Alternative Polyadenylation Regulatory Factors Signature for Survival Prediction in Kidney Renal Cell Carcinoma

Abstract

Background: Alternative polyadenylation (APA) plays a vital regulatory role in various diseases. It is widely accepted that APA is regulated by APA regulatory factors.

Objective: Whether APA regulatory factors affect the prognosis of renal cell carcinoma remains unclear, and this is the main topic of this study.

Methods: We downloaded the transcriptome and clinical data from The Cancer Genome Atlas (TCGA) database. We used the Lasso regression system to construct an APA model for analyzing the relationship between common APA regulatory factors and renal cell carcinoma. We also validated our APA model using independent GEO datasets (GSE29609, GSE76207).

Results: It was found that the expression levels of 5 APA regulatory factors (CPSF1, CPSF2, CSTF2, PABPC1, and PABPC4) were significantly associated with tumor gene mutation burden (TMB) score in renal clear cell carcinoma, and the risk score constructed using the expression level of 5 key APA regulatory factors could be used to predict the outcome of renal clear cell carcinoma. The TMB score is associated with the remodeling of the immune microenvironment.

Conclusions: By identifying key APA regulatory factors in renal cell carcinoma and constructing risk scores for key APA regulatory factors, we showed that key APA regulators affect prognosis of renal clear cell carcinoma patients. In addition, the risk score level is associated with TMB, indicating that APA may affect the efficacy of immunotherapy through immune microenvironment-related genes. This helps us better understand the mRNA processing mechanism of renal clear cell carcinoma.

Keywords: Lasso regression; Renal clear cell carcinoma; alternative polyadenylation; immunotherapy; survival.

Figure 1.

The pipeline of our method.

Figure 2.

Feature selection using the LASSO Cox regression model. (A) Heatmap showing the expression level of APA regulator in cancer and normal tissue. (B) The partial likelihood deviance was plotted versus log (lambda). The y-axis indicates the partial likelihood deviance, while the lower x-axis indicates the log (lambda) and the upper x-axis represents the average number of predictors. (C) LASSO coefficient profiles. The coefficients (y-axis) were plotted against log (lambda) and 5 features with nonzero coefficients were selected to build the radiomics signature. (D) GO-Term analysis of differently expressed genes between high-risk group and low-risk group.

Figure 3.

LASSO Cox regression model predicted TMB and gene alternative status of KIRC. (A) Mutation burden in high-risk score group versus low-risk score groups. (B) Survival curves obtained for the genes exclusively selected by the COX method, when analyzed individually. (C) Oncoprint plot showing key genes mutated in KIRC. Each column denotes an individual tumor and each row represents a gene. Colors indicate type of gene alternative as indicated in the legend below the oncoprint.

Figure 4.

Expression level of 5 APA regulators CPSF1, CPSF2, CSTF2, PABPC1, and PABPC4 is correlated with the level of immune infiltration in KIRC. P value and correlation coefficient are indicated.

Figure 5.

Kaplan-Meier survival plots representing the correlations between the expression level of 5 APA regulators expression levels in KIRC. P value is indicated.

Figure 6.

ROC curve and data validation. (A) TCGA and GSE29609 receiver operating characteristic (ROC) curve for APA risk model (red line) and 7-gene model (blue line) for the prognosis of KIRC. (B) Boxplot of gene expression level of 5 APA regulators CPSF1, CPSF2, CSTF2, PABPC1, and PABPC4 in validation datasets. P value of 2-tailed t test is indicated. Abbreviations: N, normal; T, tumor.