Background: Immune therapy is widely used in treating clear cell renal cell carcinoma (ccRCC), yet identifying patient subgroups that are expected to response remains challenging. As complement system can mediate immune effects, including the progression of tumors, a correlation between complement system and immune therapy may exist.
Methods: Based on 11 complement system associated genes (CSAGs) identified from The Cancer Genome Atlas (TCGA), we performed unsupervised clustering and classified the tumors into two different complement system (CS) patterns. The clinical significance, tumor microenvironment (TME), functional enrichment, and immune infiltration were further analyzed. A novel scoring system named CSscore was developed based on the expression levels of the 11 CSAGs.
Results: Two distinct CS patterns were identified, classified as Cluster1 and Cluster2, and Cluster1 showed poor clinical outcome. Further analysis of functional enrichment, immune cell infiltration, and genetic variation revealed that Cluster1 had high infiltration of TME immune cells, but also exhibited high immune escape. The novel prognostic model, CSscore could act as an independent prognostic factor and effectively predict patients' prognosis and distinguish the therapeutic efficacy of different immune treatment strategies. The pan-cancer analysis of the CSscore indicates its potential to be further generalized to other types of cancer.
Conclusions: Two distinct CS patterns were identified and were further analyzed in terms of infiltration of TME immune cells and immune escape, providing potential explanations for the impact on prognosis of ccRCC. Our CSscore prognostic model may offer a novel perspective in the management of ccRCC patients, and potentially other types of cancer as well.
Keywords: Clear cell renal cell carcinoma; Complement system; Drug response; Immune escape; Prognostic model.
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