COL1A1 is a prognostic biomarker and correlated with immune infiltrates in lung cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33850663", "kind": "science-record", "revision": 7, "content_hash": "sha256:f74cb0fc71a2cdc9022558a139dd0b5c85a041720bd44840aeedb266822d03d6", "created_at": "2026-11-01T15:41:32Z", "updated_at": "2026-11-01T22:03:32Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "COL1A1 is a prognostic biomarker and correlated with immune infiltrates in lung cancer.", "doi": "10.7717/peerj.11145", "pmid": "33850663", "authors": ["Qishun Geng", "Zhibo Shen", "Lifeng Li", "Jie Zhao"], "journal": "PeerJ", "year": 2021, "abstract": "OBJECTIVE: Lung cancer (LC) is one of the top ten malignant tumors and the first leading cause of cancer-related death among both men and women worldwide. It is imperative to identify immune-related biomarkers for early LC diagnosis and treatment.\nMETHODS: Three Gene Expression Omnibus (GEO) datasets were selected to acquire the differentially expressed genes(DEGs) between LC and normal lung samples through GEO2R tools of NCBI. To identify hub genes, the DEGs were performed functional enrichment analysis, the protein-protein interaction (PPI) network construction, and Lasso regression. Then, a nomogram was constructed to predict the prognosis of patients with carcinoma based on hub genes. We further evaluated the influence of COL1A1 on clinical prognosis using GSE3141, GSE31210, and TCGA database. Also, the correlations between COL1A1 and cancer immune infiltrates and the B7-CD28 family was investigated via TIMER and GEPIA. Further analysis of immunohistochemistry shown that the COL1A1 expression level is positively correlated with CD276 expression level.\nRESULTS: By difference analysis, there were 340 DEGs between LC and normal lung samples. Then, we picked out seven hub genes, which were identified as components of the risk signature to divide LC into low and high-risk groups. Among them, the expression of COL1A1 is highly correlated with overall survival(OS) and progression-free survival (PFS) (p < 0.05). Importantly, there is a moderate to strong positive relationships between COL1A1 expression level and infiltration level of CD4+ T cells, Macrophage, Neutrophil, and Dendritic cell, as well as CD276 expression level.\nCONCLUSION: These findings suggest that COL1A1 is correlated with prognosis and immune infiltrating levels, including CD4+ T cells, Macrophage, Neutrophil, and Dendritic cell, as well as CD276 expression level, indicating COL1A1 can be a potential immunity-related biomarker and therapeutic target in LC.", "claims": [{"id": "claim-data-access", "statement": "Data is available at NCBI GEO: GSE3141, GSE31210, GSE32867, GSE43458 and GSE116959.TCGA Lung Cancer dataset is available at the Cancer Genomics Browser of the University of California Santa Cruz: https://xenabrowser.net/datapages/?cohort=TCGA%20Lung%20Cancer%20(LUNG)removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33850663", "locator": "sections[21] \"Additional Information and Declarations\" paragraph 4", "quote": "Data is available at NCBI GEO: GSE3141, GSE31210, GSE32867, GSE43458 and GSE116959.TCGA Lung Cancer dataset is available at the Cancer Genomics Browser of the University of California Santa Cruz: https://xenabrowser.net/datapages/?cohort=TCGA%20Lung%20Cancer%20(LUNG)removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443."}], "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:33850663"], "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:GSE116959"], "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:GSE116959"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:33850663", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.7717/peerj.11145", "sha256": 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