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

J Transl Med. 2023 Jul 25;21(1):499. doi: 10.1186/s12967-023-04331-z.

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.

Keywords: Chemotherapy and immunotherapy; LPRI; LUAD; Prognostic model; Single cell RNA sequencing; TCGA; TME; Transcriptional regulation.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adenocarcinoma of Lung* / genetics
  • Humans
  • Lung Neoplasms* / genetics
  • Multiomics
  • Precision Medicine
  • Prognosis
  • Regulon