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It integrates multi-omics signatures to evaluate patient outcomes and immune-treatment efficacy using WGCNA for immunity-linked modules, Lasso-Cox for risk modeling, enrichment analyses (GO/KEGG), ssGSEA immune profiling, and single-cell expression inspection. Results identify immune-related gene components, yielding a RiskScore associated with immune checkpoint targets.",{"@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 & 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integration of multi-omics analysis and machine learning for the identification of prognostic assessment and immunotherapy efficacy through aging-associated genes in lung cancer  \nWei Lu1,*, Yun Zhou2,*, Ruixuan Zhao1,*, Qiushi Liu1, Wei Yang1, Tianyi Zhu1  \n1 Department of Respiratory, General Hospital of Northern Theater Command, Shenyang, China  \n2 Department of Respiratory, Wuhu Hospital, East China Normal University, The Second People’s Hospital of Wuhu, Wuhu, Anhui, China  \n* Equal contribution and shared first authorship  \nCorrespondence to: Tianyi Zhu; email: [15056591947@163.com](15056591947@163.com), [https://orcid.org/0009-0001-3061-5634](https://orcid.org/0009-0001-3061-5634)[ ](https://orcid.org/0009-0001-3061-5634)[Keywords:](Keywords: lung cancer)[ lung cancer](Keywords: lung cancer), aging, WGCNA, prognostic model, immunotherapy  \nReceived: August 29, 2023 Accepted: December 4, 2023 Published: January 22, 2024  \nCopyright: © 2024 Lu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.  \nABSTRACT  \nBackground: Recent years revealed key molecules in lung cancer research, yet their exact roles in disease onset and progression remain uncertain. Lung cancer’s heterogeneity complicates prognosis prediction. This study integrates pivotal molecules to evaluate patient prognosis and immunotherapy efficacy.  \nMethods: The WGCNA algorithm identified module genes linked to immunity. The Lasso-Cox method built a prognostic model for outcome prediction. GO and KEGG analyses explored gene pathways. ssGSEA quantified immune cell types and functions. The riskScore predicts the effectiveness of immunotherapy based on its correlation with DNA repair and immune checkpoint genes. Single-cell sequencing examined key gene expression across cell types.  \nResults: Using WGCNA, we identified the MEbrown module related to immunity. Lasso-Cox selected “BLK,”“ITGB4,” “PRKCH,” and “SNAI1” for the prognostic model. MF analysis revealed enriched functions including antigen binding, GTPase regulator activity. In terms of BP, processes like immune signaling and mitotic division were enriched. CC enrichment included immunoglobulin complexes and chromosomal regions. Enriched pathways encompassed Cell cycle, Focal adhesion, Cellular senescence, and p53 signaling. ssGSEA evaluated immune cell abundance. RiskScore correlated with CTLA4 and PD1 through MMR and immune checkpoint analysis. Single-cell analysis indicated gene expression across cell types for BLK, ITGB4, PRKCH, and SNAI1. Conclusion: In summary, our developed prognostic model utilizing age-related genes effectively predicts lung cancer prognosis and the efficacy of immune therapy.  \nINTRODUCTION  \nLung cancer is a malignant tumor that originates within lung tissue and ranks among the foremost causes of mortality worldwide [1, 2] . Therapeutic modalities for lung cancer encompass surgical intervention, radiotherapy, chemotherapy, targeted therapy, and immunotherapy, among others [3] . The realm of  \nlung cancer treatment has experienced substantial advancements, particularly in the domains of molecular targeted therapy and immunotherapeutic interventions [4] . Molecular targeted therapy hinges on aberrant signaling pathways or specific mutations inherent to tumor cells, while immunotherapy involves activating the patient ’s own immune system to combat malignant cells [5, 6] . Given the pronounced heterogeneity of  \nlung cancer tissue, treatment outcomes exhibit marked variations [7, 8] . Therefore, there is an urgent need to introduce novel biomarkers and predictive models to achieve personalized therapeutic strategies for individuals.  \nImmunotherapy, as a revolutionary treatment approach, has garnered w","cbCainmyGv1qVSdn","https://ap.wps.com/l/cbCainmyGv1qVSdn","pdf",3078455,"English","# Abstract\n## Background\n## Methods\n## Results\n## Conclusion\n# Introduction","[{\"question\":\"What is the study’s main goal in lung cancer research?\",\"answer\":\"To integrate multi-omics analysis and machine learning to identify aging-associated prognostic assessment and to evaluate immunotherapy efficacy in lung cancer patients.\"},{\"question\":\"Which computational methods are used to build the prognostic model?\",\"answer\":\"WGCNA identifies immunity-related module genes, while Lasso-Cox selects key genes and constructs the prognostic model for outcome prediction.\"},{\"question\":\"How is immunotherapy efficacy evaluated in this study?\",\"answer\":\"A RiskScore is derived and assessed for correlation with DNA repair and immune checkpoint genes, with immune profiling supported by ssGSEA and immune checkpoint analysis.\"}]","The integration of multi-omics analysis and machine learning for the identification of prognostic assessment and immunotherapy efficacy through aging-associated genes in lung cancer | PDF",1789975775,48]