{"doi":"10.1200/jco.2011.29.15_suppl.e21005","title":"Identification of serum protein biomarkers for small cell lung cancer.","abstract":"e21005 Background: Lung cancer is the most common cause of cancer mortality in the world. Small cell lung cancer (SCLC) is characterized by rapid progression, extensive metastasis, and very high mortality (95%). The fact that SCLC prognosis improves when it is detected early underscores the urgent need for a non-invasive method such as serum protein quantification for timely diagnosis. Many researchers have tried to identify serum protein biomarkers for various diseases using a proteomics approach, but challenges such as low sensitivity of mass spectrometry, variable protein concentrations in serum, small sample sizes, and clinical confounding factors still exist. We hypothesized that integrative meta-analysis of a number of SCLC gene expression data sets would increase the sample size as well as address the issue of confounding factors, which in turn will allow us to identify serum protein biomarkers for SCLC. Methods: We performed meta-analysis on 9 SCLC gene expression data sets consisting of total 749 samples. We characterized the genes discovered from meta-analysis to identify candidate serum protein biomarkers and utilized ELISA to validate our in silico findings in human SCLC patient serum samples in vivo. Results: Meta-analysis of gene expression data revealed 100 genes (false discovery rate < 0.1) that are over-expressed in SCLC. These included not only ASCL1, a well-known neuroendocrine marker, but also possible drug targets such as HDAC1 and RXRG. We demonstrated that this meta-analysis approach is more successful at identifying over-expressed genes in SCLC compared to widely used Significant Analysis of Microarrays method. We selected a subset of genes from our initial genes based on their presence in serum, expression in SCLC mouse model, and potential involvement in SCLC pathogenesis. We are currently conducting ELISA of human SCLC patient serum samples to validate our predicted serum protein biomarkers. Conclusions: Candidate SCLC serum protein biomarkers were identified using meta-analysis of gene expression profiles. The methods that we described in this study will enable us to discover serum protein biomarkers for other diseases and potentially apply them clinically for their early detection and better diagnosis in the future.","journal":"Journal of Clinical Oncology","year":2011,"id":1692,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.0366,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2011-05-20","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":19143,"name":"P. Khatri","orcid":null,"position":1,"is_corresponding":false},{"id":19144,"name":"K. Park","orcid":"0000-0002-4694-9735","position":2,"is_corresponding":false},{"id":19145,"name":"J. Sage","orcid":null,"position":4,"is_corresponding":false},{"id":48,"name":"Joel T. Dudley","orcid":"0000-0002-7036-6492","position":6,"is_corresponding":false},{"id":19146,"name":"John Sage","orcid":"0000-0003-3254-462X","position":7,"is_corresponding":false},{"id":51,"name":"Atul Janardhan Butte","orcid":"0000-0002-7433-2740","position":8,"is_corresponding":false},{"id":19142,"name":"Y. Kim","orcid":null,"position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-03-01T18:20:47.508186Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}