Journal: iScience
Article Title: Deciphering aging-associated prognosis and heterogeneity in gastric cancer through a machine learning-driven approach
doi: 10.1016/j.isci.2025.112316
Figure Lengend Snippet: The transcription factor regulatory network was established to identify subtype-specific potential target TF (A) A regulatory network inference was established using the expression profiles of TF and mRNA. (B) Univariate Cox analysis revealed two prognostically significant potential TFs. (C) Patients with higher expression levels of SOX7 showed poorer OS (patients stratified by optional value). (D) Patients with higher expression levels of ELK3 showed poorer OS (patients stratified by optional value). Survival curves were compared via log rank test. (E and F) The identified TFs, (E) ELK3 and (F) SOX7 were upregulated in the Cluster2 subtype (Data are represented as mean ± SEM), (G) could serve as diagnostic biomarkers, and (H) further detected at protein expression levels in HPA database (scale bar: 200 μm). The Wilcoxon test was used for comparisons between two groups and statistical significance was indicated with asterisks: ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001; ns denotes not significant. A p value of <0.05 was considered statistically significant.
Article Snippet: Protein levels of SOX7 and ELK3 , The Human Protein Atlas , https://www.proteinatlas.org/.
Techniques: Expressing, Diagnostic Assay