Jingjing Fan, Yunjian Tang, Kunming Wang, Shu Yang, Binlin Ma
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The nomogram model was developed using R 4.0.1, and its predictive efficacy, consistency and clinical application value in development and validation groups were evaluated using ROC, calibration and decision curves.</p><p><strong>Results: </strong>There were 44 differentially-expressed miRNAs in resistant BC patients. miR-3646, miR-4741, miR-6730-3p, miR-6831-5p and miR-8485 were candidate for resistance diagnosis in BC. Logistic multiple regression analysis showed that miR-4741 (or = 0.30, 95% CI = 0.08-0.63, P = 0.02) and miR-6831-5p (or = 0.48, 95% CI = 0.24-0.78, P = 0.01) were protective factors of BC resistance. The ROC curves showed a sensitivity of 0.884 and 0.750 for miR-4741 and miR-6831-5P as markers of resistance, suggesting that they can be used as independent risk factors for BC resistance. The other 3 miRNAs can be used as calibration factors to establish the risk prediction model of resistance in BC. In risk model, the prediction accuracy of resistance of BC is about 78%. 5-miRNA signature diagnostic models can help clinicians provide personalized treatment for NAC resistance BC patients to improve patient survival.</p><p><strong>Conclusion: </strong>MiR-4741 and miR-6831-5p are independent risk factors for breast cancer resistance. 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This study attempted to analyze serum microRNAs (miRNAs) in NAC resistant and sensitive BC patients and develop a miRNA-based nomogram model. To further help clinicians make treatment decisions for hormone receptor-positive patients.</p><p><strong>Methods: </strong>A total of 110 BC patients with NAC were recruited and assigned in sensitive and resistant group, and 4 sensitive patients and 3 resistant patients were subjected to high-throughput sequencing. The functions of their target genes were analyzed by GO and KEGG. Five BC-related reported miRNAs were selected for expression pattern measurement by RT-qPCR and multivariate logistic analysis. The nomogram model was developed using R 4.0.1, and its predictive efficacy, consistency and clinical application value in development and validation groups were evaluated using ROC, calibration and decision curves.</p><p><strong>Results: </strong>There were 44 differentially-expressed miRNAs in resistant BC patients. miR-3646, miR-4741, miR-6730-3p, miR-6831-5p and miR-8485 were candidate for resistance diagnosis in BC. Logistic multiple regression analysis showed that miR-4741 (or = 0.30, 95% CI = 0.08-0.63, P = 0.02) and miR-6831-5p (or = 0.48, 95% CI = 0.24-0.78, P = 0.01) were protective factors of BC resistance. The ROC curves showed a sensitivity of 0.884 and 0.750 for miR-4741 and miR-6831-5P as markers of resistance, suggesting that they can be used as independent risk factors for BC resistance. The other 3 miRNAs can be used as calibration factors to establish the risk prediction model of resistance in BC. In risk model, the prediction accuracy of resistance of BC is about 78%. 5-miRNA signature diagnostic models can help clinicians provide personalized treatment for NAC resistance BC patients to improve patient survival.</p><p><strong>Conclusion: </strong>MiR-4741 and miR-6831-5p are independent risk factors for breast cancer resistance. 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引用次数: 0
摘要
目的:乳腺癌(BC)患者化疗效果尚不确定。本研究试图分析NAC耐药和敏感BC患者的血清microRNAs (miRNAs)，并建立基于mirna的nomogram模型。进一步帮助临床医生对激素受体阳性患者做出治疗决定。方法:共招募110例BC NAC患者，分为敏感和耐药组，对4例敏感患者和3例耐药患者进行高通量测序。通过GO和KEGG分析其靶基因的功能。通过RT-qPCR和多因素logistic分析，选择5个bc相关的mirna进行表达模式测定。采用R 4.0.1建立nomogram模型，采用ROC曲线、校准曲线和决策曲线对开发组和验证组的预测效果、一致性和临床应用价值进行评价。结果:耐药BC患者中存在44种差异表达的mirna。miR-3646, miR-4741, miR-6730-3p, miR-6831-5p和miR-8485是BC耐药诊断的候选指标。Logistic多元回归分析显示，miR-4741 (or = 0.30, 95% CI = 0.08-0.63, P = 0.02)和miR-6831-5p (or = 0.48, 95% CI = 0.24-0.78, P = 0.01)是BC耐药的保护因素。ROC曲线显示miR-4741和miR-6831-5P作为耐药标志物的敏感性分别为0.884和0.750，提示它们可以作为BC耐药的独立危险因素。其余3种mirna可作为校正因子，建立BC耐药风险预测模型。在风险模型中，BC耐药性的预测准确率约为78%。5-miRNA特征诊断模型可以帮助临床医生为NAC耐药BC患者提供个性化治疗，提高患者生存率。结论:MiR-4741和miR-6831-5p是乳腺癌耐药的独立危险因素。本研究基于5种差异表达的血清mirna构建了BC耐药的nomogram模型。
Predictive miRNAs Patterns in Blood of Breast Cancer Patients Demonstrating Resistance Towards Neoadjuvant Chemotherapy.
Objective: The effect of chemotherapy in patients with breast cancer (BC) is uncertain. This study attempted to analyze serum microRNAs (miRNAs) in NAC resistant and sensitive BC patients and develop a miRNA-based nomogram model. To further help clinicians make treatment decisions for hormone receptor-positive patients.
Methods: A total of 110 BC patients with NAC were recruited and assigned in sensitive and resistant group, and 4 sensitive patients and 3 resistant patients were subjected to high-throughput sequencing. The functions of their target genes were analyzed by GO and KEGG. Five BC-related reported miRNAs were selected for expression pattern measurement by RT-qPCR and multivariate logistic analysis. The nomogram model was developed using R 4.0.1, and its predictive efficacy, consistency and clinical application value in development and validation groups were evaluated using ROC, calibration and decision curves.
Results: There were 44 differentially-expressed miRNAs in resistant BC patients. miR-3646, miR-4741, miR-6730-3p, miR-6831-5p and miR-8485 were candidate for resistance diagnosis in BC. Logistic multiple regression analysis showed that miR-4741 (or = 0.30, 95% CI = 0.08-0.63, P = 0.02) and miR-6831-5p (or = 0.48, 95% CI = 0.24-0.78, P = 0.01) were protective factors of BC resistance. The ROC curves showed a sensitivity of 0.884 and 0.750 for miR-4741 and miR-6831-5P as markers of resistance, suggesting that they can be used as independent risk factors for BC resistance. The other 3 miRNAs can be used as calibration factors to establish the risk prediction model of resistance in BC. In risk model, the prediction accuracy of resistance of BC is about 78%. 5-miRNA signature diagnostic models can help clinicians provide personalized treatment for NAC resistance BC patients to improve patient survival.
Conclusion: MiR-4741 and miR-6831-5p are independent risk factors for breast cancer resistance. This study constructed a nomogram model of NAC resistance in BC based on 5 differentially-expressed serum miRNAs.
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