{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Wang H"],"funding":["Health Research Project of Kunming Health Commission","Scientific Research Fund Project of Department of Education, Yunnan province","Health Science and Technology Talent Training Project and Technical Center Construction Project of Kunming City"],"pagination":["463"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9724367"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["22(1)"],"pubmed_abstract":["<h4>Background</h4>To comprehensively analyze the stemness characteristics related to prognosis and the immune microenvironment in lung adenocarcinoma (LUAD).<h4>Methods</h4>The OCLR machine learning method was used to calculate the stemness index (mRNAsi) of the LUAD samples. DEGs common between the low mRNAsi, normal, and high mRNAsi groups were screened and the immune-stemness genes were obtained. Then the PPI network was created and enrichment analyses were performed. Moreover, different subtypes based on immune-stemness genes associated with prognosis were identified, and the relationships between LUAD stemness and TIME variables were systematically analyzed, followed by TMB analysis.<h4>Results</h4>Patients in the high mRNAsi groups with poor prognosis were screened along with 144 im"],"journal":["BMC pulmonary medicine"],"pubmed_title":["Integrative stemness characteristics associated with prognosis and the immune microenvironment in lung adenocarcinoma."],"pmcid":["PMC9724367"],"funding_grant_id":["2020-04-02-111","2020-SW (Technology)-01-18","2021J0361"],"pubmed_authors":["Duan J","Yang Z","Zhang X","Luo W","Wang H","Wang Y","Cao R","Wang K"],"additional_accession":[]},"is_claimable":false,"name":"Integrative stemness characteristics associated with prognosis and the immune microenvironment in lung adenocarcinoma.","description":"<h4>Background</h4>To comprehensively analyze the stemness characteristics related to prognosis and the immune microenvironment in lung adenocarcinoma (LUAD).<h4>Methods</h4>The OCLR machine learning method was used to calculate the stemness index (mRNAsi) of the LUAD samples. DEGs common between the low mRNAsi, normal, and high mRNAsi groups were screened and the immune-stemness genes were obtained. Then the PPI network was created and enrichment analyses were performed. Moreover, different subtypes based on immune-stemness genes associated with prognosis were identified, and the relationships between LUAD stemness and TIME variables were systematically analyzed, followed by TMB analysis.<h4>Results</h4>Patients in the high mRNAsi groups with poor prognosis were screened along with 144 im","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Dec","modification":"2025-04-19T13:22:19.408Z","creation":"2025-04-19T13:22:19.408Z"},"accession":"S-EPMC9724367","cross_references":{"pubmed":["36471379"],"doi":["10.1186/s12890-022-02184-8"]}}