A prognostic index model for assessing the prognosis of ccRCC patients by using the mRNA expression profiles of AIF1L, SERPINC1 and CES1
1Department of Urology, Fujian Medical University Union Hospital, 350001 Fuzhou, Fujian, China
2Department of Urology, Southern Medical University, 510515 Guangzhou, Guangdong, China
3Department of Ultrasound, The Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, 310022 Hangzhou, Zhejiang, China
4Department of Neurology, Integrated Traditional Chinese and Western Medicine Hospital of Linping District, 310005 Hangzhou, Zhejiang, China
5Department of Urology, The Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, 310022 Hangzhou, Zhejiang, China
6The Key Laboratory of Zhejiang Province for Aptamers and Theranostics, Chinese Academy of Sciences, 310063 Hangzhou, Zhejiang, China
7Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, 310063 Hangzhou, Zhejiang, China
8The Second Clinical Medical College, Zhejiang Chinese Medical University, 310059 Hangzhou, Zhejiang, China
DOI: 10.31083/j.jomh1801025 Vol.18,Issue 1,January 2022 pp.1-9
Submitted: 13 October 2021 Accepted: 29 November 2021
Published: 31 January 2022
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.
ccRCC; Prognostic index model; AIF1L; SERPINC1; CES1
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