Working Paper Article Version 1 This version is not peer-reviewed

DNA CpGs-Pairs-Based Classification and Identification of Kidney Clear Cell Carcinoma Prognosis-Associated Subgroups

Version 1 : Received: 6 July 2020 / Approved: 7 July 2020 / Online: 7 July 2020 (17:07:01 CEST)

How to cite: Luo, Q.; Vögeli, T. DNA CpGs-Pairs-Based Classification and Identification of Kidney Clear Cell Carcinoma Prognosis-Associated Subgroups. Preprints 2020, 2020070134 Luo, Q.; Vögeli, T. DNA CpGs-Pairs-Based Classification and Identification of Kidney Clear Cell Carcinoma Prognosis-Associated Subgroups. Preprints 2020, 2020070134

Abstract

Background: A new method is based on the relative ranking of gene expression level to overcome the flaw of gene expression data scaling and normalization and has reliable results in various researches. A specific prognostic subgroup was constructed based on DNA CpGs-pairs profiles of TCGA database. Methods: In this study, specific prognostic-related subtypes based on 222 DNA CpGs-pairs. A specific prognostic subgroup was identified. Result: Based on DNA CpGs-pairs, five subgroups had a significant correlation with survival. Differences in different subtypes were associated with ages, stages, grades, T status, M status, N status, and prognosis. Then, the risk-score model was constructed based on DNA CpGs-pairs in the best prognostic subtype and verified using the testing dataset. The prognosis in the testing dataset was consistent with the best prognostic subtype of the training set. Conclusions: We developed a new method to identify KIRC molecular subtypes based on CpGs-pairs profiles. The classification based on CpGs-pairs was closely associated with the clinical characteristics of KIRC. The novel eleven-CpGs-pairs prognostic model was accurate and reliable. Therefore, our findings provide a new method for the development of personalized treatments for the new specific subtypes.

Subject Areas

DNA CpGs-pairs; Kidney Clear Cell Carcinoma (KIRC); Subtype; prognostic model

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