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Naveed M, Saad Mughal M, Aziz T, Jabeen K, Ali Khan A, Alhomrani M, Alsanie WF, Alamri AS. The Prominence of the Broad-Spectrum Protease inhibitor gene A2ML1 as a potential biomarker in cervical cancer diagnostics using Immunotherapeutic and Multi-Omics approaches. Int Immunopharmacol 2024; 142:113126. [PMID: 39265356 DOI: 10.1016/j.intimp.2024.113126] [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] [Received: 06/01/2024] [Revised: 08/27/2024] [Accepted: 09/06/2024] [Indexed: 09/14/2024]
Abstract
One of the venereal tumors that threaten human life is cervical cancer. A2ML1 is detected in advanced-stage cancer patients and is found to be strongly associated with cervical cancer. A2ML1 was shown to be substantially expressed in cervical cancer in this study, which used data from the TCGA database. Those with high A2ML1 expression had a lower chance of survival than patients with low A2ML1 expression. Both univariate and multivariate Cox regression analyses were utilized to investigate the relationship between clinical variables and overall survival rates. An investigation into the link between A2ML1 and immune infiltration was subsequently conducted. Utilizing the immune cell database, research was conducted to investigate the dispersion of 24 immune cells and their correlation to A2ML1 expression. In addition to this, the favorable correlation between immune cells and A2ML1 was validated using all three immune cell methodologies. The Genomics of Drug Sensitivity in Cancer database was used to confirm the idea that there is a link between A2ML1 expression and the efficacy of chemotherapy or immunotherapy. The findings demonstrated that A2ML1 is a potential biomarker for cervical cancer diagnostics. This biomarker may be used to chaperone immunotherapy, as well as to explain the elucidates of cervical cancer caused by the immunological microenvironment.
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Affiliation(s)
- Muhammad Naveed
- Department of Biotechnology, Faculty of Science and Technology, University of Central Punjab, Lahore 54590, Pakistan.
| | - Muhammad Saad Mughal
- Department of Biotechnology, Faculty of Science and Technology, University of Central Punjab, Lahore 54590, Pakistan
| | - Tariq Aziz
- Laboratory of Animal Health Food Hygiene and Quality University of Ioannina Arta Greece.
| | - Khizra Jabeen
- Department of Biotechnology, Faculty of Science and Technology, University of Central Punjab, Lahore 54590, Pakistan
| | - Ayaz Ali Khan
- Department of Biotechnology University of Malakand Chakdara Dir Lower
| | - Majid Alhomrani
- Department of Clinical Laboratory Sciences, The faculty of Applied Medical Sciences, Taif University, Taif, Saudi Arabia
| | - Walaa F Alsanie
- Department of Clinical Laboratory Sciences, The faculty of Applied Medical Sciences, Taif University, Taif, Saudi Arabia
| | - Abdulhakeem S Alamri
- Department of Clinical Laboratory Sciences, The faculty of Applied Medical Sciences, Taif University, Taif, Saudi Arabia
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Alam MS, Sultana A, Kibria MK, Khanam A, Wang G, Mollah MNH. Identification of Hub of the Hub-Genes From Different Individual Studies for Early Diagnosis, Prognosis, and Therapies of Breast Cancer. Bioinform Biol Insights 2024; 18:11779322241272386. [PMID: 39239087 PMCID: PMC11375675 DOI: 10.1177/11779322241272386] [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: 01/01/2024] [Accepted: 07/09/2024] [Indexed: 09/07/2024] Open
Abstract
Breast cancer (BC) is a complex disease, which causes of high mortality rate in women. Early diagnosis and therapeutic improvements may reduce the mortality rate. There were more than 74 individual studies that have suggested BC-causing hub-genes (HubGs) in the literature. However, we observed that their HubG sets are not so consistent with each other. It may be happened due to the regional and environmental variations with the sample units. Therefore, it was required to explore hub of the HubG (hHubG) sets that might be more representative for early diagnosis and therapies of BC in different country regions and their environments. In this study, we selected top-ranked 10 HubGs (CCNB1, CDK1, TOP2A, CCNA2, ESR1, EGFR, JUN, ACTB, TP53, and CCND1) as the hHubG set by the protein-protein interaction network analysis based on all of 74 individual HubG sets. The hHubG set enrichment analysis detected some crucial biological processes, molecular functions, and pathways that are significantly associated with BC progressions. The expression analysis of hHubGs by box plots in different stages of BC progression and BC prediction models indicated that the proposed hHubGs can be considered as the early diagnostic and prognostic biomarkers. Finally, we suggested hHubGs-guided top-ranked 10 candidate drug molecules (SORAFENIB, AMG-900, CHEMBL1765740, ENTRECTINIB, MK-6592, YM201636, masitinib, GSK2126458, TG-02, and PAZOPANIB) by molecular docking analysis for the treatment against BC. We investigated the stability of top-ranked 3 drug-target complexes (SORAFENIB vs ESR1, AMG-900 vs TOP2A, and CHEMBL1765740 vs EGFR) by computing their binding free energies based on 100-ns molecular dynamic (MD) simulation based Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) approach and found their stable performance. The literature review also supported our findings much more for BC compared with the results of individual studies. Therefore, the findings of this study may be useful resources for early diagnosis, prognosis, and therapies of BC.
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Affiliation(s)
- Md Shahin Alam
- Center of Translational Medicine, The First People's Hospital of Taicang, Taicang Affiliated Hospital of Soochow University, Suzhou, China
- Laboratory of Molecular Neuropathology, Department of Pharmacology, Jiangsu Key Laboratory of Neuropsychiatric Diseases and College of Pharmaceutical Sciences, Soochow University, Suzhou, China
- Bioinformatics Laboratory (Dry), Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh
| | - Adiba Sultana
- Bioinformatics Laboratory (Dry), Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh
- Medical Big Data Center, Guangdong Provincial People's Hospital/Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Md Kaderi Kibria
- Bioinformatics Laboratory (Dry), Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh
| | - Alima Khanam
- Department of Biochemistry and Molecular Biology, University of Rajshahi, Rajshahi, Bangladesh
| | - Guanghui Wang
- Center of Translational Medicine, The First People's Hospital of Taicang, Taicang Affiliated Hospital of Soochow University, Suzhou, China
| | - Md Nurul Haque Mollah
- Bioinformatics Laboratory (Dry), Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh
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Khan DA, Adhikary T, Sultana MT, Toukir IA. A comprehensive identification of potential molecular targets and small drugs candidate for melanoma cancer using bioinformatics and network-based screening approach. J Biomol Struct Dyn 2024; 42:7349-7369. [PMID: 37534476 DOI: 10.1080/07391102.2023.2240409] [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] [Received: 03/17/2023] [Accepted: 07/17/2023] [Indexed: 08/04/2023]
Abstract
Melanoma is the third most common malignant skin tumor and has increased in morbidity and mortality over the previous decade due to its rapid spread into the bloodstream or lymphatic system. This study used integrated bioinformatics and network-based methodologies to reliably identify molecular targets and small molecular medicines that may be more successful for Melanoma diagnosis, prognosis and treatment. The statistical LIMMA approach utilized for bioinformatics analysis in this study found 246 common differentially expressed genes (cDEGs) between case and control samples from two microarray gene-expression datasets (GSE130244 and GSE15605). Protein-protein interaction network study revealed 15 cDEGs (PTK2, STAT1, PNO1, CXCR4, WASL, FN1, RUNX2, SOCS3, ITGA4, GNG2, CDK6, BRAF, AGO2, GTF2H1 and AR) to be critical in the development of melanoma (KGs). According to regulatory network analysis, the most important transcriptional and post-transcriptional regulators of DEGs and hub-DEGs are ten transcription factors and three miRNAs. We discovered the pathogenetic mechanisms of MC by studying DEGs' biological processes, molecular function, cellular components and KEGG pathways. We used molecular docking and dynamics modeling to select the four most expressed genes responsible for melanoma malignancy to identify therapeutic candidates. Then, utilizing the Connectivity Map (CMap) database, we analyzed the top 4-hub-DEGs-guided repurposable drugs. We validated four melanoma cancer drugs (Fisetin, Epicatechin Gallate, 1237586-97-8 and PF 431396) using molecular dynamics simulation with their target proteins. As a result, the results of this study may provide resources to researchers and medical professionals for the wet-lab validation of MC diagnosis, prognosis and treatments.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Dhrubo Ahmed Khan
- Department of Genetic Engineering and Biotechnology, Jashore University of Science and Technology, Jashore, Bangladesh
| | - Tonmoy Adhikary
- Department of Mathematics, Jashore University of Science and Technology, Jashore, Bangladesh
| | - Mst Tania Sultana
- Department of Mathematics, Jashore University of Science and Technology, Jashore, Bangladesh
| | - Imran Ahamed Toukir
- Department of Chemical Engineering, Jashore University of Science and Technology, Jashore, Bangladesh
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Ahmmed R, Hossen MB, Ajadee A, Mahmud S, Ali MA, Mollah MMH, Reza MS, Islam MA, Mollah MNH. Bioinformatics analysis to disclose shared molecular mechanisms between type-2 diabetes and clear-cell renal-cell carcinoma, and therapeutic indications. Sci Rep 2024; 14:19133. [PMID: 39160196 PMCID: PMC11333728 DOI: 10.1038/s41598-024-69302-w] [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] [Received: 06/12/2024] [Accepted: 08/02/2024] [Indexed: 08/21/2024] Open
Abstract
Type 2 diabetes (T2D) and Clear-cell renal cell carcinoma (ccRCC) are both complicated diseases which incidence rates gradually increasing. Population based studies show that severity of ccRCC might be associated with T2D. However, so far, no researcher yet investigated about the molecular mechanisms of their association. This study explored T2D and ccRCC causing shared key genes (sKGs) from multiple transcriptomics profiles to investigate their common pathogenetic processes and associated drug molecules. We identified 259 shared differentially expressed genes (sDEGs) that can separate both T2D and ccRCC patients from control samples. Local correlation analysis based on the expressions of sDEGs indicated significant association between T2D and ccRCC. Then ten sDEGs (CDC42, SCARB1, GOT2, CXCL8, FN1, IL1B, JUN, TLR2, TLR4, and VIM) were selected as the sKGs through the protein-protein interaction (PPI) network analysis. These sKGs were found significantly associated with different CpG sites of DNA methylation that might be the cause of ccRCC. The sKGs-set enrichment analysis with Gene Ontology (GO) terms and KEGG pathways revealed some crucial shared molecular functions, biological process, cellular components and KEGG pathways that might be associated with development of both T2D and ccRCC. The regulatory network analysis of sKGs identified six post-transcriptional regulators (hsa-mir-93-5p, hsa-mir-203a-3p, hsa-mir-204-5p, hsa-mir-335-5p, hsa-mir-26b-5p, and hsa-mir-1-3p) and five transcriptional regulators (YY1, FOXL1, FOXC1, NR2F1 and GATA2) of sKGs. Finally, sKGs-guided top-ranked three repurposable drug molecules (Digoxin, Imatinib, and Dovitinib) were recommended as the common treatment for both T2D and ccRCC by molecular docking and ADME/T analysis. Therefore, the results of this study may be useful for diagnosis and therapies of ccRCC patients who are also suffering from T2D.
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Affiliation(s)
- Reaz Ahmmed
- Bioinformatics Lab (Dry), Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh
- Department of Biochemistry & Molecular Biology, University of Rajshahi, Rajshahi, 6205, Bangladesh
| | - Md Bayazid Hossen
- Bioinformatics Lab (Dry), Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh
- Department of Agricultural and Applied Statistics, Bangladesh Agricultural University, Mymensingh, 2202, Bangladesh
| | - Alvira Ajadee
- Bioinformatics Lab (Dry), Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh
| | - Sabkat Mahmud
- Bioinformatics Lab (Dry), Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh
| | - Md Ahad Ali
- Bioinformatics Lab (Dry), Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh
- Department of Chemistry, University of Rajshahi, Rajshahi, 6205, Bangladesh
| | - Md Manir Hossain Mollah
- Department of Physical Sciences, Independent University, Bangladesh (IUB), Dhaka, Bangladesh
| | - Md Selim Reza
- Bioinformatics Lab (Dry), Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh
- Division of Biomedical Informatics and Genomics, School of Medicine, Tulane University, 1440 Canal St., RM 1621C, New Orleans, LA, 70112, USA
| | - Mohammad Amirul Islam
- Department of Biochemistry & Molecular Biology, University of Rajshahi, Rajshahi, 6205, Bangladesh
| | - Md Nurul Haque Mollah
- Bioinformatics Lab (Dry), Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh.
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Afroz S, Islam N, Habib MA, Reza MS, Ashad Alam M. Multi-omics data integration and drug screening of AML cancer using Generative Adversarial Network. Methods 2024; 226:138-150. [PMID: 38670415 DOI: 10.1016/j.ymeth.2024.04.017] [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] [Received: 07/18/2023] [Revised: 04/02/2024] [Accepted: 04/20/2024] [Indexed: 04/28/2024] Open
Abstract
In the era of precision medicine, accurate disease phenotype prediction for heterogeneous diseases, such as cancer, is emerging due to advanced technologies that link genotypes and phenotypes. However, it is difficult to integrate different types of biological data because they are so varied. In this study, we focused on predicting the traits of a blood cancer called Acute Myeloid Leukemia (AML) by combining different kinds of biological data. We used a recently developed method called Omics Generative Adversarial Network (GAN) to better classify cancer outcomes. The primary advantages of a GAN include its ability to create synthetic data that is nearly indistinguishable from real data, its high flexibility, and its wide range of applications, including multi-omics data analysis. In addition, the GAN was effective at combining two types of biological data. We created synthetic datasets for gene activity and DNA methylation. Our method was more accurate in predicting disease traits than using the original data alone. The experimental results provided evidence that the creation of synthetic data through interacting multi-omics data analysis using GANs improves the overall prediction quality. Furthermore, we identified the top-ranked significant genes through statistical methods and pinpointed potential candidate drug agents through in-silico studies. The proposed drugs, also supported by other independent studies, might play a crucial role in the treatment of AML cancer. The code is available on GitHub; https://github.com/SabrinAfroz/omicsGAN_codes?fbclid=IwAR1-/stuffmlE0hyWgSu2wlXo6dYlKUei3faLdlvpxTOOUPVlmYCloXf4Uk9ejK4I.
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Affiliation(s)
- Sabrin Afroz
- Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Bangladesh
| | - Nadira Islam
- Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Bangladesh
| | - Md Ahsan Habib
- Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Bangladesh; Statistical Learning Group, Bangladesh
| | - Md Selim Reza
- Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA 70112, USA; Statistical Learning Group, Bangladesh
| | - Md Ashad Alam
- Ochsner Center for Outcomes Research, Ochsner Research, Ochsner Clinic Foundation, New Orleans, LA 70121, USA; Statistical Learning Group, Bangladesh.
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Barathikannan K, Chelliah R, Vinothkanna A, Prathiviraj R, Tyagi A, Vijayalakshmi S, Lim MJ, Jia AQ, Oh DH. Untargeted metabolomics-based network pharmacology reveals fermented brown rice towards anti-obesity efficacy. NPJ Sci Food 2024; 8:20. [PMID: 38555366 PMCID: PMC10981755 DOI: 10.1038/s41538-024-00258-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 02/14/2024] [Indexed: 04/02/2024] Open
Abstract
There is a substantial rise in the global incidence of obesity. Brown rice contains metabolic substances that can help minimize the prevalence of obesity. This study evaluated nine brown rice varieties using probiotic fermentation using Pediococcus acidilacti MNL5 to enhance bioactive metabolites and their efficacy. Among the nine varieties, FBR-1741 had the highest pancreatic lipase inhibitory efficacy (87.6 ± 1.51%), DPPH assay (358.5 ± 2.80 mg Trolox equiv./100 g, DW), and ABTS assay (362.5 ± 2.32 mg Trolox equiv./100 g, DW). Compared to other fermented brown rice and FBR-1741 varieties, UHPLC-Q-TOF-MS/MS demonstrated significant untargeted metabolite alterations. The 17 most abundant polyphenolic metabolites in the FBR-1741 variety and 132 putative targets were assessed for obesity-related target proteins, and protein interaction networks were constructed using the Cystoscope software. Network pharmacology analysis validated FBR-1741 with active metabolites in the C. elegans obesity-induced model. Administration of FBR-1741 with ferulic acid improved lifespan decreased triglycerides, and suppressed the expression of fat-related genes. The enhanced anti-obesity properties of FBR-1741 suggest its implementation in obesity-functional food.
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Affiliation(s)
- Kaliyan Barathikannan
- Agricultural and Life Science Research Institute, Kangwon National University, Chuncheon, 24341, Korea
- Saveetha School of Engineering, Saveetha (SIMATS) University, Tamil Nadu, 600124, India
| | - Ramachandran Chelliah
- Department of Food Science and Biotechnology, College of Agriculture and Life Sciences, Kangwon National University, Chuncheon, 200-701, South Korea
- Kangwon Institute of Inclusive Technology (KIIT), Kangwon National University, Chuncheon, 24341, South Korea
| | - Annadurai Vinothkanna
- School of Life Sciences, Hainan University, 570228, Haikou, China
- Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, 570311, Haikou, China
| | | | - Akanksha Tyagi
- Department of Food Science and Biotechnology, College of Agriculture and Life Sciences, Kangwon National University, Chuncheon, 200-701, South Korea
| | - Selvakumar Vijayalakshmi
- Department of Food Science and Biotechnology, College of Agriculture and Life Sciences, Kangwon National University, Chuncheon, 200-701, South Korea
| | - Min-Jin Lim
- Department of Food Science and Biotechnology, College of Agriculture and Life Sciences, Kangwon National University, Chuncheon, 200-701, South Korea
| | - Ai-Qun Jia
- Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, 570311, Haikou, China
| | - Deog- Hwan Oh
- Department of Food Science and Biotechnology, College of Agriculture and Life Sciences, Kangwon National University, Chuncheon, 200-701, South Korea.
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Zeng N, Huang F, Du J, Huang C, Yang Q, Zhan X, Xing B. Expeditious profiling of polycyclic aromatic hydrocarbons transport and obstruction mechanisms in crop xylem sap proteins via proteomics and molecular docking. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 340:122854. [PMID: 37940018 DOI: 10.1016/j.envpol.2023.122854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 10/30/2023] [Accepted: 10/31/2023] [Indexed: 11/10/2023]
Abstract
Polycyclic aromatic hydrocarbons (PAHs) pose significant environmental risks due to their toxicity and carcinogenic properties. This research seeks to pinpoint protein targets in crop xylem sap related to PAH contamination and delve into their protein-ligand interactions using computational tools. Proteomic assessment revealed differentially expressed proteins (DEPs), which were subjected to virtual high-throughput screening. Notably, the phenanthrene's influence on xylem sap proteins in maize and wheat was more pronounced than in soybean, with DEPs expression peak at 24 h post-treatment. Maize DEPs were predominantly associated with lipid biosynthesis. Phenanthrene impacted cell membrane hydrophobicity, limiting PAH adsorption and decreasing its concentration in maize xylem sap. Wheat DEPs exhibited an increase in ABC transporters after 24 h of phenanthrene exposure. ABC transporters interacted with stress-responsive proteins like C6TIY1-Co-chaperone p23 and others that either facilitate or inhibit PAH transport, including Indeno[1,2,3-cd]Pyrene and C6TIY1-Co-chaperone protein p23. Both maize and wheat created high-affinity complexes between specific proteins and PAHs, influencing their transport. This study provides insights into the mechanisms of PAH regulation and movement within plant xylem.
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Affiliation(s)
- Nengde Zeng
- College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing, Jiangsu Province, 210095, PR China; Stockbridge School of Agriculture, University of Massachusetts, Amherst, MA, 01003, USA
| | - Fei Huang
- College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing, Jiangsu Province, 210095, PR China
| | - Jiani Du
- College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing, Jiangsu Province, 210095, PR China
| | - Chenghao Huang
- College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing, Jiangsu Province, 210095, PR China
| | - Qian Yang
- College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing, Jiangsu Province, 210095, PR China
| | - Xinhua Zhan
- College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing, Jiangsu Province, 210095, PR China.
| | - Baoshan Xing
- Stockbridge School of Agriculture, University of Massachusetts, Amherst, MA, 01003, USA
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Xu G, Zhang W, Yang J, Sun N, Qu X. Identification of neutrophil extracellular traps and crosstalk genes linking inflammatory bowel disease and osteoporosis by integrated bioinformatics analysis and machine learning. Sci Rep 2023; 13:23054. [PMID: 38155235 PMCID: PMC10754907 DOI: 10.1038/s41598-023-50488-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 12/20/2023] [Indexed: 12/30/2023] Open
Abstract
Musculoskeletal deficits are among the most common extra-intestinal manifestations and complications of inflammatory bowel disease (IBD). This study aimed to identify crosstalk genes between IBD and osteoporosis (OP) and potential relationships between crosstalk and neutrophil extracellular traps (NETs)-related genes. Three common hub genes from different compared groups are actually the same, namely HDAC6, IL-8, and PPIF. ROC showed that the combined diagnostic value of HDAC6, IL-8, and PPIF was higher than each of the three key hub genes. Immune infiltration results showed that HDAC6 and IL-8 key genes negatively correlated with CD65 bright natural killer cells. USF1 was the common upstream TFs between HDAC6 and PPIF, and MYC was the common upstream TFs between IL-8 and PPIF in RegNetwork. Taken together, this study shows a linked mechanism between IBD and OP via NETs and crosstalk genes. These findings may show light on better diagnosis and treatment of IBD complicated with OP.
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Affiliation(s)
- Gang Xu
- Department of Orthopaedics, First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning Province, China.
- Key Laboratory of Molecular Mechanism for Repair and Remodeling of Orthopaedic Diseases, Dalian, Liaoning Province, China.
| | - Wanhao Zhang
- Department of Orthopaedics, First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning Province, China
| | - Jun Yang
- Department of Orthopaedics, First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning Province, China
| | - Na Sun
- Department of Pharmacy, The Third People's Hospital of Dalian, Dalian, Liaoning Province, China
| | - Xiaochen Qu
- Department of Orthopaedics, First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning Province, China.
- Key Laboratory of Molecular Mechanism for Repair and Remodeling of Orthopaedic Diseases, Dalian, Liaoning Province, China.
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Mosharaf MP, Alam K, Gow J, Mahumud RA. Exploration of key drug target proteins highlighting their related regulatory molecules, functional pathways and drug candidates associated with delirium: evidence from meta-data analyses. BMC Geriatr 2023; 23:767. [PMID: 37993790 PMCID: PMC10666371 DOI: 10.1186/s12877-023-04457-1] [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] [Received: 07/19/2023] [Accepted: 11/04/2023] [Indexed: 11/24/2023] Open
Abstract
BACKGROUND Delirium is a prevalent neuropsychiatric medical phenomenon that causes serious emergency outcomes, including mortality and morbidity. It also increases the suffering and the economic burden for families and carers. Unfortunately, the pathophysiology of delirium is still unknown, which is a major obstacle to therapeutic development. The modern network-based system biology and multi-omics analysis approach has been widely used to recover the key drug target biomolecules and signaling pathways associated with disease pathophysiology. This study aimed to identify the major drug target hub-proteins associated with delirium, their regulatory molecules with functional pathways, and repurposable drug candidates for delirium treatment. METHODS We used a comprehensive proteomic seed dataset derived from a systematic literature review and the Comparative Toxicogenomics Database (CTD). An integrated multi-omics network-based bioinformatics approach was utilized in this study. The STRING database was used to construct the protein-protein interaction (PPI) network. The gene set enrichment and signaling pathways analysis, the regulatory transcription factors and microRNAs were conducted using delirium-associated genes. Finally, hub-proteins associated repurposable drugs were retrieved from CMap database. RESULTS We have distinguished 11 drug targeted hub-proteins (MAPK1, MAPK3, TP53, JUN, STAT3, SRC, RELA, AKT1, MAPK14, HSP90AA1 and DLG4), 5 transcription factors (FOXC1, GATA2, YY1, TFAP2A and SREBF1) and 6 microRNA (miR-375, miR-17-5, miR-17-5p, miR-106a-5p, miR-125b-5p, and miR-125a-5p) associated with delirium. The functional enrichment and pathway analysis revealed the cytokines, inflammation, postoperative pain, oxidative stress-associated pathways, developmental biology, shigellosis and cellular senescence which are closely connected with delirium development and the hallmarks of aging. The hub-proteins associated computationally identified repurposable drugs were retrieved from database. The predicted drug molecules including aspirin, irbesartan, ephedrine-(racemic), nedocromil, and guanidine were characterized as anti-inflammatory, stimulating the central nervous system, neuroprotective medication based on the existing literatures. The drug molecules may play an important role for therapeutic development against delirium if they are investigated more extensively through clinical trials and various wet lab experiments. CONCLUSION This study could possibly help future research on investigating the delirium-associated therapeutic target biomarker hub-proteins and repurposed drug compounds. These results will also aid understanding of the molecular mechanisms that underlie the pathophysiology of delirium onset and molecular function.
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Affiliation(s)
- Md Parvez Mosharaf
- School of Business, Faculty of Business, Education, Law and Arts, University of Southern Queensland, Toowoomba, QLD, 4350, Australia.
- Bioinformatics Lab, Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh.
| | - Khorshed Alam
- School of Business, Faculty of Business, Education, Law and Arts, University of Southern Queensland, Toowoomba, QLD, 4350, Australia
| | - Jeff Gow
- School of Business, Faculty of Business, Education, Law and Arts, University of Southern Queensland, Toowoomba, QLD, 4350, Australia
- School of Accounting, Economics and Finance, University of KwaZulu-Natal, Durban, 4000, South Africa
| | - Rashidul Alam Mahumud
- NHMRC Clinical Trials Centre, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW, 2006, Australia
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10
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Xia WT, Qiu WR, Yu WK, Xu ZC, Zhang SH. Identifying TME signatures for cervical cancer prognosis based on GEO and TCGA databases. Heliyon 2023; 9:e15096. [PMID: 37095983 PMCID: PMC10121839 DOI: 10.1016/j.heliyon.2023.e15096] [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: 10/22/2022] [Revised: 03/24/2023] [Accepted: 03/27/2023] [Indexed: 04/26/2023] Open
Abstract
The mortality rate from cervical cancer (CESC), a malignant tumor that affects women, has increased significantly globally in recent years. The discovery of biomarkers points to a direction for the diagnosis of cervical cancer with the advancement of bioinformatics technology. The goal of this study was to look for potential biomarkers for the diagnosis and prognosis of CESC using the GEO and TCGA databases. Because of the high dimension and small sample size of the omic data, or the use of biomarkers generated from a single omic data, the diagnosis of cervical cancer may be inaccurate and unreliable. The purpose of this study was to search the GEO and TCGA databases for potential biomarkers for the diagnosis and prognosis of CESC. We begin by downloading CESC (GSE30760) DNA methylation data from GEO, then perform differential analysis on the downloaded methylation data and screen out the differential genes. Then, using estimation algorithms, we score immune cells and stromal cells in the tumor microenvironment and perform survival analysis on the gene expression profile data and the most recent clinical data of CESC from TCGA. Then, using the 'limma' package and Venn plot in R language to perform differential analysis of genes and screen out overlapping genes, these overlapping genes were then subjected to GO and KEGG functional enrichment analysis. The differential genes screened by the GEO methylation data and the differential genes screened by the TCGA gene expression data were intersected to screen out the common differential genes. A protein-protein interaction (PPI) network of gene expression data was then created in order to discover important genes. The PPI network's key genes were crossed with previously identified common differential genes to further validate them. The Kaplan-Meier curve was then used to determine the prognostic importance of the key genes. Survival analysis has shown that CD3E and CD80 are important for the identification of cervical cancer and can be considered as potential biomarkers for cervical cancer.
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Affiliation(s)
- Wen-Tao Xia
- School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen, China
| | - Wang-Ren Qiu
- School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen, China
- Corresponding author.
| | - Wang-Ke Yu
- School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen, China
| | - Zhao-Chun Xu
- School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen, China
| | - Shou-Hua Zhang
- Department of General Surgery, Jiangxi Provincial Children's Hospital, Nanchang, China
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11
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Casotti MC, Meira DD, Zetum ASS, de Araújo BC, da Silva DRC, dos Santos EDVW, Garcia FM, de Paula F, Santana GM, Louro LS, Alves LNR, Braga RFR, Trabach RSDR, Bernardes SS, Louro TES, Chiela ECF, Lenz G, de Carvalho EF, Louro ID. Computational Biology Helps Understand How Polyploid Giant Cancer Cells Drive Tumor Success. Genes (Basel) 2023; 14:801. [PMID: 37107559 PMCID: PMC10137723 DOI: 10.3390/genes14040801] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 03/17/2023] [Accepted: 03/20/2023] [Indexed: 03/29/2023] Open
Abstract
Precision and organization govern the cell cycle, ensuring normal proliferation. However, some cells may undergo abnormal cell divisions (neosis) or variations of mitotic cycles (endopolyploidy). Consequently, the formation of polyploid giant cancer cells (PGCCs), critical for tumor survival, resistance, and immortalization, can occur. Newly formed cells end up accessing numerous multicellular and unicellular programs that enable metastasis, drug resistance, tumor recurrence, and self-renewal or diverse clone formation. An integrative literature review was carried out, searching articles in several sites, including: PUBMED, NCBI-PMC, and Google Academic, published in English, indexed in referenced databases and without a publication time filter, but prioritizing articles from the last 3 years, to answer the following questions: (i) "What is the current knowledge about polyploidy in tumors?"; (ii) "What are the applications of computational studies for the understanding of cancer polyploidy?"; and (iii) "How do PGCCs contribute to tumorigenesis?"
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Affiliation(s)
- Matheus Correia Casotti
- Centro de Ciências Humanas e Naturais, Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo (UFES), Vitória 29075-910, Brazil; (M.C.C.)
| | - Débora Dummer Meira
- Centro de Ciências Humanas e Naturais, Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo (UFES), Vitória 29075-910, Brazil; (M.C.C.)
| | - Aléxia Stefani Siqueira Zetum
- Centro de Ciências Humanas e Naturais, Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo (UFES), Vitória 29075-910, Brazil; (M.C.C.)
| | - Bruno Cancian de Araújo
- Centro de Ciências Humanas e Naturais, Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo (UFES), Vitória 29075-910, Brazil; (M.C.C.)
| | - Danielle Ribeiro Campos da Silva
- Centro de Ciências Humanas e Naturais, Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo (UFES), Vitória 29075-910, Brazil; (M.C.C.)
| | | | - Fernanda Mariano Garcia
- Centro de Ciências Humanas e Naturais, Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo (UFES), Vitória 29075-910, Brazil; (M.C.C.)
| | - Flávia de Paula
- Centro de Ciências Humanas e Naturais, Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo (UFES), Vitória 29075-910, Brazil; (M.C.C.)
| | - Gabriel Mendonça Santana
- Centro de Ciências da Saúde, Curso de Medicina, Universidade Federal do Espírito Santo (UFES), Vitória 29090-040, Brazil
| | - Luana Santos Louro
- Centro de Ciências da Saúde, Curso de Medicina, Universidade Federal do Espírito Santo (UFES), Vitória 29090-040, Brazil
| | - Lyvia Neves Rebello Alves
- Centro de Ciências Humanas e Naturais, Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo (UFES), Vitória 29075-910, Brazil; (M.C.C.)
| | - Raquel Furlani Rocon Braga
- Centro de Ciências Humanas e Naturais, Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo (UFES), Vitória 29075-910, Brazil; (M.C.C.)
| | - Raquel Silva dos Reis Trabach
- Centro de Ciências Humanas e Naturais, Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo (UFES), Vitória 29075-910, Brazil; (M.C.C.)
| | - Sara Santos Bernardes
- Departamento de Patologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte 31270-901, Brazil
| | - Thomas Erik Santos Louro
- Escola Superior de Ciências da Santa Casa de Misericórdia de Vitória (EMESCAM), Vitória 29027-502, Brazil
| | - Eduardo Cremonese Filippi Chiela
- Departamento de Ciências Morfológicas, Instituto de Ciências Básicas da Saúde, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre 90035-003, Brazil
- Serviço de Pesquisa Experimental, Hospital de Clínicas de Porto Alegre, Porto Alegre 90035-903, Brazil
- Centro de Biotecnologia, Universidade Federal do Rio Grande do Sul, Porto Alegre 91501-970, Brazil
| | - Guido Lenz
- Centro de Biotecnologia, Universidade Federal do Rio Grande do Sul, Porto Alegre 91501-970, Brazil
- Departamento de Biofísica, Instituto de Biociências, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre 91501-970, Brazil
| | - Elizeu Fagundes de Carvalho
- Instituto de Biologia Roberto Alcântara Gomes (IBRAG), Universidade do Estado do Rio de Janeiro (UERJ), Rio de Janeiro 20551-030, Brazil
| | - Iúri Drumond Louro
- Centro de Ciências Humanas e Naturais, Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo (UFES), Vitória 29075-910, Brazil; (M.C.C.)
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12
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Sarker B, Rahaman MM, Islam MA, Alamin MH, Husain MM, Ferdousi F, Ahsan MA, Mollah MNH. Identification of host genomic biomarkers from multiple transcriptomics datasets for diagnosis and therapies of SARS-CoV-2 infections. PLoS One 2023; 18:e0281981. [PMID: 36913345 PMCID: PMC10010564 DOI: 10.1371/journal.pone.0281981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 02/05/2023] [Indexed: 03/14/2023] Open
Abstract
The pandemic of COVID-19 is a severe threat to human life and the global economy. Despite the success of vaccination efforts in reducing the spread of the virus, the situation remains largely uncontrolled due to the random mutation in the RNA sequence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which demands different variants of effective drugs. Disease-causing gene-mediated proteins are usually used as receptors to explore effective drug molecules. In this study, we analyzed two different RNA-Seq and one microarray gene expression profile datasets by integrating EdgeR, LIMMA, weighted gene co-expression network and robust rank aggregation approaches, which revealed SARS-CoV-2 infection causing eight hub-genes (HubGs) including HubGs; REL, AURKA, AURKB, FBXL3, OAS1, STAT4, MMP2 and IL6 as the host genomic biomarkers. Gene Ontology and pathway enrichment analyses of HubGs significantly enriched some crucial biological processes, molecular functions, cellular components and signaling pathways that are associated with the mechanisms of SARS-CoV-2 infections. Regulatory network analysis identified top-ranked 5 TFs (SRF, PBX1, MEIS1, ESR1 and MYC) and 5 miRNAs (hsa-miR-106b-5p, hsa-miR-20b-5p, hsa-miR-93-5p, hsa-miR-106a-5p and hsa-miR-20a-5p) as the key transcriptional and post-transcriptional regulators of HubGs. Then, we conducted a molecular docking analysis to determine potential drug candidates that could interact with HubGs-mediated receptors. This analysis resulted in the identification of top-ranked ten drug agents, including Nilotinib, Tegobuvir, Digoxin, Proscillaridin, Olysio, Simeprevir, Hesperidin, Oleanolic Acid, Naltrindole and Danoprevir. Finally, we investigated the binding stability of the top-ranked three drug molecules Nilotinib, Tegobuvir and Proscillaridin with the three top-ranked proposed receptors (AURKA, AURKB, OAS1) by using 100 ns MD-based MM-PBSA simulations and observed their stable performance. Therefore, the findings of this study might be useful resources for diagnosis and therapies of SARS-CoV-2 infections.
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Affiliation(s)
- Bandhan Sarker
- Faculty of Science, Department of Statistics, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj, Bangladesh
- Department of Statistics, Bioinformatics Laboratory (Dry), University of Rajshahi, Rajshahi, Bangladesh
| | - Md. Matiur Rahaman
- Faculty of Science, Department of Statistics, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj, Bangladesh
| | - Md. Ariful Islam
- Department of Statistics, Bioinformatics Laboratory (Dry), University of Rajshahi, Rajshahi, Bangladesh
| | - Muhammad Habibulla Alamin
- Faculty of Science, Department of Statistics, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj, Bangladesh
| | - Md. Maidul Husain
- Faculty of Science, Department of Statistics, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj, Bangladesh
| | - Farzana Ferdousi
- Faculty of Science, Department of Statistics, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj, Bangladesh
| | - Md. Asif Ahsan
- Department of Statistics, Bioinformatics Laboratory (Dry), University of Rajshahi, Rajshahi, Bangladesh
- Liangzhu Laboratory, Zhejiang University Medical Center, Zhejiang University, Hangzhou, Zhejiang, China
| | - Md. Nurul Haque Mollah
- Department of Statistics, Bioinformatics Laboratory (Dry), University of Rajshahi, Rajshahi, Bangladesh
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13
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Exploring Core Genes by Comparative Transcriptomics Analysis for Early Diagnosis, Prognosis, and Therapies of Colorectal Cancer. Cancers (Basel) 2023; 15:cancers15051369. [PMID: 36900162 PMCID: PMC10000172 DOI: 10.3390/cancers15051369] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 02/16/2023] [Accepted: 02/17/2023] [Indexed: 02/24/2023] Open
Abstract
Colorectal cancer (CRC) is one of the most common cancers with a high mortality rate. Early diagnosis and therapies for CRC may reduce the mortality rate. However, so far, no researchers have yet investigated core genes (CGs) rigorously for early diagnosis, prognosis, and therapies of CRC. Therefore, an attempt was made in this study to explore CRC-related CGs for early diagnosis, prognosis, and therapies. At first, we identified 252 common differentially expressed genes (cDEGs) between CRC and control samples based on three gene-expression datasets. Then, we identified ten cDEGs (AURKA, TOP2A, CDK1, PTTG1, CDKN3, CDC20, MAD2L1, CKS2, MELK, and TPX2) as the CGs, highlighting their mechanisms in CRC progression. The enrichment analysis of CGs with GO terms and KEGG pathways revealed some crucial biological processes, molecular functions, and signaling pathways that are associated with CRC progression. The survival probability curves and box-plot analyses with the expressions of CGs in different stages of CRC indicated their strong prognostic performance from the earlier stage of the disease. Then, we detected CGs-guided seven candidate drugs (Manzamine A, Cardidigin, Staurosporine, Sitosterol, Benzo[a]pyrene, Nocardiopsis sp., and Riccardin D) by molecular docking. Finally, the binding stability of four top-ranked complexes (TPX2 vs. Manzamine A, CDC20 vs. Cardidigin, MELK vs. Staurosporine, and CDK1 vs. Riccardin D) was investigated by using 100 ns molecular dynamics simulation studies, and their stable performance was observed. Therefore, the output of this study may play a vital role in developing a proper treatment plan at the earlier stages of CRC.
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14
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Hossen MB, Islam MA, Reza MS, Kibria MK, Horaira MA, Tuly KF, Faruqe MO, Kabir F, Mollah MNH. Robust identification of common genomic biomarkers from multiple gene expression profiles for the prognosis, diagnosis, and therapies of pancreatic cancer. Comput Biol Med 2023; 152:106411. [PMID: 36502691 DOI: 10.1016/j.compbiomed.2022.106411] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 11/17/2022] [Accepted: 12/03/2022] [Indexed: 12/12/2022]
Abstract
Pancreatic cancer (PC) is one of the leading causes of cancer-related death globally. So, identification of potential molecular signatures is required for diagnosis, prognosis, and therapies of PC. In this study, we detected 71 common differentially expressed genes (cDEGs) between PC and control samples from four microarray gene-expression datasets (GSE15471, GSE16515, GSE71989, and GSE22780) by using robust statistical and machine learning approaches, since microarray gene-expression datasets are often contaminated by outliers due to several steps involved in the data generating processes. Then we detected 8 cDEGs (ADAM10, COL1A2, FN1, P4HB, ITGB1, ITGB5, ANXA2, and MYOF) as the PC-causing key genes (KGs) by the protein-protein interaction (PPI) network analysis. We validated the expression patterns of KGs between case and control samples by box plot analysis with the TCGA and GTEx databases. The proposed KGs showed high prognostic power with the random forest (RF) based prediction model and Kaplan-Meier-based survival probability curve. The KGs regulatory network analysis detected few transcriptional and post-transcriptional regulators for KGs. The cDEGs-set enrichment analysis revealed some crucial PC-causing molecular functions, biological processes, cellular components, and pathways that are associated with KGs. Finally, we suggested KGs-guided five repurposable drug molecules (Linsitinib, CX5461, Irinotecan, Timosaponin AIII, and Olaparib) and a new molecule (NVP-BHG712) against PC by molecular docking. The stability of the top three protein-ligand complexes was confirmed by molecular dynamic (MD) simulation studies. The cross-validation and some literature reviews also supported our findings. Therefore, the finding of this study might be useful resources to the researchers and medical doctors for diagnosis, prognosis and therapies of PC by the wet-lab validation.
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Affiliation(s)
- Md Bayazid Hossen
- 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 Selim Reza
- 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 Abu Horaira
- Bioinformatics Lab, Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh
| | - Khanis Farhana Tuly
- Bioinformatics Lab, Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh
| | - Md Omar Faruqe
- Department of Computer Science and Engineering, University of Rajshahi, Rajshahi, 6205, Bangladesh
| | - Firoz Kabir
- Department of Ophthalmology and Visual Sciences, School of Medicine, University of Maryland, Baltimore, MD, USA
| | - Md Nurul Haque Mollah
- Bioinformatics Lab, Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh.
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15
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Reza MS, Hossen MA, Harun-Or-Roshid M, Siddika MA, Kabir MH, Mollah MNH. Metadata analysis to explore hub of the hub-genes highlighting their functions, pathways and regulators for cervical cancer diagnosis and therapies. Discov Oncol 2022; 13:79. [PMID: 35994213 PMCID: PMC9395557 DOI: 10.1007/s12672-022-00546-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Accepted: 08/11/2022] [Indexed: 11/25/2022] Open
Abstract
Cervical cancer (CC) is considered as the fourth most common women cancer globally.that shows malignant features of local infiltration and invasion into adjacent organs and tissues. There are several individual studies in the literature that explored CC-causing hub-genes (HubGs), however, we observed that their results are not so consistent. Therefore, the main objective of this study was to explore hub of the HubGs (hHubGs) that might be more representative CC-causing HubGs compare to the single study based HubGs. We reviewed 52 published articles and found 255 HubGs/studied-genes in total. Among them, we selected 10 HubGs (CDK1, CDK2, CHEK1, MKI67, TOP2A, BRCA1, PLK1, CCNA2, CCNB1, TYMS) as the hHubGs by the protein-protein interaction (PPI) network analysis. Then, we validated their differential expression patterns between CC and control samples through the GPEA database. The enrichment analysis of HubGs revealed some crucial CC-causing biological processes (BPs), molecular functions (MFs) and cellular components (CCs) by involving hHubGs. The gene regulatory network (GRN) analysis identified four TFs proteins and three miRNAs as the key transcriptional and post-transcriptional regulators of hHubGs. Then, we identified hHubGs-guided top-ranked FDA-approved 10 candidate drugs and validated them against the state-of-the-arts independent receptors by molecular docking analysis. Finally, we investigated the binding stability of the top-ranked three candidate drugs (Docetaxel, Temsirolimus, Paclitaxel) by using 100 ns MD-based MM-PBSA simulations and observed their stable performance. Therefore the finding of this study might be the useful resources for CC diagnosis and therapies.
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Affiliation(s)
- Md. Selim Reza
- Bioinformatics Lab, Department of Statistics, University of Rajshahi, Rajshahi-6205, Bangladesh
| | - Md. Alim Hossen
- Bioinformatics Lab, Department of Statistics, University of Rajshahi, Rajshahi-6205, Bangladesh
| | - Md. Harun-Or-Roshid
- Bioinformatics Lab, Department of Statistics, University of Rajshahi, Rajshahi-6205, Bangladesh
| | - Mst. Ayesha Siddika
- Microbiology Lab, Department of Veterinary and Animal Sciences, University of Rajshahi, Rajshahi-6205, Bangladesh
| | - Md. Hadiul Kabir
- Bioinformatics Lab, Department of Statistics, 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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16
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Identification of key candidate genes for IgA nephropathy using machine learning and statistics based bioinformatics models. Sci Rep 2022; 12:13963. [PMID: 35978028 PMCID: PMC9385868 DOI: 10.1038/s41598-022-18273-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 08/08/2022] [Indexed: 11/08/2022] Open
Abstract
Immunoglobulin-A-nephropathy (IgAN) is a kidney disease caused by the accumulation of IgAN deposits in the kidneys, which causes inflammation and damage to the kidney tissues. Various bioinformatics analysis-based approaches are widely used to predict novel candidate genes and pathways associated with IgAN. However, there is still some scope to clearly explore the molecular mechanisms and causes of IgAN development and progression. Therefore, the present study aimed to identify key candidate genes for IgAN using machine learning (ML) and statistics-based bioinformatics models. First, differentially expressed genes (DEGs) were identified using limma, and then enrichment analysis was performed on DEGs using DAVID. Protein-protein interaction (PPI) was constructed using STRING and Cytoscape was used to determine hub genes based on connectivity and hub modules based on MCODE scores and their associated genes from DEGs. Furthermore, ML-based algorithms, namely support vector machine (SVM), least absolute shrinkage and selection operator (LASSO), and partial least square discriminant analysis (PLS-DA) were applied to identify the discriminative genes of IgAN from DEGs. Finally, the key candidate genes (FOS, JUN, EGR1, FOSB, and DUSP1) were identified as overlapping genes among the selected hub genes, hub module genes, and discriminative genes from SVM, LASSO, and PLS-DA, respectively which can be used for the diagnosis and treatment of IgAN.
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17
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Alam MS, Sultana A, Reza MS, Amanullah M, Kabir SR, Mollah MNH. Integrated bioinformatics and statistical approaches to explore molecular biomarkers for breast cancer diagnosis, prognosis and therapies. PLoS One 2022; 17:e0268967. [PMID: 35617355 PMCID: PMC9135200 DOI: 10.1371/journal.pone.0268967] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 05/11/2022] [Indexed: 02/06/2023] Open
Abstract
Integrated bioinformatics and statistical approaches are now playing the vital role in identifying potential molecular biomarkers more accurately in presence of huge number of alternatives for disease diagnosis, prognosis and therapies by reducing time and cost compared to the wet-lab based experimental procedures. Breast cancer (BC) is one of the leading causes of cancer related deaths for women worldwide. Several dry-lab and wet-lab based studies have identified different sets of molecular biomarkers for BC. But they did not compare their results to each other so much either computationally or experimentally. In this study, an attempt was made to propose a set of molecular biomarkers that might be more effective for BC diagnosis, prognosis and therapies, by using the integrated bioinformatics and statistical approaches. At first, we identified 190 differentially expressed genes (DEGs) between BC and control samples by using the statistical LIMMA approach. Then we identified 13 DEGs (AKR1C1, IRF9, OAS1, OAS3, SLCO2A1, NT5E, NQO1, ANGPT1, FN1, ATF6B, HPGD, BCL11A, and TP53INP1) as the key genes (KGs) by protein-protein interaction (PPI) network analysis. Then we investigated the pathogenetic processes of DEGs highlighting KGs by GO terms and KEGG pathway enrichment analysis. Moreover, we disclosed the transcriptional and post-transcriptional regulatory factors of KGs by their interaction network analysis with the transcription factors (TFs) and micro-RNAs. Both supervised and unsupervised learning's including multivariate survival analysis results confirmed the strong prognostic power of the proposed KGs. Finally, we suggested KGs-guided computationally more effective seven candidate drugs (NVP-BHG712, Nilotinib, GSK2126458, YM201636, TG-02, CX-5461, AP-24534) compared to other published drugs by cross-validation with the state-of-the-art alternatives top-ranked independent receptor proteins. Thus, our findings might be played a vital role in breast cancer diagnosis, prognosis and therapies.
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Affiliation(s)
- Md. Shahin Alam
- Bioinformatics Lab (Dry), Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh
- * E-mail: (MNHM); (MSA)
| | - Adiba Sultana
- Bioinformatics Lab (Dry), Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh
- Center for Systems Biology, Soochow University, Suzhou, China
| | - Md. Selim Reza
- Bioinformatics Lab (Dry), Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh
| | - Md Amanullah
- Bioinformatics Lab (Dry), Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh
- Department of Respiratory Medicine, Sir Run Run Shaw Hospital and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Syed Rashel Kabir
- Department of Biochemistry and Molecular Biology, Rajshahi University, Rajshahi, Bangladesh
| | - Md. Nurul Haque Mollah
- Bioinformatics Lab (Dry), Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh
- * E-mail: (MNHM); (MSA)
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18
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Mosharaf MP, Reza MS, Gov E, Mahumud RA, Mollah MNH. Disclosing Potential Key Genes, Therapeutic Targets and Agents for Non-Small Cell Lung Cancer: Evidence from Integrative Bioinformatics Analysis. Vaccines (Basel) 2022; 10:vaccines10050771. [PMID: 35632527 PMCID: PMC9143695 DOI: 10.3390/vaccines10050771] [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: 03/06/2022] [Revised: 05/07/2022] [Accepted: 05/08/2022] [Indexed: 12/10/2022] Open
Abstract
Non-small-cell lung cancer (NSCLC) is considered as one of the malignant cancers that causes premature death. The present study aimed to identify a few potential novel genes highlighting their functions, pathways, and regulators for diagnosis, prognosis, and therapies of NSCLC by using the integrated bioinformatics approaches. At first, we picked out 1943 DEGs between NSCLC and control samples by using the statistical LIMMA approach. Then we selected 11 DEGs (CDK1, EGFR, FYN, UBC, MYC, CCNB1, FOS, RHOB, CDC6, CDC20, and CHEK1) as the hub-DEGs (potential key genes) by the protein–protein interaction network analysis of DEGs. The DEGs and hub-DEGs regulatory network analysis commonly revealed four transcription factors (FOXC1, GATA2, YY1, and NFIC) and five miRNAs (miR-335-5p, miR-26b-5p, miR-92a-3p, miR-155-5p, and miR-16-5p) as the key transcriptional and post-transcriptional regulators of DEGs as well as hub-DEGs. We also disclosed the pathogenetic processes of NSCLC by investigating the biological processes, molecular function, cellular components, and KEGG pathways of DEGs. The multivariate survival probability curves based on the expression of hub-DEGs in the SurvExpress web-tool and database showed the significant differences between the low- and high-risk groups, which indicates strong prognostic power of hub-DEGs. Then, we explored top-ranked 5-hub-DEGs-guided repurposable drugs based on the Connectivity Map (CMap) database. Out of the selected drugs, we validated six FDA-approved launched drugs (Dinaciclib, Afatinib, Icotinib, Bosutinib, Dasatinib, and TWS-119) by molecular docking interaction analysis with the respective target proteins for the treatment against NSCLC. The detected therapeutic targets and repurposable drugs require further attention by experimental studies to establish them as potential biomarkers for precision medicine in NSCLC treatment.
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Affiliation(s)
- Md. Parvez Mosharaf
- Bioinformatics Lab, Department of Statistics, University of Rajshahi, Rajshahi 6205, Bangladesh; (M.P.M.); (M.S.R.)
- School of Commerce, Faculty of Business, Education, Law and Arts, University of Southern Queensland, Toowoomba, QLD 4350, Australia
| | - Md. Selim Reza
- Bioinformatics Lab, Department of Statistics, University of Rajshahi, Rajshahi 6205, Bangladesh; (M.P.M.); (M.S.R.)
- Centre for High Performance Computing, Joint Engineering Research Centre for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Esra Gov
- Department of Bioengineering, Faculty of Engineering, Adana AlparslanTurkes Science and Technology University, Adana 01250, Turkey;
| | - Rashidul Alam Mahumud
- NHMRC Clinical Trials Centre, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2006, Australia;
| | - Md. Nurul Haque Mollah
- Bioinformatics Lab, Department of Statistics, University of Rajshahi, Rajshahi 6205, Bangladesh; (M.P.M.); (M.S.R.)
- Correspondence:
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