Identification of Novel Drug Candidate for Epithelial Ovarian Cancer via In Silico Investigation and In Vitro Validation

Proposing a novel molecular subtyping scheme for predicting distant recurrence-free survival in breast cancer post-neoadjuvant chemotherapy with close correlation to metabolism and senescence

Background

High relapse rates remain a clinical challenge in the management of breast cancer (BC), with distant recurrence being a major driver of patient deterioration. To optimize the surveillance regimen for distant recurrence after neoadjuvant chemotherapy (NAC), we conducted a comprehensive analysis using bioinformatics and machine learning approaches.

Materials and methods

Microarray data were retrieved from the GEO database, and differential expression analysis was performed with the R package 'Limma'. We used the Metascape tool for enrichment analyses, and 'WGCNA' was utilized to establish co-expression networks, selecting the soft threshold power with the 'pickSoftThreshold' algorithm. We integrated ten machine learning algorithms and 101 algorithm combinations to identify key genes associated with distant recurrence in BC. Unsupervised clustering was performed with the R package 'ConsensusCluster Plus'. To further screen the key gene signature of residual cancer burden (RCB), multiple knockdown studies were analyzed with the Genetic Perturbation Similarity Analysis (GPSA) database. Single-cell RNA sequencing (scRNA-seq) analysis was conducted through the Tumour Immune Single-cell Hub (TISCH) database, and the XSum algorithm was used to screen candidate small molecule drugs based on the Connectivity Map (CMAP) database. Molecular docking processes were conducted using Schrodinger software. GMT files containing gene sets associated with metabolism and senescence were obtained from GSEA MutSigDB database. The GSVA score for each gene set across diverse samples was computed using the ssGSEA function implemented in the GSVA package.

Results

Our analysis, which combined Limma, WGCNA, and machine learning approaches, identified 16 RCB-relevant gene signatures influencing distant recurrence-free survival (DRFS) in BC patients following NAC. We then screened GATA3 as the key gene signature of high RCB index using GPSA analysis. A novel molecular subtyping scheme was developed to divide patients into two clusters (C1 and C2) with different distant recurrence risks. This molecular subtyping scheme was found to be closely associated with tumor metabolism and cellular senescence. Patients in cluster C2 had a poorer DRFS than those in cluster C1 (HR: 4.04; 95% CI: 2.60-6.29; log-rank test p < 0.0001). High GATA3 expression, high levels of resting mast cell infiltration, and a high proportion of estrogen receptor (ER)-positive patients contributed to better DRFS in cluster C1. We established a nomogram based on the N stage, RCB class, and molecular subtyping. The ROC curve for 5-year DRFS showed excellent predictive value (AUC=0.91, 95% CI: 0.95-0.86), with a C-index of 0.85 (95% CI: 0.81-0.90). Entinostat was identified as a potential small molecule compound to reverse high RCB after NAC. We also provided a comprehensive review of the EDCs exposures that potentially impact the effectiveness of NAC among BC patients.

Conclusion

This study established a molecular classification scheme associated with tumor metabolism and cancer cell senescence to predict RCB and DRFS in BC patients after NAC. Furthermore, GATA3 was identified and validated as a key gene associated with BC recurrence.

Ultrasound coupled RES-loaded ultrasound microbubble inhibits the proliferation of ovarian cancer cells by expression of long non-coding RNA (lncRNA) involved in apoptosis using real-time PCR

The primary objective of this study was to evaluate the effect of low-frequency ultrasound combined with RES-loaded ultrasound microbubble contrast agents on the transcriptional and translational activities of ovarian cancer cells. After being cultures, ovarian cancer cells (OVCAR-3) and human umbilical cord endothelial cells (HUCEC) were transfected with siRNA, which was followed by RNA extraction and real-time PCR to evaluate transcriptional activity. Translational activity was determined by western blotting, which was followed by RNA interference. Proliferative and invasive activity was measured using cell proliferation, colony formation, and immunofluorescence assays. Lastly, RNA sequencing was performed. Our findings indicated that ultrasound combined with RES microbubbles inhibited cell proliferation and invasion. The expression of ING5 was enhanced, while the expression of EMT was suppressed in ovarian cancer cells. A negative correlation was observed between of the expression of ING5 and cell proliferation/migration, which were enhanced upon inhibition of ING5, suggesting dysregulation of transcriptional and translational cellular processes which could be of diagnostic and therapeutic value in ovarian cancer. Additionally, the dysregulation of lncRNAs can alter cellular homeostasis and promote ovarian cancer progression. A combination of low-frequency and RES-loaded ultrasound microbubbles was found to effectively inhibit the proliferation of OVCAR-3 ovarian cancer cells and induce apoptosis. This approach was more effective than low-frequency ultrasound combined with RES alone.

Investigation of Gold Nanoparticle Naproxen-Derived Conjugations in Ovarian Cancer

Ovarian cancer, which is one of the most diagnosed cancer types among women, maintains its significance as a global health problem. Several drug candidates have been investigated for the potential treatment of ovarian cancer. Nonsteroidal anti-inflammatory drugs (NSAIDs) demonstrated anti-cancer activity through the inhibition of cyclooxygenase 2 (COX-2) and by inhibiting COX-2-dependent prostaglandin (PG) production. Naproxen is one of the most used NSAIDs and Naproxen-derived compounds (NDCs) may show potential treatment effects on cancer as chemotherapeutic drugs. Although there are successful drug development studies, the lack of solubility of these drug candidates in aqueous media results in limited bioavailability and high variability of patient responses during treatment. Low aqueous solubility is one of the main problems in the pharmaceutical industry in terms of drug development. Nanotechnology-based strategies provide solutions to hydrophobic drug limitations by increasing dispersion and improving internalization. In this study, two different NDCs (NDC-1 and NDC-2) bearing a thiosemicarbazide/1,2,4-triazole moiety were synthesized and tested for chemotherapeutic effects on ovarian cancer cells, which have a high COX-2 expression. To overcome the limited dispersion of these hydrophobic drugs, the drug molecules were conjugated to the surface of 13 nm AuNPs. Conjugation of drugs to AuNPs increased the distribution of drugs in aqueous media, and NDC@AuNP conjugates exhibited excellent colloidal stability for up to 8 weeks. The proposed system demonstrated an increased chemotherapeutic effect than the free drug counterparts with at least 5 times lower IC50 values. NDC@AuNP nanosystems induced higher apoptosis rates, which established a simple and novel way to investigate activity of prospective drugs in drug discovery research.

Identification of Novel Drugs Targeting Cell Cycle Regulators for the Treatment of High-Grade Serous Ovarian Cancer via Integrated Bioinformatics Analysis

High-grade serous ovarian carcinoma (HGSC), the most common and aggressive histological type of ovarian cancer, remains the leading cause of cancer-related deaths among females. It is important to develop novel drugs to improve the therapeutic outcomes of HGSC patients, thereby reducing their mortality. Symmetry is one of the most important properties of the biological network, which determines the stability of a biological system. As aberrant gene expression is a critical symmetry-breaking event that perturbs the stability of biological networks and triggers tumor progression, we aim in this study to discover new candidate drugs and predict their targets for HGSC therapy based on differentially expressed genes involved in HGSC pathogenesis. Firstly, 98 up-regulated genes and 108 down-regulated genes were identified from three independent transcriptome datasets. Then, the small-molecule compounds PHA-793887, pidorubicine and lestaurtinib, which target cell-cycle-related processes, were identified as novel candidate drugs for HGSC treatment by adopting the connectivity map (CMap)-based drug repositioning approach. Furthermore, through a topological analysis of the protein–protein interaction network, cell cycle regulators CDK1, TOP2A and AURKA were identified as bottleneck nodes, and their expression patterns were validated at the mRNA and protein expression levels. Moreover, the results of molecular docking analysis showed that PHA-793887, pidorubicine and lestaurtinib had a strong binding affinity for CDK1, TOP2A and AURKA, respectively. Therefore, our study repositioned PHA-793887, pidorubicine and lestaurtinib, which can inhibit cell cycle regulators, as novel agents for HGSC treatment, thereby helping to optimize the therapeutic strategy for HGSC.