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Priyamvada P, Ramaiah S. Potential Signature Therapeutic Biomarkers TOP2A, MAD2L1, and CDK1 in Colorectal Cancer: A Systems Biomedicine-Based Approach. Biochem Genet 2024; 62:2166-2194. [PMID: 37884851 DOI: 10.1007/s10528-023-10544-0] [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: 03/17/2023] [Accepted: 10/02/2023] [Indexed: 10/28/2023]
Abstract
Colorectal cancer is the third deadliest and fourth most diagnosed cancer. It is heterogeneously driven by varied mutations and mutagens, and thus, it is challenging for targeted therapy. The rapid advancement of high-throughput technology presents considerable opportunities for discovering new colon cancer biomarkers. In the present study, we have explored and identified the biomarkers based on molecular interactions. We curated cancer datasets that were not micro-dissected and performed gene expression analysis. The protein-protein interactions were curated, and a network was constructed for the up-regulated genes. The hub genes were analyzed using 12 different topological parameters. The correlation analysis selected TOP2A, CDK1, CCNB1, AURKA, and MAD2L1 as hub genes. Further, survival analysis was performed to determine the effectiveness of the hub gene on the patient's survival rate. Our findings explore various transcription factors such as E2F4, FOXM1, E2F6, MAX, and SIN3A, along with kinases CSNK2A1, MAPK14, CDK1, CDK4, and CDK2, as potential molecular signatures and aid researchers in understanding the pathophysiological mechanisms underlying CRC development and thus providing novel therapeutic and diagnostic recourse. Furthermore, investigating miRNAs, we focused on hsa-miR-215-5p, hsa-miR-192-5p, and hsa-miR-193b-3p due to their observed impact on a diverse set of colorectal cancer genes. Thereby, the current approach brings into light CRC- related genes at the RNA and protein levels that can potentially act as novel biomarkers opening doors to diagnostic and treatment purposes.
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Affiliation(s)
- P Priyamvada
- Medical and Biological Computing Laboratory, School of Biosciences and Technology, Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, 632014, India
- Department of Bio Sciences, School of Biosciences and Technology, Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, 632014, India
| | - Sudha Ramaiah
- Medical and Biological Computing Laboratory, School of Biosciences and Technology, Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, 632014, India.
- Department of Bio Sciences, School of Biosciences and Technology, Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, 632014, India.
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2
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Turanli B, Gulfidan G, Aydogan OO, Kula C, Selvaraj G, Arga KY. Genome-scale metabolic models in translational medicine: the current status and potential of machine learning in improving the effectiveness of the models. Mol Omics 2024; 20:234-247. [PMID: 38444371 DOI: 10.1039/d3mo00152k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2024]
Abstract
The genome-scale metabolic model (GEM) has emerged as one of the leading modeling approaches for systems-level metabolic studies and has been widely explored for a broad range of organisms and applications. Owing to the development of genome sequencing technologies and available biochemical data, it is possible to reconstruct GEMs for model and non-model microorganisms as well as for multicellular organisms such as humans and animal models. GEMs will evolve in parallel with the availability of biological data, new mathematical modeling techniques and the development of automated GEM reconstruction tools. The use of high-quality, context-specific GEMs, a subset of the original GEM in which inactive reactions are removed while maintaining metabolic functions in the extracted model, for model organisms along with machine learning (ML) techniques could increase their applications and effectiveness in translational research in the near future. Here, we briefly review the current state of GEMs, discuss the potential contributions of ML approaches for more efficient and frequent application of these models in translational research, and explore the extension of GEMs to integrative cellular models.
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Affiliation(s)
- Beste Turanli
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
- Health Biotechnology Joint Research and Application Center of Excellence, Istanbul, Turkey
| | - Gizem Gulfidan
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
| | - Ozge Onluturk Aydogan
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
| | - Ceyda Kula
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
- Health Biotechnology Joint Research and Application Center of Excellence, Istanbul, Turkey
| | - Gurudeeban Selvaraj
- Concordia University, Centre for Research in Molecular Modeling & Department of Chemistry and Biochemistry, Quebec, Canada
- Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha Dental College and Hospital, Department of Biomaterials, Bioinformatics Unit, Chennai, India
| | - Kazim Yalcin Arga
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
- Health Biotechnology Joint Research and Application Center of Excellence, Istanbul, Turkey
- Marmara University, Genetic and Metabolic Diseases Research and Investigation Center, Istanbul, Turkey
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Kunhabdulla H, Manas R, Shettihalli AK, Reddy CRM, Mustak MS, Jetti R, Abdulla R, Sirigiri DR, Ramdan D, Ammarullah MI. Identifying Biomarkers and Therapeutic Targets by Multiomic Analysis for HNSCC: Precision Medicine and Healthcare Management. ACS OMEGA 2024; 9:12602-12610. [PMID: 38524437 PMCID: PMC10956120 DOI: 10.1021/acsomega.3c07206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 01/17/2024] [Accepted: 02/05/2024] [Indexed: 03/26/2024]
Abstract
Background: Head and neck squamous cell carcinoma (HNSCC) is one of the major types of cancer, with 900,000 cases and over 400,000 deaths annually. It constitutes 3-4% of all cancers in Europe and western countries. As early diagnosis is the key to treating the disease, reliable biomarkers play an important role in the precision medicine of HNSCC. Despite treatments, the survival rate of cancer patients remains unchanged, and this is mainly due to the failure to detect the disease early. Thus, the objective of this study is to identify reliable biomarkers for head and neck cancers for better healthcare management. Methods: In this study, all available, curated human genes were screened for their expression against HNSCC TCGA patient samples using genomic and proteomic data by various bioinformatic approaches and datamining. Docking studies were performed using AutoDock or online virtual screening tools for identifying potential ligands. Results: Sixty genes were short-listed, and most of them show a consistently higher expression in head and neck patient samples at both the mRNA and the protein level. Irrespective of human papillomavirus (HPV) status, all of them show a higher expression in cancer samples. The higher expression of 30 genes shows adverse effects on patient survival. Out of the 60 genes, 12 genes have crystal structures and druggable potential. We show that genes such as GTF2H4, HAUS7, MSN, and MNDA could be targets of Pembrolizumab and Nivolumab, which are approved monoclonal antibodies for HNSCC. Conclusion: Sixty genes are identified as potential biomarkers for head and neck cancers based on their consistent and statistically significantly higher expression in patient samples. Four proteins have been identified as potential drug targets based on their crystal structure. However, the utility of these candidate genes has to be further tested using patient samples.
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Affiliation(s)
- Hafeeda Kunhabdulla
- Department
of Oral Pathology, Yenepoya Dental College, Yenepoya (Deemed to be University), Deralakatte, Mangalore 575018, India
| | - Ram Manas
- Department
of Biotechnology, B.M.S. College of Engineering, Bull Temple Road, Bengaluru 560019, India
| | - Ashok Kumar Shettihalli
- Department
of Biotechnology, B.M.S. College of Engineering, Bull Temple Road, Bengaluru 560019, India
| | - Ch. Ram Mohan Reddy
- Department
of Computer Applications (MCA), B.M.S. College
of Engineering, Bull
Temple Road, Bengaluru 560019, India
| | - Mohammed S. Mustak
- Department
of Applied Zoology, Mangalore University, Mangalagangothri 574199, Karnataka, India
| | - Raghu Jetti
- Department
of Basic Medical Sciences, College of Applied Medical Sciences, King Khalid University, Abha 61421, Saudi Arabia
| | - Riaz Abdulla
- Department
of Oral Pathology, Yenepoya Dental College, Yenepoya (Deemed to be University), Deralakatte, Mangalore 575018, India
| | | | - Deden Ramdan
- Department
of Management Science, Faculty of Social Science and Political Science, Universitas Pasundan, Bandung 40261, West Java, Indonesia
| | - Muhammad Imam Ammarullah
- Department
of Mechanics and Aerospace Engineering, College of Engineering, Southern University of Science and Technology, Shenzhen 518055, Guangdong, China
- UNDIP
Biomechanics Engineering & Research Centre (UBM-ERC), Universitas Diponegoro, Semarang 50275, Central Java, Indonesia
- Biomechanics
and Biomedics Engineering Research Centre, Universitas Pasundan, Bandung 40153, West Java, Indonesia
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Yuan J, Fu Y, Liu Y. Identification of hub genes and drug candidates for NF2-related vestibular schwannoma by bioinformatics tools. Medicine (Baltimore) 2023; 102:e36696. [PMID: 38115252 PMCID: PMC10727542 DOI: 10.1097/md.0000000000036696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 10/05/2023] [Accepted: 11/27/2023] [Indexed: 12/21/2023] Open
Abstract
Neurofibromatosis type 2 (NF2)-related vestibular schwannoma (NF2-VS) is a rare genetic disorder that results in bilateral acoustic neuromas. However, the exact pathogenesis of the disease is still unclear. This study aims to use bioinformatics analyses to identify potential hub genes and therapeutic. We retrieved the mRNA expression profiles (GSE108524 and GSE141801) of NF2-VS from the database, and selected the leading 25% genes with the most variance across samples for weighted correlation network analysis. Subsequently, we conducted gene ontology term and Kyoto Encyclopedia of Genes and Genomes signaling network enrichment analyses. The STRING database was employed for protein-protein interaction (PPI) axis construction. The mRNA-miRNA modulatory network was generated via the miRTarBase database. Differentially expressed genes (DEGs) were identified via the R package "limma" in both datasets, and hub genes were screened via intersection of common DEGs, candidate hub genes from the PPI axis, and candidate hub genes from the key module. Finally, common DEGs were uploaded onto the connectivity map database to determine drug candidates. Based on our observations, the blue module exhibited the most significant relation to NF2-VS, and it included the NF2 gene. Using enrichment analysis, we demonstrated that the blue modules were intricately linked to modulations of cell proliferation, migration, adhesion, junction, and actin skeleton. Overall, 356 common DEGs were screened in both datasets, and 33 genes carrying a degree > 15 were chosen as candidate hub genes in the PPI axis. Subsequently, 4 genes, namely, GLUL, CAV1, MYH11, and CCND1 were recognized as real hub genes. In addition, 10 drugs with enrichment scores < -0.7 were identified as drug candidates. Our conclusions offered a novel insight into the potential underlying mechanisms behind NF2-VS. These findings may facilitate the identification of novel therapeutic targets in the future.
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Affiliation(s)
- Jiasheng Yuan
- Department of Otorhinolaryngology Head and Neck Surgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China
| | - Yanpeng Fu
- Department of Otorhinolaryngology Head and Neck Surgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China
| | - Yuehui Liu
- Department of Otorhinolaryngology Head and Neck Surgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China
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Meng X, Song W, Zhou B, Liang M, Gao Y. Prognostic and immune correlation analysis of mitochondrial autophagy and aging-related genes in lung adenocarcinoma. J Cancer Res Clin Oncol 2023; 149:16311-16335. [PMID: 37698683 DOI: 10.1007/s00432-023-05390-x] [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: 07/05/2023] [Accepted: 09/01/2023] [Indexed: 09/13/2023]
Abstract
PURPOSE Mitophagy and aging (MiAg) are very important pathophysiological mechanisms contributing to tumorigenesis. MiAg-related genes have prognostic value in lung adenocarcinoma (LUAD). However, prognostic, and immune correlation studies of MiAg-related genes in LUAD are lacking. METHODS MiAg differentially expressed genes (DEGs) in LUAD were obtained from public sequencing datasets. A prognostic model including MiAg DEGs was constructed according to patients divided into low- and high-risk groups. Gene Ontology, gene set enrichment analysis, gene set variation analysis, CIBERSORT immune infiltration analysis, and clinical characteristic correlation analyses were performed for functional annotation and correlation of MiAgs with prognosis in patients with LUAD. RESULTS Seven MiAg DEGs of LUAD were identified: CAV1, DSG2, DSP, MYH11, NME1, PAICS, PLOD2, and the expression levels of these genes were significantly correlated (P < 0.05). The RiskScore of the MiAg DEG prognostic model demonstrated high predictive ability of overall survival of patients diagnosed with LUAD. Patients with high and low MiAg phenotypic scores exhibited significant differences in the infiltration levels of eight types of immune cells (P < 0.05). The multi-factor DEG regression model showed higher efficacy in predicting 5-year survival than 3- and 1-year survival of patients with LUAD. CONCLUSIONS Seven MiAg-related genes were identified to be significantly associated with the prognosis of patients diagnosed with LUAD. Moreover, the identified MiAg DEGs might affect the immunotherapy strategy of patients with LUAD.
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Affiliation(s)
- Xiangzhi Meng
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Chaoyang District, Panjiayuan, Nanli 17, Beijing, 100021, People's Republic of China
| | - Weijian Song
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Chaoyang District, Panjiayuan, Nanli 17, Beijing, 100021, People's Republic of China
| | - Boxuan Zhou
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Chaoyang District, Panjiayuan, Nanli 17, Beijing, 100021, People's Republic of China
| | - Mei Liang
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Chaoyang District, Panjiayuan, Nanli 17, Beijing, 100021, People's Republic of China
| | - Yushun Gao
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Chaoyang District, Panjiayuan, Nanli 17, Beijing, 100021, People's Republic of China.
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6
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Zheng Y, Wu J, Yan B, Yang Y, Zhong H, Yi W, Cao C, Wang Q. Identification of a two metastasis-related prognostic signature in the process of predicting the survival of laryngeal squamous cell carcinoma. Sci Rep 2023; 13:13513. [PMID: 37598251 PMCID: PMC10439939 DOI: 10.1038/s41598-023-40740-2] [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: 02/04/2023] [Accepted: 08/16/2023] [Indexed: 08/21/2023] Open
Abstract
Metastasis is a major cause of treatment failure and poor outcomes in cancer patients. The data used in the current study was downloaded from TCGA and GEO databases. Differentially expressed metastasis-related genes were identified and the biological functions were implemented. Kaplan-Meier analysis univariate, and, multivariate Cox regression analyses were performed to identify robust prognostic biomarkers, followed by construction of the risk model and nomogram. Gene set enrichment analysis was performed to identify pathways enriched in low- and high-risk groups. POLR2J3 and MYH11 were treated as prognostic biomarkers in LSCC and the risk model was constructed. Receiver operating characteristic curves revealed the good performance of the risk model. A nomogram with high accuracy was constructed, as evidenced by calibration and decision curves. Moreover, we found that the expressions of POLR2J3 and MYH11 was significantly higher in metastasis tissues compared with those in non-metastasis tissues by RT-qPCR and IHC. Our study identified novel metastasis-related prognostic biomarkers in LSCC and constructed a unique nomogram for predicting the prognosis of LSCC patients. Moreover, we explored the related mechanisms of metastasis-related genes in regulating LSCC.
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Affiliation(s)
- Yuebin Zheng
- Department of Otolaryngology Head and Neck Surgery, Zigong First People's Hospital, Zigong, Sichuan, China
| | - Jun Wu
- Department of Otolaryngology Head and Neck Surgery, Zigong First People's Hospital, Zigong, Sichuan, China
| | - Bincheng Yan
- Department of Otolaryngology Head and Neck Surgery, Zigong First People's Hospital, Zigong, Sichuan, China.
| | - Yirong Yang
- Department of Otolaryngology Head and Neck Surgery, Zigong First People's Hospital, Zigong, Sichuan, China
| | - Huacai Zhong
- Department of Otolaryngology Head and Neck Surgery, Zigong First People's Hospital, Zigong, Sichuan, China
| | - Wang Yi
- Department of Otolaryngology Head and Neck Surgery, Zigong First People's Hospital, Zigong, Sichuan, China
| | - Chengjian Cao
- Department of Otolaryngology Head and Neck Surgery, Zigong First People's Hospital, Zigong, Sichuan, China
| | - Qian Wang
- Department of Otolaryngology Head and Neck Surgery, Zigong First People's Hospital, Zigong, Sichuan, China
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Kori M, Arga KY. HPV16 status predicts potential protein biomarkers and therapeutics in head and neck squamous cell carcinoma. Virology 2023; 582:90-99. [PMID: 37031657 DOI: 10.1016/j.virol.2023.03.013] [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: 01/31/2023] [Revised: 03/22/2023] [Accepted: 03/27/2023] [Indexed: 04/11/2023]
Abstract
Human papillomavirus (HPV) infection, especially HPV16, is one of the causative factors for the development of head and neck squamous cell (HNSC) carcinoma. HPV-positive and HPV-negative HNSC patients differ significantly in their molecular profiles and clinical features, so they should be evaluated differently depending on their HPV status. Given the tremendous variation in HNSC cancers depending on HPV, our goal in this study was to present biomarkers and treatment options tailored to the patient's HPV status. Gene expression levels of HPV16-positive and -negative patients were used as proxies, and the differential interactome algorithm was employed to identify the differential interacting proteins (DIPs). By assessing the prognostic capabilities and druggabilities of DIPs and their interacting partners (DIP-centered modules), we introduce eight modules as potential biomarkers specialized for either positive or negative phenotype. Finally, raloxifene was repositioned for the first time as a drug candidate for the treatment of HPV16-positive HNSC patients.
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Affiliation(s)
- Medi Kori
- Department of Bioengineering, Marmara University, Istanbul, Turkey.
| | - Kazim Yalcin Arga
- Department of Bioengineering, Marmara University, Istanbul, Turkey; Genetic and Metabolic Diseases Research and Investigation Center, Marmara University, Istanbul, Turkey.
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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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Islam T, Rezanur Rahman M, Khan A, Ali Moni M. Integration of Mendelian randomisation and systems biology models to identify novel blood-based biomarkers for stroke. J Biomed Inform 2023; 141:104345. [PMID: 36958462 DOI: 10.1016/j.jbi.2023.104345] [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: 10/29/2022] [Revised: 02/04/2023] [Accepted: 03/16/2023] [Indexed: 03/25/2023]
Abstract
Stroke is the second largest cause of mortality in the world. Genome-wide association studies (GWAS) have identified some genetic variants associated with stroke risk, but their putative functional causal genes are unknown. Hence, we aimed to identify putative functional causal gene biomarkers of stroke risk. We used a summary-based Mendelian randomisation (SMR) approach to identify the pleiotropic associations of genetically regulated traits (i.e., gene expression and DNA methylation) with stroke risk. Using SMR approach, we integrated cis-expression quantitative loci (cis-eQTLs) and cis-methylation quantitative loci (cis-mQTLs) data with GWAS summary statistics of stroke. We also utilised heterogeneity in dependent instruments (HEIDI) test to distinguish pleiotropy from linkage from the observed associations identified through SMR analysis. Our integrative SMR analyses and HEIDI test revealed 45 candidate biomarker genes (FDR < 0.05; PHEIDI>0.01) that were pleiotropically or potentially causally associated with stroke risk. Of those candidate biomarker genes, 10 genes (HTRA1, PMF1, FBN2, C9orf84, COL4A1, BAG4, NEK6, SH2B3, SH3PXD2A, ACAD10) were differentially expressed in genome-wide blood transcriptomics data from stroke and healthy individuals (FDR<0.05). Functional enrichment analysis of the identified candidate biomarker genes revealed gene ontologies and pathways involved in stroke, including "cell aging", "metal ion binding" and "oxidative damage". Based on the evidence of genetically regulated expression of genes through SMR and directly measured expression of genes in blood, our integrative analysis suggests ten genes as blood biomarkers of stroke risk. Furthermore, our study provides a better understanding of the influence of DNA methylation on the expression of genes linked to stroke risk.
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Affiliation(s)
- Tania Islam
- School of Health and Rehabilitation Sciences, Faculty of Health, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Md Rezanur Rahman
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Asaduzzaman Khan
- School of Health and Rehabilitation Sciences, Faculty of Health, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Mohammad Ali Moni
- School of Health and Rehabilitation Sciences, Faculty of Health, The University of Queensland, Brisbane, QLD 4072, Australia.
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10
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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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11
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Jumaniyazova E, Lokhonina A, Dzhalilova D, Kosyreva A, Fatkhudinov T. Immune Cells in Head-and-Neck Tumor Microenvironments. J Pers Med 2022; 12:jpm12091521. [PMID: 36143308 PMCID: PMC9506052 DOI: 10.3390/jpm12091521] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 09/09/2022] [Accepted: 09/13/2022] [Indexed: 11/16/2022] Open
Abstract
Head-and-neck cancers constitute a heterogeneous group of aggressive tumors with high incidence and low survival rates, collectively being the sixth most prevalent cancer type globally. About 90% of head-and-neck cancers are classified as squamous cell carcinomas (HNSCC). The innate and adaptive immune systems, indispensable for anti-cancer immune surveillance, largely define the rates of HNSCC emergence and progression. HNSCC microenvironments harbor multiple cell types that infiltrate the tumors and interact both with tumor cells and among themselves. Gradually, tumor cells learn to manipulate the immune system, either by adapting their own immunogenicity or through the release of immunosuppressive molecules. These interactions continuously evolve and shape the tumor microenvironment, both structurally and functionally, facilitating angiogenesis, proliferation and metastasis. Our understanding of this evolution is directly related to success in the development of advanced therapies. This review focuses on the key mechanisms that rule HNSCC infiltration, featuring particular immune cell types and their roles in the pathogenesis. A close focus on the tumor-immunity interactions will help identify new immunotherapeutic targets in patients with HNSCC.
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Affiliation(s)
- Enar Jumaniyazova
- Department of Histology, Cytology and Embryology, Peoples’ Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya Street, 117198 Moscow, Russia
- Correspondence: ; Tel.: +7-9254258360
| | - Anastasiya Lokhonina
- Department of Histology, Cytology and Embryology, Peoples’ Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya Street, 117198 Moscow, Russia
- National Medical Research Center for Obstetrics, Gynecology and Perinatology Named after Academician V.I. Kulakov of Ministry of Healthcare of Russian Federation, 4 Oparina Street, 117997 Moscow, Russia
| | - Dzhuliia Dzhalilova
- Department of Histology, Cytology and Embryology, Peoples’ Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya Street, 117198 Moscow, Russia
- Avtsyn Research Institute of Human Morphology of Petrovsky National Research Centre of Surgery, 3 Tsyurupy Street, 117418 Moscow, Russia
| | - Anna Kosyreva
- Department of Histology, Cytology and Embryology, Peoples’ Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya Street, 117198 Moscow, Russia
- Avtsyn Research Institute of Human Morphology of Petrovsky National Research Centre of Surgery, 3 Tsyurupy Street, 117418 Moscow, Russia
| | - Timur Fatkhudinov
- Department of Histology, Cytology and Embryology, Peoples’ Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya Street, 117198 Moscow, Russia
- Avtsyn Research Institute of Human Morphology of Petrovsky National Research Centre of Surgery, 3 Tsyurupy Street, 117418 Moscow, Russia
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12
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Mosharaf MP, Kibria MK, Hossen MB, Islam MA, Reza MS, Mahumud RA, Alam K, Gow J, Mollah MNH. Meta-Data Analysis to Explore the Hub of the Hub-Genes That Influence SARS-CoV-2 Infections Highlighting Their Pathogenetic Processes and Drugs Repurposing. Vaccines (Basel) 2022; 10:vaccines10081248. [PMID: 36016137 PMCID: PMC9415433 DOI: 10.3390/vaccines10081248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 07/27/2022] [Accepted: 07/30/2022] [Indexed: 01/09/2023] Open
Abstract
The pandemic of SARS-CoV-2 infections is a severe threat to human life and the world economic condition. Although vaccination has reduced the outspread, but still the situation is not under control because of the instability of RNA sequence patterns of SARS-CoV-2, which requires effective drugs. Several studies have suggested that the SARS-CoV-2 infection causing hub differentially expressed genes (Hub-DEGs). However, we observed that there was not any common hub gene (Hub-DEGs) in our analyses. Therefore, it may be difficult to take a common treatment plan against SARS-CoV-2 infections globally. The goal of this study was to examine if more representative Hub-DEGs from published studies by means of hub of Hub-DEGs (hHub-DEGs) and associated potential candidate drugs. In this study, we reviewed 41 articles on transcriptomic data analysis of SARS-CoV-2 and found 370 unique hub genes or studied genes in total. Then, we selected 14 more representative Hub-DEGs (AKT1, APP, CXCL8, EGFR, IL6, INS, JUN, MAPK1, STAT3, TNF, TP53, UBA52, UBC, VEGFA) as hHub-DEGs by their protein-protein interaction analysis. Their associated biological functional processes, transcriptional, and post-transcriptional regulatory factors. Then we detected hHub-DEGs guided top-ranked nine candidate drug agents (Digoxin, Avermectin, Simeprevir, Nelfinavir Mesylate, Proscillaridin, Linifanib, Withaferin, Amuvatinib, Atazanavir) by molecular docking and cross-validation for treatment of SARS-CoV-2 infections. Therefore, the findings of this study could be useful in formulating a common treatment plan against SARS-CoV-2 infections globally.
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Affiliation(s)
- Md. Parvez Mosharaf
- Bioinformatics Lab, Department of Statistics, University of Rajshahi, Rajshahi 6205, Bangladesh; (M.P.M.); (M.K.K.); (M.B.H.); (M.A.I.); (M.S.R.)
- School of Business, Faculty of Business, Education, Law and Arts, University of Southern Queensland, Toowoomba, QLD 4350, Australia; (K.A.); (J.G.)
| | - Md. Kaderi Kibria
- Bioinformatics Lab, Department of Statistics, University of Rajshahi, Rajshahi 6205, Bangladesh; (M.P.M.); (M.K.K.); (M.B.H.); (M.A.I.); (M.S.R.)
| | - Md. Bayazid Hossen
- Bioinformatics Lab, Department of Statistics, University of Rajshahi, Rajshahi 6205, Bangladesh; (M.P.M.); (M.K.K.); (M.B.H.); (M.A.I.); (M.S.R.)
| | - Md. Ariful Islam
- Bioinformatics Lab, Department of Statistics, University of Rajshahi, Rajshahi 6205, Bangladesh; (M.P.M.); (M.K.K.); (M.B.H.); (M.A.I.); (M.S.R.)
| | - Md. Selim Reza
- Bioinformatics Lab, Department of Statistics, University of Rajshahi, Rajshahi 6205, Bangladesh; (M.P.M.); (M.K.K.); (M.B.H.); (M.A.I.); (M.S.R.)
| | - Rashidul Alam Mahumud
- NHMRC Clinical Trials Centre, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2006, Australia;
| | - Khorshed Alam
- School of Business, Faculty of Business, Education, Law and Arts, University of Southern Queensland, Toowoomba, QLD 4350, Australia; (K.A.); (J.G.)
| | - Jeff Gow
- School of Business, Faculty of Business, Education, Law and Arts, University of Southern Queensland, Toowoomba, QLD 4350, Australia; (K.A.); (J.G.)
- School of Accounting, Economics and Finance, University of KwaZulu Natal, Durban 4001, South Africa
| | - Md. Nurul Haque Mollah
- Bioinformatics Lab, Department of Statistics, University of Rajshahi, Rajshahi 6205, Bangladesh; (M.P.M.); (M.K.K.); (M.B.H.); (M.A.I.); (M.S.R.)
- Correspondence:
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13
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Zhou Z, Liu Q, Huang Z, Zhao Y. A Bi(OTf) 3-Promoted Hydrosulfonylation of Alkenes with Sulfonyl Hydrazides: An Approach to Branched Sulfones. Org Lett 2022; 24:4433-4437. [PMID: 35678549 DOI: 10.1021/acs.orglett.2c01657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The Bi(OTf)3-promoted hydrosulfonylation of alkenes with sulfonyl hydrazides to produce branched sulfones is reported, in which various branched sulfones (>40 examples) have been prepared in moderate to good yields. The gram-scale reaction and synthesis of the experimental inhibitor precursor showed the potential application. A preliminary mechanistic study revealed that double-bond migration to form the α,β-conjugated alkene is crucial for this transformation.
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Affiliation(s)
- Zheng Zhou
- Key Laboratory of Organic Synthesis of Jiangsu Province, College of Chemistry, Chemical Engineering and Materials Science, Soochow University, Suzhou 215123, China
| | - Qianqian Liu
- Key Laboratory of Organic Synthesis of Jiangsu Province, College of Chemistry, Chemical Engineering and Materials Science, Soochow University, Suzhou 215123, China
| | - Zhibin Huang
- Key Laboratory of Organic Synthesis of Jiangsu Province, College of Chemistry, Chemical Engineering and Materials Science, Soochow University, Suzhou 215123, China
| | - Yingsheng Zhao
- Key Laboratory of Organic Synthesis of Jiangsu Province, College of Chemistry, Chemical Engineering and Materials Science, Soochow University, Suzhou 215123, China.,School of Chemistry and Chemical Engineering, Henan Normal University, Xinxiang 453000, P. R. China
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14
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Construction and Validation of a UPR-Associated Gene Prognostic Model for Head and Neck Squamous Cell Carcinoma. BIOMED RESEARCH INTERNATIONAL 2022; 2022:8677309. [PMID: 35707371 PMCID: PMC9192238 DOI: 10.1155/2022/8677309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 05/12/2022] [Indexed: 11/27/2022]
Abstract
Our study is aimed at constructing and validating a UPR-associated gene signature to predict HNSCC prognosis. We obtained 544 samples of RNA sequencing data and clinical characteristics from TCGA database and randomly grouped the samples into training and testing cohorts (1 : 1 ratio). After identifying 14 UPR-associated genes with LASSO and univariate Cox regression analysis, HNSCC samples were categorized into low-risk (LR) and high-risk (HR) subgroups depending on the risk score. Our analyses indicated that low-risk patients had a much better prognosis in the training and testing cohorts. To predict the HNSCC prognosis with the 14 UPR-associated gene signatures, we incorporated the UPR gene risk score, N stage, M stage, and age into a nomogram model. We further explored the sensitivity to anticancer drugs by using the IC50 analysis in two subgroups from the Cancer Genome Project database. The outcomes showed that the AKT inhibitor III and sorafenib were sensitive anticancer drugs in HR and LR patients, respectively. The immune cell infiltration analysis and GSEA provided strong evidence for elucidating the molecular mechanisms of UPR-associated genes affecting HNSCC. In conclusion, the UPR-associated gene risk score, N stage, M stage, and age can serve as a robust model for predicting prognosis and can improve decision-making at the individual patient level.
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15
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Alam MS, Rahaman MM, Sultana A, Wang G, Mollah MNH. Statistics and network-based approaches to identify molecular mechanisms that drive the progression of breast cancer. Comput Biol Med 2022; 145:105508. [PMID: 35447458 DOI: 10.1016/j.compbiomed.2022.105508] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 04/05/2022] [Accepted: 04/06/2022] [Indexed: 12/13/2022]
Abstract
Breast cancer (BC) is one of the most malignant tumors and the leading cause of cancer-related death in women worldwide. So, an in-depth investigation on the molecular mechanisms of BC progression is required for diagnosis, prognosis and therapies. In this study, we identified 127 common differentially expressed genes (cDEGs) between BC and control samples by analyzing five gene expression profiles with NCBI accession numbers GSE139038, GSE62931, GSE45827, GSE42568 and GSE54002, based-on two statistical methods LIMMA and SAM. Then we constructed protein-protein interaction (PPI) network of cDEGs through the STRING database and selected top-ranked 7 cDEGs (BUB1, ASPM, TTK, CCNA2, CENPF, RFC4, and CCNB1) as a set of key genes (KGs) by cytoHubba plugin in Cytoscape. Several BC-causing crucial biological processes, molecular functions, cellular components, and pathways were significantly enriched by the estimated cDEGs including at-least one KGs. The multivariate survival analysis showed that the proposed KGs have a strong prognosis power of BC. Moreover, we detected some transcriptional and post-transcriptional regulators of KGs by their regulatory network analysis. Finally, we suggested KGs-guided three repurposable candidate-drugs (Trametinib, selumetinib, and RDEA119) for BC treatment by using the GSCALite online web tool and validated them through molecular docking analysis, and found their strong binding affinities. Therefore, the findings of this study might be useful resources for BC diagnosis, prognosis and therapies.
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Affiliation(s)
- Md Shahin Alam
- Laboratory of Molecular Neuropathology, Department of Pharmacology, Jiangsu Key Laboratory of Neuropsychiatric Diseases and College of Pharmaceutical Sciences, Soochow University, 199 Ren'ai Road, Suzhou, 215123, Jiangsu, China; Bioinformatics Lab. (Dry), Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh
| | - Md Matiur Rahaman
- Department of Statistics, Faculty of Science, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj, 8100, Bangladesh; Bioinformatics Lab. (Dry), Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh
| | - Adiba Sultana
- Center for Systems Biology, Soochow University, Suzhou, 215006, China; Bioinformatics Lab. (Dry), Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh
| | - Guanghui Wang
- Laboratory of Molecular Neuropathology, Department of Pharmacology, Jiangsu Key Laboratory of Neuropsychiatric Diseases and College of Pharmaceutical Sciences, Soochow University, 199 Ren'ai Road, Suzhou, 215123, Jiangsu, China.
| | - Md Nurul Haque Mollah
- Bioinformatics Lab. (Dry), Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh.
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16
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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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17
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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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Kori M, Cig D, Arga KY, Kasavi C. Multiomics Data Integration Identifies New Molecular Signatures for Abdominal Aortic Aneurysm and Aortic Occlusive Disease: Implications for Early Diagnosis, Prognosis, and Therapeutic Targets. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2022; 26:290-304. [PMID: 35447046 DOI: 10.1089/omi.2022.0021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Cardiovascular disease (CVD) is the leading cause of death among adults in developed countries. Among CVDs, abdominal aortic aneurysm (AAA) and aortic occlusive disease (AOD) are of great public health importance because of the high mortality rate in the elderly population. Despite significant molecular insights into AAA and AOD, the molecular mechanisms of these diseases remain unclear, and the current lack of robust diagnostic and prognostic biomarkers requires novel approaches to biomarker discovery and molecular targeting. In this study, we performed a comparative analysis of genome-wide expression data from patients with large AAA (n = 29), small AAA (n = 20), AOD (n = 9), and controls (n = 10). Specifically, we identified the differentially expressed genes and associated molecular pathways and biological processes (BPs) in each disease. Using a systems science approach, these data were linked to comprehensive human biological networks (i.e., protein-protein interaction, transcriptional regulatory, and metabolic networks) to identify molecular signatures of the salient mechanisms of AAA and AOD. Significant alterations in lipid metabolism and valine, leucine, and isoleucine metabolism, as well as neurodegenerative diseases and sex differences in the pathogenesis of AAA and AOD were identified. In the presence of aneurysm, size-dependent changes in lipid metabolism were observed. In addition, molecules and signaling pathways related to immunity, inflammation, infectious disease, and oxidative phosphorylation were identified in common. The results of the comparative and integrative analyzes revealed important clues to disease mechanisms and reporter molecules at various levels that warrant future development as potential prognostic biomarkers and putative therapeutic targets.
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Affiliation(s)
- Medi Kori
- Department of Bioengineering, Faculty of Engineering, Marmara University, Istanbul, Turkey
| | - Defne Cig
- Department of Bioengineering, Faculty of Engineering, Marmara University, Istanbul, Turkey
| | - Kazim Yalcin Arga
- Department of Bioengineering, Faculty of Engineering, Marmara University, Istanbul, Turkey
- Genetic and Metabolic Diseases Research and Investigation Center (GEMHAM), Marmara University, Istanbul, Turkey
| | - Ceyda Kasavi
- Department of Bioengineering, Faculty of Engineering, Marmara University, Istanbul, Turkey
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Ahmed FF, Reza MS, Sarker MS, Islam MS, Mosharaf MP, Hasan S, Mollah MNH. Identification of host transcriptome-guided repurposable drugs for SARS-CoV-1 infections and their validation with SARS-CoV-2 infections by using the integrated bioinformatics approaches. PLoS One 2022; 17:e0266124. [PMID: 35390032 PMCID: PMC8989220 DOI: 10.1371/journal.pone.0266124] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Accepted: 03/15/2022] [Indexed: 12/18/2022] Open
Abstract
Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is one of the most severe global pandemic due to its high pathogenicity and death rate starting from the end of 2019. Though there are some vaccines available against SAER-CoV-2 infections, we are worried about their effectiveness, due to its unstable sequence patterns. Therefore, beside vaccines, globally effective supporting drugs are also required for the treatment against SARS-CoV-2 infection. To explore commonly effective repurposable drugs for the treatment against different variants of coronavirus infections, in this article, an attempt was made to explore host genomic biomarkers guided repurposable drugs for SARS-CoV-1 infections and their validation with SARS-CoV-2 infections by using the integrated bioinformatics approaches. At first, we identified 138 differentially expressed genes (DEGs) between SARS-CoV-1 infected and control samples by analyzing high throughput gene-expression profiles to select drug target key receptors. Then we identified top-ranked 11 key DEGs (SMAD4, GSK3B, SIRT1, ATM, RIPK1, PRKACB, MED17, CCT2, BIRC3, ETS1 and TXN) as hub genes (HubGs) by protein-protein interaction (PPI) network analysis of DEGs highlighting their functions, pathways, regulators and linkage with other disease risks that may influence SARS-CoV-1 infections. The DEGs-set enrichment analysis significantly detected some crucial biological processes (immune response, regulation of angiogenesis, apoptotic process, cytokine production and programmed cell death, response to hypoxia and oxidative stress), molecular functions (transcription factor binding and oxidoreductase activity) and pathways (transcriptional mis-regulation in cancer, pathways in cancer, chemokine signaling pathway) that are associated with SARS-CoV-1 infections as well as SARS-CoV-2 infections by involving HubGs. The gene regulatory network (GRN) analysis detected some transcription factors (FOXC1, GATA2, YY1, FOXL1, TP53 and SRF) and micro-RNAs (hsa-mir-92a-3p, hsa-mir-155-5p, hsa-mir-106b-5p, hsa-mir-34a-5p and hsa-mir-19b-3p) as the key transcriptional and post- transcriptional regulators of HubGs, respectively. We also detected some chemicals (Valproic Acid, Cyclosporine, Copper Sulfate and arsenic trioxide) that may regulates HubGs. The disease-HubGs interaction analysis showed that our predicted HubGs are also associated with several other diseases including different types of lung diseases. Then we considered 11 HubGs mediated proteins and their regulatory 6 key TFs proteins as the drug target proteins (receptors) and performed their docking analysis with the SARS-CoV-2 3CL protease-guided top listed 90 anti-viral drugs out of 3410. We found Rapamycin, Tacrolimus, Torin-2, Radotinib, Danoprevir, Ivermectin and Daclatasvir as the top-ranked 7 candidate-drugs with respect to our proposed target proteins for the treatment against SARS-CoV-1 infections. Then, we validated these 7 candidate-drugs against the already published top-ranked 11 target proteins associated with SARS-CoV-2 infections by molecular docking simulation and found their significant binding affinity scores with our proposed candidate-drugs. Finally, we validated all of our findings by the literature review. Therefore, the proposed candidate-drugs might play a vital role for the treatment against different variants of SARS-CoV-2 infections with comorbidities, since the proposed HubGs are also associated with several comorbidities.
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Affiliation(s)
- Fee Faysal Ahmed
- Department of Mathematics, Jashore University of Science and Technology, Jashore, Bangladesh
- Bioinformatics Lab., Department of Statistics, Rajshahi University, Rajshahi, Bangladesh
| | - Md. Selim Reza
- Bioinformatics Lab., Department of Statistics, Rajshahi University, Rajshahi, Bangladesh
| | - Md. Shahin Sarker
- Department of Pharmacy, Jashore University of Science and Technology, Jashore, Bangladesh
| | - Md. Samiul Islam
- Department of Plant Pathology, Huazhong Agricultural University, Wuhan, Hubei Province, China
| | - Md. Parvez Mosharaf
- Bioinformatics Lab., Department of Statistics, Rajshahi University, Rajshahi, Bangladesh
| | - Sohel Hasan
- Department of Biochemistry and Molecular Biology, Rajshahi University, Rajshhi, Bangladesh
| | - Md. Nurul Haque Mollah
- Bioinformatics Lab., Department of Statistics, Rajshahi University, Rajshahi, Bangladesh
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20
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Computational identification of host genomic biomarkers highlighting their functions, pathways and regulators that influence SARS-CoV-2 infections and drug repurposing. Sci Rep 2022; 12:4279. [PMID: 35277538 PMCID: PMC8915158 DOI: 10.1038/s41598-022-08073-8] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2021] [Accepted: 02/15/2022] [Indexed: 12/13/2022] Open
Abstract
The pandemic threat of COVID-19 has severely destroyed human life as well as the economy around the world. Although, the vaccination has reduced the outspread, but people are still suffering due to the unstable RNA sequence patterns of SARS-CoV-2 which demands supplementary drugs. To explore novel drug target proteins, in this study, a transcriptomics RNA-Seq data generated from SARS-CoV-2 infection and control samples were analyzed. We identified 109 differentially expressed genes (DEGs) that were utilized to identify 10 hub-genes/proteins (TLR2, USP53, GUCY1A2, SNRPD2, NEDD9, IGF2, CXCL2, KLF6, PAG1 and ZFP36) by the protein–protein interaction (PPI) network analysis. The GO functional and KEGG pathway enrichment analyses of hub-DEGs revealed some important functions and signaling pathways that are significantly associated with SARS-CoV-2 infections. The interaction network analysis identified 5 TFs proteins and 6 miRNAs as the key regulators of hub-DEGs. Considering 10 hub-proteins and 5 key TFs-proteins as drug target receptors, we performed their docking analysis with the SARS-CoV-2 3CL protease-guided top listed 90 FDA approved drugs. We found Torin-2, Rapamycin, Radotinib, Ivermectin, Thiostrepton, Tacrolimus and Daclatasvir as the top ranked seven candidate drugs. We investigated their resistance performance against the already published COVID-19 causing top-ranked 11 independent and 8 protonated receptor proteins by molecular docking analysis and found their strong binding affinities, which indicates that the proposed drugs are effective against the state-of-the-arts alternatives independent receptor proteins also. Finally, we investigated the stability of top three drugs (Torin-2, Rapamycin and Radotinib) by using 100 ns MD-based MM-PBSA simulations with the two top-ranked proposed receptors (TLR2, USP53) and independent receptors (IRF7, STAT1), and observed their stable performance. Therefore, the proposed drugs might play a vital role for the treatment against different variants of SARS-CoV-2 infections.
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21
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Wan Z, Xiong H, Tan X, Su T, Xia K, Wang D. Integrative Multi-Omics Analysis Reveals Candidate Biomarkers for Oral Squamous Cell Carcinoma. Front Oncol 2022; 11:794146. [PMID: 35096593 PMCID: PMC8795899 DOI: 10.3389/fonc.2021.794146] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 12/17/2021] [Indexed: 01/10/2023] Open
Abstract
Oral squamous cell carcinoma (OSCC) is one of the most common types of cancer worldwide. Due to the lack of early detection and treatment, the survival rate of OSCC remains poor and the incidence of OSCC has not decreased during the past decades. To explore potential biomarkers and therapeutic targets for OSCC, we analyzed differentially expressed genes (DEGs) associated with OSCC using RNA sequencing technology. Methylation-regulated and differentially expressed genes (MeDEGs) of OSCC were further identified via an integrative approach by examining publicly available methylomic datasets together with our transcriptomic data. Protein-protein interaction (PPI) networks of MeDEGs were constructed and highly connected hub MeDEGs were identified from these PPI networks. Subsequently, expression and survival analyses of hub genes were performed using The Cancer Genome Atlas (TCGA) database and the Gene Expression Profiling Interactive Analysis (GEPIA) online tool. A total of 56 upregulated MeDEGs and 170 downregulated MeDEGs were identified in OSCC. Eleven hub genes with high degree of connectivity were picked out from the PPI networks constructed by those MeDEGs. Among them, the expression level of four hub genes (CTLA4, CDSN, ACTN2, and MYH11) were found to be significantly changed in the head and neck squamous carcinoma (HNSC) patients. Three hypomethylated hub genes (CTLA4, GPR29, and TNFSF11) and one hypermethylated hub gene (ISL1) were found to be significantly associated with overall survival (OS) of HNSC patients. Therefore, these hub genes may serve as potential DNA methylation biomarkers and therapeutic targets of OSCC.
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Affiliation(s)
- Zhengqing Wan
- Hengyang Medical School, University of South China, Hengyang, China.,The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, China.,Postdoctoral Station for Basic Medicine, Hengyang Medical School, University of South China, Hengyang, China
| | - Haofeng Xiong
- Xiangya Hospital, Central South University, Changsha, China.,Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, China
| | - Xian Tan
- Hengyang Medical School, University of South China, Hengyang, China
| | - Tong Su
- Xiangya Hospital, Central South University, Changsha, China
| | - Kun Xia
- Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, China
| | - Danling Wang
- Hengyang Medical School, University of South China, Hengyang, China.,The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, China
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22
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Korfiati A, Grafanaki K, Kyriakopoulos GC, Skeparnias I, Georgiou S, Sakellaropoulos G, Stathopoulos C. Revisiting miRNA Association with Melanoma Recurrence and Metastasis from a Machine Learning Point of View. Int J Mol Sci 2022; 23:1299. [PMID: 35163222 PMCID: PMC8836065 DOI: 10.3390/ijms23031299] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 01/20/2022] [Accepted: 01/20/2022] [Indexed: 02/07/2023] Open
Abstract
The diagnostic and prognostic value of miRNAs in cutaneous melanoma (CM) has been broadly studied and supported by advanced bioinformatics tools. From early studies using miRNA arrays with several limitations, to the recent NGS-derived miRNA expression profiles, an accurate diagnostic panel of a comprehensive pre-specified set of miRNAs that could aid timely identification of specific cancer stages is still elusive, mainly because of the heterogeneity of the approaches and the samples. Herein, we summarize the existing studies that report several miRNAs as important diagnostic and prognostic biomarkers in CM. Using publicly available NGS data, we analyzed the correlation of specific miRNA expression profiles with the expression signatures of known gene targets. Combining network analytics with machine learning, we developed specific non-linear classification models that could successfully predict CM recurrence and metastasis, based on two newly identified miRNA signatures. Subsequent unbiased analyses and independent test sets (i.e., a dataset not used for training, as a validation cohort) using our prediction models resulted in 73.85% and 82.09% accuracy in predicting CM recurrence and metastasis, respectively. Overall, our approach combines detailed analysis of miRNA profiles with heuristic optimization and machine learning, which facilitates dimensionality reduction and optimization of the prediction models. Our approach provides an improved prediction strategy that could serve as an auxiliary tool towards precision treatment.
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Affiliation(s)
- Aigli Korfiati
- Department of Medical Physics, School of Medicine, University of Patras, 26504 Patras, Greece; (A.K.); (G.S.)
| | - Katerina Grafanaki
- Department of Dermatology, School of Medicine, University of Patras, 26504 Patras, Greece;
| | | | - Ilias Skeparnias
- Laboratory of Molecular Biology, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, USA;
| | - Sophia Georgiou
- Department of Dermatology, School of Medicine, University of Patras, 26504 Patras, Greece;
| | - George Sakellaropoulos
- Department of Medical Physics, School of Medicine, University of Patras, 26504 Patras, Greece; (A.K.); (G.S.)
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23
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Zheng H, Liu H, Lu Y, Li H. Identification of a Novel Signature Predicting Overall Survival in Head and Neck Squamous Cell Carcinoma. Front Surg 2021; 8:717084. [PMID: 34631779 PMCID: PMC8498039 DOI: 10.3389/fsurg.2021.717084] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Accepted: 08/27/2021] [Indexed: 12/21/2022] Open
Abstract
Background: Head and neck squamous cell carcinoma (HNSCC) is a highly heterogeneous tumor with a high incidence and poor prognosis. Therefore, effective predictive models are needed to evaluate patient outcomes and optimize treatment. Methods: Robust Rank Aggregation (RRA) method was used to identify highly robust differentially-expressed genes (DEGs) between HNSCC and normal tissue in 9 GEO and TCGA datasets. Univariate Cox regression analysis and Lasso Cox regression analysis were performed to identify DEGs related to the Overall survival (OS) and to construct a prognostic gene signature (HNSCCSig). External validation was performed using GSE65858 dataset. Moreover, comprehensive bioinformatics analyses were used to identify the association between HNSCCSig and tumor immune environment. Results: A total of 257 reliable DEGs were identified by differentially analysis result of TCGA and GSE65858 datasets. The HNSCCSig including 7 mRNAs (SLURP1, SCARA5, CLDN10, MYH11, CXCL13, HLF, and ITGA3) were developed and validated to identify high-risk group who had a worse OS than low-risk group in TCGA and GSE65858 datasets. Cox regression analysis showed that the HNSCCSig could independently predict OS in both the TCGA and the GSE65858 datasets. Further research demonstrated that the infiltration bundance of CD8 + T cells, B cells, neutrophils, and NK cells were significantly lower in the high-risk group. A nomogram was also constructed by combining the HNSCCSig and clinical characters. Conclusion: We established and validated the HNSCCSig consisting of SLURP1, SCARA5, CLDN10, MYH11, CXCL13, HLF, and ITGA3. A nomogram combining HNSCCSig and some clinical parameters was constructed to identify high-risk HNSCC-patients with poor prognosis.
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Affiliation(s)
- Haige Zheng
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Huixian Liu
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Yumin Lu
- The First Affiliated Hospital of Guangxi University of Chinese Medicine, Nanning, China
| | - Hengguo Li
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, China
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24
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Vitorino R, Choudhury M, Guedes S, Ferreira R, Thongboonkerd V, Sharma L, Amado F, Srivastava S. Peptidomics and proteogenomics: background, challenges and future needs. Expert Rev Proteomics 2021; 18:643-659. [PMID: 34517741 DOI: 10.1080/14789450.2021.1980388] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
INTRODUCTION With available genomic data and related information, it is becoming possible to better highlight mutations or genomic alterations associated with a particular disease or disorder. The advent of high-throughput sequencing technologies has greatly advanced diagnostics, prognostics, and drug development. AREAS COVERED Peptidomics and proteogenomics are the two post-genomic technologies that enable the simultaneous study of peptides and proteins/transcripts/genes. Both technologies add a remarkably large amount of data to the pool of information on various peptides associated with gene mutations or genome remodeling. Literature search was performed in the PubMed database and is up to date. EXPERT OPINION This article lists various techniques used for peptidomic and proteogenomic analyses. It also explains various bioinformatics workflows developed to understand differentially expressed peptides/proteins and their role in disease pathogenesis. Their role in deciphering disease pathways, cancer research, and biomarker discovery using biofluids is highlighted. Finally, the challenges and future requirements to overcome the current limitations for their effective clinical use are also discussed.
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Affiliation(s)
- Rui Vitorino
- Faculdade de Medicina da Universidade do Porto, Porto, Portugal.,iBiMED, Department of Medical Sciences, University of Aveiro, Aveiro, Portugal.,Laqv/requimte, Department of Chemistry, University of Aveiro, Aveiro, Portugal
| | - Manisha Choudhury
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai, Powai, India
| | - Sofia Guedes
- Laqv/requimte, Department of Chemistry, University of Aveiro, Aveiro, Portugal
| | - Rita Ferreira
- Laqv/requimte, Department of Chemistry, University of Aveiro, Aveiro, Portugal
| | - Visith Thongboonkerd
- Medical Proteomics Unit, Office for Research and Development, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | | | - Francisco Amado
- Laqv/requimte, Department of Chemistry, University of Aveiro, Aveiro, Portugal
| | - Sanjeeva Srivastava
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai, Powai, India
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25
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Wang J, Xu P, Hao Y, Yu T, Liu L, Song Y, Li Y. Interaction between DNMT3B and MYH11 via hypermethylation regulates gastric cancer progression. BMC Cancer 2021; 21:914. [PMID: 34380460 PMCID: PMC8359574 DOI: 10.1186/s12885-021-08653-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 08/05/2021] [Indexed: 01/17/2023] Open
Abstract
BACKGROUND Gastric cancer (GC) has an unwelcoming prognosis when diagnosed at an advanced stage. The purpose of this study was to examine the expression of myosin heavy chain 11 (MYH11) in GC and mechanisms related. METHODS The MYH11 expression in GC was investigated via the SangerBox platform. MYH11 expression in GC tissues and cell lines was examined by immunohistochemistry, RT-qPCR, and western blot. The relationship between MYH11 expression and patients' prognosis was analyzed. The effects of MYH11 on the biological behaviors of GC cells were investigated by gain-of-function experiments. Bioinformatics analysis was used to find genes with relevance to MYH11 expression in GC. The relationship was verified by luciferase and ChIP-qPCR assays, followed by rescue assay validation. The causes of MYH11 downregulation in GC were verified by quantitative methylation-specific PCR. Finally, the effect of MYH11 on tumor growth was examined. RESULTS MYH11 was downregulated in GC and predicted poor prognoses. MYH11 reverted the malignant phenotype of GC cells. MYH11 repressed the TNFRSF14 expression by binding to the TNFRSF14 promoter. TNFRSF14 reversed the inhibitory effect of MYH11 on the malignant phenotype of GC cells. The methylation of the MYH11 promoter was elevated in GC, which was correlated with the elevated DNMT3B in GC. Overexpression of DNMT3B repressed transcription of MYH11 by promoting its methylation. Also, MYH11 upregulation inhibited tumor growth. CONCLUSION DNMT3B inhibits MYH11 expression by promoting its DNA methylation, thereby attenuating the repressive effect of MYH11 on the transcriptional of TNFRSF14 and promoting the progression of GC.
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Affiliation(s)
- Jianhua Wang
- Department of Gastroenterology, The Yancheng Clinical College of Xuzhou Medical University, The First People's Hospital of Yancheng, Yancheng, 224001, Jiangsu, People's Republic of China
| | - Ping Xu
- Department of Gastroenterology, The Yancheng Clinical College of Xuzhou Medical University, The First People's Hospital of Yancheng, Yancheng, 224001, Jiangsu, People's Republic of China
| | - Yanping Hao
- Department of Gastroenterology, The Yancheng Clinical College of Xuzhou Medical University, The First People's Hospital of Yancheng, Yancheng, 224001, Jiangsu, People's Republic of China
| | - Tingting Yu
- Department of Gastroenterology, The Yancheng Clinical College of Xuzhou Medical University, The First People's Hospital of Yancheng, Yancheng, 224001, Jiangsu, People's Republic of China
| | - Limin Liu
- Department of Gastroenterology, The Yancheng Clinical College of Xuzhou Medical University, The First People's Hospital of Yancheng, Yancheng, 224001, Jiangsu, People's Republic of China
| | - Yan Song
- Department of Gastroenterology, The Yancheng Clinical College of Xuzhou Medical University, The First People's Hospital of Yancheng, Yancheng, 224001, Jiangsu, People's Republic of China
| | - Yan Li
- Department of Obstetrics and Gynecology, The Yancheng Clinical College of Xuzhou Medical University, The First People's Hospital of Yancheng, No. 66, Renmin South Road, Yancheng, 224001, Jiangsu, People's Republic of China.
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26
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MicroRNA-1252-5p, regulated by Myb, inhibits invasion and epithelial-mesenchymal transition of pancreatic cancer cells by targeting NEDD9. Aging (Albany NY) 2021; 13:18924-18945. [PMID: 34314382 PMCID: PMC8351675 DOI: 10.18632/aging.203344] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 05/23/2021] [Indexed: 01/14/2023]
Abstract
MicroRNAs (miRNAs) are known to be involved in the development and progression of pancreatic cancer (PAC). The expression levels and roles of miR-1252-5p in PAC remain unclear. Quantitative real-time PCR and in situ hybridization were used to detect miR-1252-5p expression in PAC cells and human tissues. We studied the gain and loss of function of miR-1252-5p in the PAC cell lines in vitro and in vivo. The direct targets of miR-1252-5p were analyzed using public databases and a dual-luciferase reporter assay. Expression levels of miR-1252-5p are downregulated in PAC cell lines and tissue samples, and its expression is negatively associated with adverse clinical features and poor prognosis. In vitro and in vivo experiments show that miR-1252-5p overexpression inhibits the proliferation, migration, invasion, and epithelial-mesenchymal transition of PAC cells, and miR-1252-5p knockdown enhances these biological behaviors. MiR-1252-5p negatively regulates neural precursor cell expressed, developmentally downregulated 9 (NEDD9) by directly binding its 3'- untranslated region. Further mechanism research revealed that the SRC/STAT3 pathway is involved in miR-1252-5p/NEDD9 mediation of PAC's biological behaviors. We also verified that Myb inhibited miR-1252-5p by directly binding at its promoter. MiR-1252-5p may act as a tumor-suppressing miRNA in PAC and may be a potential therapeutic target for PAC patients.
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27
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Comertpay B, Gulfidan G, Arga KY, Gov E. Cancer Stem Cell Transcriptome Profiling Reveals Seed Genes of Tumorigenesis: New Avenues for Cancer Precision Medicine. OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY 2021; 25:372-388. [PMID: 34037481 DOI: 10.1089/omi.2021.0021] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Cancer stem-like cells (CSCs) possess the ability to self-renew and differentiate, and they are among the major factors driving tumorigenesis, metastasis, and resistance to chemotherapy. Therefore, it is critical to understand the molecular substrates of CSC biology so as to discover novel molecular biosignatures that distinguish CSCs and tumor cells. Here, we report new findings and insights by employing four transcriptome datasets associated with CSCs, with CSC and tumor samples from breast, lung, oral, and ovarian tissues. The CSC samples were analyzed to identify differentially expressed genes between CSC and tumor phenotypes. Through comparative profiling of expression levels in different cancer types, we identified 17 "seed genes" that showed a mutual differential expression pattern. We showed that these seed genes were strongly associated with cancer-associated signaling pathways and biological processes, the immune system, and the key cancer hallmarks. Further, the seed genes presented significant changes in their expression profiles in different cancer types and diverse mutation rates, and they also demonstrated high potential as diagnostic and prognostic biomarkers in various cancers. We report a number of seed genes that represent significant potential as "systems biomarkers" for understanding the pathobiology of tumorigenesis. Seed genes offer a new innovation avenue for potential applications toward cancer precision medicine in a broad range of cancers in oncology in the future.
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Affiliation(s)
- Betul Comertpay
- Department of Bioengineering, Faculty of Engineering, Adana Alparslan Türkeş Science and Technology University, Adana, Turkey
| | - Gizem Gulfidan
- Department of Bioengineering, Marmara University, Istanbul, Turkey
| | | | - Esra Gov
- Department of Bioengineering, Faculty of Engineering, Adana Alparslan Türkeş Science and Technology University, Adana, Turkey
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28
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Erceylan ÖF, Savaş A, Göv E. Targeting the tumor stroma: integrative analysis reveal GATA2 and TORYAIP1 as novel prognostic targets in breast and ovarian cancer. Turk J Biol 2021; 45:127-137. [PMID: 33907495 PMCID: PMC8068767 DOI: 10.3906/biy-2010-39] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 03/24/2021] [Indexed: 12/19/2022] Open
Abstract
Tumor stroma interaction is known to take a crucial role in cancer growth and progression. In the present study, it was performed gene expression analysis of stroma samples with ovarian and breast cancer through an integrative analysis framework to identify common critical biomolecules at multiomics levels. Gene expression datasets were statistically analyzed to identify common differentially expressed genes (DEGs) by comparing tumor stroma and normal stroma samples. The integrative analyses of DEGs indicated that there were 59 common core genes, which might be feasible to be potential marks for cancer stroma targeted strategies. Reporter molecules (i.e. receptor, transcription factors and miRNAs) were determined through a statistical test employing the hypergeometric probability density function. Afterward, the tumor microenvironment protein-protein interaction and the generic network were reconstructed by using identified reporter molecules and common core DEGs. Through a systems medicine approach, it was determined that hub biomolecules, AR, GATA2, miR-124, TOR1AIP1, ESR1, EGFR, STAT1, miR-192, GATA3, COL1A1, in tumor microenvironment generic network. These molecules were also identified as prognostic signatures in breast and ovarian tumor samples via survival analysis. According to literature searching, GATA2 and TORYAIP1 might represent potential biomarkers and candidate drug targets for the stroma targeted cancer therapy applications.
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Affiliation(s)
- Ömer Faruk Erceylan
- Department of Bioengineering, Faculty of Engineering, Adana Alparslan Türkeş Science and Technology University, Adana Turkey
| | - Ayşe Savaş
- Department of Bioengineering, Faculty of Engineering, Adana Alparslan Türkeş Science and Technology University, Adana Turkey
| | - Esra Göv
- Department of Bioengineering, Faculty of Engineering, Adana Alparslan Türkeş Science and Technology University, Adana Turkey
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29
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Qiang W, Dai Y, Xing X, Sun X. Identification and validation of a prognostic signature and combination drug therapy for immunotherapy of head and neck squamous cell carcinoma. Comput Struct Biotechnol J 2021; 19:1263-1276. [PMID: 33717423 PMCID: PMC7921014 DOI: 10.1016/j.csbj.2021.01.046] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2020] [Revised: 01/27/2021] [Accepted: 01/30/2021] [Indexed: 02/07/2023] Open
Abstract
Immunotherapy has become a promising therapeutic option for Head and neck squamous cell carcinoma (HNSC). However, only a small percentage of patients could benefit from it, and the overall prognosis was far from satisfactory. In this study, by comprehensively computational analyses of hundreds of HNSC samples, a prognostic signature composed of 13 immune-related genes (IRGs) was constructed. The results of the analyses in multiple datasets indicated that our signature had high predictive accuracy and could serve as an independent prognostic predictor. Based on this signature and multiple clinical variables, we also established a prognostic nomogram to quantitatively predict the survival risk of individual patients. Moreover, this signature could accurately predict survival, reflect the immune microenvironment, and predict immunotherapy efficacy among HNSC patients. Two potential drugs (doxorubicin and daunorubicin) were also identified via Connectivity Map and molecular docking, which could be used for HNSC combination therapy. Taken together, we developed and validated a robust IRG-based prognostic signature to monitor the prognosis of HNSC, which could provide a solid foundation for individualized cancer immunotherapy.
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Affiliation(s)
- Weijie Qiang
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100193, PR China.,Key Laboratory of New Drug Discovery based on Classic Chinese Medicine Prescription, Chinese Academy of Medical Sciences, Beijing 100193, PR China
| | - Yifei Dai
- School of Medicine, Tsinghua University, Beijing 100084, PR China
| | - Xiaoyan Xing
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100193, PR China.,Key Laboratory of New Drug Discovery based on Classic Chinese Medicine Prescription, Chinese Academy of Medical Sciences, Beijing 100193, PR China
| | - Xiaobo Sun
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100193, PR China.,Key Laboratory of New Drug Discovery based on Classic Chinese Medicine Prescription, Chinese Academy of Medical Sciences, Beijing 100193, PR China
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30
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Hao M, Liu W, Ding C, Peng X, Zhang Y, Chen H, Dong L, Liu X, Zhao Y, Chen X, Khatoon S, Zheng Y. Identification of hub genes and small molecule therapeutic drugs related to breast cancer with comprehensive bioinformatics analysis. PeerJ 2020; 8:e9946. [PMID: 33083112 PMCID: PMC7556247 DOI: 10.7717/peerj.9946] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 08/25/2020] [Indexed: 12/21/2022] Open
Abstract
Breast cancer is one of the most common malignant tumors among women worldwide and has a high morbidity and mortality. This research aimed to identify hub genes and small molecule drugs for breast cancer by integrated bioinformatics analysis. After downloading multiple gene expression datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database, 283 overlapping differentially expressed genes (DEGs) significantly enriched in different cancer-related functions and pathways were obtained using LIMMA, VennDiagram and ClusterProfiler packages of R. We then analyzed the topology of protein–protein interaction (PPI) network with overlapping DEGs and further obtained six hub genes (RRM2, CDC20, CCNB2, BUB1B, CDK1, and CCNA2) from the network via STRING and Cytoscape. Subsequently, we conducted genes expression verification, genetic alterations evaluation, immune infiltration prediction, clinicopathological parameters analysis, identification of transcriptional and post-transcriptional regulatory molecules, and survival analysis for these hub genes. Meanwhile, 29 possible drug candidates (e.g., Cladribine, Gallium nitrate, Alvocidib, 1β-hydroxyalantolactone, Berberine hydrochloride, Nitidine chloride) were identified from the DGIdb database and the GSE85871 dataset. In addition, some transcription factors and miRNAs (e.g., E2F1, PTTG1, TP53, ZBTB16, hsa-miR-130a-3p, hsa-miR-204-5p) targeting hub genes were identified as key regulators in the progression of breast cancer. In conclusion, our study identified six hub genes and 29 potential drug candidates for breast cancer. These findings may advance understanding regarding the diagnosis, prognosis and treatment of breast cancer.
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Affiliation(s)
- Mingqian Hao
- School of Chinese Medicinal Materials, Jilin Agricultural University, Changchun, Jilin, China
| | - Wencong Liu
- School of Chinese Medicinal Materials, Jilin Agricultural University, Changchun, Jilin, China
| | - Chuanbo Ding
- School of Chinese Medicinal Materials, Jilin Agricultural University, Changchun, Jilin, China
| | - Xiaojuan Peng
- School of Chinese Medicinal Materials, Jilin Agricultural University, Changchun, Jilin, China
| | - Yue Zhang
- School of Chinese Medicinal Materials, Jilin Agricultural University, Changchun, Jilin, China
| | - Huiying Chen
- School of Chinese Medicinal Materials, Jilin Agricultural University, Changchun, Jilin, China
| | - Ling Dong
- School of Chinese Medicinal Materials, Jilin Agricultural University, Changchun, Jilin, China
| | - Xinglong Liu
- School of Chinese Medicinal Materials, Jilin Agricultural University, Changchun, Jilin, China
| | - Yingchun Zhao
- School of Chinese Medicinal Materials, Jilin Agricultural University, Changchun, Jilin, China
| | - Xueyan Chen
- School of Chinese Medicinal Materials, Jilin Agricultural University, Changchun, Jilin, China
| | - Sadia Khatoon
- School of Chinese Medicinal Materials, Jilin Agricultural University, Changchun, Jilin, China
| | - Yinan Zheng
- School of Chinese Medicinal Materials, Jilin Agricultural University, Changchun, Jilin, China
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31
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Amiri-Dashatan N, Koushki M, Jalilian A, Ahmadi NA, Rezaei-Tavirani M. Integrated Bioinformatics Analysis of mRNAs and miRNAs Identified Potential Biomarkers of Oral Squamous Cell Carcinoma. Asian Pac J Cancer Prev 2020; 21:1841-1848. [PMID: 32597160 PMCID: PMC7568896 DOI: 10.31557/apjcp.2020.21.6.1841] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Accepted: 06/28/2020] [Indexed: 12/21/2022] Open
Abstract
Background: Oral cancer is a frequently encountered neoplasm of the head and neck region, being the eighth most common type of human malignancy worldwide. Despite improvement in its control, morbidity and mortality, rates have improved little in the past decades. The present investigations about gene interaction and pathways still could not clear the appearance and development of oral squamous cell carcinoma (OSCC), completely. The aim of this study is to investigate the key genes and microRNAs interaction in OSCC. Materials and Methods: The microarray datasets GSE13601 and GSE98463, including mRNA and miRNA profiles, were extracted from the GEO database and were analyzed using GEO2R. Functional and pathway enrichment analyses were performed by using the DAVID database. The protein-protein interaction (PPI) network was constructed and analyzed using STRING database and Cytoscape software, respectively. Finally, miRDB was applied to predict the targets of the differentially expressed miRNAs (DEMs). Results: Totally, 97 differentially expressed genes (DEGs) were found in OSCC, including 66 up-regulated and 31 down-regulated genes. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses showed that up-regulated genes were significantly enriched in movement of cell or subcellular component, cell adhesion, biological adhesion, cellular localization, apoptotic signaling pathway, while the downregulated genes were enriched in muscle system process and oxidation-reduction process. From the PPI network, the top 10 nodes with the highest degree were detected as hub genes. In addition, 18 DEMs were screened, which included 7 up-regulated and 11 down-regulated miRNAs. STAT1 was potentially targeted by three miRNAs, including has-miR- 6825-5P, has-miR-4495, and has-miR-5580-3P. Conclusion: The roles of DEMs such as hsa-mir-5580-3p in OSCC through interactions with DEGs CD44, ACLY, ACTR3, STAT1, LAMC2 and YWHAZ may offer a suitable candidate biomarker pattern for diagnosis, prognosis and treatment processes in OSCC.
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Affiliation(s)
- Nasrin Amiri-Dashatan
- Proteomics Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Mehdi Koushki
- Department of Clinical Biochemistry, Faculty of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
| | - Ali Jalilian
- Department of Clinical Biochemistry, Faculty of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
| | - Nayeb Ali Ahmadi
- Proteomics Research Center, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran.
| | - Mostafa Rezaei-Tavirani
- Proteomics Research Center, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran.
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Novel molecular signatures and potential therapeutics in renal cell carcinomas: Insights from a comparative analysis of subtypes. Genomics 2020; 112:3166-3178. [PMID: 32512143 DOI: 10.1016/j.ygeno.2020.06.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 05/20/2020] [Accepted: 06/02/2020] [Indexed: 01/05/2023]
Abstract
Renal cell carcinomas (RCCs) are among the highest causes of cancer mortality. Although transcriptome profiling studies in the last decade have made significant molecular findings on RCCs, effective diagnosis and treatment strategies have yet to be achieved due to lack of adequate screening and comparative profiling of RCC subtypes. In this study, a comparative analysis was performed on RNA-seq based transcriptome data from each RCC subtype, namely clear cell RCC (KIRC), papillary RCC (KIRP) and kidney chromophobe (KICH), and mutual or subtype-specific reporter biomolecules were identified at RNA, protein, and metabolite levels by the integration of expression profiles with genome-scale biomolecular networks. This approach revealed already-known biomarkers in RCCs as well as novel biomarker candidates and potential therapeutic targets. Our findings also pointed out the incorporation of the molecular mechanisms of KIRC and KIRP, whereas KICH was shown to have distinct molecular signatures. Furthermore, considering the Dipeptidyl Peptidase 4 (DPP4) receptor as a potential therapeutic target specific to KICH, several drug candidates such as ZINC6745464 were identified through virtual screening of ZINC molecules. In this study, we reported valuable data for further experimental and clinical efforts, since the proposed molecules have significant potential for screening and therapeutic purposes in RCCs.
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Nie MJ, Pan XT, Tao HY, Xu MJ, Liu SL, Sun W, Wu J, Zou X. Clinical and prognostic significance of MYH11 in lung cancer. Oncol Lett 2020; 19:3899-3906. [PMID: 32382337 PMCID: PMC7202280 DOI: 10.3892/ol.2020.11478] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Accepted: 02/21/2020] [Indexed: 12/24/2022] Open
Abstract
Myosin heavy chain 11 (MYH11), encoded by the MYH11 gene, is a protein that participates in muscle contraction through the hydrolysis of adenosine triphosphate. Although previous studies have demonstrated that MYH11 gene expression levels are downregulated in several types of cancer, its expression levels have rarely been investigated in lung cancer. The present study aimed to explore the clinical significance and prognostic value of MYH11 expression levels in lung cancer and to further study the underlying molecular mechanisms of the function of this gene. The Oncomine database showed that the MYH11 expression levels were decreased in lung cancer compared with those noted in the normal lung tissue (P<0.05). Kaplan-Meier plotter results revealed that the decreased MYH11 expression levels were correlated with poor prognosis in lung cancer patients. Among the lung cancer cases with gene alteration of MYH11, mutation was the most common of all alteration types. Coexpedia and Metascape analyses revealed that the target genes were primarily enriched in ‘muscle contraction’, ‘contractile fiber part’, ‘actin cytoskeleton’ and the ‘adherens junction’. These results indicated that MYH11 is a potential novel drug target and prognostic indicator of lung cancer.
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Affiliation(s)
- Meng-Jun Nie
- Oncology Department, The Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, Jiangsu 210029, P.R. China.,No.1 Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210023, P.R. China
| | - Xiao-Ting Pan
- Oncology Department, The Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, Jiangsu 210029, P.R. China.,No.1 Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210023, P.R. China
| | - He-Yun Tao
- Oncology Department, The Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, Jiangsu 210029, P.R. China.,No.1 Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210023, P.R. China
| | - Meng-Jun Xu
- Oncology Department, The Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, Jiangsu 210029, P.R. China.,No.1 Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210023, P.R. China
| | - Shen-Lin Liu
- Oncology Department, The Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, Jiangsu 210029, P.R. China
| | - Wei Sun
- Oncology Department, The Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, Jiangsu 210029, P.R. China
| | - Jian Wu
- Oncology Department, The Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, Jiangsu 210029, P.R. China
| | - Xi Zou
- Oncology Department, The Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, Jiangsu 210029, P.R. China
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Comertpay B, Gov E. Identification of key biomolecules in rheumatoid arthritis through the reconstruction of comprehensive disease-specific biological networks. Autoimmunity 2020; 53:156-166. [DOI: 10.1080/08916934.2020.1722107] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Affiliation(s)
- Betul Comertpay
- Department of Bioengineering, Faculty of Engineering, Adana Alparslan Turkes Science and Technology University, Adana, Turkey
| | - Esra Gov
- Department of Bioengineering, Faculty of Engineering, Adana Alparslan Turkes Science and Technology University, Adana, Turkey
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Identification of Genetic Links of Thyroid Cancer to the Neurodegenerative and Chronic Diseases Progression: Insights from Systems Biology Approach. PROCEEDINGS OF INTERNATIONAL JOINT CONFERENCE ON COMPUTATIONAL INTELLIGENCE 2020. [DOI: 10.1007/978-981-15-3607-6_21] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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36
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Öktem EK, Yazar M, Gulfidan G, Arga KY. Cancer Drug Repositioning by Comparison of Gene Expression in Humans and Axolotl (Ambystoma mexicanum) During Wound Healing. ACTA ACUST UNITED AC 2019; 23:389-405. [DOI: 10.1089/omi.2019.0093] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Elif Kubat Öktem
- Department of Genetics and Bioengineering, Istanbul Okan University, Istanbul, Turkey
| | - Metin Yazar
- Department of Genetics and Bioengineering, Istanbul Okan University, Istanbul, Turkey
- Department of Bioengineering, Marmara University, Istanbul, Turkey
| | - Gizem Gulfidan
- Department of Bioengineering, Marmara University, Istanbul, Turkey
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Rahman MR, Islam T, Shahjaman M, Zaman T, Faruquee HM, Jamal MAHM, Huq F, Quinn JMW, Moni MA. Discovering Biomarkers and Pathways Shared by Alzheimer's Disease and Ischemic Stroke to Identify Novel Therapeutic Targets. MEDICINA (KAUNAS, LITHUANIA) 2019; 55:E191. [PMID: 31121943 PMCID: PMC6572146 DOI: 10.3390/medicina55050191] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Revised: 03/20/2019] [Accepted: 05/17/2019] [Indexed: 12/21/2022]
Abstract
Background and objectives: Alzheimer's disease (AD) is a progressive neurodegenerative disease that results in severe dementia. Having ischemic strokes (IS) is one of the risk factors of the AD, but the molecular mechanisms that underlie IS and AD are not well understood. We thus aimed to identify common molecular biomarkers and pathways in IS and AD that can help predict the progression of these diseases and provide clues to important pathological mechanisms. Materials and Methods: We have analyzed the microarray gene expression datasets of IS and AD. To obtain robust results, combinatorial statistical methods were used to analyze the datasets and 26 transcripts (22 unique genes) were identified that were abnormally expressed in both IS and AD. Results: Gene Ontology (GO) and KEGG pathway analyses indicated that these 26 common dysregulated genes identified several altered molecular pathways: Alcoholism, MAPK signaling, glycine metabolism, serine metabolism, and threonine metabolism. Further protein-protein interactions (PPI) analysis revealed pathway hub proteins PDE9A, GNAO1, DUSP16, NTRK2, PGAM2, MAG, and TXLNA. Transcriptional and post-transcriptional components were then identified, and significant transcription factors (SPIB, SMAD3, and SOX2) found. Conclusions: Protein-drug interaction analysis revealed PDE9A has interaction with drugs caffeine, γ-glutamyl glycine, and 3-isobutyl-1-methyl-7H-xanthine. Thus, we identified novel putative links between pathological processes in IS and AD at transcripts levels, and identified possible mechanistic and gene expression links between IS and AD.
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Affiliation(s)
- Md Rezanur Rahman
- Department of Biochemistry and Biotechnology, School of Biomedical Science, Khwaja Yunus Ali University, Sirajgonj 6751, Bangladesh.
| | - Tania Islam
- Department of Biotechnology and Genetic Engineering, Islamic University, Kushtia 7003, Bangladesh.
| | - Md Shahjaman
- Department of Statistics, Begum Rokeya University, Rangpur 5400, Bangladesh, .
| | - Toyfiquz Zaman
- Department of Biochemistry and Biotechnology, School of Biomedical Science, Khwaja Yunus Ali University, Sirajgonj 6751, Bangladesh.
| | - Hossain Md Faruquee
- Department of Biotechnology and Genetic Engineering, Islamic University, Kushtia 7003, Bangladesh.
| | | | - Fazlul Huq
- Discipline of Pathology, School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia.
| | - Julian M W Quinn
- Bone Biology Division, Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia.
| | - Mohammad Ali Moni
- Discipline of Pathology, School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia.
- Bone Biology Division, Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia.
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Rahman MR, Islam T, Gov E, Turanli B, Gulfidan G, Shahjaman M, Banu NA, Mollah MNH, Arga KY, Moni MA. Identification of Prognostic Biomarker Signatures and Candidate Drugs in Colorectal Cancer: Insights from Systems Biology Analysis. ACTA ACUST UNITED AC 2019; 55:medicina55010020. [PMID: 30658502 PMCID: PMC6359148 DOI: 10.3390/medicina55010020] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Revised: 12/23/2018] [Accepted: 01/14/2019] [Indexed: 12/17/2022]
Abstract
Background and objectives: Colorectal cancer (CRC) is the second most common cause of cancer-related death in the world, but early diagnosis ameliorates the survival of CRC. This report aimed to identify molecular biomarker signatures in CRC. Materials and Methods: We analyzed two microarray datasets (GSE35279 and GSE21815) from the Gene Expression Omnibus (GEO) to identify mutual differentially expressed genes (DEGs). We integrated DEGs with protein–protein interaction and transcriptional/post-transcriptional regulatory networks to identify reporter signaling and regulatory molecules; utilized functional overrepresentation and pathway enrichment analyses to elucidate their roles in biological processes and molecular pathways; performed survival analyses to evaluate their prognostic performance; and applied drug repositioning analyses through Connectivity Map (CMap) and geneXpharma tools to hypothesize possible drug candidates targeting reporter molecules. Results: A total of 727 upregulated and 99 downregulated DEGs were detected. The PI3K/Akt signaling, Wnt signaling, extracellular matrix (ECM) interaction, and cell cycle were identified as significantly enriched pathways. Ten hub proteins (ADNP, CCND1, CD44, CDK4, CEBPB, CENPA, CENPH, CENPN, MYC, and RFC2), 10 transcription factors (ETS1, ESR1, GATA1, GATA2, GATA3, AR, YBX1, FOXP3, E2F4, and PRDM14) and two microRNAs (miRNAs) (miR-193b-3p and miR-615-3p) were detected as reporter molecules. The survival analyses through Kaplan–Meier curves indicated remarkable performance of reporter molecules in the estimation of survival probability in CRC patients. In addition, several drug candidates including anti-neoplastic and immunomodulating agents were repositioned. Conclusions: This study presents biomarker signatures at protein and RNA levels with prognostic capability in CRC. We think that the molecular signatures and candidate drugs presented in this study might be useful in future studies indenting the development of accurate diagnostic and/or prognostic biomarker screens and efficient therapeutic strategies in CRC.
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Affiliation(s)
- Md Rezanur Rahman
- Department of Biotechnology and Genetic Engineering, Islamic University, Kushtia-7003, Bangladesh.
- Department of Biochemistry and Biotechnology, School of Biomedical Science, Khwaja Yunus Ali University, Sirajgonj-6751, Bangladesh.
| | - Tania Islam
- Department of Biotechnology and Genetic Engineering, Islamic University, Kushtia-7003, Bangladesh.
| | - Esra Gov
- Department of Bioengineering, Adana Science and Technology University, Adana-01250, Turkey.
| | - Beste Turanli
- Department of Bioengineering, Marmara University, Istanbul-34722, Turkey.
- Department of Bioengineering, Istanbul Medeniyet University, Istanbul-34700, Turkey.
| | - Gizem Gulfidan
- Department of Bioengineering, Marmara University, Istanbul-34722, Turkey.
| | - Md Shahjaman
- Department of Statistics, Begum Rokeya University, Rangpur-5400, Bangladesh.
| | - Nilufa Akhter Banu
- Department of Biotechnology and Genetic Engineering, Islamic University, Kushtia-7003, Bangladesh.
| | - Md Nurul Haque Mollah
- Laboratory of Bioinformatics, Department of Statistics, University of Rajshahi, Rajshahi-6205, Bangladesh.
| | - Kazim Yalcin Arga
- Department of Bioengineering, Marmara University, Istanbul-34722, Turkey.
| | - Mohammad Ali Moni
- The University of Sydney, Faculty of Medicine and Health, Sydney Medical School, Discipline of Biomedical Science, NSW 2006, Australia.
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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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Rahman MR, Islam T, Huq F, Quinn JM, Moni MA. Identification of molecular signatures and pathways common to blood cells and brain tissue of amyotrophic lateral sclerosis patients. INFORMATICS IN MEDICINE UNLOCKED 2019. [DOI: 10.1016/j.imu.2019.100193] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
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41
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Rahman MR, Islam T, Al-Mamun MA, Zaman T, Karim MR, Moni MA. The influence of depression on ovarian cancer: Discovering molecular pathways that identify novel biomarkers and therapeutic targets. INFORMATICS IN MEDICINE UNLOCKED 2019. [DOI: 10.1016/j.imu.2019.100207] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
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42
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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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43
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Islam T, Rahman MR, Karim MR, Huq F, Quinn JM, Moni MA. Detection of multiple sclerosis using blood and brain cells transcript profiles: Insights from comprehensive bioinformatics approach. INFORMATICS IN MEDICINE UNLOCKED 2019. [DOI: 10.1016/j.imu.2019.100201] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
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Rahman MR, Islam T, Turanli B, Zaman T, Faruquee HM, Rahman MM, Mollah MNH, Nanda RK, Arga KY, Gov E, Moni MA. Network-based approach to identify molecular signatures and therapeutic agents in Alzheimer's disease. Comput Biol Chem 2018; 78:431-439. [PMID: 30606694 DOI: 10.1016/j.compbiolchem.2018.12.011] [Citation(s) in RCA: 76] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2018] [Accepted: 12/25/2018] [Indexed: 01/15/2023]
Abstract
Alzheimer's disease (AD) is a dynamic degeneration of the brain with progressive dementia. Considering the uncertainties in its molecular mechanism, in the present study, we employed network-based integrative analyses, and aimed to explore the key molecules and their associations with small drugs to identify potential biomarkers and therapeutic agents for the AD. First of all, we studied a transcriptome dataset and identified 1521 differentially expressed genes (DEGs). Integration of transcriptome data with protein-protein and transcriptional regulatory interactions resulted with central (hub) proteins (UBA52, RAC1, CREBBP, AR, RPS11, SMAD3, RPS6, RPL12, RPL15, and UBC), regulatory transcription factors (FOXC1, GATA2, YY1, FOXL1, NFIC, E2F1, USF2, SRF, PPARG, and JUN) and microRNAs (mir-335-5p, mir-26b-5p, mir-93-5p, mir-124-3p, mir-17-5p, mir-16-5p, mir-20a-5p, mir-92a-3p, mir-106b-5p, and mir-192-5p) as key signaling and regulatory molecules associated with transcriptional changes for the AD. Considering these key molecules as potential therapeutic targets and Connectivity Map (CMap) architecture, candidate small molecular agents (such as STOCK1N-35696) were identified as novel potential therapeutics for the AD. This study presents molecular signatures at RNA and protein levels which might be useful in increasing discernment of the molecular mechanisms, and potential drug targets and therapeutics to design effective treatment strategies for the AD.
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Affiliation(s)
- Md Rezanur Rahman
- Department of Biotechnology and Genetic Engineering, Islamic University, Kushtia, Bangladesh; Department of Biochemistry and Biotechnology, School of Biomedical Science, Khwaja Yunus Ali University, Sirajgonj, Bangladesh
| | - Tania Islam
- Department of Biotechnology and Genetic Engineering, Islamic University, Kushtia, Bangladesh
| | - Beste Turanli
- Department of Bioengineering, Istanbul Medeniyet University, Istanbul, Turkey; Department of Bioengineering, Marmara University, Istanbul, Turkey
| | - Toyfiquz Zaman
- Department of Biochemistry and Biotechnology, School of Biomedical Science, Khwaja Yunus Ali University, Sirajgonj, Bangladesh
| | - Hossain Md Faruquee
- Department of Biotechnology and Genetic Engineering, Islamic University, Kushtia, Bangladesh; Translational Health, International Centre for Genetic Engineering and Biotechnology, New Delhi, India
| | - Md Mafizur Rahman
- Department of Biotechnology and Genetic Engineering, Islamic University, Kushtia, Bangladesh
| | - Md Nurul Haque Mollah
- Laboratory of Bioinformatics, Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh
| | - Ranjan Kumar Nanda
- Translational Health, International Centre for Genetic Engineering and Biotechnology, New Delhi, India
| | | | - Esra Gov
- Department of Bioengineering, Adana Science and Technology University, Adana, Turkey.
| | - Mohammad Ali Moni
- The University of Sydney, Sydney Medical School, School of Medical Sciences, Discipline of Biomedical Science, Sydney, New South Wales, Australia.
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