Integrative analysis of TCGA data identifies miRNAs as drug-specific survival biomarkers

• Type of Material: Article
• Authors: Shuting Lin, Georgia Institute of Technology; Jie Zhou, Georgia Institute of Technology; Yiqiong Xiao, Georgia Institute of Technology; Bridget Neary, Georgia Institute of Technology; Yong Teng, Emory University; Peng Qiu, Emory University
• Language: English
• Date: 2022-04-26
• Publisher: NATURE PORTFOLIO
• Publication Version: Final Published Version
• Copyright Statement: © The Author(s) 2022
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1038/s41598-022-10662-6
• Title of Journal or Parent Work: SCIENTIFIC REPORTS
• Volume: 12
• Issue: 1
• Start Page: 6785
• End Page: 6785
• Grant/Funding Information: This work was supported by funding from the National Science Foundation (CCF1552784 and CCF2007029). PQ is an ISAC Marylou Ingram Scholar, a Carol Ann and David D. Flanagan Faculty Fellow, and a Wallace H. Coulter Distinguished Faculty Fellow.
• Keywords: RESISTANCE; CANCER; SENSITIVITY; EXPRESSION; MICRORNA; Science & Technology; GEMCITABINE; SQUAMOUS-CELL CARCINOMA; Multidisciplinary Sciences; Science & Technology - Other Topics
• Research Categories: Health Sciences, Oncology; Engineering, Biomedical

Abstract

Biomarkers predictive of drug-specific outcomes are important tools for personalized medicine. In this study, we present an integrative analysis to identify miRNAs that are predictive of drug-specific survival outcome in cancer. Using the clinical data from TCGA, we defined subsets of cancer patients who suffered from the same cancer and received the same drug treatment, which we call cancer-drug groups. We then used the miRNA expression data in TCGA to evaluate each miRNA’s ability to predict the survival outcome of patients in each cancer-drug group. As a result, the identified miRNAs are predictive of survival outcomes in a cancer-specific and drug-specific manner. Notably, most of the drug-specific miRNA survival markers and their target genes showed consistency in terms of correlations in their expression and their correlations with survival. Some of the identified miRNAs were supported by published literature in contexts of various cancers. We explored several additional breast cancer datasets that provided miRNA expression and survival data, and showed that our drug-specific miRNA survival markers for breast cancer were able to effectively stratify the prognosis of patients in those additional datasets. Together, this analysis revealed drug-specific miRNA markers for cancer survival, which can be promising tools toward personalized medicine.