biostudies-literatureHurtado MNCI NIH HHS1394965https://www.ebi.ac.uk/biostudies/studies/S-EPMC11600981biostudies-literatureUnknown15<h4>Background</h4>Lung cancer is the leading cause of cancer death worldwide, with poor survival despite recent therapeutic advances. A better understanding of the complexity of the tumor microenvironment is needed to improve patients' outcome.<h4>Methods</h4>We applied a computational immunology approach (involving immune cell proportion estimation by deconvolution, transcription factor activity inference, pathways and immune scores estimations) in order to characterize bulk transcriptomics of 62 primary lung adenocarcinoma (LUAD) samples from patients across disease stages. Focusing specifically on early stage samples, we validated our findings using an independent LUAD cohort with 70 bulk RNAseq and 15 scRNAseq datasets and on TCGA datasets.<h4>Results</h4>Through our methodology and fFrontiers in immunologyTranscriptomics profiling of the non-small cell lung cancer microenvironment across disease stages reveals dual immune cell-type behaviors.PMC11600981U01 CA152662U01 CA196405Boutillet LSenosain MFDelfour OClermont ECruzalegui FCasanova AZou YBesse JEssabbar AKammer MZhao SCoullomb AKruczynski APassioukov AXie TPradines AGouin SBurq PMaldonado FKhajavi LChetaille EHurtado MLaunay PMahfoudi AMazieres JFavre GPancaldi VfalseTranscriptomics profiling of the non-small cell lung cancer microenvironment across disease stages reveals dual immune cell-type behaviors.<h4>Background</h4>Lung cancer is the leading cause of cancer death worldwide, with poor survival despite recent therapeutic advances. A better understanding of the complexity of the tumor microenvironment is needed to improve patients' outcome.<h4>Methods</h4>We applied a computational immunology approach (involving immune cell proportion estimation by deconvolution, transcription factor activity inference, pathways and immune scores estimations) in order to characterize bulk transcriptomics of 62 primary lung adenocarcinoma (LUAD) samples from patients across disease stages. Focusing specifically on early stage samples, we validated our findings using an independent LUAD cohort with 70 bulk RNAseq and 15 scRNAseq datasets and on TCGA datasets.<h4>Results</h4>Through our methodology and f2024-01-01T00:00:00Z20242025-04-22T20:43:07.501Z2025-04-06T03:14:29.27ZS-EPMC116009813960624010.3389/fimmu.2024.1394965