ADH1B promotes mesothelial clearance and ovarian cancer infiltration

High-grade serous carcinoma of the fallopian tube in a young woman with chromosomal 4q abnormality: A case report

BACKGROUND

Few studies have reported an association between an increased risk of acquiring cancers and survival in patients with 4q deletion syndrome. This study presents a rare association between chromosome 4q abnormalities and fallopian tube high-grade serous carcinoma (HGSC) in a young woman.

CASE SUMMARY

A 35-year-old woman presented with acute dull abdominal pain and a known chromosomal abnormality involving 4q13.3 duplication and 4q23q24 deletion. Upon arrival at the emergency room, her abdomen appeared ovoid and distended with palpable shifting dullness. Ascites were identified through abdominal ultrasound, and computed tomography revealed an omentum cake and an enlarged bilateral adnexa. Blood tests showed elevated CA-125 levels. Paracentesis was conducted, and immunohistochemistry indicated that the cancer cells favored an ovarian origin, making us suspect ovarian cancer. The patient underwent debulking surgery, which led to a diagnosis of stage IIIC HGSC of the fallopian tube. Subsequently, the patient received adjuvant chemotherapy with carboplatin and paclitaxel, resulting in stable current condition.

CONCLUSION

This study demonstrates a rare correlation between a chromosome 4q abnormality and HGSC. UBE2D3 may affect crucial cancer-related pathways, including P53, BRCA, cyclin D, and tyrosine kinase receptors, thereby possibly contributing to cancer development. In addition, ADH1 and DDIT4 may be potential influencers of both carcinogenic and therapeutic responses.

A Recipe for Successful Metastasis: Transition and Migratory Modes of Ovarian Cancer Cells

One of the characteristic features of ovarian cancer is its early dissemination. Metastasis and the invasiveness of ovarian cancer are strongly dependent on the phenotypical and molecular determinants of cancer cells. Invasive cancer cells, circulating tumor cells, and cancer stem cells, which are responsible for the metastatic process, may all undergo different modes of transition, giving rise to mesenchymal, amoeboid, and redifferentiated epithelial cells. Such variability is the result of the changing needs of cancer cells, which strive to survive and colonize new organs. This would not be possible if not for the variety of migration modes adopted by the transformed cells. The most common type of metastasis in ovarian cancer is dissemination through the transcoelomic route, but transitions in ovarian cancer cells contribute greatly to hematogenous and lymphatic dissemination. This review aims to outline the transition modes of ovarian cancer cells and discuss the migratory capabilities of those cells in light of the known ovarian cancer metastasis routes.

A prognostic prediction model for ovarian cancer using a cross-modal view correlation discovery network

Ovarian cancer is a tumor with different clinicopathological and molecular features, and the vast majority of patients have local or extensive spread at the time of diagnosis. Early diagnosis and prognostic prediction of patients can contribute to the understanding of the underlying pathogenesis of ovarian cancer and the improvement of therapeutic outcomes. The occurrence of ovarian cancer is influenced by multiple complex mechanisms, including the genome, transcriptome and proteome. Different types of omics analysis help predict the survival rate of ovarian cancer patients. Multi-omics data of ovarian cancer exhibit high-dimensional heterogeneity, and existing methods for integrating multi-omics data have not taken into account the variability and inter-correlation between different omics data. In this paper, we propose a deep learning model, MDCADON, which utilizes multi-omics data and cross-modal view correlation discovery network. We introduce random forest into LASSO regression for feature selection on mRNA expression, DNA methylation, miRNA expression and copy number variation (CNV), aiming to select important features highly correlated with ovarian cancer prognosis. A multi-modal deep neural network is used to comprehensively learn feature representations of each omics data and clinical data, and cross-modal view correlation discovery network is employed to construct the multi-omics discovery tensor, exploring the inter-relationships between different omics data. The experimental results demonstrate that MDCADON is superior to the existing methods in predicting ovarian cancer prognosis, which enables survival analysis for patients and facilitates the determination of follow-up treatment plans. Finally, we perform Gene Ontology (GO) term analysis and biological pathway analysis on the genes identified by MDCADON, revealing the underlying mechanisms of ovarian cancer and providing certain support for guiding ovarian cancer treatments.

Identification of a short, single site matriglycan that maintains neuromuscular function in the mouse

Matriglycan (-1,3-β-glucuronic acid-1,3-α-xylose-) is a polysaccharide that is synthesized on α-dystroglycan, where it functions as a high-affinity glycan receptor for extracellular proteins, such as laminin, perlecan and agrin, thus anchoring the plasma membrane to the extracellular matrix. This biological activity is closely associated with the size of matriglycan. Using high-resolution mass spectrometry and site-specific mutant mice, we show for the first time that matriglycan on the T317/T319 and T379 sites of α-dystroglycan are not identical. T379-linked matriglycan is shorter than the previously characterized T317/T319-linked matriglycan, although it maintains its laminin binding capacity. Transgenic mice with only the shorter T379-linked matriglycan exhibited mild embryonic lethality, but those that survived were healthy. The shorter T379-linked matriglycan exists in multiple tissues and maintains neuromuscular function in adult mice. In addition, the genetic transfer of α-dystroglycan carrying just the short matriglycan restored grip strength and protected skeletal muscle from eccentric contraction-induced damage in muscle-specific dystroglycan knock-out mice. Due to the effects that matriglycan imparts on the extracellular proteome and its ability to modulate cell-matrix interactions, our work suggests that differential regulation of matriglycan length in various tissues optimizes the extracellular environment for unique cell types.

Targeting metabolic fluxes reverts metastatic transitions in ovarian cancer

The formation of spheroids during epithelial ovarian cancer progression is correlated with peritoneal metastasis, disease recurrence, and poor prognosis. Although metastasis has been demonstrated to be driven by metabolic changes in transformed cells, mechanistic associations between metabolism and phenotypic transitions remain ill-explored. We performed quantitative proteomics to identify protein signatures associated with three distinct phenotypic morphologies (2D monolayers and two geometrically distinct three-dimensional spheroidal states) of the high-grade serous ovarian cancer line OVCAR-3. We obtained disease-driving phenotype-specific metabolic reaction modules and elucidated gene knockout strategies to reduce metabolic alterations that could drive phenotypic transitions. Exploring the DrugBank database, we identified and evaluated drugs that could impair such transitions and, hence, cancer progression. Finally, we experimentally validated our predictions by confirming the ability of one of our predicted drugs, the neuraminidase inhibitor oseltamivir, to inhibit spheroidogenesis in three ovarian cancer cell lines without any cytotoxic effects on untransformed stromal mesothelia.

Omentum provides a special cell microenvironment for ovarian cancer

BACKGROUND

Ovarian cancer seriously threatens women's health because of its poor prognosis and high mortality. Due to the lack of efficient early detection and screening methods, when patients seek doctors' help with complaints of abdominal distension, back pain and other nonspecific signs, the clinical results always hint at the widespread metastasis of disease. When referring to the metastasis of this disease, the omentum always takes precedence.

RECENT FINDINGS

The distinguishing feature of the omentum is adipose tissue, which satisfies the energy demand of cancer cells and supplies a more aggressive environment for ovarian cancer cells. In this review, we mainly focus on three important cell types: adipocytes, macrophages, and mesenchymal stem cells. Besides, several mechanisms underlying cancer-associated adipocytes (CAA)-facilitated ovarian cancer cell development have been revealed, including their capacities for storing lipids and endocrine function, and the release of hormones, growth factors, and adipokines. Blocking the reciprocity among cancer cells and various cells located on the omentum might contribute to ovarian cancer therapy. The inhibition of hormones, growth factors and adipokines produced by adipocytes will be a novel therapeutic strategy. However, a sufficient number of trials has not been performed. In spite of this, the therapeutic potential of metformin and the roles of exercise in ovarian cancer will be worth mentioning.

CONCLUSION

It is almost impossible to overcome completely ovarian cancer at the moment. What we can do is trying our best to improve these patients' prognoses. In this process, adipocytes may bring promising future for the therapy of ovarian cancer.

Upregulation of SPI1 in Ectopic Endometrium Contributes to an Invasive Phenotype

BACKGROUD AND AIM

Endometriosis is one of the most common gynecological diseases associated with chronic pelvic pain, infertility, and cancer. However, its molecular pathogenesis remains unclear. This study aimed to identify key genes involved in the pathogenesis of endometriosis.

METHODS

Bioinformatic analyses were perfomed to identify key differentially expressed genes (DEGs), transcription factors (TFs), and functionally enriched pathways. Effect of SPI1 on migration, invasion, expression of ADH1B, MYH11, and PLN were analyzed in human endometriotic cells.

RESULTS

By screening three transcriptome datasets from the GEO for overlapping DEGs between eutopic and ectopic endometria in patients with endometriosis, we found that the expression of ADH1B, MYH11, and PLN was markedly upregulated in the ectopic endometrium. Knockdown of ADH1B, MYH11, and PLN significantly inhibited the migration and invasion of human endometriotic 12Z cells. Additionally, gene set enrichment analysis revealed that epithelial-mesenchymal transition gene signature was positively correlated with ADH1B, MYH11, and PLN expression. Notably, the TF SPI1 was found to regulate the expression of these three genes in the endometriotic tissues and 12TZ cells. Moreover, SPI1 expression was associated with the invasion of endometriotic cells and was increased in the ectopic endometrium of patients with endometriosis.

CONCLUSION

These data suggest that SPI1 plays a key role in the progression of endometriosis by regulating ADH1B, MYH11, and PLN expression and may therefore serve as a potential prognostic and therapeutic factor for endometriosis.

Identification of copper metabolism-related subtypes and establishment of the prognostic model in ovarian cancer

BACKGROUND

Ovarian cancer (OC) is one of the most common and most malignant gynecological malignancies in gynecology. On the other hand, dysregulation of copper metabolism (CM) is closely associated with tumourigenesis and progression. Here, we investigated the impact of genes associated with copper metabolism (CMRGs) on the prognosis of OC, discovered various CM clusters, and built a risk model to evaluate patient prognosis, immunological features, and therapy response.

METHODS

15 CMRGs affecting the prognosis of OC patients were identified in The Cancer Genome Atlas (TCGA). Consensus Clustering was used to identify two CM clusters. lasso-cox methods were used to establish the copper metabolism-related gene prognostic signature (CMRGPS) based on differentially expressed genes in the two clusters. The GSE63885 cohort was used as an external validation cohort. Expression of CM risk score-associated genes was verified by single-cell sequencing and quantitative real-time PCR (qRT-PCR). Nomograms were used to visually depict the clinical value of CMRGPS. Differences in clinical traits, immune cell infiltration, and tumor mutational load (TMB) between risk groups were also extensively examined. Tumour Immune Dysfunction and Rejection (TIDE) and Immune Phenotype Score (IPS) were used to validate whether CMRGPS could predict response to immunotherapy in OC patients.

RESULTS

In the TCGA and GSE63885 cohorts, we identified two CM clusters that differed significantly in terms of overall survival (OS) and tumor microenvironment. We then created a CMRGPS containing 11 genes to predict overall survival and confirmed its reliable predictive power for OC patients. The expression of CM risk score-related genes was validated by qRT-PCR. Patients with OC were divided into low-risk (LR) and high-risk (HR) groups based on the median CM risk score, with better survival in the LR group. The 5-year AUC value reached 0.74. Enrichment analysis showed that the LR group was associated with tumor immune-related pathways. The results of TIDE and IPS showed a better response to immunotherapy in the LR group.

CONCLUSION

Our study, therefore, provides a valuable tool to further guide clinical management and tailor the treatment of patients with OC, offering new insights into individualized treatment.

Survival Analysis with High-Dimensional Omics Data Using a Threshold Gradient Descent Regularization-Based Neural Network Approach

Analysis of data with a censored survival response and high-dimensional omics measurements is now common. Most of the existing analyses are based on specific (semi)parametric models, in particular the Cox model. Such analyses may be limited by not having sufficient flexibility, for example, in accommodating nonlinearity. For categorical and continuous responses, neural networks (NNs) have provided a highly competitive alternative. Comparatively, NNs for censored survival data remain limited. Omics measurements are usually high-dimensional, and only a small subset is expected to be survival-associated. As such, regularized estimation and selection are needed. In the existing NN studies, this is usually achieved via penalization. In this article, we propose adopting the threshold gradient descent regularization (TGDR) technique, which has competitive performance (for example, when compared to penalization) and unique advantages in regression analysis, but has not been adopted with NNs. The TGDR-based NN has a highly sensible formulation and an architecture different from the unregularized and penalization-based ones. Simulations show its satisfactory performance. Its practical effectiveness is further established via the analysis of two cancer omics datasets. Overall, this study can provide a practical and useful new way in the NN paradigm for survival analysis with high-dimensional omics measurements.

Identification of Potential Biomarkers and Small Molecule Drugs for Cutaneous Melanoma Using Integrated Bioinformatic Analysis

Background: Cutaneous melanoma (CM) is a type of skin cancer with a high fatality rate, and its pathogenesis has not yet been fully elucidated.

Methods: We obtained the gene expression datasets of CM through the Gene Expression Omnibus (GEO) database. Subsequently, robust rank aggregation (RRA) method was used to identify differentially expressed genes (DEGs) between CM cases and normal skin controls. Gene functional annotation was performed to explore the potential function of the DEGs. We built the protein–protein interaction (PPI) network by the Interactive Gene database retrieval tool (STRING) and selected hub modules by Molecular Complexity Detection (MCODE). We furthered and validated our results using the TCGA-GTEX dataset. Finally, potential small molecule drugs were predicted by CMap database and verified by molecular docking method.

Results: A total of 135 DEGs were obtained by RRA synthesis analysis. GMPR, EMP3, SLC45A2, PDZD2, NPY1R, DLG5 and ADH1B were screened as potential targets for CM. Furazolidone was screened as a potential small molecule drug for the treatment of CM, and its mechanism may be related to the inhibition of CM cell proliferation by acting on GMPR.

Conclusion: We identified seven prognostic therapeutic targets associated with CM and furazolidone could be used as a potential drug for CM treatment, providing new prognostic markers, potential therapeutic targets and small molecule drugs for the treatment and prevention of CM.

Metabolic alterations and WNT signaling impact immune response in HGSOC

PURPOSE

Our study used transcriptomic and metabolomic strategies to determine the molecular profiles of HGSOC patient samples derived from primary tumor and ascites cells. These data identified clinically relevant heterogeneity among and within patients and highlighted global and patient-specific cellular responses to neoadjuvant chemotherapy (NACT).

EXPERIMENTAL DESIGN

Tissue from 61 treatment naïve patients with HGSOC were collected. In addition, 11 benign, 32 ascites, and 18 post-NACT samples (matched to the individual patient's pre-NACT sample) were collected. RNA-sequencing (RNA-seq) was performed on all samples collected. Two-dimensional spatial proteomic data was collected for two pairs of pre-and post-NACT. Untargeted metabolomics data using GCxGC-MS was generated for 30 treatment-naive tissues. Consensus clustering, analysis of differential expression, pathway enrichment, and survival analyses were performed.

RESULTS

Treatment-naïve HGSOC tissues had distinct transcriptomic and metabolomic profiles. The mesenchymal subtype harbored a metabolomic profile distinct from the other subtypes. Compared to primary tumor tissue, ascites showed significant changes in immune response and signaling pathways. NACT caused significant alterations in gene expression and WNT activity, and this corresponded to altered immune response. Overall, WNT signaling levels were inversely correlated with immune cell infiltration in HGSOC tissues and WNT signaling post-NACT was inversely correlated with progression-free survival.

CONCLUSIONS

Our study concluded that HGSOC is a heterogenous disease at baseline and growing molecular differences can be observed between primary tumor and ascites cells or within tumors in response to treatment. Our data reveal potential exploratory biomarkers relevant for treatment selection and predicting patient outcomes that warrant further research.

The Transcoelomic Ecosystem and Epithelial Ovarian Cancer Dissemination

Epithelial ovarian cancer (EOC) is considered the deadliest gynecological disease and is normally diagnosed at late stages, at which point metastasis has already occurred. Throughout disease progression, EOC will encounter various ecosystems and the communication between cancer cells and these microenvironments will promote the survival and dissemination of EOC. The primary tumor is thought to develop within the ovaries or the fallopian tubes, both of which provide a microenvironment with high risk of causing DNA damage and enhanced proliferation. EOC disseminates by direct extension from the primary tumors, as single cells or multicellular aggregates. Under the influence of cellular and non-cellular factors, EOC spheroids use the natural flow of peritoneal fluid to reach distant organs within the peritoneal cavity. These cells can then implant and seed distant organs or tissues, which develop rapidly into secondary tumor nodules. The peritoneal tissue and the omentum are two common sites of EOC metastasis, providing a microenvironment that supports EOC invasion and survival. Current treatment for EOC involves debulking surgery followed by platinum-taxane combination chemotherapy; however, most patients will relapse with a chemoresistant disease with tumors developed within the peritoneum. Therefore, understanding the role of the unique microenvironments that promote EOC transcoelomic dissemination is important in improving patient outcomes from this disease. In this review article, we address the process of ovarian cancer cellular fate at the site of its origin in the secretory cells of the fallopian tube or in the ovarian surface epithelial cells, their detachment process, how the cells survive in the peritoneal fluid avoiding cell death triggers, and how cancer- associated cells help them in the process. Finally, we report the mechanisms used by the ovarian cancer cells to adhere and migrate through the mesothelial monolayer lining the peritoneum. We also discuss the involvement of the transcoelomic ecosystem on the development of chemoresistance of EOC.

Immune infiltration and prognostic and diagnostic use of LGALS4 in colon adenocarcinoma and bladder urothelial carcinoma

Colon adenocarcinoma (COAD) is a common tumor of the gastrointestinal tract with a high mortality rate. Current research has identified many genes associated with immune infiltration that play a vital role in the development of COAD. In this study, we analysed the prognostic and diagnostic features of such immune-related genes in the context of colonic adenocarcinoma (COAD). We analysed 17 overlapping gene expression profiles of COAD and healthy samples obtained from TCGA-COAD and public single-cell sequencing resources, to identify potential therapeutic COAD targets. We evaluated the abundance of immune infiltration with those genes using the TIMER (Tumor Immune Estimation Resource) deconvolution method. Subsequently, we developed predictive and survival models to assess the prognostic value of these genes. The LGALS4 (Galectin-4) gene was found to be significantly (P<0.05) downregulated in COAD and bladder urothelial carcinoma (BLCA) compared to healthy samples. We identified LGALS4 as a prognostic and diagnostic marker for multiple cancer types, including COAD and BLCA. Our analysis reveals a series of novel candidate drug targets, as well as candidate molecular markers, that may explain the pathogenesis of COAD and BLCA. LGALS4 gene is associated with multiple cancer types and is a possible prognostic, as well as diagnostic, marker of COAD and BLCA.

Topological defects in the mesothelium suppress ovarian cancer cell clearance

We investigated an in vitro model for mesothelial clearance, wherein ovarian cancer cells invade into a layer of mesothelial cells, resulting in mesothelial retraction combined with cancer cell disaggregation and spreading. Prior to the addition of tumor cells, the mesothelial cells had an elongated morphology, causing them to align with their neighbors into well-ordered domains. Flaws in this alignment, which occur at topological defects, have been associated with altered cell density, motion, and forces. Here, we identified topological defects in the mesothelial layer and showed how they affected local cell density by producing a net flow of cells inward or outward, depending on the defect type. At locations of net inward flow, mesothelial clearance was impeded. Hence, the collective behavior of the mesothelial cells, as governed by the topological defects, affected tumor cell clearance and spreading. Importantly, our findings were consistent across multiple ovarian cancer cell types, suggesting a new physical mechanism that could impact ovarian cancer metastasis.

Effects of CXCL12 isoforms in a pancreatic pre-tumour cellular model: Microarray analysis

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of death among cancers, it is characterized by poor prognosis and strong chemoresistance. In the PDAC microenvironment, stromal cells release different extracellular components, including CXCL12. The CXCL12 is a chemokine promoting the communication between tumour and stromal cells. Six different splicing isoforms of CXCL12 are known (α, β, γ, δ, ε, θ) but their role in PDAC has not yet been characterized.

AIM

To investigate the specific role of α, β, and γ CXCL12 isoforms in PDAC onset.

METHODS

We used hTERT-HPNE E6/E7/KRasG12D (Human Pancreatic Nestin-Expressing) cell line as a pancreatic pre-tumour model and exposed it to the α, β, and γ CXCL12 isoforms. The altered expression profiles were assessed by microarray analyses and confirmed by Real-Time polymerase chain reaction. The functional enrichment analyses have been performed by Enrichr tool to highlight Gene Ontology enriched terms. In addition, wound healing assays have been carried out to assess the phenotypic changes, in terms of migration ability, induced by the α, β, and γ CXCL12 isoforms.

RESULTS

Microarray analysis of hTERT-HPNE cells treated with the three different CXCL12 isoforms highlighted that the expression of only a few genes was altered. Moreover, the α and β isoforms showed an alteration in expression of different genes, whereas γ isoform affected the expression of genes also common with α and β isoforms. The β isoform altered the expression of genes mainly involved in cell cycle regulation. In addition, all isoforms affected the expression of genes associated to cell migration, adhesion and cytoskeleton. In vitro cell migration assay confirmed that CXCL12 enhanced the migration ability of hTERT-HPNE cells. Among the CXCL12 splicing isoforms, the γ isoform showed higher induction of migration than α and β isoforms.

CONCLUSION

Our data suggests an involvement and different roles of CXCL12 isoforms in PDAC onset. However, more investigations are needed to confirm these preliminary observations.

Identification and validation of a prognostic index based on a metabolic-genomic landscape analysis of ovarian cancer

PURPOSE

Tumour metabolism has become a novel factor targeted by personalised cancer drugs. This research evaluated the prognostic significance of metabolism-related genes (MRGs) in ovarian serous cystadenocarcinoma (OSC).

METHODS

MRGs in 379 women surviving OSC were obtained using The Cancer Genome Atlas (TCGA) database. Then, several biomedical computational algorithms were employed to identify eight hub prognostic MRGs that were significantly relevant to OSC survival. These eight genes have important clinical significance and prognostic value in OSC. Subsequently, a prognostic index was constructed. Drug sensitivity analysis was used to screen the key genes in eight MRGs. Immunohistochemistry (IHC) staining confirmed the expression levels of key genes and their correlations with clinical parameters and prognosis for patients.

RESULTS

A total of 701 differentially expressed MRGs were confirmed in women with OSC by the TCGA database. The random walking with restart (RWR) algorithm and the univariate Cox and lasso regression analyses indicated a prognostic signature based on eight MRGs (i.e., ENPP1, FH, CYP2E1, HPGDS, ADCY9, NDUFA5, ADH1B and PYGB), which performed moderately well in prognostic predictions. Drug sensitivity analysis indicated that PYGB played a key role in the progression of OSC. Also, IHC staining confirmed that PYGB has a close correlation with clinical parameters and poor prognosis in OSC.

CONCLUSION

The results of the present study may help to establish a foundation for future research attempting to predict the prognosis of OSC patients and to characterise OSC metabolism.

Comparison of the Fatty Acid Metabolism Pathway in Pan-Renal Cell Carcinoma: Evidence from Bioinformatics

This study analyzed and compared the potential role of fatty acid metabolism pathways in three subtypes of renal cell carcinoma. Biological pathways that were abnormally up- and downregulated were identified through gene set variation analysis in the subtypes. Abnormal downregulation of the fatty acid metabolism pathway occurred in all three renal cell carcinoma subtypes. Alteration of the fatty acid metabolism pathway was vital in the development of pan-renal cell carcinoma. Bioinformatics methods were used to obtain a panoramic view of copy number variation, single-nucleotide variation, mRNA expression, and the survival landscape of fatty acid metabolism pathway-related genes in pan-renal cell carcinoma. Most importantly, we used genes related to the fatty acid metabolism pathway to establish a prognostic-related risk model in the three subtypes of renal cell carcinoma. The data will be valuable for future clinical treatment and scientific research.

Deep learning-based ovarian cancer subtypes identification using multi-omics data

Background

Identifying molecular subtypes of ovarian cancer is important. Compared to identify subtypes using single omics data, the multi-omics data analysis can utilize more information. Autoencoder has been widely used to construct lower dimensional representation for multi-omics feature integration. However, learning in the deep architectures in Autoencoder is difficult for achieving satisfied generalization performance. To solve this problem, we proposed a novel deep learning-based framework to robustly identify ovarian cancer subtypes by using denoising Autoencoder.

Results

In proposed method, the composite features of multi-omics data in the Cancer Genome Atlas were produced by denoising Autoencoder, and then the generated low-dimensional features were input into k-means for clustering. At last based on the clustering results, we built the light-weighted classification model with L1-penalized logistic regression method. Furthermore, we applied the differential expression analysis and WGCNA analysis to select target genes related to molecular subtypes. We identified 34 biomarkers and 19 KEGG pathways associated with ovarian cancer.

Conclusions

The independent test results in three GEO datasets proved the robustness of our model. The literature reviewing show 19 (56%) biomarkers and 8(42.1%) KEGG pathways identified based on the classification subtypes have been proved to be associated with ovarian cancer. The outcomes indicate that our proposed method is feasible and can provide reliable results.

Regulation of invasion and peritoneal dissemination of ovarian cancer by mesothelin manipulation

Peritoneal dissemination is a particular form of metastasis typically observed in ovarian cancer and the major cause for poor patient’s outcome. Identification of the molecular players involved in ovarian cancer dissemination can offer an approach to develop treatment strategies to improve clinical prognosis. Here, we identified mesothelin (MSLN) as a crucial protein in the multistep process of peritoneal dissemination of ovarian cancer. We demonstrated that MSLN is overexpressed in primary and matched peritoneal metastasis of high-grade serous carcinomas (HGSC). Using several genetically engineered ovarian cancer cell lines, resulting in loss or gain of function, we found that MSLN increased cell survival in suspension and invasion of tumor cells through the mesothelial cell layer in vitro. Intraperitoneal xenografts established with MSLN^high ovarian cancer cell lines showed enhanced tumor burden and spread within the peritoneal cavity. These findings provide strong evidences that MSLN is a key player in ovarian cancer progression by triggering peritoneal dissemination and provide support for further clinical investigation of MSLN as a therapeutic target in HGSC.

Adipocyte-Induced FABP4 Expression in Ovarian Cancer Cells Promotes Metastasis and Mediates Carboplatin Resistance

Adipocytes are critical for ovarian cancer cells to home to the omentum, but the metabolic changes initiated by this interaction are unknown. To this end, we carried out unbiased mass spectrometry-based metabolomic and proteomic profiling of cancer cells cocultured with primary human omental adipocytes. Cancer cells underwent significant proteo-metabolomic alteration(s), typified by changes in the lipidome with corresponding upregulation of lipid metabolism proteins. FABP4, a lipid chaperone protein, was identified as the critical regulator of lipid responses in ovarian cancer cells cocultured with adipocytes. Subsequently, knockdown of FABP4 resulted in increased 5-hydroxymethylcytosine levels in the DNA, downregulation of gene signatures associated with ovarian cancer metastasis, and reduced clonogenic cancer cell survival. In addition, clustered regularly interspaced short palindromic repeats (CRISPR)-mediated knockout of FABP4 in high-grade serous ovarian cancer cells reduced metastatic tumor burden in mice. Consequently, a small-molecule inhibitor of FABP4 (BMS309403) not only significantly reduced tumor burden in a syngeneic orthotopic mouse model but also increased the sensitivity of cancer cells toward carboplatin both in vitro and in vivo. Taken together, these results show that targeting FABP4 in ovarian cancer cells can inhibit their ability to adapt and colonize lipid-rich tumor microenvironments, providing an opportunity for specific metabolic targeting of ovarian cancer metastasis. SIGNIFICANCE: Ovarian cancer metastatic progression can be restricted by targeting a critical regulator of lipid responses, FABP4.

Ovarian Cancer Dissemination-A Cell Biologist's Perspective

Epithelial ovarian cancer (EOC) comprises multiple disease states representing a variety of distinct tumors that, irrespective of tissue of origin, genetic aberrations and pathological features, share common patterns of dissemination to the peritoneal cavity. EOC peritoneal dissemination is a stepwise process that includes the formation of malignant outgrowths that detach and establish widespread peritoneal metastases through adhesion to serosal membranes. The cell biology associated with outgrowth formation, detachment, and de novo adhesion is at the nexus of diverse genetic backgrounds that characterize the disease. Development of treatment for metastatic disease will require detailed characterization of cellular processes involved in each step of EOC peritoneal dissemination. This article offers a review of the literature that relates to the current stage of knowledge about distinct steps of EOC peritoneal dissemination, with emphasis on the cell biology aspects of the process.

Inhibitory effect of carbonyl reductase 1 against peritoneal progression of ovarian cancer: evaluation by ex vivo 3D-human peritoneal model

The current authors previously reported that a carbonyl reductase 1 (CR1) DNA-dendrimer complex could potentially be used in gene therapy for peritoneal metastasis of ovarian cancer. The aims of the current study were to observe the cellular dynamics of peritoneal metastasis of epithelial ovarian cancer cells and to ascertain changes in the dynamics of ovarian cancer cells as a result of transfection of CR1 DNA. (1) Artificial human peritoneal tissue (AHPT) was seeded with serous ovarian cancer cells, and the process leading to development of peritoneal carcinomatosis was observed over time. (2) Peritoneal carcinomatosis was produced in mice and compared to a model using AHPT to determine the appropriateness of AHPT. (3) CR1 DNA was transfected into cancer cells seeded on AHPT, and the dynamics of cancer cells were observed over time. (1) Cancer cells perforated the mesothelium, leaving normal mesothelium intact. However, the cells proliferated between the layers of the mesothelium, forming a mass. After 24 h, cancer cells had invaded the lymphatics, and after 48-72 h cancer cells had invaded deep into the mesothelium, where they formed a mass. (2) Invasion of the peritoneum by cancer cells in a murine model of peritoneal carcinomatosis resembled that in a model using AHPT, and results substantiated the reproducibility of peritoneal carcinomatosis in AHPT. (3) Proliferation of cells transfected with CR1 DNA was significantly inhibited on AHPT, and necrosis was evident. Nevertheless, cancer cell invasion deep into the mesothelium was not inhibited. Use of a new tool, AHPT, in an in vitro model of peritoneal metastasis revealed that CR1 DNA inhibited cancer cell proliferation. CR1 DNA does not play a role in inhibiting invasion of the mesothelium during peritoneal metastasis, but it does affect cancer cell proliferation. Results suggested that CR1 DNA inhibits cancer cell proliferation via necrosis.

Molecular Mechanisms Regulating Organ-Specific Metastases in Epithelial Ovarian Carcinoma

Epithelial ovarian carcinoma is the most predominant type of ovarian carcinoma, the deadliest gynecologic malignancy. It is typically diagnosed late when the cancer has already metastasized. Transcoelomic metastasis is the most predominant mechanism of dissemination from epithelial ovarian carcinoma, although both hematogenously and lymphogenously spread metastases also occur. In this review, we describe molecular mechanisms known to regulate organ-specific metastasis from epithelial ovarian carcinoma. We begin by discussing the sites colonized by metastatic ovarian carcinoma and rank them in the order of prevalence. Next, we review the mechanisms regulating the transcoelomic metastasis. Within this chapter, we specifically focus on the mechanisms that were demonstrated to regulate peritoneal adhesion—one of the first steps in the transcoelomic metastatic cascade. Furthermore, we describe mechanisms of the transcoelomic metastasis known to regulate colonization of specific sites within the peritoneal cavity, including the omentum. Mechanisms underlying hematogenous and lymphogenous metastatic spread are less comprehensively studied in ovarian cancer, and we summarize mechanisms that were identified to date. Lastly, we discuss the outcomes of the clinical trials that attempted to target some of the mechanisms described in this review.