A molecular subtype model of ccRCC based on immune-related genes for predicting immunotherapy response

Abstract

Abstract Purpose To construct a molecular subtype based on immunotherapy response in renal clear cell carcinoma (ccRCC) and explore the regulatory molecular mechanisms of the immune microenvironment between the subtype. Methods RNA-seq data related to immune checkpoint blockade (ICB) therapy for ccRCC-related from GSE67501 dataset and clinical information were collected from GEO and TCGA datasets. Differentially expressed genes (DEGs) were identified using the 'limma' R package. GO and KEGG pathways analysis of the DEGs were performed using 'clusterProfiler' R-package. The 'Immuneconv' package was used to assess potential ICB response and tumor mutational burden (TMB) score in different subgroups. ROC curve and survival analysis were conducted for the High-group and Low-group based on the stromal, immune, ESTIMATE scores using 'surviva' and 'survivalROC' packages. Single-cell transcriptome profiling data for ccRCC (GSE121636) was obtained from the GEO database. Immunohistochemistry was used to detect the expression of MALs in human histology. Western blot was used to detect the stable knockdown of MALs cell lines. MACS magnetic bead sorting technology was used to sort CD8+cells. Transwell and flow cytometry were used to detect the proportion of chemotactic CD8+T cell infiltration. Results A total of 311 DEGs were identified with ICB response and non-response. The renal carcinoma samples were classified into two subgroups (C1 group and C2 group) through consistency cluster analysis. A nomogram was developed based on stage, grade, immune score, and subgroup, with an area under the ROC curve of 0.732. The TIDE score of the C2 group was significantly higher than that of the C1 group, and the immune infiltration levels of B cells, neutrophils, macrophages and myeloid dendritic cells were significantly higher in group C1 compared to group C2. The immune score in the C2 group was significantly higher than that in the C1 group. The expression of MAL gene was negatively correlated with TBM score and was highly expressed in CD8+T cell group of both peripheral blood and tumor tissues. Furthermore, The immunohistochemical results showed that the expression of MAL was significantly lower in renal cancer tissues compared to adjacent tissues. Flow cytometry showed that the proportion of CD8+T cells tending towards si-MAL cells was only 13.35%, while the control group had a chemotactic proportion of 38.09%. Conclusion This study identified a correlation between the distribution of infiltrating immune cells and ccRCC subtypes, which could help clinicians to predict the efficacy of ICB. Moreover, MAL gene may play a role in the diagnosis and prognosis of ccRCC by regulating CD8+ T cells infiltration.