Construction and validation of a prognostic risk model for breast cancer based on protein expression.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35787690", "kind": "science-record", "revision": 3, "content_hash": "sha256:8514935095b9dd57627859e110ce1a8af6843faa02c0f984a30cebcf5e3bd426", "created_at": "2026-11-01T18:14:49Z", "updated_at": "2026-11-01T21:28:17Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Construction and validation of a prognostic risk model for breast cancer based on protein expression.", "doi": "10.1186/s12920-022-01299-5", "pmid": "35787690", "authors": ["Bo Huang", "Xujun Zhang", "Qingyi Cao", "Jianing Chen", "Chenhong Lin", "Tianxin Xiang", "Ping Zeng"], "journal": "BMC medical genomics", "year": 2022, "abstract": "Breast cancer (BRCA) is the primary cause of mortality among females globally. The combination of advanced genomic analysis with proteomics characterization to construct a protein prognostic model will help to screen effective biomarkers and find new therapeutic directions. This study obtained proteomics data from The Cancer Proteome Atlas (TCPA) dataset and clinical data from The Cancer Genome Atlas (TCGA) dataset. Kaplan-Meier and Cox regression analyses were used to construct a prognostic risk model, which was consisted of 6 proteins (CASPASE7CLEAVEDD198, NFKBP65-pS536, PCADHERIN, P27, X4EBP1-pT70, and EIF4G). Based on risk curves, survival curves, receiver operating characteristic curves, and independent prognostic analysis, the protein prognostic model could be viewed as an independent factor to accurately predict the survival time of BRCA patients. We further validated that this prognostic model had good predictive performance in the GSE88770 dataset. The expression of 6 proteins was significantly associated with the overall survival of BRCA patients. The 6 proteins and encoding genes were differentially expressed in normal and primary tumor tissues and in different BRCA stages. In addition, we verified the expression of 3 differential proteins by immunohistochemistry and found that CDH3 and EIF4G1 were significantly higher in breast cancer tissues. Functional enrichment analysis indicated that the 6 genes were mainly related to the HIF-1 signaling pathway and the PI3K-AKT signaling pathway. This study suggested that the prognosis-related proteins might serve as new biomarkers for BRCA diagnosis, and that the risk model could be used to predict the prognosis of BRCA patients.", "claims": [{"id": "claim-method", "statement": "Kaplan–Meier and Cox regression analyses were used to construct a prognostic risk model, which was consisted of 6 proteins (CASPASE7CLEAVEDD198, NFKBP65-pS536, PCADHERIN, P27, X4EBP1-pT70, and EIF4G).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35787690", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Kaplan–Meier and Cox regression analyses were used to construct a prognostic risk model, which was consisted of 6 proteins (CASPASE7CLEAVEDD198, NFKBP65-pS536, PCADHERIN, P27, X4EBP1-pT70, and EIF4G)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this article. These data can be found here: The TCPA database (http://www.tcpaportal.org/tcpa/). The TCGA database (http://portal.gdc.cancer.gov/). The Gene Expression Omnibus database (GEO, https://www.ncbi.nlm.nih.gov/geo/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35787690", "locator": "sections[26] \"Availability of data and materials\" paragraph 2", "quote": "Publicly available datasets were analyzed in this article. These data can be found here: The TCPA database (http://www.tcpaportal.org/tcpa/). The TCGA database (http://portal.gdc.cancer.gov/). The Gene Expression Omnibus database (GEO, https://www.ncbi.nlm.nih.gov/geo/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A prognostic risk model based on six proteins was constructed for breast cancer using Kaplan–Meier and Cox regression analyses and showed good predictive performance.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35787690", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Kaplan–Meier and Cox regression analyses were used to construct a prognostic risk model, which was consisted of 6 proteins (CASPASE7CLEAVEDD198, NFKBP65-pS536, PCADHERIN, P27, X4EBP1-pT70, and EIF4G)."}, {"asset": "source:pmid:35787690", "locator": "sections[14] \"Assessing of the protein prognostic risk model\" paragraph 2", "quote": "The area under the curve (AUC) of the risk score was 0.741, suggesting that the predictive effectiveness was sensitive and significant for the prognostic risk model (Fig. 4D)."}, {"asset": "source:pmid:35787690", "locator": "sections[15] \"Validation of the protein prognostic risk model\" paragraph 2", "quote": "The AUC of the risk score in the GSE88770 dataset was 0.712 indicating good predictive ability of the prognostic risk model (Fig. 5D)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A prognostic risk model based on six proteins was constructed for breast cancer using Kaplan–Meier and Cox regression analyses and showed good predictive performance.", "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:35787690"], "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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These data can be found here: The TCPA database (http://www.tcpaportal.org/tcpa/). The TCGA database (http://portal.gdc.cancer.gov/). The Gene Expression Omnibus database (GEO, https://www.ncbi.nlm.nih.gov/geo/).", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Publicly available datasets were analyzed in this article. These data can be found here: The TCPA database (http://www.tcpaportal.org/tcpa/). The TCGA database (http://portal.gdc.cancer.gov/). 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Moreover, we evaluated immunohistochemical images of these proteins from the Human Prot", "xpression of prognosis-related proteins with immunohistochemistry experiments. Pathology sections information", "n of 3 differential proteins was analyzed by immunohistochemistry assay Availability of data and materials Pub"], "order_basis": "paper_text_order:1331", "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": ["Kaplan–Meier and Cox regression analyses were used to construct a prognostic risk model, which was consisted of 6 proteins (CASPASE7CLEAVEDD198, NFKBP65-pS536, PCADHERIN, P27, X4EBP1-pT70, and EIF4G)."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}