A methylation-driven genes prognostic signature and the immune microenvironment in epithelial ovarian cancer.

Aberrant gene methylation has been implicated in the development and progression of tumors. In this study, we aimed to identity methylation-driven genes involved in epithelial ovarian cancer (EOC) to establish a prognostic signature for patients with EOC. We identified and verified 6 MDGs that are closely related to the prognosis of ovarian cancer. A prognostic risk score model and nomogram for predicting the prognosis of ovarian cancer were constructed based on the six MDGs. It can also effectively reflect the immune environment and immunotherapy response of ovarian cancer. These MDGs have great significance to the implementation of individualized treatment and disease monitoring of ovarian cancer patients.

Molecular subtypes based on cuproptosis-related genes and tumor microenvironment infiltration characterization in ovarian cancer.

BACKGROUND: Cuproptosis (copper death) is a recently found cell death type produced by copper iron; nonetheless, the properties of cuproptosis molecular subtypes and possible involvement of cuproptosis-related genes (CRGs) in the tumor microenvironment (TME) in ovarian cancer (OC) remain unknown. METHODS: CRG changes were characterized at the genomic and transcriptional levels in 656 OC samples, and their expression patterns were investigated using three different datasets. RESULTS: We identified three distinct molecular subtypes, and discovered that variations in molecular subtypes were linked to patient prognosis, TME cell infiltration characteristics, malignancy, and immune-related pathways. Then, in order to predict overall survival (OS), we created a risk score and tested its predictive potential in OC patients. As a result, we created a very accurate nomogram to increase risk score clinical applicability. Better OS, younger age, early stage, and immune activity were all associated with a low risk score. The hallmarks of a high-risk score are older age, advanced stage, immunosuppression, and a bad prognosis. Furthermore, risk score was linked to immune checkpoint expression (including PD-L1, CTLA4), targeted therapy gene expression (PARP, PDGFRA), cancer stem cell (CSC), chemotherapy and targeted medication sensitivity. CONCLUSIONS: Our comprehensive analysis of CRGs in OC showed their potential role in TME, clinicopathological characteristics, chemotherapy and targeted drug screening and prognosis. These discoveries could help us better understand CRGs in OC, as well as pave the path for novel ways to assess prognosis and design more effective immunotherapy strategies.

Retrospective analysis of clinicopathological features and prognosis for aggressive angiomyxoma of 27 cases in a tertiary center: a 14-year survey and related literature review.

PURPOSE: Aggressive angiomyxoma (AAM) is a rare and often misdiagnosed tumor that is characterized by frequent local recurrences. This study aimed to investigate the clinicopathological characteristics, surgical experiences, and prognosis for aggressive angiomyxoma to improve the accuracy of diagnosis and develop treatment strategies for decreasing recurrence rates. METHODS: Clinicopathological data and follow-up information for 27 patients with AAM diagnosed and treated at the Shengjing Hospital of China Medical University between January 2006 and October 2019 were retrospectively analyzed. RESULTS: The median age at disease onset among 27 patients was 39 years. The male to female ratio was 1:4.4. Painless and slow-growing mass was the most common symptom. Masses occurring in the perineum and pelvic cavity accounted for 81.5% (22/27). All of the 27 patients underwent surgical treatment. Surgical approaches included transperineal and transvaginal resection. Large pelvic masses were treated with combined abdominoperineal surgery. The postoperative recurrence rate was 37%. Kaplan-Meier survival analysis revealed that 5-year progression-free survival (PFS) rate was 64.4% and the median PFS was 132.0 ± 29.6 (95% CI 72.9-190.1). Multivariate Cox proportional analysis found that surgical margin is an independent prognostic factor for PFS (P = 0.018). None of the patients experienced distant metastasis. CONCLUSIONS: Clinical manifestations of AAM are non-specific. Laboratory testing, imaging examinations, and immunohistochemistry are helpful for diagnosis and differential diagnosis. Surgical approach can be determined according to the relationship between the tumor and adjacent organs and infiltration degree. The development of personalized treatment strategies should aim to achieve a complete resection on the premise of preserving the structure and function of important organs to maintain patient quality of life.

Identification of a pyroptosis-related long non-coding RNA Signature for prognosis and its related ceRNA regulatory network of ovarian cancer.

Aim: To identify the pyroptosis-related long non-coding RNAs (lncRNAs) in ovarian cancer and construct a prognostic signature based on them. Methods: Expression data from TCGA was used to explore differentially expressed pyroptosis-related lncRNAs in ovarian cancer. A risk signature was established by LASSO and cox regression analysis and then validated. Databases such as ESTIMATE, CIBERSORT, TIMER, XCELL were used to identify the relation between this signature and the immune microenvironment of ovarian cancer. Gene Set Enrichment Analysis was introduced to identify the pathways and functions that the signature may participate in. Based on miRcode and starBase databases, microRNAs related to the lncRNAs in our signature and the positively co-expressed pyroptosis- related genes were screened and a competing endogenous RNA (ceRNA) network was then constructed. Quantitative reverse transcription PCR was conducted to validate the expression levels of two lncRNAs in this ceRNA network. Results: A 13 pyroptosis-related lncRNA prognostic signature (MYCNOS, AL161772.1, USP30-AS1, ZNF32-AS2, AC068733.3, AC012236.1, AC015802.5, KIAA1671-AS1, AC013403.2, MIR223HG, KRT7-AS, PTPRD-AS1 and LINC01094) was constructed. Patients in high-risk group had a significantly worse prognosis than that of low-risk (P<0.0001). Immune infiltration analysis found that patients identified as high-risk had a higher infiltration of macrophages and tumor-associated fibroblasts. Further pathway analysis revealed that the signature may be involved in epithelial mesenchymal transition, extracellular matrix receptor interaction, and focal adhesion. Finally, a competitive endogenous inhibition relationship was discovered between LINC01094, KRT7-AS, MYCNOS, ZNF32-AS2, AC012236.1 and pyroptosis- related genes such as IRF1, NOD1, GSDMC, NLRP1, PLCG1, GSDME and GZMB, in which LINC01094 and KRT7-AS were found to be overexpressed in three ovarian cancer cell lines. Conclusion: We constructed a pyroptosis-related lncRNA signature and correlate it to the immune microenvironment. A ceRNA regulatory network related to pyroptosis was also constructed, which provides novel insights useful for the study of pyroptosis in ovarian cancer.

Nomograms for Predicting Overall Survival and Cancer-Specific Survival of Young Patients with Epithelial Ovarian Cancer: Analysis Based on SEER Program.

INTRODUCTION: Currently, there is no clinical prediction model for young patients (≤ 45 years old) with epithelial ovarian cancer (EOC) based on large samples of clinical data. The purpose of this study was to construct nomograms using data extracted from the Surveillance, Epidemiology, and End Results (SEER) Program to predict the overall survival (OS) and cancer-specific survival (CSS) of patients and to further guide the choice of clinical treatment options. METHODS: Data from a total of 6376 young patients with EOC collected from 1998 to 2016 were selected from the SEER database. These patients were randomly divided (7:3) into a training cohort (n = 4465) and a validation cohort (n = 1911). Cox and least absolute shrinkage and selection operator (LASSO) analyses were used to select the prognostic factors affecting OS and CSS, and the nomograms of OS and CSS were established. The performance of the nomogram models was assessed by C-index, area under the curve (AUC), calibration curves, and decision curve analysis (DCA). Sample were chosen from patients who underwent surgery in Shengjing Hospital to set external validation. Kaplan-Meier curves were plotted to compare survival outcomes between subgroups. RESULTS: Nomograms showed good predictive power and clinical practicality. The internal and external validation indicated better performance of the nomograms than the American Joint Committee on Cancer (AJCC) staging system and tumor grade system. Significant differences were observed in the survival curves of different risk subgroups. CONCLUSIONS: We constructed predictive nomograms to evaluate the OS and CSS of young patients with EOC. The nomograms will provide an individualized evaluation of OS and CSS for suitable treatment of young patients with EOC.

A Risk Signature with Nine Stemness Index-Associated Genes for Predicting Survival of Patients with Uterine Corpus Endometrial Carcinoma.

PURPOSE: To identify mRNA expression-based stemness index- (mRNAsi-) related genes and build an mRNAsi-related risk signature for endometrial cancer. METHODS: We collected mRNAsi data of endometrial cancer samples from The Cancer Genome Atlas (TCGA) and analyzed their relationship with the main clinicopathological characteristics and prognosis of endometrial cancer patients. We screened the top 50% of the genes in TCGA for weighted gene correlation network analysis (WGCNA) to explore mRNAsi-related gene sets. Among these mRNAsi-related genes, we further screened for those related to the prognosis of endometrial cancer patients via univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis. Using stepwise multivariate Cox regression analysis, a stemness index-related risk signature was constructed. Finally, we identified potential prognostic biomarkers for endometrial cancer by combining the GEO database and immunohistochemical staining. RESULTS: The mRNAsi of endometrial cancer samples was significantly higher than that of normal samples and was related to the International Federation of Gynecology and Obstetrics (FIGO) stage, pathological grade, postoperative tumor status, and overall survival of endometrial cancer patients. We identified 21 mRNAsi-related gene modules, and 1,324 genes were obtained from the most relevant module. TCGA samples were divided into training and validation cohorts, and the training cohort was used to construct a nine-mRNAsi-related gene signature (B3GAT2, CD3EAP, DMC1, FRMPD3, LINC01224, LINC02068, LY6H, NR6A1, and TLE2). High-risk and low-risk patients had significant prognostic differences, and the risk signature could accurately predict their 1-, 3-, and 5-year survival. The nomogram composed of risk score and multiple clinicopathological features could accurately predict 1-, 3-, and 5-year survival. Finally, CD3EAP was found to be a novel prognostic biomarker for endometrial cancer. CONCLUSION: Endometrial cancer cell stemness is related to patient prognosis. The nine-gene risk signature is an independent prognostic factor and can accurately predict endometrial cancer patient prognosis.

A Novel Clinical Nomogram for Predicting Cancer-Specific Survival in Adult Patients After Primary Surgery for Epithelial Ovarian Cancer: A Real-World Analysis Based on the Surveillance, Epidemiology, and End Results Database and External Validation in a Tertiary Center.

BACKGROUND: The present study aimed to construct and validate a nomogram that can be used to predict cancer-specific survival (CSS) in patients with epithelial ovarian cancer (EOC). METHODS: A total of 7,129 adult patients with EOC were extracted from the Surveillance, Epidemiology, and End Results database between 2010 and 2015. Patients were randomly divided into the training and validation cohorts (7:3). Cox regression was conducted to evaluate prognostic factors of CSS. The internal validation of the nomogram was performed using concordance index (C-index), AUC, calibration curves, and decision curve analyses (DCAs). Data from 53 adult EOC patients at Shengjing Hospital of China Medical University from 2008 to 2012 were collected for external verification. Kaplan-Meier curves were plotted to compare survival outcomes among risk subgroups. RESULTS: Age, grade, histological types, stage, residual lesion size, number of regional lymph nodes resected, number of positive lymph nodes, and chemotherapy were independent risk factors for CSS. Based on the above factors, we constructed a nomogram. The C-indices of the training cohort, internal validation cohort, and external verification group were 0.763, 0.750, and 0.920, respectively. The calibration curve indicated good agreement between the nomogram prediction and actual survival. AUC and DCA results indicated great clinical usefulness of the nomogram. The differences in the Kaplan-Meier curves among different risk subgroups were statistically significant. CONCLUSIONS: We constructed a nomogram to predict CSS in adult patients with EOC after primary surgery, which can assist in counseling and guiding treatment decision making.

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

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

MRPL15 is a novel prognostic biomarker and therapeutic target for epithelial ovarian cancer.

PURPOSE: To analyze the role of six human epididymis protein 4 (HE4)-related mitochondrial ribosomal proteins (MRPs) in ovarian cancer and selected MRPL15, which is most closely related to the tumorigenesis and prognosis of ovarian cancer, for further analyses. METHODS: Using STRING database and MCODE plugin in Cytoscape, six MRPs were identified among genes that are upregulated in response to HE4 overexpression in epithelial ovarian cancer cells. The Cancer Genome Atlas (TCGA) ovarian cancer, GTEX, Oncomine, and TISIDB were used to analyze the expression of the six MRPs. The prognostic impact and genetic variation of these six MRPs in ovarian cancer were evaluated using Kaplan-Meier Plotter and cBioPortal, respectively. MRPL15 was selected for immunohistochemistry and GEO verification. TCGA ovarian cancer data, gene set enrichment analysis, and Enrichr were used to explore the mechanism of MRPL15 in ovarian cancer. Finally, the relationship between MRPL15 expression and immune subtype, tumor-infiltrating lymphocytes, and immune regulatory factors was analyzed using TCGA ovarian cancer data and TISIDB. RESULTS: Six MRPs (MRPL10, MRPL15, MRPL36, MRPL39, MRPS16, and MRPS31) related to HE4 in ovarian cancer were selected. MRPL15 was highly expressed and amplified in ovarian cancer and was related to the poor prognosis of patients. Mechanism analysis indicated that MRPL15 plays a role in ovarian cancer through pathways such as the cell cycle, DNA repair, and mTOR 1 signaling. High expression of MRPL15 in ovarian cancer may be associated with its amplification and hypomethylation. Additionally, MRPL15 showed the lowest expression in C3 ovarian cancer and was correlated with proliferation of CD8+ T cells and dendritic cells as well as TGFßR1 and IDO1 expression. CONCLUSION: MRPL15 may be a prognostic indicator and therapeutic target for ovarian cancer. Because of its close correlation with HE4, this study provides insights into the mechanism of HE4 in ovarian cancer.

Clinical Nomograms for Predicting the Overall Survival and Cancer-specific Survival of patients with Ovarian Carcinosarcoma patients after Primary Surgery.

Background: At present, there is no clinical prediction model for ovarian carcinosarcoma (OCS) that is based on a large sample of real data. This study aimed to construct nomograms using data extracted from the Surveillance, Epidemiology, and End Results (SEER) database that can be used to predict the overall survival (OS) and cancer-specific survival (CSS) of patients with OCS and further guide the choice of clinical treatment. Methods: We selected 2753 cases of OCS from the SEER database from 1998 to 2016. Patients were randomly divided in a 7:3 ratio into a training cohort (n = 1929) and a validation cohort (n = 824). Cox analysis was used to select prognostic factors for OS and CSS, and nomograms were then established. The performance of nomogram models was assessed using the concordance index, the area under the receiver operating characteristic curve, calibration curves, and by decision curve analysis. Data from 21 OCS patients at Shengjing Hospital from 2001 to 2021 were collected for external verification. Kaplan-Meier curves were plotted to compare survival outcomes between subgroups. Results: Nomograms based on independent prognostic factors showed good predictive power and clinical practicality. Internal and external validation indicated that the nomograms performed better than staging and grading systems. Significant differences were observed in the survival curves of different risk subgroups. Conclusions: The developed nomograms will enable individualized evaluation of the OS and CSS, thus guiding the treatment of patients with OCS.

Multiomics analysis identifies key genes and pathways related to N6-methyladenosine RNA modification in ovarian cancer.

Aims: To explore the pathways and target genes related to N6-methyladenosine (m6A) methylation in ovarian cancer and their effect on patient prognosis. Methods & materials: The Cancer Genome Atlas was used to screen genes related to m6A regulators in terms of gene expression, mutation and copy number variation. These genes were subjected to pathway enrichment analysis. Prognosis-related genes were screened and involved in risk signature construction. Immunohistochemistry was used for verification. Results: We obtained 1408 genes dysregulated in parallel to m6A regulators, which were mainly involved in the platelet activation pathway. The m6A-related signature was constructed based on the expression of four prognosis-related genes (RPS6KA2, JUNB, HNF4A and P2RX1). Conclusion: This work provides new insights into the mechanism of m6A methylation in ovarian cancer.

Lay abstract N6-methyladenosine (m⁶A) methylation is the most common type of modification on mRNA. m⁶A methylation can affect the biological function of cells by affecting the protein expression level of mRNA. The process of m⁶A modification is controlled by many m⁶A regulators, which are dysregulated in ovarian cancer. Our research aims to screen the genes that are related to m⁶A regulation to analyze targets and mechanisms in ovarian cancer. We screened 1408 m⁶A-related genes, which are mainly involved in the platelet activation pathway. Among them, RPS6KA2 and JUNB were significantly related to poor prognosis of patients with ovarian cancer. RPS6KA2 was positively correlated with the m⁶A regulator METTL3 in ovarian cancer. Our study provides a basis for future mechanism studies.

Decreased PTGDS Expression Predicting Poor Survival of Endometrial Cancer by Integrating Weighted Gene Co-Expression Network Analysis and Immunohistochemical Validation.

PURPOSE: To identify key pathogenic genes and reveal the potential molecular mechanisms of endometrial cancer (EC) using bioinformatics analysis and immunohistochemistry validation. MATERIALS AND METHODS: Through weighted gene co-expression network analysis (WGCNA), a co-expression network was constructed based on the top 25% variant genes in the GSE50830 dataset downloaded from gene expression omnibus (GEO). GO and KEGG pathway enrichment analyses were performed using the DAVID online tool. Candidate genes were selected using the cytoHubba plug-in of Cytoscape, mRNA expression levels and prognostic values in EC were analyzed by Oncomine, GEPIA, and Kaplan-Meier Plotter database to determine hub genes. One hub gene was validated by immunohistochemical (IHC) staining of 116 paraffin-embedded endometrial tissues and TCGA-UCEC cohort. Genes co-expressed with this hub gene were identified by LinkedOmics. Finally, its correlation with immune infiltration was evaluated by TIMER. RESULTS: Three co-expression modules and five candidate genes in each module were obtained by WGCNA; four hub genes were identified (LGR5, SST, ZNF558, and PTGDS). The mRNA levels of LGR5 and SST were significantly upregulated in EC, whereas those of ZNF558 and PTGDS were significantly downregulated; the expression of all four genes was associated with EC prognosis. Further validation demonstrated that PTGDS was significantly downregulated in the EC group compared with the atypical hyperplasia and normal endometrial groups, and its low expression was an independent risk factor for worse prognosis of EC. Biological function analysis indicated that PTGDS might be involved in the adaptive immune response, leukocyte migration, as well as in the regulation of cell adhesion molecules and chemokine signaling. Additionally, PTGDS expression was positively correlated with immune infiltration status of B cells, CD4+ T cells and macrophages. CONCLUSION: LGR5, SST, ZNF558, and PTGDS may participate in the development, progression, and prognosis of EC, in which PTGDS may be a novel biomarker and therapeutic target for EC.