A prognostic index model for assessing the prognosis of ccRCC patients by using the mRNA expression profiles of AIF1L, SERPINC1 and CES1

³ Department of Ultrasound, The Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, 310022 Hangzhou, Zhejiang, China

⁴ Department of Neurology, Integrated Traditional Chinese and Western Medicine Hospital of Linping District, 310005 Hangzhou, Zhejiang, China

⁵ Department of Urology, The Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, 310022 Hangzhou, Zhejiang, China

⁶ The Key Laboratory of Zhejiang Province for Aptamers and Theranostics, Chinese Academy of Sciences, 310063 Hangzhou, Zhejiang, China

⁷ Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, 310063 Hangzhou, Zhejiang, China

⁸ The Second Clinical Medical College, Zhejiang Chinese Medical University, 310059 Hangzhou, Zhejiang, China

^*Correspondence: xiaoyaoyou1983@126.com (Weizhong Cai); yaoyaowu511@126.com (Yaoyao Wu); xuyp1631@zjcc.org.cn (Yipeng Xu)

J. Mens. Health 2022, 18(1), 25; https://doi.org/10.31083/j.jomh1801025

Submitted: 13 October 2021 | Accepted: 29 November 2021 | Published: 19 January 2022

This is an open access article under the CC BY 4.0 license.

Abstract

Background: Kidney carcinoma is a major cause of carcinoma-related death, with the prognosis for advanced or metastatic renal cell carcinoma still very poor. The aim of this study was to investigate feasible prognostic biomarkers that can be used to construct a prognostic index model for clear cell renal cell carcinoma (ccRCC) patients. Methods: The mRNA expression profiles of ccRCC samples were downloaded from the The Cancer Genome Atlas (TCGA) dataset and the correlation of AIF1L with malignancy, tumor stage and prognosis were evaluated. Differentially expressed genes (DEGs) between AIF1L-low and AIF1L-high expression groups were selected. Those with prognostic value as determined by univariate and multivariate Cox regression analysis were then used to construct a prognostic index model capable of predicting the outcome of ccRCC patients. Results: The expression level of AIF1L was lower in ccRCC samples than in normal kidney samples. AIF1L expression showed an inverse correlation with tumor stage and a positive association with better prognosis. ccRCC samples were divided into high- and low-expression groups according to the median value of AIF1L expression. In the AIF1L-high expression group, 165 up-regulated DEGs and 601 down-regulated DEGs were identified. Three genes (AIF1L, SERPINC1 and CES1) were selected following univariate and multivariate Cox regression analysis. The hazard ratio (HR) and 95% confidence intervals (CI) for these genes were: AIF1L (HR = 0.83, 95% CI: 0.76–0.91), SERPINC1 (HR = 1.33, 95% CI: 1.12–1.58), and CES1 (HR = 0.87, 95% CI: 0.78–0.97). A prognostic index model based on the expression level of the three genes showed good performance in predicting ccRCC patient outcome, with an area under the ROC curve (AUC) of 0.671. Conclusion: This research provides a better understanding of the correlation between AIF1L expression and ccRCC. We propose a novel prognostic index model comprising AIF1L, SERPINC1 and CES1 expression that may assist physicians in determining the prognosis of ccRCC patients.

Keywords

ccRCC

Prognostic index model

AIF1L

SERPINC1

CES1

Figures

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