Identification of the diagnostic genes and immune cell infiltration characteristics of gastric cancer using bioinformatics analysis and machine learning.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36685898", "kind": "science-record", "revision": 4, "content_hash": "sha256:d7f9b639d41ec7c89bcc99418d7c1780e92093a922b55b108457934d6cd2eb22", "created_at": "2026-11-01T19:01:14Z", "updated_at": "2026-11-01T22:51:05Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of the diagnostic genes and immune cell infiltration characteristics of gastric cancer using bioinformatics analysis and machine learning.", "doi": "10.3389/fgene.2022.1067524", "pmid": "36685898", "authors": ["Rongjun Xie", "Longfei Liu", "Xianzhou Lu", "Chengjian He", "Guoxin Li"], "journal": "Frontiers in genetics", "year": 2022, "abstract": "Background: Finding reliable diagnostic markers for gastric cancer (GC) is important. This work uses machine learning (ML) to identify GC diagnostic genes and investigate their connection with immune cell infiltration. Methods: We downloaded eight GC-related datasets from GEO, TCGA, and GTEx. GSE13911, GSE15459, GSE19826, GSE54129, and GSE79973 were used as the training set, GSE66229 as the validation set A, and TCGA & GTEx as the validation set B. First, the training set screened differentially expressed genes (DEGs), and gene ontology (GO), kyoto encyclopedia of genes and genomes (KEGG), disease Ontology (DO), and gene set enrichment analysis (GSEA) analyses were performed. Then, the candidate diagnostic genes were screened by LASSO and SVM-RFE algorithms, and receiver operating characteristic (ROC) curves evaluated the diagnostic efficacy. Then, the infiltration characteristics of immune cells in GC samples were analyzed by CIBERSORT, and correlation analysis was performed. Finally, mutation and survival analyses were performed for diagnostic genes. Results: We found 207 up-regulated genes and 349 down-regulated genes among 556 DEGs. gene ontology analysis significantly enriched 413 functional annotations, including 310 biological processes, 23 cellular components, and 80 molecular functions. Six of these biological processes are closely related to immunity. KEGG analysis significantly enriched 11 signaling pathways. 244 diseases were closely related to Ontology analysis. Multiple entries of the gene set enrichment analysis analysis were closely related to immunity. Machine learning screened eight candidate diagnostic genes and further validated them to identify ABCA8, COL4A1, FAP, LY6E, MAMDC2, and TMEM100 as diagnostic genes. Six diagnostic genes were mutated to some extent in GC. ABCA8, COL4A1, LY6E, MAMDC2, TMEM100 had prognostic value. Conclusion: We screened six diagnostic genes for gastric cancer through bioinformatic analysis and machine learning, which are intimately related to immune cell infiltration and have a definite prognostic value.", "claims": [{"id": "claim-method", "statement": "The LASSO and SVM-RFE ML algorithms were used to screen candidate diagnostic genes.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36685898", "locator": "sections[6] \"2.4 Screening and validation of diagnostic genes\" paragraph 2", "quote": "The LASSO and SVM-RFE ML algorithms were used to screen candidate diagnostic genes."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/Supplementary Material; further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36685898", "locator": "sections[21] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/Supplementary Material; further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "ABCA8, COL4A1, FAP, LY6E, MAMDC2, and TMEM100 showed consistent AUC values in the training and validation sets, and all of them were greater than 0.70, with high prediction accuracy and reliability and repeatability.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36685898", "locator": "sections[13] \"3.3 Screening and validation of diagnostic genes\" paragraph 2", "quote": "ABCA8, COL4A1, FAP, LY6E, MAMDC2, and TMEM100 showed consistent AUC values in the training and validation sets, and all of them were greater than 0.70, with high prediction accuracy and reliability and repeatability."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "ABCA8, COL4A1, FAP, LY6E, MAMDC2, and TMEM100 showed consistent AUC values in the training and validation sets, and all of them were greater than 0.70, with high prediction accuracy and reliability and repeatability.", "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:36685898"], "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. 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