Development of a novel gene signature to predict prognosis and response to PD-1 blockade in clear cell renal cell carcinoma

Abstract

Clear cell renal cell carcinoma (ccRCC) is the most common kidney malignancy characterized by a poor prognosis. The treatment efficacy of immune checkpoint inhibitors (ICIs) also varies widely in advanced ccRCC. We aim to construct a robust gene signature to improve the prognostic discrimination and prediction of ICIs for ccRCC patients. In this study, adopting differentially expressed genes from seven ccRCC datasets in GEO (Gene Expression Omnibus), a novel signature (FOXM1&TOP2A) was constructed in TCGA (The Cancer Genome Atlas) database by LASSO and Cox regression. Survival and time-dependent ROC analysis revealed the strong predictive ability of our signature in discovery set, two online validation sets and one tissue microarray (TMA) from our institution. High-risk group based on the signature comprises more high-grade (G3&G4) and advanced pathologic stage (stageIII/IV) tumors and presents hyperactivation of cell cycle process according to the functional analysis. Meanwhile, high-risk tumors demonstrate an immunosuppressive phenotype with more infiltrations of regulatory T cells (Tregs), macrophages and high expressions of genes negatively regulating anti-tumor immunity. Low-risk tumors have an improved response to anti-PD-1 therapy and the predictive ability of our signature is better than other recognized biomarkers in ccRCC. A nomogram containing this signature showed a high predictive accuracy with AUCs of 0.90 and 0.84 at 3 and 5 years. Overall, this robust signature could predict prognosis, evaluate immune microenvironment and response to anti-PD-1 therapy in ccRCC, which is very promising in clinical promotion.

An immune gene signature to predict prognosis and immunotherapeutic response in lung adenocarcinoma

“…Recent studies have shown that cell cycle-related genes are strongly associated with the prognosis of many malignant tumors. It is possible to evaluate tumor immunogenicity and response to ICIs therapy by analyzing cell cycle-related genes 47 . In this study, two genes (ANGPTL4 and PLK1) of the IGSPP that we constructed were involved in the regulation of the tumor cell cycle 36 , 39 .…”

Crosstalk Between Metabolism and Immune Activity Reveals Four Subtypes With Therapeutic Implications in Clear Cell Renal Cell Carcinoma

“…Different metabolism signatures may cause prognosis heterogeneity in ccRCCs, suggesting the possibility to classify ccRCCs from metabolic perspective. To date, variable immune-related classification methods based on gene signatures or immune components in transcriptome studies showed prediction values in ccRCC immunotherapy ( 18 – 20 ). However, the interplay of immune activity with metabolism is crucial for the regulation of the TME network.…”

Development of a Phagocytosis-Dependent Gene Signature to Predict Prognosis and Response to Checkpoint Inhibition in Clear-Cell Renal Cell Carcinoma

“…( 52 ), Yin et al. ( 53 ), and Zhang Z et al. ( 54 ) constructed gene signatures according to differential expression genes between tumor and normal tissues; Zhang et al.…”