1
|
Aalami AH, Shahriari A, Mazaheri M, Aalami F, Sahebkar A. Advancing gastrointestinal cancer diagnostics: a systematic review and meta-analysis of circulating microRNA-1246 as a non-invasive biomarker. Biomarkers 2024; 29:233-243. [PMID: 38696280 DOI: 10.1080/1354750x.2024.2350714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2024] [Accepted: 04/19/2024] [Indexed: 05/04/2024]
Abstract
BACKGROUND Despite numerous reports on the alterations of microRNA-1246 (miR-1246) expression level in digestive system cancers, its role in gastrointestinal cancers (GICs) remains unclear. This meta-analysis aimed to assess the diagnostic potential of circulating miR-1246 in GICs. METHODS Meta-disc version 1.4 and Comprehensive Meta-Analysis (CMA) version 3.7 software were used to calculate pooled sensitivity, specificity, likelihood ratios, diagnostic odds ratio (DOR), area under the curve (AUC), Q*index and summary receiver-operating characteristic (SROC). Subgroup analyses were conducted for cancer type, sample type and geographical region. Publication bias was assessed using Begg's and Egger's tests. RESULTS A total of 14 articles involving 18 studies and 1526 participants (972 cases and 554 controls) were included. The diagnostic accuracy of miRNA-1246 in GICs was as follows: pooled sensitivity: 0.81 (95% CI: 0.79 - 0.83), specificity: 0.74 (95% CI: 0.71 - 0.77), PLR: 3.315 (95% CI: 2.33 - 4.72), NLR: 0.221 (95% CI: 0.153 - 0.319), DOR: 16.87 (95% CI: 9.45 - 30.09), AUC: 0.891, and Q*-index: 0.807. No publication bias was found based on Begg's (p = 0.172) and Egger's (p = 0.113) tests. CONCLUSION Circulating miR-1246 shows promise as a non-invasive biomarker for early detection of GICs.
Collapse
Affiliation(s)
- Amir Hossein Aalami
- Department of Nutrition and Integrative Physiology, College of Health, University of Utah, Salt Lake City, UT, USA
- Division of Nephrology and Hypertension, Department of Internal Medicine, School of Medicine, University of Utah, Salt Lake City, UT, USA
| | - Ali Shahriari
- Department of Internal Medicine, Mashhad Medical Sciences Branch, Islamic Azad University, Mashhad, Iran
| | - Mohammad Mazaheri
- Department of Molecular, Cell and Systems Biology, College of Natural and Agricultural Sciences, University of California Riverside, Riverside, CA, USA
| | - Farnoosh Aalami
- Student Research Committee, Faculty of Medicine, North Khorasan University of Medical Sciences, Bojnurd, Iran
| | - Amirhossein Sahebkar
- Center for Global Health Research, Saveetha Medical College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, India
- Applied Biomedical Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
- Biotechnology Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran
| |
Collapse
|
2
|
Bhattacharjya A, Islam MM, Uddin MA, Talukder MA, Azad AKM, Aryal S, Paul BK, Tasnim W, Almoyad MAA, Moni MA. Exploring gene regulatory interaction networks and predicting therapeutic molecules for hypopharyngeal cancer and EGFR-mutated lung adenocarcinoma. FEBS Open Bio 2024; 14:1166-1191. [PMID: 38783639 PMCID: PMC11216941 DOI: 10.1002/2211-5463.13807] [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: 06/24/2023] [Revised: 01/30/2024] [Accepted: 04/16/2024] [Indexed: 05/25/2024] Open
Abstract
Hypopharyngeal cancer is a disease that is associated with EGFR-mutated lung adenocarcinoma. Here we utilized a bioinformatics approach to identify genetic commonalities between these two diseases. To this end, we examined microarray datasets from GEO (Gene Expression Omnibus) to identify differentially expressed genes, common genes, and hub genes between the selected two diseases. Our analyses identified potential therapeutic molecules for the selected diseases based on 10 hub genes with the highest interactions according to the degree topology method and the maximum clique centrality (MCC). These therapeutic molecules may have the potential for simultaneous treatment of these diseases.
Collapse
Affiliation(s)
- Abanti Bhattacharjya
- Department of Computer Science and EngineeringJagannath UniversityDhakaBangladesh
| | - Md Manowarul Islam
- Department of Computer Science and EngineeringJagannath UniversityDhakaBangladesh
| | - Md Ashraf Uddin
- School of Information TechnologyDeakin UniversityGeelongAustralia
| | - Md Alamin Talukder
- Department of Computer Science and EngineeringInternational University of Business Agriculture and TechnologyDhakaBangladesh
| | - AKM Azad
- Department of Mathematics and Statistics, Faculty of ScienceImam Mohammad Ibn Saud Islamic University (IMSIU)RiyadhSaudi Arabia
| | - Sunil Aryal
- School of Information TechnologyDeakin UniversityGeelongAustralia
| | - Bikash Kumar Paul
- Department of Information and Communication TechnologyMawlana Bhashani Science and Technology UniversityTangailBangladesh
- Department of Software EngineeringDaffodil International UniversityDhakaBangladesh
| | - Wahia Tasnim
- Department of Information and Communication TechnologyMawlana Bhashani Science and Technology UniversityTangailBangladesh
| | | | - Mohammad Ali Moni
- Artificial Intelligence & Data Science, Faculty of Health and Behavioural SciencesThe University of QueenslandBrisbaneAustralia
- AI & Digital Health Technology, Artificial Intelligence and Cyber Futures InstituteCharles Sturt UniversityBathurstAustralia
- Rural Health Research InstituteCharles Sturt UniversityOrangeAustralia
| |
Collapse
|
3
|
International BR. Retracted: Identification of Molecular Biomarkers and Key Pathways for Esophageal Carcinoma (EsC): A Bioinformatics Approach. BIOMED RESEARCH INTERNATIONAL 2024; 2024:9878069. [PMID: 38230060 PMCID: PMC10791328 DOI: 10.1155/2024/9878069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 01/08/2024] [Indexed: 01/18/2024]
Abstract
[This retracts the article DOI: 10.1155/2022/5908402.].
Collapse
|
4
|
Guo J, Zhang Y, Gao Y, Li S, Xu G, Tian Z, Xu Q, Li X, Li Y, Zhang Y. Systematical analyses of large-scale transcriptome reveal viral infection-related genes and disease comorbidities. ARTIFICIAL CELLS, NANOMEDICINE, AND BIOTECHNOLOGY 2023; 51:453-465. [PMID: 37651591 DOI: 10.1080/21691401.2023.2252477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 08/13/2023] [Accepted: 08/17/2023] [Indexed: 09/02/2023]
Abstract
Perturbation of transcriptome in viral infection patients is a recurrent theme impacting symptoms and mortality, yet a detailed understanding of pertinent transcriptome and identification of robust biomarkers is not complete. In this study, we manually collected 23 datasets related to 6,197 blood transcriptomes across 16 types of respiratory virus infections. We applied a comprehensive systems biology approach starting with whole-blood transcriptomes combined with multilevel bioinformatics analyses to characterize the expression, functional pathways, and protein-protein interaction (PPI) networks to identify robust biomarkers and disease comorbidities. Robust gene markers of infection with different viruses were identified, which can accurately classify the normal and infected patients in train and validation cohorts. The biological processes (BP) of different viruses showed great similarity and enriched in infection and immune response pathways. Network-based analyses revealed that a variety of viral infections were associated with nervous system diseases, neoplasms and metabolic diseases, and significantly correlated with brain tissues. In summary, our manually collected transcriptomes and comprehensive analyses reveal key molecular markers and disease comorbidities in the process of viral infection, which could provide a valuable theoretical basis for the prevention of subsequent public health events for respiratory virus infections.
Collapse
Affiliation(s)
- Jing Guo
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan Women and Children's Medical Center, Hainan Medical University, Haikou, Hainan, China
| | - Ya Zhang
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan Women and Children's Medical Center, Hainan Medical University, Haikou, Hainan, China
| | - Yueying Gao
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan Women and Children's Medical Center, Hainan Medical University, Haikou, Hainan, China
| | - Si Li
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan Women and Children's Medical Center, Hainan Medical University, Haikou, Hainan, China
| | - Gang Xu
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan Women and Children's Medical Center, Hainan Medical University, Haikou, Hainan, China
| | - Zhanyu Tian
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan Women and Children's Medical Center, Hainan Medical University, Haikou, Hainan, China
| | - Qi Xu
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan Women and Children's Medical Center, Hainan Medical University, Haikou, Hainan, China
| | - Xia Li
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan Women and Children's Medical Center, Hainan Medical University, Haikou, Hainan, China
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yongsheng Li
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan Women and Children's Medical Center, Hainan Medical University, Haikou, Hainan, China
| | - Yunpeng Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| |
Collapse
|
5
|
Identification of Potential Key Genes and Prognostic Biomarkers of Lung Cancer Based on Bioinformatics. BIOMED RESEARCH INTERNATIONAL 2023; 2023:2152432. [PMID: 36714024 PMCID: PMC9876670 DOI: 10.1155/2023/2152432] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 10/31/2022] [Accepted: 11/17/2022] [Indexed: 01/19/2023]
Abstract
Objective To analyze and identify the core genes related to the expression and prognosis of lung cancer including lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) by bioinformatics technology, with the aim of providing a reference for clinical treatment. Methods Five sets of gene chips, GSE7670, GSE151102, GSE33532, GSE43458, and GSE19804, were obtained from the Gene Expression Omnibus (GEO) database. After using GEO2R to analyze the differentially expressed genes (DEGs) between lung cancer and normal tissues online, the common DEGs of the five sets of chips were obtained using a Venn online tool and imported into the Database for Annotation, Visualization, and Integrated Discovery (DAVID) database for Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The protein-protein interaction (PPI) network was constructed by STRING online software for further study, and the core genes were determined by Cytoscape software and KEGG pathway enrichment analysis. The clustering heat map was drawn by Excel software to verify its accuracy. In addition, we used the University of Alabama at Birmingham Cancer (UALCAN) website to analyze the expression of core genes in P53 mutation status, confirmed the expression of crucial core genes in lung cancer tissues with Gene Expression Profiling Interactive Analysis (GEPIA) and GEPIA2 online software, and evaluated their prognostic value in lung cancer patients with the Kaplan-Meier online plotter tool. Results CHEK1, CCNB1, CCNB2, and CDK1 were selected. The expression levels of these four genes in lung cancer tissues were significantly higher than those in normal tissues. Their increased expression was negatively correlated with lung cancer patients (including LUAD and LUSC) prognosis and survival rate. Conclusion CHEK1, CCNB1, CCNB2, and CDK1 are the critical core genes of lung cancer and are highly expressed in lung cancer. They are negatively correlated with the prognosis of lung cancer patients (including LUAD and LUSC) and closely related to the formation and prediction of lung cancer. They are valuable predictors and may be predictive biomarkers of lung cancer.
Collapse
|