Machine Learning-Devised Immune-Related lncRNA Signature Panel Predicts the Prognosis and Immune Landscape in Breast Cancer Novel IRLP Signature in BRCA

机器学习设计的免疫相关lncRNA特征谱可预测乳腺癌的预后和免疫图谱;BRCA中的新型IRLP特征

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Abstract

Long noncoding RNAs (lncRNAs) actively participate in breast cancer (BRCA) tumorigenesis via epigenetic mechanisms. Our study identified immune-related lncRNA (irlncRNA) pairs and compiled them into a set of noncoding gene signatures able to stratify subtypes of BRCA associated with variable degrees of survival and immune cell infiltration. A 40 immune-related lncRNA pair (IRLP) signature including 43 irlncRNAs was built, with high sensitivity and specificity for the prediction of survival in different molecular subtypes of BRCA. Results demonstrated that the low-risk group showed a significantly longer survival rate, and this novel IRLP signature was highly associated with survival status, T stage, metastatic disease, and overall stage in BRCA. Immune infiltrating analyses found that the low-risk group has a lower expression level of macrophage M2 and a higher expression level of immunosuppressed biomarkers than the high-risk group. DEirlncRNAs were further proven to be significantly related to the MAPK signaling, Jak-STAT signaling, and ErbB signaling pathways in BRCA. In conclusion, the 40 IRLP signature showed a promising clinical prediction value in the prognosis of different molecular subtypes and immunotherapy response in BRCA, and the underlying mechanism for these IRLPs warrants further investigations.

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