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Horaira MA, Islam MA, Kibria MK, Alam MJ, Kabir SR, Mollah MNH. Bioinformatics screening of colorectal-cancer causing molecular signatures through gene expression profiles to discover therapeutic targets and candidate agents. BMC Med Genomics 2023; 16:64. [PMID: 36991484 PMCID: PMC10053149 DOI: 10.1186/s12920-023-01488-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 03/14/2023] [Indexed: 03/31/2023] Open
Abstract
BACKGROUND Detection of appropriate receptor proteins and drug agents are equally important in the case of drug discovery and development for any disease. In this study, an attempt was made to explore colorectal cancer (CRC) causing molecular signatures as receptors and drug agents as inhibitors by using integrated statistics and bioinformatics approaches. METHODS To identify the important genes that are involved in the initiation and progression of CRC, four microarray datasets (GSE9348, GSE110224, GSE23878, and GSE35279) and an RNA_Seq profiles (GSE50760) were downloaded from the Gene Expression Omnibus database. The datasets were analyzed by a statistical r-package of LIMMA to identify common differentially expressed genes (cDEGs). The key genes (KGs) of cDEGs were detected by using the five topological measures in the protein-protein interaction network analysis. Then we performed in-silico validation for CRC-causing KGs by using different web-tools and independent databases. We also disclosed the transcriptional and post-transcriptional regulatory factors of KGs by interaction network analysis of KGs with transcription factors (TFs) and micro-RNAs. Finally, we suggested our proposed KGs-guided computationally more effective candidate drug molecules compared to other published drugs by cross-validation with the state-of-the-art alternatives of top-ranked independent receptor proteins. RESULTS We identified 50 common differentially expressed genes (cDEGs) from five gene expression profile datasets, where 31 cDEGs were downregulated, and the rest 19 were up-regulated. Then we identified 11 cDEGs (CXCL8, CEMIP, MMP7, CA4, ADH1C, GUCA2A, GUCA2B, ZG16, CLCA4, MS4A12 and CLDN1) as the KGs. Different pertinent bioinformatic analyses (box plot, survival probability curves, DNA methylation, correlation with immune infiltration levels, diseases-KGs interaction, GO and KEGG pathways) based on independent databases directly or indirectly showed that these KGs are significantly associated with CRC progression. We also detected four TFs proteins (FOXC1, YY1, GATA2 and NFKB) and eight microRNAs (hsa-mir-16-5p, hsa-mir-195-5p, hsa-mir-203a-3p, hsa-mir-34a-5p, hsa-mir-107, hsa-mir-27a-3p, hsa-mir-429, and hsa-mir-335-5p) as the key transcriptional and post-transcriptional regulators of KGs. Finally, our proposed 15 molecular signatures including 11 KGs and 4 key TFs-proteins guided 9 small molecules (Cyclosporin A, Manzamine A, Cardidigin, Staurosporine, Benzo[A]Pyrene, Sitosterol, Nocardiopsis Sp, Troglitazone, and Riccardin D) were recommended as the top-ranked candidate therapeutic agents for the treatment against CRC. CONCLUSION The findings of this study recommended that our proposed target proteins and agents might be considered as the potential diagnostic, prognostic and therapeutic signatures for CRC.
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Affiliation(s)
- Md Abu Horaira
- Bioinformatics Lab, Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh
| | - Md Ariful Islam
- Bioinformatics Lab, Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh
| | - Md Kaderi Kibria
- Bioinformatics Lab, Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh
| | - Md Jahangir Alam
- Bioinformatics Lab, Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh
| | - Syed Rashel Kabir
- Department of Biochemistry and Molecular Biology, University of Rajshahi, Rajshahi, 6205, Bangladesh
| | - Md Nurul Haque Mollah
- Bioinformatics Lab, Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh.
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Ershov P, Poyarkov S, Konstantinova Y, Veselovsky E, Makarova A. Transcriptomic Signatures in Colorectal Cancer Progression. Curr Mol Med 2023; 23:239-249. [PMID: 35490318 DOI: 10.2174/1566524022666220427102048] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 11/05/2021] [Accepted: 03/09/2022] [Indexed: 02/08/2023]
Abstract
AIMS Due to a large number of identified hub-genes encoding key molecular regulators, which are involved in signal transduction and metabolic pathways in cancers, it is relevant to systemize and update these findings. BACKGROUND Colorectal cancer (CRC) is the third leading cause of cancer death in the world, with high metastatic potential. Elucidating the pathogenic mechanisms and selection of novel biomarkers in CRC is of great clinical significance. OBJECTIVE This analytical review aims at the systematization of bioinformatics and experimental identification of hub-genes associated with CRC for a more consolidated understanding of common features in networks and pathways in CRC progression as well as hub-genes selection. RESULTS In total, 301 hub-genes were derived from 40 articles. The "core" consisted of 28 hub-genes (CCNB1, LPAR1, BGN, CXCL3, COL1A2, UBE2C, NMU, COL1A1, CXCL2, CXCL11, CDK1, TOP2A, AURKA, SST, CXCL5, MMP3, CCND1, TIMP1, CXCL8, CXCL1, CXCL12, MYC, CCNA2, GCG, GUCA2A, PAICS, PYY and THBS2) mentioned in not less than three articles and having clinical significance in cancerassociated pathways. Of them, there were two discrete clusters enriched in chemokine signaling and cell cycle regulatory genes. High expression levels of BGN and TIMP1 and low expression levels of CCNB1, CXCL3, CXCL2, CXCL2 and PAICS were associated with unfavorable overall survival of patients with CRC. Differently expressed genes such as LPAR1, SST, CXCL12, GUCA2A, and PYY were shown as down regulated, whereas BGN, CXCL3, UBE2C, NMU, CXCL11, CDK1, TOP2A, AURKA, MMP3, CCND1, CXCL1, MYC, CCNA2, PAICS were up regulated genes in CRC. It was also found that MMP3, THBS2, TIMP1 and CXCL12 genes were associated with metastatic CRC. Network analysis in ONCO.IO showed that upstream master regulators RELA, STAT3, SOX2, FOXM1, SMAD3 and NF-kB were connected with "core" hub-genes. Conclusión: Results obtained are of useful fundamental information on revealing the mechanism of pathogenicity, cellular target selection for optimization of therapeutic interventions, as well as transcriptomics prognostic and predictive biomarkers development.
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Affiliation(s)
- Pavel Ershov
- Department of Analysis and Forecasting of Medical and Biological Health Risks, Federal State Budgetary Institution "Centre for Strategic Planning and Management of Biomedical Health Risks" of the Federal Medical Biological Agency, Moscow, Russia
| | - Stanislav Poyarkov
- Department of Analysis and Forecasting of Medical and Biological Health Risks, Federal State Budgetary Institution "Centre for Strategic Planning and Management of Biomedical Health Risks" of the Federal Medical Biological Agency, Moscow, Russia
| | - Yulia Konstantinova
- Oncology Department, Federal Research and Clinical Center of Specialized Kinds of Medical Care and Medical Technology of the Federal Medical Biological Agency, Moscow, Russia
| | - Egor Veselovsky
- Department of Analysis and Forecasting of Medical and Biological Health Risks, Federal State Budgetary Institution "Centre for Strategic Planning and Management of Biomedical Health Risks" of the Federal Medical Biological Agency, Moscow, Russia
| | - Anna Makarova
- Department of Analysis and Forecasting of Medical and Biological Health Risks, Federal State Budgetary Institution "Centre for Strategic Planning and Management of Biomedical Health Risks" of the Federal Medical Biological Agency, Moscow, Russia
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Dinarvand M, Koch FC, Al Mouiee D, Vuong K, Vijayan A, Tanzim AF, Azad AKM, Penesyan A, Castaño-Rodríguez N, Vafaee F. dRNASb: a systems biology approach to decipher dynamics of host-pathogen interactions using temporal dual RNA-seq data. Microb Genom 2022; 8. [PMID: 36136078 DOI: 10.1099/mgen.0.000862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Infection triggers a dynamic cascade of reciprocal events between host and pathogen wherein the host activates complex mechanisms to recognise and kill pathogens while the pathogen often adjusts its virulence and fitness to avoid eradication by the host. The interaction between the pathogen and the host results in large-scale changes in gene expression in both organisms. Dual RNA-seq, the simultaneous detection of host and pathogen transcripts, has become a leading approach to unravelling complex molecular interactions between the host and the pathogen and is particularly informative for intracellular organisms. The amount of in vitro and in vivo dual RNA-seq data is rapidly growing, which demands computational pipelines to effectively analyse such data. In particular, holistic, systems-level, and temporal analyses of dual RNA-seq data are essential to enable further insights into the host-pathogen transcriptional dynamics and potential interactions. Here, we developed an integrative network-driven bioinformatics pipeline, dRNASb, a systems biology-based computational pipeline to analyse temporal transcriptional clusters, incorporate molecular interaction networks (e.g. protein-protein interactions), identify topologically and functionally key transcripts in host and pathogen, and associate host and pathogen temporal transcriptome to decipher potential between-species interactions. The pipeline is applicable to various dual RNA-seq data from different species and experimental conditions. As a case study, we applied dRNASb to analyse temporal dual RNA-seq data of Salmonella-infected human cells, which enabled us to uncover genes contributing to the infection process and their potential functions and to identify putative associations between host and pathogen genes during infection. Overall, dRNASb has the potential to identify key genes involved in bacterial growth or host defence mechanisms for future uses as therapeutic targets.
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Affiliation(s)
- Mojdeh Dinarvand
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, Australia
| | - Forrest C Koch
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, Australia
| | - Daniel Al Mouiee
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, Australia
- Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia
- UNSW Data Science Hub, University of New South Wales, Sydney, NSW, Australia
| | - Kaylee Vuong
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, Australia
| | - Abhishek Vijayan
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, Australia
| | - Afia Fariha Tanzim
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, Australia
| | - A K M Azad
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
| | - Anahit Penesyan
- School of Natural Sciences, Faculty of Science and Engineering, Macquarie University, Sydney, NSW, Australia
| | - Natalia Castaño-Rodríguez
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, Australia
| | - Fatemeh Vafaee
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, Australia
- UNSW Data Science Hub, University of New South Wales, Sydney, NSW, Australia
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Zhang JX, Xu WH, Xing XH, Chen LL, Zhao QJ, Wang Y. ARG1 as a promising biomarker for sepsis diagnosis and prognosis: evidence from WGCNA and PPI network. Hereditas 2022; 159:27. [PMID: 35739592 PMCID: PMC9219214 DOI: 10.1186/s41065-022-00240-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 06/05/2022] [Indexed: 11/17/2022] Open
Abstract
Background Sepsis is a life-threatening multi-organ dysfunction caused by the dysregulated host response to infection. Sepsis remains a major global concern with high mortality and morbidity, while management of sepsis patients relies heavily on early recognition and rapid stratification. This study aims to identify the crucial genes and biomarkers for sepsis which could guide clinicians to make rapid diagnosis and prognostication. Methods Preliminary analysis of multiple global datasets, including 170 samples from patients with sepsis and 110 healthy control samples, revealed common differentially expressed genes (DEGs) in peripheral blood of patients with sepsis. After Gene Oncology (GO) and pathway analysis, the Weighted Gene Correlation Network Analysis (WGCNA) was used to screen for genes most related with clinical diagnosis. Also, the Protein-Protein Interaction Network (PPI Network) was constructed based on the DEGs and the hub genes were found. The results of WGCNA and PPI network were compared and one shared gene was discovered. Then more datasets of 728 experimental samples and 355 control samples were used to prove the diagnostic and prognostic value of this gene. Last, we used real-time PCR to confirm the bioinformatic results. Results Four hundred forty-four common differentially expressed genes in the blood of sepsis patients from different ethnicities were identified. Fifteen genes most related with clinical diagnosis were found by WGCNA, and 24 hub genes with most node degrees were identified by PPI network. ARG1 turned out to be the unique overlapped gene. Further analysis using more datasets showed that ARG1 was not only sharply up-regulated in sepsis than in healthy controls, but also significantly high-expressed in septic shock than in non-septic shock, significantly high-expressed in severe or lethal sepsis than in uncomplicated sepsis, and significantly high-expressed in non-responders than in responders upon early treatment. These all demonstrate the performance of ARG1 as a key biomarker. Last, the up-regulation of ARG1 in the blood was confirmed experimentally. Conclusions We identified crucial genes that may play significant roles in sepsis by WGCNA and PPI network. ARG1 was the only overlapped gene in both results and could be used to make an accurate diagnosis, discriminate the severity and predict the treatment response of sepsis. Supplementary Information The online version contains supplementary material available at 10.1186/s41065-022-00240-1.
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Affiliation(s)
- Jing-Xiang Zhang
- School of Pharmacy, Second Military Medical University, Shanghai, 200433, China
| | - Wei-Heng Xu
- School of Pharmacy, Second Military Medical University, Shanghai, 200433, China
| | - Xin-Hao Xing
- School of Pharmacy, Second Military Medical University, Shanghai, 200433, China
| | - Lin-Lin Chen
- School of Pharmacy, Second Military Medical University, Shanghai, 200433, China
| | - Qing-Jie Zhao
- School of Pharmacy, Second Military Medical University, Shanghai, 200433, China.
| | - Yan Wang
- School of Pharmacy, Second Military Medical University, Shanghai, 200433, China.
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Smell Detection Agent Optimisation Framework and Systems Biology Approach to Detect Dys-Regulated Subnetwork in Cancer Data. Biomolecules 2021; 12:biom12010037. [PMID: 35053185 PMCID: PMC8774275 DOI: 10.3390/biom12010037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Revised: 12/01/2021] [Accepted: 12/02/2021] [Indexed: 11/23/2022] Open
Abstract
Network biology has become a key tool in unravelling the mechanisms of complex diseases. Detecting dys-regulated subnetworks from molecular networks is a task that needs efficient computational methods. In this work, we constructed an integrated network using gene interaction data as well as protein–protein interaction data of differentially expressed genes derived from the microarray gene expression data. We considered the level of differential expression as well as the topological weight of proteins in interaction network to quantify dys-regulation. Then, a nature-inspired Smell Detection Agent (SDA) optimisation algorithm is designed with multiple agents traversing through various paths in the network. Finally, the algorithm provides a maximum weighted module as the optimum dys-regulated subnetwork. The analysis is performed for samples of triple-negative breast cancer as well as colorectal cancer. Biological significance analysis of module genes is also done to validate the results. The breast cancer subnetwork is found to contain (i) valid biomarkers including PIK3CA, PTEN, BRCA1, AR and EGFR; (ii) validated drug targets TOP2A, CDK4, HDAC1, IL6, BRCA1, HSP90AA1 and AR; (iii) synergistic drug targets EGFR and BIRC5. Moreover, based on the weight values assigned to nodes in the subnetwork, PLK1, CTNNB1, IGF1, AURKA, PCNA, HSPA4 and GAPDH are proposed as drug targets for further studies. For colorectal cancer module, the analysis revealed the occurrence of approved drug targets TYMS, TOP1, BRAF and EGFR. Considering the higher weight values, HSP90AA1, CCNB1, AKT1 and CXCL8 are proposed as drug targets for experimentation. The derived subnetworks possess cancer-related pathways as well. The SDA-derived breast cancer subnetwork is compared with that of tools such as MCODE and Minimum Spanning Tree, and observed a higher enrichment (75%) of significant elements. Thus, the proposed nature-inspired algorithm is a novel approach to derive the optimum dys-regulated subnetwork from huge molecular network.
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Bourdakou MM, Spyrou GM, Kolios G. Colon Cancer Progression Is Reflected to Monotonic Differentiation in Gene Expression and Pathway Deregulation Facilitating Stage-specific Drug Repurposing. Cancer Genomics Proteomics 2021; 18:757-769. [PMID: 34697067 DOI: 10.21873/cgp.20295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 09/03/2021] [Accepted: 09/16/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND/AIM Colon cancer is one of the most common cancer types and the second leading cause of death due to cancer. Many efforts have been performed towards the investigation of molecular alterations during colon cancer progression. However, the identification of stage-specific molecular markers remains a challenge. The aim of this study was to develop a novel computational methodology for the analysis of alterations in differential gene expression and pathway deregulation across colon cancer stages in order to reveal stage-specific biomarkers and reinforce drug repurposing investigation. MATERIALS AND METHODS Transcriptomic datasets of colon cancer were used to identify (a) differentially expressed genes with monotonicity in their fold changes (MEGs) and (b) perturbed pathways with ascending monotonic enrichment (MEPs) related to the number of the participating differentially expressed genes (DEGs), across the four colon cancer stages. Through an in silico drug repurposing pipeline we identified drugs that regulate the expression of MEGs and also target the resulting MEPs. RESULTS Our methodology highlighted 15 MEGs and 32 candidate repurposed drugs that affect their expression. We also found 51 MEPs divided into two groups according to their rate of DEG content alteration across colon cancer stages. Focusing on the target MEPs of the highlighted repurposed drugs, we found that one of them, the neuroactive ligand-receptor interaction, was targeted by the majority of the candidate drugs. Moreover, we observed that two of the drugs (PIK-75 and troglitazone) target the majority of the resulting MEPs. CONCLUSION These findings highlight significant genes and pathways that can be used as stage-specific biomarkers and facilitate the discovery of new potential repurposed drugs for colon cancer. We expect that the computational methodology presented can be applied in a similar way to the analysis of any progressive disease.
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Affiliation(s)
- Marilena M Bourdakou
- Laboratory of Pharmacology, Department of Medicine, Democritus University of Thrace, Alexandroupolis, Greece
| | - George M Spyrou
- The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus.,The Cyprus School of Molecular Medicine, Nicosia, Cyprus
| | - George Kolios
- Laboratory of Pharmacology, Department of Medicine, Democritus University of Thrace, Alexandroupolis, Greece;
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Patil AR, Leung MY, Roy S. Identification of Hub Genes in Different Stages of Colorectal Cancer through an Integrated Bioinformatics Approach. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:5564. [PMID: 34070979 PMCID: PMC8197092 DOI: 10.3390/ijerph18115564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 05/20/2021] [Accepted: 05/21/2021] [Indexed: 11/16/2022]
Abstract
Colorectal cancer (CRC) is the third most common cancer that contributes to cancer-related morbidity. However, the differential expression of genes in different phases of CRC is largely unknown. Moreover, very little is known about the role of stress-survival pathways in CRC. We sought to discover the hub genes and identify their roles in several key pathways, including oxidative stress and apoptosis in the different stages of CRC. To identify the hub genes that may be involved in the different stages of CRC, gene expression datasets were obtained from the gene expression omnibus (GEO) database. The differentially expressed genes (DEGs) common among the different datasets for each group were obtained using the robust rank aggregation method. Then, gene enrichment analysis was carried out with Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases. Finally, the protein-protein interaction networks were constructed using the Cytoscape software. We identified 40 hub genes and performed enrichment analysis for each group. We also used the Oncomine database to identify the DEGs related to stress-survival and apoptosis pathways involved in different stages of CRC. In conclusion, the hub genes were found to be enriched in several key pathways, including the cell cycle and p53 signaling pathway. Some of the hub genes were also reported in the stress-survival and apoptosis pathways. The hub DEGs revealed from our study may be used as biomarkers and may explain CRC development and progression mechanisms.
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Affiliation(s)
- Abhijeet R. Patil
- Computational Science Program, The University of Texas at El Paso, El Paso, TX 79968, USA; (A.R.P.); (M.-Y.L.)
| | - Ming-Ying Leung
- Computational Science Program, The University of Texas at El Paso, El Paso, TX 79968, USA; (A.R.P.); (M.-Y.L.)
- Border Biomedical Research Center, The University of Texas at El Paso, El Paso, TX 79968, USA
- Department of Mathematical Sciences, The University of Texas at El Paso, El Paso, TX 79968, USA
| | - Sourav Roy
- Border Biomedical Research Center, The University of Texas at El Paso, El Paso, TX 79968, USA
- Department of Biological Sciences, The University of Texas at El Paso, El Paso, TX 79968, USA
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Lei X, Jing J, Zhang M, Guan B, Dong Z, Wang C. Bioinformatic Identification of Hub Genes and Analysis of Prognostic Values in Colorectal Cancer. Nutr Cancer 2020; 73:2568-2578. [PMID: 33153324 DOI: 10.1080/01635581.2020.1841249] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
The purpose of this study is to discover novel hub genes which are helpful for diagnosis, prognosis, and targeted therapy in colorectal cancer (CRC) by using bioinformatics analysis. GSE74602, GSE110225, and GSE113513 were extracted from the gene expression omnibus (GEO). Differentially expressed genes (DEGs) in expression profiles were identified by GEO2R. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses of the DEGs were carried out in the Database for Annotation, Visualization, and Integrated Discovery (DAVID). String database and cytoscape were used for building protein-protein interaction (PPI) network and module analysis. The UALCAN was used for in-depth analysis of data of CRC patients from The Cancer Genome Atlas (TCGA) to identify expression levels and overall survival rates of hub genes. The DEGs included 107 up-regulation genes and 232 down-regulation genes. Twenty-nine (29) hub genes and two significant modules were screened from PPI network. The expression levels of hub genes in TCGA were verified. Survival analysis curve indicated high expression of CCNA2, CCNB1, DLGAP5, were related to high survival rates, and low expression of TIMP1 were associated with high survival rates. These results suggest that DEGs may be the hub genes of CRC, and CCNA2, CCNB1, DLGAP5, TIMP1 may be the potential prognostic markers of CRC.
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Affiliation(s)
- Xinyi Lei
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Jing Jing
- Department of Endocrinology, Municipal Hospital, Qingdao, China
| | - Miao Zhang
- Department of Respiratory, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Bingsheng Guan
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Zhiyong Dong
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Cunchuan Wang
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Jinan University, Guangzhou, China
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PAICS, a Purine Nucleotide Metabolic Enzyme, is Involved in Tumor Growth and the Metastasis of Colorectal Cancer. Cancers (Basel) 2020; 12:cancers12040772. [PMID: 32218208 PMCID: PMC7226071 DOI: 10.3390/cancers12040772] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Revised: 03/18/2020] [Accepted: 03/20/2020] [Indexed: 12/15/2022] Open
Abstract
The identification of colorectal cancer (CRC) molecular targets is needed for the development of drugs that improve patient survival. We investigated the functional role of phosphoribosylaminoimidazole carboxylase, phosphoribosylaminoimidazole succinocarboxamide synthetase (PAICS), a de novo purine biosynthetic enzyme involved in DNA synthesis, in CRC progression and metastasis by using cell and animal models. Its clinical utility was assessed in human CRC samples. The expression of PAICS was regulated by miR-128 and transcriptionally activated by Myc in CRC cells. Increased expression of PAICS was involved in proliferation, migration, growth, and invasion of CRC cells irrespective of the p53 and microsatellite status. In mice, the depletion of PAICS in CRC cells led to reduced tumor growth and metastatic cell dissemination to the liver, lungs, and bone. Positron emission tomography imaging showed significantly reduced metastatic lesions in stable PAICS knockdown CRC cells. In cells with PAICS knockdown, there was upregulation of the epithelial mesenchymal transition marker, E-cadherin, and bromodomain inhibitor, JQ1, can target its increased expression by blocking Myc. PAICS was overexpressed in 70% of CRCs, and was associated with poor 5-year survival independent of the pathologic stage, patient’s race, gender, and age. Overall, the findings point to the usefulness of PAICS targeting in the treatment of aggressive colorectal cancer.
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