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Ziyaei K, Mokhtari M, Hashemi M, Rezaei K, Abdi F. Association between exposure to water sources contaminated with polycyclic aromatic hydrocarbons and cancer risk: A systematic review. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 924:171261. [PMID: 38417520 DOI: 10.1016/j.scitotenv.2024.171261] [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: 11/29/2023] [Revised: 02/22/2024] [Accepted: 02/23/2024] [Indexed: 03/01/2024]
Abstract
The recent scientific focus on polycyclic aromatic hydrocarbons (PAHs) has stemmed from their recognized genotoxic, mutagenic, and carcinogenic properties. This systematic review seeks to evaluate the nexus between exposure to water sources contaminated with PAHs and the associated cancer risk among global populations, encompassing both children and adults. Web of Science (WoS), Cochrane Library, PubMed, ProQuest, Scopus, and Google Scholar, were searched following the PRISMA guidelines, until December 31, 2023. Quality assessment of the selected studies was performed using the Newcastle-Ottawa Scale. The increased lifetime cancer risk (ILCR) attributed to PAH exposure through ingestion and dermal absorption was thoroughly examined across diverse age groups. After extensive searching, screening, and eligibility, 30 articles were included in this review, which was conducted in different parts of the world, including Nigeria (n = 11), China (n = 7), India (n = 4), Iran (n = 3), South Africa (n = 2), Italy (n = 1), Colombia (n = 1), and Iraq (n = 1). Our analysis underscores Nigeria's alarming prevalence of PAH contamination in its rivers, groundwaters, and seawater. Remarkably, the highest cancer risk was identified among children and adults, notably in proximity to the Atlas Cove jetty (seawater) and various Nigerian rivers. This elevated risk is primarily attributed to the combined effects of ingestion and dermal absorption. Furthermore, our findings emphasize the prominent role of combustion-derived and pyrogenic sources of PAH in the examined aquatic ecosystems. This study unequivocally establishes that PAH-contaminated water sources significantly amplify the risk of cancer among both children and adults. The extent of risk variation is influenced by the specific water source, duration of exposure, and age group. Consequently, proactive identification of contaminated water sources and their pollution origins, coupled with targeted educational campaigns, holds promise for reducing the global burden of PAH-related cancer.
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Affiliation(s)
- Kobra Ziyaei
- Department of Fisheries, Faculty of Natural Resources, University of Tehran, Karaj, Iran.
| | - Majid Mokhtari
- Department of Bioinformatics, Kish International Campus, University of Tehran, Kish Island, Iran.
| | - Masoumeh Hashemi
- Department of Midwifery, Arak Branch, Islamic Azad University, Arak, Iran
| | - Kiadokht Rezaei
- Department of Fisheries, Faculty of Natural Resources, University of Tehran, Karaj, Iran.
| | - Fatemeh Abdi
- Nursing and Midwifery Care Research Center, Health Management Research Institute, Iran University of Medical Sciences, Tehran, Iran.
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Mokhtari M, Khoshbakht S, Ziyaei K, Akbari ME, Moravveji SS. New classifications for quantum bioinformatics: Q-bioinformatics, QCt-bioinformatics, QCg-bioinformatics, and QCr-bioinformatics. Brief Bioinform 2024; 25:bbae074. [PMID: 38446742 PMCID: PMC10939336 DOI: 10.1093/bib/bbae074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 11/14/2023] [Accepted: 02/07/2021] [Indexed: 03/08/2024] Open
Abstract
Bioinformatics has revolutionized biology and medicine by using computational methods to analyze and interpret biological data. Quantum mechanics has recently emerged as a promising tool for the analysis of biological systems, leading to the development of quantum bioinformatics. This new field employs the principles of quantum mechanics, quantum algorithms, and quantum computing to solve complex problems in molecular biology, drug design, and protein folding. However, the intersection of bioinformatics, biology, and quantum mechanics presents unique challenges. One significant challenge is the possibility of confusion among scientists between quantum bioinformatics and quantum biology, which have similar goals and concepts. Additionally, the diverse calculations in each field make it difficult to establish boundaries and identify purely quantum effects from other factors that may affect biological processes. This review provides an overview of the concepts of quantum biology and quantum mechanics and their intersection in quantum bioinformatics. We examine the challenges and unique features of this field and propose a classification of quantum bioinformatics to promote interdisciplinary collaboration and accelerate progress. By unlocking the full potential of quantum bioinformatics, this review aims to contribute to our understanding of quantum mechanics in biological systems.
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Affiliation(s)
- Majid Mokhtari
- Department of Bioinformatics, Kish International Campus, University of Tehran, Kish Island, Iran
| | - Samane Khoshbakht
- Department of Bioinformatics, Kish International Campus, University of Tehran, Kish Island, Iran
- Duke Molecular Physiology Institute, Duke University School of Medicine-Cardiology, Durham, NC, 27701, USA
| | - Kobra Ziyaei
- Department of Fisheries, Faculty of Natural Resources, University of Tehran, Karaj, Iran
| | | | - Sayyed Sajjad Moravveji
- Department of Bioinformatics, Kish International Campus, University of Tehran, Kish Island, Iran
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Mokhtari M, Khoshbakht S, Akbari ME, Moravveji SS. BMC3PM: bioinformatics multidrug combination protocol for personalized precision medicine and its application in cancer treatment. BMC Med Genomics 2023; 16:328. [PMID: 38087279 PMCID: PMC10717810 DOI: 10.1186/s12920-023-01745-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 11/20/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND In recent years, drug screening has been one of the most significant challenges in the field of personalized medicine, particularly in cancer treatment. However, several new platforms have been introduced to address this issue, providing reliable solutions for personalized drug validation and safety testing. In this study, we developed a personalized drug combination protocol as the primary input to such platforms. METHODS To achieve this, we utilized data from whole-genome expression profiles of 6173 breast cancer patients, 312 healthy individuals, and 691 drugs. Our approach involved developing an individual pattern of perturbed gene expression (IPPGE) for each patient, which was used as the basis for drug selection. An algorithm was designed to extract personalized drug combinations by comparing the IPPGE and drug signatures. Additionally, we employed the concept of drug repurposing, searching for new benefits of existing drugs that may regulate the desired genes. RESULTS Our study revealed that drug combinations obtained from both specialized and non-specialized cancer medicines were more effective than those extracted from only specialized medicines. Furthermore, we observed that the individual pattern of perturbed gene expression (IPPGE) was unique to each patient, akin to a fingerprint. CONCLUSIONS The personalized drug combination protocol developed in this study offers a methodological interface between drug repurposing and combination drug therapy in cancer treatment. This protocol enables personalized drug combinations to be extracted from hundreds of drugs and thousands of drug combinations, potentially offering more effective treatment options for cancer patients.
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Affiliation(s)
- Majid Mokhtari
- Department of Bioinformatics, Kish International Campus, University of Tehran, Kish Island, Iran.
| | - Samane Khoshbakht
- Department of Bioinformatics, Kish International Campus, University of Tehran, Kish Island, Iran
- Duke Molecular Physiology Institute, Duke University School of Medicine-Cardiology, Durham, NC, 27701, USA
| | | | - Sayyed Sajjad Moravveji
- Department of Bioinformatics, Kish International Campus, University of Tehran, Kish Island, Iran
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Mokhtari M, Khoshbakht S, Esmaeil Akbari M, Sayyed Sajjad M. WASF3 overexpression affects the expression of circular RNA hsa-circ-0100153, which promotes breast cancer progression by sponging hsa-miR-31, hsa-miR-767-3p, and hsa-miR-935. Heliyon 2023; 9:e22874. [PMID: 38125536 PMCID: PMC10731075 DOI: 10.1016/j.heliyon.2023.e22874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Revised: 11/01/2023] [Accepted: 11/21/2023] [Indexed: 12/23/2023] Open
Abstract
Background The WASF3 gene has been linked to promoting metastasis in breast cancer (BC) cells, and low expression reduces invasion potential. Circular RNAs (circRNAs) function as microRNA (miRNA) modulators and are involved in cancer progression, but the relationship between these factors remains unclear. Methods This study used bioinformatics methods and a computational approach to investigate the role of circRNAs and miRNAs in the context of WASF3 overexpression. Differentially expressed mRNAs, circRNAs, and miRNAs were identified using Gene Expression Omnibus (GEO) datasets. A competing endogenous RNA (ceRNA) network was constructed based on circRNA-miRNA pairs and miRNA-mRNA pairs. Functional and pathway enrichment analyses were predicted using a circRNA-miRNA-mRNA network. Results RNA expression patterns were significantly different between normal and tumor samples. A total of 190 circRNAs, 76 miRNAs, and 678 mRNAs were differentially expressed. The analysis of the circRNA-miRNA-mRNA regulatory network revealed interactions between hsa-circ-0100153, hsa-miR-31, hsa-miR-767-3p, and hsa-miR-935 with WASF3 in cancer. These interactions primarily function in DNA replication and the cell cycle. Conclusions This study reveals a mechanism by which WASF3 overexpression affects the expression of circRNAs hsa-circ-0100153, promoting BC progression by sponging hsa-miR-31/hsa-miR-767-3p /hsa-miR-935. This mechanism may increase the invasive potential of cancers, in addition to other reported molecular mechanisms involving the WASF3 gene.
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Affiliation(s)
- Majid Mokhtari
- Department of Bioinformatics, Kish International Campus, University of Tehran, Kish Island, Iran
| | - Samane Khoshbakht
- Department of Bioinformatics, Kish International Campus, University of Tehran, Kish Island, Iran
| | | | - Moravveji Sayyed Sajjad
- Department of Bioinformatics, Kish International Campus, University of Tehran, Kish Island, Iran
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Sheikh Beig Goharrizi MA, Ghodsi S, Mokhtari M, Moravveji SS. Non-invasive STEMI-related biomarkers based on meta-analysis and gene prioritization. Comput Biol Med 2023; 161:106997. [PMID: 37216774 DOI: 10.1016/j.compbiomed.2023.106997] [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: 12/04/2022] [Revised: 04/01/2023] [Accepted: 05/01/2023] [Indexed: 05/24/2023]
Abstract
BACKGROUND AND AIMS Acute ST-Segment Myocardial infarction (STEMI) is a common cardiovascular issue with a considerable burden of the disease. The underlying genetic basis and non-invasive markers were not well-established. METHODS Here, we implemented a systematic literature review and meta-analyses integration methods on 217 STEMI patients and 72 normal individuals to prioritize and detect the STEMI-related non-invasive markers. Five high-scored genes were experimentally assessed on 10 STEMI patients and 9 healthy controls. Finally, the presence of co-expressed nodes of top-score genes was explored. RESULTS The differential expression of ARGL, CLEC4E, and EIF3D were significant for Iranian patients. The ROC curve for gene CLEC4E revealed an AUC (95% CI) of 0.786 (0.686-0.886) in the prediction of STEMI. The Cox-PH model was fitted to stratify high/low risk heart failure progression (CI-index = 0.83, Likelihood-Ratio-Test = 3e-10). The SI00AI2 was a common biomarker between STEMI and NSTEMI patients. CONCLUSIONS In conclusion, the high-scored genes and prognostic model could be applicable for Iranian patients.
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Affiliation(s)
| | - Saeed Ghodsi
- Department of Cardiology, Sina Hospital, Tehran University of Medical Sciences Tehran, Iran
| | - Majid Mokhtari
- Department of Bioinformatics, Kish International Campus, University of Tehran, Kish Island, Iran; Laboratory of Personalized Precision Medicine, Bioinformatics Research Institute, Tehran, Iran
| | - Sayyed Sajjad Moravveji
- Department of Bioinformatics, Kish International Campus, University of Tehran, Kish Island, Iran
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Moravveji SS, Khoshbakht S, Mokhtari M, Salimi M, Lanjanian H, Nematzadeh S, Torkamanian-Afshar M, Masoudi-Nejad A. Impact of 5HydroxyMethylCytosine (5hmC) on reverse/direct association of cell-cycle, apoptosis, and extracellular matrix pathways in gastrointestinal cancers. BMC Genom Data 2022; 23:49. [PMID: 35768769 PMCID: PMC9241275 DOI: 10.1186/s12863-022-01061-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 06/09/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Aberrant levels of 5-hydroxymethylcytosine (5-hmC) can lead to cancer progression. Identification of 5-hmC-related biological pathways in cancer studies can produce better understanding of gastrointestinal (GI) cancers. We conducted a network-based analysis on 5-hmC levels extracted from circulating free DNAs (cfDNA) in GI cancers including colon, gastric, and pancreatic cancers, and from healthy donors. The co-5-hmC network was reconstructed using the weighted-gene co-expression network method. The cancer-related modules/subnetworks were detected. Preservation of three detected 5-hmC-related modules was assessed in an external dataset. The 5-hmC-related modules were functionally enriched, and biological pathways were identified. The relationship between modules was assessed using the Pearson correlation coefficient (p-value < 0.05). An elastic network classifier was used to assess the potential of the 5-hmC modules in distinguishing cancer patients from healthy individuals. To assess the efficiency of the model, the Area Under the Curve (AUC) was computed using five-fold cross-validation in an external dataset. RESULTS The main biological pathways were the cell cycle, apoptosis, and extracellular matrix (ECM) organization. Direct association between the cell cycle and apoptosis, inverse association between apoptosis and ECM organization, and inverse association between the cell cycle and ECM organization were detected for the 5-hmC modules in GI cancers. An AUC of 92% (0.73-1.00) was observed for the predictive model including 11 genes. CONCLUSION The intricate association between biological pathways of identified modules may reveal the hidden significance of 5-hmC in GI cancers. The identified predictive model and new biomarkers may be beneficial in cancer detection and precision medicine using liquid biopsy in the early stages.
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Affiliation(s)
- Sayyed Sajjad Moravveji
- Laboratory of Systems Biology and Bioinformatics (LBB), Department of Bioinformatics, Kish International Campus, University of Tehran, Kish Island, Iran
| | - Samane Khoshbakht
- Laboratory of Systems Biology and Bioinformatics (LBB), Department of Bioinformatics, Kish International Campus, University of Tehran, Kish Island, Iran
| | - Majid Mokhtari
- Laboratory of Systems Biology and Bioinformatics (LBB), Department of Bioinformatics, Kish International Campus, University of Tehran, Kish Island, Iran
| | - Mahdieh Salimi
- Department of Medical Genetics, Institute of Medical Biotechnology, National Institute of Genetic Engineering and Biotechnology (NIGEB), Tehran, Iran
| | - Hossein Lanjanian
- Molecular Biology and Genetics Department, Engineering and Natural Science Faculty, Istinye University, Istanbul, Turkey
| | - Sajjad Nematzadeh
- Computer Engineering Department, Architecture and Engineering Faculty, Nisantasi University, Istanbul, Turkey
| | - Mahsa Torkamanian-Afshar
- Computer Engineering Department, Architecture and Engineering Faculty, Nisantasi University, Istanbul, Turkey
| | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Department of Bioinformatics, Kish International Campus, University of Tehran, Kish Island, Iran.
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
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