Prognosis, Immune Microenvironment Infiltration and Immunotherapy Response in Clear Cell Renal Cell Carcinoma Based on Cuproptosis-related Immune Checkpoint Gene Signature.

Background: Immune checkpoint genes (ICGs), which are the cornerstone of immunotherapy, influence the incidence and progression of clear cell renal cell carcinoma (ccRCC). It is important to note that there is not much data in the literature to determine how cuproptosis and antitumor immunity are related. Methods: On the basis of The Cancer Genome Atlas ccRCC dataset (n=526), cuproptosis-related ICGs (CICGs) were used to identify distinct molecular subtypes. Using the Cox regression method, a risk signature was constructed and externally validated using the ICGC (n=91) and primary ccRCC subgroups of GSE22541 (n=24). The molecular and immune characteristics and efficacy of immunotherapy in the subgroups defined by the risk score were investigated. Four risk CICGs were verified through in vitro experiment. Results: We identified two unique molecular subgroups with substantial prognostic differences based on 17 CICGs. The two subtypes clearly differ in terms of the tumor immune microenvironment (TME). A predictive risk signature (CD276, HLA-E, LGALS9, and TNFRSF18) was created and externally confirmed, and their expressions were validated by realtime PCR. The multivariate Cox regression analysis demonstrated that this signature could independently predict survival. Thus, a credible nomogram incorporating the signature, age, stage, and grade was constructed, and discrimination was confirmed using the C-index, calibration curve, and decision curve analyses. The underlying implications for immune checkpoint inhibitors, the landscape of the TME, and single-cell level localization are depicted. In addition, its accuracy in forecasting actual immunotherapeutic results has been proven (CheckMate025 and TCGA-SKCM cohorts). The sensitivity of the two risk groups to various drug-targeted therapy methods was analyzed. Conclusions: The data provided here provide the groundwork for creating customized therapeutic options for individuals with ccRCC. The findings also suggested that researching the cuproptosis-based pathway might improve ccRCC patient better prognosis, development of anti-tumor immunity, and therapeutic strategies for immunotherapy.

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.

Coordination of Single-cell and Bulk RNA Sequencing to Construct a Cuproptosis-related Gene Prognostic Index for Endometrial Cancer Prognosis, Immune Microenvironment Infiltration, and Immunotherapy Treatment Options.

Background: This study aims to identify molecular subtypes and develop a cuproptosis-related gene prognostic index (CRGPI) for endometrial cancer (EC), in addition to outlining the immune features and the efficacy of immune checkpoint inhibitor (ICI) therapy in CRGPI-defined groups of EC. Methods: Between malignant and normal cells distinguished from single-cell RNA sequencing data GSE154763 dataset, the difference in KEGG pathways and cuproptosis-related gene (CRG) scores was intensively investigated. On the basis of TCGA dataset (n=492), CRGs were used to identify two distinct molecular subtypes. Using the Cox regression method, a CRGPI was constructed and externally validated with the IMvigor210 dataset (n=348) and GSE78220. Then, the molecular and immune characteristics and the efficacy of ICI therapy in subgroups defined by CRGPI were investigated. Results: Compared to normal cells, the expression of the TCA cycle and CRGs genes was significantly higher in malignant cells. The CRGPI was established on the premise of ATF5, FBXO46, P2RX4, SMARCD3, DAPK3, and C1orf53. Comprehensive results demonstrated a correlation between a low CRGPI score and better overall survival, younger age, early stage, immune cells and functions activation, high tumor mutation burden and high microsatellite instability, as well as better benefit from ICI therapy, and its significance for forecasting immunotherapeutic effects was verified as well (IMvigor210 cohort: HR, 1.358; 95% CI, 1.047, 1.761; p=0.02; GSE78220 cohort: HR, HR = 3.857, 95% CI, 1.009, 14.74; p=0.034). CRGPI anticipated the immunotherapy medication. Conclusions: CRGPI is a prospective biomarker to estimate the prognosis, immune and molecular characteristics, and treatment benefit of immunotherapy in EC.

Identification of a ferroptosis-related long non-coding RNA signature for prognosis prediction of ovarian cancer.

Ovarian cancer is one of the deadliest malignant tumors. Ferroptosis is closely related to various cancers, including ovarian cancer, but the genes involved in regulating ferroptosis in ovarian cancer are still unclear. The aim of this study is to construct a long non-coding RNA (lncRNA) signature related to ferroptosis and evaluate its relationship with the prognosis and clinicopathological characteristics of patients with ovarian cancer. In this study, a prognostic risk model comprising 18 lncRNAs related to ferroptosis was obtained. Compared to the low-risk group, the high-risk group based on the FerRLSig score had significantly poorer overall survival (P < 0.001). The receiver operating characteristics curve supported the accuracy of the model, established a prognostic nomogram combining FerRLSig and clinical characteristics, and showed a good prognosis and survival risk stratification predictive power. In addition, Gene Set Enrichment Analysis (GSEA) showed that FerRLSig was involved in many malignant tumor-related immunomodulatory pathways. Based on the risk model, we found that immune status and immunotherapy, chemotherapy, and targeted therapy were significantly different between the high-risk and low-risk groups. This study provided a more in-depth understanding of the molecular and signaling pathways of ferroptosis in ovarian cancer and showed the impact of tumor microenvironment on ovarian cancer, as well as provided a prognostic model for ovarian cancer patients to guide the clinical treatment of ovarian cancer.

HERPUD1 promotes ovarian cancer cell survival by sustaining autophagy and inhibit apoptosis via PI3K/AKT/mTOR and p38 MAPK signaling pathways.

HERPUD1 is an important early marker of endoplasmic reticulum stress (ERS) and is involved in the ubiquitination and degradation of several unfolded proteins. However, its role in tumorigenesis is seldom studied, and its role in ovarian cancer is unclear. Lewis y antigen is a tumor-associated sugar antigen that acts as an 'antenna' on the cell surface to receive signals from both inside and outside the cell. We previously reported that Lewis y can promote ovarian cancer by promoting autophagy and inhibiting apoptosis. In this study, we detect the expression of HERPUD1 and Lewis y antigens in 119 different ovarian cancer tissues, determine their relationship with clinicopathological parameters, analyze the correlation between these two proteins, and explore the related cancer-promoting mechanisms through MTT, flow cytometry, western blotting, and bioinformatics. HERPUD1 is highly expressed in ovarian cancer, especially in the early stage, and the expression of HERPUD1 and Lewis y antigen was positively correlated. After overexpression of Lewis y antigen, the expression level of HERPUD1 increased. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathways (KEGG) analysis showed that HERPUD1 and its related genes are enriched in regulating immunity, endoplasmic reticulum stress, ubiquitin-dependent degradation, ERS-induced apoptosis, and other key signaling pathways. We also clarified the HERPUD1 network of kinases, microRNA and transcription factor targets, and the impact of HERPUD1 mutations on prognosis. In addition, HERPUD1 promotes the proliferation of ovarian cancer cells, inhibits apoptosis, affects the cell cycle, promotes the occurrence of autophagy, and inhibits EMT and PI3K/AKT/mTOR and p38MAPK pathways. Overall, HERPUD1, regulated by the expression of tumor-associated protein Lewis y, promotes cell survival in the early stages of tumors, suggesting that HERPUD1 may play an important role in the development of ovarian cancer.

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.

Identification and clinical validation of NUSAP1 as a novel prognostic biomarker in ovarian cancer.

BACKGROUND: Nucleolar and spindle-associated protein 1 (NUSAP1) was shown to be involved in cell cycle regulation in cancer. However, its prognostic value and underlying mechanism in ovarian cancer remain unclear. METHODS: Oncomine, TCGA, CCLE, and UALCAN databases were used to analyze the expression level of NUSAP1 in ovarian cancer. The Kaplan-Meier plotter database was used to evaluate its prognostic value. The results from these analyses were further validated using immunohistochemical assay. The potential molecular mechanism of NUSAP1 in ovarian cancer was assessed with respect to homologous recombination repair, mismatch repair, and immunology using different databases. RESULTS: Database analyses and experimental results demonstrated that NUSAP1 was highly expressed in ovarian cancer, its levels being correlated with the FIGO stage. High NUSAP1 expression was an independent risk factor affecting the prognosis of patients with epithelial ovarian cancer. Moreover, NUSAP1 was associated with cell cycle, DNA replication, homologous recombination, and p53 signaling pathway. A positive correlation was identified between the expression of NUSAP1 and BRCA1/2 in ovarian cancer. In addition, NUSAP1 was associated with the expression of DNA mismatch repair genes and immune cell infiltration. CONCLUSIONS: NUSAP1 may be a valuable prognostic marker, as well as a novel biomarker for evaluating the response to immunotherapy of patients with ovarian cancer.

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.

Interaction between TMEFF1 and AHNAK proteins in ovarian cancer cells: Implications for clinical prognosis.

TMEFF1 is a newly discovered protein involved in the physiological functions of the central nervous system, embryonic development, and other biological processes. Our previous study revealed that TMEFF1 acts as a tumor-promoting gene in ovarian cancer. AHNAK, as a giant scaffolding protein, plays a role in the formation of the blood-brain barrier, cell architecture and the regulation of cardiac calcium channels. However, its role in ovarian cancer remains poorly researched. In this study, we detected the expression of AHNAK and TMEFF1 in 148 different ovarian cancer tissues, determined their relationship with pathological parameters and prognosis, clarified the interaction between the two proteins, and explored the related cancer-promoting mechanisms through immunohistochemistry, immunoprecipitation, immunofluorescence double staining, western blotting, and bioinformatics. The high expression of ANHAK and TMEFF1 in ovarian cancer indicated a higher degree of tumor malignancy and a worse prognosis. Furthermore, the expression of TMEFF1 and AHNAK was significantly positively correlated. The results also showed that AHNAK and TMEFF1 co-localized and interacted with each other in ovarian cancer tissues and cells. And knockdown of AHNAK promoted proliferation, migration and invasion of ovarian cancer cells in vitro. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses showed that AHNAK and related genes were enriched during mitosis regulation, cytoskeleton formation, gene epigenetics, etc., whereas TMEFF1 and related genes are enriched during immune regulation and other processes. We also clarified the network of kinases, microRNA, and transcription factor targets, and the impact of genetic mutations on prognosis. Notably, AHNAK was regulated by the expression of TMEFF1 and can activate the MAPK pathways. Overall, high expression of AHNAK and TMEFF1 in ovarian cancer cells indicated a higher degree of tumor malignancy and a worse prognosis. Therefore, the interaction between AHNAK and TMEFF1 may become a potential anti-tumor target for ovarian cancer treatment.

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.

Human Epididymis Protein 4 and Lewis y Enhance Chemotherapeutic Resistance in Epithelial Ovarian Cancer Through the p38 MAPK Pathway.

INTRODUCTION: Ovarian cancer has a high mortality rate due to difficulties in early detection and chemotherapy resistance. Human epididymal protein 4 (HE4) has been adopted as a novel serum biomarker for early ovarian cancer diagnosis, and the presence of Lewis y antigen modifications on HE4 in ovarian cancer cell lines has been detected in previous studies. The aim of this study was to analyze the expression of HE4 and Lewis y antigen in human ovarian cancer in order to find a correlation between them, as well as with the clinical pathological parameters of patients with ovarian cancer. METHODS: Immunohistochemistry was used to detect the respective expression of these compounds in two patient groups (chemotherapy-resistant and chemotherapy-sensitive) containing a total of 95 patients. Then, a bioinformatic approach was adopted and online large sample databases (TCGA, CCLE, and GTEx; Metascape, Cytoscape) were used to explore the potential mechanisms of action of these compounds. RESULTS: The results of this study demonstrate that high HE4 and Lewis y expression could be used as markers for chemotherapy resistance and poor prognosis in patients with ovarian cancer. These two expression events were widely correlated in various cancer tissues and are thought to act by activating the p38 mitogen-activated protein kinases (MAPK) pathway and inducing Vascular Endothelial Growth Factor A (VEGFA), Prostaglandin-Endoperoxide Synthase 2 (PTGS2), Early Growth Response 1 (EGR1), and Hypoxia-Inducible Factor 1-Alpha (HIFI1A), thereby promoting malignant biological behavior and resistance in ovarian cancer. CONCLUSIONS: These findings not only reveal the possible mechanism by which HE4 and Lewis y antigen affect ovarian cancer but also identify a four-gene signature that could be very useful in ovarian cancer detection and/or the development of new targeted therapies.

Nomogram for predicting postoperative cancer-specific early death in patients with epithelial ovarian cancer based on the SEER database: a large cohort study.

PURPOSE: Ovarian cancer is a common gynecological malignant tumor. Poor prognosis is strongly associated with early death, but there is no effective tool to predict this. This study aimed to construct a nomogram for predicting cancer-specific early death in patients with ovarian cancer. METHODS: We used data from the Surveillance, Epidemiology, and End Results database of patients with ovarian cancer registered from 1988 to 2016. Important independent prognostic factors were determined by univariate and multivariate logistic regression and LASSO Cox regression. Several risk factors were considered in constructing the nomogram. Nomogram discrimination and calibration were evaluated using C-index, internal validation, and receiver operating characteristic (ROC) curves. RESULTS: A total of 4769 patients were included. Patients were assigned to the training set (n = 3340; 70%) and validation set (n = 1429; 30%). Based on the training set, eight variables were shown to be significant factors for early death and were incorporated in the nomogram: American Joint Committee on Cancer (AJCC) stage, residual lesion size, chemotherapy, serum CA125 level, tumor size, number of lymph nodes examined, surgery of primary site, and age. The concordance indices and ROC curves showed that the nomogram had better predictive ability than the AJCC staging system and good clinical practicability. Internal validation based on validation set showed good consistency between predicted and observed values for early death. CONCLUSION: Compared with predictions made based on AJCC stage or residual lesion size, the nomogram could provide more robust predictions for early death in patients with 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.

Overexpression of TMEFF1 in Endometrial Carcinoma and the Mechanism Underlying its Promotion of Malignant Behavior in Cancer Cells.

Background: Although tomoregulin-1 (TMEFF1) is involved in embryonic development and central nervous system regulation and is a cancer suppressor gene in brain cancers, its role in endometrial carcinoma remains unclear. Methods: The expression and prognostic value of TMEFF1 were analyzed by bioinformatics methods and immunohistochemistry. An endometrial carcinoma cell line with low expression of TMEFF1 was constructed. Scratch and Transwell assays were used to determine the effect of TMEFF1 on cell invasion and migration. Changes in key proteins in the MAPK and PI3K/AKT signaling pathways and in epithelial-mesenchymal transition (EMT)-related proteins were analyzed using western blot. Chromatin immunoprecipitation assay (ChIP) was performed to identify whether the TMEFF1 promoter region binds to the transcription factor p53. Results: TMEFF1 was significantly upregulated in endometrial carcinoma, was closely associated with FIGO stage (P=0.021) and lymph node metastasis (P=0.029), and was an independent risk factor for prognosis (P=0.044). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses showed that TMEFF1 and its related genes are involved in the cell cycle, regulation of mitosis, epigenetics, neural development, cell biological signal transduction and some key signal pathways. We also identified kinases, microRNAs and a transcription factor network related to TMEFF1 and the effect of TMEFF1 mutation on prognosis. In vitro knockdown of TMEFF1 significantly inhibited cell invasion and migration. Knockdown of TMEFF1 inhibited Epithelial-mesenchymal transition (EMT) and activation of the MAPK and PI3K/AKT pathways. However, the transcription factor p53 was not found to regulate the TMEFF1 gene. Conclusion: TMEFF1 plays an important role in endometrial carcinoma and may thus be a potential anticancer therapeutic target for endometrial carcinoma.

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.

Human epididymis protein 4 promotes P­glycoprotein­mediated chemoresistance in ovarian cancer cells through interactions with Annexin II.

The aim of the present study was to investigate the effects of human epididymis protein 4 (HE4) on drug resistance and its underlying mechanisms. The associations among proteins were detected by immunoprecipitation and immunofluorescence assays. Then, stably transfected cell lines CAOV3­HE4­L and CAOV3­A2­L expressing HE4 short hairpin (sh)RNAs and ANXA2 shRNAs, respectively, were constructed. MTT assay, immunocytochemistry, western blotting, reverse transcription­quantitative polymerase chain reaction (RT­qPCR) and flow cytometry were employed to examine drug sensitivity, as well as the expression and activity of P­glycoprotein (P­gp). HE4 and P­gp in epithelial ovarian cancer tissues were assessed via immunohistochemistry. MicroRNAs that targeted the P­gp gene, ABCB1, were predicted using bioinformatics methods, and their expression was evaluated by RT­qPCR. The common signaling pathways shared by HE4, ANXA2 and P­gp were selected by Gene Set Enrichment Analysis (GSEA). The interaction of HE4, ANXA2 and P­gp were confirmed. P­gp expression was positively associated with HE4 and ANXA2 expression, respectively. Moreover, it was observed that there was no significant rescue of P­gp expression in CAOV3­A2­L cells following the administration of active HE4 protein. In addition, the expression of HE4 and P­gp in ovarian cancer tissues of drug­resistant patients were higher compared with that of the drug­sensitive group (P<0.05). Furthermore, the results revealed that hsa­miR­129­5p was significantly increased accompanied by decreased HE4 or ANXA2 expression and P­gp expression in CAOV3­HE4­L and CAOV3­A2­L cells. GSEA analyses disclosed that HE4, ANXA2 and P­gp genes were commonly enriched in the signaling pathway involved in regulating the actin cytoskeleton. These results indicated that HE4 promotes P­gp­mediated drug resistance in ovarian cancer cells through the interactions with ANXA2, and the underlying mechanism may be associated with decreased expression of hsa­miR­129­5p and dysregulation of the actin cytoskeleton signaling pathway.

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.

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.

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).