Unsupervised analysis reveals two molecular subgroups of serous ovarian cancer with distinct gene expression profiles and survival

A novel defined programmed cell death related gene signature for predicting the prognosis of serous ovarian cancer

PURPOSE

This study aims to explore the contribution of differentially expressed programmed cell death genes (DEPCDGs) to the heterogeneity of serous ovarian cancer (SOC) through single-cell RNA sequencing (scRNA-seq) and assess their potential as predictors for clinical prognosis.

METHODS

SOC scRNA-seq data were extracted from the Gene Expression Omnibus database, and the principal component analysis was used for cell clustering. Bulk RNA-seq data were employed to analyze SOC-associated immune cell subsets key genes. CIBERSORT and single-sample gene set enrichment analysis (ssGSEA) were utilized to calculate immune cell scores. Prognostic models and nomograms were developed through univariate and multivariate Cox analyses.

RESULTS

Our analysis revealed that 48 DEPCDGs are significantly correlated with apoptotic signaling and oxidative stress pathways and identified seven key DEPCDGs (CASP3, GADD45B, GNA15, GZMB, IL1B, ISG20, and RHOB) through survival analysis. Furthermore, eight distinct cell subtypes were characterized using scRNA-seq. It was found that G protein subunit alpha 15 (GNA15) exhibited low expression across these subtypes and a strong association with immune cells. Based on the DEGs identified by the GNA15 high- and low-expression groups, a prognostic model comprising eight genes with significant prognostic value was constructed, effectively predicting patient overall survival. Additionally, a nomogram incorporating the RS signature, age, grade, and stage was developed and validated using two large SOC datasets.

CONCLUSION

GNA15 emerged as an independent and excellent prognostic marker for SOC patients. This study provides valuable insights into the prognostic potential of DEPCDGs in SOC, presenting new avenues for personalized treatment strategies.

Comprehensive analysis of the interaction of antigen presentation during anti-tumour immunity and establishment of AIDPS systems in ovarian cancer

There are hundreds of prognostic models for ovarian cancer. These genes are based on different gene classes, and there are many ways to construct the models. Therefore, this paper aims to build the most stable prognostic evaluation system known to date through 101 machine learning strategies. We combined 101 algorithm combinations with 10 machine learning algorithms to create antigen presentation-associated genetic markers (AIDPS) with outstanding precision and steady performance. The inclusive set of algorithms comprises the elastic network (Enet), Ridge, stepwise Cox, Lasso, generalized enhanced regression model (GBM), random survival forest (RSF), supervised principal component (SuperPC), Cox partial least squares regression (plsRcox), survival support vector machine (Survival-SVM). Then, in the train cohort, the prediction model was fitted using a leave-one cross-validation (LOOCV) technique, which involved 101 different possible combinations of prognostic genes. Seven validation data sets (GSE26193, GSE26712, GSE30161, GSE63885, GSE9891, GSE140082 and ICGC_OV_AU) were compared and analysed, and the C-index was calculated. Finally, we collected 32 published ovarian cancer prognostic models (including mRNA and lncRNA). All data sets and prognostic models were subjected to a univariate Cox regression analysis, and the C-index was calculated to demonstrate that the antigen presentation process should be the core criterion for evaluating ovarian cancer prognosis. In a univariate Cox regression analysis, 22 prognostic genes were identified based on the expression profiles of 283 genes involved in antigen presentation and the intersection of genes (p < 0.05). AIDPS were developed by our machine learning-based integration method, which was applied to these 22 genes. One hundred and one prediction models are fitted using the LOOCV framework, and the C-index is calculated for each model across all validation sets. Interestingly, RSF + Lasso was the best model overall since it had the greatest average C-index and the highest C-index of any combination of models tested on the validated data sets. In comparing external cohorts, we found that the C-index correlated AIDPS method using the RSF + Lasso method in 101 prediction models was in contrast to other features. Notably, AIDPS outperformed the vast majority of models across all data sets. Antigen-presenting anti-tumour immune pathways can be used as a representative gene set of ovarian cancer to track the prognosis of patients with cancer. The antigen-presenting model obtained by the RSF + Lasso method has the best C-INDEX, which plays a key role in developing antigen-presenting targeted drugs in ovarian cancer and improving the treatment outcome of patients.

HIF-2α-dependent TGFBI promotes ovarian cancer chemoresistance by activating PI3K/Akt pathway to inhibit apoptosis and facilitate DNA repair process

Hypoxia-mediated chemoresistance plays a crucial role in the development of ovarian cancer (OC). However, the roles of hypoxia-related genes (HRGs) in chemoresistance and prognosis prediction and theirs underlying mechanisms remain to be further elucidated. We intended to identify and validate classifiers of hub HRGs for chemoresistance, diagnosis, prognosis as well as immune microenvironment of OC, and to explore the function of the most crucial HRG in the development of the malignant phenotypes. The RNA expression and clinical data of HRGs were systematically evaluated in OC training group. Univariate and multivariate Cox regression analysis were applied to construct hub HRGs classifiers for prognosis and diagnosis assessment. The relationship between classifiers and chemotherapy response and underlying pathways were detected by GSEA, CellMiner and CIBERSORT algorithm, respectively. OC cells were cultured under hypoxia or transfected with HIF-1α or HIF-2α plasmids, and the transcription levels of TGFBI were assessed by quantitative PCR. TGFBI was knocked down by siRNAs in OC cells, CCK8 and in vitro migration and invasion assays were performed to examine the changes in cell proliferation, motility and metastasis. The difference in TGFBI expression was examined between cisplatin-sensitive and -resistant cells, and the effects of TGFBI interference on cell apoptosis, DNA repair and key signaling molecules of cisplatin-resistant OC cells were explored. A total of 179 candidate HRGs were extracted and enrolled into univariate and multivariate Cox regression analysis. Six hub genes (TGFBI, CDKN1B, AKAP12, GPC1, TGM2 and ANGPTL4) were selected to create a HRGs prognosis classifier and four genes (TGFBI, AKAP12, GPC1 and TGM2) were selected to construct diagnosis classifiers. The HRGs prognosis classifier could precisely distinguish OC patients into high-risk and low-risk groups and estimate their clinical outcomes. Furthermore, the high-risk group had higher percentage of Macrophages M2 and exhibited higher expression of immunecheckpoints such as PD-L2. Additionally, the diagnosis classifiers could accurately distinguish OC from normal samples. TGFBI was further verified as a specific key target and demonstrated that its high expression was closely correlated with poor prognosis and chemoresistance of OC. Hypoxia upregulated the expression level of TGFBI. The hypoxia-induced factor HIF-2α but not HIF-1α could directly bind to the promoter region of TGFBI, and facilitate its transcription level. TGFBI was upregulated in cisplatin-sensitive and resistant ovarian cancer cells in a cisplatin time-dependent manner. TGFBI interference downregulated DNA repair-related markers (p-p95/NBS1, RAD51, p-DNA-PKcs, DNA Ligase IV and Artemis), apoptosis-related marker (BCL2) and PI3K/Akt pathway-related markers (PI3K-p110 and p-Akt) in cisplatin-resistant OC cells. In summary, the HRGs prognosis risk classifier could be served as a predictor for OC prognosis and efficacy evaluation. TGFBI, upregulated by HIF-2α as an HRG, promoted OC chemoresistance through activating PI3K/Akt pathway to reduce apoptosis and enhance DNA damage repair pathway.

Machine learning-derived identification of prognostic signature for improving prognosis and drug response in patients with ovarian cancer

Clinical assessments relying on pathology classification demonstrate limited effectiveness in predicting clinical outcomes and providing optimal treatment for patients with ovarian cancer (OV). Consequently, there is an urgent requirement for an ideal biomarker to facilitate precision medicine. To address this issue, we selected 15 multicentre cohorts, comprising 12 OV cohorts and 3 immunotherapy cohorts. Initially, we identified a set of robust prognostic risk genes using data from the 12 OV cohorts. Subsequently, we employed a consensus cluster analysis to identify distinct clusters based on the expression profiles of the risk genes. Finally, a machine learning-derived prognostic signature (MLDPS) was developed based on differentially expressed genes and univariate Cox regression genes between the clusters by using 10 machine-learning algorithms (101 combinations). Patients with high MLDPS had unfavourable survival rates and have good prediction performance in all cohorts and in-house cohorts. The MLDPS exhibited robust and dramatically superior capability than 21 published signatures. Of note, low MLDIS have a positive prognostic impact on patients treated with anti-PD-1 immunotherapy by driving changes in the level of infiltration of immune cells. Additionally, patients suffering from OV with low MLDIS were more sensitive to immunotherapy. Meanwhile, patients with low MLDIS might benefit from chemotherapy, and 19 compounds that may be potential agents for patients with low MLDIS were identified. MLDIS presents an appealing instrument for the identification of patients at high/low risk. This could enhance the precision treatment, ultimately guiding the clinical management of OV.

A chemotherapy response prediction model derived from tumor-promoting B and Tregs and proinflammatory macrophages in HGSOC

Background

Most patients with high-grade serous ovarian cancer (HGSOC) experienced disease recurrence with cumulative chemoresistance, leading to treatment failure. However, few biomarkers are currently available in clinical practice that can accurately predict chemotherapy response. The tumor immune microenvironment is critical for cancer development, and its transcriptomic profile may be associated with treatment response and differential outcomes. The aim of this study was to develop a new predictive signature for chemotherapy in patients with HGSOC.

Methods

Two HGSOC single-cell RNA sequencing datasets from patients receiving chemotherapy were reinvestigated. The subtypes of endoplasmic reticulum stress-related XBP1+ B cells, invasive metastasis-related ACTB+ Tregs, and proinflammatory-related macrophage subtypes with good predictive power and associated with chemotherapy response were identified. These results were verified in an independent HGSOC bulk RNA-seq dataset for chemotherapy. Further validation in clinical cohorts used quantitative real-time PCR (qRT-PCR).

Results

By combining cluster-specific genes for the aforementioned cell subtypes, we constructed a chemotherapy response prediction model containing 43 signature genes that achieved an area under the receiver operator curve (AUC) of 0.97 (p = 2.1e-07) for the GSE156699 cohort (88 samples). A huge improvement was achieved compared to existing prediction models with a maximum AUC of 0.74. In addition, its predictive capability was validated in multiple independent bulk RNA-seq datasets. The qRT-PCR results demonstrate that the expression of the six genes has the highest diagnostic value, consistent with the trend observed in the analysis of public data.

Conclusions

The developed chemotherapy response prediction model can be used as a valuable clinical decision tool to guide chemotherapy in HGSOC patients.

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.

Machine learning immune-related gene based on KLRB1 model for predicting the prognosis and immune cell infiltration of breast cancer

Objective

Breast cancer is a prevalent malignancy that predominantly affects women. The development and progression of this disease are strongly influenced by the tumor microenvironment and immune infiltration. Therefore, investigating immune-related genes associated with breast cancer prognosis is a crucial approach to enhance the diagnosis and treatment of breast cancer.

Methods

We analyzed data from the TCGA database to determine the proportion of invasive immune cells, immune components, and matrix components in breast cancer patients. Using this data, we constructed a risk prediction model to predict breast cancer prognosis and evaluated the correlation between KLRB1 expression and clinicopathological features and immune invasion. Additionally, we investigated the role of KLRB1 in breast cancer using various experimental techniques including real-time quantitative PCR, MTT assays, Transwell assays, Wound healing assays, EdU assays, and flow cytometry.

Results

The functional enrichment analysis of immune and stromal components in breast cancer revealed that T cell activation, differentiation, and regulation, as well as lymphocyte differentiation and regulation, play critical roles in determining the status of the tumor microenvironment. These DEGs are therefore considered key factors affecting TME status. Additionally, immune-related gene risk models were constructed and found to be effective predictors of breast cancer prognosis. Further analysis through KM survival analysis and univariate and multivariate Cox regression analysis demonstrated that KLRB1 is an independent prognostic factor for breast cancer. KLRB1 is closely associated with immunoinfiltrating cells. Finally, in vitro experiments confirmed that overexpression of KLRB1 inhibits breast cancer cell proliferation, migration, invasion, and DNA replication ability. KLRB1 was also found to inhibit the proliferation of breast cancer cells by blocking cell division in the G1/M phase.

Conclusion

KLRB1 may be a potential prognostic marker and therapeutic target associated with the microenzymic environment of breast cancer tumors, providing a new direction for breast cancer treatment.

Potential prognostic role of somatic mutations in a set of cancer susceptibility genes in ovarian carcinoma: A follow-up multicentric study from Pakistan

BACKGROUND

Genetic mutations, peritoneal metastasis and frequent development of chemoresistance worsen the prognosis of Ovarian carcinoma.

OBJECTIVE

The objective of the study is to determine mutations in cancer susceptibility genes in relation with chemotherapy response.

METHODS

In this follow up descriptive study, 47 consenting female patients diagnosed with surface epithelial ovarian cancer were observed for six months after completion of chemotherapy to see the treatment response. For genetic analysis, the DNA extraction was done and the genomic regions of different exons of BRCA1/2, PALB2, CHEK2, BAP1, CTNNB1, HOXB13, and PIK3CA were amplified using gene specific primers followed by Sanger Sequencing.

RESULTS

86.7% of the patients were sensitive to chemotherapy whereas 13.3% showed resistance. Genetic variants of BRCA1 in 7%, BRCA2 in 4.7%, PIK3CA in 9.3%, PALB2 in 7%, CHEK2 in 2.3%, BAP1 in 2.3%, and CTNNB1 in 2.3% of the patients were found. There was also a significant association between TNM stage and the treatment response (p< 0.01). Of the patients with no mutations, 90.9% showed chemosensitivity as opposed to 70% in mutations group.

CONCLUSION

Our study exhibits the pivotal role of genetic analysis in predicting the treatment response and paving pathway for patient tailored targeted therapy in Pakistani population.

Microfibril Associated Protein 5 (MFAP5) Is Related to Survival of Ovarian Cancer Patients but Not Useful as a Prognostic Biomarker

Ovarian cancer (OC) is usually diagnosed late due to its nonspecific symptoms and lack of reliable tools for early diagnostics and screening. OC studies concentrate on the search for new biomarkers and therapeutic targets. This study aimed to validate the MFAP5 gene, and its encoded protein, as a potential prognostic biomarker. In our previous study, we found that patients with high-grade serous OC who had higher MFAP5 mRNA levels had shorter survival, as compared with those with lower levels. Here, we used the Kaplan-Meier Plotter and CSIOVDB online tools to analyze possible associations of MFAP5 expression with survival and other clinico-pathological features. In these analyses, higher MFAP5 mRNA expression was observed in the more advanced FIGO stages and high-grade tumors, and was significantly associated with shorter overall and progression-free survival. Next, we analyzed the expression of the MFAP5 protein by immunohistochemistry (IHC) in 108 OC samples and tissue arrays. Stronger MFAP5 expression was associated with stronger desmoplastic reaction and serous vs. non-serous histology. We found no significant correlation between IHC results and survival, although there was a trend toward shorter survival in patients with the highest IHC scores. We searched for co-expressed genes/proteins using cBioPortal and analyzed potential MFAP5 interaction networks with the STRING tool. MFAP5 was shown to interact with many extracellular matrix proteins, and was connected to the Notch signaling pathway. Therefore, although not suitable as a prognostic biomarker for evaluation with a simple diagnostic tool like IHC, MFAP5 is worth further studies as a possible therapeutic target.

Prognostic value of regulatory T cells and T helper 17 cells in high grade serous ovarian carcinoma

PURPOSE

In recent years the tumor microenvironment and its interaction with the tumor has emerged into research focus with increased attention to the composition of Tumor-infiltrating lymphocytes. We wanted to quantify the composition of Regulatory T cells (Tregs) and T helper 17 cells (Th17 cells) and their prognostic impact in high-grade serous tubo-ovarian carcinoma.

METHODS

Tregs and Th17 cells were determined by immunohistochemical analysis of CD25 FoxP3 and RORγt, respectively on tissue microarrays of a cohort of 222 patients with reviewed histology and available clinical data. Expression was analyzed with Qupath for quantification and integration with clinical data enabled calculation of prognostic impact. For validation FOXP3 and RORC mRNA expression levels from 502 patients with HGSC in publicly available datasets were evaluated.

RESULTS

An average percentage of 0.93 Tregs and of 0.06 Th17 cells was detected per cells in overall tissue. Optimal cut-offs were determined and higher Tregs were associated with a better overall survival in stroma (p = 0.006), tumor area (p = 0.0012) and overall tissue (p = 0.02). After accounting for well-known prognostic factors age at diagnosis, residual tumor and FIGO stage, this association remained significant for stromal Tregs with overall survival (p = 0.02). Survival analysis for Th17 cells revealed no significant association with survival rates. Moreover, lower Th17/Treg ratios had a positive impact on patient overall survival (p = 0.025 tumor, p = 0.049 stroma and p = 0.016 overall tissue).

CONCLUSION

Our results outline a positive prognostic effect for higher Tregs but not for Th17 in high grade serous tubo-ovarian carcinoma.

Identification of a Recurrence Gene Signature for Ovarian Cancer Prognosis by Integrating Single-Cell RNA Sequencing and Bulk Expression Datasets

Ovarian cancer is one of the most common gynecological malignancies in women, with a poor prognosis and high mortality. With the expansion of single-cell RNA sequencing technologies, the inner biological mechanism involved in tumor recurrence should be explored at the single-cell level, and novel prognostic signatures derived from recurrence events were urgently identified. In this study, we identified recurrence-related genes for ovarian cancer by integrating two Gene Expression Omnibus datasets, including an ovarian cancer single-cell RNA sequencing dataset (GSE146026) and a bulk expression dataset (GSE44104). Based on these recurrence genes, we further utilized the merged expression dataset containing a total of 524 ovarian cancer samples to identify prognostic signatures and constructed a 13-gene risk model, named RMGS (recurrence marker gene signature). Based on the RMGS score, the samples were stratified into high-risk and low-risk groups, and these two groups displayed significant survival difference in two independent validation cohorts including The Cancer Genome Atlas (TCGA). Also, the RMGS score remained significantly independent in multivariate analysis after adjusting for clinical factors, including the tumor grade and stage. Furthermore, there existed close associations between the RMGS score and immune characterizations, including checkpoint inhibition, EMT signature, and T-cell infiltration. Finally, the associations between RMGS scores and molecular subtypes revealed that samples with mesenchymal subtypes displayed higher RMGS scores. In the meanwhile, the genomics characterization from these two risk groups was also identified. In conclusion, the recurrence-related RMGS model we identified could provide a new understanding of ovarian cancer prognosis at the single-cell level and offer a reference for therapy decisions for patient treatment.

Identification of key gene signatures for the overall survival of ovarian cancer

BACKGROUND

The five-year overall survival (OS) of advanced-stage ovarian cancer remains nearly 25-35%, although several treatment strategies have evolved to get better outcomes. A considerable amount of heterogeneity and complexity has been seen in ovarian cancer. This study aimed to establish gene signatures that can be used in better prognosis through risk prediction outcome for the survival of ovarian cancer patients. Different studies' heterogeneity into a single platform is presented to explore the penetrating genes for poor or better survival. The integrative analysis of multiple data sets was done to determine the genes that influence poor or better survival. A total of 6 independent data sets was considered. The Cox Proportional Hazard model was used to obtain significant genes that had an impact on ovarian cancer patients. The gene signatures were prepared by splitting the over-expressed and under-expressed genes parallelly by the variable selection technique. The data visualisation techniques were prepared to predict the overall survival, and it could support the therapeutic regime.

RESULTS

We preferred to select 20 genes in each data set as upregulated and downregulated. Irrespective of the selection of multiple genes, not even a single gene was found common among data sets for the survival of ovarian cancer patients. However, the same analytical approach adopted. The chord plot was presented to make a comprehensive understanding of the outcome.

CONCLUSIONS

This study helps us to understand the results obtained from different studies. It shows the impact of the heterogeneity from one study to another. It shows the requirement of integrated studies to make a holistic view of the gene signature for ovarian cancer survival.

OSov: An Interactive Web Server to Evaluate Prognostic Biomarkers for Ovarian Cancer

Simple Summary

The OSov web server incorporates gene expression profiles with clinical risk factors to estimate the ovarian cancers patients’ survival, and provides a tool for multiple analysis, such as forest-plot, uni/multi-variate survival analysis, Kaplan-Meier plot and nomogram construction.

Abstract

Ovarian cancer is one of the most aggressive and highly lethal gynecological cancers. The purpose of our study is to build a free prognostic web server to help researchers discover potential prognostic biomarkers by integrating gene expression profiling data and clinical follow-up information of ovarian cancer. We construct a prognostic web server OSov (Online consensus Survival analysis for Ovarian cancer) based on RNA expression profiles. OSov is a user-friendly web server which could present a Kaplan–Meier plot, forest plot, nomogram and survival summary table of queried genes in each individual cohort to evaluate the prognostic potency of each queried gene. To assess the performance of OSov web server, 163 previously published prognostic biomarkers of ovarian cancer were tested and 72% of them had their prognostic values confirmed in OSov. It is a free and valuable prognostic web server to screen and assess survival-associated biomarkers for ovarian cancer.

Repositioning Trimebutine Maleate as a Cancer Treatment Targeting Ovarian Cancer Stem Cells

The overall five-year survival rate for late-stage patients of ovarian cancer is below 29% due to disease recurrence and drug resistance. Cancer stem cells (CSCs) are known as a major contributor to drug resistance and recurrence. Accordingly, therapies targeting ovarian CSCs are needed to overcome the limitations of present treatments. This study evaluated the effect of trimebutine maleate (TM) targeting ovarian CSCs, using A2780-SP cells acquired by a sphere culture of A2780 epithelial ovarian cancer cells. TM is indicated as a gastrointestinal motility modulator and is known to as a peripheral opioid receptor agonist and a blocker for various channels. The GI50 of TM was approximately 0.4 µM in A2780-SP cells but over 100 µM in A2780 cells, demonstrating CSCs specific growth inhibition. TM induced G0/G1 arrest and increased the AV⁺/PI⁺ dead cell population in the A2780-SP samples. Furthermore, TM treatment significantly reduced tumor growth in A2780-SP xenograft mice. Voltage gated calcium channels (VGCC) and calcium-activated potassium channels (BKCa) were overexpressed on ovarian CSCs and targeted by TM; inhibition of both channels reduced A2780-SP cells viability. TM reduced stemness-related protein expression; this tendency was reproduced by the simultaneous inhibition of VGCC and BKCa compared to single channel inhibition. In addition, TM suppressed the Wnt/β-catenin, Notch, and Hedgehog pathways which contribute to many CSCs characteristics. Specifically, further suppression of the Wnt/β-catenin pathway by simultaneous inhibition of BKCa and VGCC is necessary for the effective and selective action of TM. Taken together, TM is a potential therapeutic drug for preventing ovarian cancer recurrence and drug resistance.

Proline-rich protein 11 overexpression is associated with a more aggressive phenotype and poor overall survival in ovarian cancer patients

BACKGROUND

The proline-rich protein 11 (PRR11) is a newly identified oncogene associated with a poor prognosis in several human cancers. Nonetheless, research on its role in ovarian cancer (OC) remains largely understudied. Therefore, this study aims to evaluate the expression levels of PRR11 protein and its role in human ovarian cancer.

METHODS

Immunohistochemistry analysis was used to evaluate the expression levels of PRR11 protein in human samples obtained from 49 patients diagnosed with OC and subjected to curative surgery in the First Affiliated Hospital of Wenzhou Medical University between 2007 and 2015.

RESULTS

In total, 57.1% of the primary OC tumor tissue evaluated demonstrated overexpression of PRR11. Meanwhile, the survival analysis showed that the overall survival (OS) of patients presenting overexpression of PRR11 was significantly lower than the OS of the patients with negative PRR11. In subsequent experiments, it was found that silencing the expression of PRR11 expression inhibited the proliferation of tumor cells and the migration of cells in vitro. Further, cells subjected to PRR11 knockdown exhibited a decrease in tumor growth in vivo. The downregulation of PRR11 was coupled with a decrease in N-cadherin and downregulation in the expression of early growth response protein 1 (EGR1).

CONCLUSIONS

The findings suggest that PRR11 might be considered as a potential target for prognostic assessment and gene therapy strategies for patients diagnosed with OC.

Increased Expression of UBE2T Predicting Poor Survival of Epithelial Ovarian Cancer: Based on Comprehensive Analysis of UBE2s, Clinical Samples, and the GEO Database

Ubiquitin-conjugating enzymes E2 (UBE2) have been reported in the microenvironment of various malignant tumors, but their correlation with ovarian cancer (OC) remains elusive. This study aimed to systematically analyze the expression patterns, prognostic value, genetic variation, and biological functions of 12 members of the UBE2 gene family in OC through the Oncomine, Gene Expression Profiling Interactive Analysis (GEPIA), Kaplan-Meier plotter, cBioPortal, and Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) databases, respectively. We found that the mRNA levels of UBE2C, UBE2N, UBE2S, and UBE2T were significantly upregulated in OC compared with those in normal ovarian tissue. In patients with serous ovarian cancer (SOC), UBE2A, UBE2B, UBE2C, UBE2G, UBE2R2, and UBE2T upregulation were associated with poor overall survival. Moreover, UBE2A, UBE2N, UBE2R2, and UBE2T upregulation and UBE2G downregulation were associated with poor progression-free survival. UBE2T exhibited a strong correlation with OC and was thus further examined. We found that UBE2T has a high diagnostic accuracy (area under the receiver operating characteristic curve = 0.969) in epithelial ovarian cancer (EOC). Immunohistochemical assays and the Gene Expression Omnibus (GEO) database revealed that UBE2T was significantly upregulated in EOC compared with that in borderline tumors, benign tumors, and normal ovarian tissues, and its high expression was associated with poor prognosis. The Cox model showed that UBE2T upregulation was an independent risk factor affecting the prognosis of EOC and SOC. Furthermore, UBE2T was associated with specific immune cells and was mainly involved in cell cycle-related events. Genomic analysis showed that TP53 and TTN mutations were associated with UBE2T expression. Gene copy number amplification and hypomethylation may be responsible for UBE2T upregulation in OC. In conclusion, UBE2 family members may play a role in the development of OC. Specifically, UBE2T could serve as a new prognostic marker and therapeutic target for this disease. (IRB Approval No. 2020PS533K).

Evaluation of the Role of ITGBL1 in Ovarian Cancer

In our previous microarray study we identified two subgroups of high-grade serous ovarian cancers with distinct gene expression and survival. Among differentially expressed genes was an Integrin beta-like 1 (ITGBL1), coding for a poorly characterized protein comprised of ten EGF-like repeats. Here, we have analyzed the influence of ITGBL1 on the phenotype of ovarian cancer (OC) cells. We analyzed expression of four putative ITGBL1 mRNA isoforms in five OC cell lines. OAW42 and SKOV3, having the lowest level of any ITGBL1 mRNA, were chosen to produce ITGBL1-overexpressing variants. In these cells, abundant ITGBL1 mRNA expression could be detected by RT-PCR. Immunodetection was successful only in the culture media, suggesting that ITGBL1 is efficiently secreted. We found that ITGBL1 overexpression affected cellular adhesion, migration and invasiveness, while it had no effect on proliferation rate and the cell cycle. ITGBL1-overexpressing cells were significantly more resistant to cisplatin and paclitaxel, major drugs used in OC treatment. Global gene expression analysis revealed that signaling pathways affected by ITGBL1 overexpression were mostly those related to extracellular matrix organization and function, integrin signaling, focal adhesion, cellular communication and motility; these results were consistent with the findings of our functional studies. Overall, our results indicate that higher expression of ITGBL1 in OC is associated with features that may worsen clinical course of the disease.

Upregulation of ITGBL1 predicts poor prognosis and promotes chemoresistance in ovarian cancer

BACKGROUND

Ovarian cancer remains one of the most lethal malignancies in women and the unfavorable prognosis and frequent recurrence are mainly due to the chemoresistance. However, the main mechanism underlying chemoresistance is still elusive.

OBJECTIVE

To determine the role and biological function of ITGBL1 in ovarian cancer chemoresistance.

METHODS

Immunohistochemical staining was used to determine the expression of ITGBL1 in ovarian cancer tissues. The association between ITGBL1 expression and clinicopathological features and survival was determined. Functional analysis including cell viability, apoptosis assays were performed after chemo drugs treatment to confirm the role of ITGBL1 in chemoresistance. In vivo tumor growth assay was used to detect the chemosensitivity of tumor cells. Western blot was used to detect the expression of indicated proteins.

RESULTS

We noticed that ITGBL1 expression was significantly upregulated in ovarian cancer tissues compared to that in adjacent non-cancer tissues and high expression of ITGBL1 was significantly associated with lymph node invasion and advanced FIGO stage. More importantly, high ITGBL1 was an independent prognostic factor of ovarian cancer. Further experiments demonstrated that ITGBL1 promoted tumor cell resistant to chemo drugs both in vitro and in vivo. Mechanically, we found that ITGBL1 could activate PI3K/Akt signaling and using PI3K/Akt inhibitor could abrogate ITGBL1 induced chemoresistance.

CONCLUSIONS

Our findings indicate that upregulation of ITGBL1 has important clinical significance and drives chemoresistance in ovarian cancer. Detection and depletion of ITGBL1 might be the potential approaches for diagnosis and therapy for ovarian cancer patients.

Osthole inhibits ovarian carcinoma cells through LC3-mediated autophagy and GSDME-dependent pyroptosis except for apoptosis

Ovarian carcinoma (OC) begins in the ovaries and remains a highly lethal malignancy. Despite great efforts have been made to fight against OC, there still remain limited therapeutic options owing to chemotherapy drug resistance and serious side effects. Osthole is a derivative of coumarin and extracted from Cnidium monnieri (L.) Cusson, which has been drawn more attention due to its high biological activity in various disease. However, the underlying mechanism of osthole in OC is still unclear. In this study, we aim to evaluate the mechanism of osthole against OC cells. Methodologically, Cell Counting Kit-8 (CCK-8) and LIVE/DEAD™ Cell Imaging experiments were employed to assess cell viability. 2',7'-Dichlorofluorescin diacetate (DCFH-DA) staining, flow cytometry, Hoechst staining, JC-1 staining assay and western blotting were performed to study apoptosis. Transmission electron microscopy, western blotting and monodansyl cadaverine (MDC) staining assay were used to study autophagy. Western blotting and microscopy image were employed to determine pyroptosis. Our results demonstrated that osthole could significantly suppress OC cells growth in a dose-dependent manner. We further proved that osthole could inhibit OC cells growth by mitochondria-mediated apoptosis. Meanwhile, we also discovered that osthole could trigger cell autophagy and lead to cell death. Furthermore, our study revealed that osthole could lead to pyroptosis through inducing the cleavage of gasdermin E (c-GSDME) level. Taken together, Osthole could significantly suppress the growth of OC cells and induce OC cells death via apoptosis, pyroptosis and autophagy, which is a promising new drug for the treatment of OC.

Fibronectin and Periostin as Prognostic Markers in Ovarian Cancer

Previously, based on a DNA microarray experiment, we identified a 96-gene prognostic signature associated with the shorter survival of ovarian cancer patients. We hypothesized that some differentially expressed protein-coding genes from this signature could potentially serve as prognostic markers. The present study was aimed to validate two proteins, namely fibronectin (FN1) and periostin (POSTN), in the independent set of ovarian cancer samples. Both proteins are mainly known as extracellular matrix proteins with many important functions in physiology. However, there are also indications that they are implicated in cancer, including ovarian cancer. The expression of these proteins was immunohistochemically analyzed in 108 surgical samples of advanced ovarian cancer (majority: high-grade serous) and additionally on tissue arrays representing different stages of the progression of ovarian and fallopian tube epithelial tumors, from normal epithelia, through benign tumors, to adenocarcinomas of different stages. The correlation with clinical, pathological, and molecular features was evaluated. Kaplan-Meier survival analysis and Cox-proportional hazards models were used to estimate the correlation of the expression levels these proteins with survival. We observed that the higher expression of fibronectin in the tumor stroma was highly associated with shorter overall survival (OS) (Kaplan-Meier analysis, log-rank test p = 0.003). Periostin was also associated with shorter OS (p = 0.04). When we analyzed the combined score, calculated by adding together individual scores for stromal fibronectin and periostin expression, Cox regression demonstrated that this joint FN1&POSTN score was an independent prognostic factor for OS (HR = 2.16; 95% CI: 1.02-4.60; p = 0.044). The expression of fibronectin and periostin was also associated with the source of ovarian tumor sample: metastases showed higher expression of these proteins than primary tumor samples (χ² test, p = 0.024 and p = 0.032). Elevated expression of fibronectin and periostin was also more common in fallopian cancers than in ovarian cancers. Our results support some previous observations that fibronectin and periostin have a prognostic significance in ovarian cancer. In addition, we propose the joint FN1&POSTN score as an independent prognostic factor for OS. Based on our results, it may also be speculated that these proteins are related to tumor progression and/or may indicate fallopian-epithelial origin of the tumor.

Identification of modules and hub genes associated with platinum-based chemotherapy resistance and treatment response in ovarian cancer by weighted gene co-expression network analysis

High-grade serous ovarian carcinoma (HGSOC) is the most prevalent and malignant ovarian tumor.To identify co-expression modules and hub genes correlated with platinum-based chemotherapy resistant and sensitive HGSOC, we performed weighted gene co-expression network analysis (WGCNA) on microarray data of HGSOC with 12 resistant samples and 16 sensitive samples of GSE51373 dataset.A total of 5122 genes were included in WGCNA, and 16 modules were identified. Module-trait analysis identified that the module salmon (cor = 0.50), magenta (cor = 0.49), and black (cor = 0.45) were discovered associated with chemotherapy resistant, and the significance for these platinum-resistant modules were validated in the GSE63885 dataset. Given that the black module was validated to be the most related one, hub genes of this module, alcohol dehydrogenase 1B, cadherin 11, and vestigial like family member 3were revealed to be expressional related with platinum resistance, and could serve as prognostic markers for ovarian cancer.Our analysis might provide insight for molecular mechanisms of platinum-based chemotherapy resistance and treatment response in ovarian cancer.

Genes associated with bowel metastases in ovarian cancer

OBJECTIVE

This study is designed to identify genes and pathways that could promote metastasis to the bowel in high-grade serous ovarian cancer (OC) and evaluate their associations with clinical outcomes.

METHODS

We performed RNA sequencing of OC primary tumors (PTs) and their corresponding bowel metastases (n = 21 discovery set; n = 18 replication set). Differentially expressed genes (DEGs) were those expressed at least 2-fold higher in bowel metastases (BMets) than PTs in at least 30% of patients (P < .05) with no increased expression in paired benign bowel tissue and were validated with quantitative reverse transcription PCR. Using an independent OC cohort (n = 333), associations between DEGs in PTs and surgical and clinical outcomes were performed. Immunohistochemistry and mouse xenograft studies were performed to confirm the role of LRRC15 in promoting metastasis.

RESULTS

Among 27 DEGs in the discovery set, 21 were confirmed in the replication set: SFRP2, Col11A1, LRRC15, ADAM12, ADAMTS12, MFAP5, LUM, PLPP4, FAP, POSTN, GRP, MMP11, MMP13, C1QTNF3, EPYC, DIO2, KCNA1, NETO1, NTM, MYH13, and PVALB. Higher expression of more than half of the genes in the PT was associated with an increased requirement for bowel resection at primary surgery and an inability to achieve complete cytoreduction. Increased expression of LRRC15 in BMets was confirmed by immunohistochemistry and knockdown of LRRC15 significantly inhibited tumor progression in mice.

CONCLUSIONS

We identified 21 genes that are overexpressed in bowel metastases among patients with OC. Our findings will help select potential molecular targets for the prevention and treatment of malignant bowel obstruction in OC.

Establishment and Characterization of the Novel High-Grade Serous Ovarian Cancer Cell Line OVPA8

High-grade serous ovarian carcinoma (HGSOC) is the most frequent histological type of ovarian cancer and the one with worst prognosis. Unfortunately, the majority of established ovarian cancer cell lines which are used in the research have unclear histological origin and probably do not represent HGSOC. Thus, new and reliable models of HGSOC are needed. Ascitic fluid from a patient with recurrent HGSOC was used to establish a stable cancer cell line. Cells were characterized by cytogenetic karyotyping and short tandem repeat (STR) profiling. New generation sequencing was applied to test for hot-spot mutations in 50 cancer-associated genes and fluorescence in situ hybridization (FISH) analysis was used to check for TP53 status. Cells were analyzed for expression of several marker genes/proteins by reverse-transcription polymerase chain reaction (RT-PCR), fluorescence-activated cell sorting (FACS), and immunocytochemistry (ICC). Functional tests were performed to compare OVPA8 cells with five commercially available and frequently used ovarian cancer cell lines: SKOV3, A2780, OVCAR3, ES2, and OAW42. Our newly-established OVPA8 cell line shows morphologic and genetic features consistent with HGSOC, such as epithelial morphology, multiple chromosomal aberrations, TP53 mutation, BRCA1 mutation, and loss of one copy of BRCA2. The OVPA8 line has a stable STR profile. Cells are positive for EpCAM, CK19, and CD44; they have relatively low plating efficiency/ability to form spheroids, a low migration rate, and intermediate invasiveness in matrigel, as compared to other ovarian cancer lines. OVPA8 is sensitive to paclitaxel and resistant to cisplatin. We also tested two FGFR inhibitors; OVPA8 cells were resistant to AZD4547 (AstraZeneca, London, UK), but sensitive to the new inhibitor CPL304-110-01 (Celon Pharma, Łomianki/Kiełpin, Poland). We have established and characterized a novel cell line, OVPA8, which can be a valuable preclinical model for studies on high-grade serous ovarian cancer.

Effect of combined inhibition of p110 alpha PI3K isoform and STAT3 pathway in ovarian cancer platinum-based resistance

Background

Ovarian cancer is associated with poor prognostic outcome due to late diagnosis and to intrinsic and acquired resistance to platinum-based chemotherapy in a large number of patients. This chemoresistance is acquired through the peritoneal and ascites microenvironment by several released factors, such as IL-6,. Preclinical studies have implicated the activation of PI3K pathway in chemoresistance, showing it to extend tumor cell survival and modulate multidrug resistance. We aimed to evaluate the implication of the p110 alpha PI3K subunit in ovarian cancer chemoresistance acquisition, and to evaluate whether the STAT3 pathway can mediate resistance to PI3K inhibitors through secretion of IL6.

Results

Human ovarian adenocarcinoma IGROV-1 and JHOC-5 cells cultured in ascites showed an increase in carboplatinum-based resistance. Level of chemoresistance was associated to IL6 concentration in ascites. Activation of PI3K/Akt, STAT and MAPK pathways was observed after IGROV-1 incubation with ascites and treatment with carboplatin. Neither IGROV-1 nor JHOC-5 cells exposed to ascites treated with additional IL-6 directed antibody showed any reversion of the chemoresistance.

Conclusion

IL6-related resistance was not abolished by the selective inhibition of PI3K alpha subunit coupled with the anti-IL6-receptor antibody tocilizumab. This dual inhibition requires further exploration in other ovarian cancer models such as clear cell carcinoma.

Proteoglycans-Biomarkers and Targets in Cancer Therapy

Proteoglycans (PGs), important constituents of the extracellular matrix, have been associated with cancer pathogenesis. Their unique structure consisting of a protein core and glycosaminoglycan chains endowed with fine modifications constitutes these molecules as capable cellular effectors important for homeostasis and contributing to disease progression. Indeed, differential expression of PGs and their interacting proteins has been characterized as specific for disease evolvement in various cancer types. Importantly, PGs to a large extent regulate the bioavailability of hormones, growth factors, and cytokines as well as the activation of their respective receptors which regulate phenotypic diversibility, gene expression and rates of recurrence in specific tumor types. Defining and targeting these effectors on an individual patient basis offers ground for the development of newer therapeutic approaches which may act as either supportive or a substitute treatment to the standard therapy protocols. This review discusses the roles of PGs in cancer progression, developing technologies utilized for the defining of the PG "signature" in disease, and how this may facilitate the generation of tailor-made cancer strategies.

Advances in ovarian cancer therapy

Epithelial ovarian cancer is typically diagnosed at an advanced stage. Current state-of-the-art surgery and chemotherapy result in the high incidence of complete remissions; however, the recurrence rate is also high. For most patients, the disease eventually becomes a continuum of symptom-free periods and recurrence episodes. Different targeted treatment approaches and biological drugs, currently under development, bring the promise of turning ovarian cancer into a manageable chronic disease. In this review, we discuss the current standard in the therapy for ovarian cancer, major recent studies on the new variants of conventional therapies, and new therapeutic approaches, recently approved and/or in clinical trials. The latter include anti-angiogenic therapies, polyADP-ribose polymerase (PARP) inhibitors, inhibitors of growth factor signaling, or folate receptor inhibitors, as well as several immunotherapeutic approaches. We also discuss cost-effectiveness of some novel therapies and the issue of better selection of patients for personalized treatment.

Uncovering the role of nuclear Lysyl oxidase (LOX) in advanced high grade serous ovarian cancer

OBJECTIVE

Lysyl oxidase (LOX) is an enzyme that catalyzes the cross-linking of collagen and elastin in the extracellular matrix, thus controlling the tensile strength of tissues. Along with this primary function, there are evidences supporting a role for LOX in many critical biological functions, including gene expression regulation, cell growth, adhesion and migration. Accordingly, recent studies have supported a pivotal role for LOX in cancer progression and metastasis. The current study aimed at investigating the prognostic significance and the functional role of intracellular LOX in ovarian cancer.

METHODS

To this end, we analyzed LOX expression by immunohistochemistry in archived tumor material from advanced high grade serous ovarian cancer (HGSOC) patients (n=70) and correlated data with clinicopathological parameters and with response to chemotherapy. In vitro experiments were also used to investigate the functional consequences of LOX expression on behavioral aspects of HGSOC cells.

RESULTS

Our results showed that nuclear LOX expression is associated with unfavorable outcome in advanced HGSOC, being an independent prognostic factor for disease recurrence. Besides, high nuclear levels were seen to be associated with resistance to first-line chemotherapy. Through gene expression modulation experiments in HGSOC cell lines, we demonstrate that LOX positively regulates cell proliferation, migration and anchorage-independent growth.

CONCLUSIONS

Collectively, our data suggest that LOX functions as a tumor promoter in HGSOC and positively regulates several aspects of the metastatic cascade.