Dataset Information

Synergistic AHR Binding Pathway with EMT Effects on Serous Ovarian Tumors Recognized by Multidisciplinary Integrated Analysis.

ABSTRACT: Epithelial ovarian cancers (EOCs) are fatal and obstinate among gynecological malignancies in advanced stage or relapsed status, with serous carcinomas accounting for the vast majority. Unlike EOCs, borderline ovarian tumors (BOTs), including serous BOTs, maintain a semimalignant appearance. Using gene ontology (GO)-based integrative analysis, we analyzed gene set databases of serous BOTs and serous ovarian carcinomas for dysregulated GO terms and pathways and identified multiple differentially expressed genes (DEGs) in various aspects. The SRC (SRC proto-oncogene, non-receptor tyrosine kinase) gene and dysfunctional aryl hydrocarbon receptor (AHR) binding pathway consistently influenced progression-free survival and overall survival, and immunohistochemical staining revealed elevated expression of related biomarkers (SRC, ARNT, and TBP) in serous BOT and ovarian carcinoma samples. Epithelial-mesenchymal transition (EMT) is important during tumorigenesis, and we confirmed the SNAI2 (Snail family transcriptional repressor 2, SLUG) gene showing significantly high performance by immunohistochemistry. During serous ovarian tumor formation, activated AHR in the cytoplasm could cooperate with SRC, enter cell nuclei, bind to AHR nuclear translocator (ARNT) together with TATA-Box Binding Protein (TBP), and act on DNA to initiate AHR-responsive genes to cause tumor or cancer initiation. Additionally, SNAI2 in the tumor microenvironment can facilitate EMT accompanied by tumorigenesis. Although it has not been possible to classify serous BOTs and serous ovarian carcinomas as the same EOC subtype, the key determinants of relevant DEGs (SRC, ARNT, TBP, and SNAI2) found here had a crucial role in the pathogenetic mechanism of both tumor types, implying gradual evolutionary tendencies from serous BOTs to ovarian carcinomas. In the future, targeted therapy could focus on these revealed targets together with precise detection to improve therapeutic effects and patient survival rates.

SUBMITTER: Su KM

PROVIDER: S-EPMC8389648 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundSerous ovarian cancer (SeOvCa) is an aggressive disease with differential and often inadequate therapeutic outcome after standard treatment. The Cancer Genome Atlas (TCGA) has provided rich molecular and genetic profiles from hundreds of primary surgical samples. These profiles confirm mutations of TP53 in ~100% of patients and an extraordinarily complex profile of DNA copy number changes with considerable patient-to-patient diversity. This raises the joint challenge of exploiting all new available datasets and reducing their confounding complexity for the purpose of predicting clinical outcomes and identifying disease relevant pathway alterations. We therefore set out to use multi-data type genomic profiles (mRNA, DNA methylation, DNA copy-number alteration and microRNA) available from TCGA to identify prognostic signatures for the prediction of progression-free survival (PFS) and overall survival (OS).Methodology/principal findingsWe implemented a multivariate Cox Lasso model and median time-to-event prediction algorithm and applied it to two datasets integrated from the four genomic data types. We (1) selected features through cross-validation; (2) generated a prognostic index for patient risk stratification; and (3) directly predicted continuous clinical outcome measures, that is, the time to recurrence and survival time. We used Kaplan-Meier p-values, hazard ratios (HR), and concordance probability estimates (CPE) to assess prediction performance, comparing separate and integrated datasets. Data integration resulted in the best PFS signature (withheld data: p-value = 0.008; HR = 2.83; CPE = 0.72).Conclusions/significanceWe provide a prediction tool that inputs genomic profiles of primary surgical samples and generates patient-specific predictions for the time to recurrence and survival, along with outcome risk predictions. Using integrated genomic profiles resulted in information gain for prediction of outcomes. Pathway analysis provided potential insights into functional changes affecting disease progression. The prognostic signatures, if prospectively validated, may be useful for interpreting therapeutic outcomes for clinical trials that aim to improve the therapy for SeOvCa patients.

Project description: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.

Project description:Ovarian tumors of low malignant potential and low-grade ovarian serous carcinomas are thought to represent different stages on a tumorigenic continuum and to develop along pathways distinct from high-grade ovarian serous carcinoma. We performed gene expression profiling on three normal human ovarian surface epithelia samples, and 10 low-grade and 10 high-grade ovarian serous carcinomas. Analysis of gene expression profiles of these samples has identified 80 genes upregulated and 232 genes downregulated in low-grade ovarian serous carcinomas. PAX2 was found to be one of the most upregulated genes in low-grade ovarian serous carcinoma. The upregulation of PAX2 was validated by real-time quantitative RT-PCR, western blot and immunohistochemical analyses. Real-time RT-PCR showed a statistically significant difference in PAX2 mRNA expression (expressed as fold change in comparison to normal human ovarian surface epithelia) among ovarian tumors of low malignant potential (1837.38, N=8), low-grade (183.12, N=17), and high-grade (3.72, N=23) carcinoma samples (P=0.015). Western blot analysis revealed strong PAX2 expression in ovarian tumors of low malignant potential (67%, N=3) and low-grade carcinoma samples (50%, N=10) but no PAX2 protein expression in high-grade carcinomas (0%, N=10). Using immunohistochemistry, tumors of low malignant potential (59%, N=17) and low-grade carcinoma (63%, N=16) samples expressed significantly stronger nuclear staining than high-grade ovarian carcinoma samples (9.1%, N=263). Furthermore, consistent with earlier immunohistochemical findings, PAX2 expression was expressed in the epithelial cells of fallopian tubes but not in normal ovarian surface epithelial cells. Our findings further support the two-tiered hypothesis that tumors of low malignant potential and low-grade ovarian serous carcinoma are on a continuum and are distinct from high-grade ovarian carcinomas. In addition, the absence of PAX2 expression in normal ovarian epithelia but expression in fallopian tube fimbria and ciliated epithelial inclusions would suggest the potential development of tumors of low malignant potential and of low-grade ovarian serous carcinomas from secondary Müllerian structures.

Dataset Information

Synergistic AHR Binding Pathway with EMT Effects on Serous Ovarian Tumors Recognized by Multidisciplinary Integrated Analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets