Determination of Potential Therapeutic Targets and Prognostic Markers of Ovarian Cancer by Bioinformatics Analysis

GNB4 Silencing Promotes Pyroptosis to Inhibit the Development of Glioma by Activating cGAS-STING Pathway

The induction of immunogenic cell death is a promising therapeutic option for gliomas. Pyroptosis is a type of programmed immunogenic cell death and its role in gliomas remains unclear. Differentially expressed genes (DEGs) were obtained from GSE4290 and GSE31262 datasets. Hub genes were screened from protein-protein interaction networks and assessed using principal component analysis and receiver operating characteristic curves. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to measure the mRNA expression of hub genes. Pyroptosis and pathway-related proteins were assessed using western blotting. Inflammatory factor levels were determined using enzyme-linked immunosorbent assay. The effect of guanine nucleotide-binding protein-4 (GNB4) on proliferation, migration, and invasion was evaluated using a cell viability test kit and wound-healing and transwell assays. In total, 202 DEGs were identified. Among them, F2R, GNG4, GNG3, PRKCB, and GNB4 were identified as hub genes in gliomas after comprehensive bioinformatics analysis. GNB4 was significantly upregulated in glioma cells compared to normal brain glial cells. Silencing GNB4 significantly inhibited proliferation, invasion, and migration of glioma cells. The expression of pyroptosis-related proteins increased after GNB4 silencing, with elevated levels of inflammatory factors. Pyroptosis inhibitors reversed the inhibitory effects of GNB4 silencing on cell proliferation, migration, and invasion. Additionally, GNB4 silencing activated the cGAS-STING pathway. Treatment with a cGAS-STING pathway inhibitor reversed the inhibition of proliferation, migration, and invasion while downregulating the expression of pyroptosis-related proteins. Silencing GNB4 promotes pyroptosis and thus inhibits the proliferation, migration, and invasion of glioma cells by activating the cGAS-STING pathway, which is a promising biomarker and therapeutic target for glioma.

Personalization of Therapy in High-Grade Serous Tubo-Ovarian Cancer-The Possibility or the Necessity?

High-grade serous tubo-ovarian cancer (HGSTOC) is the most lethal tumor of the female genital tract. The foregoing therapy consists of cytoreduction followed by standard platinum/taxane chemotherapy; alternatively, for primary unresectable tumors, neo-adjuvant platinum/taxane chemotherapy followed by delayed interval cytoreduction. In patients with suboptimal surgery or advanced disease, different forms of targeted therapy have been accepted or tested in clinical trials. Studies on HGSTOC discovered its genetic and proteomic heterogeneity, epigenetic regulation, and the role of the tumor microenvironment. These findings turned attention to the fact that there are several distinct primary tumor subtypes of HGSTOC and the unique biology of primary, metastatic, and recurrent tumors may result in a differential drug response. This results in both chemo-refractoriness of some primary tumors and, what is significantly more frequent and destructive, secondary chemo-resistance of metastatic and recurrent HGSTOC tumors. Treatment possibilities for platinum-resistant disease include several chemotherapeutics with moderate activity and different targeted drugs with difficult tolerable effects. Therefore, the question appears as to why different subtypes of ovarian cancer are predominantly treated based on the same therapeutic schemes and not in an individualized way, adjusted to the biology of a specific tumor subtype and temporal moment of the disease. The paper reviews the genomic, mutational, and epigenetic signatures of HGSTOC subtypes and the tumor microenvironment. The clinical trials on personalized therapy and the overall results of a new, comprehensive approach to personalized therapy for ovarian cancer have been presented and discussed.

Molecular Analysis of Prognosis and Immune Infiltration of Ovarian Cancer Based on Homeobox D Genes

Background

Homeobox D (HOXD) genes were associated with cancer pathogenesis. However, the role of HOXD genes in ovarian cancer (OC) and the possible mechanisms involved are unclear. In this study, we analyzed the function and regulatory mechanisms and functions of HOXD genes in OC based on comprehensive bioinformatics analysis.

Methods

Expression of HOXD1/3/4/8/9/10/11/12/13 mRNA was analyzed between OC tissue and normal tissue using ONCOMINE, GEO, and TCGA databases. The relationship between HOXD expression and clinical stage was studied by GEPIA. The Kaplan-Meier plotter was used to analyze prognosis. cBioPortal was used to analyze the mutation and coexpression of HOXDs. GO and KEGG analyses were performed by the DAVID software to predict the function of HOXD coexpression genes. Immune infiltration analysis was used to evaluate the relationship between the expression of HOXD genes and 24 immune infiltrating cells.

Results

The expression of HOXD3/4/8/9/10/11 was significantly lower in OC tissues than in normal ovarian tissues, while the expression of HOXD1/12/13 was significantly higher in OC tissues. The expression of HOXD genes was associated with FIGO stage, primary therapy outcome, tumor status, anatomic neoplasm subdivision, and age. The expression levels of HOXD1/3/4/8/9/10 correlated with tumor stage. HOXD1/8/9 could be served as ideal biomarkers to distinguish OC from normal tissue. Low HOXD9 expression was associated with shorter overall survival (OS) (HR: 0.75; 95% CI: 0.58-0.98; P = 0.034) and progression-free survival (PFS) (HR: 0.69; 95% CI: 0.54-0.87; P = 0.002). The HOXD coexpression genes were associated with pathways including cell cycle, TGF-beta signaling pathway, cellular senescence, and Hippo signaling pathway. HOXD genes were significantly associated with immune infiltration.

Conclusion

The expression of HOXD genes is associated with clinical characteristics. HOXD9 is a new biomarker of prognosis in OC, and HOXD1/4/8/9/10 may be potential therapeutic targets. The members of the HOXD genes may be the response to immunotherapy for OC.

Identification of key genes and pathways related to cancer-associated fibroblasts in chemoresistance of ovarian cancer cells based on GEO and TCGA databases

Background

Studies have revealed the implications of cancer-associated fibroblasts (CAFs) in tumor progression, metastasis, and treatment resistance. Here, in silico analyses were performed to reveal the key genes and pathways by which CAFs affected chemoresistance in ovarian cancer.

Methods

Candidate genes were obtained from the intersected differentially expressed genes in ovarian cancer, ovarian cancer chemoresistance, and ovarian CAF-related microarrays and chemoresistance-related genes from GeneCards databases. Kyoto Encyclopedia of Genes and Genomes enrichment analysis and Gene Set Enrichment Analysis were employed to identify the pathways engaged in ovarian cancer chemoresistance and ovarian CAF-related pathways. The top genes with high Degree in the protein-protein interaction network were intersected with the top genes enriched in the key pathways, followed by correlation analyses between key genes and chemotherapeutic response. The expression profiles of key genes were obtained from Human Protein Atlas database and TCGA-ovarian cancer data.

Results

p53, cell cycle, PI3K-Akt, and MAPK pathways were the key pathways related to the implication of CAFs in ovarian cancer chemoresistance. 276 candidate genes differentially expressed in CAFs were associated with ovarian cancer chemoresistance. MYC, IGF1, HRAS, CCND1, AKT1, RAC1, KDR, FGF2, FAS, and EGFR were enriched in the key chemoresistance-related ways. Furthermore, MYC, EGFR, CCND1 exhibited close association with chemotherapeutic response to platinum and showed a high expression in ovarian cancer tissues and platinum-resistant ovarian cancer cells.

Conclusion

The study suggests the key genes (MYC, EGFR, and CCND1) and pathways (p53, cell cycle, PI3K-Akt, and MAPK) responsible for the effect of CAFs on ovarian cancer chemoresistance.

Construction of Potential Gene Expression and Regulation Networks in Prostate Cancer Using Bioinformatics Tools

Objective

To identify the key genes involved in prostate cancer and their regulatory network.

Methods

The dataset of mRNA/miRNA transcriptome sequencing was downloaded from The Cancer Genome Atlas/the Gene Expression Omnibus database for analysis. The “edgeR” package in the R environment was used to normalize and analyze differentially expressed genes (DEGs) and miRNAs (DEmiRNAs). First, the PANTHER online tool was used to analyze the function enrichment of DEGs. Next, a protein-protein interaction (PPI) network was constructed using STRING and Cytoscape tools. Finally, miRNA-gene regulatory networks were constructed using the miRTarBase.

Results

We identified 4339 important DEGs, of which 2145 were upregulated (Up-DEGs) and 2194 were downregulated (Down-DEGs). Functional enrichment analysis showed that the Up-DEGs were related to the immune system and the cell cycle in prostate cancer, whereas the Down-DEGs were related to the nucleic acid metabolic process and metabolism pathways. Twelve core protein clusters were found in the PPI network. Further, the constructed miRNA-gene interaction network showed that 11 downregulated miRNAs regulated 16 Up-DEGs and 22 upregulated miRNAs regulated 22 Down-DEGs.

Conclusion

We identified 4339 genes and 70 miRNAs that may be involved in immune response, cell cycle, and other key pathways of the prostate cancer regulatory network. Genes such as BUB1B, ANX1A1, F5, HTR4, and MUC4 can be used as biomarkers to assist in the diagnosis and prognosis of prostate cancer.