Integration of single-cell regulon atlas and multi-omics data for prognostic stratification and personalized treatment prediction in human lung adenocarcinoma

Abstract Transcriptional programs are often dysregulated in cancers. A comprehensive investigation of potential regulons is critical to the understanding of tumorigeneses. We first constructed the regulatory networks from single-cell RNA sequencing data in human lung adenocarcinoma (LUAD). We next i...

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Bibliographic Details
Main Authors: Yi Xiong, Yihao Zhang, Na Liu, Yueshuo Li, Hongwei Liu, Qi Yang, Yu Chen, Zhizhi Xia, Xin Chen, Siyi Wanggou, Xuejun Li
Format: Article
Language:English
Published: BMC 2023-07-01
Series:Journal of Translational Medicine
Subjects:
Online Access:https://doi.org/10.1186/s12967-023-04331-z
Description
Summary:Abstract Transcriptional programs are often dysregulated in cancers. A comprehensive investigation of potential regulons is critical to the understanding of tumorigeneses. We first constructed the regulatory networks from single-cell RNA sequencing data in human lung adenocarcinoma (LUAD). We next introduce LPRI (Lung Cancer Prognostic Regulon Index), a precision oncology framework to identify new biomarkers associated with prognosis by leveraging the single cell regulon atlas and bulk RNA sequencing or microarray datasets. We confirmed that LPRI could be a robust biomarker to guide prognosis stratification across lung adenocarcinoma cohorts. Finally, a multi-omics data analysis to characterize molecular alterations associated with LPRI was performed from The Cancer Genome Atlas (TCGA) dataset. Our study provides a comprehensive chart of regulons in LUAD. Additionally, LPRI will be used to help prognostic prediction and developing personalized treatment for future studies.
ISSN:1479-5876