TL;DR
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.
This study aims to identify programmed cell death (PCD)-associated genes linked to breast cancer prognosis for the construction of a prognostic model. Transcriptomic and clinical information was imported from the The Cancer Genome Atlas (TCGA) and GEO databases. Modulatory genes related to 18 types of PCD were evaluated. Furthermore, the TCGA and GEO datasets were employed as the training and validation datasets, respectively. A risk score prognostic model based on PCD-related genes was generated via univariate, LASSO, and multivariate Cox regression analyses. Further, differences in drug sensitivity, tumor mutation burden (TMB), immune-related pathways and cell infiltration, and immune checkpoints were compared between high-risk and low-risk cohorts to elucidate the clinical applicability of the model. Lastly, the model gene’s expression was verified by RT-PCR. The data revealed 1480 PCD-related genes from the TCGA breast cancer gene expression matrix. Differential expression analysis identified 186 differentially expressed PCD genes. Furthermore, a prognostic model was generated according to the risk scores using multivariate Cox regression. The model comprised 8 PCD-related genes (BRSK2, CD24, IFNG, LAMB3, PDX1, PMAIP1, SLC7A11, and TRIML2). Moreover, breast cancer cases were divided into high-risk and low-risk cohorts per the median risk score. The results indicated that low-risk patients had better prognoses, and the model showed good predictive performance in the GEO validation cohort. The area under the curve values were 0.819, 0.731, and 0.674 for the nomogram’s 3, 5, and 8 years overall survival, respectively. Functional enrichment analysis revealed that the prognostic model was markedly linked with the modulation of the immune microenvironment and tumor progression in breast cancer. Immune infiltration assessment revealed that low-risk patients had increased activity in immune-related pathways and infiltration of immune cells. In addition, the low-risk cohort had lower TMB and elevated immune checkpoint-related levels. Correlation analysis between immune checkpoints and risk scores indicated that low-risk patients were more responsive to immunotherapy. Drug sensitivity analysis showed variations in the IC50 values between the risk cohorts, suggesting potential variations in drug efficacy across different risk cohorts. Gene expression was verified by RT-PCR.A prognostic risk model for breast cancer based on 8 PCD-related genes was constructed and its predictive value was validated. The established model may provide novel biomarkers and effective therapeutic targets for breast cancer diagnosis and treatment.
It is suggested that glycolytic activity is closely associated with both drug resistance and clinical prognosis in LUAD, and the proposed integrative model holds promise for enhancing precision treatment decision-making through optimized risk assessment and rational selection of chemotherapeutic regimens.
: 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.
Purpose Breast cancer (BRCA) is a heterogeneous disease with a complex etiology. The prognostic value of genes related to migrasomes and the tumor microenvironment (MTMERGs) in BRCA is unclear. Materials and Methods A prognostic risk model was constructed using six core signature genes identified from MTMERGs via differential expression analysis and Cox regression. Its reliability was validated in an independent cohort using Kaplan-Meier and time-dependent ROC curves. A nomogram was developed and assessed via Decision Curve Analysis (DCA). Biological functions and immune infiltration were evaluated with GSEA, CIBERSORT, and ssGSEA. Immunotherapy sensitivity was predicted using TIDE/IPS scores and the IMvigor210 cohort. Tumor Mutation Burden (TMB) analysis and the pRRophetic algorithm were used for further clinical correlation and drug sensitivity prediction. Results The six-MTMERG model effectively stratified patients into high- and low-risk groups with distinct survival outcomes. The high-risk group was associated with a predicted immunosuppressive microenvironment (estimated enrichment of M0/M2 macrophages), higher TMB, and poorer prognosis. In contrast, the low-risk group was estimated to possess an immunologically active profile and showed a better predicted response to immune checkpoint inhibitors. Predicted differential sensitivities to conventional chemotherapy were also computationally evaluated between the subgroups. Conclusion We developed a computationally derived and externally validated prognostic model for BRCA based on migrasome and tumor microenvironment features. It successfully stratifies patients into groups with divergent clinical outcomes, immune profiles, and therapeutic responses, providing insights into BRCA heterogeneity and prognosis. Further prospective and experimental validation is warranted before clinical application.
Keratins (KRTs) are intermediate filament proteins expressed in epithelial cells and serve as diagnostic cancer biomarkers. They serve pivotal roles in tumor progression and metastasis, but their prognostic value in lung adenocarcinoma (LUAD) and relationship with the tumor immune microenvironment remain unclear. Gene expression of KRTs in The Cancer Genome Atlas (TCGA)-LUAD was analyzed; candidate genes were selected using LASSO and random forest. An XGBoost classifier with SHAP interpretation distinguished tumor from normal tissues. Prognostic models were developed using 101 algorithms with 10-fold cross-validation and validated in two independent GEO cohorts. Immune cell composition and pathway activity were assessed by CIBERSORT, MCP-counter, ssGSEA and GSEA; the TIDE score estimated potential immunotherapy response. Functional validation of KRT81 was performed via shRNA knockdown in LUAD cell lines, followed by proliferation, apoptosis and migration assays. A KRT-based diagnostic and prognostic signature was established. The XGBoost model achieved high accuracy (TCGA AUC=0.996; GSE31210 AUC=0.860); SHAP identified KRT81 as a key contributor. A four-gene prognostic model (KRT27, KRT80, KRT16, KRT81) stratified patients into high- and low-risk groups. The risk score was associated with advanced tumor stage and independently predicted overall survival (multivariate HR=2.16, 95% CI 1.43–3.28, P=2.0×10-4). High-risk tumors enriched proliferation/stroma pathways (cell cycle, DNA repair, ECM-receptor interaction, focal adhesion, p53 signaling); low-risk tumors enriched immune pathways. Immune profiling revealed reduced T/B cell infiltration, increased endothelial cells and higher T-cell exclusion scores in high-risk patients, indicating an immunosuppressive microenvironment. Higher risk scores also associated with chemotherapy resistance. Functional validation confirmed that shRNA-mediated KRT81 knockdown reduced LUAD cell proliferation, migration and invasion, while promoting apoptosis. These 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. This study provides new insights for prognostic evaluation and therapeutic decision-making in LUAD.
A READ-derived response-associated gene signature for recurrence stratification and exploratory cross-cohort evaluation in additional colorectal cancer cohorts, while further exploring its association with treatment-response phenotypes.