Over the last decade, with the rapid development in the depth and quality of transcriptome sequencing, long non-coding RNAs (ln-cRNAs) longer than 200 nucleotides in length, which were once thought to be biological noise, were discovered in abundance. Research investigating lncRNAs has progressed notably in every field of medical research. 1 Accumulating evidence has demonstrated that lncRNAs are involved in diverse cellular processes, including transcription initiation, chromatin modification and transcriptional regulation, 2 by several regulatory archetypes, such as signals, decoys, guides and scaffolds, 3 and are associated with various biological systems, such as immune, metabolic and reproductive systems, in multiple human diseases, especially cancers. 4,5 Furthermore, a large number of lncRNAs have been identified as oncogenes, such as HOTAIR and H19, which were significantly positive with poor

The competing endogenous RNA (ceRNA) networks are an effective method for investigating cancer; however, construction of ceRNA networks among different subtypes of breast cancer has not been previously performed. Based on analysis of differentially expressed RNAs between 150 triple-negative breast cancer (TNBC) tissues and 823 non–triple-negative breast cancer (nTNBC) tissues downloaded from TCGA database, a ceRNA network was constructed based on database comparisons using Cytoscape. Survival analysis and receiver operating characteristic curve data were combined to screen out prognostic candidate genes, which were subsequently analyzed using co-expressed functionally related analysis, Gene Set Variation Analysis (GSVA) pathway-related analysis, and immune infiltration and tumor mutational burden immune-related analysis. A total of 190 differentially expressed lncRNAs (DElncRNAs), 48 differentially expressed mRNAs (DEmRNAs), and 13 differentially expressed miRNAs (DEmiRNAs) were included in the ceRNA network between TNBC and nTNBC subtypes. Gene ontology analysis of mRNAs coexpressed with prognostic candidate lncRNAs (AC104472.1, PSORS1C3, DSCR9, OSTN-AS1, AC012074.1, AC005035.1, SIAH2-AS1, and ERVMER61-1) were utilized for functional prediction. Consequently, OSTN-AS1 was primarily related to immunologic function, for instance, immune cell infiltration and immune-related markers coexpression. The GSVA deviation degree was increased with OSTN increased expression. In addition, many important immune molecules, such as PDCD1 and CTLA-4, were strongly correlated in terms of their quantitative expression. Competing endogenous RNA networks may identify candidate therapeutic targets and potential prognostic biomarkers in breast cancer. In particular, OSTN-AS1 serves as a novel immune-related molecule and could be involved in immunotherapy efforts in the future.

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