{"database":"iProX","file_versions":[],"scores":null,"additional":{"omics_type":["Proteomics"],"submitter":["He Huang"],"species":["Homo Sapiens"],"full_dataset_link":["http://www.iprox.org/page/project.html?id=IPX0016235000"],"submitter_email":["hhuang@simm.ac.cn"],"submitter_affiliation":["State Key Laboratory of Chemical Biology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences"],"sample_protocol":[""],"repository":["iProX"],"data_protocol":[""],"pubmed_abstract":["The response of lung cancer to chemoradiotherapy (CRT) is orchestrated by the dynamic interplay between immune microenvironment remodeling and metabolic reprogramming. However, robust biomarkers for predicting patient prognosis remain elusive. In this study, we performed proteomics profiling of 22 pairs of lung cancer specimens and compared CRT-sensitive versus CRT-resistant subtypes. Functional analysis revealed distinct pathway enrichment between the two groups: the CRT-sensitive subtype was predominantly associated with fatty acid oxidation and energy metabolism, whereas the CRT-resistant subtype exhibited elevated immune activation, inflammatory responses, and stress-related signaling. Based on these findings, we developed a LASSO regression-based predictive model that pinpointed a protein signature encompassing IKBKB interacting protein (IKBIP) and citron rho-interacting serine/threonine kinase (CIT) as optimal predictors of CRT efficacy. Subsequent survival analysis further confirmed the prognostic relevance of these proteins. Collectively, our findings emphasize the critical role of the metabolic-immune axis in modulating CRT sensitivity and provide mechanistic insights into the molecular underpinnings of treatment response, thereby offering a clinically translatable tool for guiding precision therapeutic strategies in lung cancer."],"pubmed_title":["Comprehensive Proteomic Profiling Reveals Molecular Mechanisms Mediating Chemoradiotherapy Efficacy in Lung Cancer."],"pubmed_authors":["Ouyang Weiwei W, Wang Yixuan Y, Yang Jianghua J, Cao Dongdong D, Wang Qianjing Q, Luo Yuxi Y, Tian Chuan C, Huang He H, Wang Xinbo X"],"additional_accession":[]},"is_claimable":false,"name":"Biomarker Prediction for Lung Cancer Chemoradiotherapy Efficacy Based on Proteomics","description":"The response of lung cancer to chemoradiotherapy (CRT) is orchestrated by the dynamic interplay between immune microenvironment remodeling and metabolic reprogramming. However, robust biomarkers for predicting patient prognosis remain elusive. In this study, we performed proteomics profiling of 22 pairs of lung cancer specimens and compared CRT-sensitive versus CRT-resistant subtypes. Functional analysis revealed distinct pathway enrichment between the two groups: the CRT-sensitive subtype was predominantly associated with fatty acid oxidation and energy metabolism, whereas the CRT-resistant subtype exhibited elevated immune activation, inflammatory responses, and stress-related signaling. Based on these findings, we developed a LASSO regression-based predictive model that pinpointed a protein signature encompassing IKBKB interacting protein (IKBIP) and citron rho-interacting serine/threonine kinase (CIT) as optimal predictors of CRT efficacy. Subsequent survival analysis further confirmed the prognostic relevance of these proteins. Collectively, our findings emphasize the critical role of the metabolic-immune axis in modulating CRT sensitivity and provide mechanistic insights into the molecular underpinnings of treatment response, thereby offering a clinically translatable tool for guiding precision therapeutic strategies in lung cancer.","dates":{"publication":"Thu Mar 19 00:00:00 GMT 2026"},"accession":"PXD075840","cross_references":{"TAXONOMY":["9606"],"pubmed":["42228735"]}}