Construction of the PRG score index using integrated machine learning algorithms.

<p>(A) C-index calculated for both training and validation cohorts using combinations of 10 different machine learning algorithms. (B, C) Stratification of PRG score subgroups in the training and validation cohorts. (D, E) Kaplan–Meier survival analysis of PRG score subgroups in two independen...

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Bibliographic Details
Main Author:	Xiangxin Wu (22320058) (author)
Other Authors:	Lianming Cai (22320061) (author), Yanping Liu (567079) (author), Bowen Wang (526637) (author), Tianyi Xia (7040726) (author), Zhenhua Liu (335797) (author)
Published:	2025
Subjects:	Biochemistry Cell Biology Genetics Molecular Biology Immunology Cancer Biological Sciences not elsewhere classified Information Systems not elsewhere classified prognostic model development prevalent histological subtype machine learning algorithms including western blot immune infiltration profiles explore cellular heterogeneity exhibits high heterogeneity cell rna sequencing related molecular subtypes potential therapeutic targets one key prg offering novel biomarkers biological pathway activation based prognostic signature div >< p novel prg therapeutic resistance molecular landscape biological significance vitro </ tumor biology transwell assays stomach adenocarcinoma significant differences public databases poor prognosis omics analysis gene function gastric cancer functional assays findings highlight distinct form colony formation associated genes
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Construction of the PRG score index using integrated machine learning algorithms.

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