Jul 2026· Arab Journal of Gastroenterology· Vol 27, pp. 410-424· 0 citations· 63 references
Medicine
TL;DR
MMG-based subtyping and the reliable prognostic risk score model provide novel insights for predicting prognosis and developing personalized therapy in ESCC.
Abstract
Background
AND STUDY
Aims
Immunotherapy offers promising prospects for esophageal squamous cell carcinoma (ESCC), a highly fatal malignant tumor. Given the association between manganese metabolism and tumor immunity, this study explored the prognostic value and role of manganese-metabolism-related genes (MMRGs) in the ESCC immune microenvironment to uncover their clinical potential.
PATIENTS AND
Methods
Transcriptomic and clinical data of ESCC were retrieved from TCGA and the GEO as training and validation cohorts, respectively. Patients were clustered and subtyped based on MMRGs, with survival compared. A prognostic risk model was constructed using differential analysis, PPI network, and Cox regression; its relationship with immune characteristics, immunotherapy response, and drug sensitivity was evaluated. SLC40A1 was knocked down in vitro, and its effects on cellular function and drug sensitivity were assessed via qRT-PCR, Western blot, colony formation, Transwell, and CCK-8 assays.
Results
ESCC patients were stratified into two MMRG-defined subtypes. Cluster 2 showed significantly worse overall survival (OS) than Cluster 1. An 8-gene prognostic model was established and patients were assigned to RiskScorehigh and RiskScorelow groups. The high-risk group, characterized by worse OS, displayed an immunosuppressive microenvironment with abundant M2 macrophages and high immune-checkpoint expression (PDCD1, CTLA4, TIGIT), along with a lower TIDE score, suggesting potentially greater benefit from immune-checkpoint blockade. The RiskScorehigh group was more sensitive to Gemcitabine and Oxaliplatin, whereas the RiskScorelow group responded better to BI-2536 and NU7441. Cellular functional assays confirmed high expression of SLC40A1 in ESCC cells. SLC40A1 knockdown significantly inhibited cell proliferation, migration, and invasion. Additionally, cells exhibited greater sensitivity to Gemcitabine than to BI-2536.
Conclusion
MMRG-based subtyping and the reliable prognostic risk score model provide novel insights for predicting prognosis and developing personalized therapy in ESCC.
Background Lung adenocarcinoma (LUAD), which is the leading subtype of non-small cell lung cancer (NSCLC), poses considerable difficulties in accurate prognostic assessment and targeted therapeutic options. Cell proliferation-related genes (CPGs) and mitochondrial biogenesis-related genes (MBGs) play critical roles in tumor metabolic reprogramming; however, their prognostic value and molecular mechanisms in LUAD are poorly understood. This study aims to construct a CPG/MBG-based prognostic risk model for LUAD, evaluate its clinical utility in predicting prognosis and immunotherapy response, and experimentally validate the functional role of key model genes in LUAD progression. Methods By utilizing The Cancer Genome Atlas (TCGA)-LUAD and GSE72094 datasets, this investigation formulated a risk scoring model through differential expression screening combined with least absolute shrinkage and selection operator (LASSO)-Cox regression analysis. The molecular characteristics and clinical implications of the risk model were investigated via immune microenvironment evaluation, genomic alteration analysis, and drug sensitivity prediction. The functional contributions of key genes were further substantiated using quantitative reverse transcription polymerase chain reaction (qRT-PCR), commercial assay kits, the JC-1 fluorescent probe, the Cell Counting Kit-8 (CCK-8), Transwell invasion assays, and wound healing assays. Results A risk model based on seven CPGs and MBGs (PLK1, HMMR, CYP27A1, LDHA, NPAS2, KRT17, CIDEC) showed reliable predictive performance in both GSE72094 and the TCGA-LUAD cohorts. Enhanced tumor heterogeneity and an immunosuppressive microenvironment were observed in the high-risk group. Drug sensitivity analysis indicated that the risk model could guide personalized treatment strategies; for instance, high-risk patients showed increased susceptibility to agents such as docetaxel and 5-fluorouracil. In vitro experiments demonstrated that the key gene CIDEC exhibited upregulated expression in LUAD tissues and cells. Knockdown of CIDEC led to enhanced cellular energy metabolism and increased mitochondrial membrane potential, while also effectively suppressing cell invasion, proliferation, and migration. Conclusions The established MBGs/CPGs prognostic model provides a novel tool for stratified treatment planning in LUAD, underscoring the crucial roles of cellular proliferation and mitochondrial biogenesis in tumor progression. Functional validation of CIDEC offers experimental support for the development of potential therapeutic strategies.
Findings link KRT expression to clinical outcomes, the tumor microenvironment and therapeutic response in LUAD, suggesting roles for KRTs in cancer progression, chemotherapy resistance and predictive potential for immunotherapy response.
Aohui Chen, Ting Gao, Fengqi Liu et al.· Oncology Letters· 0 citations
Background Neoadjuvant chemoimmunotherapy (NACI) improves outcomes in resectable lung squamous cell carcinoma (LUSC), yet response varies widely and current biomarkers lack precision. Novel correlates of immunotherapy sensitivity tailored to the LUSC tumor microenvironment (TME) are urgently needed. Methods Using TCGA-LUSC transcriptomic data, we constructed a 25-gene prognostic model and applied three machine learning algorithms in combination with the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm to identify core genes linked to prognosis and immunotherapy response. Immune infiltration and enrichment analyses were performed to characterize the TME. An independent pre-NACI biopsy cohort (n=36) was used for histopathological validation to explore correlations with pathological response, while single-cell RNA-seq (GSE207422) and CellChat were used to infer tumor-stromal crosstalk and explore underlying mechanisms. Results The risk score independently stratified prognosis. Among three core genes, high MBNL2 expression was associated with higher TIDE scores, lower TIDE-predicted response rates, and elevated cancer-associated fibroblast (CAF) scores. scRNA-seq revealed systematically enhanced communication between MBNL2-high tumor cells and FAP+ CAFs, with unique ligand-receptor pairs enriched in WNT and EGF pathways; FAP+ CAFs interacted with regulatory T cells via ECM remodeling and the MDK-NCL axis. Histopathological validation confirmed that low tumor-cell MBNL2 expression correlated with higher pathological response and pCR rates, reduced FAP, and decreased FOXP3 expression. Conclusion This study establishes a 25-gene prognostic model for LUSC and identifies MBNL2 as a novel correlate of poor pathological response to NACI. Elevated MBNL2 expression in tumor cells is associated with enhanced tumor-CAF crosstalk, CAF activation, and an immunosuppressive TME, laying a foundation for future mechanistic investigation.
Hao Wu, Yang Cheng, Hong-Lin Yan et al.· Frontiers in Oncology· 0 citations
A six-gene-fibrosis-based prognostic model based on six genes stratifies survival risk and correlates with immune features and drug sensitivity, but provides a preliminary framework requiring prospective clinical validation.
Yanyan Qiu, Cui Lv, Shu-Bo Ding· Clinical and Translational O...· 0 citations
Background Bladder cancer has entered an era in which immune checkpoint blockade (ICB) and antibody-drug conjugate (ADC)-based combinations are reshaping clinical management. However, transcriptomic scores that connect prognosis, tumor microenvironment state, and treatment response are incompletely defined. Methods Open-access TCGA-BLCA RNA-seq, clinical, mutation, copy-number, and RPPA data were downloaded from the Genomic Data Commons (GDC). Tumor-normal differential expressions, survival screening, LASSO-Cox modeling, train-test validation, GEO validation, pathway enrichment, immune signature scoring, mutation/CNV/RPPA support, drug sensitivity prediction, single-cell/spatial localization, and ICB validation were performed using reproducible Python and R scripts. A reduced model was derived using only genes shared by TCGA, GSE13507, and GSE31684. The fixed formula was then applied without refitting to IMvigor210 and GSE176307. Results A five-gene model composed of EMP1, AHNAK, TNFRSF14, CLEC2D, and GSDMB retained TCGA internal prognostic value (train C-index 0.693, test C-index 0.605, all-sample C-index 0.667; TCGA test log-rank p = 0.015), although GEO survival validation in GSE13507 and GSE31684 was modest. High-risk tumors were enriched for epithelial-mesenchymal transition (EMT), TNF-alpha/NF-kB signaling, inflammatory response, hypoxia, complement, CAF, macrophage, checkpoint, and cytotoxic programs. Single-cell and spatial analyses localized the score to basal tumor, endothelial, fibroblast, and perivascular compartments. In IMvigor210, risk scores were higher in ICB non-responders than responders (Wilcoxon p = 0.044; AUC for non-response = 0.580), high-risk tumors had a lower responder rate (17.6% vs. 28.0%), and high risk predicted poorer OS (log-rank p = 0.016; multivariate continuous risk HR = 3.15, p = 0.044). GSE176307 showed directionally consistent but non-significant response results (AUC = 0.576). Conclusions The five-gene score is best interpreted not as a standalone universal prognostic classifier, but as a compact stromal-EMT and immune-suppression phenotype associated with inferior ICB response. These findings support a framework linking prognosis, microenvironment biology, immunotherapy resistance, and therapeutic hypotheses in bladder cancer.
Given the substantial disease burden of stomach adenocarcinoma (STAD)—the dominant and lethal subtype of gastric cancer—and the pivotal role of folate metabolism reprogramming in malignancy and treatment response, this study aimed to construct a prognostic model utilizing folate metabolism-related genes (FMRGs). Such a model is urgently required to supplement traditional TNM staging and to guide personalized precision medicine. Interrogation of TCGA and GEO transcriptomic profiles enabled the identification of differentially expressed FMRGs. These genes subsequently facilitated the construction of a prognostic signature via LASSO-Cox regression, which was then subjected to external validation. We further evaluated the clinical relevance of this risk signature by exploring its correlations with the tumor immune microenvironment, immunotherapy efficacy, and drug sensitivity, employing CIBERSORT and ssGSEA analytical frameworks. A five-gene prognostic model established from 220 FMRGs demonstrated moderate prognostic performance in identifying high-risk stomach adenocarcinoma patients with poorer survival. The high-risk group exhibited immunosuppressive microenvironments with stromal activation, while the low-risk group demonstrated “hot” tumor phenotypes characterized by higher immunogenicity and superior responses to immunotherapy and chemotherapy. Consequently, this model serves as a robust independent prognostic indicator. This five-gene folate metabolism signature shows moderate prognostic value in STAD and may help inform future investigations of the tumor immune microenvironment and therapeutic stratification. This study identified and validated a prognostic risk model consisting of five key genes, which can predict the survival outcomes of patients with STAD. The high-risk group exhibits an immunosuppressive and stroma-remodeling phenotype, while the low-risk group demonstrates an immunologically active state with stronger responsiveness to immunotherapy and chemotherapy. The model not only performs excellently in independent prognostic assessment but also predicts patient sensitivity to immunotherapy and various chemotherapeutic drugs. This study identified and validated a prognostic risk model consisting of five key genes, which can predict the survival outcomes of patients with STAD. The high-risk group exhibits an immunosuppressive and stroma-remodeling phenotype, while the low-risk group demonstrates an immunologically active state with stronger responsiveness to immunotherapy and chemotherapy. The model not only performs excellently in independent prognostic assessment but also predicts patient sensitivity to immunotherapy and various chemotherapeutic drugs.
Liu He, Lilei Zhuang, Shenbao Wu· Discover Oncology· 0 citations
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