{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Xiong Y"],"funding":["National Natural Science Foundation of China","Special Funds for the Basic Research and Development Program in the Central Non-profit Research Institutesof China"],"pagination":["499"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10369768"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["21(1)"],"pubmed_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"],"journal":["Journal of translational medicine"],"pubmed_title":["Integration of single-cell regulon atlas and multi-omics data for prognostic stratification and personalized treatment prediction in human lung adenocarcinoma."],"pmcid":["PMC10369768"],"funding_grant_id":["81770781","82270825","2020SK2062","81472594"],"pubmed_authors":["Xiong Y","Liu H","Li X","Li Y","Xia Z","Zhang Y","Liu N","Chen Y","Yang Q","Chen X","Wanggou S"],"additional_accession":[]},"is_claimable":false,"name":"Integration of single-cell regulon atlas and multi-omics data for prognostic stratification and personalized treatment prediction in human lung adenocarcinoma.","description":"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","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Jul","modification":"2025-04-05T12:36:18.198Z","creation":"2025-04-05T12:36:18.198Z"},"accession":"S-EPMC10369768","cross_references":{"pubmed":["37491302"],"doi":["10.1186/s12967-023-04331-z"]}}