AI and Cancer Research ── Gastric ── 2026-10-22

2 Gastric papers. Lead: Supporting data for “Multi-cohort machine learning and experimental validation identify THY1 as an extracellular matrix remodeling-associated biomarker in gastric cancer”

Journals covered and how papers are chosen: see the index

Gastric cancer: treatment, trials and AI

Supporting data for “Multi-cohort machine learning and experimental validation identify THY1 as an extracellular matrix remodeling-associated biomarker in gastric cancer”
Figshare · 2026-10-20 · Evidence: Preclinical (public dataset analysis with experimental validation; deposited data record)
Abstract
This dataset contains supporting data and analysis materials for the study entitled “Multi-cohort machine learning and experimental validation identify THY1 as an extracellular matrix remodeling-associated biomarker in gastric cancer.” The deposited materials include: (1) source numerical data underlying the experimental analyses presented in Figures 7 and 8; (2) Supplementary Tables S1-S7; (3) uncropped western blot images; and (4) analysis code used for public-dataset preprocessing, machine-learning analyses, statistical analyses, and visualization. Public transcriptomic datasets analyzed in the study were obtained from GEO, TCGA-STAD, and TISCH2. GEO datasets GSE118916, GSE54129, GSE63089, and GSE65801 were used as the training/discovery cohorts; GSE146996, GSE51575, and GSE66229 were used for external validation; TCGA-STAD was used for survival and clinicopathological analyses; and GSE134520 and GSE167297 were used for single-cell transcriptomic contextualization.
Journal IF: unknown (could not be matched)
Reference: [No authors listed]. Supporting data for “Multi-cohort machine learning and experimental validation identify THY1 as an extracellular matrix remodeling-associated biomarker in gastric cancer”. Figshare. [Epub ahead of print]. doi:10.6084/m9.figshare.34715742.
Checked: Abstract only
MO40-5 PREDICT-GC/ShortOS: machine learning and regression models for predicting very short survival in advanced gastric cancer
Annals of Oncology · · Evidence: Retrospective observational (prediction model development)
Abstract
At Maharat Nakhon Ratchasima Hospital, patients with advanced gastric cancer (AGC) often have very short overall survival (OS), typically only 2-3 months. Many discontinue treatment due to rapid physical decline. Early identification of patients at risk of OS < 90 days could support timely interventions and tailored treatment strategies.
Journal IF-equivalent: 1.7 (OpenAlex 2-year mean citedness, value as of 2026-10-20, retrieved 2026-10-21; not the official Clarivate IF)
Reference: Thanestada J, Sihaban K. MO40-5 PREDICT-GC/ShortOS: machine learning and regression models for predicting very short survival in advanced gastric cancer. Annals of Oncology. 2026 Nov;37:S307. doi:10.1016/j.annonc.2026.07.186.
Checked: Abstract only

← Back to this issue