<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>16(1)</volume><submitter>Li X</submitter><funding>National Natural Science Foundation of China</funding><journal>Scientific reports</journal><pagination>3685</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC12852111</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>Integrated single-cell and machine learning analysis identifies PMAIP1 as a novel biomarker for predicting prognosis and immunotherapy response in colorectal cancer.</pubmed_title><pmcid>PMC12852111</pmcid><funding_grant_id>82360529</funding_grant_id><pubmed_authors>Li X</pubmed_authors><pubmed_authors>Ding S</pubmed_authors><pubmed_authors>Chen J</pubmed_authors><pubmed_authors>Hu Z</pubmed_authors><pubmed_authors>Zhang H</pubmed_authors><pubmed_authors>Cai Z</pubmed_authors><pubmed_authors>Gao J</pubmed_authors><pubmed_authors>Wang Y</pubmed_authors><pubmed_authors>Wu Y</pubmed_authors></additional><is_claimable>false</is_claimable><name>Integrated single-cell and machine learning analysis identifies PMAIP1 as a novel biomarker for predicting prognosis and immunotherapy response in colorectal cancer.</name><description/><dates><release>2025-01-01T00:00:00Z</release><publication>2025 Dec</publication><modification>2026-06-16T03:09:35.637Z</modification><creation>2026-06-16T03:06:53.945Z</creation></dates><accession>S-EPMC12852111</accession><cross_references><pubmed>41436831</pubmed><doi>10.1038/s41598-025-32933-8</doi></cross_references></HashMap>