A costimulatory molecule-related signature in regard to evaluation of prognosis and immune features for clear cell renal cell carcinoma

Abstract

Costimulatory molecules have been proven to enhance antitumor immune responses, but their roles in clear cell renal cell carcinoma (ccRCC) remain unexplored. In this study, we aimed to explore the gene expression profiles of costimulatory molecule genes in ccRCC and construct a prognostic signature to improve treatment decision-making and clinical outcomes. We performed the first comprehensive analysis of costimulatory molecules in patients with ccRCC and identified 13 costimulatory molecule genes with prognostic values and diagnostic values. Consensus clustering analysis based on these 13 costimulatory molecular genes showed different distribution patterns and prognostic differences for the two clusters identified. Then, a costimulatory molecule-related signature was constructed based on these 13 costimulatory molecular genes, and validated in an external dataset, showing good performance for predicting a patient's prognosis. The signature was an independent risk factor for ccRCC patients and was significantly correlated with patients' clinical factors, which could be used as a complement for clinical factors. In addition, the signature was associated with the tumor immune microenvironment and the response to immunotherapy. Patients identified as high-risk based on our signature exhibited a high mutation frequency, a high level of immune cell infiltration, and an immunosuppressive microenvironment. High-risk patients tended to have high cytolytic activity scores and immunophenoscore of CTLA4 and PD1/PD-L1/PD-L2 blocker than low-risk patients, suggesting these patients may be more suitable for immunotherapy. Therefore, our signature could provide clinicians with prognosis predictions and help guide treatment for ccRCC patients.

Fig. 1. The flowchart of the present study design.

The mRNA expression data of tumor and normal tissues were downloaded from TCGA database and ArrayExpress database (E-MTAB-3267). Survival-related costimulatory molecules were obtained and the associations with tumor immune microenvironment were evaluated.

Fig. 2. Survival analysis for thirteen costimulatory molecule genes.

The Kaplan-Meier curves for the thirteen costimulatory molecule genes in clear cell renal cell carcinoma from The Cancer Genome Atlas dataset, including TNFSF14, TNFSF4, TNFRSF25, TNFRSF6B, TNFRSF1A, RELT, LTBR,TNFRSF19, TNFRSF10A, HHLA2, EDA, CD274, and TNFSF15.

Fig. 3. Consensus clustering based on the 13 costimulatory molecule genes.

A Consensus clustering cumulative distribution function (CDF) for k = 2 to k = 10. B The relative change in area under the CDF curve for k = 2 to k = 10. C Kaplan–Meier curve for two clusters of clear cell renal cell carcinoma. D The gene set enrichment analysis showed that several immune-related pathways were significantly enriched in cluster 2.

Fig. 4. Differential expression of 13 costimulatory molecule genes between normal and tumor tissues.

Genes, including RELT, TNFSF14, TNFRSF1A, HHLA2, TNFRSF25, TNFSF4, TNFRSF6B, LTBR, and TNFRSF10A were high-expressed in tumor tissues compared with normal tissues. TNFRSF19 and TNFSF15 were low-expressed in tumor tissues compared with normal tissues. While EDA and CD274 showed no significant difference in tumor tissues compared with normal tissues. ns: P > 0.05; *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.

Fig. 5. Construction and validation of a costimulatory molecule-based prognostic signature.

Kaplan–Meier curves for prognosis evaluation in TCGA dataset (A) and E-MTAB-3267 dataset (D). Time-dependent receiver operating characteristic curves for the sensitivity and specificity of prognosis evaluation in TCGA dataset (B) and E-MTAB-3267 dataset (E). Principal component analysis for the evaluation of distribution patterns for high-risk and low-risk patients in TCGA dataset (C) and E-MTAB-3267 dataset (F).

Fig. 6. Relationship between the prognostic signature and clinicopathological factors of ccRCC patients.

A The heat map shows the expressions of the 13 costimulatory molecule genes and clinicopathological factors in the high- and low-risk groups. The univariate (B) and multivariate (C) Cox regression analyses of prognosis for the prognostic signature and clinicopathological factors. The bar chat showed that the risk score of the prognostic signature in different clinical subgroups. ns: P > 0.05, *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.

Fig. 7. Evaluation of tumor immune microenvironment and genomic alterations.

A The distributions of 28 immune cells in high-risk and low-risk patients. B Box plots showed the detailed differences for 28 immune cells between high-risk and low-risk patients. The differences of the immune score (C), stromal score (D), cytolytic activity score (E), tumor mutation burden (F), and neoAgs (G) in high-risk and low-risk patients. The mutation profile of the top 20 mutation genes in high-risk patients (H) and low-risk patients (I). The Forest plot illustrated the differences in mutation frequency of genes in high- and low-risk patients (J). Comparison of the IPS, IPS-PD1 blocker, IPS-CTLA4 blocker, and IPS-CTLA4 and PD1 blocker between high-risk and low-risk patients (K). *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.