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

Abstract

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.

Endoscopic ultrasonography-based intratumoral and peritumoral machine learning radiomics analyses for distinguishing insulinomas from non-functional pancreatic neuroendocrine tumors

“…While the excessive secretion of insulin is essential for diagnosing insulinomas, delayed or inaccurate identification of hypoglycemia and other common presentations often result in severe consequences and mortality associated with insulinomas ( 8 ). In fact, patients with insulinomas frequently endure misdiagnosis as neurological disorders over extended periods due to the diverse clinical symptoms, nonspecific biochemical tests, and low-specificity clinical prediction models ( 8 , 45 ). Currently, the 72-hour fasting test is the established diagnostic procedure for insulinomas ( 12 ).…”

“…Compared to NF-PNETs, F-PNETs have the capability to secrete various hormones or peptides such as insulin, gastrin, vasoactive intestinal peptide (VIP), glucagon, and somatostatin, resulting in distinct symptoms. Among these, insulinomas are the most prevalent subtype of F-PNETs, causing recurrent hypoglycemia as a consequence of persistent endogenous hyperinsulinism (7,8). Patients with insulinomas often face challenges in diagnosis, as the diverse clinical presentations, nonspecific biochemical tests, and lack of a specific clinical diagnostic model can lead to misdiagnosis for prolonged periods (9)(10)(11)(12).…”

DataSheet_1.pdf

“…Recurrent hypoglycemia, resulting from abnormal endogenous hyperinsulinism, is a characteristic manifestation of insulinomas. While the excessive secretion of insulin is essential for diagnosing insulinomas, delayed or inaccurate identification of hypoglycemia and other common presentations often result in severe consequences and mortality associated with insulinomas (8). In fact, patients with insulinomas frequently endure misdiagnosis as neurological disorders over extended periods due to the diverse clinical symptoms, nonspecific biochemical tests, and low-specificity clinical prediction models (8,45).…”

DataSheet_4.pdf

“…Compared to NF-PNETs, F-PNETs have the capability to secrete various hormones or peptides such as insulin, gastrin, vasoactive intestinal peptide (VIP), glucagon, and somatostatin, resulting in distinct symptoms. Among these, insulinomas are the most prevalent subtype of F-PNETs, causing recurrent hypoglycemia as a consequence of persistent endogenous hyperinsulinism (7,8). Patients with insulinomas often face challenges in diagnosis, as the diverse clinical presentations, nonspecific biochemical tests, and lack of a specific clinical diagnostic model can lead to misdiagnosis for prolonged periods (9)(10)(11)(12).…”