Circular RNA—Is the Circle Perfect?

Circular RNA (circRNA) is a distinct class of non-coding RNA produced, in principle, using a back-splicing mechanism, conserved during evolution, with increased stability and a tissue-dependent expression. Circular RNA represents a functional molecule with roles in the regulation of transcription and splicing, microRNA sponge, and the modulation of protein–protein interaction. CircRNAs are involved in essential processes of life such as apoptosis, cell cycle, and proliferation. Due to the regulatory role (upregulation/downregulation) in pathogenic mechanisms of some diseases (including cancer), its potential roles as a biomarker or therapeutic target in these diseases were studied. This review focuses on the importance of circular RNA in cancer.

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The Screening and Identification of Key Biomarkers in Adrenocortical Carcinoma: Evidence from a Bioinformatics Analysis

Journal of Biomaterials and Tissue Engineering ◽

10.1166/jbt.2022.2834 ◽

2022 ◽

Vol 12 (3) ◽

pp. 523-532

Author(s):

Xin Yan ◽

Chunfeng Liang ◽

Xinghuan Liang ◽

Li Li ◽

Zhenxing Huang ◽

...

Keyword(s):

Cell Cycle ◽

Gene Ontology ◽

Protein Interaction ◽

Adrenocortical Carcinoma ◽

Gene Expression Omnibus ◽

Heparin Binding ◽

Ppi Network ◽

Hub Genes ◽

Protein Protein Interaction ◽

Upregulated Genes

<sec> <title>Objective:</title> This study aimed to identify the potential key genes associated with the progression and prognosis of adrenocortical carcinoma (ACC). </sec> <sec> <title>Methods:</title> Differentially expressed genes (DEGs) in ACC cells and normal adrenocortical cells were assessed by microarray from the Gene Expression Omnibus database. The biological functions of the classified DEGs were examined by Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses and a protein–protein interaction (PPI) network was mapped using Cytoscape software. MCODE software was also used for the module analysis and then 4 algorithms of cytohubba software were used to screen hub genes. The overall survival (OS) examination of the hub genes was then performed by the ualcan online tool. </sec> <sec> <title>Results:</title> Two GSEs (GSE12368, GSE33371) were downloaded from GEO including 18 and 43 cases, respectively. One hundred and sixty-nine DEGs were identified, including 57 upregulated genes and 112 downregulated genes. The Gene Ontology (GO) analyses showed that the upregulated genes were significantly enriched in the mitotic cytokines is, nucleus and ATP binding, while the downregulated genes were involved in the positive regulation of cardiac muscle contraction, extracellular space, and heparin-binding (P < 0.05). The Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) pathway examination showed significant pathways including the cell cycle and the complement and coagulation cascades. The protein– protein interaction (PPI) network consisted of 162 nodes and 847 edges, including mitotic nuclear division, cytoplasmic, protein kinase binding, and cell cycle. All 4 identified hub genes (FOXM1, UBE2C, KIF11, and NDC80) were associated with the prognosis of adrenocortical carcinoma (ACC) by survival analysis. </sec> <sec> <title>Conclusions:</title> The present study offered insights into the molecular mechanism of adrenocortical carcinoma (ACC) that may be beneficial in further analyses. </sec>

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Putative Factors Interfering Cell Cycle Re-Entry in Alzheimer’s Disease: An Omics Study with Differential Expression Meta-Analytics and Co-Expression Profiling

Journal of Alzheimer s Disease ◽

10.3233/jad-215349 ◽

2021 ◽

pp. 1-26

Author(s):

Sze Chung Yuen ◽

Simon Ming-Yuen Lee ◽

Siu-wai Leung

Keyword(s):

Alzheimer’S Disease ◽

Cell Cycle ◽

Alzheimer's Disease ◽

Expression Analysis ◽

Protein Interaction ◽

Meta Analysis ◽

Differentially Expressed ◽

Tau Proteins ◽

Related Factors ◽

Protein Protein Interaction

Background: Neuronal cell cycle re-entry (CCR) is a mechanism, along with amyloid-β (Aβ) oligomers and hyperphosphorylated tau proteins, contributing to toxicity in Alzheimer’s disease (AD). Objective: This study aimed to examine the putative factors in CCR based on evidence corroboration by combining meta-analysis and co-expression analysis of omic data. Methods: The differentially expressed genes (DEGs) and CCR-related modules were obtained through the differential analysis and co-expression of transcriptomic data, respectively. Differentially expressed microRNAs (DEmiRNAs) were extracted from the differential miRNA expression studies. The dysregulations of DEGs and DEmiRNAs as binary outcomes were independently analyzed by meta-analysis based on a random-effects model. The CCR-related modules were mapped to human protein-protein interaction databases to construct a network. The importance score of each node within the network was determined by the PageRank algorithm, and nodes that fit the pre-defined criteria were treated as putative CCR-related factors. Results: The meta-analysis identified 18,261 DEGs and 36 DEmiRNAs, including genes in the ubiquitination proteasome system, mitochondrial homeostasis, and CCR, and miRNAs associated with AD pathologies. The co-expression analysis identified 156 CCR-related modules to construct a protein-protein interaction network. Five genes, UBC, ESR1, EGFR, CUL3, and KRAS, were selected as putative CCR-related factors. Their functions suggested that the combined effects of cellular dyshomeostasis and receptors mediating Aβ toxicity from impaired ubiquitination proteasome system are involved in CCR. Conclusion: This study identified five genes as putative factors and revealed the significance of cellular dyshomeostasis in the CCR of AD.

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Long non-coding RNA SLC16A1-AS1: its multiple tumorigenesis features and regulatory role in cell cycle in oral squamous cell carcinoma

Cell Cycle ◽

10.1080/15384101.2020.1762048 ◽

2020 ◽

Vol 19 (13) ◽

pp. 1641-1653

Author(s):

Hao Feng ◽

Xiaoqi Zhang ◽

Wenli Lai ◽

Jian Wang

Keyword(s):

Cell Cycle ◽

Squamous Cell Carcinoma ◽

Oral Squamous Cell Carcinoma ◽

Cell Carcinoma ◽

Squamous Cell ◽

Regulatory Role ◽

Non Coding Rna ◽

Long Non Coding Rna

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Functional Modules in the Arabidopsis Core Cell Cycle Binary Protein–Protein Interaction Network

The Plant Cell ◽

10.1105/tpc.109.073635 ◽

2010 ◽

Vol 22 (4) ◽

pp. 1264-1280 ◽

Cited By ~ 87

Author(s):

Joanna Boruc ◽

Hilde Van den Daele ◽

Jens Hollunder ◽

Stephane Rombauts ◽

Evelien Mylle ◽

...

Keyword(s):

Cell Cycle ◽

Protein Interaction ◽

Protein Interaction Network ◽

Interaction Network ◽

Functional Modules ◽

Protein Protein Interaction ◽

Protein Protein Interaction Network

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Identification of differentially expressed genes in hepatocellular carcinoma by integrated bioinformatic analysis

10.1101/570846 ◽

2019 ◽

Author(s):

Guangxin Yan ◽

Zhaoyu Liu

Keyword(s):

Gene Expression ◽

Hepatocellular Carcinoma ◽

Cell Cycle ◽

Dna Replication ◽

Liver Cancer ◽

Protein Interaction ◽

Protein Interaction Network ◽

Interaction Network ◽

Protein Protein Interaction ◽

Protein Protein Interaction Network

AbstractHepatocellular carcinoma is one of the most common tumors in the world and has a high mortality rate. This study elucidates the mechanism of hepatocellular carcinoma- (HCC) related development. The HCC gene expression profile (GSE54238, GSE84004) was downloaded from Gene Expression Omnibus for comprehensive analysis. A total of 359 genes were identified, of which 195 were upregulated and 164 were downregulated. Analysis of the condensed results showed that “extracellular allotrope” is a substantially enriched term. “Cell cycle”, “metabolic pathway” and “DNA replication” are three significantly enriched Kyoto Encyclopedia of Genes and Genomespathways. Subsequently, a protein-protein interaction network was constructed. The most important module in the protein-protein interaction network was selected for path enrichment analysis. The results showed thatCCNA2, PLK1, CDC20, UBE2CandAURKAwere identified as central genes, and the expression of these five hub genes in liver cancer was significantly increased in The Cancer Genome Atlas. Univariate regression analysis was also performed to show that the overall survival and disease-free survival of patients in the high expression group were longer than in the expression group. In addition, genes in important modules are mainly involved in “cell cycle”, “DNA replication” and “oocyte meiosis” signaling pathways. Finally, through upstream miRNA analysis, mir-300 and mir-381-3p were found to coregulateCCNA2,AURKAandUBE2C. These results provide a set of targets that can help researchers to further elucidate the underlying mechanism of liver cancer.

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Hub Genes Related to the Pathogenesis and Progression of Colorectal Cancer and Adenoma by Integrative Bioinformatics Approaches

10.21203/rs.3.rs-637295/v1 ◽

2021 ◽

Author(s):

Yuxuan HUANG ◽

Ge CUI

Keyword(s):

Colorectal Cancer ◽

Cell Cycle ◽

Differentially Expressed Genes ◽

Protein Interaction ◽

Biological Networks ◽

Interaction Network ◽

Differentially Expressed ◽

Venn Diagram ◽

Hub Genes ◽

Protein Protein Interaction

Abstract Aims: To utilize the bioinformatics to analyze the differentially expressed genes (DEGs), interaction proteins, perform gene enrichment analysis, protein-protein interaction network (PPI) and map the hub genes between colorectal cancer(CRC) and colorectal adenocarcinomas(CA).Methods: We analyzed a microarray dataset (GSE32323 and GSE4183) from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in tumor tissues and non-cancerous tissues were identified using the dplyr and Venn diagram packages of the R Studio software. Functional annotation of the DEGs was performed using the Gene Ontology (GO) website. Pathway enrichment (KEGG) used the WebGestalt to analyze the data and R Studio to generate the graph. We constructed a protein–protein interaction (PPI) network of DEGs using STRING and Cytoscape software was used for visualization. Survival analysis of the hub genes and was performed using the online platform GEPIA to determine the prognostic value of the expression of hub genes in cell lines from CRC patients. The expression of molecules with prognostic values was validated on the UALCAN database. The expression of hub genes was examined using the Human Protein Atlas. Results: Applying the GEO2R analysis and R studio, we identified a total of 471 upregulated and 278 downregulated DEGs. By using the online database WebGestalt, we identified the most relevant biological networks involving DEGs with statistically significant differences in expression were mainly associated with biological processes involved in the cell proliferation, cell cycle transition, cell homeostasis and indicated the role of each DEGs in cell cycle regulation pathways. We found 10 hub genes with prognostic values were overexpressed in the CRC and CA samples.Conclusion: we found out ten hub genes and three core genes closely associated with the pathogenesis and prognosis of CRC and CA, which is of great significance for colorectal tumor early detection and prognosis evaluation.

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