A novel hypoxia-related lncRNA risk score model for prognosis evaluation of clear cell renal cell carcinoma

Fu Liu; Xinyuan Li; Xiang Zhou; Hang Tong; Zili Hu; Xuesong Bai; Xin Gou

doi:10.2174/1386207326666230606152615

A novel hypoxia-related lncRNA risk score model for prognosis evaluation of clear cell renal cell carcinoma

Comb Chem High Throughput Screen. 2023 Jun 6. doi: 10.2174/1386207326666230606152615. Online ahead of print.

Authors

Fu Liu¹, Xinyuan Li², Xiang Zhou³, Hang Tong³, Zili Hu⁴, Xuesong Bai³, Xin Gou⁵

Affiliations

¹ The First People's Hospital of Ziyang, Sichuan, China.
² Department of Urology, The First Affiliated Hospital of Chongqing Medical University, China.
³ Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing.
⁴ Department of Urology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
⁵ Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.

PMID: 37282651
DOI: 10.2174/1386207326666230606152615

Abstract

Background: Renal cell carcinoma is the most common aggressive tumor of the genitourinary system. The main pathological subtype is clear cell renal cell carcinoma (ccRCC), and its treatment options are very limited. Therefore, identifying specific biomarkers of ccRCC is of great significance for diagnosis and prognosis.

Methods: First, we obtained transcriptome data and clinical data of 611 patients with renal clear cell carcinoma to analyze the relationship between hypoxia-related lncRNAs and overall survival (OS). We screened hypoxia-related lncRNAs through Pearson correlation and Cox regression analysis. Univariate and multivariate regression analysis were applied to assess survival-related risk factors. According to the median risk score, patients were divided into two groups. Next, a nomogram map was built, and GSEA was used for gene function annotation. RT-qPCR, Western Blot, and Flow Cytometry were used to determine the role of SNHG19 in RCC cells.

Results: By analyzing the co-expression of hypoxia genes and lncRNAs, 310 hypoxia-related genes were obtained. Four sHRlncRs (AC011445.2, PTOV1-AS2, AP004609.3, and SNHG19) with the highest prognostic values were included in the group to construct the HRRS model. The high-risk group had a shorter OS than the low-risk group. HRRS was considered to be an independent prognostic factor and associated with OS. The two groups showed different pathways in GSEA. Experiments showed that SNHG19 plays essential roles in the autophagy and apoptosis of RCC cells.

Conclusion: We constructed and validated a hypoxia-related lncRNA model for ccRCC patients. This study also provides new biomarkers for the poor prognosis of ccRCC patients.

Keywords: ccRCC; hypoxia-related genes; long non-coding RNAs; prognosis; prognosis model.