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pmid:37426414
Development of a circHIPK3-based ceRNA network and identification of mRNA signature in breast cancer patients harboring BRCA mutation.
Qi-Xin Lian · Yang Song · Lili Han · Zunxian Wang · Yinhui Song
PeerJ · 2023 doi:10.7717/peerj.15572
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Complete file
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An mRNA signature predictive of prognosis and therapy response in BRCA carriers was developed according to circular RNA homeodomain-interacting protein kinase 3 (circHIPK3)-based ceRNA network.\nMethod: We constructed a circHIPK3-based ceRNA network based on GSE173766 dataset and identified potential mRNAs that were associated with BRCA mutation patients within this ceRNA network. A total of 11 prognostic mRNAs and a risk model were identified and developed by univariate Cox regression analysis and the LASSO regression analysis as well as stepAIC method. Genomic landscape was treated by mutect2 and fisher. Immune characteristics was analyzed by ESTIMATE, MCP-counter. TIDE analysis was conducted to predict immunotherapy. The clinical treatment outcomes of BRCA mutation patients were assessed using a nomogram. The proliferation, migration and invasion in breast cancer cell lines were examined using CCK8 assay and transwell assay.\nResult: We found 241 mRNAs within the circHIPK3-based ceRNA network. An 11 mRNA-based signature was identified for prognostic model construction. High risk patients exhibited dismal prognosis, low response to immunotherapy, less immune cell infiltration and tumor mutation burden (TMB). High-risk patients were sensitive to six anti-tumor drugs, while low-risk patient were sensitive to 47 drugs. The risk score was the most effective on evaluating patients' survival. The robustness and good prediction performance were validated in The Cancer Genome Atlas (TCGA) dataset and immunotherapy datasets, respectively. In addition, circHIPK3 mRNA level was upregulated, and promoted cell viability, migration and invasion in breast cancer cell lines.\nConclusion: The current study could improve the understanding of mRNAs in relation to BRCA mutation and pave the way to develop mRNA-based therapeutic targets for breast cancer patients with BRCA mutation.", "claims": [{"id": "claim-method", "statement": "Through univariate Cox regression analysis, 40 mRNAs associated with prognosis were screened in the Train dataset (Fig. S2A). LASSO Cox regression analysis was employ to further refine the model. Independent variable coefficients that are progressively heading to zero rose as lambda was steadily increased (Fig. S2B). Figure S2C displays the confidence interval under each lambda from 10-fold cross-validation. 21 mRNAs were found when lambda = 0.0182. 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With stepwise multivariate regression analysis and stepAIC, a total of 11 mRNAs were identified, including four risk mRNAs and seven protective mRNAs (Fig. S2D)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The following information was supplied regarding data availability:\nThe data is available at OSF: Song, Yinhui. 2023. “Data Source.” OSF. May 22. doi: 10.17605/OSF.IO/DS6TA.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37426414", "locator": "sections[41] \"Data Availability Statement\" paragraphs 1-3", "quote": "The following information was supplied regarding data availability:\nThe data is available at OSF: Song, Yinhui. 2023. “Data Source.” OSF. 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