Recent Patents on Anti-Cancer Drug Discovery

STAT3 Inhibitor Napabucasin Inhibits Tumor Growth and Cooperates with Proteasome Inhibition in Human Ovarian Cancer Cells

Background: Ovarian cancer is a disease with the highest mortality in gynecologic malignancies. Activation of STAT3 pathway is well known to be associated with tumor progression and metastasis in a number of cancers, including ovarian cancer. Therefore, STAT3 may be an ideal target for ovarian cancer treatment. Objective: The present study aims to determine the antitumor activity of STAT3 inhibitor Napabucasin as a single agent or in combination with proteasome inhibitor MG-132 in ovarian cancer cells. Methods: MTT was performed to determine the anti-proliferative effect of Napabucasin on ovarian cancer SKOV-3 cells. The involved anti-tumor mechanism was explored by flow cytometry, qRTPCR and western blot. MDC staining and tandem mRFP-GFP-LC3 fluorescence microscopy were used to analyze the autophagy-inducing capability of Napabucasin with or without MG-132. The combinational anticancer effect of Napabucasin and MG-132 was evaluated according to Chou and Talalay’s method (1984). Results: Napabucasin showed obvious tumor-inhibitory effects against SKOV-3 cells. Treatment by Napabucasin arrested cell cycle progression in G2/M phase. Mechanistically, elevated expression of p21 may contribute to the blockade of the cell cycle. Moreover, we demonstrated that Napabucasin induced autophagy in SKOV-3 cells by using various assays, including MDC staining, autophagic flux examination, and detection of the autophagy markers. In addition, a combination of Napabucaisin with MG-132 exhibited a significant synergistic anti-proliferative effect, probably by inducing apoptosis through a mitochondria-dependent pathway. The two compounds induced pro-survival autophagies, and co-treatment with autophagy inhibiter might further enhance their antitumor effects. Conclusion: Napabucasin alone or in combination with MG-132 might be promising treatment strategy for ovarian cancer patients.

β-estradiol Induces Mitochondrial Apoptosis in Cervical Cancer through the Suppression of AKT/NF-κB Signaling Pathway

Background: Cervical cancer is the fourth most prevalent gynecological cancer worldwide, which threatens women's health and causes cancer-related mortality. In the search for effective anticervical cancer drugs, we discovered that β-estradiol (E2), a potent drug for estrogen deficiency syndrome treatment, displays the most potent cytotoxicity against HeLa cells. Objective: This study aims to evaluate the growth inhibitory effect of β-estradiol on HeLa cells and explore its underlying mechanisms. Methods: CCK-8 assay was used to evaluate the cytotoxicity of 6 compounds against HeLa cells. Flow cytometric analysis and Hoechst 33258 staining assay were performed to detect cell cycle arrest and apoptosis induction. The collapse of the mitochondrial potential was observed by the JC-1 staining assay. The expression levels of proteins were examined by western blotting. Results: β-Estradiol, at high concentration, displays potent cytotoxicity against HeLa cells with an IC50 value of 18.71 ± 1.57 μM for 72 h treatment. β-Estradiol induces G2/M cell cycle arrest through downregulating Cyclin B1 and p-CDK1. In addition, β-estradiol-induced apoptosis is accompanied by the loss of mitochondrial potential, activation of the Caspase family, and altered Bax/Bcl-2 ratio. β-Estradiol markedly decreased the expression level of p-AKT and p-NF-κB. Conclusion: This study demonstrated that β-estradiol induces mitochondrial apoptosis in cervical cancer through the suppression of AKT/NF-κB signaling pathway, indicating that β-estradiol may serve as a potential agent for cervical cancer treatment.

Expression and Clinical Significance of Microtubule-Actin Cross-Linking Factor 1 in Serous Ovarian Cancer

Background: Ovarian Cancer (OC) remains the first leading cause of gynecologic malignancy. The survival rate from Serous Ovarian Cancer (SOC) is very low, and the present prognostic predictors of SOC are not very sensitive or specific. Objective: The present study aimed to investigate Microtubule-Actin Cross-Linking Factor 1 (MACF1) expression in SOC tissues (including paraffin-embedded and fresh tissues) and to assess its expression and significant value in patients with SOC. Methods: A total of 18 fresh SOC tissues and their paired paratumor tissues were performed with reverse-transcription quantitative PCR analysis to detect MACF1 mRNA expression. Moreover, 175 paraffin embedded SOC tissues and 41 paratumor tissues were assessed for MACF1 expression using immunohistochemistry. Results: The mRNA and protein expression of MACF1 were both higher in cancer tissues than that in paratumor tissues, and MACF1 high expression was associated with shorter Recurrence Free Survival (RFS) and Overall Survival (OS) in patients with SOC. Furthermore, multivariate regression analysis showed that high MACF1 expression was an independent poor survival predictor of patients with SOC. Conclusion: MACF1 is upregualted in SOC, and it may be used as a useful patent of prognostic biomarker in SOC.

Eukaryotic Initiation Factor 3C Can Affect the Proliferation and Invasion of Ovarian Cancer by Regulating the p53 Signalling Pathway

Background: Eukaryotic Initiation Factor 3C (EIF3C) represents a pivotal translational initiation factor in eukaryotes and has been shown to facilitate the progression of various neoplasms. However, its mechanistic role in ovarian cancer remains elusive. Methods: In this research, the expression of EIF3C in ovarian cancer tissues was investigated using immunohistochemistry. In addition, the assessments were made on changes in cellular proliferation, invasion, and apoptotic abilities by reducing the expression of EIF3C in ovarian cancer cells. By utilizing microarray analysis, a comparison was performed between the downregulated EIF3C group and the control group of ovarian cancer cells, revealing the genes that were expressed differently. Furthermore, the signalling pathways associated with cellular proliferation were validated. The functional role of EIF3C in vivo was investigated using a xenograft tumour model. Results: The immunohistochemical analysis showed that elevated levels of EIF3C are linked to a negative prognosis in patients with ovarian cancer. Suppression of EIF3C greatly hindered the growth and spread of SK-OV-3 and HO-8910 cells while enhancing cellular programmed cell death. Following KEGG and GSEA enrichment analyses of differentially expressed genes, the p53 signalling pathway was found to be associated with EIF3C. Suppression of EIF3C resulted in the upregulation of the p53 signalling pathway, leading to the inhibition of cell proliferation and invasion and the promotion of apoptosis. In vivo experiments demonstrated that EIF3C knockdown suppressed the growth of subcutaneous tumours in nude mice. Conclusion: There is a correlation between overexpression of EIF3C in tumour tissues of ovarian cancer patients and this is associated with a poorer prognosis. By influencing the p53 signaling pathway, EIF3C facilitates the growth and infiltration of cells in ovarian cancer.

TPD52 as a Potential Prognostic Biomarker and its Correlation with Immune Infiltrates in Uterine Corpus Endometrial Carcinoma: Bioinformatic Analysis and Experimental Verification

Background: Aberrant expression of tumor protein D52 (TPD52) is associated with some tumors. The role of TPD52 in uterine corpus endometrial carcinoma (UCEC) remains uncertain. Objective: We aimed to investigate the involvement of TPD52 in the pathogenesis of UCEC. Methods: We employed bioinformatics analysis and experimental validation in our study. Results: Our findings indicated that elevated TPD52 expression in UCEC was significantly associated with various clinical factors, including clinical stage, race, weight, body mass index (BMI), histological type, histological grade, surgical approach, and age (p < 0.01). Furthermore, high TPD52 expression was a predictor of poorer overall survival (OS), progress-free survival (PFS), and disease-specific survival (DSS) (p = 0.011, p = 0.006, and p = 0.003, respectively). TPD52 exhibited a significant correlation with DSS (HR: 2.500; 95% CI: 1.153-5.419; p = 0.02). TPD52 was involved in GPCR ligand binding and formation of the cornified envelope in UCEC. Moreover, TPD52 expression was found to be associated with immune infiltration, immune checkpoints, tumor mutation burden (TMB)/ microsatellite instability (MSI), and mRNA stemness indices (mRNAsi). The somatic mutation rate of TPD52 in UCEC was 1.9%. A ceRNA network of AC011447.7/miR-1-3p/TPD52 was constructed. There was excessive TPD52 protein expression. The upregulation of TPD52 expression in UCEC cell lines was found to be statistically significant. Conclusion: TPD52 is upregulated in UCEC and may be a useful patent for prognostic biomarkers of UCEC, which may have important value for clinical treatment and supervision of UCEC patients.

N6-Methyladenosine-Related RNA Signature Predicting the Prognosis of Ovarian Cancer

Background: N6-Methyladenosine (m6A) RNA methylation is the most universal mRNA modification in eukaryotic cells. M6A mRNA modification affects almost every phases of RNA processing, including splicing, decay, export, translation and expression. Several patents have reported the application of m6A mRNA modification in cancer diagnosis and treatment. Ovarian cancer is the leading cause of death among all gynecological cancers. It is urgent to identify new biomarkers for early diagnosis and prognosis of ovarian cancer. Objective: In the current study, we aimed to evaluate the m6A RNA methylation regulators and m6A related genes and establish a new gene signature panel for the prognosis of ovarian cancer. Methods: We downloaded the mutations data, FPKM data and corresponding clinical information of 373 patients with Ovarian Cancer (OC) from the TCGA database. We performed LASSO regression analysis and multivariate cox regression analysis to develop a risk-identifying gene signature panel. Results: A total of 317 candidate m6A RNA methylation related genes were obtained. Finally, 12 - genes (WTAP, LGR6, ZC2HC1A, SLC4A8, AP2A1, NRAS, CUX1, HDAC1, CD79A, ACE2, FLG2 and LRFN1) were selected to establish the signature panel. We analyzed the genetic alterations of the selected 12 -genes in OC using cBioPortal database. Among the 373 patients, 368 patients have mutations. The results showed that all queried genes were altered in 137 of 368 cases (37.23%). The 12-gene signature panel was confirmed as an independent prognostic indicator (P =2.29E-18, HR = 1.699, 95% CI = 1.508-1.913). Conclusion: We established an effective m6A-related gene signature panel consisted of 12 -genes, which can predict the outcome of patients with OC. The high risk score indicates unfavorable survival. Our study provided novel insights into the relationship between m6A and OC. This gene signature panel will be helpful in identifying poor prognostic patients with OC and could be a promising prognostic indicator in clinical practice.

Development and Validation of a Hypoxia-related Prognostic Model for Ovarian Cancer

Background:The high heterogeneity of ovarian cancer (OC) brings great difficulties to its early diagnosis and prognostic forecast. It is an urgent need to establish a prognostic model of OC based on clinicopathological features and genomics.Methods:We identified hypoxia-related differentially expressed genes (DEGs) between OC tissues from The Cancer Genome Atlas (TCGA) and normal tissues from the Genotype-Tissue Expression (GTEx). LASSO Cox regression analysis was applied for building a prognostic model in the TCGA-GTEx cohorts, and its predictive value was validated in the GEO-OC cohort. Functional enrichment analysis was performed to investigate the underlying mechanisms. By constructing a hypoxia model of SKOV3 cell line and applying qRT-PCR, we investigated the relationship between hypoxia with two novel genes in the prognostic model (ISG20 and ANGPTL4).Results:Twelve prognostic hypoxia-related DEGs were identified and nine of them were selected to establish a prognostic model. OC patients were stratified into two risk groups, and the high-risk group showed reduced survival time compared to the low-risk group upon survival analysis. Univariate and multivariate Cox regression analysis demonstrated that the risk score acted as an independent risk factor for overall survival. The biological function of the identified prognostic hypoxia-related gene signature was involved in immune cells infiltration. Low expression of ISG20 was observed in the CoCl2-mimicked hypoxic SKOV3 cell line and negatively correlated with HIF-1α.Conclusion:Our findings showed that this hypoxia-related gene signature can serve as a satisfactory prognostic classifier for OC and will be beneficial to the research and development of targeted therapeutic strategies.

High Expression of MYL9 Indicates Poor Clinical Prognosis of Epithelial Ovarian Cancer

Background: The prognosis of Epithelial Ovarian Cancer (EOC) is poor, but the prognostic biomarkers are neither sensitive nor specific. Therefore, it is very important to search novel prognostic biomarkers for EOC. Objectives: The present study aimed to investigate Myosin Light Chain 9(MYL9) expression in Epithelial Ovarian Cancer (EOC) tissues (including paraffin-embedded and fresh tissue samples) and its relationship with clinicopathological characteristics, as well as its potential prognostic value in patients with EOC. Methods: Between March 2009 and December 2018, all of 184 paraffin-embedded cancer tissues from patients with EOC and 41 paratumor tissues, pathologically confirmed at the Memorial Hospital of Sun Yat-sen University and Integrated Hospital of Traditional Chinese Medicine, Southern Medical University, were collected for the present study and were assessed for MYL9 protein expression patterns using Immunohistochemistry (IHC). Furthermore, from August 2013 to November 2019, 16 fresh EOC tissues and their paired paratumor tissues, pathologically confirmed at the Integrated Hospital of Traditional Chinese Medicine, Southern Medical University were analyzed using Reverse-Transcription Quantitative PCR (RT-qPCR) to detect MYL9 mRNA expression levels. Results: The results showed that MYL9 expression was higher in cancer tissues compared with that in paratumor tissues, and MYL9 overexpression was associated with shorter Recurrence Free Survival (RFS) and Overall Survival (OS) of EOC patients. Furthermore, multivariate Cox model analysis indicated that MYL9 overexpression was an independent poor survival prediction in patients with EOC. Conclusion: MYL9 is upregulated in EOC and may serve as a useful patent of prognostic biomarker in EOC, and it may demonstrate an important value for the clinical treatment and supervision of patients with EOC.

Development of a Prognostic Risk Model Based on Oxidative Stress-related Genes for Platinum-resistant Ovarian Cancer Patients

Introduction: Ovarian Cancer (OC) is a heterogeneous malignancy with poor outcomes. Oxidative stress plays a crucial role in developing drug resistance. However, the relationships between Oxidative Stress-related Genes (OSRGs) and the prognosis of platinum-resistant OC remain unclear. This study aimed to develop an OSRGs-based prognostic risk model for platinum- resistant OC patients. Methods: Gene Set Enrichment Analysis (GSEA) was performed to determine the expression difference of OSRGs between platinum-resistant and -sensitive OC patients. Cox regression analyses were used to identify the prognostic OSRGs and establish a risk score model. The model was validated by using an external dataset. Machine learning was used to determine the prognostic OSRGs associated with platinum resistance. Finally, the biological functions of selected OSRG were determined via in vitro cellular experiments. Results: Three gene sets associated with oxidative stress-related pathways were enriched (p < 0.05), and 105 OSRGs were found to be differentially expressed between platinum-resistant and - sensitive OC (p < 0.05). Twenty prognosis-associated OSRGs were identified (HR: 0:562-5.437; 95% CI: 0.319-20.148; p < 0.005), and seven independent OSRGs were used to construct a prognostic risk score model, which accurately predicted the survival of OC patients (1-, 3-, and 5-year AUC=0.69, 0.75, and 0.67, respectively). The prognostic potential of this model was confirmed in the validation cohort. Machine learning showed five prognostic OSRGs (SPHK1, PXDNL, C1QA, WRN, and SETX) to be strongly correlated with platinum resistance in OC patients. Cellular experiments showed that WRN significantly promoted the malignancy and platinum resistance of OC cells. Conclusion: The OSRGs-based risk score model can efficiently predict the prognosis and platinum resistance of OC patients. This model may improve the risk stratification of OC patients in the clinic.

Identification of Immune Infiltration-related Molecular Features in Ovarian Cancer Patients and Experimental Validation of Immune Response Molecular Mechanisms through Integrated WGCNA, Machine Learning, and Single-cell Sequencing Analysis

Background: Ovarian cancer is one of the most common gynecological malignancies globally, and immunotherapy has emerged as a promising treatment strategy in recent years. However, the effectiveness of immunotherapy is often limited by immune escape mechanisms. Objective: To unravel the immune response mechanisms in ovarian cancer, this study aimed to employ integrated Weighted Gene Co-expression Network Analysis (WGCNA), machine learning, and single-- cell sequencing analysis to systematically investigate immune infiltration-related molecular features in ovarian cancer patients and experimentally validate the molecular mechanisms of the immune response. This research may provide a new theoretical foundation and treatment strategy for immune-based therapies in ovarian cancer. Methods: Relevant ovarian cancer datasets were collected from public databases. The ConsensusCluster- Plus and ggplot2 R packages were used to perform dimensionality reduction and clustering analysis of immune infiltration-related genes. Various algorithms were employed to select the best ovarian cancer prognostic model with OC consistency. The prognostic value of angiogenesis and immune-related gene expression was evaluated through Kaplan-Meier survival analysis, and the impact of immune infiltration on immune function in ovarian cancer patients was assessed. Functional pathways were identified using the Gene Set Enrichment Analysis (GSEA) method, and the infiltration abundance of immune and stromal components was inferred using the single-sample Gene Set Enrichment Analysis (ssGSEA) method. The influence of angiogenesis on the cellular level of Ovarian Cancer (OC) was explored in single- cell sequencing data, followed by in vitro cell experiments for further validation. The effect of the angiogenesis model on OC was evaluated through the above-mentioned research and experiments, aiming to investigate the mechanism of targeted therapy strategies in ovarian cancer. Results: Immune-related data were collected from ovarian cancer patients in this study. Through WGCNA analysis, the MEturquoise module was identified, and a total of 1018 hub genes were determined. A prediction model was constructed using machine learning, with CoxBoost+StepCox selected as the best model, leading to the identification of 10 genes associated with ovarian cancer. Patients with high AIDPS had shorter survival time, and GSEA analysis revealed enrichment in immune-related pathways. Single-sample gene set enrichment analysis demonstrated increased immune cell infiltration and malignant stromal changes in the high AIDPS group. Results from in vitro cell experiments showed that silencing RPL31 inhibited the proliferation and migration of ovarian cancer cells while enhancing immune response capability. Conclusion: AIDPS holds significant clinical significance in Ovarian Cancer (OC) with poor prognosis observed in patients with high AIDPS. These patients exhibit more significant genomic variations, denser immune cell infiltration, and greater tolerance toward immune therapy. Importantly, inhibiting the expression of RPL31, a key component of AIDPS, can significantly suppress the proliferation, migration, and invasive properties of ovarian cancer cells, while stimulating the cytotoxicity of effector T cells and promoting immune response, thus slowing down the progression of ovarian cancer.

Circ_0070203 Promotes Epithelial-mesenchymal Transition in Ovarian Serous Cystadenocarcinoma through miR-370-3p/TGFβR2 Axis

Introduction: Circular RNAs (circRNAs) are important biological molecules associated with the pathogenesis of multiple cancers. Objective: This work aimed to investigate the function and molecular mechanism of circ_0070203 in high-grade serous ovarian cystadenocarcinoma (HGSOC). Methods: circRNA microarray was conducted to detect the circ_0070203 expression in HGSOC tissues. Bioinformatics analysis was used to find the binding sites between circ_0070203, miR- 370-3p and TGFβR2. Real-time quantitative reverse transcription PCR (RT-qPCR) was executed to detect the expressions of circ_0070203, miR-370-3p and TGFβR2 in HGSOC tissues and SKOV3 cells. Dual-luciferase reporter gene assay was used to validate the relationships between miR-370-3p and circ_0070203 or TGFβR2. Besides, transwell assays were conducted to assess the migrative, invasive abilities of ovarian cancer (OC) cells. Western blotting was adopted to detect the expression of epithelial-mesenchymal transition (EMT)-related proteins. The related patents were also studied during the research. Results: Circ_0070203 and TGFβR2 were upregulated, while miR-370-3p was downregulated in FIGO stage Ⅲ-Ⅳ HGSOC tissues and SKOV-3 cell lines. circ_0070203 overexpression changed the expression of other EMT-related proteins and enhanced the migrative, invasive abilities of OC cells, while silencing circ_0070203 worked oppositely. Mechanistically, circ_0070203 could upregulate TGFβR2 expression in OC cells via sponging miR-370-3p. Conclusion: Circ_0070203 could promote the epithelial-mesenchymal transition, invasion, and metastasis of HGSOC via regulating the miR-370-3p/TGFβR2 axis. Our findings provided a potential biomarker for HGSOC therapy.