Bioinformatics analysis of mRNA and miRNA microarray to identify the key miRNA-mRNA pairs in cisplatin-resistant ovarian cancer

Comprehensive analysis of hub genes associated with cisplatin-resistance in ovarian cancer and screening of therapeutic drugs through bioinformatics and experimental validation

BACKGROUND

To identify key genes associated with cisplatin resistance in ovarian cancer, a comprehensive analysis was conducted on three datasets from the GEO database and through experimental validation.

METHODS

Gene expression profiles were retrieved from the GEO database. DEGs were identified by comparing gene expression profiles between cisplatin-sensitive and resistant ovarian cancer cell lines. The identified genes were further subjected to GO, KEGG, and PPI network analysis. Potential inhibitors of key genes were identified through methods such as LibDock nuclear molecular docking. In vitro assays and RT-qPCR were performed to assess the expression levels of key genes in ovarian cancer cell lines. The sensitivity of cells to chemotherapy and proliferation of key gene knockout cells were evaluated through CCK8 and Clonogenic assays.

RESULTS

Results showed that 12 genes influenced the chemosensitivity of the ovarian cancer cell line SKOV3, and 9 genes were associated with the prognosis and survival outcomes of ovarian cancer patients. RT-qPCR results revealed NDRG1, CYBRD1, MT2A, CNIH3, DPYSL3, and CARMIL1 were upregulated, whereas ERBB4, ANK3, B2M, LRRTM4, EYA4, and SLIT2 were downregulated in cisplatin-resistant cell lines. NDRG1, CYBRD1, and DPYSL3 knock-down significantly inhibited the proliferation of cisplatin-resistant cell line SKOV3. Finally, photofrin, a small-molecule compound targeting CYBRD1, was identified.

CONCLUSION

This study reveals changes in the expression level of some genes associated with cisplatin-resistant ovarian cancer. In addition, a new small molecule compound was identified for the treatment of cisplatin-resistant ovarian cancer.

Identification of upregulated exosomal miRNAs between A2780 and A2780/DDP human ovarian cancer cells by high-throughput sequencing

Exosomal miRNAs are known to play important roles in ovarian cancer and chemotherapeutic resistance. However, a systematic evaluation of characteristics of exosomal miRNAs involved in cisplatin resistance in ovarian cancer remains totally unclear. Exosomes (Exo-A2780, Exo-A2780/DDP) were extracted from cisplatin-sensitive cells (A2780) and cisplatin-resistant cells (A2780/DDP). Differential exosomal miRNA expression profiles were found by high-throughput sequencing (HTS). Target genes of the exo-miRNAs were predicted by using two online databases to increase the prediction accuracy. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were utilized to find biological relationships with chemoresistance. RT‒qPCR of three exosomal miRNAs was performed, and a protein‒protein interaction (PPI) network was established to identify the hub genes. The GDSC database was used to prove the association between hsa-miR-675-3p expression and the IC50 value. An integrated miRNA-mRNA network was constructed to predict miRNA-mRNA associations. The connection between hsa-miR-675-3p and ovarian cancer was discovered by immune microenvironment analyses. The upregulated exosomal miRNAs could regulate gene targets through signalling pathways such as the Ras, PI3K/Akt, Wnt, and ErbB pathways. GO and KEGG analyses indicated that the target genes were involved in protein binding, transcription regulator activity and DNA binding. The RT‒qPCR results were consistent with the HTS data, and the results of PPI network analysis suggested that FMR1 and CD86 were the hub genes. GDSC database analysis and construction of the integrated miRNA-mRNA network suggested that hsa-miR-675-3p was associated with drug resistance. Immune microenvironment analyses showed that hsa-miR-675-3p was crucial in ovarian cancer. The study suggested that exosomal hsa-miR-675-3p is a potential target for treating ovarian cancer and overcoming cisplatin resistance.

Development and Validation of a Prognostic Risk Model Based on Nature Killer Cells for Serous Ovarian Cancer

Nature killer (NK) cells are increasingly considered important in tumor microenvironment, but their role in predicting the prognosis of ovarian cancer has not been revealed. This study aimed to develop a prognostic risk model for ovarian cancer based on NK cells. Firstly, differentially expressed genes (DEGs) of NK cells were found by single-cell RNA-sequencing dataset analysis. Based on six NK-cell DEGs identified by univariable, Lasso and multivariable Cox regression analyses, a prognostic risk model for serous ovarian cancer was developed in the TCGA cohort. This model was then validated in three external cohorts, and evaluated as an independent prognostic factor by multivariable Cox regression analysis together with clinical characteristics. With the investigation of the underlying mechanism, a relation between a higher risk score of this model and more immune activities in tumor microenvironment was revealed. Furthermore, a detailed inspection of infiltrated immunocytes indicated that not only quantity, but also the functional state of these immunocytes might affect prognostic risk. Additionally, the potential of this model to predict immunotherapeutic response was exhibited by evaluating the functional state of cytotoxic T lymphocytes. To conclude, this study introduced a novel prognostic risk model based on NK-cell DEGs, which might provide assistance for the personalized management of serous ovarian cancer patients.

Downregulated Dual-Specificity Protein Phosphatase 1 in Ovarian Carcinoma: A Comprehensive Study With Multiple Methods

Introduction: We aimed to explore the abnormal expression of dual-specificity protein phosphatase 1 (DUSP1) and its latent molecular mechanisms in ovarian carcinoma (OVCA).

Materials and Methods: Two clinical cohorts collected from two different hospitals were used to evaluate the expression of DUSP1 protein in OVCA tissues. RNA-sequencing and microarray datasets were utilised to verify DUSP1 expression at mRNA levels in both OVCA tissues and in the peripheral blood of OVCA patients. Furthermore, an integrated calculation was performed to pool the standard mean difference (SMD) from each cohort in order to comprehensively assess the expression of DUSP1 in OVCA. Furthermore, we examined the relationship among DUSP1, tumour microenvironment (TME), and chemotherapy resistance in OVCA. Moreover, we used pathway enrichment analysis to explore the underlying mechanisms of DUSP1 in OVCA.

Results: A pooled SMD of −1.19 (95% CI [−2.00, −0.38], p = 0.004) with 1,240 samples revealed that DUSP1 was downregulated in OVCA at both mRNA and protein levels. The area under the receiver operating characteristic curve of 0.9235 indicated the downregulated DUSP1 in peripheral blood may have a non-invasive diagnostic value in OVCA. Through six algorithms, we identified that DUSP1 may related to tumour-infiltrating T cells and cancer associated fibroblasts (CAFs) in OVCA. Pathway enrichment demonstrated that DUSP1 might participate in the mitogen-activated protein kinase (MAPK) signalling pathway. Furthermore, DUSP1 may have relations with chemotherapy resistance, and a favourable combining affinity was observed in the paclitaxel-DUSP1 docking model.

Conclusion: DUSP1 was downregulated in OVCA, and this decreasing trend may affect the infiltration of CAFs. Finally, DUSP1 may have a targeting relation with paclitaxel and participate in MAPK signaling pathways.