Identifying endoplasmic reticulum stress-related genes as new diagnostic and prognostic biomarkers in clear cell renal cell carcinoma

Haitao Yu; Chunlin Zhang; Xuesong Bai; Hubin Yin; Xinyuan Li; Xiang Zhou; Weiyang He; Youlin Kuang; Xin Gou; Jie Li

doi:10.21037/tau-23-374

Identifying endoplasmic reticulum stress-related genes as new diagnostic and prognostic biomarkers in clear cell renal cell carcinoma

Transl Androl Urol. 2024 Jan 31;13(1):1-24. doi: 10.21037/tau-23-374. Epub 2024 Jan 23.

Authors

Haitao Yu^#^{1

2}, Chunlin Zhang^#^{1

2}, Xuesong Bai¹, Hubin Yin^{1

2}, Xinyuan Li^{1

2}, Xiang Zhou^{1

2}, Weiyang He¹, Youlin Kuang¹, Xin Gou¹, Jie Li¹

Affiliations

¹ Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
² Chongqing Key Laboratory of Molecular Oncology and Epigenetics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.

^# Contributed equally.

Abstract

Background: Clear cell renal cell carcinoma (ccRCC) is one of the most common cancers worldwide, and its incidence is increasing every year. Endoplasmic reticulum stress (ERS) caused by protein misfolding has broad and profound effects on the progression and metastasis of various cancers. Accumulating evidence suggests that ERS is closely related to the occurrence and progression of ccRCC. This study aimed to identify ERS-related genes for evaluating the prognosis of ccRCC.

Methods: Transcriptomic expression profiles were obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA), and clinical data were downloaded from the TCGA. First, the differentially expressed genes (DEGs) were analyzed using the limma package, and the DEGs related to ERS (ERS-DEGs) were identified from the GeneCards database. Second, a function and pathway enrichment analysis and a Gene Set Enrichment Analysis (GSEA) were performed. Third, a protein-protein interaction (PPI) network was constructed to identify the hub genes, and a gene-micro RNA (miRNA) network and gene-transcription factor (TF) network were established using the hub genes. Finally, a least absolute shrinkage and selection operator (LASSO) regression analysis was conducted to establish a diagnostic model, and a Cox analysis was used to analyze the correlations between the expression of the characteristic genes and the clinical characteristics.

Results: We identified 11 signature genes and established a diagnostic model. Further, the Cox analysis results revealed a correlation between the expression levels of the signature genes and the clinical characteristics. Ultimately, five signature genes (i.e., TNFSF13B, APOL1, COL5A3, and CDH5) were found to be associated with a poor prognosis.

Conclusions: This study suggests that TNFSF13B, APOL1, COL5A3, and CDH5 may have potential as prognostic biomarkers in ccRCC and may provide new evidence to support targeted therapy in ccRCC.

Keywords: Clear cell renal cell carcinoma (ccRCC); diagnosis; endoplasmic reticulum stress (ERS); prognosis.