Differential networking meta-analysis of gastric cancer across Asian and American racial groups.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:29745833", "kind": "science-record", "revision": 7, "content_hash": "sha256:c82712368994e0b2755069649c4c78450f955af6d5881bfc989e3e13a1164b04", "created_at": "2026-11-01T15:40:33Z", "updated_at": "2026-11-01T21:59:49Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Differential networking meta-analysis of gastric cancer across Asian and American racial groups.", "doi": "10.1186/s12918-018-0564-z", "pmid": "29745833", "authors": ["Wentao Dai", "Quanxue Li", "Bing-Ya Liu", "Yi-Xue Li", "Yuan-Yuan Li"], "journal": "BMC systems biology", "year": 2018, "abstract": "BACKGROUND: Gastric Carcinoma is one of the most lethal cancer around the world, and is also the most common cancers in Eastern Asia. A lot of differentially expressed genes have been detected as being associated with Gastric Carcinoma (GC) progression, however, little is known about the underlying dysfunctional regulation mechanisms. To address this problem, we previously developed a differential networking approach that is characterized by involving differential coexpression analysis (DCEA), stage-specific gene regulatory network (GRN) modelling and differential regulation networking (DRN) analysis.\nRESULT: In order to implement differential networking meta-analysis, we developed a novel framework which integrated the following steps. Considering the complexity and diversity of gastric carcinogenesis, we first collected three datasets (GSE54129, GSE24375 and TCGA-STAD) for Chinese, Korean and American, and aimed to investigate the common dysregulation mechanisms of gastric carcinogenesis across racial groups. Then, we constructed conditional GRNs for gastric cancer corresponding to normal and carcinoma, and prioritized differentially regulated genes (DRGs) and gene links (DRLs) from three datasets separately by using our previously developed differential networking method. Based on our integrated differential regulation information from three datasets and prior knowledge (e.g., transcription factor (TF)-target regulatory relationships and known signaling pathways), we eventually generated testable hypotheses on the regulation mechanisms of two genes, XBP1 and GIF, out of 16 common cross-racial DRGs in gastric carcinogenesis.\nCONCLUSION: The current cross-racial integrative study from the viewpoint of differential regulation networking provided useful clues for understanding the common dysfunctional regulation mechanisms of gastric cancer progression and discovering new universal drug targets or biomarkers for gastric cancer.", "claims": [{"id": "claim-method", "statement": "Permutation test showed that gastric cancer genes were significantly ranked higher in the DRG lists from three GC expression datasets, with p-values of 1.36e-05 (GSE24375), 6.54e-53 (GSE54129) and 0.00793 (TCGA-STAD).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:29745833", "locator": "sections[8] \"Construction of GRNs and identification of DRGs and DRLs\" paragraph 8", "quote": "Permutation test showed that gastric cancer genes were significantly ranked higher in the DRG lists from three GC expression datasets, with p-values of 1.36e-05 (GSE24375), 6.54e-53 (GSE54129) and 0.00793 (TCGA-STAD)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data generated or analysed during this study are included in this published article and its supplementary information files.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:29745833", "locator": "sections[16] \"Availability of data and materials\" paragraph 2", "quote": "All data generated or analysed during this study are included in this published article and its supplementary information files."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The paper applied a differential networking meta-analysis framework—integrating differential co-expression analysis, stage-specific conditional gene regulatory network modeling, and differential regulation networking—to gastric carcinoma expression data from GSE54129 (Chinese), GSE24375 (Korean), and TCGA-STAD (American), identifying 16 common cross-racial differentially regulated genes.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:29745833", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "In order to implement differential networking meta-analysis, we developed a novel framework which integrated the following steps."}, {"asset": "source:pmid:29745833", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Considering the complexity and diversity of gastric carcinogenesis, we first collected three datasets (GSE54129, GSE24375 and TCGA-STAD) for Chinese, Korean and American, and aimed to investigate the common dysregulation mechanisms of gastric carcinogenesis across racial groups."}, {"asset": "source:pmid:29745833", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Then, we constructed conditional GRNs for gastric cancer corresponding to normal and carcinoma, and prioritized differentially regulated genes (DRGs) and gene links (DRLs) from three datasets separately by using our previously developed differential networking method."}, {"asset": "source:pmid:29745833", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Based on our integrated differential regulation information from three datasets and prior knowledge (e.g., transcription factor (TF)-target regulatory relationships and known signaling pathways), we eventually generated testable hypotheses on the regulation mechanisms of two genes, XBP1 and GIF, out of 16 common cross-racial DRGs in gastric carcinogenesis."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The paper applied a differential networking meta-analysis framework—integrating differential co-expression analysis, stage-specific conditional gene regulatory network modeling, and differential regulation networking—to gastric carcinoma expression data from GSE54129 (Chinese), GSE24375 (Korean), and TCGA-STAD (American), identifying 16 common cross-racial differentially regulated genes.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:29745833"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE24375"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE24375"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:29745833", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12918-018-0564-z", "sha256": 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