Characterization of coagulation-related gene signature to predict prognosis and tumor immune microenvironment in skin cutaneous melanoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36059641", "kind": "science-record", "revision": 3, "content_hash": "sha256:b4e73bb18da28a935d69613e7095e5af25d0fe5f210d8f95b0429ad6849c77b8", "created_at": "2026-11-01T18:15:15Z", "updated_at": "2026-11-01T21:28:48Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Characterization of coagulation-related gene signature to predict prognosis and tumor immune microenvironment in skin cutaneous melanoma.", "doi": "10.3389/fonc.2022.975255", "pmid": "36059641", "authors": ["Binyu Song", "Hao Chi", "Gaoge Peng", "Yajuan Song", "Zhiwei Cui", "Yuhan Zhu", "Guo Chen", "Junzheng Wu", "Wei Liu", "Chen Dong", "Yuanyong Wang", "Ke Xu", "Zhou Yu", "Baoqiang Song"], "journal": "Frontiers in oncology", "year": 2022, "abstract": "Backgroud: Skin cutaneous melanoma (SKCM) is an extremely metastatic form of skin cancer. However, there are few valuable molecular biomarkers, and accurate diagnosis is still a challenge. Hypercoagulable state encourages the infiltration and development of tumor cells and is significantly associated with poor prognosis in cancer patients. However, the use of a coagulation-related gene (CRG) signature for prognosis in SKCM, on the other hand, has yet to be determined.\nMethod: We used data from The Cancer Genome Atlas (TCGA) and Genotype Tissue Expression (GTEx) databases to identify differentially expressed CRGs, then designed a prognostic model by using the LASSO algorithm, univariate and multivariate Cox regression analysis, and constructed a nomogram which was evaluated by calibration curves. Moreover, the Gene Expression Omnibus (GEO), GSE54467 was used as an independent validation. The correlation between risk score and clinicopathological characteristics, tumor microenvironment (TME), and immunotherapy was further analyzed.\nResults: To develop a prognostic model, seven CRGs in SKCM patients related to overall survival (OS) were selected: ANG, C1QA, CFB, DUSP6, KLKB1, MMP7, and RABIF. According to the Kaplan-Meier survival analysis, an increased OS was observed in the low-risk group than in the high-risk group (P<0.05). Immunotherapy was much more beneficial in the low-risk group, as per immune infiltration, functional enrichment, and immunotherapy analysis.\nConclusions: The prognosis of SKCM patients may now be predicted with the use of a CRG prognostic model, thus guiding the development of treatment plans for SKCM patients and promoting OS rates.", "claims": [{"id": "claim-method", "statement": "We used data from The Cancer Genome Atlas (TCGA) and Genotype Tissue Expression (GTEx) databases to identify differentially expressed CRGs, then designed a prognostic model by using the LASSO algorithm, univariate and multivariate Cox regression analysis, and constructed a nomogram which was evaluated by calibration curves.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36059641", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "We used data from The Cancer Genome Atlas (TCGA) and Genotype Tissue Expression (GTEx) databases to identify differentially expressed CRGs, then designed a prognostic model by using the LASSO algorithm, univariate and multivariate Cox regression analysis, and constructed a nomogram which was evaluated by calibration curves."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ Supplementary Material .", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36059641", "locator": "sections[27] \"Data availability statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ Supplementary Material ."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A seven-gene coagulation-related prognostic model was developed using TCGA-SKCM training data and GTEx normal skin data, and validated in GEO GSE54467; in the TCGA cohort, its time-dependent AUCs were 0.739, 0.744, 0.679, 0.708, and 0.717 at years 1, 2, 3, 4, and 5, respectively.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36059641", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "We used data from The Cancer Genome Atlas (TCGA) and Genotype Tissue Expression (GTEx) databases to identify differentially expressed CRGs, then designed a prognostic model by using the LASSO algorithm, univariate and multivariate Cox regression analysis, and constructed a nomogram which was evaluated by calibration curves. Moreover, the Gene Expression Omnibus (GEO), GSE54467 was used as an independent validation."}, {"asset": "source:pmid:36059641", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "To develop a prognostic model, seven CRGs in SKCM patients related to overall survival (OS) were selected: ANG, C1QA, CFB, DUSP6, KLKB1, MMP7, and RABIF."}, {"asset": "source:pmid:36059641", "locator": "sections[16] \"Constructing a prognostic model based on the CRGs in SKCM\" paragraph 2", "quote": "The specificity and sensitivity of the prognostic model, which had AUCs of 0.739, 0.744, 0.679, 0.708, and 0.717 at years 1, 2, 3, 4, and 5, respectively, in the TCGA cohort, were assessed using time-dependent ROC analysis ( Figure 3D )."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A seven-gene coagulation-related prognostic model was developed using TCGA-SKCM training data and GTEx normal skin data, and validated in GEO GSE54467; in the TCGA cohort, its time-dependent AUCs were 0.739, 0.744, 0.679, 0.708, and 0.717 at years 1, 2, 3, 4, and 5, respectively.", "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:36059641"], "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:GSE54467"], "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:GSE54467"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36059641", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fonc.2022.975255", "sha256": "sha256:cfc64070236f5510f53c5e479ab1a790c9cb87d8dba0964f0a8cdb7925b09982", "bytes": 42057, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fonc.2022.975255", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9434152", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE54467", "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=GSE54467", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE54467", "version": null, "note": "The datasets presented in this study can be found in online repositories. 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These seven genes mostly control apoptosis, cell cycle, EMT, DNA damage response, and o", "nd DNA damage response were inhibited, while apoptosis, EMT, Hormine AR and PI3K/AKT pathways were"], "order_basis": "paper_text_order:4971", "id": "assay:1"}, {"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["cox_ph"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["We used data from The Cancer Genome Atlas (TCGA) and Genotype Tissue Expression (GTEx) databases to identify differentially expressed CRGs, then designed a prognostic model by using the LASSO algorithm, univariate and multivariate Cox regression analysis, and constructed a nomogram which was evaluated by calibration curves."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}