OBJECTIVE

The tumor microenvironment plays a key role in regulating tumor progression. This research aimed to develop the biomarker related to tumor microenvironment in clear cell renal cell carcinoma (ccRCC).

METHODS

The ESTIMATE algorithm was used to evaluate the immune score of ccRCC cases from The Cancer Genome Atlas (TCGA). Differentially expressed genes between high and low immune scores were identified and a 13-gene signature was constructed by the LASSO Cox regression model to predict overall survival (OS) for ccRCC cases in TCGA or International Cancer Genome Consortium (ICGC) project. The immune cell fractions were calculated by the TIMER algorithm. Cell viability and gene expression were determined by CCK-8 and qRT-PCR, respectively.

RESULTS

The OS of patients with high immune scores was worse than that of patients with low immune scores. The OS between ccRCC patients from TCGA or ICGC cohort in high- and low-risk groups stratified by the gene signature was significantly different. Subgroup analysis also showed a robust prognostic ability of the gene signature. Multivariate Cox regression analysis demonstrated that this gene signature was an independent prognostic factor. The nomogram that integrated the gene signature and three clinicopathological risk factors had a favorably predictive ability in predicting 3, 5 and 10 year survival. Moreover, the high-risk group had a significantly higher abundance of B cell, T cell, CD4, neutrophil and DC infiltration. Among 13 genes, X-C motif chemokine receptor1 (XCR1) was upregulated in ccRCC cells and exerted an inhibitory effect on cell proliferation.

CONCLUSION

This study constructs a 13-gene signature as a novel prognostic marker to predict the survival of ccRCC patients and XCR1 may serve as a therapeutic target.