Prognostic Marker In Ovarian Cancer Research Articles

Identification of telomere-related lncRNAs and immunological analysis in ovarian cancer.

Ovarian cancer (OC) is a global malignancy characterized by metastatic invasiveness and recurrence. Long non-coding RNAs (lncRNAs) and Telomeres are closely connected with several cancers, but their potential as practical prognostic markers in OC is less well-defined. Relevant mRNA and clinical data for OC were sourced from The Cancer Genome Atlas (TCGA) database. The telomere-related lncRNAs (TRLs) prognostic model was established by univariate/LASSO/multivariate regression analyses. The effectiveness of the TRLs model was evaluated and measured via the nomogram. Additionally, immune infiltration, tumor mutational load (TMB), and drug sensitivity were evaluated. We validated the expression levels of prognostic genes. Subsequently, PTPRD-AS1 knockdown was utilized to perform the CCK8 assay, colony formation assay, transwell assay, and wound healing assay of CAOV3 cells. A six-TRLs prognostic model (PTPRD-AS1, SPAG5-AS1, CHRM3-AS2, AC074286.1, FAM27E3, and AC018647.3) was established, which can effectively predict patient survival rates and was successfully validated using external datasets. According to the nomogram, the model could effectively predict prognosis. Furthermore, we detected the levels of regulatory T cells and M2 macrophages were comparatively higher in the high-risk TRLs group, but the levels of activated CD8 T cells and monocytes were the opposite. Finally, the low-risk group was more sensitive to anti-cancer drugs. The mRNA levels of PTPRD-AS1, SPAG5-AS1, FAM27E3, and AC018647.3 were significantly over-expressed in OC cell lines (SKOV3, A2780, CAOV3) in comparison to normal IOSE-80 cells. AC074286.1 were over-expressed in A2780 and CAOV3 cells and CHRM3-AS2 only in A2780 cells. PTPRD-AS1 knockdown decreased the proliferation, cloning, and migration of CAOV3 cells. Our study identified potential biomarkers for the six-TRLs model related to the prognosis of OC.

Investigating the efficacy of tissue factor pathway inhibitor‑2 as a promising prognostic marker for ovarian cancer.

Tissue factor pathway inhibitor-2 (TFPI2) is a tumor marker for diagnosing ovarian cancer and ovarian clear cell carcinoma (OCCC); however, its effectiveness as a prognostic marker remains unclear. The present study aimed to investigate the utility of TFPI2 as a prognostic marker for ovarian cancer. A total of 256 cases of ovarian cancer was collected at Nara Medical University (Kashihara, Japan) from January 2008 to January 2022. The majority of cases were serous carcinoma (109, 42.6%), followed by OCCC (66, 25.8%), mucinous carcinoma (40, 15.6%), endometrial carcinoma (15, 5.9%), and other (26, 10.2%). The median preoperative serum TFPI2 for ovarian cancer was 219.0 (82.5-5,824.2) pg/ml. Overall survival (OS) of patients with non-OCCC and OCCC was calculated using the cut-off value determined obtained through receiver operating characteristic curve analysis. Cut-off values of TFPI2 for OS were 201 for non-OCCC and 255 pg/ml for OCCC. In univariate analysis, OS was significantly elevated in patients with non-OCCC and OCCC who had TFPI2 levels ≥201 pg/ml (P<0.001) and ≥255 pg/ml (P=0.036), respectively. Progression-free survival (PFS) was significantly elevated in patients with non-OCCC and OCCC who had TFPI2 levels ≥201 and ≥255 pg/ml (both P<0.001), respectively. Multivariate analysis revealed that OS was significantly higher in patients with non-OCCC who had TFPI2 levels ≥201 pg/ml (P=0.021), while PFS was significantly higher in patients with OCCC who had TFPI2 levels ≥255 pg/ml (P=0.020). These findings suggest that TFPI2 is a potential prognostic marker for ovarian carcinoma.

Contribution of sphingomyelin phosphodiesterase acid-like 3B to the proliferation, migration, and invasion of ovarian cancer cells.

Cancer has the highest mortality rate among gynecological cancers and poses a serious threat to women's lives. However, the treatment options for ovarian cancer are still limited, and exploring effective targeted biomarkers is particularly important for predicting and treating ovarian cancer. Therefore, it is necessary to explore the molecular mechanisms of the occurrence and development of ovarian cancer. This investigation encompassed the analysis of gene expression profiles, measurement of transcription levels of potential target genes in peripheral blood samples from ovarian cancer patients and characterization of the ovarian cancer-related secretory protein sphingomyelin phosphodiesterase acid-like 3B (SMPDL3B). Through bioinformatics analysis, potential target genes were identified, and their association with overall survival (OS) and progression-free survival (PFS) in ovarian cancer patients was assessed utilizing relevant databases. Subsequently, differences in target gene expression in ovarian cancer tissue samples were validated through protein blotting and quantitative real-time PCR (qRT-qPCR). Cell proliferation assays using the cell count kit-8 (CCK-8) method, as well as transwell chamber assay and pre coated matrix gel chamber assay were employed to elucidate the role of SMPDL3B in ovarian cancer cell migration and invasion. This study revealed a substantial upregulation of SMPDL3B in the serum of ovarian cancer patients, correlating with an unfavorable prognosis. High SMPDL3B expression was linked not only to increased proliferation of ovarian cancer cells, but also enhanced migration and invasion. Remarkably, the knockdown the human alkaline ceramidase 2 (ACER2) gene in cancer cells with heightened SMPDL3B expression significantly inhibited cell proliferation, migration, and invasion induced by SMPDL3B activation (P<0.05), highlighting the functional interplay between ACER2 and SMPDL3B in ovarian cancer. In summary, this study proposes SMPDL3B as a prognostic marker for ovarian cancer, with implications for potential therapeutic intervention targeting the ACER2-SMPDL3B axis.

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.

Emerging role of m6A modification in ovarian cancer: progression, drug resistance, and therapeutic prospects.

Ovarian Cancer (OC) ranks as a prominent contributor to mortality among female reproductive system associated cancers, particularly the prevalent subtype epithelial Ovarian Cancer (EOC). Despite advancements in treatment modalities, the prognosis for OC patients remains grim due to limitation of current therapeutic methodology such as high cytotoxicity of chemotherapeutic agents and tumor relapse making existing chemotherapy ineffective. Recognizing the limitations of a broad-spectrum approach to treating OC, a shift toward targeted therapies aligning with unique molecular features is imperative. This shift stems from an incomplete understanding of OC's origin, distinguishing it from extensively researched malignancies such as cervical or colon cancer. At the molecular level, postsynthetic modifications-DNA, RNA, and protein-shape transcriptional, posttranscriptional, and posttranslational processes. Posttranscriptional regulatory mechanisms, including RNA modifications are termed epitranscriptomic and play critical roles in this process. For more than five decades, 100+ RNA post-synthetic modifications, notably N6-methyladenosine (m6A), most prevalent RNA modification in mammals, dynamically regulate messenger RNA (mRNA), and non-coding RNA (ncRNA) life orchestrated via writers, erasers, and readers. The disruption of m6A modifications are found in several cancers, including OC, underscores pivotal role of m6A. This review focused on m6A modifications in coding and non-coding RNAs, emphasizing their role as prognostic markers in OC and their impact on development, migration, invasion, and drug resistance. Additionally, RNA-modified regulators have been explored as potential molecular and therapeutic targets, offering an innovative approach to combatting this challenging malignancy.

Correlation between pre-operative platelets count and serum cancer antigen-125 level in epithelial ovarian carcer

Cancer antigen 125 (Ca-125) is currently used as an adjunct to ovarian cancer diagnosis, prognosis, and monitoring. Platelet (PLT) count and Ca-125 levels are both prognostic markers in ovarian cancer that are linked to inflammation and immune evasion.To determine the relationship between pre-operative platelet count and serum Ca-125 level, and their diagnostic accuracy for the prediction of stage IV epithelial ovarian cancer.The study included forty-two women with histologic diagnosis of epithelial ovarian cancer managed at the University of Port Harcourt Teaching Hospital between January 1, 2016, and December 31, 2020. Following informed consent, a data collection form was used to obtain socio-demographic and clinical characteristics. Pretreatment levels of Cancer Antigen 125 (Ca-125) and Platelets count (PLT) were determined from blood samples. The Spearman correlation coefficient was used to determine the relationship between PLT and Ca-125, and the Receiver Operating Characteristic (ROC) curve analysis was used to assess the predictive accuracy of PLT count alone and PLT-Ca-125. The sample median platelet count was 308 (307) x 10/L and median Ca-125 was 286µ/ml (397). Pre-operative platelets count was significantly associated with Ca-125 (rho= 0.28 p-value = 0.03). Ca-125 had a statistically significant relationship with ovarian cancer histology (X2:19.522; p-value 0.001). PLT-Ca-125 (0.51) and PLT only (0.29) had a statistically significant positive correlation with ovarian cancer stage (p 0.001). Since it had an area under the curve (AUC) greater than 0.7, PLT-Ca-125 can be used as a predictive model to correctly stage patients with epithelial ovarian cancer. Ca-125 level (z:-2.24; p-value = 0.025) was significantly associated with thrombocytosis in ovarian cancer patients.Platelet count and Ca-125 levels do correlate in blood samples taken from ovarian cancer patients prior to treatment. Furthermore, PLT-Ca-125 levels could be used to predict advanced stage disease.

CX3CR1 is a potential biomarker of immune microenvironment and prognosis in epithelial ovarian cancer.

Immunotherapy is less efficient for epithelial ovarian cancer and lacks ideal biomarkers to select the best beneficiaries for immunotherapy. CX3CR1 as chemokine receptor mainly expressed on immune cell membranes, and combined with its unique ligand CX3CL1, mediates tissue chemotaxis and adhesion of immune cells. However, the immune functional and prognostic value of CX3CR1 in epithelial ovarian cancer has not been clarified. A comprehensive retrospective analysis was performed by using the online database to identify the underlying immunological mechanisms and prognostic value of CX3CR1. The Human Protein Atlas, gene expression profiling interactive analysis, and TISIDB (an integrated repository portal for tumor-immune system interactions) database showed that CX3CR1 expressed higher in epithelial ovarian cancer than that in normal ovarian tissue. Four hundred twenty-two cases from Gene Expression Profiling Interactive Analysis and 1656 cases from Kaplan-Meier plotter database showed higher expression of CX3CR1 (above median) was associated with unfavorable overall survival. TIMER, UALCAN, and TISIDB database were applied to validate CX3CR1 negative impact on overall survival. In addition, correlation analysis showed that the expression level of CX3CR1 was positive association with infiltrating levels of B cells (R = 0.31, P = 3.10e-12), CD8+ T cells (R = 0.26, P = 7.93e-09), CD4+ T cells (R = 0.11, P = 1.41e-02), macrophages (R = 0.32, P = 4.29e-13), dendritic cells (R = 0.27, P = 2.98e-09), and neutrophil (R = 0.25, P = 3.25e-08) in epithelial ovarian cancer. Therefore, CX3CR1 involved in reshaping the immune microenvironment for epithelial ovarian cancer and maybe a potential immunotherapy target and prognostic marker for ovarian cancer.

Targeting NEAT1 Affects the Sensitivity to PARPi in Serous Ovarian Cancer by Regulating the Homologous Recombination Repair Pathway.

Objective: Patients initially sensitive to PARPi (PARP inhibitor) regain resistance because of homologous recombination (HR) restoration, although PARPi has a synthetic lethality effect on serous ovarian cancer cells with BRCA1/2 mutations. This study aimed to investigate the role of NEAT1 in HR function and whether targeting NEAT1 in serous ovarian cancer cells could increase PARPi sensitivity. Methods: Ovarian cancer patients' clinical information and the expression of NEAT1 were collected from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC). Ovarian cancer (OC) cells HeyA8 and SKOV3 were silenced by transfecting NEAT1 ASO. QRT-PCR confirmed the mRNA expression of RAD51, FOXM1, NEAT1_1 and NEAT1_2. We assessed the expression of RAD51, FOXM1, and γ-H2AX by Western blotting and Immunofluorescence. Comet Assays were used to detect DNA double-strand damage levels. In OC cells transfected with NEAT1 ASO or co-transfected overexpression RAD51/empty vector and si-NEAT1/si-ctrl, the sensitivity to Olaparib was determined using CCK8 assay. The Kaplan-Meier survival curves assessed the prognostic and predictive roles of NEAT1 in OC. Results: NEAT1 was an independent prognostic marker of ovarian cancer. NEAT1 knockdown reduced the expression of NEAT1_1, NEAT1_2, RAD51, and FOXM1 and increased the expression of γ-H2AX. In addition, Olaparib increased the expression of RAD51, representing HR repair efficiency, which was inhibited by NEAT1 knockdown. Moreover, the knockdown of NEAT1 increased the DNA damage caused by Olaparib, demonstrated by increased nuclear γ-H2AX foci, DNA in the tail, and expression of γ-H2AX. NEAT1 knockdown sensitized ovarian cancer cells to Olaparib by targeting RAD51-HR. NEAT1 expression could predict response to chemotherapy for ovarian cancer. Conclusions: NEAT1 knockdown inhibited HR capacity and increased DNA damage caused by Olaparib in serous ovarian cancer cells, making them more sensitive to Olaparib and providing a crucial therapeutic advantage of increasing sensitivity to Olaparib.

ULK2 suppresses ovarian cancer cell migration and invasion by elevating IGFBP3.

Ovarian cancer is an aggressive malignancy with high mortality known for its considerable metastatic potential. This study aimed to explore the expression and functional role of Unc-51 like autophagy activating kinase 2 (ULK2) in the progression of ovarian cancer. ULK2 expression patterns in ovarian cancer tissues as well as benign tumor control samples obtained from our institution were evaluated using immunohistochemistry. Cell counting kit 8 and Transwell assays were applied to assess the effects of ULK2 overexpression on cell proliferation, migration and invasion, respectively. RNA sequencing was performed to explore potential mechanisms of action of ULK2 beyond its classical autophagy modulation. Our experiments showed significant downregulation of ULK2 in ovarian cancer tissues. Importantly, low expression of ULK2 was markedly correlated with decreased overall survival. In vitro functional studies further demonstrated that overexpression of ULK2 significantly suppressed tumor cell proliferation, migration, and invasion. RNA sequencing analysis revealed a potential regulatory role of ULK2 in the insulin signaling pathway through upregulation of insulin-like growth factor binding protein-3 (IGFBP3) in ovarian cancer cells. In summary, the collective data indicated that ULK2 acted as a tumor suppressor in ovarian cancer by upregulating the expression of IGFBP3. Our study underscores the potential utility of ULK2 as a valuable prognostic marker for ovarian cancer.

HADHA promotes ovarian cancer outgrowth via up-regulating CDK1

BackgroundOvarian cancer, a prevalent cause of cancer-related mortality among gynecological cancers, still lacks a clear understanding of its pathogenesis. In this study, our objective was to investigate the functional roles and pathogenic mechanisms of HADHA in ovarian cancer.MethodsWe utilized an ovarian cancer tissue microarray and three ovarian cancer cell lines (HO-8910, A2780, and SK-OV-3) for our analysis. Lentiviral-mediated short hairpin RNA (shRNA) was employed to interfere with HADHA expression in ovarian cancer cells. Various cellular events associated with tumor development were assessed using techniques such as Celigo cell counting assay, wound healing assay, Transwell assay, and flow cytometry analysis. Additionally, xenograft tumor models were developed to visualize the impacts of HADHA/CDK1 on ovarian cancer progression.ResultsOur data revealed significant HADHA overexpression in both ovarian cancer tissues and cell lines. Patients with elevated HADHA levels tended to experience poor survival outcomes. Moreover, HADHA upregulation correlated with several pathological parameters, including pathological stage, tumor size, tumor infiltrate, metastasis, and recurrence. Loss-of-function experiments targeting HADHA demonstrated that its suppression in ovarian cancer cells hindered cell growth and migration, while promoting apoptosis. To elucidate the underlying mechanism by which HADHA regulates ovarian cancer, we identified CDK1 as a target of HADHA. HADHA upregulated CDK1 expression by inhibiting its ubiquitination-dependent proteasomal degradation. Significantly, the overexpression of CDK1 reversed the impaired cell development caused by HADHA depletion, both in vitro and in vivo.ConclusionOur study highlights the involvement of HADHA in ovarian cancer tumorigenesis and suggests its potential as a promising prognostic marker in ovarian cancer. Through its regulation of CDK1, HADHA influences critical cellular processes in ovarian cancer, providing insights into its pathogenic mechanism.

The splicing factor SNRPB promotes ovarian cancer progression through regulating aberrant exon skipping of POLA1 and BRCA2.

Splicing factors play a crucial role in the initiation and development of various human cancers. SNRPB, a core spliceosome component, regulates pre-mRNA alternative splicing. However, its function and underlying mechanism in ovarian cancer remain unclear. This study identified SNRPB as a critical driver of ovarian cancer through TCGA and CPTAC database analysis. SNRPB was highly upregulated in fresh frozen ovarian cancer tissues compared with normal fallopian tubes. Immunohistochemistry revealed that SNRPB expression was increased in formalin-fixed, paraffin-embedded ovarian cancer sections and was positively correlated with a poor prognosis for ovarian cancer. Functionally, SNRPB knockdown suppressed ovarian cancer cell proliferation and invasion, and overexpression exerted opposite effects. SNRPB expression increased after cisplatin treatment, and silencing SNRPB sensitized ovarian cancer cells to cisplatin. KEGG pathway analysis revealed that the differentially expressed genes (DEGs) were mainly enriched in DNA replication and homologous recombination, and almost all DEGs related to DNA replication and homologous recombination were downregulated after SNRPB knockdown according to RNA-seq. Exon 3 skipping of the DEGs DNA polymerase alpha 1 (POLA1) and BRCA2 was induced by SNRPB silencing. Exon 3 skipping of POLA1 yielded premature termination codons and led to nonsense-mediated RNA decay (NMD); exon 3 skipping of BRCA2 led to loss of the PALB2 binding domain, which is necessary for homologous recombination, and increased ovarian cancer cell cisplatin sensitivity. POLA1 or BRCA2 knockdown partially impaired the increased malignancy of SNRPB-overexpressing ovarian cancer cells. Moreover, miR-654-5p was found to reduce SNRPB mRNA expression by directly binding to the SNRPB 3'-UTR. Overall, SNRPB was identified as an important oncogenic driver that promotes ovarian cancer progression by repressing exon 3 skipping of POLA1 and BRCA2. Thus, SNRPB is a potential treatment target and prognostic marker for ovarian cancer.

Pathogenicity of PKCγ Genetic Variants-Possible Function as a Non-Invasive Diagnostic Biomarker in Ovarian Cancer.

Ovarian cancer has the highest mortality rate among gynecologic malignancies, owing to its misdiagnosis or late diagnosis. Identification of its genetic determinants could improve disease outcomes. Conventional Protein Kinase C-γ (PKCγ) dysregulation is reported in several cancers. Similarly, its variant rs1331262028 is also reported to have an association with hepatocellular carcinoma. Therefore, the aim of the present study was to analyze the variant rs1331262028 association with ovarian cancer and to determine its impact on PKCγ's protein interactions. Association of variation was determined through genotyping PCR (cohort size:100). Protein-protein docking and molecular dynamic simulation were carried out to study the variant impact of PKCγ interactions. The study outcome indicated the positive association of variant rs1331262028 with ovarian cancer and its clinicopathological features. Molecular dynamics simulation depicted the potential influence of variation on PKCγ molecular signaling. Hence, this study provided the foundations for assessing variant rs1331262028 as a potential prognostic marker for ovarian cancer. Through further validation, it can be applied at the clinical level.

Prognostic Role of Vascular Endothelial Growth Factor and Correlation with Oxidative Stress Markers in Locally Advanced and Metastatic Ovarian Cancer Patients

Vascular endothelial growth factor (VEGF) plays an important role in tumor progression in ovarian cancer, but the complex mechanism and interaction with oxidative stress are not fully understood. A prospective study included 52 patients with ovarian adenocarcinoma stage IIIA-IV. Serum VEGF and reactive oxygen species (ROS) such as malondialdehyde and ceruloplasmin were measured. VEGF levels were elevated (mean 1014.7 ± 165 pg/mL), especially in patients with macroscopic residual disease (1058 vs. 810 pg/mL, p = 0.0001). Median progression-free survival (PFS) and overall survival (OS) were 6 and 40 months in patients with a very high VEGF (over 1200 pg/mL), 11 and 48 months in patients with VEGF between 1000-1200 pg/mL, 18 and 84 months in patients with VEGF between 800-1000 pg/mL, and not reached in patients with normal VEGF. Increased VEGF values were associated with a 2.6-fold increased risk of disease progression (HR = 2.60, 95% CI 1.69-3.99), and a 1.4-fold increased risk of death (HR = 1.4, 95% CI 1.15-1.91, p = 0.002). Receiver operator characteristic (ROC) curves were used to validate VEGF as a prognostic factor and the area under the curve (AUC) was 0.814, p = 0.036 for PFS and 0.729, p = 0.043, for OS. There was a positive correlation between VEGF and malondialdehyde, Pearson coefficient of 0.35, p = 0.0001. VEGF and malondialdehyde are important prognostic markers in ovarian cancer, especially in macroscopic residual disease, and there is a positive correlation between angiogenesis and oxidative stress.

Mitochondrial AAA protease gene associated with immune infiltration is a prognostic biomarker in human ovarian cancer.

Early diagnosis and identification of prognostic markers for ovarian cancer can significantly improve survival and reduce mortality. The role of the YME1L1 signaling axis in genetic alterations and immune infiltration of the tumor microenvironment remains unclear. Bioinformatics web resources, including GEPIA2, cBioPortal, Oncomine, Kaplan-Meier Plotter, and TIMER, were used to analyze the expression profile, prognostic value, and immune infiltration of YME1L1. We further performed a tissue microarray analysis of paraffin-embedded tissues from 60 patients with ovarian cancer, recorded at the FIGO/TNM cancer staging. Here, we used multi-omics analysis of multiple histological datasets to map the role of epigenetic and genetic alterations of YME1L1 in tumor immune infiltration and the prognosis of cancer patients. We explored YME1L1 gene expression profiles by systematically analyzing the association between YME1L1 expression and the prognosis of patients with ovarian cancer confirmed in multiple databases. High expression levels of YME1L1 were associated with poor overall and disease-free survival. Together, our studies suggest that YME1L1 may modulate tumor survival and immune features and contribute to tumor immune invasion, poor prognosis, and immunotherapy failure. Our findings may have clinical implications for the design of treatment strategies, prognosis assessment, and follow-up management of patients receiving immunotherapy for various cancers.

Identification of Novel Immunologic Checkpoint Gene Prognostic Markers for Ovarian Cancer.

Ovarian cancer has a higher resistance to chemotherapy, displaying the highest mortality rate among gynecological cancers. Recently, immune checkpoint inhibitor therapy is an effective treatment for selected patients. However, a low response rate for immune checkpoint treatment was observed for ovarian cancer patients. Therefore, it is necessary to identify ovarian cancer patients who might gain benefits from immune checkpoint treatment. Datasets containing ovarian cancer samples with mRNA-seq and clinical follow-up data were downloaded from different databases like The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The researchers applied the univariate analysis for selecting the immune checkpoint genes (ICGs) at a significance level of P < 0.05 as the candidate ICGs. The Spearman correlation coefficients were calculated to compare the correlation between tumor mutation burden and candidate ICGs, and the Kaplan-Meier plots were generated. They also assessed the external validation datasets and the results of immunohistochemical staining. 46 and 35 ICGs were extracted from the TCGA and GEO datasets, respectively, and we categorized the ICGs into 3 expression patterns. Nine (TCGA) and three (GEO) ICGs were significantly related to the prognosis. Univariate survival analysis indicated a significant prognostic relationship between the expression levels of ICOS, TIGIT, and TNFRSF8 and overall survival (OS). Moreover, the expression of ICOS and TIGIT also presented a significantly positive relationship with the CD8A expression. Importantly, patients with a higher CD8A and ICOS expression level (ICOS-H/CD8A-H) showed a better survival rate compared to other patients. Stratified analysis using TIGIT, TNFRSF8, and CD8A expression also showed an improved prognosis for the high TIGIT/high CD8A expression subgroup and the low TNFRSF8/low CD8A expression subgroup compared to the other subgroups. This study identified different immune subtypes that can predict the OS of ovarian cancer patients. This data could prove to be beneficial for making important clinical decisions and designing individual immunotherapeutic strategies.

Integration of Transcriptome and Epigenome to Identify and Develop Prognostic Markers for Ovarian Cancer.

DNA methylation is a widely researched epigenetic modification. It is associated with the occurrence and development of cancer and has helped evaluate patients' prognoses. However, most existing DNA methylation prognosis models have not simultaneously considered the changes of the downstream transcriptome. Methods. The RNA-Sequencing data and DNA methylation omics data of ovarian cancer patients were downloaded from The Cancer Genome Atlas (TCGA) database. The Consensus Cluster Plus algorithm was used to construct the methylated molecular subtypes of the ovary. Lasso regression was employed to build a multi-gene signature. An independent data set was applied to verify the prognostic value of the signature. The Gene Set Variation Analysis (GSVA) was used to carry out the enrichment analysis of the pathways linked to the gene signature. The IMvigor 210 cohort was used to explore the predictive efficacy of the gene signature for immunotherapy response. Results. We distinguished ovarian cancer samples into two subtypes with different prognosis, based on the omics data of DNA methylation. Differentially expressed genes and enrichment analysis among subtypes indicated that DNA methylation was related to fatty acid metabolism and the extracellular matrix (ECM)-receptor. Furthermore, we constructed an 8-gene signature, which proved to be efficient and stable in predicting prognostics in ovarian cancer patients with different data sets and distinctive pathological characteristics. Finally, the 8-gene signature could predict patients' responses to immunotherapy. The polymerase chain reaction experiment was further used to verify the expression of 8 genes. Conclusion. We analyzed the prognostic value of the related genes of methylation in ovarian cancer. The 8-gene signature predicted the prognosis and immunotherapy response of ovarian cancer patients well and is expected to be valuable in clinical application.

Exploration of the Immunotyping Landscape and ImmuneInfiltration-Related Prognostic Markers in Ovarian Cancer Patients.

BackgroundIncreasing evidence indicates that immune cell infiltration (ICI) affects the prognosis of multiple cancers. This study aims to explore the immunotypes and ICI-related biomarkers in ovarian cancer.MethodsThe ICI levels were quantified with the CIBERSORT and ESTIMATE algorithms. The unsupervised consensus clustering method determined immunotypes based on the ICI profiles. Characteristic genes were identified with the Boruta algorithm. Then, the ICI score, a novel prognostic marker, was generated with the principal component analysis of the characteristic genes. The relationships between the ICI scores and clinical features were revealed. Further, an ICI signature was integrated after the univariate Cox, lasso, and stepwise regression analyses. The accuracy and robustness of the model were tested by three independent cohorts. The roles of the model in the immunophenoscores (IPS), tumor immune dysfunction and exclusion (TIDE) scores, and immunotherapy responses were also explored. Finally, risk genes (GBP1P1, TGFBI, PLA2G2D) and immune cell marker genes (CD11B, NOS2, CD206, CD8A) were tested by qRT-PCR in clinical tissues.ResultsThree immunotypes were identified, and ICI scores were generated based on the 75 characteristic genes. CD8 TCR pathways, chemokine-related pathways, and lymphocyte activation were critical to immunophenotyping. Higher ICI scores contributed to better prognoses. An independent prognostic factor, a three-gene signature, was integrated to calculate patients’ risk scores. Higher TIDE scores, lower ICI scores, lower IPS, lower immunotherapy responses, and worse prognoses were revealed in high-risk patients. Macrophage polarization and CD8 T cell infiltration were indicated to play potentially important roles in the development of ovarian cancer in the clinical validation cohort.ConclusionsOur study characterized the immunotyping landscape and provided novel immune infiltration-related prognostic markers in ovarian cancer.

RAD21 Confers Poor Prognosis and Affects Ovarian Cancer Sensitivity to Poly(ADP-Ribose)Polymerase Inhibitors Through DNA Damage Repair.

BackgroundPoly(ADP-ribose)polymerase (PARP) inhibitors are a class of molecular-targeted cancer drugs. Synthetic lethality is a phenomenon that renders homologous recombination repair defective cells more sensitive to PARP inhibitors. As a component of the cohesin complex, RAD21 regulates DNA damage repair. However, the biological roles of RAD21 in ovarian cancer and their underlying mechanisms remain unclear.MethodsAn immunohistochemical assay was used to validate the expression of RAD21 in ovarian cancer and its correlation with prognosis. The effects of RAD21 were evaluated through Cell Counting Kit-8 (CCK8), wound-healing, and invasion assays in vitro and the tumor growth in vivo. Furthermore, CCK8 assay and immunofluorescence assay were used to detect the effect of RAD21 on cell sensitivity to PARP inhibitors and their mechanism. The pathway changes were detected by Western blotting.ResultsRAD21 was markedly upregulated in ovarian cancer samples. High RAD21 expression was correlated with poor differentiation and poor prognosis in patients with ovarian cancer. Functionally, RAD21 overexpression promoted cancer cell proliferation, migration, and invasion. Moreover, RAD21 knockdown increased the sensitivity of ovarian cancer cells to three kinds of PARP inhibitors by affecting DNA damage repair. In vivo experiments indicated that RAD21 promoted tumor growth. Mechanistically, the overexpression of RAD21 led to increased phosphorylation levels of Akt and mTOR. Blocking the Akt/mTOR signaling pathway reversed RAD21 overexpression-induced cancer progression and drug resistance.ConclusionsRAD21 can serve as a valuable prognostic marker for ovarian cancer and has the potential as a therapeutic target that can expand the utility of PARP inhibitors.

Diagnostic and prognostic role of TFF3, Romo-1, NF-кB and SFRP4 as biomarkers for endometrial and ovarian cancers: a prospective observational translational study

PurposeThis study aimed to evaluate trefoil factor 3 (TFF3), secreted frizzled-related protein 4 (sFRP4), reactive oxygen species modulator 1 (Romo1) and nuclear factor kappa B (NF-κB) as diagnostic and prognostic markers of endometrial cancer (EC) and ovarian cancer (OC).MethodsThirty-one patients with EC and 30 patients with OC undergone surgical treatment were enrolled together with 30 healthy controls in a prospective study. Commercial ELISA kits determined serum TFF-3, Romo-1, NF-кB and sFRP-4 concentrations.ResultsSerum TFF-3, Romo-1 and NF-кB levels were significantly higher in patients with EC and OC than those without cancer. Regarding EC, none of the serum biomarkers differs significantly between endometrial and non-endometrioid endometrial carcinomas. Mean serum TFF-3 and NF-кB levels were significantly higher in advanced stages. Increased serum levels of TFF-3 and NF-кB were found in those with a higher grade of the disease. Regarding OC, none of the serum biomarkers differed significantly among histological subtypes. Significantly increased serum levels of NF-кB were observed in patients with advanced-stage OC than those with stage I and II diseases. No difference in serum biomarker levels was found between those who had a recurrence and those who had not. The sensibility and specificity of these four biomarkers in discriminating EC and OC from the control group showed encouraging values, although no one reached 70%.ConclusionsTFF-3, Romo-1, NF-кB and SFRP4 could represent new diagnostic and prognostic markers for OC and EC. Further studies are needed to validate our results.

RIPK4 Is an Immune Regulating-Associated Biomarker for Ovarian Cancer and Possesses Generalization Value in Pan-Cancer.

Ovarian cancer (OC) is the most lethal gynecologic cancer. Many studies have reported that RIPK4 (receptor interacting serine/threonine kinase 4) displayed a dysregulated level in many types of tumors. However, its expressions and functions in OC were rarely reported. The levels of RIPK4 were detected in OC and nontumor specimens using TCGA and GEO datasets. The prognostic values of RIPK4 in patients were determined using Kaplan-Meier methods and Kaplan-Meier assays. GO assays and KEGG pathway assays were carried out for functional enrichments. CIBERSORT was applied for estimating the fractions of immune cell types. Finally, RIPK4 was validated in pan-cancer. In this study, our group found that RIPK4 exhibited a higher level of RIPK4 in OC specimens than nontumor specimens. Survival studies revealed that patients with high RIPK4 expressions showed a shorter overall survival than those with low RIPK4 expression. Multivariate assays further confirmed that RIPK4 expression was an independent prognostic element for OC. KEGG pathway analysis displayed that the dysregulated genes in specimens with high RIPK4 expressions were enriched in focal adhesion, proteoglycans in cancer, central carbon metabolism in cancer, and insulin secretion. Correlation analyses showed that several TICs were positively correlated with RIPK4 expression. The pan-cancer validation results showed that RIPK4 was associated with survival in five tumors. Overall, our findings suggested RIPK4 as a prognostic marker in OC.