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PubMed2026

Multimodal alignment improves generalizability of genomic biomarker prediction in computational pathology.

Computational pathology models that use digitized histopathology whole-slide images have the potential to become a cost-effective and scalable alternative to molecular assays for the prediction of genomic biomarkers, a key task in precision oncology. However, as new genomic biomarkers are discovered or quantified, large, labeled datasets must be prospectively collected to train new models. To address this challenge, we developed multimodal alignment for biomarker learning and generalization (MARBLE), a multimodal contrastive pretraining strategy that integrates structured biomarker knowledge into representation learning of histopathology images. MARBLE aligns histopathology-derived representations with representations of genomic biomarkers generated by a large language model (LLM) and a protein language model (PLM). This biologically informed alignment enables data-efficient generalization to novel, out-of-distribution biomarkers. Using the MSK-IMPACT cohort of over 40,000 patients across multiple biomarker panel versions, we design experiments grounded in real-world data to demonstrate the value of our proposed approach.

CP: computational biology↗

PubMed2026

Machine learning-ready genomic biomarkers: ATF3 polymorphisms predict postoperative analgesic demand through AI-compatible phenotyping.

PURPOSE: To determine whether ATF3 polymorphisms can serve as genetic biomarkers for machine learning-based precision analgesia by establishing a genotype-phenotype association suitable for predictive modeling of postoperative opioid requirements. METHODS: In a prospective cohort of 167 adults undergoing abdominal surgery, ATF3 SNPs rs3122721 and rs3125293 were genotyped. A structured dataset architecture was developed to represent genetic profiles as input features for supervised learning models, enabling translational analysis of genotype‑dependent opioid consumption over 72 h. RESULTS: Patients with homozygous genotypes of the ATF3 SNPs had significantly higher opioid requirements than non‑carriers, despite reporting similar subjective pain scores. This consistent genotype‑dependent pattern provided a clinically relevant phenotype suitable for integration into predictive algorithms. CONCLUSION: ATF3 genotyping offers a promising biomarker for computationally informed precision analgesia. By linking genomic variability to clinically meaningful outcomes within a structured clinical and genomic framework, this approach supports the future development of risk-stratified clinical decision-support systems to optimize postoperative pain management.Trial registration ChiCTR1900021991, registered 30 April 2019. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at https://doi.org/10.1007/s13755-026-00480-9.

ATF3↗

PubMed2026

Occupational exposure to 2,4-dichlorophenoxyacetic acid (2,4-D) and associated oxidative and genomic biomarkers among soybean farmers: a cross-sectional study.

BACKGROUND: 2,4-Dichlorophenoxyacetic acid (2,4-D) is a herbicide widely used for weed control in soybean cultivation. This study aimed to investigate hepatic and genetic damage biomarkers in farmers occupationally exposed to 2,4-D, widely used in soybean cultivation in southern Brazil (Rio Grande do Sul), in addition to estimate urinary 2,4-D concentrations as an indicator of recent exposure. METHODS: A cross-sectional study was conducted, including 54 occupationally exposed farmers and 51 non-exposed controls (organic farmers). RESULTS: Urinary 2,4-D was detected in 88.5% of the exposed group versus 25% of the controls (p < 0.001). Creatine kinase (CK) was significantly elevated in the exposed group (p < 0.05), suggesting possible muscle injury, while AST and ALT (classical liver enzymes) did not differ between groups. Oxidative stress markers showed a clear pattern of redox imbalance, with increased TBARS (p < 0.001), increased CAT activity (p < 0.001), and reduced SOD activity (p < 0.001). Telomere length was significantly shorter in the exposed group (p = 0.001). Use of personal protective equipment (PPE) was reported to be inadequate. CONCLUSION: Occupational exposure to 2,4-D is associated with systemic oxidative stress and telomere shortening, even in the absence of transaminase elevation. This points to early hepatocellular vulnerability mediated by oxidative mechanisms rather than overt cytolysis. The study emphasizes the need for continuous monitoring of populations chronically exposed to chlorophenoxy herbicides.

2,4-dichlorophenoxyacetic acid↗

PubMed2026

Intramuscular patient-derived xenografts achieve high engraftment rates in gastric cancer: implications for pharmacodynamic testing and genomic biomarker discovery.

BACKGROUND: Gastric cancer (GC) exhibits marked inter-patient heterogeneity, limiting empirical chemotherapy efficacy. Patient-derived xenograft (PDX) models preserve the molecular features of parental tumors and can serve as pharmacodynamic surrogates, but conventional subcutaneous PDX suffers from low engraftment rates. This study evaluated an optimized intramuscular PDX platform for individualized drug testing in GC and applied whole exome sequencing (WES) for biomarker identification (Clinical trial registry: ChiCTR-OOC-17012731). MATERIALS AND METHODS: Ninety-eight treatment-naive GC patients were enrolled between April 2018 and December 2020. Fresh tumor tissues were engrafted into NCG mice by intramuscular transplantation. Drug efficacy was evaluated using tumor cell necrosis rate and Ki-67 expression. WES was performed on 32 engrafted tumorgrafts to characterize driver mutations in fast- and slow-growing subgroups. RESULTS: An engraftment rate of 71.7% (43/60) was achieved, substantially exceeding rates reported in prior studies. Clinical characteristics were independent of engraftment success and outgrowth time (all p > 0.05). Fast- and slow-growing tumorgrafts diverged in frequently altered genes: KMT2C, APOB, CDK12 and MSH2 predominated in fast-growing grafts, whereas TP53, CHD3 and TET2 were enriched in slow-growing grafts. Slow-growing tumorgrafts correlated with longer progression-free survival (p = 0.02). PDX-guided treatment was associated with improved prognosis. CONCLUSIONS: Intramuscular transplantation into NCG mice yields high engraftment rates for GC PDX. PDX-guided chemotherapy selection is associated with favorable outcomes. Driver mutation divergence between fast- and slow-growing tumorgrafts provides candidate prognostic biomarkers.

Animals↗

PubMed2026

Beyond genes: EpiSwitch® and Orion platform-powered 3D genome architecture biomarkers reveal shared biology across ME/CFS, long COVID, PTSD, rheumatoid arthritis, and multiple sclerosis.

BACKGROUND: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), Long COVID (LC19), post-traumatic stress disorder (PTSD), rheumatoid arthritis (RA), and multiple sclerosis (MS) are clinically distinct disorders that share substantial symptom overlap, including persistent fatigue, cognitive impairment, autonomic dysfunction, and immune dysregulation. Although these conditions differ in diagnosis and clinical presentation, their underlying biological mechanisms remain poorly understood and may involve convergent regulatory pathways. METHODS: The EpiSwitch® 3D genomics platform and Orion knowledgebase were used to integrate chromosome conformation signatures with genome-wide association study (GWAS)-derived datasets across ME/CFS, LC19, PTSD, RA, and MS. Three-dimensional genomic anchors were mapped to coding genes and analysed using STRING protein-protein interaction networks and Cytoscape-based systems biology approaches. Disease-specific anchor datasets were generated and compared at both gene and network levels to identify shared biological processes and regulatory mechanisms. RESULTS: Analysis of the ME/CFS dataset identified 552 unique 3D genomic anchors mapped to 567 genes, with analogous disease-specific anchor sets generated for LC19, PTSD, RA, and MS. Direct overlap between disease-associated genes was limited; however, higher-order network analyses revealed substantial interconnectivity and convergence across conditions. Shared biological pathways included immune and cytokine signalling, interferon responses, mitochondrial function, metabolic regulation, and neuroendocrine processes. Highly connected hub genes included immune regulatory nodes such as LAG3 and components of the mTOR signalling pathway, implicating T-cell exhaustion, chronic immune activation, and immunometabolic dysregulation as common mechanisms underlying these disorders. CONCLUSIONS: These findings support a systems-level model in which clinically overlapping fatigue-associated syndromes arise from perturbations of interconnected regulatory networks rather than discrete disease-specific pathways. Despite limited genetic overlap, substantial convergence at the network level suggests shared biological architecture across ME/CFS, LC19, PTSD, RA, and MS. The identification of common regulatory pathways provides a mechanistic framework for the development of cross-disease diagnostic and therapeutic strategies. By capturing dynamic regulatory states, 3D genomic biomarkers offer significant potential for objective blood-based diagnostics, patient stratification, and the identification of shared therapeutic targets across complex chronic disorders. These findings support the application of precision medicine approaches and may accelerate the development of novel interventions for fatigue-associated multisystem diseases.

Humans↗

PubMed2026

Genomic Analysis and Clinical Correlation of Non-Small Cell Lung Cancer with Special Reference to Brain Metastasis.

BACKGROUND: Next-generation sequencing (NGS) has improved genomic analysis depth in precision oncology. This study analyzed genomic biomarker testing in stage IV NSCLC, focusing on brain metastasis and clinicopathological correlations. OBJECTIVE: To study molecular markers and clinicopathological correlations in stage IV NSCLC patients, with and without brain metastasis. METHODS: A total of 169 stage IV NSCLC patients were studied from April 2023 to May 2025. Demographic data, clinical presentations, and mutation analyses were assessed using NGS on tissue blocks or liquid biopsies. RESULTS: Among 169 patients, 41.42% (n = 70) had brain metastasis (NSCLC-BM), while 58.58% (n = 99) had no brain metastasis (mNSCLC). Median ages were 51.5 and 56 years, respectively. Adenocarcinoma comprised 95.27% (n = 161) of cases. The cerebral hemisphere was the most common intracranial metastatic site, while skeletal involvement was the most common extracranial site. Headache was the predominant neurological symptom. EGFR mutations were the most common overall. EGFR > TP53 > ALK > other mutations were observed in NSCLC-BM, while EGFR > TP53 > KRAS > other mutations were seen in mNSCLC. Mutation analysis stratified by smoking history (χ²(1) = 1.347, p = 0.245) and sex (χ²(1) = 0.0302, p = 0.862) was not statistically significant. The benefit of gefitinib plus chemotherapy in EGFR exon 19 and exon 21 L858R mutations was greater in mNSCLC (log-rank χ²(1) = 10.813, p = 0.001) than in NSCLC-BM (log-rank χ²(1) = 3.100, p = 0.078). Median survival was 11 months (95% CI: 7.506-14.494) for NSCLC-BM versus 21 months (95% CI: 8.365-33.635) for mNSCLC, with a statistically significant difference (log-rank χ²(1) = 8.639, p = 0.003). CONCLUSION: NSCLC-BM showed higher genomic biomarker enrichment (80% vs. 68.68%) but poorer outcomes than mNSCLC. EGFR was the most common targetable mutation, followed by ALK in NSCLC-BM and KRAS in mNSCLC.

Humans↗

PubMed2026

Predictive Biomarkers for Immune Checkpoint Inhibitor Efficacy: Challenges, Innovations, and a Pathway to Precision Medicine in the Era of Cancer Immunotherapy.

BACKGROUND: Immune checkpoint inhibitors (ICIs) have transformed oncology practice. However, treatment response remains heterogeneous, rendering predictive biomarkers critical for optimal patient care. The 3 established biomarkers, programmed death-ligand 1, tumor mutational burden (TMB), and microsatellite instability-high/deficient mismatch repair, are approved and clinically validated but are modest predictors of benefit. As a result, multiple novel predictive biomarkers remain under investigation. CONTENT: This review highlights established and investigational predictive ICI efficacy biomarkers. For established biomarkers, we describe biology, assay modalities, approved companion diagnostics, landmark studies, and notable limitations. Due to the multisystem nature of antitumor immune effects, investigational biomarkers span multiple domains, including tumor genomic biomarkers (e.g., mutational signatures, TMB, neoantigen clonality), tumor microenvironment (e.g., tumor-infiltrating lymphocytes [TILs], tertiary lymphoid structures), systemic immune biomarkers (e.g., cytokines, autoantibodies, glycoproteins, peripheral blood mononuclear cells), and the microbiome (e.g., gastrointestinal microbial diversity, responder-enriched taxa). SUMMARY: The established biomarkers PD-L1, TMB, and microsatellite instability-high/deficient mismatch repair inform ICI use in clinical practice but have important limitations. Multiple investigational biomarkers show promise in refining patient selection and optimizing therapy. Moving forward, increased assay harmonization, prospective validation, and standardized parameters may improve performance. Composite models integrating complementary signals across domains may further individualize treatment and lead to an era of personalized cancer immunotherapy.

Humans↗

PubMed2026

Ex Vivo Tumor-Derived Organoid Pharmacotyping Identifies Personalized Therapeutic Options for Patients with Biliary Tract Cancer.

UNLABELLED: Biliary tract cancers (BTC) pose clinical challenges due to poor chemotherapy response and aggressive disease course. We evaluated patient-derived tumor organoid-based drug sensitivity testing as a tool to guide therapy. In this multicenter study, 26 tumor organoids were successfully derived from 43 patients with BTC and tested with an average of 50 cancer-directed therapies using the Clinical Laboratory Improvement Amendments-certified PARIS assay. Despite most organoids being from late-stage disease, 24/26 (92.3%) exhibited strong sensitivity to one or more targeted agents. Active drugs included inhibitors of EGFR/HER2, MEK, ERK, BCR-ABL and SRC family, mTOR, PI3K, MDM2, BCL2, and BET. Drug sensitivities aligned with known genetic biomarkers but were also observed in cultures lacking them, indicating ex vivo testing can expand actionability beyond genomics. In five cases, results guided therapy; one patient with an FGFR-BICC1 fusion refractory to FGFR inhibitors responded to dasatinib, achieving symptomatic improvement, stable disease, and >8-month survival. SIGNIFICANCE: Ex vivo drug testing of tumor-derived organoids is clinically feasible and can be used to identify personalized treatment options for patients with BTC, to evaluate the functional relevance of genomic biomarkers, and to guide treatment in real time.

Humans↗

PubMed2026

Salvage therapy for radiorecurrent prostate cancer: beyond equipoise - a call for biomarker-driven stratification.

The increasing incidence of localized radiorecurrent prostate cancer demands a shift from modality-centric comparisons toward biomarker-driven patient selection. Light et al. provide a matched comparison of salvage focal therapy (sFT) versus salvage radical prostatectomy (sRP), reporting comparable 10-year cancer-specific survival but fewer complications with sFT. However, the study lacks integration of modern PSMA PET/CT restaging and genomic risk stratification (e.g., Decipher classifier), both of which could profoundly influence therapeutic outcomes. Moreover, salvage reirradiation-a promising third option-is omitted. We argue that equipoise is no longer sufficient; the field needs prospective registries or trials that stratify by imaging and genomic biomarkers, with coprimary endpoints of metastasis-free survival and patient-reported functional outcomes. We also discuss the limitations of PSMA PET/CT for small-volume lesions and the importance of validating genomic thresholds specifically in the salvage setting. Only such an approach will enable truly personalized salvage therapy.

Biomarker stratification↗

PubMed2026

Mutual Information-based Prognostic Biomarker Discovery in Cancer Genomics: Conceptual Framework and Representative Applications of MI-POG.

Mutual information (MI)-based approaches have increasingly been applied to cancer genomics; however, their use for genome-wide prognostic biomarker discovery remains relatively underexplored. The present article summarizes the conceptual workflow of Mutual Information-based Prognostic Omics Gene (MI-POG) based on previously published applications in breast cancer, lower-grade glioma, and other cancer datasets. The framework consists of clinical endpoint discretization, genome-wide MI-based screening, candidate ranking, and downstream validation using conventional survival-analysis approaches. Previous MI-POG applications identified solute carrier family 20 member 1 (SLC20A1) as a prognostic biomarker in hormone receptor-positive breast cancer. Elevated SLC20A1 expression was associated with unfavorable survival outcomes and was independently validated in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) cohort. Methodological analyses demonstrated how survival endpoints can be integrated into an information-theoretic framework through fixed-time outcome discretization, enabling model-independent assessment of molecular-clinical dependencies. Applications across multiple cancer datasets suggested the potential applicability of the framework across biologically distinct tumor types, although further validation will be required to establish its robustness and generalizability. In conclusion, MI-POG can be formalized as an information-theoretic framework for genome-wide identification of prognostic biomarkers by quantifying molecular-clinical dependencies using mutual information. Representative applications from previously published studies suggest that MI-POG may complement conventional survival-analysis approaches and provide a useful strategy for biomarker discovery, although additional benchmarking and prospective validation will be required.

Humans↗

PubMed2025

CoxKAN: Kolmogorov-Arnold networks for interpretable, high-performance survival analysis.

MOTIVATION: Survival analysis is a branch of statistics that is crucial in medicine for modeling the time to critical events such as death or relapse, in order to improve treatment strategies and patient outcomes. Selecting survival models often involves a trade-off between performance and interpretability; deep learning models offer high performance but lack the transparency of more traditional approaches. This poses a significant issue in medicine, where practitioners are reluctant to use black-box models for critical patient decisions. RESULTS: We introduce CoxKAN, a Cox proportional hazards Kolmogorov-Arnold Network for interpretable, high-performance survival analysis. Kolmogorov-Arnold Networks (KANs) were recently proposed as an interpretable and accurate alternative to multi-layer perceptrons. We evaluated CoxKAN on four synthetic and nine real datasets, including five cohorts with clinical data and four with genomics biomarkers. In synthetic experiments, CoxKAN accurately recovered interpretable hazard function formulae and excelled in automatic feature selection. Evaluations on real datasets showed that CoxKAN consistently outperformed the traditional Cox proportional hazards model (by up to 4% in C-index) and matched or surpassed the performance of deep learning-based models. Importantly, CoxKAN revealed complex interactions between predictor variables and uncovered symbolic formulae, which are key capabilities that other survival analysis methods lack, to provide clear insights into the impact of key biomarkers on patient risk. AVAILABILITY AND IMPLEMENTATION: CoxKAN is available at GitHub and Zenodo.

Humans↗

PubMed2026

Big data in multiple sclerosis.

PURPOSE OF REVIEW: This review summarizes recent key advancements in multiple sclerosis (MS) achieved through the utilization of big data from diverse sources and advanced analytical techniques. RECENT FINDINGS: Real-world evidence (RWE) derived from MS big data has significantly enhanced treatment strategies, redefined the concept of disease progression, refined prognostic models, and facilitated personalized medicine. RWE has highlighted the long-term benefits of early intensive treatment compared to escalation strategies, the unfavorable risk profile associated with treatment de-escalation and the importance of managing treatments during pregnancy. Additionally, it has revealed similarities and differences in the effectiveness and safety of specific high-efficacy therapies, as well as key predictors for switching treatments. RWE has also emphasized the central role of progression independent of relapse activity as a significant driver of disability and predictor of unfavorable long-term outcomes in both adult and pediatric onset MS. A data-driven approach utilizing artificial intelligence and big data has established a comprehensive framework for understanding the disease's evolution. Multimodal big data frameworks - encompassing clinical data, MRI, genomics, biomarkers, and app-based metrics - have demonstrated their ability to enhance diagnostic performance and risk stratification in MS. SUMMARY: Big data approaches are transforming MS research and clinical practice by providing stronger RWE to guide therapeutic decision-making, refining models of disease progression, and developing more precise prognostic tools.

Humans↗

PubMed2026

The Puerperium in the Modern Dairy Cow: A Review.

The puerperium represents a critical physiological period during which the bovine reproductive tract transitions from pregnancy to renewed fertility. In the modern high-producing dairy cow, this transition is challenged by profound metabolic, endocrine, immunological, and structural demands that collectively influence uterine health, ovarian function, and subsequent reproductive performance. This review examines current understanding of the physiology of the puerperium in dairy cattle, with particular emphasis on uterine involution, immune clearance of postpartum contamination, endocrine regulation, and resumption of ovarian cyclicity. Further, it contrasts high-yielding Holsteins with fertility selected dairy populations. Normal uterine involution involves coordinated myometrial contraction, tissue remodelling, endometrial regeneration, and tightly regulated inflammatory responses. Failure of these processes predisposes cows to postpartum uterine disorders, including retained fetal membranes, metritis, endometritis (purulent vaginal discharge with cytological confirmation), and pyometra, which remain major contributors to subfertility and economic loss. Central to the pathophysiology of puerperal disease is negative energy balance, which disrupts immune competence, alters hepatic steroid metabolism, impairs ovarian signalling, and compromises oocyte and embryo quality. Emerging evidence highlights the complex interplay between metabolism, immunity, and the uterine microbiome, shifting current perspectives away from pathogen-centric models toward host resilience. Advances in biomarkers, genomic selection, and precision monitoring offer new opportunities for targeted reproductive management. Ultimately, optimisation of transition period management remains the cornerstone of supporting physiological puerperal recovery and sustaining reproductive efficiency in modern dairy systems.

Animals↗

PubMed2026

Epilepsy: Bridging Epidemiological Landscapes, Molecular Mechanisms, and Emerging Precision Therapeutics.

Epilepsy ranks among the most prevalent neurological disorders worldwide, and recent years have witnessed significant advancements in understanding its epidemiological features, pathophysiological mechanisms, diagnostic methodologies, and therapeutic approaches. This review systematically examines the epidemiology of epilepsy, highlighting pronounced regional and population-based disparities, particularly the substantial treatment gap observed in low-income countries. Regarding pathogenesis, epilepsy development involves aberrant ion channel function, neuroinflammatory processes, dysregulation of the mTOR signaling pathway, and genetic predispositions. Diagnostic innovations, including ultra-high field magnetic resonance imaging, artificial intelligence-enhanced electroencephalogram analysis, and liquid biopsy techniques, have markedly enhanced the precision of epileptogenic focus localization and etiological identification. Genetic investigations have uncovered numerous epilepsy-associated genes, thereby underpinning the advancement of targeted therapies. Therapeutically, novel antiepileptic drugs, neuromodulation modalities such as vagus nerve stimulation and deep brain stimulation, alongside gene therapy, have expanded treatment options for refractory epilepsy. Nonetheless, global epilepsy management continues to confront challenges including limited drug accessibility, social stigma, and pharmacoresistance. The future trajectory emphasizes individualized and precision medicine approaches, integrating genomics, biomarker discovery, and intelligent monitoring technologies to foster comprehensive improvements in epilepsy diagnosis and treatment.

epidemiology↗

PubMed2025

Genome-wide methylation biomarkers and biological aging in patients with bipolar disorder characterized for lithium response.

BACKGROUND: Epigenetic mechanisms might play a role in modulating susceptibility to bipolar disorder (BD) and response to lithium, the mainstay treatment for BD. Additionally, individuals with BD experience accelerated biological aging. METHODS: We compared blood DNA methylation profiles measured with EPIC v.2.0 arrays between patients with BD (33 lithium responders and 31 nonresponders) and nonpsychiatric controls (n = 32), as well as based on long-term lithium response. In addition, we compared cellular aging between these groups using epigenetic age, pace of aging, and, for the first time, transcriptional age acceleration based on bulk RNA sequencing in 93 patients and 56 controls. RESULTS: We identified 191 differentially methylated positions (DMPs) and 8 differentially methylated regions between patients with BD and controls, located in genes enriched for "Postsynaptic Density" (odds ratio = 6.81, p = 0.001). No DMP was significantly associated with lithium response after multiple testing correction. Patients showed a significantly higher biological age acceleration than controls based on two epigenetic clocks (GrimAge, Mann-Whitney U = 551, p = 0.0009; GrimAge2: U = 477, p = 9.0E-05) and pace of aging (DunedinPACE, t = 3.01, p = 0.003), but not on transcriptional age. While we observed no significant difference in epigenetic aging based on lithium response, lithium responders showed lower epigenetic acceleration using all clocks, with a trend observed using the PhenoAge clock (t = 1.97, p = 0.053). CONCLUSIONS: Our findings point to methylation patterns characterizing BD and support the hypothesis of accelerated cellular aging in BD.

Humans↗

PubMed2007

Ovarian cancer biomarkers: a focus on genomic and proteomic findings.

Among the gynaecological malignancies, ovarian cancer is one of the neoplastic forms with the poorest prognosis and with the bad overall and disease-free survival rates than other gynaecological cancers; several studies, analyzing clinical data and pathological features on ovarian cancers, have focused on the identification of both diagnostic and prognostic markers for applications in clinical practice. High-throughput technologies have accelerated the process of biomarker discovery, but their validity should be still demonstrated by extensive researches on sensibility and sensitivity of ovarian cancer novel biomarkers, determining whether gene profiling and proteomics could help differentiate between patients with metastatic ovarian cancer and primary ovarian carcinomas, and their potential impact on management.Therefore, considerable interest lies in identifying molecular prognostic biomarkers and protein indicators to guide treatment decisions and clinical follow up; the current state of knowledge about the potential clinical value of gene expression profiling in ovarian cancer is discussed, focusing on three main areas: distinguishing normal ovarian tissue from ovarian tumors, identifying different subtypes of ovarian cancer and identifying cancer likely to be responsive to therapy.In this elaborate we discuss the use of novel molecules, discovered by proteomics and genomics approaches, as potential protein biomarkers in the management of ovarian cancer, to improve the anticancer therapy for malignant ovarian tumors and to monitor the clinical follow up.

Ovarian cancer↗

PubMed2026

MET Exon 14 Skipping Mutation in NSCLC: From Genomic Discovery to Biomarker-Guided Therapeutic Innovation.

INTRODUCTION: Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, and the MET exon 14 skipping mutation is a key oncogenic driver, which promotes tumor progression and provides a new direction for precision therapy. METHODS: A systematic search of English-language literature and clinical trial data related to the MET exon 14 skipping mutation from 2020-2025 was performed to summarize the role of the mutation and therapeutic advances. RESULTS: DNA-based next-generation sequencing (NGS), RNA-based NGS, and RT-qPCR were employed as the main detection methods. Preclinical models confirmed that mutations promote tumor progression by activating the RAS/MAPK pathway. Clinical trials have reported objective remission rates (ORR) of 46-68% for first-line treatment with MET inhibitors in NSCLC patients harboring MET exon 14 skipping mutations. DISCUSSION: MET exon 14 skipping mutation as a therapeutic target for NSCLC has made significant progress, and MET inhibitors are more advantageous than chemotherapy and immunotherapy, and have been recommended by national and international guidelines as a first-line treatment option. Additionally, NGS technology has the potential to dynamically monitor tumor evolution and drugresistant mutations, thereby helping to realize precision medicine. CONCLUSION: The MET exon 14 skipping mutation is an important target for the precision treatment of NSCLC, and MET-TKIs have remarkable efficacy but a prominent problem with drug resistance. The construction of a precision medicine system encompassing diagnosis, treatment, and drug resistance management through multi-omics research, technological innovation, and international collaboration is a key direction for improving prognosis.

Humans↗

PubMed2025

Epstein-Barr Virus-Associated Gastric Cancer: A Histopathologic Study With Comprehensive Molecular Profiling.

A subset of gastric cancers (GCs) is linked to Epstein-Barr virus (EBV) infection. This study aims to characterize the histopathological and molecular features of EBV-associated GCs (EBVaGCs), focusing on predictive biomarkers and genomic and transcriptomic analysis. A total of 35 primary EBVaGCs were considered. The presence of EBV was confirmed with in situ hybridization. Immunohistochemical analyses for HER2, PD-L1, claudin 18.2, and mismatch repair proteins were performed. Genomic and transcriptomic profiles were assessed using AmoyDx Master Panel, which can identify single-nucleotide variants, InDels, and copy number variations on 571 hot genes, as well as microsatellite status, tumor molecular burden, and homologous recombination deficiency at the DNA level; however, at the RNA level, it identifies rearrangements/fusions in 45 genes and also quantifies the expression of 2396 cancer-related transcripts. The following histotypes were identified: carcinoma with lymphoid stroma (CLS; 69%), tubular (20%), and mixed (11%). Most cases were associated with atrophic gastritis (71%), and only 11% with dysplasia. The vast majority (94%) of EBVaGCs expressed EBV-encoded RNA in all tumor cells. Mismatch repair deficiency and HER2 overexpression were each observed in 6% of cases, whereas all tumors had a PD-L1-combined positive score ≥10. Sixty-six percent of cases showed moderate/strong claudin 18.2 expression in ≥75% of cancer cells. The most frequently altered genes were PIK3CA (41%) and ARID1A (17%). Transcriptomic analysis revealed substantial differential gene expression between EBVaGCs and EBV-negative controls, with upregulation of genes involved in antigen presentation, natural killer cell-mediated cytotoxicity, and cytokine-cytokine receptor interaction in EBVaGCs. Within EBVaGC, CLS showed higher expression of immune-related transcripts and higher PD-L1 expression than other histotypes. This study establishes EBVaGC as a distinct molecular class, with a distinctive profile of genomic alterations and expression of predictive biomarkers, and also with a unique immune microenvironment with enhanced cytotoxic activity. The findings highlight EBV's role in early tumor development and EBVaG-CLS as a distinct subgroup within EBVaGC, characterized by unique morphologic features and a pronounced immune activation profile.

Humans↗