<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Liu C</submitter><funding>Natural Science Foundation of China</funding><funding>Research Funds for the Central Universities</funding><funding>National Key Research and Development Program of China</funding><pagination>1610</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC10000504</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>15(5)</volume><pubmed_abstract>&lt;h4>Background&lt;/h4>Gastric cancer is a malignant tumor with high morbidity and mortality. Therefore, the accurate recognition of prognostic molecular markers is the key to improving treatment efficacy and prognosis.&lt;h4>Methods&lt;/h4>In this study, we developed a stable and robust signature through a series of processes using machine-learning approaches. This PRGS was further experimentally validated in clinical samples and a gastric cancer cell line.&lt;h4>Results&lt;/h4>The PRGS is an independent risk factor for overall survival that performs reliably and has a robust utility. Notably, PRGS proteins promote cancer cell proliferation by regulating the cell cycle. Besides, the high-risk group displayed a lower tumor purity, higher immune cell infiltration, and lower oncogenic mutation than the low-PRGS group.&lt;h4>Conclusions&lt;/h4>This PRGS could be a powerful and robust tool to improve clinical outcomes for individual gastric cancer patients.</pubmed_abstract><journal>Cancers</journal><pubmed_title>Development and Experimental Validation of a Novel Prognostic Signature for Gastric Cancer.</pubmed_title><pmcid>PMC10000504</pmcid><funding_grant_id>2019RC045</funding_grant_id><funding_grant_id>62003028</funding_grant_id><funding_grant_id>2017YFA0505503</funding_grant_id><pubmed_authors>Li X</pubmed_authors><pubmed_authors>Yin F</pubmed_authors><pubmed_authors>Shi M</pubmed_authors><pubmed_authors>Huo Y</pubmed_authors><pubmed_authors>Zhang Y</pubmed_authors><pubmed_authors>Gao J</pubmed_authors><pubmed_authors>Liu C</pubmed_authors><pubmed_authors>Jin P</pubmed_authors><pubmed_authors>Wang Z</pubmed_authors><pubmed_authors>Zhang MQ</pubmed_authors><pubmed_authors>Chen T</pubmed_authors></additional><is_claimable>false</is_claimable><name>Development and Experimental Validation of a Novel Prognostic Signature for Gastric Cancer.</name><description>&lt;h4>Background&lt;/h4>Gastric cancer is a malignant tumor with high morbidity and mortality. Therefore, the accurate recognition of prognostic molecular markers is the key to improving treatment efficacy and prognosis.&lt;h4>Methods&lt;/h4>In this study, we developed a stable and robust signature through a series of processes using machine-learning approaches. This PRGS was further experimentally validated in clinical samples and a gastric cancer cell line.&lt;h4>Results&lt;/h4>The PRGS is an independent risk factor for overall survival that performs reliably and has a robust utility. Notably, PRGS proteins promote cancer cell proliferation by regulating the cell cycle. Besides, the high-risk group displayed a lower tumor purity, higher immune cell infiltration, and lower oncogenic mutation than the low-PRGS group.&lt;h4>Conclusions&lt;/h4>This PRGS could be a powerful and robust tool to improve clinical outcomes for individual gastric cancer patients.</description><dates><release>2023-01-01T00:00:00Z</release><publication>2023 Mar</publication><modification>2024-12-03T21:24:12.089Z</modification><creation>2024-12-03T21:24:12.089Z</creation></dates><accession>S-EPMC10000504</accession><cross_references><pubmed>36900401</pubmed><doi>10.3390/cancers15051610</doi></cross_references></HashMap>