Journal
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
Volume 22, Issue 8, Pages -Publisher
MDPI
DOI: 10.3390/ijms22084105
Keywords
borderline ovarian tumors (BOTs); gene ontology (GO); functionome-based and data-driven analysis; immune and inflammatory response; cell membrane and transporter; cell cycle and signaling; cell metabolism; galactose catabolism
Funding
- Ministry of Science and Technology [MOST 107-2314-B-016-036]
- Tri-Service General Hospital [TSGH-C108-115, TSGH-D-109106, TSGH-D-109189, TSGH-D-110172]
- Teh-Tzer Study Group for Human Medical Research Foundation
- Taipei Veterans General Hospital [V109E-005-5(110)]
- Taipei Veterans General Hospital, Tri-Service General Hospital, Academia Sinica Joint Research Programs [VTA110-T-4-1, VTA110-V5-1-2]
- Cheng-Hsin General Hospital
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This study utilized integrated analysis to explore gene expression profiles of BOTs and its subtypes. Key dysregulated biomolecular functions were identified, including immune response, cell membrane, cell cycle, and cell metabolism-related functions, contributing to the pathogenesis of BOTs. Relevant genes involved in these functions were identified, highlighting the potential roles of immunological functionome and metabolic pathways in the tumorigenesis of BOTs.
The pathogenesis and molecular mechanisms of ovarian low malignant potential (LMP) tumors or borderline ovarian tumors (BOTs) have not been fully elucidated to date. Surgery remains the cornerstone of treatment for this disease, and diagnosis is mainly made by histopathology to date. However, there is no integrated analysis investigating the tumorigenesis of BOTs with open experimental data. Therefore, we first utilized a functionome-based speculative model from the aggregated obtainable datasets to explore the expression profiling data among all BOTs and two major subtypes of BOTs, serous BOTs (SBOTs) and mucinous BOTs (MBOTs), by analyzing the functional regularity patterns and clustering the separate gene sets. We next prospected and assembled the association between these targeted biomolecular functions and their related genes. Our research found that BOTs can be accurately recognized by gene expression profiles by means of integrative polygenic analytics among all BOTs, SBOTs, and MBOTs; the results exhibited the top 41 common dysregulated biomolecular functions, which were sorted into four major categories: immune and inflammatory response-related functions, cell membrane- and transporter-related functions, cell cycle- and signaling-related functions, and cell metabolism-related functions, which were the key elements involved in its pathogenesis. In contrast to previous research, we identified 19 representative genes from the above classified categories (IL6, CCR2 for immune and inflammatory response-related functions; IFNG, ATP1B1, GAS6, and PSEN1 for cell membrane- and transporter-related functions; CTNNB1, GATA3, and IL1B for cell cycle- and signaling-related functions; and AKT1, SIRT1, IL4, PDGFB, MAPK3, SRC, TWIST1, TGFB1, ADIPOQ, and PPARGC1A for cell metabolism-related functions) that were relevant in the cause and development of BOTs. We also noticed that a dysfunctional pathway of galactose catabolism had taken place among all BOTs, SBOTs, and MBOTs from the analyzed gene set databases of canonical pathways. With the help of immunostaining, we verified significantly higher performance of interleukin 6 (IL6) and galactose-1-phosphate uridylyltransferase (GALT) among BOTs than the controls. In conclusion, a bioinformatic platform of gene-set integrative molecular functionomes and biophysiological pathways was constructed in this study to interpret the complicated pathogenic pathways of BOTs, and these important findings demonstrated the dysregulated immunological functionome and dysfunctional metabolic pathway as potential roles during the tumorigenesis of BOTs and may be helpful for the diagnosis and therapy of BOTs in the future.
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