Article citationsMore>>

Hudson, J.I., Hudson, Y., Kanyama, G., Schnabel, J., Javaras, K.N., Kaufman, M.J., et al. (2024) Causal Factors in Childhood and Adolescence Leading to Anabolic-Androgenic Steroid Use: A Machine Learning Approach. Drug and Alcohol Dependence Reports, 10, Article 100215.
https://doi.org/10.1016/j.dadr.2023.100215

has been cited by the following article:

• TITLE: Analysis of CSF2RA Expression and Its Functional and Clinical Significance in Gastric Cancer Based on Bioinformatics

  AUTHORS: Rong He, Yongle Li, Lin Jiang, Mingtao Luo, Shiquan Wei, Lihe Jiang

  KEYWORDS: Bioinformatics, Gastric Cancer, CSF2RA, Prognostic Biomarker, Tumor Immune Microenvironment, Machine Learning

  JOURNAL NAME: Journal of Biosciences and Medicines, Vol.13 No.9, September 22, 2025

  ABSTRACT: Objective: To investigate the expression characteristics, clinical significance, and immune regulatory mechanisms of colony stimulating factor receptor α (CSF2RA) in gastric cancer (GC) based on multi-omics data. Methods: The TCGA GC dataset was used for integration. Key modules were identified using differential analysis and weighted gene co-expression network analysis (WGCNA). Three machine learning algorithms—LASSO regression, Random Forest, and Gradient Boosting Machine (GBM)—were combined to identify core genes. The expression patterns were validated using the TIMER2 database. Kaplan-Meier survival analysis and a nomogram model were used to assess the prognostic value. Immune microenvironment characteristics were dissected using CIBERSORT and ESTIMATE algorithms. Potential functions were explored based on Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses and protein-protein interaction (PPI) network construction. Results: CSF2RA was the sole signature gene consistently identified and cross-validated using all three machine-learning algorithms. It was significantly overexpressed in GC tissues (P