{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Cai L"],"funding":["U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)","U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences","Cancer Prevention and Research Institute of Texas (Cancer Prevention Research Institute of Texas)","U.S. Department of Health & Human Services | NIH | National Cancer Institute","NCI NIH HHS","U.S. Department of Health & Human Services | National Institutes of Health (NIH)","Cancer Prevention and Research Institute of Texas","U.S. Department of Health & Human Services | National Institutes of Health","NIGMS NIH HHS","U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS)"],"pagination":["2551-2564"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC6477796"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["38(14)"],"pubmed_abstract":["We constructed a lung cancer-specific database housing expression data and clinical data from over 6700 patients in 56 studies. Expression data from 23 genome-wide platforms were carefully processed and quality controlled, whereas clinical data were standardized and rigorously curated. Empowered by this lung cancer database, we created an open access web resource-the Lung Cancer Explorer (LCE), which enables researchers and clinicians to explore these data and perform analyses. Users can perform meta-analyses on LCE to gain a quick overview of the results on tumor vs non-malignant tissue (normal) differential gene expression and expression-survival association. Individual dataset-based survival analysis, comparative analysis, and correlation analysis are also provided with flexible options to allow for customized analyses from the user."],"journal":["Oncogene"],"pubmed_title":["LCE: an open web portal to explore gene expression and clinical associations in lung cancer."],"pmcid":["PMC6477796"],"funding_grant_id":["5P30CA142543","RP120732","P50 CA070907","RP150596","R01 CA172211","1R01CA172211","5R01CA152301","R01 CA152301","P30 CA142543","P50CA70907","1R01GM115473","R01 GM115473"],"pubmed_authors":["Minna J","Lin S","Yang L","Zhou Q","Xie Y","Ci B","Cai L","Zhou Y","Luo D","Tang H","Wistuba I","Heymach J","Allen J","Huffman K","Gazdar A","Girard L","Xiao G","Yao B"],"additional_accession":[]},"is_claimable":false,"name":"LCE: an open web portal to explore gene expression and clinical associations in lung cancer.","description":"We constructed a lung cancer-specific database housing expression data and clinical data from over 6700 patients in 56 studies. Expression data from 23 genome-wide platforms were carefully processed and quality controlled, whereas clinical data were standardized and rigorously curated. Empowered by this lung cancer database, we created an open access web resource-the Lung Cancer Explorer (LCE), which enables researchers and clinicians to explore these data and perform analyses. Users can perform meta-analyses on LCE to gain a quick overview of the results on tumor vs non-malignant tissue (normal) differential gene expression and expression-survival association. Individual dataset-based survival analysis, comparative analysis, and correlation analysis are also provided with flexible options to allow for customized analyses from the user.","dates":{"release":"2019-01-01T00:00:00Z","publication":"2019 Apr","modification":"2024-02-15T08:48:58.792Z","creation":"2019-07-25T07:14:05Z"},"accession":"S-EPMC6477796","cross_references":{"pubmed":["30532070"],"doi":["10.1038/s41388-018-0588-2"]}}