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Here, we developed and validated a 13\u2010gene signature via weighted gene coexpression network analysis (WGCNA) of chemotherapy\u2010resistant and sensitive OS transcriptomes. The risk model demonstrated robust prognostic value across four independent cohorts (TARGET\u2010OS, GSE21257, GSE16091, and GSE39055). Multiscale analysis revealed that high\u2010risk tumors exhibit proliferative hyperactivation, genomic instability with elevated TMB, and a paradoxical immune\u2010cold phenotype despite the high mutational load. Single\u2010cell and spatial transcriptomics demonstrated that high\u2010risk malignant cells display dedifferentiated, stem\u2010like properties, preferentially localize to hypoxic necrotic peripheries, and engage in extensive tumor\u2013stromal crosstalk. Drug sensitivity predictions confirmed resistance to first\u2010line chemotherapy agents. Collectively, these findings establish a clinically actionable 13\u2010gene biomarker and provide a mechanistic framework linking transcriptional profiles to chemoresistance biology, exposing novel therapeutic vulnerabilities in OS.<\/jats:p>","DOI":"10.1155\/humu\/8885469","type":"journal-article","created":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T09:16:59Z","timestamp":1780132619000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Integrative Multiomics Analysis Identifies a Novel Gene Signature That Predicts Chemotherapy Resistance and Poor Survival in 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