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Mares-Quiñones MD, Galán-Vásquez E, Pérez-Rueda E, Pérez-Ishiwara DG, Medel-Flores MO, Gómez-García MDC. Identification of modules and key genes associated with breast cancer subtypes through network analysis. Sci Rep 2024; 14:12350. [PMID: 38811600 PMCID: PMC11137066 DOI: 10.1038/s41598-024-61908-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 05/10/2024] [Indexed: 05/31/2024] Open
Abstract
Breast cancer is the most common malignancy in women around the world. Intratumor and intertumoral heterogeneity persist in mammary tumors. Therefore, the identification of biomarkers is essential for the treatment of this malignancy. This study analyzed 28,143 genes expressed in 49 breast cancer cell lines using a Weighted Gene Co-expression Network Analysis to determine specific target proteins for Basal A, Basal B, Luminal A, Luminal B, and HER2 ampl breast cancer subtypes. Sixty-five modules were identified, of which five were characterized as having a high correlation with breast cancer subtypes. Genes overexpressed in the tumor were found to participate in the following mechanisms: regulation of the apoptotic process, transcriptional regulation, angiogenesis, signaling, and cellular survival. In particular, we identified the following genes, considered as hubs: IFIT3, an inhibitor of viral and cellular processes; ETS1, a transcription factor involved in cell death and tumorigenesis; ENSG00000259723 lncRNA, expressed in cancers; AL033519.3, a hypothetical gene; and TMEM86A, important for regulating keratinocyte membrane properties, considered as a key in Basal A, Basal B, Luminal A, Luminal B, and HER2 ampl breast cancer subtypes, respectively. The modules and genes identified in this work can be used to identify possible biomarkers or therapeutic targets in different breast cancer subtypes.
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Affiliation(s)
- María Daniela Mares-Quiñones
- Laboratorio de Biomedicina Molecular, Programa de Doctorado en Biotecnología, Escuela Nacional de Medicina y Homeopatía, Instituto Politécnico Nacional, Ciudad de México, Mexico
| | - Edgardo Galán-Vásquez
- Departamento de Ingeniería de Sistemas Computacionales y Automatización, Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, Ciudad Universitaria, Ciudad de México, Mexico
| | - Ernesto Pérez-Rueda
- Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, Unidad Académica del Estado de Yucatán, Mérida, Mexico
| | - D Guillermo Pérez-Ishiwara
- Laboratorio de Biomedicina Molecular, Programa de Doctorado en Biotecnología, Escuela Nacional de Medicina y Homeopatía, Instituto Politécnico Nacional, Ciudad de México, Mexico
| | - María Olivia Medel-Flores
- Laboratorio de Biomedicina Molecular, Programa de Doctorado en Biotecnología, Escuela Nacional de Medicina y Homeopatía, Instituto Politécnico Nacional, Ciudad de México, Mexico
| | - María Del Consuelo Gómez-García
- Laboratorio de Biomedicina Molecular, Programa de Doctorado en Biotecnología, Escuela Nacional de Medicina y Homeopatía, Instituto Politécnico Nacional, Ciudad de México, Mexico.
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Yu Y, Li L, Luo B, Chen D, Yin C, Jian C, You Q, Wang J, Fang L, Cai D, Sun J. Predicting potential therapeutic targets and small molecule drugs for early-stage lung adenocarcinoma. Biomed Pharmacother 2024; 174:116528. [PMID: 38555814 DOI: 10.1016/j.biopha.2024.116528] [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: 01/16/2024] [Revised: 03/28/2024] [Accepted: 03/28/2024] [Indexed: 04/02/2024] Open
Abstract
Lung cancer is a leading cause of cancer-related mortality worldwide, with non-small cell lung cancer (NSCLC) constituting the majority, and its main subtype being lung adenocarcinoma (LUAD). Despite substantial advances in LUAD diagnosis and treatment, early diagnostic biomarkers inadequately fulfill clinical requirements. Thus, we conducted bioinformatics analysis to identify potential biomarkers and corresponding therapeutic drugs for early-stage LUAD patients. Here we identified a total of 10 differentially expressed genes (DEGs) with survival significance through the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Subsequently, we identified a promising small molecule drug, Aminopurvalanol A, based on the 10 key genes using the L1000FWD application, which was validated by molecular docking followed by in vivo and in vitro experiments. The results highlighted TOP2A, CDH3, ASPM, CENPF, SLC2A1, and PRC1 as potential detection biomarkers for early LUAD. We confirmed the efficacy and safety of Aminopurvalanol A, providing valuable insights for the clinical management of LUAD.
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Affiliation(s)
- Yongxin Yu
- Institute of Cancer, Xinqiao Hospital, Army Medical University, Chongqing 400037, China
| | - Lingchen Li
- Institute of Cancer, Xinqiao Hospital, Army Medical University, Chongqing 400037, China
| | - Bangyu Luo
- Institute of Cancer, Xinqiao Hospital, Army Medical University, Chongqing 400037, China
| | - Diangang Chen
- Institute of Cancer, Xinqiao Hospital, Army Medical University, Chongqing 400037, China
| | - Chenrui Yin
- Institute of Cancer, Xinqiao Hospital, Army Medical University, Chongqing 400037, China
| | - Chunli Jian
- Institute of Cancer, Xinqiao Hospital, Army Medical University, Chongqing 400037, China
| | - Qiai You
- Institute of Cancer, Xinqiao Hospital, Army Medical University, Chongqing 400037, China
| | - Jianmin Wang
- Department of Oncology, Chongqing Hospital of Traditional Chinese Medicine, Chongqing 400021, China
| | - Ling Fang
- Institute of Cancer, Xinqiao Hospital, Army Medical University, Chongqing 400037, China
| | - Dingqin Cai
- Institute of Cancer, Xinqiao Hospital, Army Medical University, Chongqing 400037, China
| | - Jianguo Sun
- Institute of Cancer, Xinqiao Hospital, Army Medical University, Chongqing 400037, China.
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Zarrella JA, Tsurumi A. Genome-wide transcriptome profiling and development of age prediction models in the human brain. Aging (Albany NY) 2024; 16:4075-4094. [PMID: 38428408 DOI: 10.18632/aging.205609] [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: 05/02/2022] [Accepted: 03/28/2023] [Indexed: 03/03/2024]
Abstract
Aging-related transcriptome changes in various regions of the healthy human brain have been explored in previous works, however, a study to develop prediction models for age based on the expression levels of specific panels of transcripts is lacking. Moreover, studies that have assessed sexually dimorphic gene activities in the aging brain have reported discrepant results, suggesting that additional studies would be advantageous. The prefrontal cortex (PFC) region was previously shown to have a particularly large number of significant transcriptome alterations during healthy aging in a study that compared different regions in the human brain. We harmonized neuropathologically normal PFC transcriptome datasets obtained from the Gene Expression Omnibus (GEO) repository, ranging in age from 21 to 105 years, and found a large number of differentially regulated transcripts in the old and elderly, compared to young samples overall, and compared female and male-specific expression alterations. We assessed the genes that were associated with age by employing ontology, pathway, and network analyses. Furthermore, we applied various established (least absolute shrinkage and selection operator (Lasso) and Elastic Net (EN)) and recent (eXtreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM)) machine learning algorithms to develop accurate prediction models for chronological age and validated them. Studies to further validate these models in other large populations and molecular studies to elucidate the potential mechanisms by which the transcripts identified may be related to aging phenotypes would be advantageous.
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Affiliation(s)
- Joseph A Zarrella
- Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Amy Tsurumi
- Department of Surgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Shriner's Hospitals for Children-Boston, Boston, MA 02114, USA
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Muthamilselvan S, Palaniappan A. BrcaDx: precise identification of breast cancer from expression data using a minimal set of features. FRONTIERS IN BIOINFORMATICS 2023; 3:1103493. [PMID: 37287543 PMCID: PMC10242386 DOI: 10.3389/fbinf.2023.1103493] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Accepted: 05/15/2023] [Indexed: 06/09/2023] Open
Abstract
Background: Breast cancer is the foremost cancer in worldwide incidence, surpassing lung cancer notwithstanding the gender bias. One in four cancer cases among women are attributable to cancers of the breast, which are also the leading cause of death in women. Reliable options for early detection of breast cancer are needed. Methods: Using public-domain datasets, we screened transcriptomic profiles of breast cancer samples, and identified progression-significant linear and ordinal model genes using stage-informed models. We then applied a sequence of machine learning techniques, namely, feature selection, principal components analysis, and k-means clustering, to train a learner to discriminate "cancer" from "normal" based on expression levels of identified biomarkers. Results: Our computational pipeline yielded an optimal set of nine biomarker features for training the learner, namely, NEK2, PKMYT1, MMP11, CPA1, COL10A1, HSD17B13, CA4, MYOC, and LYVE1. Validation of the learned model on an independent test dataset yielded a performance of 99.5% accuracy. Blind validation on an out-of-domain external dataset yielded a balanced accuracy of 95.5%, demonstrating that the model has effectively reduced the dimensionality of the problem, and learnt the solution. The model was rebuilt using the full dataset, and then deployed as a web app for non-profit purposes at: https://apalania.shinyapps.io/brcadx/. To our knowledge, this is the best-performing freely available tool for the high-confidence diagnosis of breast cancer, and represents a promising aid to medical diagnosis.
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Asiedu E, Larbi A, Adankwah E, Yambah JK, Sakyi SA, Kwarteng EVS, Obiri-Yeboah D, Kwarteng A. Transcriptomic profiling identifies host-derived biomarker panels for assessing cerebral malaria. GENE REPORTS 2022. [DOI: 10.1016/j.genrep.2022.101650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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