Multi-database analysis showed that genes associated with inflammatory response are important for prognosis prediction of renal clear cell carcinoma

Abstract

Background Clear cell carcinoma of the kidney is the largest subtype of kidney tumor. Inflammatory responses are involved in all stages of the tumor. The relationship between genes related to inflammatory response and renal clear cell carcinoma is expected to help the diagnosis and treatment of tumor patients. Methods First, we obtained all the data needed for this study free of charge from a public database. After differential analysis and COX regression, we obtained genes that were used to build the model. In addition, data from multiple databases were included in this study. To make the data from different sources comparable, we standardize all the data using the SVA package. Next, through LASSO regression, we constructed a prognostic model of genes related to inflammation (IRGM). The model contains 10 gene model signatures related to the inflammatory response (IRGMS). The data used for modeling and internal validation came from the TCGA database and the GSE29609 dataset. Clear cell renal carcinoma data from the ICGC database will be used for external validation. Tumor data from E-MTAB-1980 cohort will provide an additional external validation. The GSE40453 dataset and the GSE53757 dataset will be used to verify the differential expression of IRGMS. The immunohistochemistry of IRGMS will be queried through the HPA database. After adequate validation of IRGM, we explored the application of IRGM in greater depth by constructing nomograms, pathway enrichment analysis, immunocorrelation analysis, drug susceptibility analysis, and subtype identification. Results IRGM can robustly predict the prognosis of patient samples with clear cell carcinoma of the kidney from different databases. IRGMS (IGFBP3, SCNN1B, IFI16, LRRC19, GSTM3, IFI44, APOLD1, HPGD, CPA3, PROM1) is expected to become a new biomarker associated with clear cell carcinoma of the kidney. The construction of nomogram can use IRGM to predict patient survival more accurately, so as to adopt more reasonable treatment methods. Pathway enrichment analysis showed that patients in the HR group were associated with a variety of tumorigenesis biological processes. Immune-related analysis and drug susceptibility analysis suggest that patients with higher IRGM scores have more treatment options. The subtype identification results are conducive to further refinement of treatment. Conclusion IRGMS (IGFBP3, SCNN1B, IFI16, LRRC19, GSTM3, IFI44, APOLD1, HPGD, CPA3, PROM1) is valuable in predicting the prognosis of clear cell carcinoma of the kidney. Patients with higher IRGM scores may be better candidates for treatment with immune checkpoint inhibitors and have more chemotherapy options.