[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-331796-105":3,"doc-detail-331796-en":81,"detail-sidebar-cat-0-en-105":99},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","immune-metabolic-landscapes-of-prognostic-signatures-for-lung-adenocarcinoma-based-on-a-novel-deep-learning-framework","Immune, metabolic landscapes of prognostic signatures for lung adenocarcinoma based on a novel deep learning framework","","Lung adenocarcinoma (LUAD) is a highly lethal malignancy, and this study aims to identify prognostic biomarkers. A discovery cohort using the TCGA-LUAD dataset supports a joint deep learning framework, VAEjMLP, combining a variational autoencoder with a multilayer perceptron. Shapley Additive Explanations (SHAP) quantifies the contribution of feature genes to classification and enables a biomarker potential scoring algorithm. Nineteen biomarkers are linked to immune and metabolic regulation. A Cox-derived risk model using HLA-DRB1, SCGB1A1, and HLA-DRB5 stratifies patients into high- and low-risk groups, with external validation and distinct immune and metabolic pathway enrichment. The low-risk group shows stronger immune pathway enrichment and infiltration, while the high-risk group exhibits heightened metabolic activity and differences in aerobic glycolysis, amino acids, lipids, angiogenesis, epithelial-mesenchymal transition, cytokines, and inflammatory responses. High-risk patients are predicted to be more sensitive to Afatinib, Gefitinib, and Gemcitabine.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/immune-metabolic-landscapes-of-prognostic-signatures-for-lung-adenocarcinoma-based-on-a-novel-deep-learning-framework/331796/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/immune-metabolic-landscapes-of-prognostic-signatures-for-lung-adenocarcinoma-based-on-a-novel-deep-learning-framework/331796.png","ImageObject",300,407,{"name":42,"@type":43},"Jiven","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-11-04","2026-09-21",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",10,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What deep learning framework is used to identify prognostic biomarkers for LUAD?","Question",{"text":63,"@type":64},"The study proposes VAEjMLP, a joint framework combining a variational autoencoder (VAE) with a multilayer perceptron (MLP) using the TCGA-LUAD dataset as a discovery cohort.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How does the study evaluate the contribution of genes to the classification decision?",{"text":68,"@type":64},"It applies Shapley Additive Explanations (SHAP) to assess each feature gene’s contribution, supporting development of a biologically meaningful biomarker potential scoring algorithm.",{"name":70,"@type":61,"acceptedAnswer":71},"How is the LUAD prognostic risk model constructed and validated?",{"text":72,"@type":64},"The model is built using Cox regression screened biomarkers HLA-DRB1, SCGB1A1, and HLA-DRB5, stratifying patients into high- and low-risk groups; it is validated using external datasets.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},331796,1790502141,{"code":4,"msg":82,"data":83},"success",{"doc_id":79,"user_id":84,"nickname":42,"user_avatar":85,"doc_module":4,"category_id":86,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":87,"file_id":88,"file_url":89,"file_type":90,"file_size":91,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":92,"language":93,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":94,"faqs":95,"seo_title":96,"seo_description":12,"update_tm":97,"read_time":98,"preview_page_count":4},1099513958607,"https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nImmune, metabolic landscapes of prognostic signatures for lung adenocarcinoma based on a novel deep learning framework  \nShimei Qin1,2, Shibin Sun1,2, Yahui Wang1,2, Chao Li1, Lei Fu1, Ming Wu1, Jinxing Yan1, Wan Li1, Junjie Lv 1* & Lina Chen1*  \nLung adenocarcinoma (LUAD) is a malignant tumor with high lethality, and the aim of this study was to identify promising biomarkers for LUAD. Using theTCGA-LUAD dataset as a discovery cohort, a novel joint framework VAEjMLP based on variational autoencoder (VAE) and multilayer perceptron (MLP) was proposed. And the Shapley Additive Explanations (SHAP) method was introduced to evaluate the contribution of feature genes to the classification decision, which helped us to develop a biologically meaningful biomarker potential scoring algorithm. Nineteen potential biomarkers for LUAD were identified, which were involved in the regulation of immune and metabolic functions in  \nLUAD. A prognostic risk model for LUAD was constructed by the biomarkers HLA-DRB1, SCGB1A1, and HLA-DRB5 screened by Cox regression analysis, dividing the patients into high-risk and lowrisk groups. The prognostic risk model was validated with external datasets. The low-risk group was characterized by enrichment of immune pathways and higher immune infiltration compared to the high-risk group. While, the high-risk group was accompanied by an increase in metabolic pathway activity. There were significant differences between the high-and low-risk groups in metabolic reprogramming of aerobic glycolysis, amino acids, and lipids, as well as in angiogenic activity, epithelial-mesenchymal transition, tumorigenic cytokines, and inflammatory response. Furthermore, high-risk patients were more sensitive toAfatinib, Gefitinib, and Gemcitabine as predicted by the pRRophetic algorithm. This study provides prognostic signatures capable of revealing the immune and metabolic landscapes for LUAD, and may shed light on the identification of other cancer biomarkers.  \nCancer is still a global major public health problem1. According to the latest global cancer statistics estimates, lung cancer remains the leading cause of cancer death and the second most commonly diagnosed cancer, accounting for approximately 20% of cancer-related mortality and 10% of incidence2. Non-small cell lung cancer (NSCLC) accounts for approximately 85% of lung cancer cases3, with lung adenocarcinoma (LUAD) as its main histologic subtype and its incidence is still increasing4. Despite significant advances in diagnosis and treatment, the 5-year survival rate of LUAD is only 4–17%5. Therefore, screening and poor prognosis of lung adenocarcinoma remains an ongoing challenge. Accumulating evidence suggests that biomarker identification and application are of major importance for timely diagnosis and accurate prognosis of cancer6,7.  \nMachine learning has been widely used in biomarker discovery studies for cancer8–10. For example, based on breast cancer gene expression datasets, Zare et al.11 identified 59 novel inflammatory breast cancer-specific gene signatures using a random forest approach. Zhang et al.12 used a support vector machine algorithm to excavate a m6A target miRNAs diagnostic signature for cancer detection and successfully implemented it for lung cancer. Machine learning algorithms were deployed to develop tumor-infiltrating immune cell associated RNAs to predict survival outcomes in LUAD patients13. Deep learning has gained increasing attention in the research of cancer signatures identification14, 15. The denoising autoencoder, an unsupervised deep learning algorithm, has been successfully applied to LUAD molecular signature mining16. Divate et al.17 presented a robust neural network model providing gene signatures for accurate classification of cancers based on gene expression data. Three feed-forward neural networks were employed to RNA-seq samples from 18 solid tumor typ","cbCairlndgXzzpnx","https://ap.wps.com/l/cbCairlndgXzzpnx","pdf",16926958,22,"English","# Background\n## Lung adenocarcinoma clinical challenge and biomarker need\n## Machine learning and deep learning for cancer signatures\n# Methods\n## TCGA-LUAD discovery cohort and VAEjMLP framework\n## SHAP-based feature gene contribution evaluation\n## Cox regression prognostic risk model and patient stratification\n## External validation and pathway/phenotype comparisons\n# Results and Implications\n## Nineteen immune-metabolic biomarkers\n## Immune infiltration and metabolic reprogramming differences\n## Predicted drug sensitivity and clinical relevance","[{\"question\":\"What deep learning framework is used to identify prognostic biomarkers for LUAD?\",\"answer\":\"The study proposes VAEjMLP, a joint framework combining a variational autoencoder (VAE) with a multilayer perceptron (MLP) using the TCGA-LUAD dataset as a discovery cohort.\"},{\"question\":\"How does the study evaluate the contribution of genes to the classification decision?\",\"answer\":\"It applies Shapley Additive Explanations (SHAP) to assess each feature gene’s contribution, supporting development of a biologically meaningful biomarker potential scoring algorithm.\"},{\"question\":\"How is the LUAD prognostic risk model constructed and validated?\",\"answer\":\"The model is built using Cox regression screened biomarkers HLA-DRB1, SCGB1A1, and HLA-DRB5, stratifying patients into high- and low-risk groups; it is validated using external datasets.\"}]","Immune, metabolic landscapes of prognostic signatures for lung adenocarcinoma based on a novel deep learning framework | PDF",1789995200,55,{"code":4,"msg":82,"data":100},[101,105,109,113,118,123,128,131,136,139,142],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":102,"show_sort_weight":103,"slug":104},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":106,"show_sort_weight":107,"slug":108},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":110,"show_sort_weight":111,"slug":112},"Exam",70,"exam",{"id":114,"doc_module":4,"doc_module_name":25,"category_name":115,"show_sort_weight":116,"slug":117},5,"Comic",60,"comic",{"id":119,"doc_module":4,"doc_module_name":25,"category_name":120,"show_sort_weight":121,"slug":122},6,"Technology",50,"technology",{"id":124,"doc_module":4,"doc_module_name":25,"category_name":125,"show_sort_weight":126,"slug":127},7,"Healthcare",40,"healthcare",{"id":86,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":129,"slug":130},30,"research-report",{"id":132,"doc_module":4,"doc_module_name":25,"category_name":133,"show_sort_weight":134,"slug":135},9,"Religion & Spirituality",20,"religion-spirituality",{"id":134,"doc_module":4,"doc_module_name":25,"category_name":137,"show_sort_weight":134,"slug":138},"World Cup","world-cup",{"id":55,"doc_module":4,"doc_module_name":25,"category_name":140,"show_sort_weight":55,"slug":141},"Lifestyle","lifestyle",{"id":143,"doc_module":4,"doc_module_name":25,"category_name":144,"show_sort_weight":114,"slug":145},19,"General","general"]