scholarly journals Comprehensive Analysis Reveals the Potential Regulatory Mechanism Between Ub–Proteasome System and Cell Cycle in Colorectal Cancer

2021 ◽  
Vol 9 ◽  
Author(s):  
Zhiyuan Zhang ◽  
Jingwen Chen ◽  
Wentao Tang ◽  
Qingyang Feng ◽  
Jianmin Xu ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Cell Cycle ◽  
Regulatory Mechanism ◽  
Differentially Expressed ◽  
Potential Mechanism ◽  
Ppi Network ◽  
Protein Coding ◽  
Protein Protein Interaction ◽  
Differentially Expressed Protein ◽  
Cycle System

The ubiquitin (Ub)–proteasome system (UPS) is an important regulatory component in colorectal cancer (CRC), and the cell cycle is also characterized to play a significant role in CRC. In this present study, we firstly identified UPS-associated differentially expressed genes and all the differentially expressed protein-coding genes in CRC through three differential analyses. UPS-associated genes were also further analyzed via survival analysis. A weighted gene co-expression network analysis (WGCNA) was used to identify the cell cycle-associated genes. We used protein–protein interaction (PPI) network to comprehensively mine the potential mechanism of the UPS–cell cycle regulatory axis. Moreover, we constructed a signature based on UPS-associated genes to predict the overall survival of CRC patients. Our research provides a novel insight view of the UPS and cell cycle system in CRC.

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

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.

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

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>

scholarly journals CircRNA-miRNA-mRNA Regulatory Network in Colorectal Cancer

2021 ◽  
Author(s):  
Liyuan Liu ◽  
Shan Wu ◽  
Dan Jiang ◽  
Yuliang Qu ◽  
Hongxia Wang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Regulatory Network ◽  
Research Direction ◽  
Differentially Expressed ◽  
Circular Rnas ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Qrt Pcr ◽  
Cancer Tissues ◽  
Chip Analysis

Abstract Background: Abnormal expression of Circular RNAs (circRNAs) occurs in the occurrence and progression of colorectal cancer (CRC) and plays an important role in the pathogenesis of tumors. We combined bioinformatics and laboratory-validated methods to search for key circRNAs and possible potential mechanisms. Methods: Colorectal cancer tissues and normal paracancerous tissues were detected by microarray analysis and qRT-PCR validation, and differentially expressed circRNAs were screened and identified. The circRNA-miRNA-mRNA regulatory network (cirReNET) was constructed, Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used to ascertain the functions of circRNAs in CRCs. In addition, a protein-protein interaction (PPI) network of hub genes which acquired by string and plugin app CytoHubba in cytoscape was established. Validation of expression of hub genes was identified by GEPIA database. Results: 564 differentially expressed circRNAs which include 207 up-regulated and 357 down-regulated circRNAs were detected. The top 3 up-regulated circRNAs (hsa_circRNA_100833, hsa_circRNA_103828, hsa_circRNA_103831) and the top 3 down-regulated circRNAs (hsa_circRNA_103752, hsa_circRNA_071106, hsa_circRNA_102293) in chip analysis were chosen to be verified in 33 pairs of CRCs by qRT-PCR. The cirReNET include of 6 circRNAs, 19 miRNAs and 210 mRNA. And the targeted mRNAs were associated with cellular metabolic process, cell cycle and glandular epithelial cell differentiation and so on. 12 and 10 target hub genes were shown separately in upregulated circRNA-downregulated miRNA-upregulated mRNA (UcDiUm-RNA) group and downregulated circRNA-upregulated miRNA-downregulated mRNA (DcUiDm-RNA) group. Finally, we may have predicted and discovered several critical circRNA-miRNA-mRNA regulatory axes (cirReAXEs) which may play important roles in colorectal cancer. Conclusion: We constructed a cirReNET including 6 candidate circRNAs, which were crucial in CRCs, may become potential diagnostic markers and predictive indicators of CRCs, and we may provide a research direction for the pathogenesis of colorectal cancer.

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

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.

scholarly journals Transcriptional modulator ZBED6 affects cell cycle and growth of human colorectal cancer cells

2015 ◽  
Vol 112 (25) ◽  
pp. 7743-7748 ◽  
Author(s):  
Muhammad Akhtar Ali ◽  
Shady Younis ◽  
Ola Wallerman ◽  
Rajesh Gupta ◽  
Leif Andersson ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Cell Cycle ◽  
Cell Lines ◽  
Differentially Expressed Genes ◽  
Target Genes ◽  
Egf Receptor ◽  
Differentially Expressed ◽  
Striking Difference ◽  
Human Colorectal Cancer ◽  
Chip Sequencing

The transcription factor ZBED6 (zinc finger, BED-type containing 6) is a repressor of IGF2 whose action impacts development, cell proliferation, and growth in placental mammals. In human colorectal cancers, IGF2 overexpression is mutually exclusive with somatic mutations in PI3K signaling components, providing genetic evidence for a role in the PI3K pathway. To understand the role of ZBED6 in tumorigenesis, we engineered and validated somatic cell ZBED6 knock-outs in the human colorectal cancer cell lines RKO and HCT116. Ablation of ZBED6 affected the cell cycle and led to increased growth rate in RKO cells but reduced growth in HCT116 cells. This striking difference was reflected in the transcriptome analyses, which revealed enrichment of cell-cycle–related processes among differentially expressed genes in both cell lines, but the direction of change often differed between the cell lines. ChIP sequencing analyses displayed enrichment of ZBED6 binding at genes up-regulated in ZBED6-knockout clones, consistent with the view that ZBED6 modulates gene expression primarily by repressing transcription. Ten differentially expressed genes were identified as putative direct gene targets, and their down-regulation by ZBED6 was validated experimentally. Eight of these genes were linked to the Wnt, Hippo, TGF-β, EGF receptor, or PI3K pathways, all involved in colorectal cancer development. The results of this study show that the effect of ZBED6 on tumor development depends on the genetic background and the transcriptional state of its target genes.

scholarly journals Identification of potential biomarkers with colorectal cancer based on bioinformatics analysis and machine learning

2021 ◽  
Vol 18 (6) ◽  
pp. 8997-9015
Author(s):  
Ahmed Hammad ◽  
◽  
Mohamed Elshaer ◽  
Xiuwen Tang ◽  
◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Machine Learning ◽  
Roc Curve ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Ppi Network ◽  
Hub Genes ◽  
Survival Analyses ◽  
Potential Biomarkers

<abstract> <p>Colorectal cancer (CRC) is one of the most common malignancies worldwide. Biomarker discovery is critical to improve CRC diagnosis, however, machine learning offers a new platform to study the etiology of CRC for this purpose. Therefore, the current study aimed to perform an integrated bioinformatics and machine learning analyses to explore novel biomarkers for CRC prognosis. In this study, we acquired gene expression microarray data from Gene Expression Omnibus (GEO) database. The microarray expressions GSE103512 dataset was downloaded and integrated. Subsequently, differentially expressed genes (DEGs) were identified and functionally analyzed via Gene Ontology (GO) and Kyoto Enrichment of Genes and Genomes (KEGG). Furthermore, protein protein interaction (PPI) network analysis was conducted using the STRING database and Cytoscape software to identify hub genes; however, the hub genes were subjected to Support Vector Machine (SVM), Receiver operating characteristic curve (ROC) and survival analyses to explore their diagnostic values. Meanwhile, TCGA transcriptomics data in Gene Expression Profiling Interactive Analysis (GEPIA) database and the pathology data presented by in the human protein atlas (HPA) database were used to verify our transcriptomic analyses. A total of 105 DEGs were identified in this study. Functional enrichment analysis showed that these genes were significantly enriched in biological processes related to cancer progression. Thereafter, PPI network explored a total of 10 significant hub genes. The ROC curve was used to predict the potential application of biomarkers in CRC diagnosis, with an area under ROC curve (AUC) of these genes exceeding 0.92 suggesting that this risk classifier can discriminate between CRC patients and normal controls. Moreover, the prognostic values of these hub genes were confirmed by survival analyses using different CRC patient cohorts. Our results demonstrated that these 10 differentially expressed hub genes could be used as potential biomarkers for CRC diagnosis.</p> </abstract>

scholarly journals Bioinformatics Analysis of Key Genes and circRNA-miRNA-mRNA Regulatory Network in Gastric Cancer

2020 ◽  
Vol 2020 ◽  
pp. 1-16 ◽  
Author(s):  
Yiting Tian ◽  
Yang Xing ◽  
Zheng Zhang ◽  
Rui Peng ◽  
Luyu Zhang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Extracellular Matrix ◽  
Expression Profiles ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Receptor Interaction ◽  
Future Research ◽  
Circular Rnas ◽  
Ppi Network ◽  
Protein Protein Interaction

Gastric cancer (GC) is one of the most common malignancies in the world, with morbidity and mortality ranking second among all cancers. Accumulating evidences indicate that circular RNAs (circRNAs) are closely correlated with tumorigenesis. However, the mechanisms of circRNAs still remain unclear. This study is aimed at determining hub genes and circRNAs and analyzing their potential biological functions in GC. Expression profiles of mRNAs and circRNAs were downloaded from the Gene Expression Omnibus (GEO) data sets of GC and paracancer tissues. Differentially expressed genes (DEGs) and differentially expressed circRNAs (DE-circRNAs) were identified. The target miRNAs of DE-circRNAs and the bidirectional interaction between target miRNAs and DEGs were predicted. Functional analysis was performed, and the protein-protein interaction (PPI) network and the circRNA-miRNA-mRNA network were established. A total of 456 DEGs and 2 DE-circRNAs were identified with 3 mRNA expression profiles and 2 circRNA expression profiles. GO analysis indicated that DEGs were mainly enriched in extracellular matrix and cell adhesion, and KEGG confirmed that DEGs were mainly associated with focal adhesion, the PI3K-Akt signaling pathway, extracellular matrix- (ECM)- receptor interaction, and gastric acid secretion. 15 hub DEGs (BGN, COL1A1, COL1A2, FBN1, FN1, SPARC, SPP1, TIMP1, UBE2C, CCNB1, CD44, CXCL8, COL3A1, COL5A2, and THBS1) were identified from the PPI network. Furthermore, the survival analysis indicate that GC patients with a high expression of the following 9 hub DEGs, namely, BGN, COL1A1, COL1A2, FBN1, FN1, SPARC, SPP1, TIMP1, and UBE2C, had significantly worse overall survival. The circRNA-miRNA-mRNA network was constructed based on 1 circRNA, 15 miRNAs, and 45 DEGs. In addition, the 45 DEGs included 5 hub DEGs. These results suggested that hub DEGs and circRNAs could be implicated in the pathogenesis and development of GC. Our findings provide novel evidence on the circRNA-miRNA-mRNA network and lay the foundation for future research of circRNAs in GC.

scholarly journals Network analysis of KLF5 targets showing the potential oncogenic role of SNHG12 in colorectal cancer

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Qi Liao ◽  
Linbo Chen ◽  
Ning Zhang ◽  
Yang Xi ◽  
Shiyun Hu ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Cell Cycle ◽  
Network Analysis ◽  
Expression Pattern ◽  
Cell Lines ◽  
Regulatory Network ◽  
Bioinformatics Analysis ◽  
Regulatory Relationship ◽  
Protein Coding ◽  
Tumorigenesis And Progression

Abstract Background KLF5 is a member of the Kruppel-like factor, subfamily of zinc finger proteins that are involved in cancers. KLF5 functions as a transcription factor and regulates the diverse protein-coding genes (PCGs) in colorectal cancer (CRC). However, the long non-coding RNAs (lncRNAs) regulated by KLF5 in CRC are currently unknown. Methods In this study, we first designed a computational pipeline to determine the PCG and lncRNA targets of KLF5 in CRC. Then we analyzed the motif pattern of the binding regions for the lncRNA targets. The regulatory co-factors of KLF5 were then searched for through bioinformatics analysis. We also constructed a regulatory network for KLF5 and annotated its functions. Finally, one of the KLF5 lncRNA targets, SNHG12, was selected to further explore its expression pattern and functions in CRC. Results We were able to identify 19 lncRNA targets of KLF5 and found that the motifs of the lncRNA binding sites were GC-enriched. Next, we pinpointed the transcription factors AR and HSF1 as the regulatory co-factors of KLF5 through bioinformatics analysis. Then, through the analysis of the regulatory network, we found that KLF5 may be involved in DNA replication, DNA repair, and the cell cycle. Furthermore, in the cell cycle module, the SNHG12 up-regulating expression pattern was verified in the CRC cell lines and tissues, associating it to CRC invasion and distal metastasis. This indicates that SNHG12 may play a critical part in CRC tumorigenesis and progression. Additionally, expression of SNHG12 was found to be down-regulated in CRC cell lines when KLF5 expression was knocked-down by siRNA; and a strong correlation was observed between the expression levels of SNHG12 and KLF5, further alluding to their regulatory relationship. Conclusions In conclusion, the network analysis of KLF5 targets indicates that SNHG12 may be a significant lncRNA in CRC.

scholarly journals A Network Pharmacology Approach to Uncover the Potential Mechanism of Yinchensini Decoction

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Guoming Chen ◽  
Chuyao Huang ◽  
Yunyun Liu ◽  
Tengyu Chen ◽  
Ruilan Huang ◽  
...  
Keyword(s):  
Rna Polymerase Ii ◽  
Enrichment Analysis ◽  
Drug Binding ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Regulation Of Transcription ◽  
Potential Mechanism ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
System Pharmacology

Objective. To predict and explore the potential mechanism of Yinchensini decoction (YCSND) based on systemic pharmacology. Method. TCMSP database was searched for the active constituents and related target proteins of YCSND. Cytoscape 3.5.1 was used to construct the active ingredient-target interaction of YCSND and network topology analysis, with STRING online database for protein-protein interaction (PPI) network construction and analysis; and collection from the UniProt database of target protein gene name, with the DAVID database for the gene ontology (GO) functional analysis, KEGG pathway enrichment analysis mechanism and targets of YCSND. Results. The results indicate the core compounds of YCSND, namely, kaempferol, 7-Methoxy-2-methyl isoflavone, and formononetin. And its core targets are prostaglandin G/H synthase 2, estrogen receptor, Calmodulin, heat shock protein HSP 90, etc. PPI network analysis shows that the key components of the active ingredients of YCSND are JUN, TP53, MARK1, RELA, MYC, and so on. The results of the GO analysis demonstrate that extracellular space, cytosol, and plasma membrane are the main cellular components of YCSND. Its molecular functions are mainly acting on enzyme binding, protein heterodimerization activity, and drug binding. The biological process of YCSND is focused on response to drug, positive regulation of transcription from RNA polymerase II promoter, the response to ethanol, etc. KEGG results suggest that the pathways, including pathways in cancer, hepatitis B, and pancreatic cancer, play a key role in YCSND. Conclusion. YCSND exerts its drug effect through various signaling pathways and acts on kinds of targets. By system pharmacology, the potential role of drugs and the mechanism of action can be well predicted.

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