[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123466-en":3,"doc-seo-123466-105":30,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123466,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Identification of Prognostic Biomarkers for Stage III Non-Small Cell Lung Carcinoma in Female Nonsmokers Using Machine Learning","Lung cancer remains a major cause of cancer-related mortality, and stage III non-small cell lung cancer (NSCLC) requires improved prognostic stratification for personalized care. This study identifies prognostic biomarkers in non-smoking females with stage III NSCLC using gene expression profiling from the GDS3837 dataset. XGBoost machine learning achieves strong predictive performance (AUC=0.835), highlighting C/EBPα, LDHA, UNC-45B, CHK1, and HIF-1α as literature-supported markers linked to lung cancer. Results suggest biomarker-driven early diagnosis and tailored therapy through combining machine learning with molecular profiling.","Identification of Prognostic Biomarkers for Stage III Non-Small Cell Lung Carcinoma in Female Nonsmokers Using Machine Learning  \nHuili Zheng 1a,* , Qimin Zhang 1b , Yiru Gong2a , Zheyan Liu2b , and Shaohan Chen2c  \n1, 2 Department of Biostatistics, Columbia University, New York, NY 10032, USA [hz2710@caa.columbia.edu](hz2710@caa.columbia.edu1a)[1a](hz2710@caa.columbia.edu1a),* , [qimin.zhang@columbia.edu](qimin.zhang@columbia.edu1b)[1b](qimin.zhang@columbia.edu1b) , [yiru.g@columbia.edu](yiru.g@columbia.edu2a)[2a](yiru.g@columbia.edu2a) , [zl3119@caa.columbia.edu](zl3119@caa.columbia.edu2b)[2b](zl3119@caa.columbia.edu2b) , [shaohan.chen@caa.columbia.edu](shaohan.chen@caa.columbia.edu2c)[2c](shaohan.chen@caa.columbia.edu2c)  \narXiv :2408 . 16068v2 [ q-bio .GN] 30 Aug 2024  \nAbstract—Lung cancer remains a leading cause of cancerrelated deaths globally, with non-small cell lung cancer (NSCLC) being the most common subtype. This study aimed to identify key biomarkers associated with stage III NSCLC in non-smoking females using gene expression profiling from the GDS3837 dataset. Utilizing XGBoost, a machine learning algorithm, the analysis achieved a strong predictive performance with an AUC score of 0.835. The top biomarkers identified—CCAAT enhancer binding protein alpha (C/EBPα), lactate dehydrogenase A4 (LDHA), UNC-45 myosin chaperone B (UNC-45B), checkpoint kinase 1 (CHK1), and hypoxia-inducible factor 1 subunit alpha (HIF- 1α)—have been validated in the literature as being significantly linked to lung cancer. These findings highlight the potential of these biomarkers for early diagnosis and personalized therapy, emphasizing the value of integrating machine learning with molecular profiling in cancer research.  \nIndex Terms—Lung cancer biomarkers, Non-small cell lung cancer (NSCLC), Bioinformatics, Machine learning  \nI. INTRODUCTION  \nLung cancer is a significant health challenge globally, being among the most common and deadly cancers. It accounts for approximately 12% of all new cancer diagnoses and nearly 20% of all cancer deaths annually. It is primarily classified into two major types: non-small cell lung cancer (NSCLC), which represents about 82% of cases, and small cell lung cancer (SCLC), which is less common but more aggressive. Despite advancements in early detection and treatment, the prognosis for lung cancer patients remains poor, with a five-year survival rate of approximately 25%, underscoring the critical need for continued research and innovation in this field [1] .  \nSmoking is widely recognized as the leading risk factor for lung cancer, responsible for approximately 80% to 90% of all lung cancer cases. The carcinogenic compounds in tobacco smoke contribute significantly to the development of both NSCLC and SCLC. However, an increasing number of lung cancer cases are being diagnosed in individuals who have never smoked, particularly among women. This trend suggests that other factors, like genetic predispositions, environmental exposures (e.g., radon and air pollution), and hormonal influences, may play a critical role in lung cancer development among non-smokers. To enhance survival rates in non-smoking lung cancer patients, it is crucial to conduct a thorough analysis of the molecular mechanisms underlying  \ncarcinogenesis in NSCLC. This approach will help identify more effective biomarkers for early diagnosis and uncover new targets for drug development. [1], [2] .  \nStage III non-small cell lung cancer is a complex disease, where accurate prognostic evaluation is key to personalized treatment. Detecting prognostic biomarkers can guide therapeutic decisions, potentially benefiting patients eligible for immunotherapy or targeted therapies. Recent studies, including a review on stage III NSCLC management, highlight the potential of biomarker-driven approaches to refine patient selection, improve outcomes, and advance personalized treatment strategies in this challenging lung cancer subset [3] . Given the p","cbCaid4YVo8dGjbY","https://ap.wps.com/l/cbCaid4YVo8dGjbY","pdf",911674,1,4,"English","en",105,"# Abstract\n# Introduction\n## Background and clinical need\n## Smoking and non-smoker risk factors\n## Role of gene expression profiling\n## Machine learning in bioinformatics\n# Data","[{\"question\":\"What dataset and data type were used to identify biomarkers in this study?\",\"answer\":\"The study used the publicly available GDS3837 gene expression dataset from NCBI GEO, containing expression profiles from tumor and adjacent normal lung tissue.\"},{\"question\":\"Which machine learning method delivered the predictive results, and how was performance measured?\",\"answer\":\"XGBoost was used, and performance was evaluated using AUC, achieving an AUC score of 0.835.\"},{\"question\":\"What biomarkers were identified as top prognostic candidates?\",\"answer\":\"The top biomarkers were C/EBPα, LDHA, UNC-45B, CHK1, and HIF-1α, each validated in the literature as significantly associated with lung cancer.\"}]","Identification of Prognostic Biomarkers for Stage III Non-Small Cell Lung Carcinoma in Female Nonsmokers Using Machine Learning | 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dataset and data type were used to identify biomarkers in this study?","Question",{"text":74,"@type":75},"The study used the publicly available GDS3837 gene expression dataset from NCBI GEO, containing expression profiles from tumor and adjacent normal lung tissue.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Which machine learning method delivered the predictive results, and how was performance measured?",{"text":79,"@type":75},"XGBoost was used, and performance was evaluated using AUC, achieving an AUC score of 0.835.",{"name":81,"@type":72,"acceptedAnswer":82},"What biomarkers were identified as top prognostic candidates?",{"text":83,"@type":75},"The top biomarkers were C/EBPα, LDHA, UNC-45B, CHK1, and HIF-1α, each validated in the literature as significantly associated with lung 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