Risk SNP in a transcript of RP11-638I2.4 increases lncRNA-YY1 interaction and pancreatic cancer susceptibility.
Ming ZhangYanmin LiFuwei ZhangHui GengYimin CaiZequn LuBin LiCaibo NingWenzhuo WangHaijie LiJianbo TianYing ZhuXiaoping MiaoPublished in: Archives of toxicology (2023)
Tens of thousands of long non-coding RNAs (lncRNAs) have been identified through RNA-seq analysis, but the biological and pathological significance remains unclear. By integrating the genome-wide lncRNA data with a cross-ancestry meta-analysis of PDAC GWASs, we depicted a comprehensive atlas of pancreatic ductal adenocarcinoma (PDAC)-associated lncRNAs, containing 1,204 lncRNA (445 novel lncRNAs and 759 GENCODE annotated lncRNAs) and 4,368 variants. Furthermore, we found that PDAC-associated lncRNAs could function by altering chromatin activity, transcription factors, and RNA-binding proteins binding affinity. Importantly, genetic variants linked to PDAC are preferentially found at PDAC-associated lncRNA regions, supporting the biological and clinical relevance of PDAC-associated lncRNAs. Finally, we prioritized a novel transcript (MICT00000110172.1) of RP11-638I2.4 as a potential tumor promoter. MICT00000110172.1 is able to reinforce the interaction with YY1, which could reverse the effect of YY1 on pancreatic cancer cell cycle arrest to promote the pancreatic cancer growth. G > A change at rs2757535 in the second exon of MICT00000110172.1 induces a spatial structural change and creates a target region for YY1 binding, which enforces the effect of MICT00000110172.1 in an allele-specific manner, and thus confers susceptibility to tumorigenesis. In summary, our results extend the repertoire of PDAC-associated lncRNAs that could act as a starting point for future functional explorations, and the identification of lncRNA-based target therapy.
Keyphrases
- long non coding rna
- rna seq
- genome wide
- genome wide identification
- network analysis
- transcription factor
- single cell
- genome wide analysis
- dna methylation
- long noncoding rna
- poor prognosis
- cell cycle arrest
- gene expression
- copy number
- cell death
- dna binding
- stem cells
- dna damage
- machine learning
- cell proliferation
- risk assessment
- pi k akt
- deep learning
- binding protein
- data analysis
- bioinformatics analysis