficacy.

Preoperative evaluation revealed that approximately 76.6% of patients were CTC-positive, indicating a substantial presence of circulating tumor cells prior to surgery. After the surgical procedure, this positivity rate dropped to 56.8%, signifying that surgery can effectively reduce but not entirely eliminate the presence of CTCs. This residual positivity hints at possible microscopic disease persistence or early dissemination, which may influence long-term outcomes.

A key aspect of research in oncology involves understanding predictors of lymph node metastasis (LNM), a known harbinger of poorer prognosis in colorectal cancer. The study’s univariate analysis identified multiple factors associated with increased risk for LNM, including advanced tumor stage (T3-4), nerve invasion, vascular invasion, and preoperative positivity of circulating tumor cells. These findings collectively reinforce the multifaceted nature of tumor spread, involving both histopathological characteristics and circulating biomarkers.

Multivariate logistic regression further refined these associations, demonstrating that vascular invasion and preoperative CTC positivity independently increase the likelihood of lymphatic spread. Specifically, vascular invasion presented an odds ratio exceeding 20, while preoperative CTC positivity conferred an eightfold increase in risk for lymph node metastasis. This statistical rigor affirms the robustness of CTCs as predictive indicators, potentially outperforming traditional histological markers alone.

From a prognostic perspective, the status of CTCs after surgery emerged as a crucial factor. Patients who were negative for circulating tumor cells in their postoperative blood samples exhibited significantly improved disease-free survival (DFS). This observation suggests that postoperative CTC negativity may serve as an early surrogate marker for reduced recurrence risk, enabling clinicians to stratify patients according to their likelihood of favorable outcomes.

The study advances the concept that perioperative monitoring of CTC dynamics could transform clinical management of colorectal cancer. By integrating CTC quantification into pre- and postoperative assessment, oncologists may be equipped with a powerful tool to tailor treatment strategies. For instance, patients exhibiting persistent postoperative CTC positivity might benefit from intensified adjuvant therapy or vigilant surveillance schedules to catch early signs of relapse.

Central to the study’s innovation is the application of microfluidic chip technology for CTC capture and analysis. This cutting-edge methodology leverages the physical and biological properties of tumor cells to isolate them from peripheral blood with high specificity and throughput. The technology not only facilitates accurate enumeration but also supports downstream molecular characterization, which could uncover mutational landscapes essential for personalized medicine.

Colorectal cancer remains a dominant cause of cancer-related mortality worldwide, and its prognosis heavily hinges on early detection of metastasis and recurrence. Traditional imaging and pathological evaluation, while indispensable, often fall short in identifying microscopic residual disease. The findings of this research underscore the complementary role of liquid biopsies, with CTCs acting as a minimally invasive biomarker reflecting ongoing tumor biology.

In terms of biological insight, the study highlights the aggressive phenotype of tumors shedding large numbers of circulating cells into the bloodstream. Preoperative CTC positivity correlates with established pathological features such as vascular invasion, implicating a tumor’s capacity to intravasate and colonize distant sites. Such insights refine our understanding of metastatic cascade, emphasizing the interplay between tumor microenvironment and systemic dissemination.

The decline in CTCs following surgery also emphasizes the impact of surgical manipulation in potentially reducing tumor cell dissemination. However, the persistence of CTCs in a significant subset of patients postoperatively raises questions regarding the biological characteristics of these surviving cells. Are they representative of chemotherapy-resistant clones, or do they possess unique attributes facilitating immune evasion and dormancy? These questions remain an exciting field for future inquiry.

Importantly, this study’s prospective design and sizable cohort lend credibility and generalizability to the conclusions. Conducted at Jiangyin People’s Hospital over a two-year period, the investigation’s rigorous methodology and comprehensive follow-up data strengthen the evidence base linking CTC dynamics with clinical outcomes. The prospective nature particularly mitigates biases inherent to retrospective analyses, facilitating a clearer causal interpretation.

While these findings are promising, the authors prudently note the exploratory nature of the association between postoperative CTC positivity and disease-free survival. Larger, multicenter trials with extended follow-up are warranted to firmly establish CTCs as definitive prognostic biomarkers and to evaluate their utility in guiding adjuvant treatment decisions. Such investigations may eventually facilitate incorporation into standardized clinical protocols.

In sum, this seminal research underscores the clinical relevance of perioperative circulating tumor cell kinetics in resectable colorectal cancer. By illuminating the prognostic significance of pre- and postoperative CTC levels, it opens pathways for precision oncology interventions aimed at mitigating metastatic spread and improving long-term survival. This work exemplifies the transformative potential of liquid biopsy technologies in reshaping cancer diagnostics and therapeutics in the near future.

Subject of Research: The study focuses on the clinical implications of perioperative changes in circulating tumor cells in patients with resectable colorectal cancer and their associations with lymph node metastasis and prognosis.

Article Title: Clinical relevance of perioperative changes in circulating tumor cells in resectable colorectal cancer

Article References:
Feng, Q., Ni, C., Zhou, F. et al. Clinical relevance of perioperative changes in circulating tumor cells in resectable colorectal cancer. BMC Cancer 25, 1689 (2025). https://doi.org/10.1186/s12885-025-15087-8

Image Credits: Scienmag.com

DOI: https://doi.org/10.1186/s12885-025-15087-8

The significance of this biomarker lies in the previously overlooked role of histone genes within the context of cell proliferation. Histones are proteins that package and order DNA into structural units called nucleosomes, and their regulation is integral to cellular replication and function. Traditionally, cancer research has focused on other genetic factors, largely overlooking the histone genes as potential indicators of tumor behavior. However, researchers have now uncovered that elevated levels of RNAPII binding to histone genes correlate strongly with aggressive tumor growth, offering a new perspective on tumor biology.

The study utilized a cutting-edge genomic technology called Cleavage Under Targeted Accessible Chromatin (CUTAC), developed by a team led by Dr. Steven Henikoff, a prominent figure in the field. This innovative technique allows scientists to analyze gene expression and transcription activities from formalin-fixed, paraffin-embedded (FFPE) samples, which are typically archived tissue biopsies. While such samples are invaluable for research, traditional RNA sequencing methods have struggled to accurately detect histone RNAs due to their unique molecular structure. The development of CUTAC marks a significant technological advance, enabling a more nuanced understanding of gene regulation in cancer.

Through the application of CUTAC, the researchers examined RNAPII levels in tumor and normal tissues across various cancer types. Strikingly, they found that histone gene expression was significantly elevated in tumor samples compared to their normal counterparts. This finding suggests that the aggressive proliferation typical of cancer cells is matched by an increased demand for histone production, as these proteins are essential for the proper organization of DNA during cell division. As a result, the ability to measure RNAPII activity could provide insights into the overall activity of histone genes in patients.

To validate their findings, the research team conducted extensive profiling on 36 FFPE samples from patients diagnosed with meningioma. This common yet benign brain tumor serves as an excellent model for investigating the correlation between RNAPII activity and tumor progression. The results showed that RNAPII activity not only distinguished between cancerous and non-cancerous samples but also aligned tightly with clinical grading, highlighting its potential as a prognostic tool.

One of the most significant implications of this discovery is its ability to accurately predict which tumors are likely to recur after treatment. The researchers demonstrated that hyper-elevated RNAPII levels on histone genes were associated with a greater likelihood of rapid recurrence and genomic instability, characterized by whole-arm chromosome loss. By effectively pinpointing these biomarkers, healthcare providers could tailor treatment strategies to mitigate the risk of recurrence, ultimately improving patient outcomes.

Dr. Ye Zheng, a co-first author and assistant professor in Bioinformatics and Computational Biology at MD Anderson, emphasized the importance of the research, stating that the study sheds light on the "’overlooked mechanism of cancer aggressiveness’". This new understanding of histone gene regulation presents a unique opportunity for the development of diagnostic tests.

The integration of computational approaches alongside experimental techniques has enabled the researchers to create a comprehensive methodology that leverages diverse biopsy samples. By merging their findings with nearly 1,300 public clinical datasets, the team has significantly bolstered the reliability of their conclusions, paving the way for future research.

Among the critical takeaways from this study is the ability to utilize existing FFPE samples, which have long been stored and often regarded as low-yield due to RNA degradation. By employing the CUTAC technology, researchers not only reestablish the utility of these specimens but also enhance data quality, revolutionizing how historical sample data can inform contemporary cancer research.

The potential applications of this research extend beyond simply understanding cancer biology; they encompass the broader landscape of precision medicine. Customizing treatment plans based on individual genomic profiles stands to redefine cancer therapy, allowing for targeted interventions that cater to the unique characteristics of each patient’s tumor.

Future studies are set to explore the application of CUTAC technology on FFPE samples from an even wider array of cancer types. As Zheng and his colleagues continue to validate their methods and findings, the prospect of incorporating this biomarker into clinical settings becomes increasingly viable, offering hope for improved cancer diagnostics and therapeutics.

The implications of this research cannot be overstated. By unearthing a new mechanism underlying cancer aggressiveness, the findings hold the promise of developing innovative tests that could not only diagnose cancer more accurately but also inform treatment decisions. As the scientific community continues to delve into the genetic intricacies of tumors, it is crucial to recognize the vital intersection of technology, genetics, and cancer research that pushes the boundaries of what is achievable in oncology.

In conclusion, this study highlights the transformative potential of advanced genomic technologies in cancer research. By focusing on RNAPII’s role in histone gene expression, researchers are paving the way for a new diagnostic framework that may significantly impact the future of cancer treatment, particularly for meningioma and breast cancers. As more discoveries emerge from such innovative research, the hope for improved patient outcomes grows, underscoring the essential role of collaboration in the fight against cancer.

Subject of Research: Cancer biomarkers, RNA Polymerase II, histone genes
Article Title: Researchers Discover Novel Biomarker for Cancer Prognosis
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Image Credits: Credit: The University of Texas MD Anderson Cancer Center

Keywords: Cancer research, RNA Polymerase II, histone genes, meningioma, breast cancer, genomic technology, CUTAC, precision medicine, prognostic biomarkers, tumor recurrence, tumor aggressiveness, oncology.