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

整合单细胞调控图谱和多组学数据对人类肺腺癌进行预后分层和个性化治疗预测

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作者:Yi Xiong #, Yihao Zhang #, Na Liu, Yueshuo Li, Hongwei Liu, Qi Yang, Yu Chen, Zhizhi Xia, Xin Chen, Siyi Wanggou, Xuejun Li0

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.

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