Ovarian cancer and the evolution of subtype classifications using transcriptional profiling†

Missing cell types in single-cell references impact deconvolution of bulk data but are detectable

BACKGROUND

Advancements in RNA sequencing have expanded our ability to study gene expression profiles of biological samples in bulk tissue and single cells. Deconvolution of bulk data with single-cell references provides the ability to study relative cell-type proportions, but most methods assume a reference is present for every cell type in bulk data. This is not true in all circumstances-cell types can be missing in single-cell profiles for many reasons. In this study, we examine the impact of missing cell types on deconvolution methods.

RESULTS

Using paired single-cell and single-nucleus data, we simulate realistic scenarios where cell types are missing since single-nucleus RNA sequencing is able to capture cell types that would otherwise be missing in a single-cell counterpart. Single-nucleus sequencing captures cell types absent in single-cell counterparts, allowing us to study their effects on deconvolution. We evaluate three different methods and find that performance is influenced by both the number and similarity of missing cell types. Additionally, missing cell-type profiles can be recovered from residuals using a simple non-negative matrix factorization strategy. We also analyzed real bulk data of cancerous and non-cancerous samples. We observe results consistent with simulation, namely that expression patterns from cell types likely to be missing appear present in residuals.

CONCLUSIONS

We expect our results to provide a starting point for those developing new deconvolution methods and help improve their to better account for the presence of missing cell types. Our results suggest that deconvolution methods should consider the possibility of missing cell types.

Evaluation of the recently established Dutch nationwide Archipelago of Ovarian Cancer Research biobank

Fundamental and translational research in ovarian cancer aims to enhance understanding of disease mechanisms and improve treatment and survival outcomes. To support this, we established the Dutch multicenter, interdisciplinary Archipelago of Ovarian Cancer Research (AOCR) infrastructure, which includes a nationwide biobank. In this study, we share our experiences in establishing the infrastructure, offer guidance for similar initiatives, and evaluate the AOCR patient cohort. Key challenges included obtaining Data Protection Impact Assessment (DPIA) clearance, drafting the consortium agreement, and securing ethical approval from all hospitals. Over three years, 1093 patients were enrolled across 17 hospitals, resulting in the collection of 1339 tissue samples and 2280 blood samples. Of the 523 patients with currently available clinical and pathological data, 74 % (n = 387) had primary ovarian cancer. Among these patients, 73.4 % was diagnosed with high-grade serous ovarian carcinoma, and 80.9 % presented with advanced-stage disease. Surgery was performed on 93 % of patients with primary ovarian cancer, and chemotherapy was administered to 90.4 % of these patients. In conclusion, the AOCR biobank has established a robust foundation for future fundamental and translational ovarian cancer research. This manuscript provides valuable insights and guidance for developing future research infrastructures and biobanks, and contains detailed information about the AOCR patient cohort to date.

Activation of ferritin light chain (FTL) by transcription factor salmonella pathogenicity island 1 modulates glycolysis to drive metastasis of ovarian cancer cells

BACKGROUND

Ovarian cancer (OC) is the most lethal gynecological cancer often diagnosed at an advanced stage due to a lack of effective biomarkers. Ferritin light chain (FTL) is implicated in the development of various cancers, but its impact on OC remains unknown.

RESEARCH DESIGN AND METHODS

Bioinformatics methods were utilized to analyze FTL. Quantitative real-time polymerase chain reaction, western blot, and immunohistochemistry were employed for expression detection, and cell counting kit- 8, and transwell assays were for cell biological functions assessment. Extracellular acidification rate, oxygen consumption rate, and glycolytic metabolite contents were measured. Dual-luciferase and chromatin immunoprecipitation assay validated binding relationship. Xenografted tumor models in nude mice verified the role of FTL in vivo.

RESULTS

Cell function experiments revealed that FTL facilitated proliferation, migration, and invasion of OC cells. Rescue experiments unveiled that 2-Deoxy-D-glucose attenuated stimulation on OC cell metastasis and glycolysis by FTL overexpression. Salmonella pathogenicity island 1 (SPI1) up-regulated FTL expression to promote glycolysis and metastasis. FTL knockdown inhibited tumor growth and suppressed glycolysis and cell metastasis in vivo, while SPI1 overexpression attenuated these effects.

CONCLUSIONS

This study demonstrated pro-metastatic mechanisms of transcription factor SPI1/FTL axis in OC and suggested it as a potential target for treating OC metastasis.

Improving histotyping precision: The impact of immunohistochemical algorithms on epithelial ovarian cancer classification

To improve the precision of epithelial ovarian cancer histotyping, Köbel et al. (2016) developed immunohistochemical decision-tree algorithms. These included a six- and four-split algorithm, and separate six-split algorithms for early- and advanced stage disease. In this study, we evaluated the efficacy of these algorithms. A gynecological pathologist determined the hematoxylin and eosin (H&E)-based histotypes of 230 patients. Subsequently, the final histotypes were established by re-evaluating the H&E-stained sections and immunohistochemistry outcomes. For histotype prediction using the algorithms, the immunohistochemical markers Napsin A, p16, p53, progesterone receptor (PR), trefoil factor 3 (TFF3), and Wilms' tumor 1 (WT1) were scored. The algorithmic predictions were compared with the final histotypes to assess their precision, for which the early- and advanced stage algorithms were assessed together as six-split-stages algorithm. The six-split algorithm demonstrated 96.1% precision, whereas the six-split-stages and four-split algorithms showed 93.5% precision. Of the 230 cases, 16 (7%) showed discordant original and final diagnoses; the algorithms concurred with the final diagnosis in 14/16 cases (87.5%). In 12.4%-13.3% of cases, the H&E-based histotype changed based on the algorithmic outcome. The six-split stages algorithm had a lower sensitivity for low-grade serous carcinoma (80% versus 100%), while the four-split stages algorithm showed reduced sensitivity for endometrioid carcinoma (78% versus 92.7-97.6%). Considering the higher sensitivity of the six-split algorithm for endometrioid and low-grade serous carcinoma compared with the four-split and six-split-stages algorithms, respectively, we recommend the adoption of the six-split algorithm for histotyping epithelial ovarian cancer in clinical practice.

Why Is Wnt/β-Catenin Not Yet Targeted in Routine Cancer Care?

Despite significant progress in cancer prevention, screening, and treatment, the still limited number of therapeutic options is an obstacle towards increasing the cancer cure rate. In recent years, many efforts were put forth to develop therapeutics that selectively target different components of the oncogenic Wnt/β-catenin signaling pathway. These include small molecule inhibitors, antibodies, and more recently, gene-based approaches. Although some of them showed promising outcomes in clinical trials, the Wnt/β-catenin pathway is still not targeted in routine clinical practice for cancer management. As for most anticancer treatments, a critical limitation to the use of Wnt/β-catenin inhibitors is their therapeutic index, i.e., the difficulty of combining effective anticancer activity with acceptable toxicity. Protecting healthy tissues from the effects of Wnt/β-catenin inhibitors is a major issue due to the vital role of the Wnt/β-catenin signaling pathway in adult tissue homeostasis and regeneration. In this review, we provide an up-to-date summary of clinical trials on Wnt/β-catenin pathway inhibitors, examine their anti-tumor activity and associated adverse events, and explore strategies under development to improve the benefit/risk profile of this therapeutic approach.

Identification and validation of immunity- and disulfidptosis-related genes signature for predicting prognosis in ovarian cancer

Background

Ovarian cancer (OC) ranks as the fifth most prevalent neoplasm in women and exhibits an unfavorable prognosis. To improve the OC patient's prognosis, a pioneering risk signature was formulated by amalgamating disulfidptosis-related genes.

Methods

A comparative analysis of OC tissues and normal tissues was carried out, and differentially expressed disulfidptosis-related genes (DRGs) were found using the criteria of |log2 (fold change) | > 0.585 and adjusted P-value <0.05. Subsequently, the TCGA training set was utilized to create a prognostic risk signature, which was validated by employing both the TCGA testing set and the GEO dataset. Moreover, the immune cell infiltration, mutational load, response to chemotherapy, and response to immunotherapy were analyzed. To further validate these findings, QRT-PCR analysis was conducted on ovarian tumor cell lines.

Results

A risk signature was created using fourteen differentially expressed genes (DEGs) associated with disulfidptosis, enabling the classification of ovarian cancer (OC) patients into high-risk group (HRG) and low-risk group (LRG). The HRG exhibited a lower overall survival (OS) compared to the LRG. In addition, the risk score remained an independent predictor even after incorporating clinical factors. Furthermore, the LRG displayed lower stromal, immune, and estimated scores compared to the HRG, suggesting a possible connection between the risk signature, immune cell infiltration, and mutational load. Finally, the QRT-PCR experiments revealed that eight genes were upregulated in the human OC cell line SKOV3 compared with the human normal OC line IOSE80, while six genes were down-regulated.

Conclusions

A fourteen-biomarker signature composed of disulfidptosis-related genes could serve as a valuable risk stratification tool in OC, facilitating the identification of patients who may benefit from individualized treatment and follow-up management.

Paradigm Shift: A Comprehensive Review of Ovarian Cancer Management in an Era of Advancements

Ovarian cancer (OC) is the female genital malignancy with the highest lethality. Patients present a poor prognosis mainly due to the late clinical presentation allied with the common acquisition of chemoresistance and a high rate of tumour recurrence. Effective screening, accurate diagnosis, and personalised multidisciplinary treatments are crucial for improving patients' survival and quality of life. This comprehensive narrative review aims to describe the current knowledge on the aetiology, prevention, diagnosis, and treatment of OC, highlighting the latest significant advancements and future directions. Traditionally, OC treatment involves the combination of cytoreductive surgery and platinum-based chemotherapy. Although more therapeutical approaches have been developed, the lack of established predictive biomarkers to guide disease management has led to only marginal improvements in progression-free survival (PFS) while patients face an increasing level of toxicity. Fortunately, because of a better overall understanding of ovarian tumourigenesis and advancements in the disease's (epi)genetic and molecular profiling, a paradigm shift has emerged with the identification of new disease biomarkers and the proposal of targeted therapeutic approaches to postpone disease recurrence and decrease side effects, while increasing patients' survival. Despite this progress, several challenges in disease management, including disease heterogeneity and drug resistance, still need to be overcome.

Evaluation of secondary cytoreduction surgery in platinum-resistant ovarian cancer patients within three-line recurrent: a multicenter, randomized controlled study

BACKGROUND

Epithelial ovarian cancer is the leading cause of death among gynecological malignancies. Platinum resistance remains a dilemma and bottleneck in treatment, and salvage chemotherapy has limited effectiveness. Recently, the role of secondary cytoreductive surgery (SCS) in patients with platinum-resistant recurrent ovarian cancer (ROC) has caused attention especially in patients with oligometastases. However, there is neither high-quality evidence-based evidence nor standardized criteria for selecting SCS for patients with platinum-resistant ROC until now.

METHODS

This multicenter, randomized, controlled clinical trial is to evaluate the value of SCS and to clarify reliable criteria of utilizing SCS in women with ROC, which is led by Gynecologic Oncology Group, Women's Hospital, Zhejiang University School of Medicine. Recruitment has started on January 1st, 2023, and is scheduled to end in December 2026. One hundred and forty participants with platinum-resistant ROC who meet the "RSCS criteria" will be randomized assigned at a ratio of 1:1 to either the experimental arm or the standard arm. Patients in the experimental arm will receive SCS followed by non-platinum single agent chemotherapy (paclitaxel, gemcitabine or liposomal adriamycin) for at least 4 cycles while patients in the standard arm will be provided with only non-platinum single agent chemotherapy. The primary outcome is progression-free survival. The secondary outcomes are overall survival, adverse events and health-related cancer-specific quality of life.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT05633199.

Long noncoding RNA H19 in ovarian biology and placenta development

As the first long noncoding RNA to be discovered, H19 has gained substantial attention as a key regulator of several biological processes and its roles in female reproductive biology are gradually getting revealed. Herein, we have summarized the current evidence regarding H19 expression pattern and involvement in the developmental and pathological processes associated with the ovary and the placenta. The findings indicate that within the ovaries, H19 is expressed in the antral and cystic atretic follicles as well as in the corpora lutea but absent in the primordial, primary, and secondary follicles. Its normal expression promotes the maturation of antral follicles and prevents their premature selection for the ovulatory journey while its aberrant induction promotes polycystic ovary syndrome development and ovarian cancer metastasis. In the placenta, H19 is highly expressed in the cytotrophoblasts and extravillous trophoblasts but weakly expressed in the syncytiotrophoblast layer and potentially controls trophoblast cell fate decisions during placenta development. Abnormal expression of H19 is observed in the placental villi of pregnancies affected by pre-eclampsia and fetal growth restriction. Therefore, dysregulated H19 is a candidate biomarker and therapeutic target for the mitigation of ovarian and placenta-associated diseases.

Ovarian cancer classification and prognosis assessment model based on prognostic target genes in key microRNA-target gene networks

BACKGROUND

The present study was designed to screen key microRNA (miRNA)-target gene networks for ovarian cancer (OC) and to classify and construct a risk assessment system for OC based on the target genes.

METHODS

OC sample data of The Cancer Genome Atlas dataset and GSE26193, GSE30161, GSE63885 and GSE9891 datasets were retrospectively collected. Pearson correlation analysis and targeted analysis of miRNA and target gene were performed to screen key miRNA-target gene networks. Target genes associated with the prognosis of OC were screened from key miRNA-target gene networks for consensus clustering and least absolute shrinkage and selection operator-based regression machine learning analysis of OC samples.

RESULTS

Twenty target genes of 2651 key miRNA-target gene pairs had significant prognostic correlation in each OC cohort, and OC was divided into three clusters. There were differences in prognostic outcome, biological pathways, immune cell abundance and susceptibility to immune checkpoint blockade (ICB) therapy and anti-tumor drugs among the three molecular clusters. S2 exhibited the least advantage in prognosis and immunotherapy response rate in the three molecular clusters, and the pathways regulating immunity, hypoxia, metabolism and promoting malignant progression of cancer, as well as infiltrating immune and stromal cell population abundance, were the highest in this cluster. An eight-target gene prognostic model was created, and the risk index obtained by using this model not only significantly distinguished the immune characteristics of the sample, but also predicted the response of the sample to ICB treatment, and helped to screen 36 potential anti-OC drugs.

CONCLUSIONS

The present study provides a classification strategy for OC based on prognostic target genes in key miRNA-target gene networks, and creates a risk assessment system for predicting prognosis and response to ICB therapy in OC patients, providing molecular basis for prognosis and precise treatment of OC.

Construction and validation of molecular subtype and signature of immune cell-related telomeric genes and prediction of prognosis and immunotherapy efficacy in ovarian cancer patients

BACKGROUND

Ovarian cancer (OVC) has emerged as a fatal gynecological malignancy as a result of a lack of reliable methods for early detection, limited biomarkers and few treatment options. Immune cell-related telomeric genes (ICRTGs) show promise as potential biomarkers.

METHODS

ICRTGs were discovered using weighted gene co-expression network analysis (WGCNA). ICRTGs were screened for significant prognosis using one-way Cox regression analysis. Subsequently, molecular subtypes of prognosis-relevant ICRTGs were constructed and validated for OVC, and the immune microenvironment's landscape across subtypes was compared. OVC prognostic models were built and validated using prognosis-relevant ICRTGs. Additionally, chemotherapy susceptibility drugs for OVC patients in the low- and high-risk groups of ICRTGs were screened using genomics of drug susceptibility to cancer (GDSC). Finally, the immunotherapy response in the low- and high-risk groups was detected using the data from GSE78220. We conducted an immune index correlation analysis of ICRTGs with significant prognoses. The MAP3K4 gene, for which the prognostic correlation coefficient is the highest, was validated using tissue microarrays for a prognostic-immune index correlation.

RESULTS

WGCNA analysis constructed a gene set of ICRTGs and screened 22 genes with prognostic significance. Unsupervised clustering analysis revealed the best molecular typing for two subtypes. The Gene Set Variation Analysis algorithm was used to calculate telomere scores and validate the molecular subtyping. A prognostic model was constructed using 17 ICRTGs. In the The Cancer Genome Atlas-OVC training set and the Gene Expression Omnibus validation set (GSE30161), the risk score model's predicted risk groups and the actual prognosis were shown to be significantly correlated. GDSC screened Axitinib, Bexarotene, Embelin and the GSE78220 datasets and demonstrated that ICRTGs effectively distinguished the group that responds to immunotherapy from the non-responsive group. Additionally, tissue microarray validation results revealed that MAP3K4 significantly predicted patient prognosis. Furthermore, MAP3K4 exhibited a positive association with PD-L1 and a negative relationship with the M1 macrophage markers CD86 and INOS.

CONCLUSIONS

ICRTGs may be reliable biomarkers for the molecular typing of patients with OVC, enabling the prediction of prognosis and immunotherapy efficacy.

m6A-modified circASXL1 promotes proliferation and migration of ovarian cancer through the miR-320d/RACGAP1 axis

Ovarian cancer (OC) is one of the most common malignant tumors in women. Circular RNAs (circRNAs) can potentially regulate the development of OC. Therefore, this study investigated the role of circASXL1 in OC progression. Cell functions were assessed by MTT, colony formation, wound healing, and transwell assays. RIP and dual luciferase reporter assays confirmed the relationship between miR-320d and circASXL1 or RACGAP1. MeRIP was utilized to detect m6A levels. Xenograft tumor was established for in vivo experiments. CircASXL1 and RACGAP1 levels were increased in OC tissues and cells, whereas miR-320d expression was decreased. Upregulation of circASXL1 was associated with poor prognosis in OC patients. CircASXL1 silencing suppressed OC cell proliferation, migration and invasion in vitro and in vivo. Mechanistically, METTL3/IGF2BP1-mediated m6A modification maintained circASXL1 stability and upregulated its expression. CircASXL1 was a ceRNA that sequestrated miR-320d from RACGAP1, leading to increased RACGAP1 expression. CircASXL1 promoted OC cell proliferation, migration and invasion via the miR-320d/RACGAP1 axis. Therefore, m6A-modified circASXL1 acts as an oncogene in OC by targeting miR-320d and activating RACGAP1/PI3K/Akt pathway, which provides novel promising biomarkers for OC diagnosis.

Homologous Recombination Deficiency (HRD) Scoring, by Means of Two Different Shallow Whole-Genome Sequencing Pipelines (sWGS), in Ovarian Cancer Patients: A Comparison with Myriad MyChoice Assay

High-grade serous ovarian cancer (HGSOC) patients carrying the BRCA1/2 mutation or deficient in the homologous recombination repair system (HRD) generally benefit from treatment with PARP inhibitors. Some international recommendations suggest that BRCA1/2 genetic testing should be offered for all newly diagnosed epithelial ovarian cancer, along with HRD assessment. Academic tests (ATs) are continuously under development, in order to break down the barriers patients encounter in accessing HRD testing. Two different methods for shallow whole-genome sequencing (sWGS) were compared to the reference assay, Myriad. All these three assays were performed on 20 retrospective HGSOC samples. Moreover, HRD results were correlated with the progression-free survival rate (PFS). Both sWGS chemistries showed good correlation with each other and a complete agreement, even when compared to the Myriad score. Our academic HRD assay categorized patients as HRD-Deficient, HRM-Mild and HRN-Negative. These three groups were matched with PFS, providing interesting findings in terms of HRD scoring and months of survival. Both our sWGS assays and the Myriad test correlated with the patient's response to treatments. Finally, our AT confirms its capability of determining HRD status, with the advantage of being faster, cheaper, and easier to carry out. Our results showed a prognostic value for the HRD score.

Comparative analysis of syngeneic mouse models of high-grade serous ovarian cancer

Ovarian cancers exhibit high rates of recurrence and poor treatment response. Preclinical models that recapitulate human disease are critical to develop new therapeutic approaches. Syngeneic mouse models allow for the generation of tumours comprising the full repertoire of non-malignant cell types but have expanded in number, varying in the cell type of origin, method for transformation, and ultimately, the properties of the tumours they produce. Here we have performed a comparative analysis of high-grade serous ovarian cancer models based on transcriptomic profiling of 22 cell line models, and intrabursal and intraperitoneal tumours from 12. Among cell lines, we identify distinct signalling activity, such as elevated inflammatory signalling in STOSE and OVE16 models, and MAPK/ERK signalling in ID8 and OVE4 models; metabolic differences, such as reduced glycolysis-associated expression in several engineered ID8 subclones; and relevant functional properties, including differences in EMT activation, PD-L1 and MHC class I expression, and predicted chemosensitivity. Among tumour samples, we observe increased variability and stromal content among intrabursal tumours. Finally, we predict differences in the microenvironment of ID8 models engineered with clinically relevant mutations. We anticipate that this work will serve as a valuable resource, providing new insight to help select models for specific experimental objectives.

Performance of computational algorithms to deconvolve heterogeneous bulk ovarian tumor tissue depends on experimental factors

BACKGROUND

Single-cell gene expression profiling provides unique opportunities to understand tumor heterogeneity and the tumor microenvironment. Because of cost and feasibility, profiling bulk tumors remains the primary population-scale analytical strategy. Many algorithms can deconvolve these tumors using single-cell profiles to infer their composition. While experimental choices do not change the true underlying composition of the tumor, they can affect the measurements produced by the assay.

RESULTS

We generated a dataset of high-grade serous ovarian tumors with paired expression profiles from using multiple strategies to examine the extent to which experimental factors impact the results of downstream tumor deconvolution methods. We find that pooling samples for single-cell sequencing and subsequent demultiplexing has a minimal effect. We identify dissociation-induced differences that affect cell composition, leading to changes that may compromise the assumptions underlying some deconvolution algorithms. We also observe differences across mRNA enrichment methods that introduce additional discrepancies between the two data types. We also find that experimental factors change cell composition estimates and that the impact differs by method.

CONCLUSIONS

Previous benchmarks of deconvolution methods have largely ignored experimental factors. We find that methods vary in their robustness to experimental factors. We provide recommendations for methods developers seeking to produce the next generation of deconvolution approaches and for scientists designing experiments using deconvolution to study tumor heterogeneity.

MMDAE-HGSOC: A novel method for high-grade serous ovarian cancer molecular subtypes classification based on multi-modal deep autoencoder

High-grade serous ovarian cancer (HGSOC) is a type of ovarian cancer developed from serous tubal intraepithelial carcinoma. The intrinsic differences among molecular subtypes are closely associated with prognosis and pathological characteristics. At present, multi-omics data integration methods include early integration and late integration. Most existing HGSOC molecular subtypes classification methods are based on the early integration of multi-omics data. The mutual interference among multi-omics data is ignored, which affects the effectiveness of feature learning. High-dimensional multi-omics data contains genes unassociated with HGSOC molecular subtypes, resulting in redundant information, which is not conducive to model training. In this paper, we propose a multi-modal deep autoencoder learning method, MMDAE-HGSOC. MiRNA expression, DNA methylation, and copy number variation (CNV) are integrated with mRNA expression data to construct a multi-omics feature space. The multi-modal deep autoencoder network is used to learn the high-level feature representation of multi-omics data. The superposition LASSO (S-LASSO) regression algorithm is proposed to fully obtain the associated genes of HGSOC molecular subtypes. The experimental results show that MMDAE-HGSOC is superior to the existing classification methods. Finally, we analyze the enrichment gene ontology (GO) terms and biological pathways of these significant genes, which are discovered during the gene selection process.

Identification of necroptosis subtypes and development of necroptosis-related risk score model for in ovarian cancer

Background: ith the ongoing development of targeted therapy, non-apoptotic cell death, including necroptosis, has become a popular topic in the field of prevention and treatment. The purpose of this study was to explore the effect of necroptosis-related genes (NRGs) on the classification of ovarian cancer (OV) subtypes and to develop a necroptosis-related risk score (NRRS) classification system. Methods: 74 NRGs were obtained from the published studies, and univariate COX regression analysis was carried out between them and OV survival. Consensus clustering analysis was performed on OV samples according to the expression of NRGs related to prognosis. Furthermore, the NRRS model was developed by combining Weighted Gene Co-Expression Network Analysis (WGCNA) with least absolute shrinkage and selection operator (Lasso)-penalized Cox regression and multivariate Cox regression analysis. And the decision tree model was constructed based on the principle of random forest screening factors principle. Results: According to the post-related NRGs, OV was divided into two necroptosis subtypes. Compared with Cluster 1 (C1), the overall survival (OS) of Cluster 2 (C2) was significantly shorter, stromal score and immune score, the infiltration level of tumor associated immune cells and the expression of 20 immune checkpoints were significantly higher. WGCNA identified the blue module most related to necroptosis subtype, and 12 genes in the module were used to construct NRRS. NRRS was an independent prognostic variable of OV. The OS of samples with lower NRRS was significantly longer, and tumor mutation burden and homologous recombination defect were more obvious. Conclusion: This study showed that necroptosis plays an important role in the classification, prognosis, immune infiltration and biological characteristics of OV subtypes. The evaluation of tumor necroptosis may provide a new perspective for OV treatment.

The Effect of Bone Marrow Mesenchymal Stem Cells-Exosomes (BMSC-EXO) on Tumor Angiogenesis and Its Mechanism in Ovarian Cancer Microenvironment

In ovarian cancer microenvironment, BMSC cells can differentiate into a variety of stem cells, thereby reducing the damage to tissues, and this effect lies in the exosomal substances secreted by BMSC cells. Then, in ovarian cancer microenvironment, whether BMSC-exo exhibited an effect on angiogenesis at the tumor site, and its possible molecular mechanism remains unclear. BALA nude mice and ovarian cancer tumor tissues were collected to isolate vascular endothelial cells which were then assigned into Control group, 40 μg/ml BMSC-exo group, 80 μg/ml BMSC-exo group, 120 μg/ml BMSC-exo group in the presence of Wnt/β-catenin inhibitor (PNU-74654) followed by analysis of proliferation and migration of ovarian cancer vascular endothelial cells (OCVECs) and the angiogenesis. 40 μg/ml and 80 μg/mlBMSC-exo group showed significantly higher cell proliferation than control group with higher cell number in 80 μg/ml BMSC-exo group than 40 μg/ml BMSC-exo group (P < 0.05). The number of cell migration after BMSC-exo treatment was increased (P < 0.05) and the tumor tissue showed obvious angiogenesis with more CD31-positive cells (P < 0.05). PNU-74654 group showed significantly downregulated Wnt and β-catenin proteins (P < 0.05) and lower cell number and higher migration rate of vascular endothelial cells (P < 0.05). In conclusion, exosomes secreted by BMSC can repair damaged tissues possibly through activation of Wnt/β-catenin signaling pathway.

IGF2BP3‑stabilized SIX4 promotes the proliferation, migration, invasion and tube formation of ovarian cancer cells

The mortality rate of ovarian cancer (OC) is the highest among the different types of female reproductive system cancers. SIX homeobox 4 (SIX4), a member of the homeobox family, subfamily SIX, fulfills an important role in metastasis and angiogenesis in a variety of types of cancer. The aim of the present study was to investigate both the effects and the underlying mechanism of SIX4 on angiogenesis in OC. The Gene Expression Profiling Interactive Analysis and Encyclopedia of RNA Interactomes databases were employed to predict the expression levels of SIX4 in OC tissues, and its association with the overall survival (OS) rate of patients with OC. The expression levels of SIX4 in OC cell lines were detected by reverse transcription‑quantitative PCR (RT‑qPCR) and western blot analysis. Following silencing of SIX4, the proliferation, invasion, migration and angiogenesis of OC cells were investigated via Cell Counting Kit‑8, colony formation, wound healing, Transwell and tube formation assays. Subsequently, the levels of insulin‑like growth factor 2 mRNA binding protein 3 (IGF2BP3) in OC cell lines were detected by RT‑qPCR and western blot analysis. The ability of IGF2BP3 to bind to SIX4 mRNA was detected via an RNA immunoprecipitation assay, and the stability of SIX4 mRNA was assessed by RT‑qPCR following Actinomycin D treatment. Finally, the effects of transfection of sh‑SIX4 and overexpression of IGF2BP3 simultaneously were examined to further delineate the mechanism involved. It was revealed that SIX4 was highly expressed in OC tissues and cells, and its expression was associated with low OS rates in patients with OC. SIX4 knockdown with short hairpin RNA inhibited the proliferation, migration and invasion of cells, as well as angiogenesis. In addition, IGF2BP3 overexpression led to an improvement in the stability of SIX4 mRNA. Overexpression of IGF2BP3 also reversed the inhibitory effect of SIX4 interference on the malignant phenotypes of OC cells. Taken together, the results of the present study demonstrated that IGF2BP3‑stabilized SIX4 promoted the proliferation, metastasis and angiogenesis of SKOV3 cells.

CAF-Associated Paracrine Signaling Worsens Outcome and Potentially Contributes to Chemoresistance in Epithelial Ovarian Cancer

Background

High-grade serous ovarian cancer (HGSOC) is the predominant and deadliest form of ovarian cancer. Some of its histological subtypes can be distinguished by frequent occurrence of cancer-associated myofibroblasts (CAFs) and desmoplastic stroma reaction (DSR). In this study, we want to explore the relationship between therapy outcome and the activity of CAF-associated signaling pathways in a homogeneous HGSOC patient collective. Furthermore, we want to validate these findings in a general Epithelial ovarian cancer (EOC) cohort.

Methods

The investigation cohort consists of 24 HGSOC patients. All of them were treated with platinum-based components and clinical follow-up was available. The validation cohort was comprised of 303 patients. Sequencing data (whole transcriptome) and clinical data were extracted from The Cancer Genome Atlas (TCGA). RNA of HGSOC patients was isolated using a Maxwell RSC instrument and the appropriate RNA isolation kit. For digital expression analysis a custom-designed gene panel was employed. All genes were linked to various DSR- and CAF- associated pathways. Expression analysis was performed on the NanoString nCounter platform. Finally, data were explored using the R programming environment (v. 4.0.3).

Result

In total, 15 CAF-associated genes were associated with patients’ survival. More specifically, 6 genes (MMP13, CGA, EPHA3, PSMD9, PITX2, PHLPP1) were linked to poor therapy outcome. Though a variety of different pathways appeared to be associated with therapy failure, many were related to CAF paracrine signaling, including MAPK, Ras and TGF-β pathways. Similar results were obtained from the validation cohort.

Discussion

In this study, we could successfully link CAF-associated pathways, as shown by increased Ras, MAPK and PI3K-Akt signaling to therapy failure (chemotherapy) in HGSOC and EOCs in general. As platinum-based chemotherapy has been the state-of-the-art therapy to treat HGSOC for decades, it is necessary to unveil the reasons behind resistance developments and poor outcome. In this work, CAF-associated signaling is shown to compromise therapy response. In the validation cohort, CAF-associated signaling is also associated with therapy failure in general EOC, possibly hinting towards a conserved mechanism. Therefore, it may be helpful to stratify HGSOC patients for CAF activity and consider alternative treatment options.

Machine Learning analysis of high-grade serous ovarian cancer proteomic dataset reveals novel candidate biomarkers

Ovarian cancer is one of the most common gynecological malignancies, ranking third after cervical and uterine cancer. High-grade serous ovarian cancer (HGSOC) is one of the most aggressive subtype, and the late onset of its symptoms leads in most cases to an unfavourable prognosis. Current predictive algorithms used to estimate the risk of having Ovarian Cancer fail to provide sufficient sensitivity and specificity to be used widely in clinical practice. The use of additional biomarkers or parameters such as age or menopausal status to overcome these issues showed only weak improvements. It is necessary to identify novel molecular signatures and the development of new predictive algorithms able to support the diagnosis of HGSOC, and at the same time, deepen the understanding of this elusive disease, with the final goal of improving patient survival. Here, we apply a Machine Learning-based pipeline to an open-source HGSOC Proteomic dataset to develop a decision support system (DSS) that displayed high discerning ability on a dataset of HGSOC biopsies. The proposed DSS consists of a double-step feature selection and a decision tree, with the resulting output consisting of a combination of three highly discriminating proteins: TOP1, PDIA4, and OGN, that could be of interest for further clinical and experimental validation. Furthermore, we took advantage of the ranked list of proteins generated during the feature selection steps to perform a pathway analysis to provide a snapshot of the main deregulated pathways of HGSOC. The datasets used for this study are available in the Clinical Proteomic Tumor Analysis Consortium (CPTAC) data portal (https://cptac-data-portal.georgetown.edu/).

Establishment and Validation of a Genetic Label Associated With M2 Macrophage Infiltration to Predict Survival in Patients With Colon Cancer and to Assist in Immunotherapy

Background

Colon cancer is a malignant tumor with high morbidity and mortality. Researchers have tried to interpret it from different perspectives and divided it into different subtypes to facilitate individualized treatment. With the rise in the use of immunotherapy, its value in the field of tumor has begun to emerge. From the perspective of immune infiltration, this study classified colon cancer according to the infiltration of M2 macrophages in patients with colon cancer and further explored the same.

Methods

Cibersort algorithm was used to analyze the level of immune cell infiltration in patients with colon cancer in The Cancer Genome Atlas (TCGA) database. Weighted gene co-expression network analysis (WGCNA), Consensus Clustering analysis, Lasso analysis, and univariate Kaplan-Meier analysis were used to screen and verify the hub genes associated with M2 macrophages. Principal component analysis (PCA) was used to establish the M2 macrophage-related score (M2I Score). The correlation between M2I Score and somatic cell variation and microsatellite instability (MSI) were analyzed. Furthermore, the correlation between M2 macrophage score and differences in immunotherapy sensitivity was also explored.

Results

M2 macrophage infiltration was associated with poor prognosis. Four hub genes (ANKS4B, CTSD, TIMP1, and ZNF703) were identified as the progression-related genes associated with M2 macrophages. A stable and accurate M2I Score for M2 macrophages used in colon adenocarcinoma was determined based on four hub genes. The M2I Score was positively correlated with the tumor mutation load (TMB). The M2I Score of the group with high instability of microsatellites was higher than that of the group with low instability of microsatellites and microsatellite-stable group. Combined with the Cancer Immunome Atlas database, we concluded that patients with high M2I Scores were more sensitive to programmed cell death protein 1 (PD-1) inhibitors and PD-1 inhibitors combined with cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) inhibitors. The low-rating group may have better efficacy without immune checkpoint inhibitors or with CTLA4 inhibitors alone.

Conclusion

Four prognostic hub genes associated with M2 macrophages were screened to establish the M2I Score. Patients were divided into two subgroups: high M2I Score group and low M2I Score group. TMB, MSI, and sensitivity to immunotherapy were higher in the high-rated group. PD-1 inhibitors or PD-1 combined with CTLA-4 inhibitors are preferred for patients in the high-rated group who are more sensitive to immunotherapy.

Applications of single-cell sequencing in cancer research: progress and perspectives

Single-cell sequencing, including genomics, transcriptomics, epigenomics, proteomics and metabolomics sequencing, is a powerful tool to decipher the cellular and molecular landscape at a single-cell resolution, unlike bulk sequencing, which provides averaged data. The use of single-cell sequencing in cancer research has revolutionized our understanding of the biological characteristics and dynamics within cancer lesions. In this review, we summarize emerging single-cell sequencing technologies and recent cancer research progress obtained by single-cell sequencing, including information related to the landscapes of malignant cells and immune cells, tumor heterogeneity, circulating tumor cells and the underlying mechanisms of tumor biological behaviors. Overall, the prospects of single-cell sequencing in facilitating diagnosis, targeted therapy and prognostic prediction among a spectrum of tumors are bright. In the near future, advances in single-cell sequencing will undoubtedly improve our understanding of the biological characteristics of tumors and highlight potential precise therapeutic targets for patients.

Construction autophagy-related prognostic risk signature to facilitate survival prediction, individual treatment and biomarker excavation of epithelial ovarian cancer patients

Background

Existing clinical methods for prognosis evaluating for Epithelial Ovarian Cancer (EOC) patients had defects of invasive, unsystematic and subjective and little data are available for individualizing treatment, therefore, to identify potential prognostic markers and new therapeutic targets for EOC is urgently required.

Results

Expression of 232 autophagy-related genes (ARGs) in 354 EOC and 56 human ovarian surface epithelial specimens from 7 independent laboratories were analyzed, 31 mRNAs were identified as DEARGs. We did functional and pathway enrichment analysis and constructed protein–protein interaction network for all DEARGs. To screen out candidate DEARGs related to EOC patients’ survival and construct an autophagy-related prognostic risk signature, univariate and multivariate Cox proportional hazards models were established separately. Finally, 5 optimal independent prognostic DEARGs (PEX3, DNAJB9, RB1, HSP90AB1 and CXCR4) were confirmed and the autophagy-related risk model was established by the 5 prognostic DEARGs. The accuracy and robustness of the prognostic risk model for survival prediction were evaluated and verified by analyzing the correlation between EOC patients’ survival status, clinicopathological features and risk scores.

Conclusions

The autophagy-related prognostic risk model can be independently used to predict overall survival in EOC patients, it can also potentially assist in individualizing treatment and biomarker development.

How Autophagy Shapes the Tumor Microenvironment in Ovarian Cancer

Ovarian cancer (OC) is characterized by a high mortality rate due to the late diagnosis and the elevated metastatic potential. Autophagy, a lysosomal-driven catabolic process, contributes to the macromolecular turnover, cell homeostasis, and survival, and as such, it represents a pathway targetable for anti-cancer therapies. It is now recognized that the vascularization and the cellular composition of the tumor microenvironment influence the development and progression of OC by controlling the availability of nutrients, oxygen, growth factors, and inflammatory and immune-regulatory soluble factors that ultimately impinge on autophagy regulation in cancer cells. An increasing body of evidence indicates that OC carcinogenesis is associated, at least in the early stages, to insufficient autophagy. On the other hand, when the tumor is already established, autophagy activation provides a survival advantage to the cancer cells that face metabolic stress and protects from the macromolecules and organelles damages induced by chemo- and radiotherapy. Additionally, upregulation of autophagy may lead cancer cells to a non-proliferative dormant state that protects the cells from toxic injuries while preserving their stem-like properties. Further to complicate the picture, autophagy is deregulated also in stromal cells. Thus, changes in the tumor microenvironment reflect on the metabolic crosstalk between cancer and stromal cells impacting on their autophagy levels and, consequently, on cancer progression. Here, we present a brief overview of the role of autophagy in OC hallmarks, including tumor dormancy, chemoresistance, metastasis, and cell metabolism, with an emphasis on the bidirectional metabolic crosstalk between cancer cells and stromal cells in shaping the OC microenvironment.

Expanding and Remixing the Metadata Landscape

Genomic data sharing accelerates research. Data are most valuable when they are accompanied by detailed metadata. To date, metadata are often human-annotated descriptions of samples and their handling. We discuss how machine learning-derived elements complement such descriptions to enhance the research ecosystem around genomic data.

Development and Validation of the Gene Expression Predictor of High-grade Serous Ovarian Carcinoma Molecular SubTYPE (PrOTYPE)

PURPOSE

Gene expression-based molecular subtypes of high-grade serous tubo-ovarian cancer (HGSOC), demonstrated across multiple studies, may provide improved stratification for molecularly targeted trials. However, evaluation of clinical utility has been hindered by nonstandardized methods, which are not applicable in a clinical setting. We sought to generate a clinical grade minimal gene set assay for classification of individual tumor specimens into HGSOC subtypes and confirm previously reported subtype-associated features.

EXPERIMENTAL DESIGN

Adopting two independent approaches, we derived and internally validated algorithms for subtype prediction using published gene expression data from 1,650 tumors. We applied resulting models to NanoString data on 3,829 HGSOCs from the Ovarian Tumor Tissue Analysis consortium. We further developed, confirmed, and validated a reduced, minimal gene set predictor, with methods suitable for a single-patient setting.

RESULTS

Gene expression data were used to derive the predictor of high-grade serous ovarian carcinoma molecular subtype (PrOTYPE) assay. We established a de facto standard as a consensus of two parallel approaches. PrOTYPE subtypes are significantly associated with age, stage, residual disease, tumor-infiltrating lymphocytes, and outcome. The locked-down clinical grade PrOTYPE test includes a model with 55 genes that predicted gene expression subtype with >95% accuracy that was maintained in all analytic and biological validations.

CONCLUSIONS

We validated the PrOTYPE assay following the Institute of Medicine guidelines for the development of omics-based tests. This fully defined and locked-down clinical grade assay will enable trial design with molecular subtype stratification and allow for objective assessment of the predictive value of HGSOC molecular subtypes in precision medicine applications.See related commentary by McMullen et al., p. 5271.

Role of microRNA/Epithelial-to-Mesenchymal Transition Axis in the Metastasis of Bladder Cancer

Bladder cancer (BC) is the 11th most common diagnosed cancer, and a number of factors including environmental and genetic ones participate in BC development. Metastasis of BC cells into neighboring and distant tissues significantly reduces overall survival of patients with this life-threatening disorder. Recently, studies have focused on revealing molecular pathways involved in metastasis of BC cells, and in this review, we focus on microRNAs (miRNAs) and their regulatory effect on epithelial-to-mesenchymal transition (EMT) mechanisms that can regulate metastasis. EMT is a vital process for migration of BC cells, and inhibition of this mechanism restricts invasion of BC cells. MiRNAs are endogenous non-coding RNAs with 19-24 nucleotides capable of regulating different cellular events, and EMT is one of them. In BC cells, miRNAs are able to both induce and/or inhibit EMT. For regulation of EMT, miRNAs affect different molecular pathways such as transforming growth factor-beta (TGF-β), Snail, Slug, ZEB1/2, CD44, NSBP1, which are, discussed in detail this review. Besides, miRNA/EMT axis can also be regulated by upstream mediators such as lncRNAs, circRNAs and targeted by diverse anti-tumor agents. These topics are also discussed here to reveal diverse molecular pathways involved in migration of BC cells and strategies to target them to develop effective therapeutics.

Activin and inhibin signaling: From regulation of physiology to involvement in the pathology of the female reproductive system

Activins and inhibins - comprising activin A, B, AB, C and E, and inhibin A and B isoforms - belong to the transforming growth factor beta (TGFβ) superfamily. They regulate several biological processes, including cellular proliferation, differentiation and invasiveness, to enhance the formation and functioning of many human tissues and organs. In this review, we have discussed the role of activin and inhibin signaling in the physiological and female-specific pathological events that occur in the female reproductive system. The up-to-date evidence indicates that these cytokines regulate germ cell development, follicular development, ovulation, uterine receptivity, decidualization and placentation through the activation of several signaling pathways; and that their dysregulated expression is involved in the pathogenesis and pathophysiology of the numerous diseases, including pregnancy complications, that disturb reproduction. Hence, some of the isoforms have been suggested as potential biomarkers and therapeutic targets for the management of some of these diseases. Tackling the research directions highlighted in this review will enhance a detailed comprehension and the clinical utility of these cytokines.

EMT Factors and Metabolic Pathways in Cancer

The epithelial-mesenchymal transition (EMT) represents a biological program during which epithelial cells lose their cell identity and acquire a mesenchymal phenotype. EMT is normally observed during organismal development, wound healing and tissue fibrosis. However, this process can be hijacked by cancer cells and is often associated with resistance to apoptosis, acquisition of tissue invasiveness, cancer stem cell characteristics, and cancer treatment resistance. It is becoming evident that EMT is a complex, multifactorial spectrum, often involving episodic, transient or partial events. Multiple factors have been causally implicated in EMT including transcription factors (e.g., SNAIL, TWIST, ZEB), epigenetic modifications, microRNAs (e.g., miR-200 family) and more recently, long non-coding RNAs. However, the relevance of metabolic pathways in EMT is only recently being recognized. Importantly, alterations in key metabolic pathways affect cancer development and progression. In this review, we report the roles of key EMT factors and describe their interactions and interconnectedness. We introduce metabolic pathways that are involved in EMT, including glycolysis, the TCA cycle, lipid and amino acid metabolism, and characterize the relationship between EMT factors and cancer metabolism. Finally, we present therapeutic opportunities involving EMT, with particular focus on cancer metabolic pathways.