Prognostic Implications of the Complement Protein C1Q and Its Correlation with Immune Infiltrates in Osteosarcoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33707956", "kind": "science-record", "revision": 8, "content_hash": "sha256:4a9a9213355b985b9463364fc7e22eae6d156748b313e25a965b985c1342dae2", "created_at": "2026-11-01T15:41:28Z", "updated_at": "2026-11-01T22:03:18Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Prognostic Implications of the Complement Protein C1Q and Its Correlation with Immune Infiltrates in Osteosarcoma.", "doi": "10.2147/OTT.S295063", "pmid": "33707956", "authors": ["Hanji Huang", "Manli Tan", "Li Zheng", "Guohua Yan", "Kanglu Li", "Dejie Lu", "Xiaofei Cui", "Si He", "Danqing Lei", "Bo Zhu", "Jinmin Zhao"], "journal": "OncoTargets and therapy", "year": 2021, "abstract": "BACKGROUND: Osteosarcoma (OS) is the most widespread bone tumour among childhood cancers, and distant metastasis is the dominant factor in poor prognosis for patients with OS. Therefore, it is necessary to identify new prognostic biomarkers for identifying patients with aggressive disease.\nMETHODS: Two OS datasets (GSE21257 and GSE33383) were downloaded from the Gene Expression Omnibus (GEO) and subsequently subjected to weighted gene co-expression network analysis (WGCNA) and differential gene expression analysis (DGE) to screen candidate genes. A prognostic model was constructed using OS data derived from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program to further screen key genes and perform gene ontology (GO) analysis. The prognostic values of key genes were assessed using the Kaplan-Meier (KM) plotter. The GEO dataset was used for immune infiltration analysis and association analysis of key genes. In addition, quantitative real-time polymerase chain reaction (qRT-PCR) was employed to validate the expression levels of potentially crucial genes in OS cell lines.\nRESULTS: In the present study, we found 114 genes with a highly significant correlation in the module and 44 downregulated genes; 25 candidate genes overlapped in the two parts of the genes. Among these, three key genes, C1QA, C1QB, and C1QC, were the most significant hub genes, which had the highest node degrees, were clustered into one group, and implicated in most significant biological processes (regulation of immune effector process). Moreover, these three key genes were negatively associated with the prognosis of OS and positively associated with three immune cells (follicular helper T cells, memory B cells, and CD8 T cells). Additionally, compared to non-metastatic OS cell lines, the expression of three key genes was significantly downregulated in metastatic OS cell lines.\nCONCLUSION: Our results revealed that three key genes (C1QA, C1QB, and C1QC) were implicated in tumour immune infiltration and may be promising biomarkers for predicting metastasis and prognosis of patients with OS.", "claims": [{"id": "claim-method", "statement": "Finally, Pearson correlation analysis was used to explore the correlation between screened candidate genes and differentially expressed immune cells.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33707956", "locator": "sections[11] \"Evaluation of Infiltrating Immune Cells\" paragraph 2", "quote": "Finally, Pearson correlation analysis was used to explore the correlation between screened candidate genes and differentially expressed immune cells."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Two OS datasets (GSE21257 and GSE33383) were downloaded from the Gene Expression Omnibus (GEO) and subsequently subjected to weighted gene co-expression network analysis (WGCNA) and differential gene expression analysis (DGE) to screen candidate genes. First, the datasets GSE21257 (34 metastatic and 19 non-metastatic samples) and GSE33383 (34 metastatic and 19 non-metastatic samples) were acquired from the GEO database.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33707956", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Two OS datasets (GSE21257 and GSE33383) were downloaded from the Gene Expression Omnibus (GEO) and subsequently subjected to weighted gene co-expression network analysis (WGCNA) and differential gene expression analysis (DGE) to screen candidate genes."}, {"asset": "source:pmid:33707956", "locator": "sections[16] \"Data Integration and Removal of Batch Effects\" paragraph 2", "quote": "First, the datasets GSE21257 (34 metastatic and 19 non-metastatic samples) and GSE33383 (34 metastatic and 19 non-metastatic samples) were acquired from the GEO database."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "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:33707956"], "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:GSE21257"], "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:GSE21257"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:33707956", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.2147/OTT.S295063", "sha256": "sha256:5338f28f913631e60e060e3274a436035dea212780f33afe6b64c9d1b78f6bfa", "bytes": 42463, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.2147/OTT.S295063", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC7943548", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE21257", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE21257", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE21257", "version": null, "note": "Two OS datasets (GSE21257 and GSE33383) were downloaded from the Gene Expression Omnibus (GEO) and subsequently subjected to weighted gene co-expression network analysis (WGCNA) and differential gene expression analysis (DGE) to screen candidate genes. 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