Expression of non-coding RNAs in early stage breast cancer and their prognostic implications
Session type: Poster / e-Poster / Silent Theatre session
Invasive ductal carcinoma (IDC) and ductal carcinoma in situ (DCIS) are most common breast cancer lesions among women. Understanding dysregulated expression pattern of non-coding RNAs in early stage breast cancers can help elucidate its functional role in carcinogenesis. Few studies have investigated expression profile of long non-coding RNAs (lncRNAs) and microRNAs in early stage breast cancer.
Expression profile of total RNA (DESeq) and microRNA (miRge) from early stage IDC (n=6), paired normal (n=6), DCIS (n=7), and unmatched apparent normal (n=5) breast tissue samples from Indian patients was evaluated using Illumina HiSeq 2500 platform. Differential expression was validated using qRT-PCR and promoter methylation was evaluated using bisulfite sequencing.
We identified 375 statistically significant differentially expressed lncRNAs with >2 fold change. We validated expression of ADAMTS9-AS2, EPB41L4A-AS1, WDFY3-AS2, RP11-295M3.4, RP11-161M6.2, RP11-490M8.1, CTB-92J24.3, and FAM83H-AS1 in 52 IDC and paired normal tissue using qRT-PCR. ADAMTS9‐AS2 was found to be the most commonly downregulated lncRNA in tumour tissues (13‐fold). Bisulphite sequencing revealed hypermethylation of ADAMTS9-AS2 promoter (P < 0.0001) in tumour compared to paired normal samples. Integrative analysis of differentially expressed miRNAs and mRNAs that are experimentally proven targets listed in miRTarBase led to identification of 82 miRNAs and 176 mRNAs with inverse expression pattern. We report novel differentially expressed miRNAs miR-301a-3p (>4 fold; p-value<0.05) and miR-1260b (>3.3 fold; p-value<0.05) in early stage breast cancer.
This study highlights expression profile of non-coding RNAs in early stage breast cancer. ADAMTS9‐AS2 was consistently downregulated in patient samples due to promoter methylation. We propose miR-301a-3p as a promising prognostic marker in early stage breast cancer as its overexpression is significantly associated with poor survival in TCGA and Metabric datasets.