lncRNA Gene Signatures for Prediction of Breast Cancer Intrinsic Subtypes and Prognosis

Abstract

Background: Breast cancer is intrinsically heterogeneous and is commonly classified into four main subtypes associated with distinct biological features and clinical outcomes. However, currently available data resources and methods are limited in identifying molecular subtyping on protein-coding genes, and little is known about the roles of long non-coding RNAs (lncRNAs), which occupies 98% of the whole genome. lncRNAs may also play important roles in subgrouping cancer patients and are associated with clinical phenotypes. Methods: The purpose of this project was to identify lncRNA gene signatures that are associated with breast cancer subtypes and clinical outcomes. We identified lncRNA gene signatures from The Cancer Genome Atlas (TCGA )RNAseq data that are associated with breast cancer subtypes by an optimized 1-Norm SVM feature selection algorithm. We evaluated the prognostic performance of these gene signatures with a semi-supervised principal component (superPC) method. Results: Although lncRNAs can independently predict breast cancer subtypes with satisfactory accuracy, a combined gene signature including both coding and non-coding genes will give the best clinically relevant prediction performance. We highlighted eight potential biomarkers (three from coding genes and five from non-coding genes) that are significantly associated with survival outcomes. Conclusion: Our proposed methods are a novel means of identifying subtype-specific coding and non-coding potential biomarkers that are both clinically relevant and biologically significant.

Long non‑coding RNA SNHG1 promotes breast cancer progression by regulation of LMO4

“…Subsequent experimental analysis identified several lncRNAs which act as oncogenes or tumor suppressors in this type of cancer (38)(39)(40). Moreover, the overexpression or downregulation of lncRNAs has been used as a breast cancer biomarker, aiming to improve detection of this disease at an early stage or predict the sensitivity of the patients to chemotherapy (41)(42)(43). The present study demonstrated that lncRNA small nucleolar RNA host gene 1 (SNHG1) was overexpressed in breast cancer and that it promoted breast cancer cell growth, cell cycle and migration, suggesting its oncogenic role.…”

Long non‐coding RNAs differential expression in breast cancer subtypes: What do we know?

“…Associating mRNA and lncRNAs expression information from TCGA, Zhang et al showed that a combined gene signature improves the clinically relevant prediction performance. They highlighted eight potential biomarkers, three from coding genes and five from non‐coding genes, among that: CTD‐2616 J11.9, RP1‐140 K8.1, RP11‐546 K22.1, AC000095.9 and SCGB1D5P.…”

“…Among lncRNAs that were deregulated HOTAIRM1 was significantly overexpressed in the basal-like related cluster, HOTAIR was up-regulated in ERBB2-overexpressing tumors and, both RP11-53O19.2 + RP11-473 L15.3 were overexpressed in luminal A and B related clusters. 18 Associating mRNA and lncRNAs expression information from TCGA, Zhang et al 20 showed that a combined gene signature improves the clinically relevant prediction performance. They highlighted eight potential biomarkers, three from coding genes and five from non-coding genes, among that: CTD-2616 J11.9, RP1-140 K8.1, RP11-546 K22.1, AC000095.9 and SCGB1D5P.…”

Eight immune-related genes predict survival outcomes and immune characteristics in breast cancer

“…It is classified into four molecular subtypes; luminal A, luminal B, triple-negative breast cancer (TNBC), and human epidermal growth factor receptor type 2 (HER2) positive [ 2 , 3 ]. Treatment methods are very different for each subtype [ 4 ]. Endocrine therapy is effective for treating luminal A and B subtypes, which are therefore associated with good prognoses.…”