<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>16</volume><submitter>Li Z</submitter><pubmed_abstract>&lt;h4>Background&lt;/h4>Clear cell renal cell carcinoma (ccRCC) exhibits marked heterogeneity and variable benefit from immune checkpoint inhibitors (ICIs). Post-translational modifications (PTMs) regulate immune signaling and tumor behavior, yet PTM-informed biomarkers for ccRCC remain underexplored.&lt;h4>Methods&lt;/h4>We intersected immune-related genes, PTM-related genes, and differentially expressed genes in TCGA-KIRC to derive candidates and built a prognostic model across TCGA and E-MTAB-1980 using multiple algorithms, selecting a random survival forest-based post-translational modification-related signature (PTMRS) with the best performance. Prognostic value and independence were evaluated by time-dependent ROC, Kaplan-Meier, and multivariate Cox analyses. Tumor immune context was profiled b</pubmed_abstract><journal>Frontiers in immunology</journal><pagination>1707375</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC12702869</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>Integrative multi-omics analysis identifies a PTM-related immune signature and IRF9 as a driver in ccRCC.</pubmed_title><pmcid>PMC12702869</pmcid><pubmed_authors>Li X</pubmed_authors><pubmed_authors>Hu S</pubmed_authors><pubmed_authors>Li Z</pubmed_authors><pubmed_authors>Jiang S</pubmed_authors><pubmed_authors>Wang J</pubmed_authors></additional><is_claimable>false</is_claimable><name>Integrative multi-omics analysis identifies a PTM-related immune signature and IRF9 as a driver in ccRCC.</name><description>&lt;h4>Background&lt;/h4>Clear cell renal cell carcinoma (ccRCC) exhibits marked heterogeneity and variable benefit from immune checkpoint inhibitors (ICIs). Post-translational modifications (PTMs) regulate immune signaling and tumor behavior, yet PTM-informed biomarkers for ccRCC remain underexplored.&lt;h4>Methods&lt;/h4>We intersected immune-related genes, PTM-related genes, and differentially expressed genes in TCGA-KIRC to derive candidates and built a prognostic model across TCGA and E-MTAB-1980 using multiple algorithms, selecting a random survival forest-based post-translational modification-related signature (PTMRS) with the best performance. Prognostic value and independence were evaluated by time-dependent ROC, Kaplan-Meier, and multivariate Cox analyses. Tumor immune context was profiled b</description><dates><release>2025-01-01T00:00:00Z</release><publication>2025</publication><modification>2026-06-06T04:19:26.927Z</modification><creation>2026-05-25T03:12:01.618Z</creation></dates><accession>S-EPMC12702869</accession><cross_references><pubmed>41403935</pubmed><doi>10.3389/fimmu.2025.1707375</doi></cross_references></HashMap>