A seven-gene immune-related prognostic signature that, combined with clinicopathological parameters, provides a robust tool for individualized survival prediction and may guide precision management in CRC patients is developed and validated.
Abstract
: Backgrounds: Colorectal cancer (CRC) prognosis remains difficult due to molecular heterogeneity and interaction between tumor cells and the immune microenvironment. This study aimed to identify transcriptomic and immune-cell patterns associated with overall survival (OS) and to develop an integrated prognostic model to improve risk stratification. Methods: RNA-sequencing was performed on 131 primary CRC samples and matched normal tissues. Differentially expressed genes (DEGs) were identified and functionally characterized through gene ontology, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, and protein–protein interaction (PPI) network analysis. Immune-cell composition was estimated using CIBERSORTx deconvolution and evaluated for its association with OS. Prognostic DEGs were screened using univariate Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) analysis to construct a risk score. The model was validated in the Cancer Genome Atlas Database (TCGA)-COAD (colon cancer) and READ (rectal cancer) cohorts. A nomogram integrating molecular and clinicopathological variables were generated. Results: A total of 5589 DEGs were identified between CRC and normal tissues, enriched in pathways related to cell cycle, Tumor Protein P53 (TP53), WNT Family Member (WNT), Janus kinase/signal transducer and activator of transcription 3 (JAK/STAT), calcium signaling, metabolism, and immune regulation. PPI analysis highlighted ten upregulated hub genes involved in mitotic spindle formation and chromosomal stability. Immune infiltration analysys indicated that higher proportions of plasma cells ( p = 6.9 × 10 − 4 ), naïve B cells ( p = 0.019), resting CD4 + memory T cells ( p = 0.02), M0 macrophages ( p = 0.0077), and activated dendritic cells ( p = 1.89 × 10 − 5 ) were associated with improved OS, whereas monocytes ( p = 0.012), neutrophils ( p = 0.041), activated mast cells ( p = 0.0066), and M2 macrophages ( p = 0.014) were linked to poorer survival. A seven-gene signature including aspartate beta-hydroxylase (ASPH), bradykinin receptor B1 (BDKRB1), calcium voltage-gated channel auxiliary subunit beta 1 (CACNB1), C-C motif chemokine receptor 8 (CCR8), cyclic nucleotide gated channel subunit alpha 3 (CNGA3), microtubule associated protein 1A (MAP1A) and oxytocin receptor (OXTR) stratified patients into high-and low-risk groups with significant OS differences. The model demonstrated strong predictive performance (AUC: 0.84 at 1 year) and was validated in TCGA cohorts. Multivariate analysis confirmed the risk score as an independent prognostic factor. The integrated nomogram accurately predicted 1, 3-, and 5-year survival (C-index = 0.757; 95% CI 0.724–0.791). Conclusions: We developed and validated a seven-gene immune-related prognostic signature that, combined with clinicopathological parameters, provides a robust tool for individualized survival prediction and may guide precision management in CRC patients.
Hepatocellular carcinoma (HCC) is one of the malignant tumors with high incidence and mortality rates worldwide. Given the poor prognosis of patients with HCC, it is crucial to explore the molecular mechanisms underlying HCC development and to evaluate prognostic markers. Differential expression analysis followed by univariate Cox, LASSO, and multivariate Cox regression identified four genes (EPO, SOCS2, IL18RAP, and KPNA2), and a Cox-based risk score was evaluated in the TCGA-LIHC cohort and externally in GSE14520 using Kaplan–Meier and time-dependent ROC analyses. Bulk, single-cell, and protein resources provided convergent expression context. Survival machine-learning analysis using observed overall-survival time and censoring status identified Cox–Ridge as the best-performing model in TCGA-LIHC, with more modest performance in GSE14520, and immune profiling revealed risk-group-associated differences in estimated immune and stromal components, immune-cell composition, and immune-checkpoint expression. The oncoPredict/GDSC2 screen highlighted five potential drug candidates for experimental prioritization. Because the drug screen is based on computationally predicted sensitivities, these findings should be regarded as hypothesis-generating and require validation in prospective cohorts and experimental systems before clinical translation.
Yu-Xian Liu, Xing-Jie Chen, Junyuan Zhang et al.· International Journal of Mol...· 0 citations
A dual-gene model based on neutrophil heterogeneity demonstrated strong predictive performance and functioned as an independent prognostic indicator, and distinct immune and prognostic differences were identified among molecular subtypes.
BACKGROUND
Immunotherapy has demonstrated outstanding therapeutic success in solid cancers by regulating immunity through immunological components in the tumor microenvironment. However, the immunological phenotypes and immunosuppressive processes in glioblastoma (GBM) remain unknown.
METHODS
The relative abundance of immune cells was determined, which was used to classify 167 GBM samples into high- and low-immune subtypes with ssGSEA analysis. Differentially expressed immune-related genes (DE-IRGs) were determined between these two immune subtypes, which were used for gene oncology, pathway network, survival, and nonnegative matrix factorization cluster analyses. Survival-related DE-IRGs was used to create DE-IRG signature with LASSO regression. DE-IRG signature-based risk score was calculated for determing high- and low-risk score groups. Differentially expressed genes (DEGs) were determined between high-and low-risk score groups, which were used for WGCNA coexpression gene modules analysis. A ggalluvial plot was used to examine the cross-talk between the LASSO groups and NMF clusters. Furthermore, DE-IRGs data were integrated with quantitative proteomics data of human GBMs to obtain key molecules, followed by functional analysis of key molecule in GBM cell models.
RESULTS
A total of 115 DE-IRGs were identified in high- vs. low-immune subtypes in GBM. These DE-IRGs were mapped into 6 KEGG pathways, 88 important biological processes, and 25 important molecular functions. Seven DE-IRG prognostic signatures (GBX1, PF4V1, RETN, SOAT2, SUMO1P1, TNS4, and TTC22) based on DE-IRGs were generated via LASSO regression to identify GBM samples as high- or low-risk score groups. This signature was closely correlated with overall survival, clinical characteristics, immune cells, immunological scores, immune checkpoints, tumor mutation burden, gene mutations, and drug sensitivity in GBM patients. The LASSO groups had a substantial correlation with three NMF-based unique clusters in GBM, and a WGCNA of 452 DEGs was performed to distinguish high- and low-risk score groups. Furthermore, integrative analysis of 115 DE-IRGs and 608 differentially expressed proteins (DEPs) found a overlapped molecule C4BPA that promoted the phenotype of GBM tumor cells.
CONCLUSION
This study provided the complete IRG landscape and distribution of tumor microenvironment cells in GBM, which are promising indicators of prognosis and survival, and have the potential to monitor treatment schedules.
Jianbang Han, Linting Luo, Ke Yu et al.· BMC Cancer· 0 citations
Aim: Colorectal cancer (CRC) ranks among the most prevalent malignancies across the globe, with treatment resistance often closely linked to the complexity of the tumor microenvironment (TME). This study aims to establish a gene signature associated with epithelial-mesenchymal transition (EMT) that integrates TME dynamics, prognosis prediction, and drug resistance assessment in CRC. Methods: We employed the Cancer Genome Atlas (TCGA) resource and bulk RNA-sequencing profiles linked to EMT to identify common differentially expressed genes (DEGs). An eight-gene signature was constructed using multivariable Cox regression analysis. The correlations of risk groups (scores) with overall survival, biological characteristics, and drug sensitivity were analyzed in CRC patients. Results: Patients in the high-risk group exhibited significantly worse clinical outcomes than those in the low-risk group. Moreover, a lower risk score was significantly correlated with increased responsiveness to both immune checkpoint inhibitors and 5-fluorouracil in CRC patients. Additionally, experiments in vitro and in vivo confirmed that FABP4 functions as an oncogene in CRC by facilitating cell growth, migration, and chemotherapy resistance. Conclusion: The EMT-associated gene signature holds significant value for predicting clinical prognosis, immunotherapy responsiveness, and chemotherapy sensitivity in CRC.
Xiaonan Shen, Hao Zhong, Wenqing Jia et al.· Cancer Drug Resistance· 0 citations
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.
Jun-Ze Chen, Jia-Mei Li, Zhi-Yong Lin et al.· Journal of Gastrointestinal...· 0 citations
OBJECTIVE
To investigate FUBP1 expression, its prognostic value, impact on the tumor microenvironment (TME), and drug sensitivity in colorectal cancer (CRC), and to explore its potential underlying mechanisms.
METHODS
Using data from The Cancer Genome Atlas (TCGA), we analyzed FUBP1 expression in CRC, evaluated its prognostic value via survival analysis and nomogram construction, performed pathway enrichment analysis, and assessed immune cell infiltration and Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) scores. We further conducted immunohistochemistry (IHC) validation on an independent institutional cohort and analyzed the Gene Expression Omnibus (GEO) single-cell RNA sequencing dataset GSE132465 (n = 63,689 cells) to examine the association between FUBP1 expression and the tumor immune microenvironment at single-cell resolution.
RESULTS
FUBP1 was highly expressed in CRC (P < 0.001), particularly in younger patients. High FUBP1 expression was associated with improved overall survival in univariate analysis (HR = 0.68, P = 0.028), yet this association was not maintained as an independent prognostic factor after adjustment for other clinical covariates (HR = 0.722, P = 0.098). Immunohistochemistry results from the independent cohort confirmed upregulated FUBP1 protein in 90% of CRC specimens. Single-cell analysis revealed that FUBP1-high cell clusters exhibited markedly reduced immune cell infiltration (35.97%vs 62.96%, P < 0.001), indicating an immunosuppressive "cold" tumor microenvironment. Tumors with high FUBP1 expression also displayed elevated PD-L1, PD-1, and CTLA4 expression, lower half-maximal inhibitory concentration (IC50) values for oxaliplatin, irinotecan, and 5-fluorouracil, and higher Immune Phenotype Score (IPS) for anti-PD-1 monotherapy or combined anti-CTLA-4 immunotherapy. FUBP1 expression was correlated with increased expression of MYC, TP53, and their downstream target genes (CCND1, CDK4, BAX, CDKN1A).
CONCLUSION
FUBP1 is highly expressed in CRC and associated with an immunosuppressive "cold" tumor microenvironment characterized by decreased immune cell infiltration, while it correlates with favorable chemotherapeutic sensitivity. FUBP1 may serve as a potential predictive biomarker for responses to chemotherapy and immunotherapy, rather than an independent prognostic indicator for survival. Its linkage to MYC and TP53 signaling pathways warrants further mechanistic investigation.
Zhen-Xiang Li, Yan-Fang Zhao, Yan-Li Si et al.· Cancer Treatment and Researc...· 0 citations
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