Construction of a three-component regulatory network of transcribed ultraconserved regions for the identification of prognostic biomarkers in gastric cancer
Although altered expression and functional roles of the transcribed ultraconserved regions (T-UCRs) in the pathophysiology of neoplasms has already been investigated, relevance of the functions for T-UCRs in gastric cancer (GC) is still the subject of inquiry. In the current study, The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD) dataset was used as a resource for the RNA-sequencing data. Differential expression analysis was conducted using DESeq2. Interactions between T-UCRs, miRNAs, and mRNAs were combined into a three-component network. The Survival package was utilized to identify survival-related differentially-expressed T-UCRs (DET-UCRs). Using an in-house cohort of GC tissues, expression of two DET-UCRs was experimentally verified. Thirty-four T-UCRs were dysregulated in TCGA-STAD tumoral samples compared to non-tumoral counterparts. The network was composed of 34 DET-UCRs, 275 miRNAs and 796 mRNAs nodes. Five T-UCRs were significantly correlated with the overall survival. While no expression of uc.232 was observed in our in-house cohort of GC tissues, uc.343 showed an increased expression in gastric tumoral tissues. The constructed three-component regulatory network of T-UCRs in GC presents a comprehensive understanding of the underlying gene expression regulation processes involved in tumor development and can serve as a basis to investigate potential prognostic biomarkers and therapeutic targets. Simple summaryGastric cancer (GC) is one of the most common cancers in the world and is considered as a highly heterogeneous disease based on subtypes and genetic alterations. GC is mostly detected in the advanced stages, hence, identifying diagnostic and prognostic biomarkers is of urgent need. Transcribed ultraconserved regions (T-UCRs) are a type of long non-coding RNAs which are linked to human carcinogenesis. Their mechanisms of action and the factors regulating their expression in cancers are poorly understood. In the current study, by applying a systems biology approach and constructing a regulatory network, we have presented a T-UCR as a potential diagnostic biomarker. Additionally, five T-UCRs with significant correlation with patients overall survival were found, which can be potentially used as prognostic biomarkers in future.