Integrated Bioinformatics Analysis for the Identification of Key lncRNAs, mRNAs, and Potential Drugs in Clear Cell Renal Cell Carcinomas

Sheng Liu; Guanyun Shi; Zhengbo Pan; Weisong Cheng; Linfei Xu; Xingzhang Lin; Yongfeng Lin; Liming Zhang; Guanghua Ji; Xin Lv; Dongguo Wang

doi:10.2147/IJGM.S409711

Integrated Bioinformatics Analysis for the Identification of Key lncRNAs, mRNAs, and Potential Drugs in Clear Cell Renal Cell Carcinomas

Int J Gen Med. 2023 May 29:16:2063-2080. doi: 10.2147/IJGM.S409711. eCollection 2023.

Authors

Sheng Liu^#¹, Guanyun Shi¹, Zhengbo Pan¹, Weisong Cheng¹, Linfei Xu¹, Xingzhang Lin¹, Yongfeng Lin¹, Liming Zhang¹, Guanghua Ji¹, Xin Lv¹, Dongguo Wang^#²

Affiliations

¹ Department of Urinary Surgery, Taizhou Municipal Hospital Affiliated with Taizhou University, Taizhou, Zhejiang Province, People's Republic of China.
² Department of Central Laboratory, Taizhou Municipal Hospital Affiliated with Taizhou University, Taizhou, Zhejiang Province, People's Republic of China.

^# Contributed equally.

Abstract

Purpose: The overall survival of clear cell renal cell carcinoma (ccRCC) is poor. Markers for early detection and progression could improve disease outcomes. This study aims to reveal the potential pathogenesis of ccRCC by integrative bioinformatics analysis and to further develop new therapeutic strategies.

Patients and methods: RNA-seq data of 530 ccRCC cases in TCGA were downloaded, and a comprehensive analysis was carried out using bioinformatics tools. Another 14 tissue samples were included to verify the expression of selected lncRNAs by qRT-PCR. DGIdb database was used to screen out potential drugs, and molecular docking was used to explore the interaction and mechanism between candidate drugs and targets.

Results: A total of 58 differentially expressed lncRNAs (DElncRNAs) and 660 differentially expressed mRNAs (DEmRNAs) were identified in ccRCC. LINC02038, FAM242C, LINC01762, and PVT1 were identified as the optimal diagnostic lncRNAs, of which PVT1 was significantly correlated with the survival rate of ccRCC. GO analysis of cell components showed that DEmRNAs co-expressed with 4 DElncRNAs were mainly distributed in the extracellular area and the plasma membrane, involved in the transport of metal ions, the transport of proteins across membranes, and the binding of immunoglobulins. Immune infiltration analysis showed that MDSC was the most correlated immune cells with PVT1 and key mRNA SIGLEC8. Validation analysis showed that GABRD, SIGLEC8 and CDKN2A were significantly overexpressed, while ESRRB, ELF5 and UMOD were significantly down-regulated, which was consistent with the expression in our analysis. Furthermore, 84 potential drugs were screened by 6 key mRNAs, of which ABEMACICLIB and RIBOCICLIB were selected for molecular docking with CDKN2A, with stable binding affinity.

Conclusion: In summary, 4 key lncRNAs and key mRNAs of ccRCC were identified by integrative bioinformatics analysis. Potential drugs were screened for the treatment of ccRCC, providing a new perspective for disease diagnosis and treatment.

Keywords: bioinformatics analysis; clear cell renal cell carcinoma; drugs prediction; lncRNA; mRNA; molecular docking.

Grants and funding

There is no funding to report.