Dataset Information

Screening differentially expressed genes between endometriosis and ovarian cancer to find new biomarkers for endometriosis.

ABSTRACT:

Aim

Endometriosis is one of the most common reproductive system diseases, but the mechanisms of disease progression are still unclear. Due to its high recurrence rate, searching for potential therapeutic biomarkers involved in the pathogenesis of endometriosis is an urgent issue.

Methods

Due to the similarities between endometriosis and ovarian cancer, four endometriosis datasets and one ovarian cancer dataset were downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified, followed by gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and protein-protein interaction (PPI) analyses. Then, we validated gene expression and performed survival analysis with ovarian serous cystadenocarcinoma (OV) datasets in TCGA/GTEx database, and searched for potential drugs in the Drug-Gene Interaction Database. Finally, we explored the miRNAs of key genes to find biomarkers associated with the recurrence of endometriosis.

Results

In total, 104 DEGs were identified in the endometriosis datasets, and the main enriched GO functions included cell adhesion, extracellular exosome and actin binding. Fifty DEGs were identified between endometriosis and ovarian cancer datasets including 11 consistently regulated genes, and nine DEGs with significant expression in TCGA/GTEx. Only IGHM had both significant expression and an association with survival, three module DEGs and two significantly expressed DEGs had drug associations, and 10 DEGs had druggability.

Conclusions

ITGA7, ITGBL1 and SORBS1 may help us understand the invasive nature of endometriosis, and IGHM might be related to recurrence; moreover, these genes all may be potential therapeutic targets.KEY MESSAGEThis manuscript used a bioinformatics approach to find target genes for the treatment of endometriosis.This manuscript used a new approach to find target genes by drawing on common characteristics between ovarian cancer and endometriosis.We screened relevant therapeutic agents for target genes in the drug database, and performed histological validation of target genes with both expression and survival analysis difference in cancer databases.

SUBMITTER: Lu Z

PROVIDER: S-EPMC8381947 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Wilms tumor (WT) is the most common pediatric renal malignant tumor in the world. Overall, the prognosis of Wilms tumor is very good. However, the prognosis of patients with anaplastic tumor histology or disease relapse is still poor, and their recurrence rate, metastasis rate and mortality are significantly increased compared with others. Currently, the combination of histopathological examination and molecular biology is essential to predict prognosis and guide the treatment. However, the molecular mechanism has not been well studied. Genetic profiling may be helpful in some way. Hence, we sought to identify novel promising biomarkers of WT by integrating bioinformatics analysis and to identify genes associated with the pathogenesis of WT. In the presented study, the NCBI Gene Expression Omnibus was used to download two datasets of gene expression profiles related to WT patients for the purpose of detecting overlapped differentially expressed genes (DEGs). The DEGs were then uploaded to DAVID database for enrichment analysis. In addition, the functional interactions between proteins were evaluated by simulating the protein-protein interaction (PPI) network of DEGs. The impact of selected hub genes on survival in WT patients was analyzed by using the online tool R2: Genomics Analysis and Visualization Platform. The correlation between gene expression and the degree of immune infiltration was assessed by the Estimation of Stromal and Immune cells in Malignant Tumor tissues using the Expression (ESTIMATE) algorithm and the single sample GSEA. Top 12 genes were identified for further study after constructing a PPI network and screening hub gene modules. Kinesin family member 2C (KIF2C) was identified as the most significant gene predicting the overall survival of WT patients. The expression of KIF2C in WT was further verified by quantitative real-time polymerase chain reaction and immunohistochemistry. Furthermore, we found that KIF2C was significantly correlated with immune cell infiltration in WT. Our present study demonstrated that altered expression of KIF2C may be involved in WT and serve as a potential prognostic biomarker for WT patients.

Project description:BACKGROUND:To screen and analyze differentially expressed genes in pancreatic carcinoma tissues taken from Mongolian and Han patients by Affymetrix Genechip. METHODS:Pancreatic ductal cell carcinoma tissues were collected from the Mongolian and Han patients undergoing resection in the Second Affiliated Hospital of Nanchang University from March 2015 to May 2018 and the total RNA was extracted. Differentially expressed genes were selected from the total RNA qualified by Nanodrop 2000 and Agilent 2100 using Affymetrix and a cartogram was drawn; The gene ontology (GO) analysis and Pathway analysis were used for the collection and analysis of biological information of these differentially expressed genes. Finally, some differentially expressed genes were verified by real-time PCR. RESULTS:Through the microarray analysis of gene expression, 970 differentially expressed genes were detected by comparing pancreatic cancer tissue samples between Mongolian and Han patients. A total of 257 genes were significantly up-regulated in pancreatic cancer tissue samples in Mongolian patients; while a total of 713 genes were down-regulated. In the Gene Ontology database, 815 differentially expressed genes were identified with clear GO classification, and CPB1 gene showed the highest increase in expression level (multiple difference: 31.76). The pathway analysis detected 28 signaling pathways that included these differentially expressed genes, involving a total of 178 genes. Among these pathways, the enrichment of differentially expressed genes in the FAK signaling pathway was the strongest and COL11A1 gene showed the highest multiple difference (multiple difference: 5.02). The expression of differentially expressed genes CPB1, COL11A1?ITGA4?BIRC3?PAK4?CPA1?CLPS?PIK3CG and HLA-DPA1 determined by real-time PCR were consistent with the results of gene microarray analysis. CONCLUSIONS:The results of microarray analysis of gene expression profiles showed that there are a large number of differentially expressed genes in pancreatic cancer tissue samples comparing Mongolian and Han population. These genes are closely related to the cell proliferation, differentiation, invasion, metastasis and multi-drug resistance in pancreatic cancer. They are also involved in the regulation of multiple important signaling pathways in organisms.

Project description:OBJECTIVE:There is an increased risk of developing ovarian cancer (OC) in patients with endometriosis. Hence, development of new biomarkers may provide a positive clinical outcome for early detection. MicroRNAs (miRNAs) are small non-coding RNAs that play an important role in biological and pathological process and are currently used as diagnostic and prognostic markers in various cancers. In the current study, we assessed the differential expression of miRNAs from 19 paired ovarian cancer and its associated endometriosis tissue samples. In addition we also analyzed the downstream targets of those miRNAs. METHODS:Nineteen paired cases of ovarian cancer and endometriosis foci were identified by a gynecologic pathologist and macro-dissected. The total RNAs were extracted and subjected to comprehensive miRNA profiling from the pooled samples of these two different entities using microarray analysis. Later, the abnormal expressions of few selected miRNAs were validated in individual cases by quantitative real-time PCR (qRT-PCR). Ingenuity pathway analysis revealed target mRNAs which were validated by qRT-PCR. RESULTS:The miRNA profiling identified deregulation of greater than 1156 miRNAs in OC, of which the top seven were further validated by qRT-PCR. The expression of miR-1, miR-133a, and miR-451 were reduced significantly (p<0.0001) in the OC patients compared to its associated endometriosis. In contrast, the expression of miR-141, miR-200a, miR-200c, and miR-3613 were elevated significantly (p<0.05) in most of the OC patients. Furthermore, among the downstream mRNAs of these miRNAs, the level of PTEN expression was significantly (p<0.05) reduced in OC compared to endometriosis while no significant difference was observed in NF-?B expression. CONCLUSION:The expression of miRNAs and mRNAs in OC were significantly different compared to its concurrent endometriosis. These differential expressed miRNAs may serve as potential diagnostic and prognostic biomarkers for OC associated with endometriosis.