Изучение причинно-следственной связи и патогенеза множественных склерозов рака груди с помощью биоинформатического анализа.
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Wang Yanfeng, Ji Haitao, Wang Huxia, Song Zhangjun
Аннотация
Оригинал публикации
Investigating the causality and pathogenesis of multiple sclerosis in breast cancer via bioinformatics analysis.
BACKGROUND: Breast cancer (BC) is one of the most prevalent cancers among women in the worldwide. Multiple sclerosis (MS) plays an important role not only in pathogenesis but also in the antitumour response. Therefore, we investigated the causal relationship and pathogenesis between BC and MS via integrated Mendelian randomization (MR) and bioinformatics analysis.
METHODS: We performed MR analyses to investigate the potential causal relationship between BC and MS via the IEU database. A comprehensive strategy among least absolute shrinkage and selection operator regression, support vector machine, univariate and multivariate Cox regression analysis, time ROC regression, and survival analysis was adopted to construct and validate the prognostic model. Immune cell infiltration and chemotherapy sensitivity were used to evaluate the prognostic model.
RESULTS: MR analysis revealed that MS was causally associated with the incidence risk of BC. Patients in the high-risk group were related to worse survival, immune infiltration, and increased sensitivity to paclitaxel, methotrexate, doxorubicin, gemcitabine, and sunitinib. Furthermore, KLRC1 was expressed at low levels in BC tissues and correlated with poor prognosis and immune cell infiltration.
CONCLUSIONS: Our findings suggest a potential causal association between MS and BC. KLRC1 is a robust and promising biomarker for predicting the prognosis of patients with MS and BC.