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At least 19 recordsLinked to original sources

PubMed2026

cfMethDB: A Comprehensive cfDNA Methylation Data Resource for Cancer Biomarkers.

Cancer is a major global health threat, and early detection is crucial for improving patient outcomes. DNA methylation in circulating cell-free DNA (cfDNA) has emerged as a promising biomarker for non-invasive cancer diagnosis. However, the integration and utilization of existing cfDNA methylation data have been limited, hindering comprehensive research efforts, particularly in the discovery of cfDNA methylation biomarkers. To address this challenge, we introduced cfMethDB, a comprehensive database dedicated to cfDNA methylation in cancer that encompasses 4828 publicly available datasets. Through standardized analysis, we identified 1,048,770 differentially methylated cytosines (DMCs) as candidate biomarkers across seven cancer types. With cfMethDB, we not only identified known cfDNA methylation biomarkers, but also discovered several genes, such as ZIC4, that could be novel biomarkers. Moreover, cfMethDB offers a suite of user-friendly tools, including biomarker evaluation, pan-cancer search, and end motif analysis. We hope that cfMethDB will serve as a valuable platform for the discovery of novel cancer cfDNA methylation biomarkers and facilitate cancer research and clinical applications. cfMethDB is publicly available at https://cfmethdb.hzau.edu.cn/home.

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↗

PubMed2025

Real-world deployment of a fine-tuned pathology foundation model for lung cancer biomarker detection.

Artificial intelligence models using digital histopathology slides stained with hematoxylin and eosin offer promising, tissue-preserving diagnostic tools for patients with cancer. Despite their advantages, their clinical utility in real-world settings remains unproven. Assessing EGFR mutations in lung adenocarcinoma demands rapid, accurate and cost-effective tests that preserve tissue for genomic sequencing. PCR-based assays provide rapid results but with reduced accuracy compared with next-generation sequencing and require additional tissue. Computational biomarkers leveraging modern foundation models can address these limitations. Here we assembled a large international clinical dataset of digital lung adenocarcinoma slides (N = 8,461) to develop a computational EGFR biomarker. Our model fine-tunes an open-source foundation model, improving task-specific performance with out-of-center generalization and clinical-grade accuracy on primary and metastatic specimens (mean area under the curve: internal 0.847, external 0.870). To evaluate real-world clinical translation, we conducted a prospective silent trial of the biomarker on primary samples, achieving an area under the curve of 0.890. The artificial-intelligence-assisted workflow reduced the number of rapid molecular tests needed by up to 43% while maintaining the current clinical standard performance. Our retrospective and prospective analyses demonstrate the real-world clinical utility of a computational pathology biomarker.

Humans↗

PubMed2026

A Boveri perspective on cancer biomarker testing using artificial intelligence.

Artificial intelligence (AI) can predict genomic alterations from histology, yet its adoption is slowed by a lack of trust. We argue that deliberate morphology (i.e., a cognitive understanding of histological features supported by standardized annotations) creates a bidirectional feedback loop between clinical practice and model outputs.We translate these observations into an actionable hypothesis for clinical and computational teams: that by enhancing explainability, deliberate morphology could facilitate the responsible deployment of AI biomarkers in oncology.

Journal Article↗

PubMed2026

The future of blood-based biomarkers in liver cancer.

Liquid biomarkers hold substantial promise in liver cancer, with potential applications in risk stratification, surveillance and early detection, therapeutic decision-making, and treatment-response monitoring. In parallel with oncologic advances, liquid biopsy has gained increasing attention. However, despite expanded research efforts, prospective clinical validation remains limited. While cell-free DNA-based detection of actionable alterations has entered clinical practice in select contexts, most candidate liquid biomarkers still require rigorous evaluation through translational research embedded in clinical trials and prospective cohort studies. In this review, we summarise the current landscape of blood-based biomarkers across the cancer care continuum for individuals at risk, or diagnosed with hepatocellular carcinoma and biliary tract cancers, and discuss the key challenges and opportunities that lie ahead.

Humans↗

PubMed2026

Using cancer profiles to identify synthetic lethal therapeutic targets and predictive biomarkers in cancer gene dependency data.

MOTIVATION: Large scale loss-of-function screens utilising CRISPR or siRNA can provide profound insights into the importance of individual genes for the survival of a cancer cell and can drive the identification of therapeutic targets and biomarkers, and the development of targeted drugs. However, the analysis of these data and the substantial bodies of metadata that relate to them, is technically challenging and typically requires substantial expertise in data science and computer coding. RESULTS: To facilitate the analysis of cancer gene dependency data by cancer biologists and clinical scientists, we have developed DepMine-a computational toolkit providing a powerful system for framing complex queries relating cancer gene dependency to the underlying genetic changes that occur in cancer cells. DepMine identifies synthetic lethal relationships between putative target genes and complex 'cancer profiles' built from user-specified combinations of mutations, copy-number variation, and expression levels, and can refine these to optimal biomarker definitions for target dependency. AVAILABILITY: The Python implementation of DepMine and associated data files can be obtained at https://github.com/UOSbioinformaticslab/depmine and is free to academics and Not-For-Profit organisations. The DepMine release referenced in this paper is archived as DOI: 10.5281/zenodo.19570601.

Humans↗

PubMed2026

Proteomic Analysis of Extracellular Vesicles Reveals Vitronectin and Laminin Subunit Alpha-3 as Candidate Biomarkers for Gastric Cancer.

BACKGROUND/AIMS: Clinically useful noninvasive biomarkers for gastric cancer remain limited. Extracellular vesicles (EVs) carry a molecular cargo reflective of their cells of origin and have emerged as promising candidates for blood-based cancer biomarkers. We aimed to identify EV-associated protein biomarkers for gastric cancer via a proteomic approach. METHODS: Proteomic profiling of EVs was performed using one normal gastric cell line (Hs738st/int) and two gastric cancer cell lines (AGS and NCI-N87). Selected proteins were validated in blood-derived EVs isolated from plasma samples of 10 healthy controls and 36 patients with gastric cancer. RESULTS: Proteomic analysis identified 224 differentially expressed proteins whose expression was consistently altered in gastric cancer cell line-derived EVs. Among these, vitronectin (VTN) and laminin subunit alpha-3 (LAMA3) were selected based on their consistent upregulation. EV-associated LAMA3 levels were significantly higher in patients with gastric cancer than in healthy controls (p=0.003), with significant elevations observed from stage II onward (p=0.041, p=0.017, and p=0.004 for stages II, III, and IV, respectively). EV-associated VTN levels were not significantly different overall (p=0.089); however, stage-specific analysis demonstrated significant increases in VTN levels in patients with stage III (p=0.036) and stage IV (p=0.005) gastric cancer. Both EV-associated VTN and LAMA3 levels showed significant positive correlations with the cancer stage (ρ=0.564 and ρ=0.611, respectively; both p<0.001). CONCLUSIONS: The levels of EV-associated VTN and LAMA3 appear to be more closely associated with disease progression than with early-stage detection of gastric cancer. These findings suggest that EV-based proteomic biomarkers may have clinical utility for monitoring tumor progression in patients with clinically advanced gastric cancer.

Humans↗

PubMed2026

Predictive biomarkers in cancer immunotherapy for genitourinary malignancies.

Immunotherapy has transformed the management of genitourinary cancers, offering durable responses in selected patient groups. However, the clinical benefit of immune checkpoint inhibitors varies significantly across renal cell carcinoma, urothelial carcinoma, and prostate cancer, underscoring the need for reliable predictive biomarkers. This review summarizes current knowledge on established and emerging biomarkers, including PD L1 expression, tumor mutational burden, molecular subtypes, genomic alterations, tumor microenvironment characteristics, circulating biomarkers, microbiome influences, and multi omic integrative approaches. We discuss their potential clinical relevance, limitations, and applicability across different tumor types. Future directions emphasize the development of composite biomarkers, standardization of testing platforms, real time monitoring strategies, and the integration of advanced technologies such as artificial intelligence and spatial profiling. Understanding and validating these biomarkers will be essential for optimizing personalized immunotherapy in genitourinary cancers.

Circulating tumor DNA↗

PubMed2026

Antibody-drug conjugates against multidrug-resistant cancers: Biomarker-guided patient selection, payload engineering, linker chemistry, and bystander effects.

Antibody-drug conjugates (ADCs) are one of the most significant advancements in modern cancer therapeutics. Combining the target selectivity of monoclonal antibodies with the cytotoxic potential of payloads, ADCs effectively kill cancer cells and offer hope to patients with even refractory cancer types. Beyond simply increasing the number of therapeutic options available for cancer patients, ADCs have become a powerful frontline agent in overcoming multidrug resistance (MDR). As one of the most challenging obstacles to effective cancer care, MDR is mediated by ATP-binding cassette (ABC) transporter-mediated drug efflux, target-based mutations, and dysregulated apoptosis. The clinical success of ADCs specifically engineered to overcome MDR, including in heterogeneous tumors and cancer cells that exhibit bypass signaling, is well established. This is especially evident with trastuzumab deruxtecan (T-DXd) in HER2-low, HER2-positive, and HER2-mutant cancers; sacituzumab govitecan (SG) in TROP2-expressing triple-negative breast cancer (TNBC) and urothelial carcinoma; and enfortumab vedotin in Nectin-4-positive bladder cancer. By overcoming MDR, ADCs have enabled more effective treatment algorithms across multiple malignancies. Most importantly, the clinical application of ADCs has become inextricably linked to cancer genomics. HER2 testing has evolved from a two-tiered system to a continuous spectrum including HER2-ultralow, HER2-low, HER2-positive, and ERBB2-mutant categories. Each of these categories exhibits different eligibility guidelines for ADC patient selection. As cancer cells continue to evolve and develop resistance to even ADCs through mutations and variants, researchers and clinicians have used pharmacogenomics to predict ADC response and resistance. To define the genomic architecture of ADC-resistant tumor subpopulations, single-cell transcriptomic studies and liquid biopsy approaches are being used to enable real-time examination of the tumor genome during ADC therapy, thereby optimizing treatment and circumventing resistance driven by emerging mutations and variants. This review provides a comprehensive analysis of the molecular structure of ADCs, the pharmacological principles underlying their potent cytotoxic activity against MDR cancer cells, the genomic and transcriptomic biomarkers that guide ADC patient selection, and the emerging resistance mechanisms that will shape the next generation of promising ADC development.

Humans↗

PubMed2026

Hyperprogression Upon Cemiplimab Alone or With Short Course Chemotherapy in PD-L1 ≥ 50% Non-small Cell Lung Cancer: A Biomarker Guided Multicenter International Phase 2 Trial-HYPERBOLIC Study.

BACKGROUND: Immune checkpoint inhibitor (ICI) monotherapy is the standard first-line treatment for advanced non-small cell lung cancer (NSCLC) with PD-L1 ≥ 50%; however, up to 30% of patients experience early progression or death, including cases of hyperprogressive disease (HPD). High baseline levels (≥ 30.5%) of circulating CD10- low-density neutrophils (LDNs) have been associated with increased HPD occurrence. Emerging evidence suggests that combining ICI with platinum-based chemotherapy (PCT) may mitigate the risk of HPD. Currently, no prospective studies have addressed HPD prevention in this context. PATIENTS AND METHODS: HYPERBOLIC (NCT07274384) is a phase 2, randomized, open-label, multicenter, international trial evaluating whether adding 3 cycles of PCT to first-line cemiplimab reduces HPD rate in stage IV NSCLC with PD-L1 ≥ 50% and CD10- LDNs (identified by flow cytometry as CD15⁺CD11b⁺ within the PBMC fraction, with immature cells defined by loss of CD10) ≥ 30.5%. Seventy-four patients will be randomized (1:1 ratio) to receive cemiplimab alone or cemiplimab plus 3 PCT cycles, followed by cemiplimab maintenance. Randomization will be stratified by Lung Immune Prognostic Index. The first computed tomography scan at week 7 after treatment start will assess HPD occurrence, defined as RECIST v 1.1. disease progression with a delta tumor growth rate (ΔTGR) ≥ 50% and/or TGR ratio ≥ 2. The primary endpoint will be the combined rate of HPD and early death (death within 12 weeks with no radiological evaluation). Secondary endpoints will be HPD rate according to alternative definitions, overall survival, progression free survival, objective response rate, and safety. An extensive translational research platform will include spatial transcriptomics of tumor tissue, single-cell RNA sequencing of PBMCs, circulating-free DNA and plasma factors profiling, and saliva/stool microbiome genomics and metabolomics, to longitudinally explore tumor-host dynamic interactions during treatment. CONCLUSION: to our knowledge, HYPERBOLIC is the first prospective, biomarker-driven trial investigating early treatment escalation based on HPD risk in PD-L1-high NSCLC.

CD10↗

PubMed2025

UALCAN Mobile, an app for cancer proteogenomic data analysis.

Cancer is a complex disease affecting various organs and is a major cause of death worldwide. During cancer initiation, disease progression, and tumor metastasis, various genomic and proteomic alterations are observed. Recent technological advances have led to the generation of large amounts of molecular data, including genomics and transcriptomics. These large-scale datasets can be utilized to analyze and identify sub-class-specific cancer biomarkers and targets. However, there is a need for the development of user-friendly tools for large-scale data analysis, disseminating the analyzed data in a visualizable format to cancer researchers with no programming skills. We developed UALCAN, a comprehensive platform that allows users to integrate disparate data to better understand the genes, proteins, and pathways perturbed in cancer and make discoveries of potential biomarkers and targets. In the current study, we describe the development of the UALCAN Mobile application (app) that will provide cancer transcriptomic data obtained from The Cancer Genome Atlas (TCGA) project to evaluate protein-coding gene expression based on various stratifications, including stage, grade, race, gender, and molecular-subtypes across over 30 types of cancers. In addition, the UALCAN mobile provides data analysis options for epigenetic changes due to DNA promoter methylation and Clinical Proteomic Tumor Analysis Consortium (CPTAC) cancer proteomic data. The app provides access to large cancer molecular datasets on the go. To find changes in the expression of causative genes and proteins and to identify biomarkers and therapeutic targets, UALCAN mobile app will be extremely valuable. The "UALCAN Mobile" app is free to use and can be downloaded from both the iOS/Apple and the Android Play Store and has been downloaded over 100 times in each of iOS and android app stores.

app↗

PubMed2025

Molecular Landscape, Genomic Shift, and Prediction in the Neoadjuvant Setting of Human Epidermal Growth Factor Receptor 2-Positive Breast Cancer.

The amplification or overexpression of human epidermal growth factor receptor 2 (HER2) defines a breast cancer subtype, which benefits from neoadjuvant HER2-targeted therapy. However, at least 40% of patients respond poorly or do not respond to treatment. We analyzed the main genomic alterations of 64 HER2+ patients by next-generation sequencing to identify new predictors of response and correlate them with clinicopathological parameters. We also compared the genomic alterations between primary and residual tumors after neoadjuvant treatment. The TP53 gene was the most frequently mutated gene, and in combination with ERBB2 overexpression, the 2 were predictive of residual cancer burden (P = .001). Furthermore, the combination of their immunohistochemical counterpart (p53 mutant and score 3+ for HER2) can predict complete pathological response and the grade of response (P = .038 and P = .031, respectively). Therefore, p53 could be included in the initial panel of breast cancer biomarkers to help therapeutic decision-making in HER2+ cases.

Humans↗

PubMed2024

Pan-cancer analysis of biallelic inactivation in tumor suppressor genes identifies KEAP1 zygosity as a predictive biomarker in lung cancer.

The canonical model of tumor suppressor gene (TSG)-mediated oncogenesis posits that loss of both alleles is necessary for inactivation. Here, through allele-specific analysis of sequencing data from 48,179 cancer patients, we define the prevalence, selective pressure for, and functional consequences of biallelic inactivation across TSGs. TSGs largely assort into distinct classes associated with either pan-cancer (Class 1) or lineage-specific (Class 2) patterns of selection for biallelic loss, although some TSGs are predominantly monoallelically inactivated (Class 3/4). We demonstrate that selection for biallelic inactivation can be utilized to identify driver genes in non-canonical contexts, including among variants of unknown significance (VUSs) of several TSGs such as KEAP1. Genomic, functional, and clinical data collectively indicate that KEAP1 VUSs phenocopy established KEAP1 oncogenic alleles and that zygosity, rather than variant classification, is predictive of therapeutic response. TSG zygosity is therefore a fundamental determinant of disease etiology and therapeutic sensitivity.

Kelch-Like ECH-Associated Protein 1↗

PubMed2026

PRDX1 as a novel urinary biomarker for bladder cancer: Development of an integrated fiber optic sensing platform.

In this study, integrated proteomic and transcriptomic analyses identified peroxiredoxin 1 (PRDX1) as a novel urinary biomarker for bladder cancer (BC). PRDX1 was significantly upregulated in BC tissues and was associated with poorer overall survival. In vitro experiments further demonstrated that PRDX1 promotes malignant phenotypes of BC cells, including proliferation, migration, and invasion. Silencing PRDX1 in BC cells significantly reduced the invasiveness and proliferation ability.To address the clinical need for rapid and non-invasive detection, we developed an innovative optical fiber biosensor based on surface plasmon resonance (SPR) technology for the quantitative detection of urinary PRDX1. The biosensor exhibited excellent analytical performance, including high sensitivity (limit of detection: 0.06 ng/mL), a wide linear range (0-25 ng/mL), rapid response (∼14 s), as well as good stability and selectivity. In clinical validation involving 97 BC patients and 30 healthy controls, the biosensor demonstrated outstanding diagnostic performance, with an area under the receiver operating characteristic curve (AUC) of 0.91 and an overall diagnostic accuracy of 86.6%, outperforming conventional enzyme-linked immunosorbent assay (ELISA). Collectively, this study not only identifies PRDX1 as a promising biomarker for non-invasive diagnosis and prognostic evaluation of BC, but also establishes an efficient SPR-based optical fiber sensing platform, providing new insights into both clinical detection and the functional role of PRDX1 in BC progression.

Humans↗

PubMed2026

Identifying multigenic modules under selection in the tumor genome.

MOTIVATION: Genomic alterations in cancer arise from selective pressures acting on hallmark molecular modules, layered over a background of random mutagenic events. Methods to detect selection at the level of modules, as opposed to genes or nucleotides, are relatively underdeveloped. RESULTS: Here we present CanSRMaPP (Cancer Selection Recovery by Maximum Posterior Probability), a Bayesian model of the cancer genome that infers mutational selection on single genes and multi-genic modules while simultaneously modeling background events. Applying CanSRMaPP to lung adenocarcinoma genomes, we identify positive selection on 63 modules, yielding a model that parsimoniously explains the observed pattern of genetic alterations observed in new cancer cohorts. We further show that CanSRMaPP is adaptable to more tumor types and to alternative module definitions. We show that these modules serve as an effective scaffold for translating the cancer genome to molecular states, with prediction of cancer biomarker status as demonstration. AVAILABILITY: CanSRMaPP is freely available on GitHub. SUPPLEMENTARY INFORMATION: Supplementary Figs. S1-5, Supplementary Tables S1-5, and Supplementary Notes 1 and 2 are available at Bioinformatics online.

Journal Article↗

PubMed2026

Non-coding RNAs in cancer: multi-omics insights, liquid biopsy advances, drug resistance mechanisms, and the road to clinical translation.

For most of the twentieth century, the transcriptional output of the human genome was thought to be biologically inert-a characterization that has been proven wrong in almost every important respect. Non-coding RNAs (ncRNAs) such as microRNAs (miRNAs), long non-coding RNAs (lncRNAs), circular RNAs (circRNAs), small nucleolar RNAs (snoRNAs) and PIWI-interacting RNAs (piRNAs) are now thought of as vital regulators of gene expression in all the stages of cancer pathogenesis, including the initial epigenetic changes, metastatic spread and the development of therapeutic resistance. This review highlights four areas where the clinical potential of ncRNAs is most promising: reconstruction of ncRNA regulatory networks by multi-omics integration; circulating ncRNAs as minimally invasive cancer biomarkers; causal roles of ncRNAs in drug resistance through epithelial-mesenchymal plasticity, metabolic reprogramming, and stromal communication; and translation of ncRNA targeting strategies to clinical trials. We will need to invest equally in mechanistic rigor and translational infrastructure to move forward.

antisense oligonucleotides↗

PubMed2026

Development of a Computational Histology Artificial Intelligence-Powered Prognostic Biomarker in Colorectal Cancer in The Cancer Genome Atlas.

BACKGROUND: Risk stratification in colorectal cancer (CRC) plays an important role in treatment decision-making. As such, prognostic biomarkers that can augment risk stratification have clinical value. Quantitative histologic features from routine hematoxylin and eosin (H&E)-stained whole slide images (WSIs) provide a novel avenue for biomarker discovery. In this study, we explored the potential for a computational histology artificial intelligence (CHAI) platform to develop and validate a prognostic biomarker in CRC. METHODS: The Cancer Genome Atlas Colorectal Adenocarcinoma project was utilized for this study, with inclusion of all subjects (stage I-IV) with available digitized H&E specimens. The cohort was split into development and validation cohorts by a stratified random split. The previously developed CHAI platform was applied in the development cohort to construct a continuous risk score from histologic features associated with progression-free interval (PFI) that was dichotomized based on an optimized cutpoint for distinguishing PFI into a high risk CHAI (+) and lower risk CHAI (-). PFI was compared between CHAI (+) and CHAI (-) patients in the validation cohort in multivariable Cox proportional hazards models. Time-dependent area under the curve (tdAUC) and C-indices were also calculated for PFI. RESULTS: A total of 583 participants were included in the study, with 409 assigned to the validation cohort. The CHAI biomarker classified 229 participants (56%) as CHAI (+) and 180 (44%) as CHAI (-) in the validation set. CHAI (+) participants had worse PFI in a multivariable analysis adjusting for available clinicopathologic variables (hazard ratio (HR) = 2.65; 95% confidence interval (CI), 1.63-4.30). TdAUC for the CHAI biomarker was 0.60 (95% CI, 0.53-0.67) at 12 months, 0.62 (0.55-0.69) at 36 months, and 0.67 (0.55-0.79) at 60 months; the C-index was 0.62 (95% CI, 0.58-0.67). CONCLUSIONS: The CHAI platform was used to develop a prognostic digital pathology biomarker in CRC. This demonstrates the feasibility and potential to apply this artificial intelligence-based digital pathology biomarker platform for risk stratification in CRC and supports its further study.

Artificial intelligence↗

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↗