METHODS: COL1A1 mRNA expression was analysed in 2509 patients with breast cancer obtained from the cBioPortal database. Type I collagen protein expression was studied by immunohistochemistry in 1395 women diagnosed with early invasive breast cancer.
RESULTS: Low COL1A1 mRNA and protein levels correlated with poor prognosis features, such as hormone receptor negativity, high histological grade, triple-negative subtype, node positivity, and tumour size. In unadjusted analysis, high stromal type I collagen protein expression was associated with improved overall survival (OS) (HR = 0.78, 95% CI = 0.61-0.99, p = .043) and trended towards improved breast cancer-specific survival (BCSS) (HR = 0.65, 95% CI = 0.42-1.01, P = 0.053), although these findings were lost after adjustment for other clinical variables. In unadjusted analysis, high expression of type I collagen was associated with better OS (HR = 0.70, 95% CI = 0.55-0.90, P = .006) and BCSS (HR = 0.55, 95% CI = 0.34-0.88, P = .014) among patients not receiving chemotherapy. Strikingly, the opposite was observed among patients receiving chemotherapy. There, high expression of type I collagen was instead associated with worse OS (HR = 1.83, 95% CI = 0.65-5.14, P = .25) and BCSS (HR = 1.72, 95% CI = 0.54-5.50, P = .357).
CONCLUSIONS: Low stromal type I collagen mRNA and protein expression are associated with unfavourable tumour characteristics in breast cancer. Stromal type I collagen might predict chemotherapy response.
方法:分析了从cBioPortal数据库获得的2509例乳腺癌患者的COL1A1mRNA表达。通过免疫组织化学研究了1395例诊断为早期浸润性乳腺癌的女性中I型胶原蛋白的表达。
结果:低COL1A1mRNA和蛋白水平与不良预后特征相关,如激素受体阴性,组织学分级高,三阴性亚型,节点阳性,和肿瘤大小。在未经调整的分析中,高基质I型胶原蛋白表达与总生存率(OS)改善相关(HR=0.78,95%CI=0.61-0.99,p=.043),并倾向于改善乳腺癌特异性生存率(BCSS)(HR=0.65,95%CI=0.42-1.01,P=0.053),尽管这些发现在校正其他临床变量后丢失了.在未经调整的分析中,在未接受化疗的患者中,I型胶原的高表达与更好的OS(HR=0.70,95%CI=0.55-0.90,P=.006)和BCSS(HR=0.55,95%CI=0.34-0.88,P=.014)相关.引人注目的是,在接受化疗的患者中观察到相反的情况.在那里,而I型胶原的高表达与较差的OS(HR=1.83,95%CI=0.65-5.14,P=.25)和BCSS(HR=1.72,95%CI=0.54-5.50,P=.357)相关。
结论:低基质I型胶原mRNA和蛋白表达与乳腺癌的不良肿瘤特征相关。基质I型胶原可能预测化疗反应。