Non-coding small nucleolar RNA SNORD17 promotes the progression of hepatocellular carcinoma through a positive feedback loop upon p53 inactivation

Liang, Jintai; Li, Ganxun; Liao, Jinli; Huang, Zhao; Wen, Jingyuan; Wang, Yu; Chen, Zeyu; Cai, Guangzhen; Xu, Weiqi; Ding, Zhao; Liang, Hang; Datta, Pran K.; Chu, Liang; Chen, Xiaoping; Zhang, Bixiang

doi:10.1038/s41418-022-00929-w

Cited by 28 publications

(25 citation statements)

References 45 publications

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“…This snoRNA has upregulated in most cancer patients but is down-expressed in patients with melanoma [38,39]. According to the literature, SNORD17 is overexpressed in cases of hepatocellular carcinoma, and its upregulation is usually associated with poor clinical outcomes [40,41].…”

Section: Discussionmentioning

confidence: 99%

Screening and identification of potential biomarkers for pancreatic cancer: An integrated bioinformatics analysis

Jafari,

Ravan,

Karimi-Sani

et al. 2022

Preprint

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Background Pancreatic cancer is one of the highly invasive and the seventh most common cause of death among cancers worldwide. To identify essential genes and the involved mechanisms in pancreatic cancer, we used bioinformatics analysis to identify potential biomarkers for pancreatic cancer management. Methods Gene expression profiles of pancreatic cancer patients and normal tissues were screened and downloaded from The Cancer Genome Atlas (TCGA) bioinformatics database. The Differentially expressed genes (DEGs) were identified among gene expression signatures of normal and pancreatic cancer, using R software. Enrichment analysis of the DEGs, including Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, was performed by an interactive and collaborative HTML5 gene list enrichment analysis tool. The protein-protein interaction (PPI) network was also constructed using the Search Tool for the Retrieval of Interacting Genes (STRING) database and followed by identifying hub genes of the top 100 DEGs in pancreatic cancer using Cytoscape software. Results Over 2000 DEGs with variable log2 fold (LFC) were identified among 34,706 genes. Principal component analysis showed that the top 20 DEGs, including H1-4, H1-5, H4C3, H4C2, RN7SL2, RN7SL3, RN7SL4P, RN7SKP80, SCARNA12, SCARNA10, SCARNA5, SCARNA7, SCARNA6, SCARNA21, SCARNA9, SCARNA13, SNORA73B, SNORA53, SNORA54 might distinguish pancreatic cancer from normal tissue. GO analysis showed that the top DEGs have more enriched in the negative regulation of gene silencing, negative regulation of chromatin organization, negative regulation of chromatin silencing, nucleosome positioning, regulation of chromatin silencing, and nucleosomal DNA binding. KEGG analysis identified an association between pancreatic cancer and systemic lupus erythematosus, alcoholism, neutrophil extracellular trap formation, and viral carcinogenesis. In PPI network analysis, we found that the different types of histone-encoding genes are involved as hub genes in the carcinogenesis of pancreatic cancer. Conclusion In conclusion, our bioinformatics analysis identified genes that were significantly related to the prognosis of pancreatic cancer patients. These genes and pathways could serve as new potential prognostic markers and be used to develop treatments for pancreatic cancer patients.

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Section: Discussionmentioning

confidence: 99%

Screening and identification of potential biomarkers for pancreatic cancer: An integrated bioinformatics analysis

Jafari,

Ravan,

Karimi-Sani

et al. 2022

Preprint

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“…This snoRNA has upregulated in most cancer patients, but down-expressed in patients with melanoma (38,39). According to the literature, SNORD17 is also overexpressed in cases of hepatocellular carcinoma and its upregulation is usually associated with poor clinical outcomes (40,41).…”

Section: Discussionmentioning

confidence: 99%

Identification of new biomarkers for pancreatic cancer management: A bioinformatics analysis

Jafari

Ravan

Aria

et al. 2022

Preprint

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Background Pancreatic adenocarcinoma is one of the highly invasive and the seventh most common cause of death among cancers worldwide. To identify key genes and the involved mechanisms in pancreatic adenocarcinoma, we used bioinformatics analyzes in our study to introduce potential biomarkers in pancreatic cancer management. Methods In this study, gene expression profiles of pancreatic adenocarcinoma patients and normal adjacent tissues were screened and downloaded from The Cancer Genom Atlas (TCGA) bioinformatics database. Differentially expressed genes (DEGs) were identified between normal and pancreatic cancer gene expression signatures using R software. Then, Enrichment analysis of DEGs [including Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis] was performed by an enrichr (interactive and collaborative HTML5 gene list enrichment analysis) web-based tool. The protein-protein interaction (PPI) network was also constructed using STRING (Search Tool for the Retrieval of Interacting Genes) and Cytoscape software to identify the hub genes according to the top 100 DEGs in pancreatic adenocarcinoma. Results In our study, more than 2000 DEGs with variable log2 fold (LFC) were identified among 34,706 genes. Principal component analysis showed that the top 20 DEGs, including H1-4, H1-5, H4C3, H4C2, RN7SL2, RN7SL3, RN7SL4P, RN7SKP80, SCARNA12, SCARNA10, SCARNA5, SCARNA7, SCARNA6, SCARNA21, SCARNA9, SCARNA13, SNORA73B, SNORA53, SNORA54 with 99.91% probability might distinguish pancreatic adenocarcinoma from normal tissue. GO analysis of these 20 top DEGs showed that they have more enriched in negative regulation of gene silencing, negative regulation of chromatin organization, negative regulation of chromatin silencing, nucleosome positioning, regulation of chromatin silencing and nucleosomal DNA binding. KEGG analysis identified an association between pancreatic adenocarcinoma and systemic lupus erythematosus, alcoholism, neutrophil extracellular trap formation, and viral carcinogenesis. In protein-protein interaction (PPI) network analysis, we found that different types of histone-encoding genes are involved as hub genes in the carcinogenesis of pancreatic adenocarcinoma. Conclusions Our bioinformatics analysis showed that the DEGs and hub genes as key genes identified in this study may serve as new biomarkers in the near future for better management of pancreatic cancer. Although, H1.3 is currently one of the prognostic biomarkers in pancreatic cancer.

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“…Ectopic expression of SNORA72 activates stem cell transformation of ovarian cancer cells through the Notch1/c-Myc pathway [ 27 ]. SnoRNAs can also act as the downstream molecule of p53 and MYC responsible for tumorigenesis [ 28 , 29 ]. Furthermore, a large group of sdRNAs is significantly associated with the characteristics of the tumor-immune microenvironment, and some of the sdRNAs function similar to the microRNAs involved in cancer progression and chemoresistance [ 30 , 31 ].…”

Section: Biological Functions Of Snornasmentioning

confidence: 99%

“…They identified 54 up-regulated and 14 down-regulated snoRNAs enriched in the ribosome pathway, the cell cycle, and DNA replication [ 32 ]. Liang et al analyzed two HCC cohorts from the Gene Expression Omnibus database, in which 54 differentially expressed snoRNAs were identified [ 28 ]. Using high-throughput small RNA sequencing of six pairs of HCC tumor tissues and corresponding noncancerous liver tissues, Wang et al identified 10 up-regulated and 7 down-regulated snoRNAs [ 33 ].…”

Section: Snorna Expression Profiling In Hcc and Associated Diseasesmentioning

confidence: 99%

“…Several studies have shown that snoRNAs act as a tumor promoter or suppressor in HCC ( Table 1 ). SnoRNAs could significantly affect pathophysiological processes, including tumor initiation, progression, metastasis, and drug resistance [ 28 , 54 , 55 ]. Some molecular mechanisms have been intensively investigated ( Figure 3 ).…”

Section: Snornas and Liver Carcinogenesismentioning

confidence: 99%

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Small Nucleolar RNAs and Their Comprehensive Biological Functions in Hepatocellular Carcinoma

Liu

Xie

Meng

et al. 2022

Cells

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Small nucleolar RNAs (snoRNAs) are a class of highly conserved, stable non-coding RNAs involved in both post-transcriptional modification of RNA and in ribosome biogenesis. Recent research shows that the dysfunction of snoRNAs plays a pivotal role in hepatocellular carcinoma (HCC) and related etiologies, such as hepatitis B virus (HBV), hepatitis C virus (HCV), and non-alcoholic fatty liver disease (NAFLD). Growing evidence suggests that snoRNAs act as oncogenes or tumor suppressors in hepatocellular carcinoma (HCC) through multiple mechanisms. Furthermore, snoRNAs are characterized by their stability in body fluids and their clinical relevance and represent promising tools as diagnostic and prognostic biomarkers. SnoRNAs represent an emerging area of cancer research. In this review, we summarize the classification, biogenesis, activity, and functions of snoRNAs, as well as highlight the mechanism and roles of snoRNAs in HCC and related diseases. Our findings will aid in the understanding of complex processes of tumor occurrence and development, as well as suggest potential diagnostic markers and treatment targets. Furthermore, we discuss several limitations and suggest future research and application directions.

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Non-coding small nucleolar RNA SNORD17 promotes the progression of hepatocellular carcinoma through a positive feedback loop upon p53 inactivation

Cited by 28 publications

References 45 publications

Screening and identification of potential biomarkers for pancreatic cancer: An integrated bioinformatics analysis

Screening and identification of potential biomarkers for pancreatic cancer: An integrated bioinformatics analysis

Identification of new biomarkers for pancreatic cancer management: A bioinformatics analysis

Small Nucleolar RNAs and Their Comprehensive Biological Functions in Hepatocellular Carcinoma

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