Model-based analysis uncovers mutations altering autophagy selectivity in human cancer.
Ontology highlight
ABSTRACT:
SUBMITTER: Han Z
PROVIDER: S-EPMC8166871 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:RNA secondary structure may influence many cellular processes, including RNA processing, stability, localization, and translation. Single-nucleotide variations (SNVs) that alter RNA secondary structure, referred to as riboSNitches, are potentially causative of human diseases, especially in untranslated regions (UTRs) and noncoding RNAs (ncRNAs). The functions of somatic mutations that act as riboSNitches in cancer development remain poorly understood. In this study, we developed a computational pipeline called SNIPER (riboSNitch-enriched or depleted elements in cancer genomes), which employs MeanDiff and EucDiff to detect riboSNitches and then identifies riboSNitch-enriched or riboSNitch-depleted non-coding elements across tumors. SNIPER is available at github: https://github.com/suzhixi/SNIPER/ . We found that riboSNitches were more likely to be pathogenic. Moreover, we predicted several UTRs and lncRNAs (long non-coding RNA) that significantly enriched or depleted riboSNitches in cancer genomes, indicative of potential cancer driver or essential noncoding elements. Our study highlights the possibly neglected importance of RNA secondary structure in cancer genomes and provides a new strategy to identify new cancer-associated genes.
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Project description:Background: The vault RNAs (vtRNAs) are a class of 84-141-nt eukaryotic non-coding RNAs transcribed by RNA polymerase III, associated to the ribonucleoprotein complex known as vault particle. Of the four human vtRNA genes, vtRNA1-1, vtRNA1-2 and vtRNA1-3, clustered at locus 1, are integral components of the vault particle, while vtRNA2-1 is a more divergent homologue located in a second locus. Gene expression studies of vtRNAs in large cohorts have been hindered by their unsuccessful sequencing using conventional transcriptomic approaches. Methods: VtRNA expression in The Cancer Genome Atlas (TCGA) Pan-Cancer cohort was estimated using the genome-wide DNA methylation and chromatin accessibility data (ATAC-seq) of their genes as surrogate variables. The association between vtRNA expression and patient clinical outcome, immune subtypes and transcriptionally co-regulated gene programs was analyzed in the dataset. Results: VtRNAs promoters are enriched in transcription factors related to viral infection. VtRNA2-1 is likely the most independently regulated homologue. VtRNA1-1 has the most accessible chromatin, followed by vtRNA1-2, vtRNA2-1 and vtRNA1-3. VtRNA1-1 and vtRNA1-3 chromatin status does not significantly change in cancer tissues. Meanwhile, vtRNA2-1 and vtRNA1-2 expression is widely deregulated in neoplastic tissues and its alteration is compatible with a broad oncogenic role for vtRNA1-2, and both tumor suppressor and oncogenic functions for vtRNA2-1. Yet, vtRNA1-1, vtRNA1-2 and vtRNA2-1 promoter DNA methylation predicts a shorter patient overall survival cancer-wide. In addition, gene ontology analyses of vtRNAs co-regulated genes identify a chromosome regulatory domain, epithelial differentiation, immune and thyroid cancer gene sets for specific vtRNAs. Furthermore, vtRNA expression patterns are associated with cancer immune subtypes and vtRNA1-2 expression is positively associated with cell proliferation and wound healing. Conclusions: Our study presents the landscape of vtRNA chromatin status cancer-wide, identifying co-regulated gene networks and ontological pathways associated with the different vtRNA genes that may account for their diverse roles in cancer.
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Project description:BackgroundAutophagy, a process of self-digestion, is closely related to multiple biological processes of colon cancer. This study aimed to construct and evaluate prognostic signature of autophagy-related genes (ARGs) to predict overall survival (OS) in colon cancer patients.Materials and methodsFirst, a total of 234 ARGs were downloaded via The Cancer Genome Atlas (TCGA) database. Based on the TCGA dataset, differentially expressed ARGs were identified in colon cancer. The univariate and multivariate Cox regression analysis was performed to screen prognostic ARGs to construct the prognostic model. The feasibility of the prognostic model was evaluated using receiver operating characteristic curves and Kaplan-Meier curves. A prognostic model integrating the gene signature with clinical parameters was established with a nomogram.ResultsWe developed an autophagy risk signature based on the 6 ARGs (ULK3, ATG101, MAP1LC3C, TSC1, DAPK1, and SERPINA1). The risk score was positively correlated with poor outcome and could independently predict prognosis. Furthermore, the autophagy-related signature could effectively reflect the levels of immune cell type fractions and indicate an immunosuppressive microenvironment.ConclusionWe innovatively identified and validated 6 autophagy-related gene signature that can independently predict prognosis and reflect overall immune response intensity in the colon cancer microenvironment.
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