Welcome to the InfoSci Platform
Integration of Multi-Omics Data to Identify Cancer Biomarkers

Integration of Multi-Omics Data to Identify Cancer Biomarkers

Peng Li, Bo Sun
Copyright: © 2022 |Volume: 15 |Issue: 1 |Pages: 15
ISSN: 1938-7857|EISSN: 1938-7865|EISBN13: 9781683180340|DOI: 10.4018/JITR.2022010105
Cite Article Cite Article

MLA

Li, Peng, and Bo Sun. "Integration of Multi-Omics Data to Identify Cancer Biomarkers." JITR vol.15, no.1 2022: pp.1-15. http://doi.org/10.4018/JITR.2022010105

APA

Li, P. & Sun, B. (2022). Integration of Multi-Omics Data to Identify Cancer Biomarkers. Journal of Information Technology Research (JITR), 15(1), 1-15. http://doi.org/10.4018/JITR.2022010105

Chicago

Li, Peng, and Bo Sun. "Integration of Multi-Omics Data to Identify Cancer Biomarkers," Journal of Information Technology Research (JITR) 15, no.1: 1-15. http://doi.org/10.4018/JITR.2022010105

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

A novel method for integrating multi-omics data, including gene expression, copy number variation, DNA methylation, and miRNA data, is proposed to identify biomarkers of cancer prognosis. First, survival analysis was performed for these four types of omics data to obtain survival-related genes. Next, survival-related genes detected in at least two types of omics data were selected as candidate genes. The four types of omics data only composed of candidate genes were subjected to dimension reduction using an autoencoder to obtain a one-dimensional data representation. The mRMR algorithm was used to screen for key genes. This method was applied to lung squamous cell carcinoma and 20 cancer-related genes were identified. Gene function analysis revealed that the genes were related to cancer. Using survival analysis, the genes were verified to distinguish between high- and low-risk groups. These results indicate that the genes can be used as biomarkers for cancer.