A Genomic Instability-Derived Risk Index Predicts Clinical Outcome And Immunotherapy Response For Clear Cell Renal Cell Carcinoma

Objective Genomic instability and immune checkpoint blockade（ICB）therapy are important for the prognosis and progression of clear cell renal cell carcinoma（cc RCC）.However,few studies have simultaneously explored the genomic instability-derived risk index（GIRI）associated with cc RCC prognosis and ICB therapy response.This study aimed to construct the GIRI through bioinformatic methods and to explore its potential causes affecting the prognosis of cc RCC.Methods Transcriptome data and clinical prognostic information related to cc RCC were downloaded from The Cancer Genome Atlas（TCGA）and International Cancer Genome Consortium（ICGC）databases.In addition,cumulative mutation counts for337 cc RCC samples and transcriptome data for 72 adjacent normal samples were obtained from the TCGA database.The TCGA tumor samples were sorted in descending order based on cumulative mutation counts.The 25% of TCGA samples with the highest mutation counts were assigned to the genomic unstable（GU）group,while the 25% of TCGA samples with the lowest mutation counts were assigned to the genomic stable（GS）group.Using the “limma” R package,differential expression analysis was performed between the GS group and the GU group to identify genomic instability-associated genes（GAGs）.Using univariate Cox proportional hazards regression analysis,Kaplan-Meier survival analysis,and least absolute shrinkage and selection operator（LASSO）Cox regression analysis,GAGs were rigorously screened to obtain candidate prognostic-related genes.The TCGA cc RCC samples were randomly divided into a training cohort and a test cohort with a ratio of 5:5.Based on the TCGA training cohort,a multivariate Cox proportional hazards regression model was used to analyze and select the best GAGs to construct the GIRI.The median GIRI of the TCGA training cohort was used to distinguish high or low GIRI groups from the TCGA and ICGC cohorts.Kaplan-Meier survival analysis was used to identify prognostic differences between high and low GIRI subtypes.Receiver operating characteristic（ROC）curve analysis was performed to generate ROC curves for age,tumor grade,clinical stage,and GIRI to assess the prognostic value of GIRI and other clinical parameters.Gene set variation analysis（GSVA）,tumor mutation burden（TMB）analysis,and tumor microenvironment analysis were performed in the TCGA dataset to explore possible reasons for prognostic differences in GIRI subgroups（high GIRI group or low GIRI group）.Finally,the TIDE score of TCGA samples was assessed by the Tumor Immune Dysfunction and Exclusion（TIDE）online website to predict whether the patient would benefit from ICB therapy.Results We obtained 414 differentially expressed GAGs between the GS group（n=85）and the GU group（n=85）,and finally screened two genes（PLCL1 and TBC1D1）to construct the GIRI.Both TCGA and ICGC cohorts demonstrated significantly lower overall survival in the high GIRI population compared to the low GIRI population.The high GIRI group displayed higher levels of cumulative mutation counts,TMB,immune scores,immune checkpoint（PD-1,CTLA4,and LAG3）expression,and pathway activities associated with genomic instability（cell cycle,mismatch repair,and DNA replication）.Compared with that in the low GIRI group,the lower TIDE score for the high GIRI group implied that this group is more likely to benefit from ICB therapy.Conclusions The newly developed genomic instability-derived risk index is an effective cancer biomarker for predicting prognosis in cc RCC.High GIRI predicts poor prognosis and unstable genomic signature and may provide a reference value for predicting patient response to ICB therapy.