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pmid:33859479

Integrated Profiling Identifies CCNA2 as a Potential Biomarker of Immunotherapy in Breast Cancer.

Yichao Wang · Qianyi Zhong · Zhaoyun Li · Zhu Lin · Hanjun Chen · Pan Wang

OncoTargets and therapy · 2021 doi:10.2147/OTT.S296373

Paper

complete

Experiment

complete

Data

partial

Analysis

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Integrated Profiling Identifies CCNA2 as a Potential Biomarker of Immunotherapy in Breast Cancer.
doi
10.2147/OTT.S296373
pmid
33859479
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Yichao Wang
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Qianyi Zhong
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Zhaoyun Li
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Zhu Lin
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Hanjun Chen
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Pan Wang
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OncoTargets and therapy
year
2021
abstract
INTRODUCTION: Breast cancer is the main reason for cancer-related deaths in women and the most common malignant cancer among women. In recent years, immunosuppressive factors have become a new type of treatment for cancer. However, there are no effective biomarkers for breast cancer immunotherapy. Therefore, exploring immune-related biomarkers is presently an important topic in breast cancer. METHODS: Gene expression profile data of breast cancer from The Cancer Genome Atlas (TCGA) was downloaded. Scale-free gene co-expression networks were built with weighted gene co-expression network analysis. The correlation of genes was performed with Pearson's correlation values. The potential associations between clinical features and gene sets were studied, and the hub genes were screened out. Gene Ontology and gene set enrichment analysis were used to reveal the function of hub gene in breast cancer. The gene expression profiles of GSE15852, downloaded from the Gene Expression Omnibus database, were used for hub gene verification. In addition, candidate biomarkers expression in breast cancer was studied. Survival analysis was performed using Log rank test and Kaplan-Meier. Immunohistochemistry was used to analyze the expression of CCNA2. RESULTS: A total of 6 modules related to immune cell infiltration were identified via the average linkage hierarchical clustering. According to the threshold criteria (module membership >0.9 and gene significance >0.35), a significant module consisting of 13 genes associated with immune cells infiltration were identified as candidate hub genes after performed with the human protein interaction network. And 3 genes with high correlation to clinical traits were identified as hub genes, which were negatively associated with the overall survival. Among them, the expression of CCNA2 was increased in metastatic breast cancer compare with non-metastatic breast cancer, who underwent immunotherapy. Immunohistochemistry results showed that CCNA2 expression in carcinoma tissues was elevated compared with normal control. DISCUSSION: CCNA2 identified as a potential immune therapy marker in breast cancer, which were first reported here and deserved further research.
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formed using Log rank test and Kaplan-Meier. Immunohistochemistry was used to analyze the expression of CCNA2.
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breast cancer, who underwent immunotherapy. Immunohistochemistry results showed that CCNA2 expression in carc
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{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33859479", "kind": "science-record", "revision": 6, "content_hash": "sha256:654eafe55cb008d826effcaedbb0e96813bef8a4af81c0969e19210e9d5b4a65", "created_at": "2026-11-01T15:41:32Z", "updated_at": "2026-11-01T21:25:08Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integrated Profiling Identifies CCNA2 as a Potential Biomarker of Immunotherapy in Breast Cancer.", "doi": "10.2147/OTT.S296373", "pmid": "33859479", "authors": ["Yichao Wang", "Qianyi Zhong", "Zhaoyun Li", "Zhu Lin", "Hanjun Chen", "Pan Wang"], "journal": "OncoTargets and therapy", "year": 2021, "abstract": "INTRODUCTION: Breast cancer is the main reason for cancer-related deaths in women and the most common malignant cancer among women. In recent years, immunosuppressive factors have become a new type of treatment for cancer. However, there are no effective biomarkers for breast cancer immunotherapy. Therefore, exploring immune-related biomarkers is presently an important topic in breast cancer.\nMETHODS: Gene expression profile data of breast cancer from The Cancer Genome Atlas (TCGA) was downloaded. Scale-free gene co-expression networks were built with weighted gene co-expression network analysis. The correlation of genes was performed with Pearson's correlation values. The potential associations between clinical features and gene sets were studied, and the hub genes were screened out. Gene Ontology and gene set enrichment analysis were used to reveal the function of hub gene in breast cancer. The gene expression profiles of GSE15852, downloaded from the Gene Expression Omnibus database, were used for hub gene verification. In addition, candidate biomarkers expression in breast cancer was studied. Survival analysis was performed using Log rank test and Kaplan-Meier. Immunohistochemistry was used to analyze the expression of CCNA2.\nRESULTS: A total of 6 modules related to immune cell infiltration were identified via the average linkage hierarchical clustering. According to the threshold criteria (module membership >0.9 and gene significance >0.35), a significant module consisting of 13 genes associated with immune cells infiltration were identified as candidate hub genes after performed with the human protein interaction network. And 3 genes with high correlation to clinical traits were identified as hub genes, which were negatively associated with the overall survival. Among them, the expression of CCNA2 was increased in metastatic breast cancer compare with non-metastatic breast cancer, who underwent immunotherapy. Immunohistochemistry results showed that CCNA2 expression in carcinoma tissues was elevated compared with normal control.\nDISCUSSION: CCNA2 identified as a potential immune therapy marker in breast cancer, which were first reported here and deserved further research.", "claims": [{"id": "claim-method", "statement": "the Mann–Whitney U-test was used for 2 sample comparisons that the data were non-normal distribution.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33859479", "locator": "sections[9] \"Statistics\" paragraph 2", "quote": "the Mann–Whitney U-test was used for 2 sample comparisons that the data were non-normal distribution."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data, models, and code generated or used during the study appear in the submitted article.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33859479", "locator": "sections[19] \"Data Sharing Statement\" paragraph 2", "quote": "All data, models, and code generated or used during the study appear in the submitted article."}], "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:33859479"], "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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