Integrated Bioinformatics Analysis for Identifying Hub Genes and Therapeutic Targets in Recurrent Breast Cancer Liver Metastasis

first_page Download PDF settings Order Article Reprints Font Type: Arial Georgia Verdana Font Size: Aa Aa Aa Line Spacing:    Column Width:    Background: Open AccessAbstract Integrated Bioinformatics Analysis for Identifying Hub Genes and Therapeutic Targets in Recurrent Breast Cancer Liver Metastasis † by Yuet-Hei Tyler KwokYuet-Hei Tyler Kwok SciProfiles Scilit Preprints.org Google Scholar and Kumaraswamy Naidu ChitralaKumaraswamy Naidu Chitrala SciProfiles Scilit Preprints.org Google Scholar * Department of Engineering Technology, University of Houston, Sugar Land, TX 77479, USA * Author to whom correspondence should be addressed. † Presented at the 3rd International Electronic Conference on Biomolecules, 23–25 April 2024; Available online: https://sciforum.net/event/IECBM2024. Proceedings 2024, 103(1), 35; https://doi.org/10.3390/proceedings2024103035 Published: 12 April 2024 (This article belongs to the Proceedings of The 3rd International Electronic Conference on Biomolecules) Download keyboard_arrow_down Download PDF Download PDF with Cover Download XML Download Epub Versions Notes Keywords: bioinformatics; hub genes; breast cancer; metastasis Introduction: Breast cancer during advancement to the metastatic stage involves the liver, thereby diminishing the survival rate among 50% of cases. Currently, there are few therapeutic protocols for breast cancer liver metastasis (BCLM) available, thereby necessitating a deeper understanding of the molecular patterns governing this molecular mechanism. Therefore, in the present study, by analyzing the differentially expressed genes (DEGs) between primary breast tumors and BCLM lesions, we aim to shed light on the diversities of this process.Methods: In this study, we investigated breast cancer liver metastasis relapse by employing a comprehensive approach that integrates data filtering, Gene Ontology and KEGG Pathway Analysis, Overall Survival analysis, identification of the alterations in the DEGs, visualization of the protein–protein interaction network, Signor 2.0, identification of positively correlated genes, screening results of the function of hub genes, immune cell infiltration analysis, copy number variant analysis, gene-to-mRNA interactions, transcription factor analysis, and identification of potential treatment targets.Results: Our results showed two genes, PCK1 and LPL, were differentially expressed between primary breast tumors and BCLM lesions. PCK1 is a regulator of gluconeogenesis, and LPL, involved in lipid metabolism, impacts cancer cell energetics and biosynthetic capabilities. Elevated PCK1 levels show a correlation with a poorer prognosis, indicating an aggressive phenotype. The transcription factors (AR, PPARA, RXRA, RXRB, and RXRG) regulating both genes offer insights into energy homeostasis, metabolism, and cell growth. Immune infiltration analysis suggests their collective role in modulating the tumor microenvironment, influencing immunosurveillance and evasion.Conclusion: This study's integrative approach unveils metabolic reprogramming, suggesting altered PCK1 and LPL expression are key in breast cancer metastasis recurrence. Author ContributionsConceptualization, K.N.C.; data curation, Y.-H.T.K. and K.N.C.; formal analysis, Y.-H.T.K. and K.N.C.; funding acquisition, K.N.C.; investigation, Y.-H.T.K. and K.N.C.; methodology, Y.-H.T.K. and K.N.C.; project administration, K.N.C.; resources, K.N.C.; software, Y.-H.T.K. and K.N.C.; supervision, K.N.C.; validation, Y.-H.T.K. and K.N.C.; visualization, Y.-H.T.K. and K.N.C.; writing—original draft, Y.-H.T.K. and K.N.C.; writing, review and editing, Y.-H.T.K. and K.N.C. All authors have read and agreed to the published version of the manuscript.FundingWork on the manuscript and its revisions was supported by the National Institute on Minority Health and Health Disparities (NIMHD) of the National Institutes of Health (NIH) to the University of Houston under award number U54MD015946. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.Institutional Review Board StatementEthical review and approval were waived for this study due to the usage of existing datasets and the study did not involve experiments related to human subjects or animals.Informed Consent StatementPatient consent was waived due to the usage of existing datasets and the study did not involve experiments related to human subjects or animals.Data Availability StatementPublicly available datasets were analyzed in this study.Conflicts of InterestThe authors declare no conflicts of interest.Disclaimer/Publisher's Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Share and Cite MDPI and ACS Style Kwok, Y.-H.T.; Chitrala, K.N. Integrated Bioinformatics Analysis for Identifying Hub Genes and Therapeutic Targets in Recurrent Breast Cancer Liver Metastasis. Proceedings 2024, 103, 35. https://doi.org/10.3390/proceedings2024103035 AMA Style Kwok Y-HT, Chitrala KN. Integrated Bioinformatics Analysis for Identifying Hub Genes and Therapeutic Targets in Recurrent Breast Cancer Liver Metastasis. Proceedings. 2024; 103(1):35. https://doi.org/10.3390/proceedings2024103035 Chicago/Turabian Style Kwok, Yuet-Hei Tyler, and Kumaraswamy Naidu Chitrala. 2024. "Integrated Bioinformatics Analysis for Identifying Hub Genes and Therapeutic Targets in Recurrent Breast Cancer Liver Metastasis" Proceedings 103, no. 1: 35. https://doi.org/10.3390/proceedings2024103035 Article Metrics No No Article Access Statistics Multiple requests from the same IP address are counted as one view.