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PubMed2026

Development and internal validation of a six-gene prognostic model based on galactose metabolism for overall survival in lung adenocarcinoma.

BACKGROUND: Lung cancer remains a leading cause of cancer incidence and mortality globally. Metabolic reprogramming promotes tumor progression and shapes an immunosuppressive tumor microenvironment. Galactose metabolism is involved in multiple malignancies, but its prognostic value in lung adenocarcinoma (LUAD) remains unclear. This study aimed to develop and internally validate a galactose metabolism-related multigene prognostic model for LUAD. METHODS: A retrospective prognostic model development and internal validation study was performed using RNA sequencing (RNA-seq) and clinical data from 585 LUAD patients in The Cancer Genome Atlas (TCGA). Differential expression, functional enrichment, univariate and multivariate Cox regression were applied to construct a prognostic gene signature. Internal validation was performed using bootstrap resampling. Model performance was evaluated by time-dependent receiver operating characteristic (ROC), C-index, calibration, and Kaplan-Meier analysis. Associations between the model and immune infiltration, immunotherapy responsiveness, and tumor stemness were also analyzed. RESULTS: A six-gene prognostic model (GALT, GANC, PGM1, GALM, B4GALT1, PGM2) was developed. The model showed good discrimination with 1-, 3-, and 5-year area under the curve (AUC) values of 0.719, 0.693, and 0.684, respectively. The low-risk group exhibited significantly longer survival, increased antitumor immune infiltration (CD8+ T cells, M1 macrophages, activated CD4+ memory T cells), higher expression of T cell proliferation-related genes, lower immune checkpoint expression, better predicted immunotherapy response, and lower tumor stemness compared with the high-risk group. CONCLUSIONS: We developed and internally validated a six-gene prognostic model for LUAD based on galactose metabolism. The model shows moderate prognostic performance and is associated with antitumor immunity and tumor stemness. It may be used for prognostic risk stratification and to guide personalized immunotherapy in LUAD.

Galactose metabolism↗

PubMed2025

Identification of Prognostic Gene Signatures for Survival of Patients With Phaeochromocytoma, Paraganglioma, and Other Tumor Types.

BACKGROUND/AIM: Tumor treatments remain unsatisfactory, as many patients continue to die despite therapy. There is an urgent need for novel drug targets, particularly for rare tumors. In this study, we sought to identify genes with prognostic significance for survival in patients with phaeochromocytoma or paraganglioma. We also examined whether these genes are relevant in other tumor entities. PATIENTS AND METHODS: We mined the TCGA-based KM Plotter and studied 186 risk genes for phaeochromocytoma and paraganglioma. RESULTS: Using Kaplan-Meier statistics, we performed 3,163 calculations based on 7,489 tumor biopsies and identified a 2-gene signature for phaeochromocytoma/paraganglioma (AQP4, FAM84H). Since the 186 risk genes are not exclusively related to the development of phaeochromocytoma/paraganglioma alone, we also investigated their prognostic relevance in 17 other tumor types. A clustered 12-gene signature has been found common in four other tumor entities (liver hepatocellular carcinoma, renal clear cell carcinoma, renal papillary cell carcinoma, lung adenocarcinoma). This signature consisted of BUB1, BUB1B, CDK1, CENPA, CKAP2L, IQGAP3, MKI67, NDC80, PBK, RRM2, TOP2A, and TTK. CONCLUSION: Our analysis provides a basis for the development of a novel prognostic test to predict the survival time of patients.

Kaplan-Meier analysis↗

PubMed2025

Identification and validation of prognostic genes associated with mitochondrial nuclear genes in gastric cancer.

Mitochondrial-related nuclear genes (MNGs) have shown great importance in cancer diagnosis and prognosis, but their role in gastric cancer (GC) remains unclear. GC-related transcriptome data from the gene expression omnibus and cancer genome atlas databases were analyzed to identify differentially expressed MNGs. A prognostic risk model was constructed through univariate Cox and least absolute shrinkage and selection operator regression, validated by Kaplan-Meier (K-M) survival curve and receiver operating characteristic curve. This was followed by immune infiltration analysis, independent prognostic analysis, functional enrichment analysis, drug sensitivity analysis, drug prediction, molecular docking and construction of regulatory networks. Three prognostic genes (ATP8A2, COX15 and TARS2) were identified. The expression of TARS2 and COX15 was positively correlated with CNV, while ATP8A2 was unaffected. The risk model and nomogram, integrating risk score and clinicopathological factors, exhibited excellent predictive performance. A significant correlation was observed between prognostic genes and differential immune cells, such as T cells, B cells, and NK cells. BMS-754807, Gefitinib, JQ1, Lapatinib, and Sapitinib exhibited significant differences in sensitivity between the high-risk group and the low-risk group. The results of molecular docking showed TP8A2 has stable binding ability with cytosine, COX15 with indomethacin, and TARS2 with bisacodyl. RT-qPCR revealed downregulation of ATP8A2 and upregulation of COX15 and TARS2 in GC samples. MNGs, including ATP8A2, COX15, and TARS2, demonstrated significant associations with immune infiltration, CNV, and prognostic outcomes of GC.

Humans↗

PubMed2025

Exploring prognostic genes in the immune microenvironment of acute myeloid leukemia via weighted gene co-expression network analysis.

BACKGROUND: Acute myeloid leukemia (AML) is a heterogeneous blood cancer that arises from transformed myeloid precursor cells in a compromised bone marrow microenvironment. This environment is essential for AML initiation, progression, and relapse. Alongside oncogenic changes in hematopoietic cells, immunological dysregulation also contributes to leukemogenesis. The present study is aimed to identify prognostic genes in stromal and immune cells associated with AML using the weighted gene co-expression network analysis (WGCNA). METHODS: Gene expression profiles were retrieved from The Cancer Genome Atlas database, and immune and stromal cell scores were calculated using the ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data) method. These scores helped identify differentially expressed genes (DEGs), which were then used to create gene clusters through WGCNA. To explore the functions of genes linked to AML subtypes, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed. A protein-protein interaction network was developed to identify hub genes. The top 18 hub genes were identified using the cytoHubba plug-in in Cytoscape software, and survival analysis was conducted with the Gene Expression Profiling Interactive Analysis 2 online tool. RESULTS: A total of 1097 DEGs were identified, with 601 being upregulated and 496 downregulated. WGCNA analysis indicated that the gray module, comprising 165 genes, had the strongest association with AML subtypes (Cor > 0.3; P < .05). Gene Ontology enrichment analysis demonstrated that the 18 identified hub genes were predominantly associated with neutrophil activation, immune response, secretory granule membrane, and pattern recognition receptor activity. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis revealed that the DEGs were mainly involved in pathways related to phagosome, lysosome, tuberculosis, leishmaniasis, and neutrophil extracellular trap formation. Kaplan-Meier survival analysis of the top 18 hub genes indicated that ITGAM, IL10, and CD163 were significantly correlated with survival outcomes in AML. CONCLUSION: Key stromal and immune-related genes influencing AML patient outcomes were identified, highlighting their potential as therapeutic targets. These discoveries provide deeper insights into the molecular mechanisms driving AML pathogenesis and subtype differentiation.

Leukemia, Myeloid, Acute↗

PubMed2026

Exploration and experimental verification of triaptosis-related prognostic genes and cells in gastric cancer.

BACKGROUND: Triaptosis is a recently characterized form of programmed cell death with unclear implications in cancer. This study aimed to investigate the prognostic significance and biological relevance of triaptosis in gastric cancer (GC). METHODS: Transcriptomic and clinical data from TCGA-STAD and GSE62254, and single-cell RNA sequencing data from GSE183904 were analyzed. Triaptosis-related gene (TRG) scores were calculated using single-sample gene set enrichment analysis. Differentially expressed genes identified in TRG-score and GC-versus-normal comparisons underwent functional enrichment, Cox regression, and least absolute shrinkage and selection operator regression to develop an externally validated signature. Immune profiles, pathway activity, somatic mutations, tumor mutational burden (TMB), predicted drug sensitivity, and clinical features were compared by risk group. Single-cell analyses assessed TRG activity, prognostic gene expression, cell-cell communication, and pseudotime. Reverse transcription-quantitative PCR and Western blotting assessed mRNA expression and protein levels, respectively. RESULTS: A TRG-based prognostic model comprising ASPN, GRB14, and VTN was developed and externally validated, effectively distinguishing patients into two distinct risk groups with notably different survival outcomes. mRNA expression of all three genes and their protein levels were significantly higher in SGC-7901 cells than in GES-1 cells. High-risk patients had higher stromal scores and distinct immune profiles; 15 immune cell types differed between groups. Single-cell analysis revealed fibroblasts and pericytes among high-TRG-active cell types. Prognostic genes were significantly overexpressed in fibroblasts, which also showed high TRG activity. Fibroblasts demonstrated enhanced communication with pericytes, whereas tumor-derived fibroblasts showed weaker communication with macrophages, indicating immune microenvironment remodeling. CONCLUSION: The three-gene prognostic signature predicted GC prognosis and was associated with distinct immune and genomic features, suggesting potential value for risk stratification and personalized treatment.

Humans↗

PubMed2026

Sodium Overload-Related Molecular Subtypes and a Four-Gene Prognostic Signature Predict Survival, Immune Landscape, and Therapeutic Response in Acute Myeloid Leukemia.

Sodium overload has recently emerged as a critical metabolic stressor involved in cancer progression; however, its molecular characteristics and clinical relevance in acute myeloid leukemia (AML) remain unexplored. RNA-seq data sets, clinical annotations, and mutational profiles of AML patients were annotations from The Cancer Genome Atlas and integrated with Genotype-Tissue Expression normal samples. Sodium overload-related genes (SORGs) were obtained from GeneCards. Differentially expressed SORGs (DESORGs) screened by applying the limma statistical model, followed by univariate Cox proportional hazards regression, consensus clustering, functional enrichment, immune infiltration analysis, and pathway evaluation. A prognostic signature was developed through least absolute shrinkage and selection operator regression followed by multivariate Cox modeling. The model's performance was further verified in two external GEO data sets (GSE71014 and GSE37642). Nomogram construction, subgroup analysis, tumor mutational burden (TMB) assessment, drug sensitivity prediction, transcription factor (TF) analysis, and competing endogenous RNA (ceRNA) network analyses were also performed. A total of 57 DESORGs were identified, and 2 sodium overload-related molecular subtypes exhibited distinct survival, immune infiltration, and inflammatory pathway activation. A robust four-gene signature (DOCK1, GABRE, HTR7, ACSM1) stratified patients into high- and low-risk categories with significantly different survival across training and validation cohorts. High-risk patients displayed increased immune infiltration, higher TMB, reduced sensitivity to multiple chemotherapeutic drugs, and inferior predicted response to PD-L1 blockade. TF and ceRNA networks revealed multilayered transcriptional and post-transcriptional regulation of the signature genes. This study identifies sodium overload-related molecular heterogeneity in AML and establishes a validated four-gene prognostic signature that integrates genomic, immunologic, and therapeutic features, offering potential utility for personalized risk assessment and treatment optimization.

Humans↗

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

TFPI-high myofibroblast states and a meta-program-related five-gene prognostic signature in breast cancer.

Intratumoral heterogeneity and tumor-microenvironment interactions limit prognostic stratification in breast cancer, but the prognostic relevance and cellular context of recurrent transcriptional meta-programs remain unclear. We aimed to derive a meta-program-related prognostic signature and characterize its component transcripts at single-cell resolution. Six paired institutional tumors and adjacent non-tumor tissues served as a proof-of-concept comparison. Univariable Cox screening and least absolute shrinkage and selection operator Cox regression were used to derive a five-gene score from a prespecified meta-program-related candidate set in The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) training cohort; the score was tested internally and assessed in GSE20685 using fixed coefficients and cohort-specific median cutoffs. GSE161529 single-cell transcriptomic data were used to map signature transcripts across 136,526 quality-controlled cells, while donor-aware pseudobulk analysis compared upper- and lower-quartile TFPI expression states in annotated myofibroblasts. The score comprised TCN1, FOXJ1, PIGR, SLAIN1, and TFPI and was associated with overall survival in the training, testing, and external cohorts, with concordance indices of 0.782, 0.756, and 0.721, respectively. TFPI transcripts were detected across endothelial, fibroblast, and myofibroblast compartments. TFPI-high myofibroblasts showed transcriptional enrichment of extracellular matrix and collagen fibril organization, transforming growth factor beta signaling, epithelial-mesenchymal transition, and myogenesis, together with lower oxidative phosphorylation and fatty acid metabolism programs. In bulk TCGA-BRCA tissue, TFPI expression correlated positively with stromal (r = 0.48), immune (r = 0.25), and composite microenvironment scores (r = 0.40; all p < 0.001). These findings identify a hypothesis-generating five-gene bulk-tissue prognostic signature and an expression-associated TFPI-high myofibroblast state but do not establish a discrete lineage, the cellular source of bulk TFPI, a TFPI-dependent mechanism, or clinical utility. Independent prospective cohorts, spatial and protein-level validation, and functional perturbation studies are required.

Journal Article↗

PubMed2026

Integrative analysis of single-cell sequencing identifies CD8+ TIM3+ CD101+ T cell-associated genes as prognostic biomarkers in breast cancer.

BACKGROUND: Breast cancer is a prevalent and deadly malignancy that significantly impacts women's quality of life and imposes financial burdens. Despite therapeutic advancements, tumour heterogeneity and frequent relapses remain major challenges. Accordingly, this study aimed to characterize immune features associated with CD8+ TIM3+ CD101+ T cells and develop a prognostic signature for breast cancer. METHODS: This study integrated single-cell and bulk transcriptomic datasets to characterize CD8+ TIM3+ CD101+ T cell (CCT)-related immune features and construct a prognostic signature in breast cancer. Single-cell RNA-seq data were sourced from the Gene Expression Omnibus (GEO) repository, and bulk transcriptomic data were from The Cancer Genome Atlas (TCGA) and GEO databases. Analytical methods included pseudo-time trajectory reconstruction (Monocle2), intercellular signalling analysis (CellChat), functional enrichment (ClusterProfiler), and immune profiling (ssGSEA). Prognostic modeling was conducted using least absolute shrinkage and selection operator (LASSO) Cox regression, with validation via Kaplan-Meier and time-dependent receiver operating characteristic (ROC) analyses. RESULTS: Single-cell analysis identified 17 clusters spanning seven cell types, including T cells, myeloid cells, and epithelial cells. T-cell sub-clustering revealed four subtypes. Pseudotime analysis suggested a potential state-transition relationship between CD8+ CD101- TIM3+ and CD8+ CD101+ TIM3+ T-cell states. A total of 121 differentially expressed genes were enriched in vital biological processes. An 11-gene prognostic model showed strong predictive power across cohorts. Single-cell T-cell reclustering identified a CD8+ CD101+ TIM3+ T-cell subpopulation, which was primarily characterized by the expression of markers such as CD101 and HAVCR2/TIM3. CONCLUSIONS: This study maps cellular heterogeneity and molecular networks in breast cancer, offering insights for targeted therapy and improved prognosis.

Breast invasive carcinoma↗

PubMed2026

Identifying gene expression signatures for risk stratification of postoperative adjuvant chemotherapy in colorectal cancer.

Clinical risk stratification for postoperative recurrence in patients with pathological stage II (pStage II) colorectal cancer (CRC) is essential for guiding the use of postoperative adjuvant chemotherapy (ACT). In this study, we identified novel prognostic gene expression biomarkers in patients with pStage II CRC and developed a new risk stratification framework for ACT decision-making. First, genome-wide biomarker discovery was conducted to identify prognostic gene expression biomarkers associated with recurrence risk in pStage II CRC. This analysis identified 10 differentially expressed genes as potential biomarkers for recurrence. The efficacy of these biomarkers was then tested using 188 clinical surgical specimens obtained from patients with pStage II CRC. A predictive panel was developed using qRT-PCR and used to assess 93 clinical specimens with an area under the curve (AUC) of 0.82, and its performance was further validated in an independent cohort (n = 95). By incorporating key clinicopathological features, a Gene expression-based Prediction of Recurrence in pStage II CRC (GPRSC) signature was developed, which robustly predicted postoperative recurrence (AUC: 0.80). Finally, combining the GPRSC signature, microsatellite instability status, and conventional criteria, we developed a novel risk stratification system for postoperative ACT decision-making in pStage II CRC. Overall, we identified novel gene expression biomarkers and developed a prognostic signature that informs clinical decision-making regarding postoperative ACT in patients with pStage II CRC.

Humans↗

PubMed2026

TCGA-based identification of prognostic biomarkers and candidate traditional Chinese medicine compounds in papillary thyroid carcinoma: An observational study.

This study aimed to identify prognostic genes associated with papillary thyroid carcinoma (PTC) and explore candidate traditional Chinese medicine (TCM) compounds using integrated bioinformatics and molecular docking. In this observational study, PTC gene expression profiles and clinical data were obtained from The Cancer Genome Atlas. Differentially expressed genes were screened using differential-expression sequencing (DESeq2), followed by protein-protein interaction network analysis to identify hub genes. Their expression, diagnostic value, immune relevance, prognostic significance, protein-level validation, and single-cell distribution were assessed using gene expression profiling interactive analysis, receiver operating characteristic analysis, immune infiltration analysis, Kaplan-Meier survival analysis, the human protein atlas, and single-cell RNA-sequencing data. Candidate TCM compounds were predicted using symptom mapping (SymMap) and the TCM Systems Pharmacology Database and Analysis Platform, and molecular docking was performed to evaluate potential ligand-target interactions. Five hub genes, colony-stimulating factor 2, apolipoprotein E, fibronectin 1 (FN1), collagen type I alpha 1 chain (COL1A1), and intercellular adhesion molecule 1, were identified and found to be significantly upregulated in PTC tissues, with diagnostic value in receiver operating characteristic analysis. Immune infiltration analysis showed associations with macrophages, dendritic cells, and T helper 1 cells, whereas single-cell analysis demonstrated heterogeneous expression across immune and stromal cell populations, including fibroblasts. Higher FN1 and COL1A1 expression was associated with poorer outcomes. Immunohistochemistry supported the expression patterns, while single-cell analysis provided exploratory cell-type-level context for the cellular distribution of selected genes. Ginseng and Smilax glabra were predicted as common candidate TCMs, and docking suggested favorable binding between their active compounds and selected hub targets. Colony-stimulating factor 2, apolipoprotein E, FN1, COL1A1, and intercellular adhesion molecule 1 may be biologically relevant hub genes in PTC, while FN1 and COL1A1 may have prognostic value. Predicted TCM compounds provide preliminary computational evidence for possible compound-target interactions, requiring experimental and clinical validation.

Female↗

PubMed2025

Carbonic Anhydrase Inhibition Sensitizes Group 3 Medulloblastoma to Radiotherapy.

UNLABELLED: Group 3 (G3) medulloblastoma constitutes the most aggressive molecular subgroup, and nearly all patients present with metastases upon recurrence. Treatment for newly diagnosed medulloblastoma relies on a combination of maximal safe surgical resection, followed by chemotherapy and ionizing radiation, and no therapies have been shown to confer a survival benefit at the time of recurrence. Given the limited therapeutic options available for patients with medulloblastoma, especially at recurrence, and the incomplete understanding of the molecular mechanisms underlying resistance to treatment, we sought to uncover actionable targets and biomarkers that could help refine patient selection and treatment of newly diagnosed medulloblastoma to reduce the risk of recurrence. In clinically relevant mouse models of G3 medulloblastoma, CT-guided fractionated radiotherapy extended overall survival and induced the clonal selection of radioresistant subpopulations of tumor cells that drove medulloblastoma recurrence. Comparison of recurrent tumors with treatment-naïve newly diagnosed tumors revealed a gene expression signature that was found to be a biomarker of radioresistance and poor prognosis. This prognostic gene signature was shown to be subgroup specific in a large patient cohort. Recurrent tumors had elevated expression of carbonic anhydrase 4, and genetic and pharmacologic modulation of carbonic anhydrase 4 could promote or reduce resistance to radiotherapy. These data suggest that the FDA-approved carbonic anhydrase inhibitor acetazolamide may be a useful radiosensitizer to improve the efficacy of the treatment of newly diagnosed G3 medulloblastoma that could reduce the risk of tumor recurrence and improve survival in pediatric patients. SIGNIFICANCE: G3 medulloblastoma features a prognostic subgroup-specific gene expression signature and can be targeted with a carbonic anhydrase inhibitor to enhance radiosensitivity, reducing the risk of recurrence and improving survival.

Medulloblastoma↗

PubMed2026

Investigating the mechanisms of PhIP-induced colorectal cancer through network toxicology, machine learning, and molecular dynamics simulation.

BACKGROUND: Over the past few years, 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP)- a compound from grilled or processed meats-has emerged as a major player in cancer development, especially colorectal cancer (CRC). This work dives into its potential links to CRC and uncovers the key genes that bridge this connection. METHODS: We tapped into various databases to pinpoint target genes tied to PhIP and CRC, then ran protein-protein interaction (PPI) analyses for visualization. Next, we explored underlying mechanisms through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. To nail down predictions, we tested 107 machine learning pipelines and picked the best one, validating its accuracy and the core genes' prognostic value across datasets. Next, molecular docking and dynamics simulations probed the interactions between these genes and PhIP. Finally, cell proliferation was assessed using Cell Counting Kit-8 (CCK-8) and 5-ethynyl-2'-deoxyuridine (EdU) assays, and polymerase chain reaction (PCR) was performed to validate the expression levels of the hub genes. RESULTS: Our analysis identified 39 overlapping genes, from which a machine learning model (glmBoost + Enet) identified six candidate targets: CDK4, CEBPB, COMT, SOX9, TIMP1, and TOP2A. To prioritize these, a hierarchical screening framework was applied. Molecular docking and dynamics simulations identified CDK4, COMT, and TIMP1 as the most stable interactors with PhIP. Functional assays confirmed that PhIP treatment significantly enhanced the proliferation of CRC cells. Crucially, quantitative PCR (qPCR) validation in multiple CRC cell lines identified TIMP1 as the primary target, showing the most consistent and significant upregulation upon PhIP exposure. CONCLUSIONS: In essence, these genes drive PhIP is role in CRC, offering novel insights into its molecular pathways. This could reshape how we tackle food-related pollutants, paving the way for better prevention and targeted therapies.

Colorectal cancer (CRC)↗

PubMed2025

Integrating necroptosis and immune landscapes: a multi-omics-derived NecropImmScore stratifies prognosis and therapy in ovarian cancer.

BACKGROUND: Ovarian cancer (OC) remains the deadliest gynecologic malignancy, largely due to its immunosuppressive tumor microenvironment (TME) and resistance to therapy. Necroptosis, a regulated lytic cell death pathway mediated by the RIPK1-RIPK3-MLKL axis, can trigger immunogenic cell death, but its specific role in shaping the OC immune landscape and its clinical translation potential are posorly understood. METHODS: We employed multi-omics analysis (transcriptomics, genomics, clinical data) from TCGA-OV (n = 380), ICGC OV-AU, and IMvigor210 cohorts, combined with rigorous in vitro functional validation using OC cell lines (SKOV3, HEY), macrophages (THP-1 derived), and T cells (Jurkat). Computational immunology approaches (ESTIMATE, CIBERSORT, ssGSEA) quantified immune infiltration. We identified MLKL-associated immune genes, performed survival analysis (Kaplan-Meier, Cox regression), and constructed a necroptosis-immune signature (NecropImmScore) using consensus clustering and PCA of 102 prognostic genes. Drug sensitivity was predicted via pRRophetic and CellMiner. RESULTS: MLKL emerged as a protective prognostic biomarker (p = 0.018), significantly correlated with enhanced immune infiltration (ImmuneScore, StromalScore, ESTIMATEScore; p < 2.22e-16), M1 macrophage polarization (p = 0.006), activated CD4 + T cells (p = 0.003), and elevated immune checkpoint expression (PD-L1, CTLA4, LAG3, TIGIT). In vitro, MLKL overexpression in OC cells promoted M1 polarization (p < 0.05), activated Jurkat T cells (upregulated CCR4/5/7/9, CD69, CD3D/E, GZMB; p < 0.05), and induced key chemokines (CXCL9/10/11/13) critical for immune cell recruitment. Integration of MLKL-related and immune-related DEGs (n = 632) revealed enrichment in T-cell activation, chemokine signaling, and antigen presentation pathways (FDR < 0.05). Consensus clustering based on 102 survival-associated genes defined three molecular subtypes (Clusters A-C) with divergent survival (p = 0.019), necroptosis activity, and immune infiltration (Cluster C: best prognosis, highest MLKL/ImmuneScore). The derived NecropImmScore robustly stratified patients: high-score correlated with superior overall survival (TCGA: p < 0.001; ICGC: p = 0.014), inflamed TME phenotype, elevated checkpoint expression, and improved response to anti-PD-L1 in IMvigor210. Critically, high NecropImmScore predicted higher BRCA1 mutation frequency (AUC = 0.802), synergy with BRCA1 status for prognosis, higher homologous recombination deficiency (HRD) score, sensitivity to cisplatin (p = 0.014), paclitaxel (p = 0.016), gemcitabine (p = 0.017), and provided superior prognostic stratification when combined with TMB and HRD score (p < 0.001). CONCLUSION: This study establishes MLKL as a master regulator of anti-tumor immunity in OC, driving chemokine-mediated immune cell recruitment and TME reprogramming. The novel NecropImmScore is a multifaceted biomarker that effectively predicts prognosis, immunotherapy response, BRCA1 deficiency, and chemosensitivity, offering significant potential for guiding precision therapeutic strategies in OC.

Humans↗

PubMed2026

RCoxNet: A Deep Learning Framework Integrating Random Walk with Restart, Mutation, and Clinical Data for Cancer Survival Prediction.

Accurate survival prediction in cancer remains challenging due to the sparsity of somatic mutation profiles and the failure of existing models to capture higher-order gene-gene dependencies. Network diffusion methods such as Random Walk with Restart (RWR) can propagate mutation signals across protein-protein interaction (PPI) networks to address sparsity, yet their integration within a deep learning Cox survival framework has not been comprehensively benchmarked across multiple cancer cohorts. We present RCoxNet, a deep learning framework that maps somatic mutation profiles onto a ConsensusPathDB-derived PPI network via RWR, selects prognostic genes by log-rank filtering, and processes network-informed mutation scores through three fully connected hidden layers feeding into a Cox proportional hazards output. RCoxNet was evaluated on The Cancer Genome Atlas (TCGA) cohorts for four cancer types (breast invasive carcinoma [BRCA], lung adenocarcinoma [LUNG], glioblastoma multiforme [GBM], and ovarian serous cystadenocarcinoma [OV]) using 20 independent random splits. The model achieved mean C-index values of 0.807 ± 0.044 (BRCA), 0.750 ± 0.039 (LUNG), 0.704 ± 0.041 (GBM), and 0.668 ± 0.036 (OV), consistently outperforming DeepSurv, Cox-nnet, SurvivalNet, Cox Elastic-Net (Cox-EN), and DeepHit, with statistically significant gains over Cox-EN, Cox-nnet, SurvivalNet, and DeepHit across the majority of cohorts. RCoxNet demonstrates that embedding sparse mutation profiles into a PPI network context substantially improves cancer survival prediction and yields biologically interpretable prognostic features relevant to precision oncology.

cancer survival prediction↗

PubMed2026

Radiogenomics predicts immune microenvironment heterogeneity and response to combination immunotherapy in hepatocellular carcinoma.

BACKGROUND: The combination of immune checkpoint inhibitors (ICIs) with anti-angiogenic agents is the preferred first-line therapy option for patients with advanced hepatocellular carcinoma (HCC), yet only a subset of patients responds, urging the quest for prediction biomarkers. We aimed to integrate genomics with radiology to propose an immune-derived radiogenomics biomarker of response to such combination immunotherapy and evaluate its added value in clinical context. METHODS: We integrated bulk RNA sequencing (RNA-seq) and proteomics data of 994 HCC patients with single-cell RNA-seq data of 11 samples across multiple datasets to identify an immune-related signature (IRS) that may influence sensitivity or resistance to such combined immunotherapy strategy, followed by verification of selected marker genes using immunohistochemistry and cytological experiments. We then trained/validated a cross-modality radiogenomics biomarker using machine learning based on TCIA database that was further tested in multi-scale independent cohorts covering 754 HCC patients. RESULTS: Integrative multi-omics analysis identifed a parsimonious 2-gene prognostic signature including KPNA2 and SMG5 that was significantly associated with immune heterogeneity and response to combination immunotherapy. Machine-learning pipeline exported the optimal 4-feature radiogenomics biomarker using support vector machine that significantly discriminated prognosis (hazard ratio 1.415–1.890; p < 0.05 for all) and modestly predicted response to ICI plus anti-angiogenic therapy (area under the curve 0.720–0.829) in independent retrospective series across major imaging modalities (computed tomography/magnetic resonance imaging). In a prospective neoadjuvant cohort, this biomarker also showed favorable performance for predicting pathological response and tumor recurrence, accompanied by biological validation through single-cell RNA-seq analysis of pre-treatment biopsies. CONCLUSIONS: Our study provides a cross-device-cross-modal radiogenomics biomarker that can improve patient selection for emerging ICI plus anti-angiogenic therapy with novel potential therapeutic targets in HCC.

Humans↗

PubMed2026

Dietary Polyphenol Acteoside-Related Molecular Signatures in Clear Cell Renal Cell Carcinoma: Multi-Omics Profiling and Functional Validation of IMPDH1.

Clear cell renal cell carcinoma (ccRCC) is characterized by substantial metabolic and molecular heterogeneity, but the disease-relevant programs associated with acteoside, a dietary polyphenol, remain poorly understood. We integrated predicted acteoside targets with bulk, single-cell, and spatial transcriptomic data from ccRCC and combined molecular subtyping with cross-cohort machine-learning analysis. Acteoside-related signatures were preferentially enriched in malignant compartments and increased with tumor grade and stage. Consensus clustering identified two molecular subtypes with distinct biological and clinical features. C1 was associated with immune activation, metabolic activity, and more favorable survival, whereas C2 showed greater genomic instability, reduced renal epithelial differentiation, and poorer outcomes. We further benchmarked multiple machine-learning strategies and established a 10-gene prognostic model that retained predictive performance across independent cohorts, with IMPDH1 emerging as the strongest risk-associated feature. Functional experiments confirmed the biological relevance of IMPDH1: its knockdown suppressed ccRCC cell proliferation, DNA synthesis, colony formation, and migration, whereas overexpression produced the opposite effects. Together, these findings indicate that acteoside-related molecular signatures capture clinically relevant heterogeneity in ccRCC and provide a framework for linking dietary-polyphenol-related molecular space with tumor biology. The identification and functional validation of IMPDH1 further highlight its potential importance in ccRCC progression.

IMPDH1↗

PubMed2026

The prognostic value and molecular mechanisms of Porphyromonas gingivalis infection-associated differentially expressed genes in oral squamous cell carcinoma.

BACKGROUND: Increasing evidence suggests that Porphyromonas gingivalis (Pg) is associated with oral squamous cell carcinoma (OSCC) development and progression. This study aimed to identify Pg-associated genes with prognostic relevance in OSCC through integrated bioinformatics analysis. METHODS: OSCC-related differentially expressed genes (DEGs) were identified from the The Cancer Genome Atlas (TCGA)-OSCC cohort and intersected with Pg supernatant-associated DEGs from GSE192887. Raw count data were analyzed with DESeq2, whereas transcripts per million (TPM)-transformed expression values were used for downstream visualization and model construction. Weighted gene co-expression network analysis (WGCNA), univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression, and multivariable Cox modeling were used to develop a seven-gene prognostic signature, which was externally evaluated in GSE41613. Additional analyses examined treatment-associated expression changes in the seven model genes, pairwise correlations among the model genes, and correlations between Pg supernatant-associated differentially expressed gene (PgSDEG)-derived module eigengenes and immune-cell fractions. Quantitative reverse-transcription polymerase chain reaction (qRT-PCR) was performed in eight paired OSCC and adjacent non-tumor tissues and in supplemented-brain heart infusion (BHI) vehicle-control and Pg culture-supernatant-treated HOK, HSC-3, and CAL-27 cells. RESULTS: A prognostic signature comprising CXCL8, GAST, HBQ1, PADI3, STC1, TEX19, and TMEM92 was established. The signature showed limited-to-moderate discrimination in the TCGA training cohort, with 1-, 3-, and 5-year areas under the curve (AUCs) of 0.68, 0.69, and 0.69, respectively, and limited discrimination in the GSE41613 external cohort (AUCs: 0.66, 0.67, and 0.61). Kaplan-Meier analysis showed poorer survival in the high-risk group in both cohorts. The GSE192887 analysis showed significant treatment-associated expression changes in all seven genes after Pg culture-supernatant exposure. In paired tissues, CXCL8 and TMEM92 were significantly higher in OSCC tissues, whereas STC1 was not significant after Holm correction. In CAL-27 cells, CXCL8, STC1, and TMEM92 increased significantly after culture-supernatant treatment, whereas the corresponding comparisons were not significant in HOK or HSC-3 cells after adjustment. CONCLUSIONS: This study developed a seven-gene Pg-associated prognostic signature for OSCC and provided complementary transcriptomic, immune-correlation, tissue, and cell-based evidence that placed the signature in biological context. The model showed limited-to-moderate discrimination and is not ready for clinical use. The enrichment, gene-correlation, and immune-correlation findings are hypothesis-generating rather than mechanistic evidence. Further independent validation and dedicated functional studies are required.

Oral squamous cell carcinoma (OSCC)↗