Bioinformatics analysis and verification of molecular targets in ovarian cancer stem-like cells

Spheroids formation in large drops suspended in superhydrophobic paper cones

The utilization of 3D cell culture for spheroid formation holds significant implications in cancer research, contributing to a fundamental understanding of the disease and aiding drug development. Conventional methods such as the hanging drop technique and other alternatives encounter limitations due to smaller drop volumes, leading to nutrient starvation and restricted culture duration. In this study, we present a straightforward approach to creating superhydrophobic paper cones capable of accommodating large volumes of culture media drops. These paper cones have sterility, autoclavability, and bacterial repellent properties. Leveraging these attributes, we successfully generate large spheroids of ovarian cancer cells and, as a proof of concept, conduct drug screening to assess the impact of carboplatin. Thus, our method enables the preparation of flexible superhydrophobic surfaces for laboratory applications in an expeditious manner, exemplified here through spheroid formation and drug screening demonstrations.

Exploring the tumor micro-environment in primary and metastatic tumors of different ovarian cancer histotypes

Ovarian cancer is a highly heterogeneous disease consisting of at least five different histological subtypes with varying clinical features, cells of origin, molecular composition, risk factors, and treatments. While most single-cell studies have focused on High grade serous ovarian cancer, a comprehensive landscape of the constituent cell types and their interactions within the tumor microenvironment are yet to be established in the different ovarian cancer histotypes. Further characterization of tumor progression, metastasis, and various histotypes are also needed to connect molecular signatures to pathological grading for personalized diagnosis and tailored treatment. In this study, we leveraged high-resolution single-cell RNA sequencing technology to elucidate the cellular compositions on 21 solid tumor samples collected from 12 patients with six ovarian cancer histotypes and both primary (ovaries) and metastatic (omentum, rectum) sites. The diverse collection allowed us to deconstruct the histotypes and tumor site-specific expression patterns of cells in the tumor, and identify key marker genes and ligand-receptor pairs that are active in the ovarian tumor microenvironment. Our findings can be used in improving precision disease stratification and optimizing treatment options.

Construction of miRNA-lncRNA-mRNA co-expression network affecting EMT-mediated cisplatin resistance in ovarian cancer

Platinum resistance is one of the major concerns in ovarian cancer treatment. Recent evidence shows the critical role of epithelial-mesenchymal transition (EMT) in this resistance. Epithelial-like ovarian cancer cells show decreased sensitivity to cisplatin after cisplatin treatment. Our study prospected the association between epithelial phenotype and response to cisplatin in ovarian cancer. Microarray dataset GSE47856 was acquired from the GEO database. After identifying differentially expressed genes (DEGs) between epithelial-like and mesenchymal-like cells, the module identification analysis was performed using weighted gene co-expression network analysis (WGCNA). The gene ontology (GO) and pathway analyses of the most considerable modules were performed. The protein-protein interaction network was also constructed. The hub genes were specified using Cytoscape plugins MCODE and cytoHubba, followed by the survival analysis and data validation. Finally, the co-expression of miRNA-lncRNA-TF with the hub genes was reconstructed. The co-expression network analysis suggests 20 modules relating to the Epithelial phenotype. The antiquewhite4, brown and darkmagenta modules are the most significant non-preserved modules in the Epithelial phenotype and contain the most differentially expressed genes. GO, and KEGG pathway enrichment analyses on these modules divulge that these genes were primarily enriched in the focal adhesion, DNA replication pathways and stress response processes. ROC curve and overall survival rate analysis show that the co-expression pattern of the brown module's hub genes could be a potential prognostic biomarker for ovarian cancer cisplatin resistance.

GBP5 and ACSS3: two potential biomarkers of high-grade ovarian cancer identified through downstream analysis of microarray data

Among all malignancies of the reproductive organs, ovarian cancer is the sixth leading cause of death for women. Several factors contribute to the uncontrolled expression of certain genes in cancer thus making them act as oncogenes or tumour suppressors. In this study, we have examined four microarray datasets of high-grade ovarian cancer cells to identify differentially expressed genes (DEGs). 362 and 94 common DEGs were identified as up-regulated and down-regulated, respectively from 119 disease and 31 control samples. The DEGs were further analysed for their gene ontologies (GO), pathway, protein-protein interactions and co-expression. Most of the biological processes were associated with cellular processes, biological regulation, metabolic processes, and developmental processes. Further, regulatory networks were constructed by the DEGs which are also co-expressed and the hub genes were identified. The hub genes targeted by a large number of microRNAs (miRNAs) were further analyzed to reveal their role in the overall survival of cancer patients. Finally, GBP5 and ACSS3 were highlighted as potential biomarkers for ovarian cancer research.Communicated by Ramaswamy H. Sarma.

A Combined Long Noncoding RNA Signature as a Candidate Prognostic Biomarker for Ovarian Cancer

Aims

Dysregulated long noncoding RNAs (lncRNAs) contributing to ovarian cancer (OC) development may serve as prognostic biomarker. We aimed to explore a lncRNA signature to serve as prognostic biomarker of OC.

Methods

Univariate Cox regression was conducted on the lncRNA expression dataset from the TCGA cohort, and 246 genes significantly associated with survival were retained for building a model. A random forest survival model was carried out, and a model was developed using 6 genes with the highest frequency. The selected genes were applied in a Cox multivariate regression model for prognostic prediction by calculating the risk score. We also used CCK-8, EdU, and colony formation assays to validate the function of these lncRNAs in OC cells.

Results

This study confirmed that the 6-lncRNA combined signature was related to OC prognosis. Systematic analysis demonstrated that lncRNA-associated genes were enriched in oncogenic signalling pathways. Five out of the 6 lncRNAs participated in OC proliferation.

Conclusion

We established a 6-lncRNA combined signature for OC prognosis, which may serve as powerful prognostic biomarker for OC after further validation.