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Hossain MJ, Chowdhury UN, Islam MB, Uddin S, Ahmed MB, Quinn JMW, Moni MA. Machine learning and network-based models to identify genetic risk factors to the progression and survival of colorectal cancer. Comput Biol Med 2021; 135:104539. [PMID: 34153790 DOI: 10.1016/j.compbiomed.2021.104539] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 05/12/2021] [Accepted: 05/26/2021] [Indexed: 01/04/2023]
Abstract
Colorectal cancer (CRC) is one of the most common and lethal malignant lesions. Determining how the identified risk factors drive the formation and development of CRC could be an essential means for effective therapeutic development. Aiming this, we investigated how the altered gene expression resulting from exposure to putative CRC risk factors contribute to prognostic biomarker identification. Differentially expressed genes (DEGs) were first identified for CRC and other eight risk factors. Gene set enrichment analysis (GSEA) through the molecular pathway and gene ontology (GO), as well as protein-protein interaction (PPI) network, were then conducted to predict the functions of these DEGs. Our identified genes were explored through the dbGaP and OMIM databases to compare with the already identified and known prognostic CRC biomarkers. The survival time of CRC patients was also examined using a Cox Proportional Hazard regression-based prognostic model by integrating transcriptome data from The Cancer Genome Atlas (TCGA). In this study, PPI analysis identified 4 sub-networks and 8 hub genes that may be potential therapeutic targets, including CXCL8, ICAM1, SOD2, CXCL2, CCL20, OIP5, BUB1, ASPM and IL1RN. We also identified seven signature genes (PRR5.ARHGAP8, CA7, NEDD4L, GFR2, ARHGAP8, SMTN, OIP5) in independent analysis and among which PRR5. ARHGAP8 was found in both multivariate analyses and in analyses that combined gene expression and clinical information. This approach provides both mechanistic information and, when combined with predictive clinical information, good evidence that the identified genes are significant biomarkers of processes involved in CRC progression and survival.
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Affiliation(s)
- Md Jakir Hossain
- Department of Electrical and Electronic Engineering, University of Rajshahi, Rajshahi 6205, Bangladesh
| | - Utpala Nanda Chowdhury
- Department of Computer Science and Engineering, University of Rajshahi, Rajshahi 6205, Bangladesh
| | - M Babul Islam
- Department of Electrical and Electronic Engineering, University of Rajshahi, Rajshahi 6205, Bangladesh
| | - Shahadat Uddin
- Complex Systems Research Group & Project Management Program, Faculty of Engineering, The University of Sydney, NSW, 2006, Australia
| | - Mohammad Boshir Ahmed
- School of Material Science and Engineering, Gwangju Institute of Science and Technology, Gwangju, 61005, Republic of Korea
| | - Julian M W Quinn
- Healthy Ageing Theme, Garvan Institute of Medical Research, Darlinghurst, NSW, 2010, Australia
| | - Mohammad Ali Moni
- Healthy Ageing Theme, Garvan Institute of Medical Research, Darlinghurst, NSW, 2010, Australia; WHO Collaborating Centre on eHealth, School of Public Health and Community Medicine, Faculty of Medicine, UNSW Sydney, NSW, 2052, Australia.
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Al-Mustanjid M, Mahmud SMH, Royel MRI, Rahman MH, Islam T, Rahman MR, Moni MA. Detection of molecular signatures and pathways shared in inflammatory bowel disease and colorectal cancer: A bioinformatics and systems biology approach. Genomics 2020; 112:3416-3426. [PMID: 32535071 DOI: 10.1016/j.ygeno.2020.06.001] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Revised: 05/03/2020] [Accepted: 06/02/2020] [Indexed: 02/07/2023]
Abstract
Emerging evidence indicates IBD is a risk factor for the increasing incidence of colorectal cancer (CRC) development. We used a system biology approach to identify common molecular signatures and pathways that interact between IBD and CRC and the indispensable pathological mechanisms. First, we identified 177 common differentially expressed genes (DEGs) between IBD and CRC. Gene set enrichment, protein-protein, DEGs-transcription factors, DEGs-microRNAs, protein-drug interaction, gene-disease association, Gene Ontology, pathway enrichment analyses were conducted to these common genes. The inclusion of common DEGs with bimolecular networks disclosed hub proteins (LYN, PLCB1, NPSR1, WNT5A, CDC25B, CD44, RIPK2, ASAP1), transcription factors (SCD, SLC7A5, IKZF3, SLC16A1, SLC7A11) and miRNAs (mir-335-5p, mir-26b-5p, mir-124-3p, mir-16-5p, mir-192-5p, mir-548c-3p, mir-29b-3p, mir-155-5p, mir-21-5p, mir-15a-5p). Analysis of the interaction between protein and drug discovered ASAP1 interacts with cysteine sulfonic acid and double oxidized cysteine drug compounds. Gene-disease association analysis retrieved ASAP1 also associated with pulmonary and bladder neoplasm diseases.
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Affiliation(s)
- Md Al-Mustanjid
- Department of Software Engineering, Faculty of Science and Information Technology, Daffodil International University, Dhaka 1207, Bangladesh
| | - S M Hasan Mahmud
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
| | - Md Rejaul Islam Royel
- Department of Software Engineering, Faculty of Science and Information Technology, Daffodil International University, Dhaka 1207, Bangladesh
| | - Md Habibur Rahman
- Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh
| | - Tania Islam
- Department of Biotechnology and Genetic Engineering, Faculty of Biological Sciences, Islamic University, Kushtia 7003, Bangladesh
| | - Md Rezanur Rahman
- Department of Biochemistry and Biotechnology, School of Biomedical Science, Khwaja Yunus Ali, University, Enayetpur, Sirajganj 6751, Bangladesh
| | - Mohammad Ali Moni
- WHO Collaborating Centre on eHealth, UNSW Digital Health, School of Public Health and Community Medicine, Faculty of Medicine, UNSW Sydney, Australia.
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Hossain MA, Asa TA, Rahman MM, Uddin S, Moustafa AA, Quinn JMW, Moni MA. Network-Based Genetic Profiling Reveals Cellular Pathway Differences Between Follicular Thyroid Carcinoma and Follicular Thyroid Adenoma. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:E1373. [PMID: 32093341 PMCID: PMC7068514 DOI: 10.3390/ijerph17041373] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2019] [Revised: 02/05/2020] [Accepted: 02/12/2020] [Indexed: 12/11/2022]
Abstract
Molecular mechanisms underlying the pathogenesis and progression of malignant thyroid cancers, such as follicular thyroid carcinomas (FTCs), and how these differ from benign thyroid lesions, are poorly understood. In this study, we employed network-based integrative analyses of FTC and benign follicular thyroid adenoma (FTA) lesion transcriptomes to identify key genes and pathways that differ between them. We first analysed a microarray gene expression dataset (Gene Expression Omnibus GSE82208, n = 52) obtained from FTC and FTA tissues to identify differentially expressed genes (DEGs). Pathway analyses of these DEGs were then performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) resources to identify potentially important pathways, and protein-protein interactions (PPIs) were examined to identify pathway hub genes. Our data analysis identified 598 DEGs, 133 genes with higher and 465 genes with lower expression in FTCs. We identified four significant pathways (one carbon pool by folate, p53 signalling, progesterone-mediated oocyte maturation signalling, and cell cycle pathways) connected to DEGs with high FTC expression; eight pathways were connected to DEGs with lower relative FTC expression. Ten GO groups were significantly connected with FTC-high expression DEGs and 80 with low-FTC expression DEGs. PPI analysis then identified 12 potential hub genes based on degree and betweenness centrality; namely, TOP2A, JUN, EGFR, CDK1, FOS, CDKN3, EZH2, TYMS, PBK, CDH1, UBE2C, and CCNB2. Moreover, transcription factors (TFs) were identified that may underlie gene expression differences observed between FTC and FTA, including FOXC1, GATA2, YY1, FOXL1, E2F1, NFIC, SRF, TFAP2A, HINFP, and CREB1. We also identified microRNA (miRNAs) that may also affect transcript levels of DEGs; these included hsa-mir-335-5p, -26b-5p, -124-3p, -16-5p, -192-5p, -1-3p, -17-5p, -92a-3p, -215-5p, and -20a-5p. Thus, our study identified DEGs, molecular pathways, TFs, and miRNAs that reflect molecular mechanisms that differ between FTC and benign FTA. Given the general similarities of these lesions and common tissue origin, some of these differences may reflect malignant progression potential, and include useful candidate biomarkers for FTC and identifying factors important for FTC pathogenesis.
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Affiliation(s)
- Md. Ali Hossain
- Department of Computer Science & Engineering, Manarat International University, Khagan, Dhaka 1343, Bangladesh;
| | - Tania Akter Asa
- Electrical and Electronic Engineering, Islamic University, Kushtia 7005, Bangladesh;
| | - Md. Mijanur Rahman
- Computer Science & Engineering, Jatiya Kabi Kazi Nazrul Islam University, Mymensingh 2205, Bangladesh;
| | - Shahadat Uddin
- Complex Systems Research Group & Project Management Program, Faculty of Engineering, The University of Sydney, Sydney, NSW 2006, Australia;
| | - Ahmed A. Moustafa
- Marcs Institute for Brain and Behaviour and School of Psychology, Western Sydney University, Sydney, NSW 2751, Australia;
| | - Julian M. W. Quinn
- Bone Biology Divisions, Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia;
| | - Mohammad Ali Moni
- Bone Biology Divisions, Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia;
- WHO Collaborating Centre on eHealth, School of Public Health and Community Medicine, Faculty of Medicine, The University of New South Wales, Sydney, NSW 2052, Australia
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Rana HK, Akhtar MR, Islam MB, Ahmed MB, Lió P, Huq F, Quinn JMW, Moni MA. Machine Learning and Bioinformatics Models to Identify Pathways that Mediate Influences of Welding Fumes on Cancer Progression. Sci Rep 2020; 10:2795. [PMID: 32066756 PMCID: PMC7026442 DOI: 10.1038/s41598-020-57916-9] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Accepted: 12/21/2019] [Indexed: 12/13/2022] Open
Abstract
Welding generates and releases fumes that are hazardous to human health. Welding fumes (WFs) are a complex mix of metallic oxides, fluorides and silicates that can cause or exacerbate health problems in exposed individuals. In particular, WF inhalation over an extended period carries an increased risk of cancer, but how WFs may influence cancer behaviour or growth is unclear. To address this issue we employed a quantitative analytical framework to identify the gene expression effects of WFs that may affect the subsequent behaviour of the cancers. We examined datasets of transcript analyses made using microarray studies of WF-exposed tissues and of cancers, including datasets from colorectal cancer (CC), prostate cancer (PC), lung cancer (LC) and gastric cancer (GC). We constructed gene-disease association networks, identified signaling and ontological pathways, clustered protein-protein interaction network using multilayer network topology, and analyzed survival function of the significant genes using Cox proportional hazards (Cox PH) model and product-limit (PL) estimator. We observed that WF exposure causes altered expression of many genes (36, 13, 25 and 17 respectively) whose expression are also altered in CC, PC, LC and GC. Gene-disease association networks, signaling and ontological pathways, protein-protein interaction network, and survival functions of the significant genes suggest ways that WFs may influence the progression of CC, PC, LC and GC. This quantitative analytical framework has identified potentially novel mechanisms by which tissue WF exposure may lead to gene expression changes in tissue gene expression that affect cancer behaviour and, thus, cancer progression, growth or establishment.
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Affiliation(s)
- Humayan Kabir Rana
- Department of Computer Science and Engineering, Green University of Bangladesh, Dhaka, Bangladesh
| | - Mst Rashida Akhtar
- Department of Computer Science and Engineering, Varendra University, Rajshahi, Bangladesh
| | - M Babul Islam
- Department of Electrical and Electronic Engineering, University of Rajshahi, Rajshahi, Bangladesh
| | - Mohammad Boshir Ahmed
- Bio-electronics Materials Laboratory, School of Materials Science and Engineering, Gwangju Institute of Science and Technology, 261 Cheomdan-gwagiro, Buk-gu, Gwangju, 500-712, Republic of Korea
| | - Pietro Lió
- Computer Laboratory, Department of Computer Science and Technology, University of Cambridge, 15 JJ Thomson Avenue, Cambridge, CB3 0FD, UK
| | - Fazlul Huq
- Discipline of Pathology, School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
| | - Julian M W Quinn
- Bone Biology Division, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia
| | - Mohammad Ali Moni
- Discipline of Pathology, School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia. .,Bone Biology Division, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia.
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Network-based identification of genetic factors in ageing, lifestyle and type 2 diabetes that influence to the progression of Alzheimer's disease. INFORMATICS IN MEDICINE UNLOCKED 2020. [DOI: 10.1016/j.imu.2020.100309] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
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Hossain MA, Saiful Islam SM, Quinn JM, Huq F, Moni MA. Machine learning and bioinformatics models to identify gene expression patterns of ovarian cancer associated with disease progression and mortality. J Biomed Inform 2019; 100:103313. [DOI: 10.1016/j.jbi.2019.103313] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2019] [Revised: 09/20/2019] [Accepted: 10/13/2019] [Indexed: 02/07/2023]
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Yi Y, Liu Y, Wu K, Wu W, Zhang W. The core genes involved in the promotion of depression in patients with ovarian cancer. Oncol Lett 2019; 18:5995-6007. [PMID: 31788074 PMCID: PMC6865084 DOI: 10.3892/ol.2019.10934] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Accepted: 08/08/2019] [Indexed: 12/09/2022] Open
Abstract
The present study aimed to identify the core genes and pathways involved in depression in patients with ovarian cancer (OC) who suffer from high or low-grade depression. The dataset GSE9116 from Gene Expression Omnibus database was analyzed to identify differentially expressed genes (DEGs) in these patients. To elucidate how certain genes could promote depression in patients with OC, pathway crosstalk, protein-protein interaction (PPI) and comprehensive gene-pathway analyses were determined using WebGestalt, ToppGene and Search Tool for the Retrieval of Interacting Genes and gene ontology analysis. Key genes and pathways were extracted from the gene-pathway network, and gene expression and survival analysis were evaluated. A total of 93 DEGs were identified from GSE9116 dataset, including 84 upregulated genes and nine downregulated genes. The PPI, pathway crosstalk and comprehensive gene-pathway analyses highlighted C-C motif chemokine ligand 2 (CCL2), Fos proto-oncogene, AP-1 transcription factor subunit (FOS), serpin family E member 1 (SERPINE1) and serpin family G member 1 (SERPING1) as core genes involved in the promotion of depression in patients with OC. These core genes were involved in the following four pathways 'Ensemble of genes encoding ECM-associated proteins including ECM-affiliated proteins', 'ECM regulators and secreted factors', 'Ensemble of genes encoding extracellular matrix and extracellular matrix-associated proteins' and 'MAPK signaling pathway and IL-17 signaling pathway'. The results from gene expression and survival analysis demonstrated that these four key genes were upregulated in patients with OC and high-grade depression and could worsen patients' survival. These results suggested that CCL2, FOS, SERPINE1 and SERPING1 may serve a crucial role in the promotion of depression in patients with OC. This finding may provide novel markers for predicting and treating depression in patients with OC; however, the underlying mechanisms remain unknown and require further investigation.
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Affiliation(s)
- Yuexiong Yi
- Department of Obstetrics and Gynecology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei 430071, P.R. China
| | - Yanyan Liu
- Department of Obstetrics and Gynecology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei 430071, P.R. China
| | - Kejia Wu
- Department of Obstetrics and Gynecology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei 430071, P.R. China
| | - Wanrong Wu
- Department of Obstetrics and Gynecology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei 430071, P.R. China
| | - Wei Zhang
- Department of Obstetrics and Gynecology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei 430071, P.R. China
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Rahman MF, Rahman MR, Islam T, Zaman T, Shuvo MAH, Hossain MT, Islam MR, Karim MR, Moni MA. A bioinformatics approach to decode core genes and molecular pathways shared by breast cancer and endometrial cancer. INFORMATICS IN MEDICINE UNLOCKED 2019. [DOI: 10.1016/j.imu.2019.100274] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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Hossain MA, Asa TA, Rahman MR, Moni MA. Network-based approach to identify key candidate genes and pathways shared by thyroid cancer and chronic kidney disease. INFORMATICS IN MEDICINE UNLOCKED 2019. [DOI: 10.1016/j.imu.2019.100240] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
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Drug repositioning and biomarkers in low-grade glioma via bioinformatics approach. INFORMATICS IN MEDICINE UNLOCKED 2019. [DOI: 10.1016/j.imu.2019.100250] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
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