Cargando…

Identifying target ion channel-related genes to construct a diagnosis model for insulinoma

Background: Insulinoma is the most common functional pancreatic neuroendocrine tumor (PNET) with abnormal insulin hypersecretion. The etiopathogenesis of insulinoma remains indefinable. Based on multiple bioinformatics methods and machine learning algorithms, this study proposed exploring the molecu...

Descripción completa

Detalles Bibliográficos
Autores principales: Mo, Shuangyang, Wang, Yingwei, Wu, Wenhong, Zhao, Huaying, Jiang, Haixing, Qin, Shanyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10523017/
https://www.ncbi.nlm.nih.gov/pubmed/37772258
http://dx.doi.org/10.3389/fgene.2023.1181307
_version_ 1785110476275843072
author Mo, Shuangyang
Wang, Yingwei
Wu, Wenhong
Zhao, Huaying
Jiang, Haixing
Qin, Shanyu
author_facet Mo, Shuangyang
Wang, Yingwei
Wu, Wenhong
Zhao, Huaying
Jiang, Haixing
Qin, Shanyu
author_sort Mo, Shuangyang
collection PubMed
description Background: Insulinoma is the most common functional pancreatic neuroendocrine tumor (PNET) with abnormal insulin hypersecretion. The etiopathogenesis of insulinoma remains indefinable. Based on multiple bioinformatics methods and machine learning algorithms, this study proposed exploring the molecular mechanism from ion channel-related genes to establish a genetic diagnosis model for insulinoma. Methods: The mRNA expression profile dataset of GSE73338 was applied to the analysis, which contains 17 insulinoma samples, 63 nonfunctional PNET (NFPNET) samples, and four normal islet samples. Differently expressed ion channel-related genes (DEICRGs) enrichment analyses were performed. We utilized the protein–protein interaction (PPI) analysis and machine learning of LASSO and support vector machine-recursive feature elimination (SVM-RFE) to identify the target genes. Based on these target genes, a nomogram diagnostic model was constructed and verified by a receiver operating characteristic (ROC) curve. Moreover, immune infiltration analysis, single-gene gene set enrichment analysis (GSEA), and gene set variation analysis (GSVA) were executed. Finally, a drug–gene interaction network was constructed. Results: We identified 29 DEICRGs, and enrichment analyses indicated they were primarily enriched in ion transport, cellular ion homeostasis, pancreatic secretion, and lysosome. Moreover, the PPI network and machine learning recognized three target genes (MCOLN1, ATP6V0E1, and ATP4A). Based on these target genes, we constructed an efficiently predictable diagnosis model for identifying insulinomas with a nomogram and validated it with the ROC curve (AUC = 0.801, 95% CI 0.674–0.898). Then, single-gene GSEA analysis revealed that these target genes had a significantly positive correlation with insulin secretion and lysosome. In contrast, the TGF-beta signaling pathway was negatively associated with them. Furthermore, statistically significant discrepancies in immune infiltration were revealed. Conclusion: We identified three ion channel-related genes and constructed an efficiently predictable diagnosis model to offer a novel approach for diagnosing insulinoma.
format Online
Article
Text
id pubmed-10523017
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-105230172023-09-28 Identifying target ion channel-related genes to construct a diagnosis model for insulinoma Mo, Shuangyang Wang, Yingwei Wu, Wenhong Zhao, Huaying Jiang, Haixing Qin, Shanyu Front Genet Genetics Background: Insulinoma is the most common functional pancreatic neuroendocrine tumor (PNET) with abnormal insulin hypersecretion. The etiopathogenesis of insulinoma remains indefinable. Based on multiple bioinformatics methods and machine learning algorithms, this study proposed exploring the molecular mechanism from ion channel-related genes to establish a genetic diagnosis model for insulinoma. Methods: The mRNA expression profile dataset of GSE73338 was applied to the analysis, which contains 17 insulinoma samples, 63 nonfunctional PNET (NFPNET) samples, and four normal islet samples. Differently expressed ion channel-related genes (DEICRGs) enrichment analyses were performed. We utilized the protein–protein interaction (PPI) analysis and machine learning of LASSO and support vector machine-recursive feature elimination (SVM-RFE) to identify the target genes. Based on these target genes, a nomogram diagnostic model was constructed and verified by a receiver operating characteristic (ROC) curve. Moreover, immune infiltration analysis, single-gene gene set enrichment analysis (GSEA), and gene set variation analysis (GSVA) were executed. Finally, a drug–gene interaction network was constructed. Results: We identified 29 DEICRGs, and enrichment analyses indicated they were primarily enriched in ion transport, cellular ion homeostasis, pancreatic secretion, and lysosome. Moreover, the PPI network and machine learning recognized three target genes (MCOLN1, ATP6V0E1, and ATP4A). Based on these target genes, we constructed an efficiently predictable diagnosis model for identifying insulinomas with a nomogram and validated it with the ROC curve (AUC = 0.801, 95% CI 0.674–0.898). Then, single-gene GSEA analysis revealed that these target genes had a significantly positive correlation with insulin secretion and lysosome. In contrast, the TGF-beta signaling pathway was negatively associated with them. Furthermore, statistically significant discrepancies in immune infiltration were revealed. Conclusion: We identified three ion channel-related genes and constructed an efficiently predictable diagnosis model to offer a novel approach for diagnosing insulinoma. Frontiers Media S.A. 2023-09-12 /pmc/articles/PMC10523017/ /pubmed/37772258 http://dx.doi.org/10.3389/fgene.2023.1181307 Text en Copyright © 2023 Mo, Wang, Wu, Zhao, Jiang and Qin. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Mo, Shuangyang
Wang, Yingwei
Wu, Wenhong
Zhao, Huaying
Jiang, Haixing
Qin, Shanyu
Identifying target ion channel-related genes to construct a diagnosis model for insulinoma
title Identifying target ion channel-related genes to construct a diagnosis model for insulinoma
title_full Identifying target ion channel-related genes to construct a diagnosis model for insulinoma
title_fullStr Identifying target ion channel-related genes to construct a diagnosis model for insulinoma
title_full_unstemmed Identifying target ion channel-related genes to construct a diagnosis model for insulinoma
title_short Identifying target ion channel-related genes to construct a diagnosis model for insulinoma
title_sort identifying target ion channel-related genes to construct a diagnosis model for insulinoma
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10523017/
https://www.ncbi.nlm.nih.gov/pubmed/37772258
http://dx.doi.org/10.3389/fgene.2023.1181307
work_keys_str_mv AT moshuangyang identifyingtargetionchannelrelatedgenestoconstructadiagnosismodelforinsulinoma
AT wangyingwei identifyingtargetionchannelrelatedgenestoconstructadiagnosismodelforinsulinoma
AT wuwenhong identifyingtargetionchannelrelatedgenestoconstructadiagnosismodelforinsulinoma
AT zhaohuaying identifyingtargetionchannelrelatedgenestoconstructadiagnosismodelforinsulinoma
AT jianghaixing identifyingtargetionchannelrelatedgenestoconstructadiagnosismodelforinsulinoma
AT qinshanyu identifyingtargetionchannelrelatedgenestoconstructadiagnosismodelforinsulinoma