DHRS7 is an immune-related prognostic biomarker of KIRC and pan-cancer

Development and implementation of a prognostic model for clear cell renal cell carcinoma based on heterogeneous TLR4 expression

Objective

Clear cell renal cell carcinoma (ccRCC) is the most common subtype among renal cell carcinomas and has the worst prognosis, originating from renal tubular epithelial cells. Toll-like receptor 4 (TLR4) plays a crucial role in ccRCC proliferation, infiltration, and metastasis. The aim of this study was to construct a prognostic scoring model for ccRCC based on TLR4 expression heterogeneity and to explore its association with immune infiltration, thereby providing insights for the treatment and prognostic evaluation of ccRCC.

Methods

Using R software, a differential analysis was conducted on normal samples and ccRCC samples, and in conjunction with the KEGG database, a correlation analysis for the clear cell renal cell carcinoma pathway (hsa05211) was carried out. We observed the expression heterogeneity of TLR4 in the TCGA-KIRC cohort and identified its related differential genes (TRGs). Based on the expression levels of TRGs, consensus clustering was employed to identify TLR4-related subtypes, and further clustering heatmaps, principal component, and single-sample gene set enrichment analyses were conducted. Overlapping differential genes (ODEGs) between subtypes were analysed, and combined with survival data, univariate Cox regression, LASSO, and multivariate Cox regression were used to establish a prognostic risk model for ccRCC. This model was subsequently evaluated through ROC analysis, risk factor correlation analysis, independent prognostic factor analysis, and intergroup differential analysis. The ssGSEA model was employed to explore immune heterogeneity in ccRCC, and the performance of the model in predicting patient prognosis was evaluated using box plots and the oncoPredict software package.

Results

In the TCGA-KIRC cohort, TLR4 expression was notably elevated in ccRCC samples compared to normal samples, correlating with improved survival in the high-expression group. The study identified distinct TLR4-related differential genes and categorized ccRCC into three subtypes with varied survival outcomes. A risk prognosis model based on overlapping differential genes was established, showing significant associations with immune cell infiltration and key immune checkpoints (PD-1, PD-L1, CTLA4). Additionally, drug sensitivity differences were observed between risk groups.

Conclusion

In the TCGA-KIRC cohort, the expression of TLR4 in ccRCC samples exhibited significant heterogeneity. Through clustering analysis, we identified that the primary immune cells across subtypes are myeloid-derived suppressor cells, central memory CD4 T cells, and regulatory T cells. Furthermore, we successfully constructed a prognostic risk model for ccRCC composed of 17 genes. This model provides valuable references for the prognosis prediction and treatment of ccRCC patients.

Integrating single-cell and bulk RNA sequencing to predict prognosis and immunotherapy response in prostate cancer

Prostate cancer (PCa) stands as a prominent contributor to morbidity and mortality among males on a global scale. Cancer-associated fibroblasts (CAFs) are considered to be closely connected to tumour growth, invasion, and metastasis. We explored the role and characteristics of CAFs in PCa through bioinformatics analysis and built a CAFs-based risk model to predict prognostic treatment and treatment response in PCa patients. First, we downloaded the scRNA-seq data for PCa from the GEO. We extracted bulk RNA-seq data for PCa from the TCGA and GEO and adopted "ComBat" to remove batch effects. Then, we created a Seurat object for the scRNA-seq data using the package "Seurat" in R and identified CAF clusters based on the CAF-related genes (CAFRGs). Based on CAFRGs, a prognostic model was constructed by univariate Cox, LASSO, and multivariate Cox analyses. And the model was validated internally and externally by Kaplan-Meier analysis, respectively. We further performed GO and KEGG analyses of DEGs between risk groups. Besides, we investigated differences in somatic mutations between different risk groups. We explored differences in the immune microenvironment landscape and ICG expression levels in the different groups. Finally, we predicted the response to immunotherapy and the sensitivity of antitumour drugs between the different groups. We screened 4 CAF clusters and identified 463 CAFRGs in PCa scRNA-seq. We constructed a model containing 10 prognostic CAFRGs by univariate Cox, LASSO, and multivariate Cox analysis. Somatic mutation analysis revealed that TTN and TP53 were significantly more mutated in the high-risk group. Finally, we screened 31 chemotherapeutic drugs and targeted therapeutic drugs for PCa. In conclusion, we identified four clusters based on CAFs and constructed a new CAFs-based prognostic signature that could predict PCa patient prognosis and response to immunotherapy and might suggest meaningful clinical options for the treatment of PCa.