A novel 7-chemokine-genes predictive signature for prognosis and therapeutic response in renal clear cell carcinoma

Ming-Jie Lin; Xiu-Xiao Tang; Gao-Sheng Yao; Zhi-Ping Tan; Lei Dai; Ying-Han Wang; Jiang-Quan Zhu; Quan-Hui Xu; Mukhtar Adan Mumin; Hui Liang; Zhu Wang; Qiong Deng; Jun-Hang Luo; Jin-Huan Wei; Jia-Zheng Cao

doi:10.3389/fphar.2023.1120562

A novel 7-chemokine-genes predictive signature for prognosis and therapeutic response in renal clear cell carcinoma

Front Pharmacol. 2023 Mar 20:14:1120562. doi: 10.3389/fphar.2023.1120562. eCollection 2023.

Authors

Affiliations

¹ Department of Urology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
² Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China.
³ Department of Urology, Affiliated Longhua People's Hospital, Southern Medical University, Shenzhen, China.
⁴ Institute of Precision Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
⁵ Department of Urology, Jiangmen Central Hospital, Jiangmen, Guangdong, China.

Abstract

Background: Renal clear cell carcinoma (ccRCC) is one of the most prevailing type of malignancies, which is affected by chemokines. Chemokines can form a local network to regulate the movement of immune cells and are essential for tumor proliferation and metastasis as well as for the interaction between tumor cells and mesenchymal cells. Establishing a chemokine genes signature to assess prognosis and therapy responsiveness in ccRCC is the goal of this effort. Methods: mRNA sequencing data and clinicopathological data on 526 individuals with ccRCC were gathered from the The Cancer Genome Atlas database for this investigation (263 training group samples and 263 validation group samples). Utilizing the LASSO algorithm in conjunction with univariate Cox analysis, the gene signature was constructed. The Gene Expression Omnibus (GEO) database provided the single cell RNA sequencing (scRNA-seq) data, and the R package "Seurat" was applied to analyze the scRNA-seq data. In addition, the enrichment scores of 28 immune cells in the tumor microenvironment (TME) were calculated using the "ssGSEA" algorithm. In order to develop possible medications for patients with high-risk ccRCC, the "pRRophetic" package is employed. Results: High-risk patients had lower overall survival in this model for predicting prognosis, which was supported by the validation cohort. In both cohorts, it served as an independent prognostic factor. Annotation of the predicted signature's biological function revealed that it was correlated with immune-related pathways, and the riskscore was positively correlated with immune cell infiltration and several immune checkpoints (ICs), including CD47, PDCD1, TIGIT, and LAG-3, while it was negatively correlated with TNFRSF14. The CXCL2, CXCL12, and CX3CL1 genes of this signature were shown to be significantly expressed in monocytes and cancer cells, according to scRNA-seq analysis. Furthermore, the high expression of CD47 in cancer cells suggested us that this could be a promising immune checkpoint. For patients who had high riskscore, we predicted 12 potential medications. Conclusion: Overall, our findings show that a putative 7-chemokine-gene signature might predict a patient's prognosis for ccRCC and reflect the disease's complicated immunological environment. Additionally, it offers suggestions on how to treat ccRCC using precision treatment and focused risk assessment.

Keywords: chemokine; gene signature; immunotherapy; renal clear cell carcinoma; tumor microenvironment.