SEARCH · PubMed Health

Results for “METABRIC”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

13 recordsLinked to original sources

PubMed2026

Tertiary lymphoid structure transcriptomic signatures show limited and cohort-dependent value for predicting axillary nodal involvement in oestrogen receptor-positive luminal breast cancer.

Tertiary lymphoid structures (TLS) are associated with prognosis in solid tumours. Their value for predicting axillary nodal involvement in oestrogen receptor-positive luminal breast cancer remains uncertain. Three published TLS signatures were scored by single-sample gene set enrichment analysis in oestrogen receptor-positive luminal tumours. The Cancer Genome Atlas Breast Invasive Carcinoma cohort (TCGA-BRCA) included 632 cases, of which 379 met strict consensus. METABRIC included 1086 cases, of which 663 met strict consensus. Logistic models adjusted for age and pathological tumour stage. Strict consensus, majority vote, and continuous scores were compared. Performance assessment included bootstrapped changes in area under the receiver-operating-characteristic curve, Brier scores, calibration, and decision-curve analysis. Survival was evaluated in METABRIC and explored in TCGA-BRCA. Strict-consensus TLS status was not associated with nodal positivity in TCGA-BRCA (adjusted odds ratio: 0.95, 95% confidence interval: 0.62-1.45, P = 0.822). METABRIC was similar (odds ratio: 0.76, 95% confidence interval: 0.55-1.06, P = 0.105). Full-cohort METABRIC analyses detected small majority-vote and continuous-score associations, absent in TCGA-BRCA. Across specifications, bootstrapped changes in area under the receiver-operating-characteristic curve ranged from 0.0002 to 0.0089, with minimal Brier-score improvement and no stable decision-curve benefit. In METABRIC, the univariable overall survival association attenuated after age adjustment (hazard ratio: 1.33-1.10). TCGA-BRCA survival analyses were nonsignificant. TLS transcriptomic signals showed small, cohort-dependent associations with nodal status but no reproducible or clinically meaningful incremental predictive value. These data do not support replacing sentinel lymph node biopsy with a TLS signature in oestrogen receptor-positive luminal breast cancer.

breast cancer↗

PubMed2026

PGR expression as a pharmacogenomic companion biomarker to GENE70-derived genomic risk in ER-positive/HER2-negative breast cancer.

BACKGROUND: The biology of the estrogen receptor-positive (ER+) and human epidermal growth factor receptor 2-negative (HER2-) breast cancers is heterogeneous even when they are categorized by their risk via genomics. Transcriptomic PGR expression reflects endocrine pathway activity and may provide complementary biological information within established GENE70-derived genomic-risk categories. Whether this molecular marker improves the biological interpretation of genomic-risk stratification beyond conventional clinicopathological assessment remains uncertain. OBJECTIVES: The aim of this study was to determine whether transcriptomic PGR expression provides complementary biological and prognostic information within reconstructed GENE70-derived genomic-risk categories and refines the characterization of endocrine-related tumour biology in ER-positive/HER2-negative breast cancer. METHODS: This study analysed publicly available transcriptomic and clinical data from three cohorts: METABRIC (discovery cohort), GSE96058/SCAN-B cohort (validation cohort) and TCGA-BRCA cohort (molecular validation cohort). The GENE70-derived genomic-risk score was reconstructed for each cohort using matched genes. Cox regression, Kaplan-Meier analysis and subgroup comparisons were used to assess relationships between PGR expression, clinicopathologic variables, molecular features and survival outcomes. RESULTS: Across the three independent cohorts, low transcriptomic PGR expression was consistently associated with higher GENE70-derived genomic risk, increased MKI67 expression, reduced ESR1 expression and enrichment of the Luminal B subtype. Survival findings differed between cohorts. In the discovery METABRIC cohort, transcriptomic PGR expression showed heterogeneous associations with survival, particularly within GENE70-derived high-risk subgroups, whereas the external GSE96058/SCAN-B validation cohort demonstrated consistent associations between low PGR expression and poorer overall survival in both the overall ER-positive/HER2-negative population and GENE70-derived high-risk subgroups. CONCLUSION: These findings suggest that transcriptomic PGR provides complementary biological and prognostic information within GENE70-derived genomic-risk categories. However, because treatment response was not evaluated in the present study, the findings should not be interpreted as evidence of predictive or pharmacogenomic utility and prospective studies incorporating treatment-response analyses are required before such applications can be established.

Humans↗

PubMed2026

Unveiling novel transcriptomic prognostic biomarkers for specific breast cancer subtypes and treatment regimens.

BACKGROUND: Breast cancer (BRCA) is the most common cancer in women worldwide, yet current gene expression panels offer limited insight into treatment responses across different subtypes and therapies. This study aimed to identify reliable biomarkers for predicting treatment outcomes in specific BRCA subtypes and treatment regimens. METHODS: This study analyzed transcriptomic data from The Cancer Genome Atlas to identify differentially expressed genes (DEGs) in patient groups treated with different combinations of hormone therapy (H), chemotherapy (C), radiotherapy (R), and targeted therapy (T). Non-negative matrix factorization clustering was performed to stratify patients into clusters representing different BRCA subtypes. Functional enrichment analysis was performed, and survival assessments were conducted using the METABRIC dataset. RESULTS: A total of 1,148 DEGs were identified across treatment regimens, with 75 common DEGs shared across multiple regimens. Among these, 12 candidate biomarkers were associated with luminal subtypes treated with H, including LRP1B, of which high expression predicted cancer recurrence. In triple-negative breast cancer (TNBC) treated with C, 76 candidate biomarkers were identified, including TTYH1 for recurrence and ANXA8L1 and MPZ for non-recurrence. Functional analyses identified intermediate filament organization and keratinization as pathways associated with specific candidate biomarkers of TNBC following C. Survival analysis using METABRIC strengthened the prognostic ability of LRP1B and TTYH1 to predict worse survival and ANXA8L1 and MPZ to predict prolonged survival, with four additional prognostic biomarkers. CONCLUSION: This study identified gene expression prognostic biomarkers for luminal and TNBC subtypes, thereby supporting personalized therapies. Further experimental validation is required to confirm these findings for clinical application. CLINICAL TRIAL REGISTRY: No.

Breast cancer↗

PubMed2026

Integrin α3 (ITGA3) expression across breast cancer subtypes: Prognosis and therapeutic relevance.

BACKGROUND: Integrin α3 (ITGA3), which heterodimerizes with integrin β1, has emerged as a potential biomarker and therapeutic target in several epithelial malignancies; however, its clinical relevance in breast cancer remains incompletely characterized. This study evaluated ITGA3 expression across breast cancer molecular subtypes and assessed its prognostic and predictive significance. METHODS: Immunohistochemistry (IHC) was performed on archival breast cancer specimens using tissue microarrays (n = 148) and whole-tissue sections (n = 21). Complete clinicopathologic and outcome data were available for 108 patients, including hormone receptor-positive/human epidermal growth factor receptor 2-negative, HER2-positive, and triple-negative breast cancer (TNBC) subtypes. ITGA3 expression was quantified using H-scores and correlated with clinicopathologic features and survival outcomes. Independent transcriptomic analyses were conducted using the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and the Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) cohorts to evaluate ITGA3 mRNA expression, co-expressed signaling pathways, and associations with therapeutic response. RESULTS: ITGA3 protein expression was detected in 85.2% of breast cancer specimens and was significantly higher in HR-positive/HER2-negative and HER2-positive tumors compared with TNBC (p < 0.0050). High ITGA3 expression was associated with shorter recurrence-free survival (p < 0.0001). In the METABRIC cohort, tumors with ITGA3 alterations demonstrated significantly worse relapse-free survival (p < 0.0001) and overall survival (p < 0.0500). Transcriptomic analyses revealed that ITGA3 co-expressed with estrogen receptor 1(ESR1), erb-b2 receptor tyrosine kinase 2 (ERBB2), and luminal markers, along with enrichment of estrogen receptor and phosphoinositide 3-kinase-protein kinase B-mechanistic target of rapamycin (PI3K/AKT/mTOR) signaling pathways. ITGA3 expression was not predictive of response to tamoxifen or trastuzumab. CONCLUSION: Elevated ITGA3 expression is associated with breast cancer recurrence and poor clinical outcomes, supporting its potential role as a prognostic biomarker and candidate therapeutic target.

Biomarkers↗

PubMed2025

PKCζ, CTNNBIP1 and ALDH1A3 Expression in Luminal B Breast Cancer Indicates Decreased Hormone Therapy Effectiveness.

BACKGROUND/AIM: The role of catenin β interacting protein 1 (CTNNBIP1), a negative regulator of the canonical Wnt/β-catenin signaling pathway, in luminal A and B breast cancer stem cells treated with hormone therapy is unknown. This study investigated the relationship between CTNNBIP1 and aldehyde dehydrogenase 1 family member A3 (ALDH1A3) expression and its impact on disease-specific survival in luminal A and B breast cancer. Given that high protein kinase ζ (PKCζ) expression, together with elevated CTNNBIP1 or ALDH1A3, is linked to poor prognosis in luminal B tumors, we also examined their combined influence. MATERIALS AND METHODS: Gene expression and clinical data from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC; n=2,509) were analyzed using Kaplan-Meier and Cox proportional hazards models. Findings were validated with The Cancer Genome Atlas Pan-Cancer Atlas (TCGA; n=1,084). RESULTS: CTNNBIP1 high ALDH1A3 high indicated a poor prognosis in patients with luminal B breast cancer treated with hormone therapy in the METABRIC dataset and aromatase inhibitors as hormone therapy in the TCGA data set, suggesting that high CTNNBIP1 and ALDH1A3 expression contributed to decreased effectiveness of hormone therapy in patients with luminal B breast cancer. PKC ζ high CTNNBIP1 high ALDH1A3 high was associated with a poor prognosis in patients with luminal B breast cancer treated with hormone therapy and aromatase inhibitors, suggesting that high PKC ζ , CTNNBIP1 and ALDH1A3 expression contributed to decreased effectiveness of hormone therapy in patients with luminal B breast cancer. CONCLUSION: PKC ζ and CTNNBIP1 may be involved in the progression of ALDH1A3-positive luminal B breast cancer. In luminal B breast cancer, PKC ζ , CTNNBIP1 and ALDH1A3 could serve as molecular drug targets and prognostic biomarkers to predict the effectiveness of hormone therapy.

ALDH1A3↗

PubMed2026

Integrated multi-omics analysis and functional experiments reveals PPAP2C as a potential prognostic biomarker and therapeutic target in breast cancer.

BACKGROUND: This study aims to systematically elucidate the clinical significance and biological function of the phospholipid phosphatase (PLPP) family member (PPAP2C) phosphatidic acid phosphatase type 2C in breast cancer, and to evaluate its potential as a prognostic biomarker and therapeutic target. METHODS: Gene expression data from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Cancer Cell Line Encyclopedia (CCLE) databases were integrated to characterize the expression profile of PLPP family members, focusing on PPAP2C in breast cancer. The prognostic value of PPAP2C, initially identified at the mRNA level (TCGA, (METABRIC) Molecular Taxonomy of Breast Cancer International Consortium, Gene Expression Omnibus (GEO)), was confirmed at the protein level by immunohistochemistry (IHC) on tissue microarrays (TMA). The oncogenic functions of PPAP2C were investigated in triple-negative breast cancer (TNBC) cells through CRISPR-Cas9-mediated knockout and ectopic overexpression, with assessment of key phenotypes including proliferation, colony formation, migration, and invasion. In vivo validation was subsequently performed using an MDA-MB-231 xenograft model. RESULTS: PPAP2C exhibits the most significant overexpression pattern across 33 cancer types (upregulated in 16 cancers, downregulated in only 3). Compared with normal tissues, PPAP2C showed specific overexpression in breast cancer tissues and was significantly associated with advanced clinical stages and aggressive subtypes (HER2+ and TNBC). Survival analysis demonstrated that high PPAP2C expression correlated with significantly shorter overall survival and disease-free survival, which was further validated in METABRIC and GEO cohorts. Tissue microarray analysis confirmed higher PPAP2C protein positivity in tumor tissues (94.7%) than in adjacent normal tissues (59.7%), with worse OS and RFS in high-expression groups. Multivariate analysis identified PPAP2C as an independent prognostic factor for OS. Functional experiments revealed that PPAP2C knockout (via 5-bp/1-bp frameshift mutations) suppressed TNBC cell proliferation, colony formation, migration, and invasion, while overexpression enhanced these phenotypes. In vivo studies further demonstrated complete tumor regression in MDA-MB-231 xenografts upon PPAP2C knockout. CONCLUSION: This study identifies PPAP2C as a key oncogenic driver and a robust independent prognostic biomarker in breast cancer. The findings provide compelling evidence that PPAP2C represents a promising therapeutic target, offering a new strategic avenue for precision therapy, particularly for aggressive breast cancer subtypes.

PLPP2↗

PubMed2026

Age-Associated Four-Gene Prognostic Signature in Breast Cancer.

BACKGROUND: Young-onset breast cancer is associated with inferior disease-free survival (DFS), but the contribution of additional molecular heterogeneity remains unclear. AIMS: To identify an exploratory age-associated gene expression signature linked to recurrence-related outcomes and evaluate its prognostic association. METHODS AND RESULTS: We analyzed clinicopathological and RNA-sequencing data from 821 patients with Stages I-III invasive ductal or lobular carcinoma in The Cancer Genome Atlas, including 142 patients aged ≤ 45 years. Genes associated with both age and DFS were screened, followed by LASSO-Cox and stepwise multivariable Cox regression. A four-gene signature (Sig4: C4orf14 [NOA1], LINC01124, ZNF704, and AGFG2) was identified. Young patients had significantly worse DFS than older patients, whereas overall and disease-specific survival did not differ significantly. After adjustment for clinicopathological factors, young age remained associated with worse DFS. Following inclusion of the continuous Sig4 score, the age association was attenuated and no longer statistically significant, while Sig4 remained independently associated with worse DFS. Sig4-high tumors were enriched for proliferation, cell-cycle, DNA-repair, metabolic, and stress-response pathways. In METABRIC, the fixed TCGA-derived Sig4 score was associated with worse relapse-free survival in the overall cohort but not in patients aged ≤ 45 years. CONCLUSION: Sig4 is an exploratory age-associated four-gene signature with potential general prognostic relevance in breast cancer. Its utility for risk stratification specifically in young-onset breast cancer was not externally validated and requires confirmation in independent prospective cohorts enriched for young patients.

Humans↗

PubMed2026

Intratumoral collagen correlates with histological grade and patient prognosis in breast cancer.

Histological grading, using the Nottingham Grading System (NGS), is a major prognostic indicator for breast cancer. NGS involves the scoring of cancer cell-related morphological features, yet it overlooks tumor microenvironment (TME) components such as collagen. Collagen proteins, integral to the extracellular matrix (ECM), influence tumor architecture and progression but their relationship with histological grade is not fully characterized. Here, we assessed intratumoral collagen deposition using Masson Trichrome staining of whole slides (n = 166), proteomic profiling (n = 2) and transcriptomic analyses of the METABRIC (n = 1827) and TCGA-BRCA (n = 753) cohorts. We showed that low-grade tumors display significantly higher intratumoral collagen deposition compared to high-grade tumors. Moreover, we demonstrated that collagen expression at the transcript and protein levels (Masson Trichrome) could discriminate Grade II carcinomas into distinct prognostic groups, in which patients with Grade II carcinomas with elevated levels of collagen expression were associated with lower pTNM stage and better survival outcomes. Our results support the inclusion of TME features, such as collagen deposition, to enhance prognostic accuracy in breast cancer.

Humans↗

PubMed2025

Epigenetic regulation of HOXA2 expression affects tumor progression and predicts breast cancer patient survival.

Accumulating evidence suggests that genetic and epigenetic biomarkers hold potential for enhancing the early detection and monitoring of breast cancer (BC). Epigenetic alterations of the Homeobox A2 (HOXA2) gene have recently garnered significant attention in the clinical management of various malignancies. However, the precise role of HOXA2 in breast tumorigenesis has remained elusive. To address this point, we conducted high-throughput RNA sequencing and DNA methylation array studies on laser-microdissected human BC samples, paired with normal tissue samples. Additionally, we performed comprehensive in silico analyses using large public datasets: TCGA and METABRIC. The diagnostic performance of HOXA2 was calculated by means of receiver operator characteristic curves. Its prognostic significance was assessed through immunohistochemical studies and Kaplan-Meier Plotter database interrogation. Moreover, we explored the function of HOXA2 and its role in breast carcinogenesis through in silico, in vitro, and in vivo investigations. Our work revealed significant hypermethylation and downregulation of HOXA2 in human BC tissues. Low HOXA2 expression correlated with increased BC aggressiveness and unfavorable patient survival outcomes. Suppression of HOXA2 expression significantly heightened cell proliferation, migration, and invasion in BC cells, and promoted tumor growth in mice. Conversely, transgenic HOXA2 overexpression suppressed these cellular processes and promoted apoptosis of cancer cells. Interestingly, a strategy of pharmacological demethylation successfully restored HOXA2 expression in malignant cells, reducing their neoplastic characteristics. Bioinformatics analyses, corroborated by in vitro experimentations, unveiled a novel implication of HOXA2 in the lipid metabolism of BC. Specifically, depletion of HOXA2 leaded to a concomitantly decreased expression of PPARγ and its target CIDEC, a master regulator of lipid droplet (LD) accumulation, thereby resulting in reduced LD abundance in BC cells. In summary, our study identifies HOXA2 as a novel prognosis-relevant tumor suppressor in the mammary gland.

Humans↗

PubMed2026

TFAP2B and FOXC1 are associated with biologically and clinically distinct differentiation states in triple-negative breast cancer.

Triple-negative breast cancer (TNBC) comprises biologically distinct subtypes, including luminal androgen receptor (LAR) tumors, but routine immunohistochemical markers incompletely capture lineage-associated differentiation states. We investigated TFAP2B, an AP-2 family transcription factor linked to epithelial differentiation, and FOXC1, a basal-like-associated transcription factor, as complementary markers of luminal and basal differentiation in TNBC. In a tissue microarray of 105 TNBCs, tumors were stratified as TFAP2B-dominant, FOXC1-dominant, or double-negative according to relative marker predominance and characterized using lineage, proliferation, molecular, immune, and chemotherapy-response parameters. TFAP2B-dominant tumors were associated with a coherent luminal phenotype, including apocrine/lobular enrichment, strong MUCL1 and AR expression, high CK18, low SOX10, reduced proliferation, frequent wild-type p53 patterns and retained RB1 expression, and low PD-L1 expression. FOXC1-dominant tumors showed contrasting basal-like features. These associations were independently supported at the transcriptional and genomic levels in the METABRIC TNBC cohort. FOXC1-dominant tumors also showed more frequent chemotherapy-induced downstaging than TFAP2B-dominant tumors. Together, we identify TFAP2B as a marker associated with luminal differentiation in TNBC and support combined TFAP2B/FOXC1 assessment as a practical framework for identifying clinically relevant TNBC differentiation states.

Humans↗

PubMed2025

Transcriptome-wide analysis reveals potential roles of CFD and ANGPTL4 in fibroblasts regulating B cell lineage for extracellular matrix-driven clustering and novel avenues for immunotherapy in breast cancer.

BACKGROUND: The remodeling of the extracellular matrix (ECM) plays a pivotal role in tumor progression and drug resistance. However, the compositional patterns of ECM in breast cancer and their underlying biological functions remain elusive. METHODS: Transcriptome and genome data of breast cancer patients from TCGA database was downloaded. Patients were classified into different clusters by using non-negative matrix factorization (NMF) based on signatures of ECM components and regulators. Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify core genes related to ECM clusters. Additional 10 independent public cohorts including Metabric, SCAN_B, GSE12276, GSE16446, GSE19615, GSE20685, GSE21653, GSE58644, GSE58812, and GSE88770 were collected to construct Training or Testing cohort, following machine learning calculating ECM correlated index (ECI) for survival analysis. Pathway enrichment and correlation analysis were used to explore the relationship among ECM clusters, ECI and TME. Single-cell transcriptome data from GSE161529 was processed for uncovering the differences among ECM clusters. RESULTS: Using NMF, we identified three ECM clusters in the TCGA database: C1 (Neuron), C2 (ECM), and C3 (Immune). Subsequently, WGCNA was employed to pinpoint cluster-specific genes and develop a prognostic model. This model demonstrated robust predictive power for breast cancer patient survival in both the Training cohort (n = 5,392, AUC = 0.861) and the Testing cohort (n = 1,344, AUC = 0.711). Upon analyzing the tumor microenvironment (TME), we discovered that fibroblasts and B cell lineage were the core cell types associated with the ECM cluster phenotypes. Single-cell RNA sequencing data further revealed that angiopoietin like 4 (ANGPTL4)+ fibroblasts were specifically linked to the C2 phenotype, while complement factor D (CFD)+ fibroblasts characterized the other ECM clusters. CellChat analysis indicated that ANGPTL4+ and CFD+ fibroblasts regulate B cell lineage via distinct signaling pathways. Additionally, analysis using the Kaplan-Meier Plotter website showed that CFD was favorable for immunotherapy response, whereas ANGPTL4 negatively impacted the outcomes of cancer patients receiving immunotherapy. CONCLUSION: We identified distinct ECM clusters in breast cancer patients, irrespective of molecular subtypes. Additionally, we constructed an effective prognostic model based on these ECM clusters and recognized ANGPTL4+ and CFD+ fibroblasts as potential biomarkers for immunotherapy in breast cancer.

Humans↗

PubMed2025

High p62 and ALDH1A3 Reduce the Effectiveness of Endocrine Therapy in Luminal B Breast Cancer.

BACKGROUND/AIM: High expression of p62 and ALDH1A3 indicates a poor clinical outcome in luminal B breast cancer, and p62 is involved in the progression of ALDH1-positive luminal B breast cancer stem cells. However, the association between endocrine therapy and high p62 and ALDH1A3 expression, in luminal B breast cancer remains unclear. MATERIALS AND METHODS: Two datasets with gene expression and clinical data for patients with primary breast cancer (METABRIC, n=2,509; The Cancer Genome Atlas, n=1,084) were downloaded and statistically analyzed. To evaluate the association between the p62 and ALDH1A3 expression levels and endocrine therapy, including tamoxifen and aromatase inhibitor, in patients with luminal B breast cancer, disease-specific survival was examined using Kaplan-Meier and multivariate Cox regression analyses. RESULTS: Patients with p62 high ALDH1A3 high luminal B breast cancer treated with endocrine therapy exhibited a poor prognosis. Moreover, patients with p62 high ALDH1A3 high luminal B breast cancer treated with tamoxifen showed a trend towards a poor prognosis, but those treated with aromatase inhibitors showed a significantly poor prognosis. These results suggest that endocrine therapy, especially aromatase inhibitors, exhibits a reduced effectiveness against p62 high ALDH1A3 high luminal B tumors. CONCLUSION: p62 and ALDH1A3 could be used together as a prognostic biomarker for predicting the efficacy of endocrine therapy for luminal B breast cancer.

ALDH1A3↗

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↗