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Results for “prognostic stratification”

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PubMed2026

Dynamic evolution of chaperone-mediated autophagy is associated with tumor microenvironment remodeling and prognostic stratification in lung adenocarcinoma: insights from single-cell transcriptomics, ensemble machine learning, and experimental validation.

BACKGROUND: Lung adenocarcinoma (LUAD) shows prognostic heterogeneity, and tumor-node-metastasis (TNM) staging is limited for individualized management. Chaperone-mediated autophagy (CMA) maintains proteostasis, but its role during adenocarcinoma in situ (AIS)-minimally invasive adenocarcinoma (MIA)-invasive adenocarcinoma (IAC) progression remains unclear. METHODS: Single-cell RNA sequencing (scRNA-seq) data from GSE189357 and bulk transcriptomes from The Cancer Genome Atlas (TCGA)-LUAD and Gene Expression Omnibus (GEO) cohorts were integrated. CMA activity, cell-cell communication, weighted gene co-expression network analysis (WGCNA), tumor-normal differential expression, machine-learning survival modeling, tumor microenvironment (TME) features, drug sensitivity, and EPC1 function were analyzed. RESULTS: CMA-high tumor epithelial cells increased from AIS (58.1%) to MIA (65.7%) but declined in IAC (44.4%; p < 0.001). CMA-low cells preferentially received fibroblast-derived extracellular matrix cues. A CMA-negatively correlated module identified 69 core genes. Random survival forest (RSF) performed best among 117 machine-learning combinations (mean concordance index > 0.873). High-risk patients had worse survival across cohorts, and the risk score was independently associated with overall survival (hazard ratio = 16.013, 95% confidence interval: 9.579-26.768, p < 0.001). High-risk tumors showed proliferative activation and M0 macrophage enrichment, whereas low-risk tumors showed stronger immune-related signaling. EPC1 overexpression suppressed malignant phenotypes in A549 cells. CONCLUSION: CMA dynamics are associated with stromal and immune remodeling during LUAD progression. A CMA-based model provides robust prognostic stratification and may offer a basis for future TME-guided studies.

Chaperone-mediated autophagy↗

PubMed2026

Kynurenine metabolism-related gene signature for prognostic stratification in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) remains a major global health burden with high mortality rates and limited therapeutic options. The identification of reliable biomarkers for early diagnosis and prognosis prediction is urgently needed. Kynurenine metabolism, a critical pathway in immune regulation and tumor progression, has been implicated in various cancers. However, its prognostic value in HCC has not been fully elucidated. This study aimed to develop a prognostic risk model based on kynurenine metabolism-related genes (KMRGs) for HCC patients. METHODS: Transcriptomic and clinical data of HCC patients were retrieved from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) databases. A prognostic risk model was established using least absolute shrinkage and selection operator (LASSO) and Cox regression analyses. Survival analysis and functional enrichment analysis were conducted to validate the predictive performance of the model and to investigate the underlying mechanisms. ALDH8A1 was ultimately identified as a target gene based on survival analysis, and its impact on tumor cell migration was assessed using the HCC cell line. RESULTS: A prognostic model based on seven KMRGs was established. The high-risk group exhibited significantly worse overall survival compared to the low-risk group. Functional enrichment analysis in high-risk patients highlighted significant enrichment in core biological processes, including spliceosome assembly and ribonucleoprotein complex biogenesis. Furthermore, a nomogram integrating the risk score and clinical pathological features was developed, demonstrating moderate predictive performance for HCC prognosis. CONCLUSIONS: This study successfully constructed a prognostic risk model based on seven KMRGs, providing a valuable tool for predicting clinical outcomes in HCC patients. These findings highlight the potential role of kynurenine metabolism in HCC progression and offer new insights for future therapeutic strategies.

ALDH8A1↗

PubMed2026

Identification of a novel signature for prognostic stratification and integrative analyses in lung adenocarcinoma.

BACKGROUND: Recently, research has revealed that the Golgi apparatus is involved in the development process of cancer; however, the specific effect of Golgi apparatus-related genes (GAGs) in lung adenocarcinoma (LUAD) remains unclear. This study aims to construct a more concise and practical risk model in LUAD using GAG. METHODS: The gene expression profiles of patients with LUAD were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and GAGs were downloaded from the Gene Set Enrichment Analysis (GSEA) database. Univariate Cox and least absolute shrinkage and selection operator (LASSO) analyses were performed to identify the prognostic GAG signature. Kaplan-Meier and receiver operating characteristic (ROC) curves were plotted to validate the predictive effect of the prognostic signatures. The correlation between the risk model and the immune landscape was examined using CIBERSORT and TIDE analyses. Also, the genes in the signature were assessed by single-cell RNA sequencing (scRNA-seq). RESULTS: A prognostic signature comprising 5 GAG genes (GNPNAT1, RGS20, CAV3, NTSR1, and FURIN) was established after LASSO and multi-Cox analyses. Both the Kaplan-Meier analysis and the ROC curves supported the strong predictive utility of the risk model. Specifically, the former yielded significant stratification in all three validation datasets (P=1.2001e-05, P=0.006, and P=0.04), while the latter provided further evidence of its predictive precision through the area under the curve. In addition, we found that the low-risk group responded better to immunotherapy than the high-risk group (P<0.0001). scRNA-seq analysis revealed the distribution patterns of the 5 GAG genes in cells. Finally, we assessed the situation of tumor mutation burden (TMB) and performed functional analysis based on the risk model of GAGs. CONCLUSIONS: The risk model based on GAGs can effectively stratify the prognosis of patients and predict immunotherapy responses in LUAD.

Golgi apparatus↗

PubMed2026

Immune-Like Malignant Epithelial Programs Shape Tumor-Immune Interactions and Inform Prognostic Stratification in Lung Adenocarcinoma.

Lung adenocarcinoma (LUAD) is characterized by marked cellular heterogeneity, yet how malignant epithelial states contribute to immune regulation and clinical outcomes remains incompletely defined. We integrated single-cell RNA-sequencing data to map the cellular landscape of LUAD and identify malignant epithelial cells based on inferred copy-number alterations. Epithelial states were further examined through trajectory inference, transcription factor analysis, and cell-cell communication profiling. Single-cell-derived genes were subsequently integrated with TCGA and independent GEO cohorts to construct and validate a machine learning-based prognostic signature. Malignant epithelial cells displayed distinct functional programs, including an immune-like state associated with genomic instability, immune-related transcriptional activity, tumor-immune communication, and patient outcomes. The resulting immune-like malignant epithelial cell signature (IMEC-Sig) consistently stratified survival across multiple cohorts. Low IMEC-Sig scores were accompanied by greater immune infiltration, higher immune checkpoint expression, and increased immunophenoscore, whereas high scores were linked to a comparatively immunosuppressive phenotype. Pan-cancer analyses further identified KRT8 as a gene associated with unfavorable prognosis, and functional experiments showed that KRT8 silencing suppressed proliferation, migration, invasion, and colony formation in LUAD cells. Together, these findings connect malignant epithelial heterogeneity with the immune context and clinical outcomes, support IMEC-Sig as a biologically informed prognostic tool, and nominate KRT8 as a potential therapeutic target in LUAD.

Humans↗

PubMed2026

Construction of molecular signatures based on the co-expression network of NECSO-related gene TRPM4 and its prognostic value in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) demonstrates significant prognostic variability that is not entirely accounted for by traditional staging systems. Necrosis by sodium overload (NECSO) is an emerging programmed cell death pathway, but its clinical relevance in HCC remains undefined. Therefore, this study aimed to identify TRPM4-associated core genes, develop and validate a prognostic signature, and investigate its relationship with the tumor immune microenvironment, tumor mutational burden, and single-cell expression patterns in HCC. METHODS: We integrated transcriptomic, clinical, and mutational datasets from The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) (n=421) and Gene Expression Omnibus (GEO) cohorts (n=115) to identify genes co-expressed with TRPM4-a key NECSO mediator-and those differentially expressed in HCC. A prognostic signature was developed using least absolute shrinkage and selection operator (LASSO)-Cox regression and validated through survival analysis, time-dependent receiver operating characteristic (ROC) curves, and multivariate Cox regression analysis. The immune landscape was characterized using CIBERSORT, somatic mutation data were used to calculate tumor mutational burden (TMB) and assess its correlation with the risk score, and single-cell RNA sequencing (scRNA-seq) resolved cell-type-specific expression patterns. RESULTS: From 294 TRPM4-associated core genes, we identified an 11-gene signature (BRSK1, MMP1, GRIN2D, GP6, MYOM2, N4BP3, CCDC112, TSEN54, MAP3K9, SPP1, B3GNT4) that independently predicted overall survival (OS) (hazard ratio =5.419, P<0.001) with areas under the curve (AUCs) of 0.779, 0.693, and 0.701 at 1, 3, and 5 years. These values were superior or comparable to conventional clinicopathologic variables after direct comparison. High-risk patients exhibited an immunosuppressive microenvironment, characterized by enrichment of M0 macrophage, a higher M2/M1 ratio (P<0.001) and distinct immune checkpoint profiles. When integrated with TMB, the prognostic stratification was further refined: high-TMB/high-risk patients had poorest outcomes (median OS, 15.3 months), while low-TMB/low-risk patients had the most favorable survival (median OS, 68.7 months). Single-cell analysis revealed that MMP1 was induced in cancer-associated fibroblasts (CAFs) and SPP1 was downregulated in macrophages, single-cell risk scores confirmed TAFs and macrophages as the main contributors to the prognostic model. CONCLUSIONS: The TRPM4-centered 11-gene signature provides robust and independent prognostic stratification in HCC by integrating immune, mutational, and single-cell features. This signature serves as a potential tool for prognostic evaluation and may help inform immunotherapeutic strategies for HCC.

Hepatocellular carcinoma (HCC)↗

PubMed2026

Lymphangiogenesis-related gene signature-based risk model for prognostic assessment of cervical cancer: immune-metabolic characterization and molecular subtype analysis.

BACKGROUND: Lymphangiogenesis promotes tumor dissemination and may shape the immune contexture of cervical cancer, yet lymphangiogenesis-related prognostic stratification and its immunometabolic implications remain insufficiently defined in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC). METHODS: TCGA-CESC transcriptomes and clinical data were obtained from UCSC Xena and integrated with normal cervix tissues from the Genotype-Tissue Expression Project after batch correction. Prognostic LYMRGs were first identified from the differentially expressed set using univariable Cox proportional hazards analysis. Candidate genes were then reduced using an L1-regularized Cox model (Least Absolute Shrinkage and Selection Operator), and the remaining markers were entered into a multivariable Cox regression to obtain the final coefficients and compute an individualized risk score. The model's prognostic value was further assessed in an independent Gene Expression Omnibus dataset. In addition, expression patterns of the signature genes were leveraged for molecular subtyping of TCGA samples via non-negative matrix factorization (NMF). Immune infiltration and immunotherapy-associated characteristics were interrogated through a multi-algorithm strategy (single-sample gene set enrichment analysis, CIBERSORT, ESTIMATE, Tumor Immune Dysfunction and Exclusion (TIDE), and Immunophenoscore . Additional analyses included pathway enrichment (GSEA/GO/KEGG), drug sensitivity prediction (pRRophetic/CellMiner), and ceRNA network analysis. RESULTS: A six-gene LYMRG signature robustly stratified survival. High-risk patients had significantly worse overall survival in The Cancer Genome Atlas with AUCs of 0.819/0.801/0.801 at 1/3/5 years, and in GSE52903 (P = 0.001) with AUCs of 0.733/0.719/0.725. NMF identified two subtypes with distinct prognosis (P = 0.01) and divergent immune landscapes. Risk groups and subtypes exhibited consistent differences in immune infiltration, checkpoint expression, TIDE/IPS patterns, and pathway enrichment. Predicted chemosensitivity differed by risk group, and the ceRNA network suggested candidate upstream lncRNA regulators of the signature. CONCLUSION: A lymphangiogenesis-related six-gene model enables clinically meaningful prognostic stratification of CESC and links lymphangiogenesis programs to distinct tumor immune phenotypes and therapeutic vulnerabilities.

cancer↗

PubMed2026

A pragmatic adaptation of the RANO clinical risk score for IDH-Wildtype glioblastoma in the absence of MGMT promoter methylation testing.

BACKGROUND: The novel RANO risk score provides prognostic stratification for patients with IDH-wildtype glioblastoma(GBM) and includes age, Karnofsky Performance Scale(KPS), RANO resection class(RRC), and MGMT promoter methylation(MGMTm). However, MGMTm testing is unavailable in many countries. We aimed to explore the prognostic performance of a RANO-adapted clinical score excluding MGMTm. METHODS: We applied the same scoring system established by original RANO score, excluding MGMTm. Three risk classes were defined as numerical scores derived through tertiles. The primary endpoint was overall survival(OS), and the secondary exploratory endpoint was progression-free survival(PFS). RESULTS: One-hundred twenty patients were included. Three risk classes were identified:low-risk(0-1 points,n:47, 39.2%), intermediate-risk(2-3 points,n:27, 22.5%), and high-risk(≥4 points,n:46, 38.3%). The median OS was 35.5 months(95% CI:21.1-49.9) for the low-risk group, 16 months(95% CI:9.9-22.1) for the intermediate-risk group, and 5 months(95% CI:3.6-6.3) for the high-risk group(p < 0.001). Similarly, the median PFS was 16.3 months(95% CI:12.3-20.3) for the low-risk group, 9.9 months(95% CI:7.2-12.6) for the intermediate-risk group, and 4.1 months(95% CI:3.5-4.7) for the high-risk group(p < 0.001). CONCLUSION: This simplified RANO-adapted score demonstrated promising prognostic stratification using basic clinical parameters. As a pragmatic adaptation study, external validation is required before clinical application, particularly in settings where MGMTm testing is unavailable.

Humans↗

PubMed2026

Thrombus Metabolism-Based Molecular Subtyping for Prognostic Risk Stratification in Acute Ischemic Stroke: A Preliminary Study.

AIMS: To preliminarily characterize metabolic molecular subtypes of cerebral thromboemboli and evaluate their clinical significance in anterior circulation acute ischemic stroke due to large vessel occlusion (AIS-LVO). METHODS: Untargeted metabolomics was performed on thromboemboli retrieved from 36 patients with anterior circulation AIS-LVO using ultra-performance coupled liquid chromatography with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS). Unsupervised hierarchical clustering was employed to identify distinct metabolic molecular subtypes, and their associations with stroke etiology, radiographic severity, and functional outcomes were analyzed. RESULTS: Two distinct thrombus metabolic molecular subtypes (C1 and C2) were identified based on 12 metabolites significantly associated with both short-term (7-day ∆NIHSS) and long-term (90-day mRS) functional outcomes. The C1 subtype, predominantly cardioembolic, exhibited enhanced lipid metabolism, whereas the C2 subtype, primarily atherothrombotic, demonstrated increased folate metabolism. Patients with C1 thromboemboli presented more severe admission ischemic lesions (as indicated by ASPECTS) and experienced poorer short-term and long-term outcomes. A six-metabolite signature derived from LASSO regression was identified for exploratory discrimination of thrombus metabolic subtypes, etiological subtypes, and 90-day outcomes. CONCLUSION: This preliminary exploratory study identifies two metabolically distinct thrombus molecular subtypes with clinical implications in anterior circulation AIS-LVO, providing a novel basis for risk stratification and personalized secondary prevention and warrants further investigation.

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

scRNA-seq and bulk RNA-seq reveal the characteristics of macrophage copper metabolism and establish a risk signature in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is a prevalent malignancy with an urgent need for improved prognostic stratification and treatment-response prediction. This study aimed to explore a macrophage copper metabolism-associated prognostic model and to investigate the relationship between this risk model and the tumor immune microenvironment. METHODS: The FindClusters function was used to analyze cell clusters, and CellChat and CellPhoneDB/LIANA were employed for cell-cell communication analysis. Copper metabolism-related genes were sourced from the MSigDB database. A prognostic risk model was established using least absolute shrinkage and selection operator (LASSO) analysis and multivariate Cox regression analysis, and a nomogram was constructed by integrating the prognostic model with clinicopathological factors. Additional analyses were performed to map the seven model genes in single-cell data, assess model uncertainty and robustness, evaluate macrophage/copper/cuproptosis-related transcriptional programs, and examine the correlations between risk score, immune infiltration and predicted drug sensitivity. RESULTS: Using single-cell RNA sequencing (scRNA-seq) data, we identified four macrophage subpopulations. Macrophages with high SPP1 expression showed close interaction with T cell populations and were associated with copper ion metabolism. By incorporating 141 copper metabolism-related genes and using The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) cohort, we constructed a seven-gene risk prediction model. Additional single-cell mapping showed that the model genes were detectable in the HCC single-cell dataset and showed a macrophage-associated expression pattern. The model showed moderate prognostic discrimination in TCGA-LIHC, whereas its external performance was heterogeneous and remained evaluable across external cohorts, with performance varying among datasets. Immune and mechanism-related analyses suggested that the risk signature was associated with macrophage-related infiltration, copper metabolism and cuproptosis-related transcriptional programs. Drug sensitivity analysis nominated Daporinad as a computationally predicted candidate compound, supporting Daporinad as a pharmacogenomic candidate for follow-up investigation. CONCLUSIONS: By integrating scRNA-seq and bulk RNA sequencing (RNA-seq) data, we constructed a macrophage copper metabolism-associated prognostic signature for HCC. The risk score was associated with survival, immune microenvironment features and predicted drug response, providing a transcriptomic framework for risk stratification and therapeutic hypothesis generation.

Hepatocellular carcinoma (HCC)↗

PubMed2026

Reduced VEPH1 expression is associated with an invasive phenotype and poor prognosis in clear cell renal cell carcinoma.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) remains a clinically heterogeneous urologic malignancy, and improved biomarkers are needed to refine prognostic stratification. VEPH1 has been implicated in cancer biology, but its role in ccRCC is incompletely defined. This study aimed to investigate the expression, prognostic relevance, and functional effects of VEPH1 in ccRCC. METHODS: VEPH1 transcript expression and prognostic relevance were evaluated using The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) dataset and the University of Alabama at Birmingham Cancer Data Analysis Portal (UALCAN) and validated in paired ccRCC and adjacent normal renal tissues. The ability of VEPH1 transcript expression to distinguish tumor from normal tissues within the TCGA-KIRC dataset was assessed by receiver operating characteristic analysis. Gain- and loss-of-function experiments were performed in 786-O and 769-P ccRCC cells to determine the effects of VEPH1 on epithelial-mesenchymal transition (EMT)-related markers, migration, and invasion. AKT and ERK phosphorylation was evaluated by western blotting. RESULTS: VEPH1 transcript expression was significantly lower in ccRCC tissues than in normal renal tissues and distinguished tumor from normal samples within the TCGA-KIRC dataset. Low VEPH1 transcript expression was associated with poorer overall survival. Validation in 11 paired clinical specimens confirmed reduced VEPH1 messenger RNA (mRNA) and VEPH1 protein expression in tumor tissues. Functionally, VEPH1 overexpression increased E-cadherin, decreased N-cadherin, and suppressed migration and invasion, whereas partial VEPH1 knockdown produced the opposite changes. In exploratory signaling analyses, VEPH1 overexpression was associated with reduced AKT and ERK phosphorylation without altering total AKT or ERK levels. CONCLUSIONS: Reduced VEPH1 transcript expression was associated with poorer overall survival, whereas experimental VEPH1 depletion was associated with invasive and EMT-related features in ccRCC cells. VEPH1 may represent a candidate prognostic indicator in ccRCC; however, its relationship with AKT and ERK signaling and its clinical relevance require further mechanistic and independent-cohort validation.

Clear cell renal cell carcinoma (ccRCC)↗

PubMed2026

Pretreatment EBV-DNA/TLG-Based Risk Stratification Is Associated With Survival Outcomes in Nonmetastatic Nasopharyngeal Carcinoma: An Exploratory Study.

Whether combining pretreatment plasma Epstein-Barr virus DNA (EBV-DNA) with 18F-FDG PET/CT-derived total lesion glycolysis (TLG) improves prognostic stratification in nonmetastatic nasopharyngeal carcinoma (NPC) is unclear, particularly in nonendemic populations. We retrospectively analyzed 86 eligible nonmetastatic NPC patients treated with definitive radiotherapy (2010-2024) at a single nonendemic-region institution. EBV-DNA (prespecified cutoff 3500 copies/mL) and TLG (cutoff 200, ROC-derived within this cohort) were dichotomized. Both were available in 59/86 patients (68.6%), who differed from the rest in nodal and overall stage and in RT technique. Baseline PET/CT was in-house in 57 of 86 patients, and a robustness analysis in that subgroup is reported. Given limited events (13 PFS, 9 OS), Cox analyses are exploratory and were supplemented with penalized regression and bootstrap validation. At a median follow-up of 75.5 months, 5-year PFS and OS for the whole cohort (n = 86) were 81.1% and 85.9%. The EBV-DNAhigh/TLGhigh subgroup remained associated with inferior PFS after adjustment in an exploratory model (adjusted HR = 3.97, 95% CI: 1.32-11.93) and, in a single-variable model, with inferior OS (HR = 4.13, 95% CI: 1.10-15.52). Discrimination was comparable to the individual-biomarker model for PFS and lower for OS. Five-year PFS fell monotonically across the four risk groups in the complete-case cohort (n = 59; 89.7%-58.3%). OS differed across groups (log-rank p = 0.044) but was not strictly monotonic, with wide, overlapping confidence intervals. This two-biomarker model is hypothesis-generating and needs prospective, multicenter validation before any consideration of risk-adapted treatment.

Epstein–Barr virus DNA↗

PubMed2026

Systematic characterization of neurotransmitter receptor dysregulation identifies a neural-related prognostic signature associated with biochemical recurrence in prostate cancer.

BACKGROUND: The nervous system is increasingly recognized to play a critical role in tumor initiation and progression. Central to this complex relationship are the interactions between neurotransmitters secreted by neurons and their receptors (neurotransmitter receptors, NTRs) expressed on cancer cells, which activate multiple intracellular signaling pathways. However, the spectrum of NTR dysregulation and its association with biochemical recurrence (BCR) in prostate cancer (PCa) has not been explored. Therefore, the aim of this study was to fill this gap. METHODS: We systematically characterized the expression profiles of 130 NTR genes by integrating bulk and single-cell transcriptomic data. Consistently dysregulated NTR (cdNTR) genes were identified and used to construct a PCa signature (PCaSig) using elastic-net regression. The robustness of PCaSig was evaluated across three independent cohorts. In addition, the associations of PCaSig with clinicopathological characteristics, genomic alterations, tumor immune-related characteristics, and biological pathways were comprehensively investigated. RESULTS: Thirteen cdNTR genes with strong cell-type specificity, particularly in luminal epithelial cells, were identified. PCaSig robustly stratified patients into distinct BCR risk groups across multiple independent cohorts and remained an independent predictor after adjustment for clinicopathological factors. High PCaSig scores were associated with aggressive clinicopathological features, elevated tumor mutation burden (TMB), suppression of neurotransmitter-related signaling, and activation of cell-cycle and immune-related pathways. Notably, PCaSig refined prognostic stratification regardless of TMB status and was associated with distinct immune-related characteristics, including immune checkpoint expression and immune cell infiltration. Incorporation of PCaSig into a clinical nomogram significantly improved prognostic accuracy and clinical net benefit. CONCLUSIONS: These findings establish NTR dysregulation as a previously underappreciated dimension of PCa and support PCaSig as a clinically relevant tool for personalized management.

Neurotransmitter receptor (NTR)↗

PubMed2026

An oxidative stress - and immunotherapy-related six-gene signature defines immune subtypes and predicts prognosis and immunotherapy response in hepatocellular carcinoma.

BACKGROUND: Oxidative stress and the tumor immune microenvironment jointly shape hepatocellular carcinoma (HCC) progression and response to immunotherapy, yet integrated biomarkers linking these processes are lacking. METHODS: Transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets were used to identify oxidative stress- and immunotherapyrelated differentially expressed genes (OSIRDEGs). Functional enrichment, weighted gene co-expression network analysis (WGCNA) and LASSO-Cox regression were used to construct a prognostic signature. Consensus clustering, TIDE, CIBERSORT and ssGSEA characterized immune phenotypes. Somatic mutation, copy-number and drug-response data were integrated to assess genomic alterations and drug sensitivity. Expression of model genes was validated by qRT-PCR and western blotting in HCC cell lines. RESULTS: We identified 24 OSIRDEGs enriched in cell-cycle and mitotic pathways. WGCNA intersection yielded 18 module genes, from which a six-gene signature (BUB1B, CDKN2A, CENPE, HMMR, PTTG1, SPP1) was derived. The signature robustly stratified patients into high- and low-risk groups with significantly different progression-free and disease-free survival in both TCGA-LIHC and GSE14520. Based on signature expression, two molecular subtypes were defined, exhibiting distinct survival, immune landscapes and predicted immunotherapy responsiveness. Model genes harbored recurrent alterations and showed significant correlations with anticancer agents. All six genes were upregulated at mRNA and protein levels in metastatic HCC cell lines versus normal hepatocytes. CONCLUSIONS: We systematically explored the landscape of OSIRDEGs in HCC, and proposed a validated six-gene signature that refines prognostic stratification, delineates immunerelevant HCC subtypes and highlights candidate biomarkers for therapeutic selection and mechanistic investigation.

Humans↗

PubMed2026

Circulating tumor cells identify a disseminated genomic high-risk phenotype within IMS-IMWG 2025 staging in newly diagnosed multiple myeloma.

The 2025 IMS-IMWG consensus genomic staging (CGS) system has improved genomic risk stratification in newly diagnosed multiple myeloma (NDMM), yet does not capture whether high-risk clones have acquired a disseminated phenotype. We investigated whether circulating tumor cells (CTC) refine CGS and enable longitudinal residual disease monitoring. We retrospectively analyzed 631 MM patients from the NICHE cohort (NCT04645199) who underwent CTC assessment across disease phases. Among 410 patients assessed at diagnosis, CTC were detectable in 63.9% and correlated with both bone marrow plasma cell infiltration and accumulation of high-risk cytogenetic abnormalities. In 359 NDMM patients with adequate follow-up, a cohort-derived CTC threshold of 0.38% independently predicted inferior progression-free survival (PFS) after multivariable adjustment. Importantly, CTC refined prognostic stratification specifically within the CGS high-risk subgroup. Patients with CGS high-risk/CTC-high disease had the shortest PFS, thereby defining a disseminated genomic high-risk phenotype comprising 12.4% (39/314) of evaluable NDMM patients. In follow-up cohorts, detectable CTC were associated with inferior outcomes in 127 patients assessed during non-progressive disease states, whereas combined CTC and bone marrow minimal residual disease assessment stratified outcomes in 120 patients with paired measurements. Overall, CTC-integrated CGS supports minimally invasive baseline risk stratification and longitudinal disease monitoring.

Journal Article↗

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

Recent Advances in Surveillance Strategies for Nasopharyngeal Carcinoma.

PURPOSE OF REVIEW: Nasopharyngeal carcinoma (NPC) is a malignant tumor characterized by a distinct geographical distribution. Effective surveillance is crucial for the early detection of recurrence or metastasis and for improving patient prognosis.This review systematically examines current NPC follow-up protocols and recent developments to inform individualized precision surveillance. RECENT FINDINGS: This review focuses on two main aspects. 1) We compare and analyze current major NPC follow-up guidelines, with key discussions covering follow-up frequency, imaging modalities (including magnetic resonance imaging [MRI] and positron emission tomography [PET]), plasma Epstein-Barr virus DNA (EBV-DNA) monitoring, and functional assessments. 2)We elaborate on the application prospects and research progress of genomics, radiomics, and artificial intelligence in NPC surveillance. Studies suggest that risk-stratified, individualized follow-up strategies, such as those based on conditional survival models, can enhance the cost-effectiveness of surveillance. Additionally, emerging technologies, including radiomics and artificial intelligence, show promise for improving recurrence risk assessment, prognostic stratification, and individualized surveillance in NPC. Concurrently, advances in genomics and radiomics offer new opportunities for predicting complications and guiding treatment adjustments. Future efforts should focus on integrating multidisciplinary expertise to develop dynamic monitoring systems that enable precise follow-up and ultimately improve patient survival outcomes.

Humans↗

PubMed2026

A spotlight on PTEN alterations in prostate cancer: A narrative review.

Among the molecular alterations observed in prostate cancer, PTEN loss represents a key event, functionally linked to aberrant activation of the PI3K/AKT/mTOR pathway. Loss of PTEN function contributes to disease progression, therapy resistance and poor prognosis. This review highlights the biological significance and the prognostic role of PTEN in prostate cancer, the biologically relevant crosstalk between the PI3K and androgen receptor pathways, and the implications of this interaction for tumour adaptation and treatment resistance. We also discuss current methodologies for PTEN assessment, including immunohistochemistry and genomic techniques, and provide an overview of clinical trials targeting the PI3K/AKT axis in prostate cancer. Understanding PTEN alterations is essential for improving prognostic stratification and guiding precision oncology approaches.

Humans↗