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Abedini SS, Akhavantabasi S, Liang Y, Heng JIT, Alizadehsani R, Dehzangi I, Bauer DC, Alinejad-Rokny H. A critical review of the impact of candidate copy number variants on autism spectrum disorder. MUTATION RESEARCH. REVIEWS IN MUTATION RESEARCH 2024; 794:108509. [PMID: 38977176 DOI: 10.1016/j.mrrev.2024.108509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Revised: 04/14/2024] [Accepted: 07/02/2024] [Indexed: 07/10/2024]
Abstract
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder (NDD) influenced by genetic, epigenetic, and environmental factors. Recent advancements in genomic analysis have shed light on numerous genes associated with ASD, highlighting the significant role of both common and rare genetic mutations, as well as copy number variations (CNVs), single nucleotide polymorphisms (SNPs) and unique de novo variants. These genetic variations disrupt neurodevelopmental pathways, contributing to the disorder's complexity. Notably, CNVs are present in 10 %-20 % of individuals with autism, with 3 %-7 % detectable through cytogenetic methods. While the role of submicroscopic CNVs in ASD has been recently studied, their association with genomic loci and genes has not been thoroughly explored. In this review, we focus on 47 CNV regions linked to ASD, encompassing 1632 genes, including protein-coding genes and long non-coding RNAs (lncRNAs), of which 659 show significant brain expression. Using a list of ASD-associated genes from SFARI, we detect 17 regions harboring at least one known ASD-related protein-coding gene. Of the remaining 30 regions, we identify 24 regions containing at least one protein-coding gene with brain-enriched expression and a nervous system phenotype in mouse mutants, and one lncRNA with both brain-enriched expression and upregulation in iPSC to neuron differentiation. This review not only expands our understanding of the genetic diversity associated with ASD but also underscores the potential of lncRNAs in contributing to its etiology. Additionally, the discovered CNVs will be a valuable resource for future diagnostic, therapeutic, and research endeavors aimed at prioritizing genetic variations in ASD.
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Affiliation(s)
- Seyedeh Sedigheh Abedini
- UNSW BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW 2052, Australia; School of Biotechnology & Biomolecular Sciences, UNSW Sydney, Sydney, NSW 2052, Australia
| | - Shiva Akhavantabasi
- Department of Molecular Biology and Genetics, Yeni Yuzyil University, Istanbul, Turkey; Ghiaseddin Jamshid Kashani University, Andisheh University Town, Danesh Blvd, 3441356611, Abyek, Qazvin, Iran
| | - Yuheng Liang
- UNSW BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW 2052, Australia
| | - Julian Ik-Tsen Heng
- Curtin Health Innovation Research Institute, Curtin University, Bentley 6845, Australia
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria, Australia
| | - Iman Dehzangi
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ 08102, USA; Department of Computer Science, Rutgers University, Camden, NJ 08102, USA
| | - Denis C Bauer
- Transformational Bioinformatics, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Sydney, Australia; Applied BioSciences, Faculty of Science and Engineering, Macquarie University, Macquarie Park, Australia
| | - Hamid Alinejad-Rokny
- UNSW BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW 2052, Australia; Tyree Institute of Health Engineering (IHealthE), UNSW Sydney, Sydney, NSW 2052, Australia.
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Salido-Guadarrama I, Romero-Cordoba SL, Rueda-Zarazua B. Multi-Omics Mining of lncRNAs with Biological and Clinical Relevance in Cancer. Int J Mol Sci 2023; 24:16600. [PMID: 38068923 PMCID: PMC10706612 DOI: 10.3390/ijms242316600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 11/14/2023] [Accepted: 11/15/2023] [Indexed: 12/18/2023] Open
Abstract
In this review, we provide a general overview of the current panorama of mining strategies for multi-omics data to investigate lncRNAs with an actual or potential role as biological markers in cancer. Several multi-omics studies focusing on lncRNAs have been performed in the past with varying scopes. Nevertheless, many questions remain regarding the pragmatic application of different molecular technologies and bioinformatics algorithms for mining multi-omics data. Here, we attempt to address some of the less discussed aspects of the practical applications using different study designs for incorporating bioinformatics and statistical analyses of multi-omics data. Finally, we discuss the potential improvements and new paradigms aimed at unraveling the role and utility of lncRNAs in cancer and their potential use as molecular markers for cancer diagnosis and outcome prediction.
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Affiliation(s)
- Ivan Salido-Guadarrama
- Departamento de Bioinformatìca y Análisis Estadísticos, Instituto Nacional de Perinatología Isidro Espinosa de los Reyes, Mexico City 11000, Mexico
| | - Sandra L. Romero-Cordoba
- Departamento de Medicina Genómica y Toxicología Ambiental, Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico;
- Biochemistry Department, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City 14080, Mexico
| | - Bertha Rueda-Zarazua
- Posgrado en Ciencias Biológicas, Facultad de Medicina, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico;
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Swaminathan H, Saravanamurali K, Yadav SA. Extensive review on breast cancer its etiology, progression, prognostic markers, and treatment. Med Oncol 2023; 40:238. [PMID: 37442848 DOI: 10.1007/s12032-023-02111-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 06/30/2023] [Indexed: 07/15/2023]
Abstract
As the most frequent and vulnerable malignancy among women, breast cancer universally manifests a formidable healthcare challenge. From a biological and molecular perspective, it is a heterogenous disease and is stratified based on the etiological factors driving breast carcinogenesis. Notably, genetic predispositions and epigenetic impacts often constitute the heterogeneity of this disease. Typically, breast cancer is classified intrinsically into histological subtypes in clinical landscapes. These stratifications empower physicians to tailor precise treatments among the spectrum of breast cancer therapeutics. In this pursuit, numerous prognostic algorithms are extensively characterized, drastically changing how breast cancer is portrayed. Therefore, it is a basic requisite to comprehend the multidisciplinary rationales of breast cancer to assist the evolution of novel therapeutic strategies. This review aims at highlighting the molecular and genetic grounds of cancer additionally with therapeutic and phytotherapeutic context. Substantially, it also renders researchers with an insight into the breast cancer cell lines as a model paradigm for breast cancer research interventions.
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Affiliation(s)
- Harshini Swaminathan
- Department of Biotechnology, Karpagam Academy of Higher Education, Coimbatore, 641021, Tamil Nadu, India
| | - K Saravanamurali
- Virus Research and Diagnostics Laboratory, Department of Microbiology, Coimbatore Medical College, Coimbatore, India
| | - Sangilimuthu Alagar Yadav
- Department of Biotechnology, Karpagam Academy of Higher Education, Coimbatore, 641021, Tamil Nadu, India.
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Yang S, Kim SH, Kang M, Joo JY. Harnessing deep learning into hidden mutations of neurological disorders for therapeutic challenges. Arch Pharm Res 2023:10.1007/s12272-023-01450-5. [PMID: 37261600 DOI: 10.1007/s12272-023-01450-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 05/26/2023] [Indexed: 06/02/2023]
Abstract
The relevant study of transcriptome-wide variations and neurological disorders in the evolved field of genomic data science is on the rise. Deep learning has been highlighted utilizing algorithms on massive amounts of data in a human-like manner, and is expected to predict the dependency or druggability of hidden mutations within the genome. Enormous mutational variants in coding and noncoding transcripts have been discovered along the genome by far, despite of the fine-tuned genetic proofreading machinery. These variants could be capable of inducing various pathological conditions, including neurological disorders, which require lifelong care. Several limitations and questions emerge, including the use of conventional processes via limited patient-driven sequence acquisitions and decoding-based inferences as well as how rare variants can be deduced as a population-specific etiology. These puzzles require harnessing of advanced systems for precise disease prediction, drug development and drug applications. In this review, we summarize the pathophysiological discoveries of pathogenic variants in both coding and noncoding transcripts in neurological disorders, and the current advantage of deep learning applications. In addition, we discuss the challenges encountered and how to outperform them with advancing interpretation.
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Affiliation(s)
- Sumin Yang
- Department of Pharmacy, College of Pharmacy, Hanyang University, Rm 407, Bldg.42, 55 Hanyangdaehak-Ro, Sangnok-Gu Ansan, Ansan, Gyeonggi-Do, 15588, Republic of Korea
| | - Sung-Hyun Kim
- Department of Pharmacy, College of Pharmacy, Hanyang University, Rm 407, Bldg.42, 55 Hanyangdaehak-Ro, Sangnok-Gu Ansan, Ansan, Gyeonggi-Do, 15588, Republic of Korea
| | - Mingon Kang
- Department of Computer Science, University of Nevada, Las Vegas, NV, 89154, USA
| | - Jae-Yeol Joo
- Department of Pharmacy, College of Pharmacy, Hanyang University, Rm 407, Bldg.42, 55 Hanyangdaehak-Ro, Sangnok-Gu Ansan, Ansan, Gyeonggi-Do, 15588, Republic of Korea.
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