Open AccessA novel prognostic model based on multi‐omics features predicts the prognosis of colon cancer patients
Author(s) -
Yang Haojie,
Jin Wei,
Liu Hua,
Wang Xiaoxue,
Wu Jiong,
Gan Dan,
Cui Can,
Han Yilin,
Han Changpeng,
Wang Zhenyi
Publication year - 2020
Publication title -
molecular genetics and genomic medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.765
H-Index - 29
ISSN - 2324-9269
DOI - 10.1002/mgg3.1255
Subject(s) - omics , nomogram , copy number variation , dna methylation , colorectal cancer , survival analysis , microrna , computational biology , bioinformatics , phenotype , biology , cancer , gene , oncology , medicine , gene expression , genome , genetics
Background As a common malignant tumor in the colon, colon cancer (CC) has high incidence and recurrence rates. This study is designed to build a prognostic model for CC. Methods The gene expression dataset, microRNA‐seq dataset, copy number variation (CNV) dataset, DNA methylation dataset, and transcription factor (TF) dataset of CC were downloaded from UCSC Xena database. Using limma package, the differentially methylated genes (DMGs), and differentially expressed genes (DEGs) and miRNAs (DEMs) were identified. Based on random forest method, prognostic model for each omics dataset were constructed. After the omics features related to prognosis were selected using logrank test, the prognostic model based on multi‐omics features was built. Finally, the clinical phenotypes correlated with prognosis were screened using Kaplan–Meier survival analysis, and the nomogram model was established. Results There were 1625 DEGs, 268 DEMs, and 386 DMGs between the tumor and normal samples. A total of 105, 29, 159, five, and six genes/sites significantly correlated with prognosis were identified in the gene expression dataset ( GABRD ), miRNA‐seq dataset (miR‐1271), CNV dataset ( RN7SKP247 ), DNA methylation dataset (cg09170112 methylation site [located in SFSWAP ]), and TF dataset ( SIX5 ), respectively. The prognostic model based on multi‐omics features was more effective than those based on single omics dataset. The number of lymph nodes, pathologic_M stage, and pathologic_T stage were the clinical phenotypes correlated with prognosis, based on which the nomogram model was constructed. Conclusion The prognostic model based on multi‐omics features and the nomogram model might be valuable for the prognostic prediction of CC.