ArticleMolecular genetics & genomic medicine2020
A novel prognostic model based on multi-omics features predicts the prognosis of colon cancer patients.
Article in Molecular genetics & genomic medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Survival prediction landscape: an in-depth systematic literature review on activities, methods, tools, diseases, and databases.Frontiers in artificial intelligence · 2024Pooled it
- GABRD as an Emerging Oncogene: Exploring Functions and Therapeutic Implications Across Cancers.Life (Basel, Switzerland) · 2026Review
- Baicalein Alleviates Lithium-Pilocarpine-Induced Status Epilepticus by Regulating DNMT1/GABRD Pathway in Rats.Organogenesis · 2025Article
- Functional impact of multi-omic interactions in lung cancer.Frontiers in genetics · 2024Article
- KCNJ14 knockdown significantly inhibited the proliferation and migration of colorectal cells.BMC medical genomics · 2022Article
- Review
- Research for Expression and Prognostic Value of GABRD in Colon Cancer and Coexpressed Gene Network Construction Based on Data Mining.Computational and mathematical methods in medicine · 2021Article
- A novel prognostic model based on multi-omics features predicts the prognosis of colon cancer patients.Molecular genetics & genomic medicine · 2020Article
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10 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundAs 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.
methodsThe 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.
resultsThere 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.
conclusionThe prognostic model based on multi-omics features and the nomogram model might be valuable for the prognostic prediction of CC.
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