{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T06:49:31Z","timestamp":1765349371288,"version":"3.46.0"},"reference-count":106,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T00:00:00Z","timestamp":1765324800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Bioinform."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>With increased drug resistance and tumor heterogeneity accounting for limited therapeutic strategies, gastric cancer remains one of the major causes of cancer-related mortality around the globe. Targeting the components of the tumor microenvironment (TME) has become a promising therapeutic strategy due to their crucial roles in cancer cell proliferation, progression, and metastasis. One of the limitations of the previously identified therapeutic targets is their limited applicability to a broader patient population.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study aims to identify (TME)-related therapeutic targets using an integrated bioinformatics and molecular docking approach that involves a larger number of datasets to cover a broader cohort of gastric cancer patients. It analyzed multiple publicly available transcriptomic datasets using Robust Rank Aggregation (RRA) meta-analysis and Weighted Gene Co-expression Network Analysis (WGCNA) to identify significant hub genes. Furthermore, protein-protein interaction (PPI) network analyses, conducted using multiple methods such as Cytohubba topology analysis and ClusterONE module analysis, refined the potential therapeutic candidates. Functional enrichment analyses were performed to identify vital genes involved in TME interactions and ECM remodeling.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The enriched genes were validated for their significant dysregulation in the Cancer Genome Atlas gastric adenocarcinoma dataset (TCGA-STAD) and three independent GEO datasets to ensure differential expression across distinct cohorts. Genes with consistent dysregulation were used in survival analyses across TCGA and two GEO datasets to prioritize hub genes with prognostic significance. Finally, a targeted literature survey ensured the exclusion of previously targeted genes, and molecular docking analyses conducted using phytocompounds identified potential therapeutic leads with strong affinities for the identified targets.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>This integrated approach revealed notable, promising targets in the TME and natural compounds for developing potential personalized therapeutic strategies in gastric cancer.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fbinf.2025.1654326","type":"journal-article","created":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T06:48:02Z","timestamp":1765349282000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Identification and validation of tumor microenvironment-related therapeutic targets in gastric cancer using integrated multi-omics and molecular docking approaches"],"prefix":"10.3389","volume":"5","author":[{"given":"Mohamed Kalith","family":"Oli M.","sequence":"first","affiliation":[]},{"given":"Jafar Ali Ibrahim","family":"Syed Masood","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2025,12,10]]},"reference":[{"key":"B3","doi-asserted-by":"publisher","first-page":"W172","DOI":"10.1093\/nar\/gkaf368","article-title":"ClusterONE web: a tool for discovering and analyzing overlapping protein complexes","volume":"53","author":"Baez","year":"2025","journal-title":"Nucleic Acids Res."},{"key":"B4","doi-asserted-by":"publisher","first-page":"W513","DOI":"10.1093\/nar\/gkae303","article-title":"ProTox 3.0: a webserver for the prediction of toxicity of chemicals","volume":"52","author":"Banerjee","year":"2024","journal-title":"Nucleic Acids Res."},{"key":"B5","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1186\/s13046-025-03278-x","article-title":"SNORA37\/CMTR1\/ELAVL1 feedback loop drives gastric cancer progression via facilitating CD44 alternative splicing","volume":"44","author":"Bao","year":"2025","journal-title":"J. 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