Prognostic Biomarker For Ovarian Cancer Research Articles

Identification and validation of a prognostic model based on immune-related genes in ovarian carcinoma.

A novel valuable prognostic model has been developed on the basis of immune-related genes (IRGs), which could be used to estimate overall survival (OS) in ovarian cancer (OC) patients in The Cancer Genome Atlas (TCGA) dataset and the International Cancer Genome Consortium (ICGC) dataset. This prognostic model was engineered by employing LASSO regression in training cohort (TCGA dataset). The corresponding growth predictive values of this model for individualized survival was evaluated using survival analysis, receiver operating characteristic curve (ROC curve), and risk curve analysis. Combined with clinical characteristics, a model risk score nomogram for OS was well built. Thereafter, depended on the model risk score, patients were divided into high and low risk subgroups. The survival difference between these subgroups was measured using Kaplan-Meier survival method. In addition, correlations containing pathway enrichment, treatment, immune cell infiltration and the prognostic model were also analyzed. We established the ovarian cancer cell line W038 for this study and identified the performances of GBP1P1 knockdown on a series of activities including cellular proliferation, apoptosis, migration, and invasion of W038 cells in vitro. We constructed a 25-genes prognostic model (TNFAIP8L3, PI3, TMEM181, GBP1P1 (LOC400759), STX18, KIF26B, MRPS11, CACNA1C, PACSIN3, GMPR, MANF, PYGB, SNRPA1, ST7L, ZBP1, BMPR1B-DT, STAC2, LINC02585, LYPD6, NSG1, ACOT13, FAM120B, LEFTY1, SULT1A2, FZD3). The areas under the curves (AUC) of 1, 2 and 3 years were 0.806, 0.773 and 0.762, in the TCGA cohort, respectively. Besides, the effectiveness of the model was verified using ICGC testing data. Univariate and multivariate Cox regression analysis exposes the risk score as an independent prognosis predictor for OS both in the TCGA and ICGC cohort. In summary, we utilized comprehensive bioinformatics analysis to build an effective prognostic gene model for OC patients. These bioinformatic results suggested that GBP1P1 could act as a novel biomarker for OC. GBP1P1 knockdown substantially inhibited the proliferation, migration, and invasion of W038 cells in vitro, and increased the percentage of apoptotic W038 cells. The analyses of genetic status of patients with 25-genes model might improve the ability to predict the prognosis of patients with OC and help to select patients suit able to therapies. Immune-related gene GBP1P1 might serve as prognostic biomarker for OC.

Lemur tail kinase 3 serves as a predictor of patient outcomes and a target for the treatment of ovarian cancer

Lemur tail kinase 3 (LMTK3) belongs to a family of tyrosine kinases that are known to correlate with tumor grade and patient survival in some cancers. Here, we validated LMTK3 as a specific target and a prognostic biomarker in ovarian cancer (OC). In samples from 204 stage I-II OC patients, immunohistochemical studies revealed a higher cytoplasmic-to-nuclear staining intensity of LMTK3, which correlated with worse overall survival (p<0.001). Efficacy studies utilizing novel LMTK3 binding peptides (LMTK3BPs) showed that all chemosensitive and chemoresistant OC cells were killed without affecting normal cells (p<0.005), with synergistic effects shown following cisplatin and docetaxel treatment. In an orthotopic xenograft mouse model of OC, we saw a 35% tumor reduction in response to intravenous injections of 2mg/kg LMTK3BP given three times a week for 3weeks. Furthermore, invivo safety studies showed no signs of toxicity after LMTK3BP treatment, even at doses as high as 40mg/kg. This study highlights LMTK3 as a predictor of patient clinical outcomes. More importantly, novel LMTK3BPs represent potential safe treatment options, either alone or in combination with therapies, for OC.

Current data and future perspectives on DNA methylation in ovarian cancer (Review).

Ovarian cancer (OC) represents the most prevalent malignancy of the female reproductive system. Its distinguishing features include a high aggressiveness, substantial morbidity and mortality, and a lack of apparent symptoms, which collectively pose significant challenges for early detection. Given that aberrant DNA methylation events leading to altered gene expression are characteristic of numerous tumor types, there has been extensive research into epigenetic mechanisms, particularly DNA methylation, in human cancers. In the context of OC, DNA methylation is often associated with the regulation of critical genes, such as BRCA1/2 and Ras‑association domain family 1A. Methylation modifications within the promoter regions of these genes not only contribute to the pathogenesis of OC, but also induce medication resistance and influence the prognosis of patients with OC. As such, a more in‑depth understanding of DNA methylation underpinning carcinogenesis could potentially facilitate the development of more effective therapeutic approaches for this intricate disease. The present review focuses on classical tumor suppressor genes, oncogenes, signaling pathways and associated microRNAs in an aim to elucidate the influence of DNA methylation on the development and progression of OC. The advantages and limitations of employing DNA methylation in the diagnosis, treatment and prevention of OC are also discussed. On the whole, the present literature review indicates that the DNA methylation of specific genes could potentially serve as a prognostic biomarker for OC and a therapeutic target for personalized treatment strategies. Further investigations in this field may yield more efficacious diagnostic and therapeutic alternatives for patients with OC.

Abstract 2273: Development of a prognostic biomarker for ovarian cancer based on a high-resolution digital pathology platform for spatial statistical analysis of platelets and blood vessels

Abstract Purpose: A third of ovarian cancer patients have elevated platelet counts that are associated with poor prognosis. However, the exact mechanism by which platelets preferentially extravasate into tumor microenvironment is unknown, partly owing to lack of effective tools that can accurately segment and measure platelet counts found in tumor tissues. In this study, we developed a high-resolution digital pathology (DP) platform with whole tissue confocal imaging and deep learning model that can be used to automatically segment out platelets and blood vessels to perform spatial statistics (SA) of platelets with high precision and consistency. We utilized our SADP platform to study an interaction between tumor secreted SDF-1 and CXCR4 on platelets which initiates platelet chemotaxis towards tumor microenvironment. Methods: A deep learning algorithm to accurately segment out platelets was developed based on the U-Net structure using our original datasets from the murine ovarian cancer model. Tumor-bearing mice were injected with 5 mg/kg of Plerixafor, a CXCR4 inhibitor, daily for 4 weeks. Then, tumor nodules were resected, stained with CD41a and CD31 antibodies, and imaged using a whole slide confocal microscopy. We performed manual annotation of these images to generate the mask dataset and performed further data augmentation to increase the diversity of datasets. We trained our model using 85% of these images and performed validation using the remaining 15%. Results: Using our SADP platform, the validation dataset achieved pixel-wise accuracy of 98.8%. A receiver operating characteristic (ROC) curve demonstrated an area under the ROC curve (AUC) of 0.99 with high sensitivity and specificity. Intersection over union (IoU) score of 0.765 was obtained. A statistically significant (p=0.0467), 7.03% reduction in the platelet density was shown in the Plerixafor-treated mouse ovarian tumor tissues (n=8) compared to the control tumor tissues (n=9). We also found that platelets are spatially concentrated near blood vessels in the control tumor tissues compared to the Plerixafor-treated tumor tissues (p=0.0377). Conclusions: We developed a SADP platform to segment and quantitate tens of thousands of platelets from whole slide mouse ovarian cancer tissues automatically with high accuracy and consistency and incorporated spatial statistics to understand their spatial relationships and patterns that would not be apparent in non-spatial analyses. Applying the SADP platform, we demonstrated that treatment with Plerixafor leads to overall reduction in platelet infiltration within ovarian murine tumors. Our ongoing effort is to utilize our SADP platform on human tumor tissue images from a whole slide digital scanner to further characterize spatial distribution of platelets as a potential prognostic marker for ovarian cancer. Citation Format: Ju Young Ahn, Wendolyn Carlos-Alcalde, Matthew Vasquez, Min Soon Cho, Vahid Afshar-Kharghan, Stephen T. Wong. Development of a prognostic biomarker for ovarian cancer based on a high-resolution digital pathology platform for spatial statistical analysis of platelets and blood vessels [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2273.

SPARC is a Novel Prognostic Biomarker for Ovarian Cancer and Associated with Immune Signatures and Drug Response

SPARC is a Novel Prognostic Biomarker for Ovarian Cancer and Associated with Immune Signatures and Drug Response

Activity of Potassium Channels in CD8+ T Lymphocytes: Diagnostic and Prognostic Biomarker in Ovarian Cancer?

CD8+ T cells play a role in the suppression of tumor growth and immunotherapy. Ion channels control the Ca2+-dependent function of CD8+ lymphocytes such as cytokine/granzyme production and tumor killing. Kv1.3 and KCa3.1 K+ channels stabilize the negative membrane potential of T cells to maintain Ca2+ influx through CRAC channels. We assessed the expression of Kv1.3, KCa3.1 and CRAC in CD8+ cells from ovarian cancer (OC) patients (n = 7). We found that the expression level of Kv1.3 was higher in patients with malignant tumors than in control or benign tumor groups while the KCa3.1 activity was lower in the malignant tumor group as compared to the others. We demonstrated that the Ca2+ response in malignant tumor patients is higher compared to control groups. We propose that altered Kv1.3 and KCa3.1 expression in CD8+ cells in OC could be a reporter and may serve as a biomarker in diagnostics and that increased Ca2+ response through CRAC may contribute to the impaired CD8+ function.

Prognostic role of long non-coding RNA USP30-AS1 in ovarian cancer: insights into immune cell infiltration in the tumor microenvironment.

Ovarian cancer represents a formidable gynecologic malignancy bearing a dismal prognosis owing to the dearth of reliable early detection approaches and a high recurrence rate. Long non-coding RNAs (lncRNAs) have garnered immense attention as key orchestrators involved in diverse biological processes and take part in cancer initiation and progression. The present study investigated the potential significance of lncRNA USP30-AS1 in ovarian cancer prognosis, as well as its putative association with immune cell infiltration in tumor immune microenvironment (TIME). By analyzing publicly available datasets, we identified six lncRNAs with prognostic prediction ability, including USP30-AS1. The results revealed a significant positive correlation of USP30-AS1 expression with the infiltration of immune cells such as Th1 cells, TFH, CD8 T cells, B cells, antigen-presenting dendritic cells (aDC), and plasmacytoid dendritic cells (pDC) in ovarian cancer specimens. These findings provide compelling evidence of the potential involvement of lncRNA in the regulation of the TME in ovarian carcinoma. The outcomes from this study underscore the potential of USP30-AS1 as a promising prognostic biomarker for ovarian cancer. Additionally, the findings offer significant insights into the plausible role of lncRNAs in modulating immune activities, thus adding to our understanding of the disease biology. Additional investigations are necessary to unravel the molecular mechanisms underpinning these connections and validate the results seen in independent cohorts and experimental models.

323P Platelet-to-lymphocyte ratio, neutrophil-to-lymphocyte ratio, and lymphocyte-to-monocyte ratio as prognostic biomarkers in ovarian cancer among the Asian population: A meta-analysis

323P Platelet-to-lymphocyte ratio, neutrophil-to-lymphocyte ratio, and lymphocyte-to-monocyte ratio as prognostic biomarkers in ovarian cancer among the Asian population: A meta-analysis

From seeds to survival rates: investigating Linum usitatissimum’s potential against ovarian cancer through network pharmacology

Ovarian cancer is a malignant tumor that primarily forms in the ovaries. It often goes undetected until it has spread to the pelvis and abdomen, making it more challenging to treat and often fatal. Historically, natural products and their structural analogues have played a pivotal role in pharmacotherapy, especially for cancer. Numerous studies have demonstrated the therapeutic potential of Linum usitatissimum against ovarian cancer, but the specific molecular mechanisms remain elusive. This study combines data mining, network pharmacology, and molecular docking analysis to pioneer an innovative approach for ovarian cancer treatment by identifying potent phytochemicals. Findings of current study revealed that Apigenin, Vitamin E, Palmitic acid, Riboflavin, Isolariciresinol, 5-Dehydro-avenasterol, Cholesterol, Pantothenic acid, Nicotinic acid, Campesterol, Beta-Sitosterol, Stigmasterol, Daucosterol, and Vitexin suppress tumor growth by influencing AKT1, JUN, EGFR, and VEGFA. Kaplan–Meier survival analysis spotlighted AKT1, JUN, EGFR, and VEGFA as potential diagnostic and prognostic biomarkers for ovarian cancer. However, it is imperative to conduct in vivo and in vitro examinations to ascertain the pharmacokinetics and biosafety profiles, bolstering the candidacy of L. usitatissimum in ovarian cancer therapeutics.

Identifying Serum Small Extracellular Vesicle MicroRNA as a Noninvasive Diagnostic and Prognostic Biomarker for Ovarian Cancer.

There remains a lack of effective and noninvasive methods for the diagnosis and prognosis prediction of epithelial ovarian carcinoma (EOC). Here, we investigated the possibility of serum-derived small extracellular vesicle (sEV) microRNAs (miRNAs) as potential biomarkers for distinguishing between benign and malignant adnexal masses and predicting the prognosis of EOC patients. A serum sEV miRNA model for identifying the EOC (sEVmiR-EOC) was successfully established in the training cohort. Furthermore, the sEVmiR-EOC model was confirmed in the testing cohort and validation cohort, demonstrating robust diagnostic accuracy. The sEVmiR-EOC model showed better performance than carbohydrate antigen 125 (CA125) in discriminating patients with stage I EOC from benign patients. Using EOC samples and follow-up data, we identified miR-141-3p and miR-200c-3p as potential prognostic predictors. Finally, we confirmed the change of the sEVmiR-EOC RiskScore between the preoperative and postoperative samples and found that the sEVmiR-EOC model could predict the prognosis of EOC patients.

Role of Syndecans in Ovarian Cancer: New Diagnostic and Prognostic Biomarkers and Potential Therapeutic Targets

Ovarian cancer (OC) is the eighth cancer both in prevalence and mortality in women and represents the deadliest female reproductive cancer. Due to generally vague symptoms, OC is frequently diagnosed only at a late and advanced stage, resulting in high mortality. The tumor extracellular matrix and cellular matrix receptors play a key role in the pathogenesis of tumor progression. Syndecans are a family of four transmembrane heparan sulfate proteoglycans (PG), including syndecan-1, -2, -3, and -4, which are dysregulated in a myriad of cancers, including OC. Many clinicopathological studies suggest that these proteins are promising diagnostic and prognostic biomarkers for OC. Furthermore, functions of the syndecan family in the regulation of cellular processes make it an interesting pharmacological target for anticancer therapies.

Combination of PARPi and anti-PD-L1 therapies in ovarian cancer

Ovarian cancer affects one in 72 female patients in America, and nearly half of the females who carry a BRCA1/2 mutation will be diagnosed in their lifetime. At present, treatment options such as immune checkpoint inhibitors (ICI) and poly (ADP-ribose) polymerase inhibitors (PARPi) are effective against ovarian cancer in a subset of the population. In this article, we review important combination therapeutics that maximize the benefits of these agents in as many patients as possible. PARPi targets deoxyribonucleic acid (DNA) repair mechanisms inside malignant cells, inducing cell death through synthetic lethality. ICIs target immunogenic antigens expressed on the surface of malignant cells so that the immune system can eliminate cancer cells. There is a direct relationship between the degree of DNA damage, also known as the tumor mutational burden and the effectiveness of ICIs. This principle suggests that treatments combining PARPi and ICI may allow DNA damage to accumulate by interrupting repair mechanisms, which may result in newly expressed antigens that could be targeted by the bolstered immune system. In addition, heat-shock proteins (Hsps) are upregulated during cellular stress, such as the stress elicited by the immense metabolic demand of cancer cells. Hsp has potential as prognostic biomarkers, and further, study is required to see how they interact with treatment options. More specifically, both Hsp60 and Hsp10 may represent a prognostic biomarker for ovarian cancer, and further, research into their mechanisms is important. ICI and PARPi combinatorial therapies for ovarian cancer may extend the benefits of each drug to a larger population, and Hsp represents an opportunity for predicting outcomes and tracking responses in cancer patients.

SPON1 is an independent prognostic biomarker for ovarian cancer

BackgroundOvarian cancer has the worst outcome among gynecological malignancies; therefore, biomarkers that could contribute to the early diagnosis and/or prognosis prediction are urgently required. In the present study, we focused on the secreted protein spondin-1 (SPON1) and clarified the prognostic relevance in ovarian cancer.MethodsWe developed a monoclonal antibody (mAb) that selectively recognizes SPON1. Using this specific mAb, we determined the expression of SPON1 protein in the normal ovary, serous tubal intraepithelial carcinoma (STIC), and ovarian cancer tissues, as well as in various normal adult tissues by immunohistochemistry, and verified its clinicopathological significance in ovarian cancer.ResultsThe normal ovarian tissue was barely positive for SPON1, and no immunoreactive signals were detected in other healthy tissues examined, which was in good agreement with data obtained from gene expression databases. By contrast, upon semi-quantification, 22 of 242 ovarian cancer cases (9.1%) exhibited high SPON1 expression, whereas 64 (26.4%), 87 (36.0%), and 69 (28.5%) cases, which were designated as SPON1-low, possessed the moderate, weak, and negative SPON1 expression, respectively. The STIC tissues also possessed SPON1-positive signals. The 5-year recurrence-free survival (RFS) rate in the SPON1-high group (13.6%) was significantly lower than that in the SPON1-low group (51.2%). In addition, high SPON1 expression was significantly associated with several clinicopathological variables. Multivariable analysis revealed that high SPON1 was an independent prognostic factor for RFS of ovarian cancer.ConclusionsSPON1 represents a prognostic biomarker for ovarian cancer, and the anti-SPON1 mAb could be valuable as an outcome predictor.

Pan-Cancer Analysis Reveals PPRC1 as a Novel Prognostic Biomarker in Ovarian Cancer and Hepatocellular Carcinoma.

Background and Objectives: As is well understood, peroxisome proliferator-activated receptor gamma cofactor-related 1 (PPRC1) plays a central role in the transcriptional control of the mitochondrial biogenesis and oxidative phosphorylation (OXPHOS) process, yet its critical role in pan-cancer remains unclear. Materials and Methods: In this paper, the expression levels of PPRC1 in different tumor tissues and corresponding adjacent normal tissues were analyzed based on four databases: The Genotype-Tissue Expression (GTEx), Cancer Cell Line Encyclopedia (CCLE), The Cancer Genome Atlas (TCGA), and Tumor Immune Estimation Resource (TIMER). Meanwhile, the prognostic value of PPRC1 was inferred using Kaplan-Meier plotter and forest-plot studies. In addition, the correlation between PPRC1 expression and tumor immune cell infiltration, immune checkpoints, and the tumor-stemness index was analyzed using TCGA and TIMER databases. Results: According to our findings, the expression level of PPRC1 was found to be different in different cancer types and there was a positive correlation between PPRC1 expression and prognosis in several tumor types. In addition, PPRC1 expression was found to be significantly correlated with immune cell infiltration, immune checkpoints, and the tumor-stemness index in both ovarian and hepatocellular carcinoma. Conclusions: PPRC1 demonstrated promising potential as a novel biomarker in pan-cancer due to its potential association with immune cell infiltration, expression of immune checkpoints, and the tumor-stemness index.

Abstract 5406: A deep learning enabled digital pathology platform for characterization of cancer-associated thrombosis of ovarian cancer

Abstract Purpose: Cancer-associated thrombosis (CAT), characterized by activation, extravasation, and aggregation of platelets in tumor microenvironment, is a common complication observed in ovarian cancer patients. A third of ovarian cancer patients have elevated platelet counts that are associated with poor prognosis. However, the exact mechanism by which platelets preferentially extravasate into tumor microenvironment is unknown, partly owing to lack of tools that can accurately segment and measure platelet counts found in tumor tissues. In this study, we hypothesize that tumor secreted SDF-1 interacts with CXCR4 receptors on platelets and initiates platelet chemotaxis towards tumor microenvironment. We developed a digital pathology (DP) platform with automated whole tissue confocal imaging and deep learning (DL) model that can be used to automatically segment out platelets and calculate platelet density with high precision and consistency in the mouse ovarian cancer tissue sections for supporting hypothesis evaluation. Methods: A DL algorithm to accurately segment out platelets was developed based on the U-Net structure using our original datasets from the murine ovarian cancer model. Tumor-bearing mice were injected with 5 mg/kg of Plerixafor, a CXCR4 inhibitor, daily for 4 weeks. Then, tumor nodules were resected, stained with CD41a antibody, and imaged using the whole slide confocal microscopy. Using these high-resolution images, we performed manual annotation of platelets to generate the mask dataset and performed further data augmentation to increase the diversity of datasets. We trained our model using 85% of these images and performed validation using the remaining 15%. Results: Using our DLDP platform, the validation dataset achieved pixel-wise accuracy of 98.7%. A receiver operating characteristic (ROC) curve demonstrated an area under the ROC curve (AUC) of 0.99 with high sensitivity and specificity. Intersection over union (IoU) score of 0.724 was obtained. A statistically significant (p=0.0467), 7.03% reduction in the platelet density was shown in the Plerixafor-treated mouse ovarian tumor tissues (n=8) compared to the control tumor tissues (n=9). Note that the traditional threshold method was not able to find the statistical significance (p=0.0966) in the same dataset. Conclusions: We developed a DLDP platform to segment and quantitate tens of thousands of platelets from whole slide mouse ovarian cancer tissues automatically with high accuracy and consistency. Applying the DLDP platform, we demonstrated that treatment with Plerixafor leads to overall reduction in platelet infiltration within ovarian murine tumors. With high-throughput, accurate, and automated detection of platelets, our DLDP platform can serve as a powerful tool for characterization of CAT by evaluation of platelet density as a potential prognostic biomarker for ovarian cancer. Citation Format: Ju Young Ahn, Wendolyn Carlos-Alcalde, Matthew Vasquez, Min Soon Cho, Vahid Afshar-Kharghan, Stephen T. Wong. A deep learning enabled digital pathology platform for characterization of cancer-associated thrombosis of ovarian cancer. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5406.

Identifying ITGB2 as a Potential Prognostic Biomarker in Ovarian Cancer.

Epithelial ovarian cancer is by far the most lethal gynecological malignancy. The exploration of promising immunomarkers to predict prognosis in ovarian cancer patients remains challenging. In our research, we carried out an integrated bioinformatic analysis of genome expressions and their immune characteristics in the ovarian cancer microenvironment with validation in different experiments. We filtrated 332 differentially expressed genes with 10 upregulated hub genes from the Gene Expression Omnibus database. These genes were closely related to ovarian tumorigenesis. Subsequently, the survival and immune infiltration analysis demonstrated that the upregulation of five candidate genes, ITGB2, VEGFA, CLDN4, OCLN, and SPP1, were correlated with an unfavorable clinical outcome and increased immune cell infiltration in ovarian cancer. Of these genes, ITGB2 tended to be the gene most correlated with various immune cell infiltrations and had a strong correlation with significant M2 macrophages infiltration (r = 0.707, p = 4.71 × 10-39), while it had a moderate correlation with CD4+/CD8+ T cells and B cells. This characteristic explains why the high expression of ITGB2 was accompanied by immune activation but did not reverse carcinogenesis. Additionally, we confirmed that ITGB2 was over-expressed in ovarian cancer tissues and was mainly located in cytoplasm, detected by Western blotting and the immunohistochemical method. In summary, ITGB2 may serve as a prognostic immunomarker for ovarian cancer patients.

Molecular mechanisms of miR-1236 in the assessment of tumor lymphangiogenesis in human ovarian cancer patients.

Tumor lymphangiogenesis is a critical component in the progression of cancers and specific microRNAs have been reported to be implicated in this process. Recent studies revealed the involvement of miR-1236 in lymphangiogenic signaling by targeting vascular endothelial growth factor receptor 3 (VEGFR3). However, the prognostic importance of miR-1236 and its clinical relevance for lymphangiogenesis in ovarian cancer (OC) remains unclear. The study included 52 ovarian tumors and 28 normal ovarian tissues. Quantitative real-time PCR was utilized to analyze the VEGFR3, VEGF-C, LYVE-1 and PROX1 mRNA expression as well as miR-1236. VEGFR3 protein expression was measured by immunohistochemistry staining. Immunohistochemistry for the podoplanin marker (D2-40) was performed to measure lymphatic vessel density (LVD). In addition, diagnostic evaluation based on the receiver-operating characteristic (ROC) curve was performed. The influence of miR-1236 on overall survival was evaluated by Kaplan-Meier method. Here, we show that miR-1236 expression was significantly decreased in ovarian tumors compared with control tissues (p < 0.001) and correlated with advanced clinical stage, lymph node metastasis, distant metastasis and patient survival (All P < 0.05). Moreover, in ovarian tumors, LVD as well as the gene expression of VEGFR3, VEGF-C and LYVE-1, but not PROX1, were found to be remarkably higher compared with control tissues. We also detected a more robust positive staining for VEGFR3 in OC tissues than in control tissues. Furthermore, our results demonstrated an inverse association of miR-1236 expression with LVD, VEGFR3, LYVE-1 and PROX1 expression in OC tissues. The ROC curve analysis indicated that miR-1236 expression has the potential to be used as a diagnostic and prognostic biomarker in OC. Survival analysis further verified a lowered overall survival rate in patients with low miR-1236 expression than in those with high expression. Our results provide evidence for the translational involvement of miR-1236 in the lymphangiogenesis of OC by regulating lymphangiogenesis-related factors and support the clinical importance of miR-1236 as a new diagnostic and prognostic biomarker for OC.

P-Element-Induced Wimpy Testis Proteins and P-Element-Induced Wimpy Testis-Interacting RNAs Expression in Ovarian Cancer Stem Cells.

Background: P-element-induced wimpy testis (PIWI)-interacting RNAs (piRNAs) are a type of noncoding RNA and are predominantly expressed in germline cells. piRNAs function as gene regulators and potential biomarkers for the development of a number of malignancies. The biological importance of piRNAs in ovarian cancer is still unknown. In this study, we investigated the expression of piRNAs in ovarian cancer stem cells and compared it with that in adherent cells. Methods: To assess changes in the expression levels of PIWIL1/HIWI, PIWIL2/HILI, PIWIL3, and PIWIL4/HIWI2, we used quantitative reverse-transcription polymerase chain reaction (RT-qPCR) analysis. Changes in piRNA expression levels in ovarian cancer stem cells were analyzed using Arraystar piRNA microarray screening. Gene Ontology (GO) enrichment analysis was conducted to determine the potential functions of piRNAs. Results: Using microarray analysis, we identified a cohort of differentially expressed piRNAs. Fifteen piRNAs, including DQ570763 and DQ597396, were downregulated, and 58 piRNAs were upregulated when compared with those in adherent A2780 and SKOV3 cells (p > 0.05, >2.0, respectively). GO functions of the downregulated piRNAs (DQ570763 and DQ570797) suggest that their roles are commonly associated with the Golgi apparatus. In addition, A2780-SP and SKOV3-SP cells had higher PIWIL3 and PIWIL4 mRNA levels than adherent cells (A2780 and SKOV3). Moreover, we determined, using receiver operating characteristic plot, that the expression level of PIWIL4 was lower in responders than in nonresponders after treatment with platins in patients with ovarian cancer. Finally, in ovarian cancer, PIWIL4 expression was associated with somatic mutations of dynein axonemal heavy chain 2, signal induced proliferation associated 1 like 2, YTH N6-methyladenosine RNA-binding protein 1, TBC1 domain family member 8, and LPS responsive Beige-like anchor protein. Conclusion: Our study showed that PIWI proteins and piRNAs are potential diagnostic and prognostic biomarkers for ovarian cancer.

Systemic inflammation factors as survival prognosis markers in ovarian neoplasm and the relationship with cancer-associated inflammatory mediators-a review.

At the level of the genital system, ovarian neoplasm is the most frequent cause of morbidity and mortality. In the specialized literature, the coexistence of an inflammatory process is admitted from the early stages of the evolution of this pathology. Starting from the importance of this process, both in determinism and in the evolution of carcinogenesis and summarizing the field of knowledge, for this study we considered two objectives: the first was the presentation of the pathogenic mechanism, through which chronic +ovarian inflammation is involved in the process of carcinogenesis, and the second is the justification of the clinical utility of the three parameters, accepted as biomarkers of systemic inflammation: neutrophil-lymphocyte ratio, platelet lymphocyte ratio, and lymphocyte-monocyte ratio in the assessment of prognosis. The study highlights the acceptance of these hematological parameters, with practical utility, as prognostic biomarkers in ovarian cancer, based on the intrinsic link with cancer-associated inflammatory mediators. Based on the data from the specialized literature, the conclusion is that in ovarian cancer, the inflammatory process induced by the presence of the tumor, induces changes in the types of circulating leukocytes, with immediate effects on the markers of systemic inflammation.

Integrative multi-omics and machine learning approach reveals tumor microenvironment-associated prognostic biomarkers in ovarian cancer

Integrative multi-omics and machine learning approach reveals tumor microenvironment-associated prognostic biomarkers in ovarian cancer