Prognostic implication and immunotherapy response prediction of a costimulatory molecule signature in kidney renal clear cell carcinoma

A Novel KIF4A-related Model for Predicting Immunotherapy Response and Prognosis in Kidney Renal Clear Cell Carcinoma

BACKGROUND

The efficacy of chemotherapy in treating Kidney Renal Clear Cell Carcinoma (KIRC) is limited, whereas immunotherapy has shown some promising clinical outcomes. In this context, KIF4A is considered a potential therapeutic target for various cancers. Therefore, identifying the mechanism of KIF4A that can predict the prognosis and immunotherapy response of KIRC would be of significant importance.

METHODS

Based on the TCGA Pan-Cancer dataset, the prognostic significance of the KIF4A expression across 33 cancer types was analyzed by univariate Cox algorithm. Furthermore, overlapping differentially expressed genes (DEGs1) between the KIF4A high- and lowexpression groups and DEGs2 between the KIRC and normal groups were also analyzed. Machine learning and Cox regression algorithms were performed to obtain biomarkers and construct a prognostic model. Finally, the role of KIF4A in KIRC was analyzed using quantitative real-time PCR, transwell assay, and EdU experiment.

RESULTS

Our analysis revealed that KIF4A was significant for the prognosis of 13 cancer types. The highest correlation with KIF4A was found for KICH among the tumour mutation burden (TMB) indicators. Subsequently, a prognostic model developed with UBE2C, OTX1, PPP2R2C, and RFLNA was obtained and verified with the Renal Cell Cancer-EU/FR dataset. There was a positive correlation between risk score and immunotherapy. Furthermore, the experiment results indicated that KIF4A expression was considerably increased in the KIRC group. Besides, the proliferation, migration, and invasion abilities of KIRC tumor cells were significantly weakened after KIF4A was knocked out.

CONCLUSION

We identified four KIF4A-related biomarkers that hold potential for prognostic assessment in KIRC. Specifically, early implementation of immunotherapy targeting these biomarkers may yield improved outcomes for patients with KIRC.

SLC11A1 promotes kidney renal clear cell carcinoma (KIRC) progression by remodeling the tumor microenvironment

Kidney renal clear cell carcinoma (KIRC) is a highly immune-infiltrated kidney cancer with the highest mortality rate and the greatest potential for invasion and metastasis. Solute carrier family 11 member1 (SLC11A1) is a phagosomal membrane protein located in monocytes and plays a role in innate immunity, autoimmune diseases, and infection, but its expression and biological role in KIRC is still unknown. In this study, we sought to investigate the potential value of SLC11A1 according to tumor growth and immune response in KIRC. TIMER and UALCAN database was used to analyze the expression feature and prognostic significance of SLC11A1 and its correlation with immune-related biomarkers in KIRC. Proliferation, migration, and invasion were measured using colony formation, EdU, and transwell assays. Role of SLC11A1 on KIRC tumor growth was examined by the xenograft tumor model in vivo. Effects of KIRC cells on macrophage polarization and the proliferation and apoptosis of CD8+ T cells were analyzed using flow cytometry assays. Herein, SLC11A1 was highly expressed in KIRC tissues and cell lines. SLC11A1 downregulation repressed KIRC cell proliferation, migration, invasion, macrophage, and lymphocyte immunity in vitro, as well as hindered tumor growth in vivo. SLC11A1 is significantly correlated with immune cell infiltration and immune-related biomarkers. In KIRC patients, SLC11A1 is highly expressed and positively correlated with the immune-related factors CCL2 and PD-L1. SLC11A1 induced CCL2 and PD-L1 expression, thereby activating the JAK/STAT3 pathway. SLC11A1 deficiency constrained KIRC cell malignant phenotypes and immune response via regulating CCL2 and PD-L1-mediated JAK/STAT3 pathway, providing a promising therapeutic target for KIRC treatment.

Prognostic costimulatory molecule-related signature risk model correlates with immunotherapy response in colon cancer

Costimulatory molecules can promote the activation and proliferation of T cells and play an essential role in immunotherapy. However, their role in the prognosis of colon adenocarcinoma remains elusive. In this study, the expression data of costimulatory molecules and clinicopathological information of 429 patients with colon adenocarcinoma were obtained from The Cancer Genome Atlas database. The patients were divided into training and verification cohorts. Correlation, Cox regression, and Lasso regression analyses were performed to identify costimulatory molecules related to prognosis. After mentioning the construction of the risk mode, a nomogram integrating the clinical characteristics and risk scores of patients was constructed to predict prognosis. Eventually, three prognostic costimulatory molecules were identified and used for constructing a risk model. High expression of these three molecules indicated a poor prognosis. The predictive accuracy of the risk model was verified in the GSE17536 dataset. Subsequently, multivariate regression analysis showed that the signature based on the three costimulatory molecules was an independent risk factor in the training cohort (HR = 2.12; 95% CI = 1.26, 3.56). Based on the risk model and clinicopathological data, the AUC values for predicting the 1-, 3-, and 5-year survival probability of patients with colon adenocarcinoma were 0.77, 0.77, and 0.71, respectively. To the best of our knowledge, this study is the first to report a risk signature constructed based on the costimulatory molecules TNFRSF10c, TNFRSF13c, and TNFRSF11a. This risk signature can serve as a prognostic biomarker for colon adenocarcinoma and is related to the immunotherapeutic response of patients.

A Signature Based on Costimulatory Molecules for the Assessment of Prognosis and Immune Characteristics in Patients With Stomach Adenocarcinoma

Although costimulatory molecules have been shown to boost antitumor immune responses, their significance in stomach adenocarcinoma (STAD) remains unknown. The purpose of this study was to examine the gene expression patterns of costimulatory molecule genes in patients with STAD and develop a predictive signature to aid in therapy selection and outcome prediction. We used 60 costimulatory family genes from prior research to conduct the first complete costimulatory molecular analysis in patients with STAD. In the two study groups, consensus clustering analysis based on these 60 genes indicated unique distribution patterns and prognostic differences. Using the least absolute shrinkage and selection operator and Cox regression analysis, we identified nine costimulatory molecular gene pairs (CMGPs) with prognostic value. With these nine CMGPs, we were able to develop a costimulatory molecule-related prognostic signature that performed well in an external dataset. For the patients with STAD, the signature was proven to be a risk factor independent of the clinical characteristics, indicating that this signature may be employed in conjunction with clinical considerations. A further connection between the signature and immunotherapy response was discovered. The patients with high mutation rates, an abundance of infiltrating immune cells, and an immunosuppressive milieu were classified as high-risk patients. It is possible that these high-risk patients have a better prognosis for immunotherapy since they have higher cytolytic activity scores and immunophenoscores of CTLA4 and PD-L1/PD-L2 blockers. Therefore, our signature may help clinicians in assessing patient prognosis and developing treatment plans.