A novel lipid metabolism gene signature for clear cell renal cell carcinoma using integrated bioinformatics analysis

Ke Li; Yan Zhu; Jiawei Cheng; Anlei Li; Yuxing Liu; Xinyi Yang; Hao Huang; Zhangzhe Peng; Hui Xu

doi:10.3389/fcell.2023.1078759

A novel lipid metabolism gene signature for clear cell renal cell carcinoma using integrated bioinformatics analysis

Front Cell Dev Biol. 2023 Feb 14:11:1078759. doi: 10.3389/fcell.2023.1078759. eCollection 2023.

Authors

Ke Li^{1

2

3}, Yan Zhu⁴, Jiawei Cheng^{1

3

5}, Anlei Li⁶, Yuxing Liu⁶, Xinyi Yang^{3

5}, Hao Huang^{1

3

5}, Zhangzhe Peng^{1

3

5}, Hui Xu^{1

3

5}

Affiliations

¹ Department of Nephrology, Xiangya Hospital, Central South University, Changsha, China.
² Department of Urology, Xiangya Hospital, Central South University, Changsha, China.
³ National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.
⁴ Foreign Languages Institute, China University of Geosciences Wuhan, Wuhan, China.
⁵ Hunan Key Laboratory of Organ Fibrosis, Central South University, Changsha, China.
⁶ Department of Cell Biology, School of Life Sciences, Central South University, Changsha, China.

Abstract

Background: Clear cell renal cell carcinoma (ccRCC), which is the most prevalent type of renal cell carcinoma, has a high mortality rate. Lipid metabolism reprogramming is a hallmark of ccRCC progression, but its specific mechanism remains unclear. Here, the relationship between dysregulated lipid metabolism genes (LMGs) and ccRCC progression was investigated. Methods: The ccRCC transcriptome data and patients' clinical traits were obtained from several databases. A list of LMGs was selected, differentially expressed gene screening performed to detect differential LMGs, survival analysis performed, a prognostic model established, and immune landscape evaluated using the CIBERSORT algorithm. Gene Set Variation Analysis and Gene set enrichment analysis were conducted to explore the mechanism by which LMGs affect ccRCC progression. Single-cell RNA-sequencing data were obtained from relevant datasets. Immunohistochemistry and RT-PCR were used to validate the expression of prognostic LMGs. Results: Seventy-one differential LMGs were identified between ccRCC and control samples, and a novel risk score model established comprising 11 LMGs (ABCB4, DPEP1, IL4I1, ENO2, PLD4, CEL, HSD11B2, ACADSB, ELOVL2, LPA, and PIK3R6); this risk model could predict ccRCC survival. The high-risk group had worse prognoses and higher immune pathway activation and cancer development. Conclusion: Our results showed that this prognostic model can affect ccRCC progression.

Keywords: clear cell renal cell carcinoma; differentially expressed genes; early diagnosis; lipid metabolism genes; prognostic genes; single-cell analysis.

Grants and funding

This study was supported by the National Natural Science Foundation of China (82270730, 82090024 and 82173877); General Project of Hunan Provincial Department of Finance (422000025); The China Postdoctoral Science Foundation (2020TQ0363 and 2020M682598); The Natural Science Foundation of Hunan, China (2020JJ4917 and 2021JJ40992); The Key Research and Development Program of Hunan Province (2021SK 2015); the Outstanding Youth Foundation of Natural Science Foundation of Hunan Province (2022JJ10100); The Fundamental Research Funds for the Central Universities of Central South University (2021zzts0079) and the Youth Science Foundation of Xiangya Hospital (2021Q11).