Gene expression profiling supports the hypothesis that human ovarian surface epithelia are multipotent and capable of serving as ovarian cancer initiating cells

SLC45A4 is involved in malignant progression of ovarian cancer through glycolytic metabolic reprogramming

Tumor cells promote malignant behaviors such as proliferation, invasion, and metastasis of cancer cells through glucose metabolic reprogramming, but the role of the H-dependent sugar cotransporter SLC45A4 in regulating metabolic reprogramming in ovarian cancer (OC) remains largely unknown. This study aimed to investigate the effects of SLC45A4 silencing on the transcriptome spectrum of ovarian cancer cells (OCC), glucose uptake, lactic acid production, intracellular ATP levels, and the expression and activity of HIF-α glycolysis signaling pathway. The results showed that SLC45A4 is overexpressed in OC and its elevated expression correlates with adverse clinical outcomes in OC patients. Silencing of SLC45A4 significantly inhibited the proliferation, invasion, and metastasis of OCC by suppressing glucose uptake and glycolysis, and it also reduced the expression of HIF-α glycolysis signaling pathway in OC tissues. In vivo experiments using shRNA to knock down SLC45A4 in xenograft models in nude mice demonstrated a significant inhibition of tumor growth. These findings suggest that SLC45A4 silencing can restrain the malignant progression of OC by inhibiting glucose uptake in OCC and affecting the reprogramming of glycolytic energy metabolism, indicating that SLC45A4 may serve as a potential therapeutic target for OC intervention.

Low androgen/progesterone or high oestrogen/androgen receptors ratio in serous ovarian cancer predicts longer survival

The treatment of ovarian cancer (OC) remains one of the greatest challenges in gynaecological oncology. The presence of classic steroid receptors in OC makes hormone therapy an attractive option; however, the response of OC to hormone therapy is modest. Here, we compared the expression patterns of progesterone (PGR), androgen (AR) and oestrogen alpha (ERα) receptors between serous OC cell lines and non-cancer ovarian cells. These data were analysed in relation to steroid receptor expression profiles from patient tumour samples and survival outcomes using a bioinformatics approach. The results showed that ERα, PGR and AR were co-expressed in OC cell lines, and patient samples from high-grade and low-grade OC co-expressed at least two steroid receptors. High AR expression was negatively correlated, whereas ERα and PGR expression was positively correlated with patient survival. AR showed the opposite expression pattern to that of ERα and PGR in type 1 (SKOV-3) and 2 (OVCAR-3) OC cell lines compared with non-cancer (HOSEpiC) ovarian cells, with AR downregulated in type 1 and upregulated in type 2 OC. A low AR/PGR ratio and a high ESR1/AR ratio were associated with favourable survival outcomes in OC compared with other receptor ratios. Although the results must be interpreted with caution because of the small number of primary tumour samples analysed, they nevertheless suggest that the evaluation of ERα, AR and PGR by immunohistochemistry should be performed in patient biological material to plan future clinical trials.

Identification of a G-protein coupled receptor-related gene signature through bioinformatics analysis to construct a risk model for ovarian cancer prognosis

BACKGROUND

Ovarian cancer (OV) is a common malignant tumor of the female reproductive system with a 5-year survival rate of ∼30 %. Inefficient early diagnosis and prognosis leads to poor survival in most patients. G protein-coupled receptors (GPCRs, the largest family of human cell surface receptors) are associated with OV. We aimed to identify GPCR-related gene (GPCRRG) signatures and develop a novel model to predict OV prognosis.

METHOD

We downloaded data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Prognostic GPCRRGs were screened using least absolute shrinkage and selection operator (LASSO) Cox regression analysis, and a prognostic model was constructed. The predictive ability of the model was evaluated by Kaplan-Meier (K-M) survival analysis. The levels of GPCRRGs were examined in normal and OV cell lines using quantitative reverse-Etranscription polymerase chain reaction. The immunological characteristics of the high- and low-risk groups were analyzed using single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT.

RESULTS

Based on the risks scores, 17 GPCRRGs were associated with OV prognosis. CXCR4, GPR34, LGR6, LPAR3, and RGS2 were significantly expressed in three OV datasets and enabled accurate OV diagnosis. K-M analysis of the prognostic model showed that it could differentiate high- and low-risk patients, which correspond to poorer and better prognoses, respectively. GPCRRG expression was correlated with immune infiltration rates.

CONCLUSIONS

Our prognostic model elaborates on the roles of GPCRRGs in OV and provides a new tool for prognosis and immune response prediction in patients with OV.

Discovery of differentially expressed proteins for CAR-T therapy of ovarian cancers with a bioinformatics analysis

Target antigens are crucial for developing chimeric antigen receptor (CAR)-T cells, but their application to ovarian cancers is limited. This study aimed to identify potential genes as CAR-T-cell antigen candidates for ovarian cancers. A differential gene expression analysis was performed on ovarian cancer samples from four datasets obtained from the GEO datasets. Functional annotation, pathway analysis, protein localization, and gene expression analysis were conducted using various datasets and tools. An oncogenicity analysis and network analysis were also performed. In total, 153 differentially expressed genes were identified in ovarian cancer samples, with 60 differentially expressed genes expressing plasma membrane proteins suitable for CAR-T-cell antigens. Among them, 21 plasma membrane proteins were predicted to be oncogenes in ovarian cancers, with nine proteins playing crucial roles in the network. Key genes identified in the oncogenic pathways of ovarian cancers included MUC1, CXCR4, EPCAM, RACGAP1, UBE2C, PRAME, SORT1, JUP, and CLDN3, suggesting them as recommended antigens for CAR-T-cell therapy for ovarian cancers. This study sheds light on potential targets for immunotherapy in ovarian cancers.

MET overexpression in ovarian cancer via CD24-induced downregulation of miR-181a: A signalling for cellular quiescence-like state and chemoresistance in ovarian CSCs

Increased expression of CD24 and MET, markers for cancer stem-like cells (CSCs), are each associated with ovarian cancer severity. However, whether CD24 and MET are co-expressed in ovarian CSCs and, if so, how they are related to CSC phenotype manifestation remains unknown. Our immunohistochemistry analysis showed that the co-expression of CD24 and MET was associated with poorer patient survival in ovarian cancer than those without. In addition, analyses using KM plotter and ROC plotter presented that the overexpression of CD24 or MET in ovarian cancer patients was associated with resistance to platinum-based chemotherapy. In our miRNA transcriptome and putative target genes analyses, miR-181a was downregulated in CD24-high ovarian cancer cells compared to CD24-low and predicted to bind to CD24 and MET 3'UTRs. In OV90 and SK-OV-3 cells, CD24 downregulated miR-181a expression by Src-mediated YY1 activation, leading to increased expression of MET. And, CD24 or MET knockdown or miR-181a overexpression inhibited the manifestation of CSC phenotypes, cellular quiescence-like state and chemoresistance, in OV90 and SK-OV-3 cells: increased colony formation, decreased G0/G1 phase cell population and increased sensitivity to Cisplatin and Carboplatin. Our findings suggest that CD24-miR-181a-MET may consist of a signalling route for ovarian CSCs, therefore being a combinatory set of markers and therapeutic targets for ovarian CSCs.

Intra-sample reversed pairs based on differentially ranked genes reveal biosignature for ovarian cancer

Ovarian cancer, a major gynecological malignancy, often remains undetected until advanced stages, necessitating more effective early screening methods. Existing biomarker based on differential genes often suffers from variations in clinical practice. To overcome the limitations of absolute gene expression values including batch effects and biological heterogeneity, we introduced a pairwise biosignature leveraging intra-sample differentially ranked genes (DRGs) and machine learning for ovarian cancer detection across diverse cohorts. We analyzed ten cohorts comprising 872 samples with 796 ovarian cancer and 76 normal. Our method, DRGpair, involves three stages: intra-sample ranking differential analysis, reversed gene pair analysis, and iterative LASSO regression. We identified four DRG pairs, demonstrating superior diagnostic performance compared to current state-of-the-art biomarkers and differentially expressed genes in seven independent cohorts. This rank-based approach not only reduced computational complexity but also enhanced the specificity and effectiveness of biomarkers, revealing DRGs as promising candidates for ovarian cancer detection and offering a scalable model adaptable to varying cohort characteristics.

Identification and validation of IRF6 related to ovarian cancer and biological function and prognostic value

BACKGROUND

Ovarian cancer (OC) is a severe gynecological malignancy with significant diagnostic and therapeutic challenges. The discovery of reliable cancer biomarkers can be used to adjust diagnosis and improve patient care. However, serous OC lacks effective biomarkers. We aimed to identify novel biomarkers for OC and their pathogenic causes.

METHODS

The present study used the differentially expressed genes (DEGs) obtained from the "Limma" package and WGCNA modules for intersection analysis to obtain DEGs in OC. Three hub genes were identified-claudin 3 (CLDN3), interferon regulatory factor 6 (IRF6), and prostasin (PRSS8)-by searching for hub genes through the PPI network and verifying them in GSE14407, GSE18520, GSE66957, and TCGA + GTEx databases. The correlation between IRF6 and the prognosis of OC patients was further confirmed in Kaplan-Miller Plotter. RT-qPCR and IHC confirmed the RNA and protein levels of IRF6 in the OC samples. The effect of IRF6 on OC was explored using transwell invasion and scratch wound assays. Finally, we constructed a ceRNA network of hub genes and used bioinformatics tools to predict drug sensitivity.

RESULTS

The joint analysis results of TCGA, GTEx, and GEO databases indicated that IRF6 RNA and protein levels were significantly upregulated in serous OC and were associated with OS and PFS. Cell function experiments revealed that IRF6 knockdown inhibited SKOV3 cell proliferation, migration and invasion.

CONCLUSION

IRF6 is closely correlated with OC development and progression and could be considered a novel biomarker and therapeutic target for OC patients.

Identification and characterization of multipotential stem cells in immortalized normal ovarian surface epithelial cells

The ovarian surface epithelium (OSE) is a single layer of squamous-to-cuboidal epithelial cells that experience repetitive ovulatory rupture and subsequent repair. However, the characteristics of human immortalized ovarian surface epithelial cells (IOSE80) remain elusive. This study aims to determine whether IOSE80 cells have the characteristics of stem cell proliferation and multilineage differentiation and their application in regenerative medicine. IOSE80 cells are sequenced by high-throughput transcriptome analysis, and 5 sets of public data are used to compare the differences between IOSE80 cells and bone marrow mesenchymal stem cells, pluripotent stem cells, and oocytes in transcriptome profiling. The IOSE80 cells present a cobblestone-like monolayer and express the epithelial cell marker KRT18; the stem cell markers IFITM3, ALDH1A1, and VIM; lowly express stem cell marker LGR5 and germ cell markers DDX4 and DAZL. In addition, the GO terms "regulation of stem cell proliferation", "epithelial cell proliferation", etc., are significantly enriched ( P<0.05). IOSE80 cells have the potential to act as mesenchymal stem cells to differentiate into adipocytes with lipid droplets, osteoblasts, and chondroblasts in vitro. IOSE80 cells express pluripotent stem cell markers, including OCT4, SSEA4, TRA-1-60, and TRA-1-81, and they can be induced into three germ layers in vitro. IOSE80 cells also form oocyte-like cells in vitro and in vivo. In addition, IOSE80 cells exhibit robust proliferation, migration, and ovarian repair functions after in vivo transplantation. This study demonstrates that IOSE80 cells have the characteristics of pluripotent/multipotent stem cells, indicating their important role in tissue engineering and regenerative medicine.

Ovarian cancer relies on the PDGFRβ-fibronectin axis for tumorsphere formation and metastatic spread

High-grade serous ovarian cancer (HGSOC) is the deadliest gynecological malignancy. The most common form of metastatic spread of HGSOC is transcoelomic dissemination. In this process, detached cells from the primary tumor aggregate as tumorspheres and promote the accumulation of peritoneal ascites. This represents an early event in HGSOC development and is indicative of poor prognosis. In this study, based on tumorspheres isolated from ascitic liquid samples from HGSOC patients, ovarian cancer spheroid 3D cultures, and in vivo models, we describe a key signal for tumorsphere formation in HGSOC. We report that platelet-derived growth factor receptor beta (PDGFRβ) is essential for fibronectin-mediated cell clustering of ovarian cancer cells into tumorspheres. This effect is mediated by the kinase NUAK family SNF1-like kinase 1 (NUAK1) and blocked by PDGFRβ pharmacological or genetic inhibition. In the absence of PDGFRβ, ovarian cancer cells can be provided with fibronectin by cancer-associated fibroblasts to generate chimeric spheroids. This work provides new insights that uncover potential targets to prevent peritoneal dissemination, the main cause of advanced disease in HGSOC patients.

Calcium signals and potential therapy targets in ovarian cancer (Review)

Ovarian cancer (OC) is a deadly disease. The poor prognosis and high lethality of OC are attributed to its high degrees of aggressiveness, resistance to chemotherapy and recurrence rates. Calcium ion (Ca2+) signaling has received attention in recent years, as it appears to form an essential part of various aspects of cancer pathophysiology and is a potential therapeutic target for OC treatment. Disruption of normal Ca2+ signaling pathways can induce changes in cell cycle progression, apoptosis, proliferation and migration and invasion, leading to the development of the malignant phenotype of tumors. In the present review, the main roles of ion channel/receptor/pump‑triggered Ca2+ signaling pathways located at the plasma membrane and organelle Ca2+ transport in OC are summarized. In addition, the potential of Ca2+ signaling as a novel target for the development of effective treatment strategies for OC was discussed. Furthering the understanding into the role of Ca2+ signaling in OC is expected to facilitated the identification of novel therapeutic targets and improved clinical outcomes for patients.

KRT19 is a Promising Prognostic Biomarker and Associates with Immune Infiltrates in Serous Ovarian Cystadenocarcinoma

Background

Ovarian cancer (OV) is the highest prevalent gynecologic tumor with complicated pathogenesis; high-grade serous ovarian cystadenocarcinoma (HGSOC) is the most epidemiological and malignant subtype of OV. Keratin type I cytoskeleton 19 (KRT19) is an intermediate filament protein which plays essential roles in the maintenance of epithelial cells. However, its role in OV remains largely unknown.

Methods

Bioinformatic analysis with various databases was conducted in this study. In details, KRT19 expression was assessed using databases including The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), Gene Expression Omnibus (GEO) and Human Protein Atlas (HPA). GO-KEGG and GSEA analysis were performed by R packages. The biological function of KRT19 was analyzed based on the single-cell sequencing information from CancerSEA database. The association of KRT19 expression with immunomodulator and chemokine was predicted via the TISIDB database.

Results

The expression of KRT19 was significantly upregulated in ovarian samples compared with normal controls. KRT19 expression was negatively associated with prognosis in OV, and further analysis revealed that KRT19 had promising diagnostic significance in distinguishing OV cancer from normal samples. GO-KEGG and GSEA analysis indicated that KRT19 was associated with multiple biological functions and pathways including epidermis development, apical junction, inflammatory response, and epithelial mesenchymal transition. By using different GEO series, we found that KRT19 was differentially expressed in OV-associated tissues. Furthermore, the increased KRT19 expression was positively correlated with the immune infiltration levels of the most immune cells in OV.

Conclusion

This study demonstrated that KRT19 is a promising prognosis and diagnosis biomarker that determines cancer progression and is correlated with tumor immune cells infiltration in OV, suggesting being a molecular target for immunotherapies.

RBM15‑mediating MDR1 mRNA m6A methylation regulated by the TGF‑β signaling pathway in paclitaxel‑resistant ovarian cancer

Ovarian cancer (OC) lacks effective biomarkers for diagnosis at an early stage and often develops chemoresistance after the initial treatment at an advanced stage. RNA‑binding motif protein 15 (RBM15) is an RNA m6A methylation mediator that serves an oncogenic role in some cancers. However, the function and molecular mechanisms of RBM15 in ovarian tumorigenesis and chemoresistance remain to be elucidated. The present study identified the overexpression of RBM15 in OC tissues and paclitaxel (PTX)‑resistant cells using reverse transcription‑quantitative (q)PCR, western blotting and immunohistochemistry. Clinical data analyses showed that high expression of RBM15 was associated with poor prognosis in patients with OC. Overexpression of RBM15 led to an increase in cell viability and colony formation and a decrease in cell sensitivity to PTX and apoptosis, whereas the knockdown of RBM15 resulted in the inhibition of cell viability and colony formation in vitro and tumor formation in vivo and increased cell apoptosis and sensitivity to PTX in a time‑ and dose‑dependent manner. Furthermore, RBM15 knockdown reduced the spheroid formation of PTX‑resistant OC cells. Silencing of RBM15 decreased multidrug resistance 1 (MDR1) mRNA m6A methylation detected by the methylated RNA immunoprecipitation‑qPCR assay and downregulated the expression of a chemo‑drug efflux pump MDR1 at the mRNA and protein levels. Finally, RBM15 expression was suppressed by the activation of the TGF‑β signaling pathway. Thus, the findings revealed a TGF‑β/RBM15/MDR1 regulatory mechanism. Targeting RBM15 may provide a novel therapeutic strategy for the treatment of PTX‑resistant OC.

PTEN deficiency exposes a requirement for an ARF GTPase module for integrin-dependent invasion in ovarian cancer

Dysregulation of the PI3K/AKT pathway is a common occurrence in high-grade serous ovarian carcinoma (HGSOC), with the loss of the tumour suppressor PTEN in HGSOC being associated with poor prognosis. The cellular mechanisms of how PTEN loss contributes to HGSOC are largely unknown. We here utilise time-lapse imaging of HGSOC spheroids coupled to a machine learning approach to classify the phenotype of PTEN loss. PTEN deficiency induces PI(3,4,5)P3 -rich and -dependent membrane protrusions into the extracellular matrix (ECM), resulting in a collective invasion phenotype. We identify the small GTPase ARF6 as a crucial vulnerability of HGSOC cells upon PTEN loss. Through a functional proteomic CRISPR screen of ARF6 interactors, we identify the ARF GTPase-activating protein (GAP) AGAP1 and the ECM receptor β1-integrin (ITGB1) as key ARF6 interactors in HGSOC regulating PTEN loss-associated invasion. ARF6 functions to promote invasion by controlling the recycling of internalised, active β1-integrin to maintain invasive activity into the ECM. The expression of the CYTH2-ARF6-AGAP1 complex in HGSOC patients is inversely associated with outcome, allowing the identification of patient groups with improved versus poor outcome. ARF6 may represent a therapeutic vulnerability in PTEN-depleted HGSOC.

Altered expression of the L-arginine/nitric oxide pathway in ovarian cancer: metabolic biomarkers and biological implications

MOTIVATION

Ovarian cancer (OC) is a highly lethal gynecological malignancy. Extensive research has shown that OC cells undergo significant metabolic alterations during tumorigenesis. In this study, we aim to leverage these metabolic changes as potential biomarkers for assessing ovarian cancer.

METHODS

A functional module-based approach was utilized to identify key gene expression pathways that distinguish different stages of ovarian cancer (OC) within a tissue biopsy cohort. This cohort consisted of control samples (n = 79), stage I/II samples (n = 280), and stage III/IV samples (n = 1016). To further explore these altered molecular pathways, minimal spanning tree (MST) analysis was applied, leading to the formulation of metabolic biomarker hypotheses for OC liquid biopsy. To validate, a multiple reaction monitoring (MRM) based quantitative LCMS/MS method was developed. This method allowed for the precise quantification of targeted metabolite biomarkers using an OC blood cohort comprising control samples (n = 464), benign samples (n = 3), and OC samples (n = 13).

RESULTS

Eleven functional modules were identified as significant differentiators (false discovery rate, FDR < 0.05) between normal and early-stage, or early-stage and late-stage ovarian cancer (OC) tumor tissues. MST analysis revealed that the metabolic L-arginine/nitric oxide (L-ARG/NO) pathway was reprogrammed, and the modules related to "DNA replication" and "DNA repair and recombination" served as anchor modules connecting the other nine modules. Based on this analysis, symmetric dimethylarginine (SDMA) and arginine were proposed as potential liquid biopsy biomarkers for OC assessment. Our quantitative LCMS/MS analysis on our OC blood cohort provided direct evidence supporting the use of the SDMA-to-arginine ratio as a liquid biopsy panel to distinguish between normal and OC samples, with an area under the ROC curve (AUC) of 98.3%.

CONCLUSION

Our comprehensive analysis of tissue genomics and blood quantitative LC/MSMS metabolic data shed light on the metabolic reprogramming underlying OC pathophysiology. These findings offer new insights into the potential diagnostic utility of the SDMA-to-arginine ratio for OC assessment. Further validation studies using adequately powered OC cohorts are warranted to fully establish the clinical effectiveness of this diagnostic test.

Identification of lncRNAs Deregulated in Epithelial Ovarian Cancer Based on a Gene Expression Profiling Meta-Analysis

Epithelial ovarian cancer (EOC) is one of the deadliest gynecological cancers worldwide, mainly because of its initially asymptomatic nature and consequently late diagnosis. Long non-coding RNAs (lncRNA) are non-coding transcripts of more than 200 nucleotides, whose deregulation is involved in pathologies such as EOC, and are therefore envisaged as future biomarkers. We present a meta-analysis of available gene expression profiling (microarray and RNA sequencing) studies from EOC patients to identify lncRNA genes with diagnostic and prognostic value. In this meta-analysis, we include 46 independent cohorts, along with available expression profiling data from EOC cell lines. Differential expression analyses were conducted to identify those lncRNAs that are deregulated in (i) EOC versus healthy ovary tissue, (ii) unfavorable versus more favorable prognosis, (iii) metastatic versus primary tumors, (iv) chemoresistant versus chemosensitive EOC, and (v) correlation to specific histological subtypes of EOC. From the results of this meta-analysis, we established a panel of lncRNAs that are highly correlated with EOC. The panel includes several lncRNAs that are already known and even functionally characterized in EOC, but also lncRNAs that have not been previously correlated with this cancer, and which are discussed in relation to their putative role in EOC and their potential use as clinically relevant tools.

RNA sequencing and expression analysis reveal a role for Lhx9 in the haploinsufficient adult mouse ovary

Understanding the molecular pathways that underpin ovarian development and function is vital for improving the research approaches to investigating fertility. Despite a significant improvement in our knowledge of molecular activity in the ovary, many questions remain unanswered in the quest to understand factors influencing fertility and ovarian pathologies such as cancer. Here, we present an investigation into the expression and function of the developmental transcription factor LIM Homeobox 9 (LHX9) in the adult mouse ovary. We have characterized Lhx9 expression in several cell types of the mature ovary across follicle stages. To evaluate possible LHX9 function in the adult ovary, we investigated ovarian anatomy and transcription in an Lhx9^+/- knockout mouse model displaying subfertility. Despite a lack of gross anatomical differences between genotypes, RNA-sequencing found that 90 differentially expressed genes between Lhx9^{+/ -} and Lhx9^+/+ mice. Gene ontology analyses revealed a reduced expression of genes with major roles in ovarian steroidogenesis and an increased expression of genes associated with ovarian cancer. Analysis of the ovarian epithelium revealed Lhx9^{+/ -} mice have a disorganized epithelial phenotype, corresponding to a significant increase in epithelial marker gene expression. These results provide an analysis of Lhx9 in the adult mouse ovary, suggesting a role in fertility and ovarian epithelial cancer.

Long non-coding RNA SLC25A21-AS1 inhibits the development of epithelial ovarian cancer by specifically inducing PTBP3 degradation

BACKGROUND

Epithelial ovarian cancer (EOC) is a highly prevalent disease that rapidly metastasizes and has poor prognosis. Most women are in the middle or late stages when diagnosed and have low survival rates. Recently, long non-coding RNAs (lncRNAs) were recognized to play pivotal roles in the development of EOC.

METHODS

The expression of SLC25A21 antisense RNA 1 (SLC25A21-AS1) and Polypyrimidine Tract Binding Protein 3 (PTBP3) in EOC cells was assessed via qPCR. The proliferation activity of these cells was detected by EdU and Cell counting kit-8 (CCK8) assays, while the death rate of apoptotic cells and the cell cycle were detected by flow cytometry. Detection of cell transfer rate by transwell assay. Protein expression was measured through western blotting. Interactions between SLC25A21-AS1 and PTBP3 were detected through RNA immunoprecipitation (RIP), IF-FISH co-localization experiments and electrophoretic mobility shift assay (EMSA). The in vivo importance of SLC25A21-AS1 as a tumor suppressor modulator was assessed using murine xenograft models.

RESULTS

The lncRNA SLC25A21-AS1 has negligible expression in ovarian cancer tissues compared with that in normal ovarian tissues. A series of functional experiments revealed that the upregulation of SLC25A21-AS1 markedly blocked the proliferation and metastasis of EOC cells in vitro, while its downregulation had the opposite effect. Overexpression of SLC25A21-AS1 in a nude mouse model of EOC in vivo resulted in slower tumor growth and weakened metastatic potential. Moreover, SLC25A21-AS1 reduced the protein stability of PTBP3 and promoted its degradation. A series of subsequent experiments found that SLC25A21-AS1 inhibits EOC cell proliferation and metastasis by modulating PTBP3 through the ubiquitin-proteasome pathway and that the combination of SLC25A21-AS1 and PTBP3 provides the necessary conditions for the for the function to be realized.

CONCLUSIONS

Our research reveals the effect of SLC25A21-AS1 in EOC development and suggests SLC25A21-AS1 can serve as a prognostic target by promoting the degradation of PTBP3 to improve patient survival.

Gene co-expression network analysis revealed novel biomarkers for ovarian cancer

Ovarian cancer is the second most common gynecologic cancer and remains the leading cause of death of all gynecologic oncologic disease. Therefore, understanding the molecular mechanisms underlying the disease, and the identification of effective and predictive biomarkers are invaluable for the development of diagnostic and treatment strategies. In the present study, a differential co-expression network analysis was performed via meta-analysis of three transcriptome datasets of serous ovarian adenocarcinoma to identify novel candidate biomarker signatures, i.e. genes and miRNAs. We identified 439 common differentially expressed genes (DEGs), and reconstructed differential co-expression networks using common DEGs and considering two conditions, i.e. healthy ovarian surface epithelia samples and serous ovarian adenocarcinoma epithelia samples. The modular analyses of the constructed networks indicated a co-expressed gene module consisting of 17 genes. A total of 11 biomarker candidates were determined through receiver operating characteristic (ROC) curves of gene expression of module genes, and miRNAs targeting these genes were identified. As a result, six genes (CDT1, CNIH4, CRLS1, LIMCH1, POC1A, and SNX13), and two miRNAs (mir-147a, and mir-103a-3p) were suggested as novel candidate prognostic biomarkers for ovarian cancer. Further experimental and clinical validation of the proposed biomarkers could help future development of potential diagnostic and therapeutic innovations in ovarian cancer.

TWIST1 induces proteasomal degradation of β-catenin during the differentiation of ovarian cancer stem-like cells

Ovarian cancer (OC) is one of the leading gynecologic cancers worldwide. Cancer stem-like cells are correlated with relapse and resistance to chemotherapy. Twist1, which is involved in ovarian cancer stem-like cell differentiation, is positively correlated with CTNNB1 in different differentiation stages of ovarian cancer cells: primary epithelial ovarian cancer cells (primary EOC cells), mesenchymal spheroid-forming cells (MSFCs) and secondary epithelial ovarian cancer cells (sEOC cells). However, the expression of β-catenin is inversed compared to CTNNB1 in these 3 cell states. We further demonstrated that β-catenin is regulated by the protein degradation system in MSFCs and secondary EOC but not in primary EOC cells. The differentiation process from primary EOC cells to MSFCs and sEOC cells might be due to the downregulation of β-catenin protein levels. Finally, we found that TWIST1 can enhance β-catenin degradation by upregulating Axin2.

SLC7A1 Overexpression Is Involved in Energy Metabolism Reprogramming to Induce Tumor Progression in Epithelial Ovarian Cancer and Is Associated with Immune-Infiltrating Cells

Cationic amino acid transporters (SLC7A1/CAT1) are highly expressed in human ovarian cancer (OC) tissues. However, the specific biological functions and mechanisms involved remain unclear. We used bioinformatics analysis to explore SLC7A1 expression level, prognostic value, and tumor mutation burden (TMB) in ovarian cancer (OC) tissues. We performed in vitro experiments to identify the expression and biological function of SLC7A1 in epithelial ovarian cancer (EOC) tissues and cells. An amino acid autoanalyzer was used to detect the effect of SLC7A1 on amino acid metabolism in EOC cells. Finally, SLC7A1 in OC was evaluated for cell-to-cell signalling and immune infiltration using online databases. We found that increased SLC7A1 expression in EOC cells and tissues was associated with poorer survival outcomes (P < 0.05) but not with tumor stage or grade of OC (P > 0.05). SLC7A1 is involved in the transport of phenylalanine and arginine in EOC cells, and its knockdown reduced the proliferation and migration of EOC cells and the resistance of cells to cisplatin. Furthermore, the TIMER database indicated that SLC7A1 overexpression was significantly positively correlated with levels of CD4⁺ memory resting cells, CD8⁺ effector memory cells, M0 macrophages, and cancer-associated fibroblasts (CAFs) in OC (P < 0.05) and significantly negatively correlated with CD4⁺ memory-activated cells (P < 0.05). Cell immunofluorescence indicated that SLC7A1 overexpression may affect the distribution of immune-infiltrating lymphocytes in tumors by inhibiting the expression of CCL4. Therefore, we concluded that SLC7A1 is involved in the metabolic remodelling of amino acids in EOC to promote tumor development and cisplatin resistance and is related to the tumor-infiltrating immune microenvironment of OC. SLC7A1 is a biomarker for predicting EOC progression and cisplatin resistance and represents a promising target for EOC treatment.

Identification of New Molecular Biomarkers in Ovarian Cancer Using the Gene Expression Profile

Ovarian cancer is a common cause of death among women worldwide. The current diagnostic and prognostic procedures available for the treatment of ovarian cancer are either not specific or are very expensive. Gene expression profiling has proved to be a very effective tool in the exploration of new molecular markers in patients with ovarian cancer, although the link between such markers and patient survival and clinical outcomes is still elusive. We are looking for genes that may function in the development and progression of ovarian cancer. The aim of our study was to evaluate the expression of selected suppressor genes (ATM, BRCA1, BRCA2), proto-oncogenes (KRAS, c-JUN, c-FOS), pro-apoptotic genes (NOXA, PUMA), genes related to chromatin remodeling (MEN1), and genes related to carcinogenesis (NOD2, CHEK2, EGFR). Tissue samples from 30 normal ovaries and 60 ovarian carcinoma tumors were provided for analysis of the gene and protein expression. Gene expression analysis was performed using the real-time PCR method. The protein concentrations from tissue homogenates were determined using the ELISA technique according to the manufacturers’ protocols. An increase in the expression level of mRNA and protein in women with ovarian cancer was observed for KRAS, c-FOS, PUMA, and EGFR. No significant changes in the transcriptional levels we observed for BRCA1, BRCA2, NOD2, or CHEK2. In conclusion, we suggest that KRAS, NOXA, PUMA, c-FOS, and c-JUN may be associated with poor prognosis in ovarian cancer.

cGAS-STING signalings potentiate tumor progression via sustaining cancer stemness

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Intrinsic activation of cGAS-STING signalings potentiate tumorigenesis.

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cGAS-STING signalings promote tumor progression by sustaining cancer stemness.

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STAT3 may act as a downstream effector of cGAS-STING signalings to stimulate cancer stemness.

The cytosolic DNA-sensing cGAS-STING pathway has been proved to be involved in tumor progression and influence the effect of cancer immunotherapy. However, little attentions have been paid to the role of cGAS-STING pathway on cancer stemness. Herein, we found that the cGAS-STING pathway was activated in different tumor cells. cGAS- or STING-knockout impaired the capability of tumor formation in vivo and tumorsphere formation in vitro. In addition, loss of cGAS-STING cascade promoted tumor apoptosis, but inhibited tumor growth and metastasis. We further demonstrated that cGAS-STING pathway potentiated tumor formation by sustaining cancer stemness. Moreover, analysis of RNA-seq showed that cGAS-STING pathway maintained cancer stemness probably by activating STAT3. Our findings highlight the role of intrinsic activation of cGAS-STING pathway in tumorigenesis, and reveal a new mechanism of its regulation of tumor progression via sustaining cancer stemness through STAT3 activation.

Four calcium signaling pathway-related genes were upregulated in microcystic adnexal carcinoma: transcriptome analysis and immunohistochemical validation

BACKGROUND

Microcystic adnexal carcinoma (MAC) is a skin cancer with challenges in diagnosis and management. This study was aimed to detect molecular alterations of MAC and guide its pathologic diagnosis and treatment.

METHODS

We performed transcriptome analysis on 5 MAC and 5 normal skin tissues, identified the differentially expressed genes, and verified them by immunohistochemistry.

RESULTS

Three hundred four differentially expressed genes (DEGs) in MAC were identified by next-generation transcriptome sequencing, among which 225 genes were upregulated and 79 genes were downregulated. Four genes of the calcium signaling pathway, including calcium voltage-gated channel subunit alpha 1 S (CACNA1S), ATPase sarcoplasmic/endoplasmic reticulum Ca2+ transporting 1 (ATP2A1), ryanodine receptor 1 (RYR1), and myosin light chain kinase 3 (MYLK3), were upregulated and then been verified by immunohistochemistry. The expression of CACNA1S, ATP2A1, RYR1, and MYLK3 was upregulated in MAC compared with normal sweat glands and syringoma tumor cells and was generally negative in trichoepithelioma and infundibulocystic type basal cell carcinoma.

CONCLUSIONS

The four genes of the calcium signaling pathway were upregulated in MAC at both RNA and protein levels. CACNA1S, ATP2A1, RYR1, and MYLK3 may be new diagnostic molecular markers and therapeutic targets for MAC.

Bioinformatic Analysis for Influential Core Gene Identification and Prognostic Significance in Advanced Serous Ovarian Carcinoma

Background and objectives: Ovarian cancer is one of the leading causes of death among women worldwide. Most newly diagnosed ovarian cancer patients are diagnosed in advanced stages of the disease. Despite various treatments, most patients with advanced stage ovarian cancer, including serous ovarian cancer (the most common subtype of ovarian cancer), experience recurrence, which is associated with extremely poor prognoses. In the present study, we aimed to identify core genes involved in ovarian cancer and their associated molecular mechanisms, as well as to investigate related clinicopathological implications in ovarian cancer. Materials and methods: Three gene expression cohorts (GSE14407, GSE36668, and GSE38666) were obtained from the Gene Expression Omnibus databases to explore potential therapeutic biomarkers for ovarian cancer. Nine up-regulated and six down-regulated genes were screened. Three publicly available gene expression datasets (GSE14407, GSE36668, and GSE38666) were analyzed. Results: A total of 14 differently expressed genes (DEGs) were identified, among which nine genes were upregulated (BIRC5, CDCA3, CENPF, KIF4A, NCAPG, RRM2, UBE2C, VEGFA, and NR2F6) and were found to be significantly enriched in cell cycle regulation by gene ontology analysis. Further protein–protein interaction network analysis revealed seven hub genes among these DEGs. Moreover, Kaplan–Meier survival analysis showed that a higher expression of CDCA3 and UBE2C was associated with poor overall patient survival regardless of tumor stage and a higher tumor histologic grade. Conclusion: Altogether, our study suggests that CDCA3 and UBE2C may be valuable biomarkers for predicting the outcome of patients with advanced serous ovarian cancer.

Long non‑coding RNA HAND2‑AS1/miR‑106a/PTEN axis re‑sensitizes cisplatin‑resistant ovarian cells to cisplatin treatment

Cisplatin (DDP) resistance in patients suffering from ovarian cancer is a considerable hurdle to successful treatment. The present study aimed to identify a possible long non‑coding RNA (lncRNA)‑microRNA (miRNA)‑mRNA axis participating in ovarian cancer DDP‑resistance based on the critical roles of non‑coding RNAs, including lncRNAs and miRNAs, in carcinogenesis. According to online data and experimental results, lncRNA HAND2‑AS1 expression was significantly downregulated within ovarian carcinoma, especially within recurrent and DDP‑resistant ovarian carcinoma. The expression of HAND2‑AS1 was also shown to be markedly inhibited in SKOV3/DDP (DDP) cells with resistance to DDP. In SKOV3/DDP cells, HAND2‑AS1 overexpression inhibited cell viability and promoted cell apoptosis upon DDP treatment through the Bcl‑2/caspase‑3 apoptotic signaling. It was hypothesized that PTEN mRNA expression was also markedly inhibited in SKOV3/DDP ovarian cancer cells, while HAND2‑AS1 overexpression rescued PTEN proteins and blocked PI3K/AKT signaling activation. Moreover, miR‑106a was found to bind directly to PTEN 3' UTR and HAND2‑AS1. Upon DDP treatment, miR‑106a overexpression in SKOV3/DDP cells promoted cell viability. It inhibited cell apoptosis through the Bcl‑2/caspase‑3 apoptotic signaling pathway and downregulated the protein levels of PTEN and upregulated PI3K/AKT signaling activity. Furthermore, miR‑106a overexpression partially reversed the effect of HAND2‑AS1 overexpression upon PTEN proteins and SKOV3/DDP cell proliferation upon DDP treatment. In conclusion, a lncRNA HAND2‑AS1/miR‑106a/PTEN axis that re‑sensitizes DDP‑resistant SKOV3/DDP cells to DDP treatment has been established.

Common miRNAs, candidate genes and their interaction network across four subtypes of epithelial ovarian cancer

Epithelial ovarian cancer (EOC) is categorized into four major histological subtypes such as clear cell carcinoma (CCC), endometrioid carcinoma (EC), mucinous carcinoma (MC), and serous carcinoma (SC). Heterogeneity of the EOC leads to different clinical outcomes of the disease, although all the subtypes are originated from the same layer of tissue. Therefore, it is of interest to identify the common candidate genes, miRNA and their interaction network in four the subtypes of EOC. A comparative gene expression analysis identified 248 common differentially expressed genes (DEGs) in the four subtypes of EOC. Identified common DEGs were found to be enriched in cancer specific pathways. A protein-protein interaction (PPI) network of the common DEGs were constructed, and subsequent module and survival analyses identified seven key candidate genes (CCNB1, CENPM, CEP55, RACGAP1, TPX2, UBE2C, and ZWINT). We also documented 10 key candidate miRNAs (hsa-mir-16-5p, hsa-mir-23b-3p, hsa-mir-34a-5p, hsa-mir-103a-3p, hsa-mir-107, hsa-mir-124-3p, hsa-mir-129-2-3p, hsa-mir-147a, hsa-mir-205-5p, and hsa-mir-195-5p) linked to the candidate genes. These derived data find application in the understanding of EOC.

Meta-analysis identifying epithelial-derived transcriptomes predicts poor clinical outcome and immune infiltrations in ovarian cancer

BACKGROUND

Previous studies revealed that the epithelial component is associated with the modulation of the ovarian tumor microenvironment (TME). However, the identification of key transcriptional signatures of laser capture microdissected human ovarian cancer epithelia remains lacking.

METHODS

We identified the differentially expressed transcriptional signatures of human ovarian cancer epithelia by meta-analysis of GSE14407, GSE2765, GSE38666, GSE40595, and GSE54388. Then we investigated the enrichment of KEGG pathways that are associated with epithelia-derived transcriptomes. Finally, we investigated the correlation of key epithelia-hub genes with the survival prognosis and immune infiltrations. Finally, we investigated the genetic alterations of key prognostic hub genes and their diagnostic efficacy in ovarian cancer epithelia.

RESULTS

We identified 1339 differentially expressed genes (DEGs) in ovarian cancer epithelia including 541upregulated and 798 downregulated genes. We identified 21 (such as E2F4, FOXM1, TFDP1, E2F1, and SIN3A) and 11 (such as JUN, DDX4, FOSL1, NOC2L, and HMGA1) master transcriptional regulators (MTRs) that are interacted with upregulated and the downregulated genes in ovarian tumor epithelium, respectively. The STRING-based analysis identified hub genes (such as CDK1, CCNB1, AURKA, CDC20, and CCNA2) in ovarian cancer epithelia. The significant clusters of identified hub genes are associated with the enrichment of KEGG pathways including cell cycle, DNA replication, cytokine-cytokine receptor interaction, pathways in cancer, and focal adhesion. The upregulation of SCNN1A and CDCA3 and the downregulation of SOX6 are correlated with a shorter survival prognosis in ovarian cancer (OV). The expression level of SOX6 is negatively correlated with immune score and positively correlated with tumor purity in OV. Moreover, SOX6 is negatively correlated with the infiltration of TILs, CD8+ T cells, CD4+ Regulatory T cells, cytolytic activity, T cell activation, pDC, neutrophils, and macrophages in OV. Also, SOX6 is negatively correlated with various immune markers including CD8A, PRF1, GZMA, GZMB, NKG7, CCL3, and CCL4, indicating the immune regulatory efficiency of SOX6 in the TME of OV. Furthermore, SCNN1A, CDCA3, and SOX6 genes are genetically altered in OV and the expression levels of SCNN1A and SOX6 genes showed diagnostic efficacy in ovarian cancer epithelia.

CONCLUSIONS

The identified ovarian cancer epithelial-derived key transcriptional signatures are significantly correlated with survival prognosis and immune infiltrations, and may provide new insight into the diagnosis and treatment of epithelial ovarian cancer.

Differential expression analysis in ovarian cancer: A functional genomics and systems biology approach

BACKGROUND

Ovarian cancer is one of the rarest lethal oncologic diseases that have hardly any specific biomarkers. The availability of high-throughput genomic data and advancement in bioinformatics tools allow us to predict gene biomarkers and apply systems biology approaches to get better diagnosis, and prognosis of the disease with a tentative drug that may be repurposed.

OBJECTIVE

To perform genome-wide association studies using microarray gene expression of ovarian cancer and identify gene biomarkers, construction and analyze networks, perform survival analysis, and drug interaction studies for better diagnosis, prognosis, and treatment of ovarian cancer.

METHOD

The gene expression profiles of both healthy and serous ovarian cancer epithelial samples were considered. We applied a series of bioinformatics methods and tools, including fold-change statistics for differential expression analysis, DisGeNET and NCBI-Gene databases for gene-disease association mapping, DAVID 6.8 for GO enrichment analysis, GeneMANIA for network construction, Cytoscape 3.8 with its plugins for network visualization, analysis, and module detection, the UALCAN for patient survival analysis, and PubChem, DrugBank and DGIdb for gene-drug interaction.

RESULTS

We identified 8 seed genes that were subjected for drug-gene interaction studies. Because of over-expression in all the four stages of ovarian cancer, we discern that genes HMGA1 and PSAT1 are potential therapeutic biomarkers for its diagnosis at an early stage (stage I). Our analysis suggests that there are 11 drugs common in the seed genes. However, hypermethylated seed genes HMGA1 and PSAT1 showcased a good interaction affinity with drugs cisplatin, cyclosporin, bisphenol A, progesterone, and sunitinib, and are crucial in the proliferation of ovarian cancer.

CONCLUSION

Our study reveals that HMGA1 and PSAT1 can be deployed for initial screening of ovarian cancer and drugs cisplatin, bisphenol A, cyclosporin, progesterone, and sunitinib are effective in curbing the epigenetic alteration.

Screening and identification of key biomarkers in alimentary tract cancers: A bioinformatic analysis

BACKGROUND

Alimentary tract cancers (ATCs) are the most malignant cancers in the world. Numerous studies have revealed the tumorigenesis, diagnosis and treatment of ATCs, but many mechanisms remain to be explored.

METHODS

To identify the key genes of ATCs, microarray datasets of oesophageal cancer, gastric cancer and colorectal cancer were obtained from the Gene Expression Omnibus (GEO) database. In total, 207 differentially expressed genes (DEGs) were screened. KEGG and GO function enrichment analyses were conducted, and a protein-protein interaction (PPI) network was generated and gene modules analysis was performed using STRING and Cytoscape.

RESULTS

Five hub genes were screened, and the associated biological processes indicated that these genes were mainly enriched in cellular processes, protein binding and metabolic processes. Clinical survival analysis showed that COL10A1 and KIF14 may be significantly associated with the tumorigenesis or pathology grade of ATCs. In addition, relative human ATC cell lines along with blood samples and tumour tissues of ATC patients were obtained. The data proved that high expression of COL10A1 and KIF14 was associated with tumorigenesis and could be detected in blood.

CONCLUSION

In conclusion, the identification of hub genes in the present study helped us to elucidate the molecular mechanisms of tumorigenesis and identify potential diagnostic indicators and targeted treatment for ATCs.

Human Granulosa Cells-Stemness Properties, Molecular Cross-Talk and Follicular Angiogenesis

The ovarian follicle is the basic functional unit of the ovary, comprising theca cells and granulosa cells (GCs). Two different types of GCs, mural GCs and cumulus cells (CCs), serve different functions during folliculogenesis. Mural GCs produce oestrogen during the follicular phase and progesterone after ovulation, while CCs surround the oocyte tightly and form the cumulus oophurus and corona radiata inner cell layer. CCs are also engaged in bi-directional metabolite exchange with the oocyte, as they form gap-junctions, which are crucial for both the oocyte’s proper maturation and GC proliferation. However, the function of both GCs and CCs is dependent on proper follicular angiogenesis. Aside from participating in complex molecular interplay with the oocyte, the ovarian follicular cells exhibit stem-like properties, characteristic of mesenchymal stem cells (MSCs). Both GCs and CCs remain under the influence of various miRNAs, and some of them may contribute to polycystic ovary syndrome (PCOS) or premature ovarian insufficiency (POI) occurrence. Considering increasing female fertility problems worldwide, it is of interest to develop new strategies enhancing assisted reproductive techniques. Therefore, it is important to carefully consider GCs as ovarian stem cells in terms of the cellular features and molecular pathways involved in their development and interactions as well as outline their possible application in translational medicine.

Meta-analysis based gene expression profiling reveals functional genes in ovarian cancer

Background: Ovarian cancer causes high mortality rate worldwide, and despite numerous attempts, the outcome for patients with ovarian cancer are still not well improved. Microarray-based gene expressional analysis provides with valuable information for discriminating functional genes in ovarian cancer development and progression. However, due to the differences in experimental design, the results varied significantly across individual datasets.

Methods: In the present study, the data of gene expression in ovarian cancer were downloaded from Gene Expression Omnibus (GEO) and 16 studies were included. A meta-analysis based gene expression analysis was performed to identify differentially expressed genes (DEGs). The most differentially expressed genes in our meta-analysis were selected for gene expression and gene function validation.

Results: A total of 972 DEGs with P-value < 0.001 were identified in ovarian cancer, including 541 up-regulated genes and 431 down-regulated genes, among which 92 additional DEGs were found as gained DEGs. Top five up- and down-regulated genes were selected for the validation of gene expression profiling. Among these genes, up-regulated CD24 molecule (CD24), SRY (sex determining region Y)-box transcription factor 17 (SOX17), WFDC2, epithelial cell adhesion molecule (EPCAM), innate immunity activator (INAVA), and down-regulated aldehyde oxidase 1 (AOX1) were revealed to be with consistent expressional patterns in clinical patient samples of ovarian cancer. Gene functional analysis demonstrated that up-regulated WFDC2 and INAVA promoted ovarian cancer cell migration, WFDC2 enhanced cell proliferation, while down-regulated AOX1 was functional in inducing cell apoptosis of ovarian cancer.

Conclusion: Our study shed light on the molecular mechanisms underlying the development of ovarian cancer, and facilitated the understanding of novel diagnostic and therapeutic targets in ovarian cancer.

The RNA-Binding Protein CELF2 Inhibits Ovarian Cancer Progression by Stabilizing FAM198B

An increasing number of studies have clarified the functional roles of RNA-binding proteins (RBPs) in driving post-transcriptional mechanisms of cancer progression. In this study, we integrated data from the RBP database and Gene Expression Omnibus (GEO) data with RNA sequencing (RNA-seq) data from 10 ovarian cancer tissues and 8 normal ovarian tissues and identified an RBP, CUGBP- and ETR-3-like family 2 (CELF2). We found that CELF2 expression was downregulated in ovarian cancer and positively correlated with the overall survival (OS) and progression-free survival (PFS) of patients with ovarian cancer. Altered CELF2 expression led to changes in the proliferation, migration, and invasion of ovarian cancer cells in vitro and in vivo. CELF2 expression increased the stability of its target, FAM198B, by binding to AU/U-rich elements (AREs) in the 3' untranslated region (3' UTR). FAM198B knockdown restored the CELF2-mediated suppression of proliferation and migration. We also found that CELF2/FAM198B may repress ovarian cancer progression by inhibiting the mitogen-activated protein kinase/extracellular-regulated protein kinase (MAPK/ERK) signaling pathway. Finally, a curcumin-induced increase in CELF2 expression resulted in increased ovarian cancer cell sensitivity to cisplatin. Our study elucidated a novel mechanism by which the CELF2/FAM198B axis regulates proliferation and metastasis in ovarian cancer, providing novel, potential therapeutic targets for ovarian cancer.

Endothelin-1 drives invadopodia and interaction with mesothelial cells through ILK

Cancer cells use actin-based membrane protrusions, invadopodia, to degrade stroma and invade. In serous ovarian cancer (SOC), the endothelin A receptor (ETAR) drives invadopodia by a not fully explored coordinated function of β-arrestin1 (β-arr1). Here, we report that β-arr1 links the integrin-linked kinase (ILK)/βPIX complex to activate Rac3 GTPase, acting as a central node in the adhesion-based extracellular matrix (ECM) sensing and degradation. Downstream, Rac3 phosphorylates PAK1 and cofilin and promotes invadopodium-dependent ECM proteolysis and invasion. Furthermore, ETAR/ILK/Rac3 signaling supports the communication between cancer and mesothelial cells, favoring SOC cell adhesion and transmigration. In vivo, ambrisentan, an ETAR antagonist, inhibits the adhesion and spreading of tumor cells to intraperitoneal organs, and invadopodium marker expression. As prognostic factors, high EDNRA/ILK expression correlates with poor SOC clinical outcome. These findings provide a framework for the ET-1R/β-arr1 pathway as an integrator of ILK/Rac3-dependent adhesive and proteolytic signaling to invadopodia, favoring cancer/stroma interactions and metastatic behavior.

LINC00452 promotes ovarian carcinogenesis through increasing ROCK1 by sponging miR-501-3p and suppressing ubiquitin-mediated degradation

Ovarian cancer refers to all sorts of cancerous growth that starts from the ovary. Dysregulation of long non-coding RNAs (lncRNAs) is associated with ovarian cancer development and progression. Cellular expression and localization of LINC00452 in ovarian cancer cells were detected by qPCR and FISH. The roles of LINC00452 in ovarian carcinogenesis were characterized by MTT, transwell and colony-formation assays in vitro as well as xenograft mouse model. The underlying mechanism was explored by microarray, RIP, Co-IP and luciferase reporter assays. This study identified a novel lncRNA LINC00452 being elevated in both ovarian cancer cells and tumor tissues in patients. Such aberrant expression of LINC00452 was negatively correlated with relapse-free survival of ovarian cancer patients. Overexpression of LINC00452 potentiated CaOV3 cell viability, migration and invasion in vitro as well as xenograft tumor growth in vivo. Evidence from the current study suggests that the carcinogenicity of LINC00452 is partially due to competitive sponging of miR-501-3p followed with release of repression on the ROCK1, a key effector in Rho signaling pathway. Irrespective of its miRNA sponge function, LINC00452 is capable of preventing ROCK1 protein from ubiquitin/proteasome-mediated degradation via their mutual physical interaction. Our study makes LINC00452 a potential therapeutic target for ovarian cancer.

Neural Stem Cells Improve the Delivery of Oncolytic Chimeric Orthopoxvirus in a Metastatic Ovarian Cancer Model

Oncolytic virotherapy represents a promising approach for treating recurrent and/or drug-resistant ovarian cancer. However, its successful application in the clinic has been hampered by rapid immune-mediated clearance, which reduces viral delivery to the tumor. Patient-derived mesenchymal stem cells that home to tumors have been used as viral delivery tools, but variability associated with autologous cell isolations limits the clinical applicability of this approach. We previously developed an allogeneic, clonal neural stem cell (NSC) line (HB1.F3.CD21) that can be used to deliver viral cargo. Here, we demonstrate that this NSC line can improve the delivery of a thymidine kinase gene-deficient conditionally replication-competent orthopoxvirus, CF33, in a preclinical cisplatin-resistant peritoneal ovarian metastases model. Overall, our findings provide the basis for using off-the-shelf allogeneic cell-based delivery platforms for oncolytic viruses, thus providing a more efficient delivery alternative compared with the free virus administration approach.

Identification and validation of core genes for serous ovarian adenocarcinoma via bioinformatics analysis

Ovarian cancer is a fatal gynaecological malignancy in women worldwide, and serous ovarian cancer (SOC) is considered the most common histological subtype of this malignancy. Thus, the present study aimed to identify the core genes for SOC via bioinformatics analysis. The GSE18520 and GSE14407 datasets were downloaded from the Gene Expression Omnibus (GEO) database to screen for differentially expressed genes (DEGs) and perform gene set enrichment analysis (GSEA). A protein-protein interaction (PPI) network was constructed to identify the core genes, while The Cancer Genome Atlas (TCGA) database was used to screen for prognosis-associated DEGs. Furthermore, clinical samples were collected for further validation of kinesin family member 11 (KIF11) gene. In the GEO analysis, a total of 198 DEGs were identified, including 81 upregulated and 117 downregulated genes compared SOC to normal tissue. GSEA across the two datasets demonstrated that 16 gene sets, including those involved in the cell cycle and DNA replication, were notably associated with SOC. A PPI network of the DEGs was constructed with 130 nodes and 387 edges. Subsequently, 20 core genes involved in the same top-ranked module were filtered out by submodule analysis. Survival analysis identified three predictive genes for SOC prognosis, including KIF11, CLDN3 and FGF13. KIF11 was identified as a core and predictive gene and thus was further validated using clinical samples. The results demonstrated that KIF11 was upregulated in tumour tissues compared with adjacent normal tissues and was associated with aggressive factors, including tumour grade, TNM stage and lymph node invasion. In conclusions, the present study identified the core genes and gene sets for SOC, thus extending the understanding of SOC occurrence and progression. Furthermore, KIF11 was identified as a promising tumour-promoting gene and a potential target for the diagnosis and treatment of SOC.

Identification of KIF23 as a prognostic signature for ovarian cancer based on large-scale sampling and clinical validation

The purpose of this study is to explore the expression and clinical significance of KIF23 in ovarian cancer (OV) and identify potential targets for clinical treatment. Oncomine, GEO, and TCGA databases were used to analysis the expression of KIF23 in OV. The prognostic value of KIF23 gene was analyzed by the Kaplan-Meier plotter database. The molecular mechanism of KIF23 activity was analyzed from the perspective of immunology, gene mutation, copy number variation (CNV). Finally, immunohistochemistry was conducted to validate the expression of KIF23, univariable and multivariate cox analysis were used to determine its relationship with clinical characteristics and OV prognosis. It showed that highly expressed KIF23 is an adverse independent prognostic biomarker for OV patients. Genomics analysis showed that KIF23 expression was associated with mutations such as FLG2 and TTN, and was significantly enriched in DNA replication and the cell cycle tumor-related signaling pathways. Immunology analysis showed that KIF23 is closely related to the immune infiltration. KIF23 can not only performed as a prognosis signature in OV but also as a target of immune molecular therapeutics.

Assessing the Effect of SNPs on Litter Traits in Pigs

The reproductive ability of sows is the principle of continuous and efficient production, based on such traits as the number of piglets, the total number of parities, and the period of economic use. Currently, SNPs associated with the TNB and NBA are presented in the PigQTLdb. The aim of this work was the assessment of the SNP effects on the litter traits in Large White (LW, n = 502) and Landrace (LN, n = 432) sow breeds in a farm in Russia. 9 SNPs (SNP_1: rs80956812; SNP_2: rs81471381; SNP_3: rs80891106; SNP_4: rs81399474; SNP_5: rs81421148; SNP_6: rs81242222; SNP_7: rs81319839; SNP_8: rs81312912; SNP_9: rs80962240) were selected for the study. Associative analysis was performed using the GLM procedure in R version 3.5.1. The analysis of reproductive traits was carried out according to the results of the first parity, the second and subsequent parities, and totals for lifetime of sows. The significant effect on litter traits in LW was determined for SNP rs80956812, SNP rs81471381, SNP rs81421148, and SNP rs81399474. The significant effect on litter traits in LN was determined for SNP rs81421148 and SNP rs81319839. AKT3 gene was identified as perspective candidate gene, whose biological functions, as well as the results obtained in our work and in other studies, indicate its potential role in the reproductive process regulation in pigs. In general, the data obtained help to explain the genetic mechanisms of reproductive traits.

Transcriptome analyses of ovarian stroma: tunica albuginea, interstitium and theca interna

The ovary has specialised stromal compartments, including the tunica albuginea, interstitial stroma and theca interna, which develops concurrently with the follicular antrum. To characterise the molecular determinants of these compartments, stroma adjacent to preantral follicles (pre-theca), interstitium and tunica albuginea were laser microdissected (n = 4 per group) and theca interna was dissected from bovine antral follicles (n = 6). RNA microarray analysis showed minimal differences between interstitial stroma and pre-theca, and these were combined for some analyses and referred to as stroma. Genes significantly upregulated in theca interna compared to stroma included INSL3, LHCGR, HSD3B1, CYP17A1, ALDH1A1, OGN, POSTN and ASPN. Quantitative RT-PCR showed significantly greater expression of OGN and LGALS1 in interstitial stroma and theca interna versus tunica and greater expression of ACD in tunica compared to theca interna. PLN was significantly higher in interstitial stroma compared to tunica and theca. Ingenuity pathway, network and upstream regulator analyses were undertaken. Cell survival was also upregulated in theca interna. The tunica albuginea was associated with GPCR and cAMP signalling, suggesting tunica contractility. It was also associated with TGF-β signalling and increased fibrous matrix. Western immunoblotting was positive for OGN, LGALS1, ALDH1A1, ACD and PLN with PLN and OGN highly expressed in tunica and interstitial stroma (each n = 6), but not in theca interna from antral follicles (n = 24). Immunohistochemistry localised LGALS1 and POSTN to extracellular matrix and PLN to smooth muscle cells. These results have identified novel differences between the ovarian stromal compartments.

Screening and identification of key biomarkers for retinoblastoma: Evidence from bioinformatics analysis

BACKGROUND

Retinoblastoma (RB) is one of the most common malignant tumors in pediatrics; to clarify the cause of RB, a lot of manpower and material resources have been invested but have not been well explained.

METHODS

To identify the candidate genes in the occurrence and development of the disease, we downloaded the microarray datasets GSE97508, GSE92987, and GSE24673 from the gene expression database (GEO). The differentially expressed gene (DEG) was identified and functional enrichment analysis was performed. The protein-protein interaction network was constructed and analyzed by String and Cytoscape.

RESULTS

A total of 74 DEGs were identified, including 40 up-regulated genes and 34 down-regulated genes. The rich functions and pathways of DEG include regulating mitosis, cell cycle, DNA transcription process, promoting protein phosphorylation, regulating energy metabolism in vivo, promoting the binding of some macromolecular complexes, and regulating the cell cycle. Twenty-four HUB genes were identified. Biological process analysis showed that these genes were mainly enriched in regulating energy metabolism in vivo, promoting the binding of some small molecules and regulating the cell cycle. Survival analysis showed that DGPDC1, NDC80, SHCBP, TOP2A, and DLGAP5 may be involved in the occurrence, invasion, or recurrence of RB.

CONCLUSION

In conclusion, screening DEGs and HUB genes in RB can help us to better understand the mechanism of the occurrence and development of RB at the molecular level, and provide candidate targets for the diagnosis and treatment of RB.

Identification of Differentially Expressed Genes (DEGs) Relevant to Prognosis of Ovarian Cancer by Use of Integrated Bioinformatics Analysis and Validation by Immunohistochemistry Assay

Background

The aim of this study was to investigate the differentially expressed genes (DEGs) relevant to prognosis of ovarian cancer by use of integrated bioinformatics analysis.

Material/Methods

The DEGs between normal ovariy tissue and ovarian cancer tissue were screened in GSE54388, GSE14407, and GSE18520 datasets and the overlapping DEGs were then indentified. GO and KEEG enrichment were performed to analyze the biological functions and pathways of the DEGs. A protein–protein interaction (PPI) network of the identified DEGs was constructed using the STRING database. Differences in prognosis between low and high expression of the hub DEGs were also evaluated using the Kaplan-Meier Plotter database. Protein expression of 2 hub genes – BUB1B and KIF201A – was assessed by immunohistochemistry assay and evaluated with the patient’s clinical pathology characteristics.

Results

We identified 361 DEGs, mainly involving oncogene-induced cell senescence, cyclin B1-CDK1 complex, protein kinase A catalytic subunit binding, cell cycle, and p53 signaling pathway. Ten hub genes were identified from among the 361 DEGs. The overall survival (OS) and progression-free survival (PFS) of these 10 hub genes were evaluated in the Kaplan-Meier-plotter database. Three (BUB1B, KIF11, and KIF20A) of the 10 hub genes were found to be correlated with ovarian cancer OS and PFS. BUB1B expression level was correlated with ovarian FIGO stage (p<0.05) and tumor differentiation (p<0.05). For KIF20A, the expression level was correlated with FIGO stage (p<0.05) and intraperitoneal metastasis (p<0.05).

Conclusions

DEGs can participate in ovarian cancer development and can be used as biomarkers for prognosis. Patients with upregulated BUB1B, KIF11, and KIF20A tend to have worse overall survival and disease-free survival compared with patients who have low expression.

ATAD2 predicts poor outcomes in patients with ovarian cancer and is a marker of proliferation

The oncogene ATPase family AAA domain-containing protein 2 (ATAD2) has been demonstrated to promote malignancy in a number of different types of tumor; however, its expression and role in ovarian cancer (OC) remain unknown. In the present study, it was demonstrated that ATAD2 acts as both a marker and a driver of cell proliferation in OC. Immunohistochemistry (IHC) and bioinformatics analyses were used to evaluate ATAD2 expression in OC, and multi-omics integrated analyses were used to dissect which factor resulted in its upregulation. Multiplex IHC assay was used to reveal the specific expression of ATAD2 in proliferating OC cells. CRISPR-Cas9-mediated gene editing was performed to investigate the effect of ATAD2 deletion on OC proliferation. The results demonstrated that ATAD2 is elevated in primary OC tissues compared with the adjacent normal tissue and metastases from the stomach. Genetic copy number amplification is a primary cause resulting in upregulation of ATAD2, and this was most frequently observed in OC. High ATAD2 expression was associated with advanced progression and predicted an unfavorable prognosis. ATAD2 could be used to identify cases of OC with a high proliferation signature and could label proliferating cells in OC. CRISPR-Cas9-mediated ATAD2 deletion resulted in a significant decrease in both cell proliferation and colony formation ability. Mechanistically, ATAD2-knockdown resulted in deactivation of the mitogen-activated protein kinase (MAPK) pathways, particularly the JNK-MAPK pathway, resulting in suppression of proliferation. Collectively, the data from the present study demonstrated that the ATD2 gene was frequently amplified and protein expression levels were upregulated in OC. Therefore, ATAD2 may serve as an attractive diagnostic and prognostic OC marker, which may be used to identify patients with primary OC, whom are most likely to benefit from ATAD2 gene-targeted proliferation intervention therapies.

Identification of Differentially Expressed Genes to Establish New Biomarker for Cancer Prediction

The goal of the human genome project is to integrate genetic information into different clinical therapies. To achieve this goal, different computational algorithms are devised for identifying the biomarker genes, cause of complex diseases. However, most of the methods developed so far using DNA microarray data lack in interpreting biological findings and are less accurate in disease prediction. In the paper, we propose two parameters risk_factor and confusion_factor to identify the biologically significant genes for cancer development. First, we evaluate risk_factor of each gene and the genes with nonzero risk_factor result misclassification of data, therefore removed. Next, we calculate confusion_factor of the remaining genes which determines confusion of a gene in prediction due to closeness of the samples in the cancer and normal classes. We apply nondominated sorting genetic algorithm (NSGA-II) to select the maximally uncorrelated differentially expressed genes in the cancer class with minimum confusion_factor. The proposed Gene Selection Explore (GSE) algorithm is compared to well established feature selection algorithms using 10 microarray data with respect to sensitivity, specificity, and accuracy. The identified genes appear in KEGG pathway and have several biological importance.

Evaluation of core serous epithelial ovarian cancer genes as potential prognostic markers and indicators of the underlying molecular mechanisms using an integrated bioinformatics analysis

Ovarian cancer is a major cause of mortality in women. However, the molecular events underlying the pathogenesis of the disease are yet to be fully elucidated. In the present study, an integrated bioinformatics analysis was performed to identify core genes involved in serous epithelial ovarian cancer. A total of three expression datasets were downloaded from the Gene Expression Omnibus database, and included 46 serous epithelial ovarian cancer and 30 ovarian surface epithelium samples. The three datasets were merged, and batch normalization was performed. The normalized merged data were subsequently analyzed for differentially expressed genes (DEGs). In total, 2,212 DEGs were identified, including 1,300 upregulated and 912 downregulated genes. Gene Ontology analysis revealed that these DEGs were primarily involved in ‘regulation of cell cycle’, ‘mitosis’, ‘DNA packaging’ and ‘nucleosome assembly’. The main cellular components included ‘extracellular region part’, ‘chromosome’, ‘extracellular matrix’ and ‘condensed chromosome kinetochore’, whereas the molecular functions included ‘Calcium ion binding’, ‘polysaccharide binding’, ‘enzyme inhibitor activity’, ‘growth factor activity’, ‘cyclin-dependent protein kinase regulator activity’, ‘microtubule motor activity’ and ‘Wnt receptor activity’. Kyoto Encyclopedia of Genes and Genomes pathway analysis revealed that these DEGs were predominantly involved in ‘Wnt signaling pathway’, ‘pathways in cancer’, ‘PI3K-Akt signaling pathway’, ‘cell cycle’, ‘ECM-receptor interaction’, ‘p53 signaling pathway’ and ‘focal adhesion’. The 20 most significant DEGs were identified from the protein-protein interaction network, and Oncomine analysis of these core genes revealed that 13 were upregulated and two were downregulated in serous epithelial ovarian cancer. Survival analysis revealed that cyclin B1, polo like kinase 1, G protein subunit γ transducin 1 and G protein subunit γ 12 are key molecules that may be involved in the prognosis of serous epithelial ovarian cancer. These core genes may provide novel treatment targets, although their roles in the carcinogenesis and prognosis of serous epithelial ovarian cancer require further study.