Identifying Diagnostic and Prognostic Biomarkers and Candidate Therapeutic Drugs of Gastric Cancer Based on Transcriptomics and Single-Cell Sequencing.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34899080", "kind": "science-record", "revision": 4, "content_hash": "sha256:ea072c523e6ab36634467ae5cf5ab6866358694ea6e290492ca0802eed6253b1", "created_at": "2026-11-01T17:36:36Z", "updated_at": "2026-11-01T22:05:20Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identifying Diagnostic and Prognostic Biomarkers and Candidate Therapeutic Drugs of Gastric Cancer Based on Transcriptomics and Single-Cell Sequencing.", "doi": "10.3389/pore.2021.1609955", "pmid": "34899080", "authors": ["Xu Zhao", "Shuang Wu", "Jingjing Jing"], "journal": "Pathology oncology research : POR", "year": 2021, "abstract": "Background and Objective: Gastric cancer (GC) is an important health burden and the prognosis of GC is poor. We aimed to explore new diagnostic and prognostic indicators as well as potential therapeutic targets for GC in the current study. Methods: We screened the overlapped differentially expressed genes (DEGs) from GSE54129 and TCGA STAD datasets. Protein-protein interaction network analysis recognized the hub genes among the DEGs. The roles of these genes in diagnosis, prognosis, and their relationship with immune infiltrates and drug sensitivity of GC were analyzed using R studio. Finally, the clinically significant hub genes were verified using single-cell RNA sequencing (scRNA-seq) data. Results: A total of 222 overlapping genes were screened, which were enriched in extracellular matrix-related pathways. Further, 17 hub genes were identified, and our findings demonstrated that BGN, COMP, COL5A2, and SPARC might be important diagnostic and prognostic indicators of GC, which were also correlated with immune cell infiltration, tumor mutation burden (TMB), microsatellite instability (MSI), and sensitivity of therapeutic drugs. The scRNA-seq results further confirmed that all four hub genes were highly expressed in GC. Conclusion: Based on transcriptomics and single-cell sequencing, we identified four diagnostic and prognostic biomarkers of GC, including BGN, COMP, COL5A2, and SPARC, which can help predict drug sensitivity for GC as well.", "claims": [{"id": "claim-method", "statement": "The Kaplan-Meier method was used to evaluate the correlation between gene expression and total survival time, and the log rank test was used for comparison.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34899080", "locator": "sections[12] \"Statistical Analysis\" paragraph 2", "quote": "The Kaplan-Meier method was used to evaluate the correlation between gene expression and total survival time, and the log rank test was used for comparison."}], "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 authors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34899080", "locator": "sections[26] \"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 authors."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Based on transcriptomics and single-cell sequencing, we identified four diagnostic and prognostic biomarkers of GC, including BGN, COMP, COL5A2, and SPARC, which can help predict drug sensitivity for GC as well.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:34899080", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "Based on transcriptomics and single-cell sequencing, we identified four diagnostic and prognostic biomarkers of GC, including BGN, COMP, COL5A2, and SPARC, which can help predict drug sensitivity for GC as well."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Based on transcriptomics and single-cell sequencing, we identified four diagnostic and prognostic biomarkers of GC, including BGN, COMP, COL5A2, and SPARC, which can help predict drug sensitivity for GC as well.", "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:34899080"], "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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Conclusion: Based on transcriptomics and single-cell sequencing, we identified fo", "nt hub genes were verified using single-cell RNA sequencing (scRNA-seq) data. Results: A total of 222 ov"], "order_basis": "paper_text_order:63", "id": "assay:1"}, {"role": "data_generation", "definition_id": "scrna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "unsupported", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["ly significant hub genes were verified using single-cell RNA sequencing (scRNA-seq) data. Results: A total of 222 ov", "e verified using single-cell RNA sequencing (scRNA-seq) data. Results: A total of 222 overlapping g", "), and sensitivity of therapeutic drugs. The scRNA-seq results further confirmed that all four hub", "arately. The quality control (QC) process of scRNA-seq data was performed using Seurat (version 3.0"], "order_basis": "paper_text_order:765", "id": "assay:2"}, {"role": "data_generation", "definition_id": "microarray", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["ne software, we analyzed the raw data of the microarray in GSE54129 to identify the DEGs. The TCGA S"], "order_basis": "paper_text_order:1674", "id": "assay:3"}, {"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["survival_km"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 0.8, "evidence": ["Kaplan-Meier"], "order_basis": "consumes_earlier_output", "id": "assay:4"}]}