Gene Expression Subtyping Reveals Immune alterations:TCGA Database for Prognosis in Ovarian Serous Cystadenocarcinoma.
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ABSTRACT:
SUBMITTER: Feng C
PROVIDER: S-EPMC8497788 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundOvarian cancer is one of the leading causes of female deaths worldwide. Ovarian serous cystadenocarcinoma occupies about 90% of it. Effective and accurate biomarkers for diagnosis, outcome prediction and personalized treatment are needed urgently.MethodsGene expression profile for OSC patients was obtained from the TCGA database. The ESTIMATE algorithm was used to calculate immune scores and stromal scores of expression data of ovarian serous cystadenocarcinoma samples. Survival results between high and low groups of immune and stromal score were compared and differentially expressed genes (DEGs) were screened out by limma package. The Gene Ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and the protein-protein interaction (PPI) network analysis were performed with the g:Profiler database, the Cytoscape and Search Tool for the Retrieval of Interacting Genes (STRING-DB). Survival results between high and low immune and stromal score groups were compared. Kaplan-Meier plots based on TCGA follow up information were generated to evaluate patients' overall survival.ResultsEighty-six upregulated DEGs and one downregulated DEG were identified. Three modules, which included 49 nodes were chosen as important networks. Seven DEGs (VSIG4, TGFBI, DCN, F13A1, ALOX5AP, GPX3, SFRP4) were considered to be correlated with poor overall survival.ConclusionSeven DEGs (VSIG4, TGFBI, DCN, F13A1, ALOX5AP, GPX3, SFRP4) were correlated with poor overall survival in our study. This new set of genes can become strong predictor of survival, individually or combined. Further investigation of these genes is needed to validate the conclusion to provide novel understanding of tumor microenvironment with ovarian serous cystadenocarcinoma prognosis and treatment.
| S-EPMC8103916 | biostudies-literature
Project description:BackgroundOvarian serous cystadenocarcinoma (OSCC) is a life-threatening malignancy with poor prognosis. Therefore, the identification of immune-related genes associated with OSCC prognosis may reveal new targets of immunotherapy for OSCC.Patients and methodsThe gene expression profiles of overlapped genes were extracted by weighted gene co-expression network analysis (WGCNA) to identify immune-related modules. Significant genes were identified by univariate Cox regression analysis of model genes. Model characteristic genes were obtained by least absolute shrinkage and selection operator (LASSO) analysis and used to calculate a "signature index". The model's ability to predict prognosis in OSCC patients was assessed using time-dependent receiver operator characteristic curves. Differences in the biological processes and Kyoto Encyclopedia of Genes and Genomes pathways between groups with high or low signature index were assessed using gene set enrichment analysis (GSEA). The types of immune cells and their abundance in the two index groups were explored by single-sample GSEA.ResultsThe expression profiles of 3517 overlapped genes were extracted by WGCNA, and nine modules related to the immune system of OSCC were obtained. The expression profiles of 114 hub genes were then subjected to LASSO analysis. Among them, 10 immune-related genes were significant, of which six were identified as model characteristic genes and were used to calculate the signature index. Moreover, 24 types of immune cells were identified in the tumor microenvironment, and their abundance was explored in high- and low-signature index groups of two datasets.ConclusionARHGEF18, PLEKHA7, MTOR, VPS45, BRCA1, and HINT2 were identified as characteristic genes and used to develop a new immune-related gene-based signature as a promising prognostic biomarker for OSCC.
| S-EPMC8331220 | biostudies-literature
Project description:TCGA Analysis of RNA Expression for Ovarian Serous Cystadenocarcinoma Using Affymetrix HT_HG-U133A
2016-06-04 | GSE82191 | GEO
Project description:TCGA Analysis of RNA Expression for Glioblastoma Multiforme Using Affymetrix HT_HG-U133A
2015-05-08 | GSE68661 | GEO
Project description:Ovarian cancer (OV) is a common type of carcinoma in females. Many studies have reported that ferroptosis is associated with the prognosis of OV patients. However, the mechanism by which this occurs is not well understood. We utilized Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) to identify ferroptosis-related genes in OV. In the present study, we applied Cox regression analysis to select hub genes and used the least absolute shrinkage and selection operator to construct a prognosis prediction model with mRNA expression profiles and clinical data from TCGA. A series of analyses for this signature was performed in TCGA. We then verified the identified signature using International Cancer Genome Consortium (ICGC) data. After a series of analyses, we identified six hub genes (DNAJB6, RB1, VIMP/ SELENOS, STEAP3, BACH1, and ALOX12) that were then used to construct a model using a training data set. The model was then tested using a validation data set and was found to have high sensitivity and specificity. The identified ferroptosis-related hub genes might play a critical role in the mechanism of OV development. The gene signature we identified may be useful for future clinical applications.
| S-EPMC8169824 | biostudies-literature
Project description:BackgroundOvarian serous cystadenocarcinoma (OSC) is the most common and lethal gynecological cancer in women worldwide; however, biomarkers to diagnose and predict prognosis of OSC remain limited. Therefore, the present study aimed to investigate whether sodium/potassium adenosine triphosphate (Na+/K+-ATP)ase α-subunits (ATP1As) are helpful diagnostic and prognostic markers of OSC.MethodsGene expression data (RNA-Seq) of 376 patients with OSC were downloaded from The Cancer Genome Atlas (TCGA) program database. Additional databases used in our analysis included the Gene Expression Omnibus, International Cancer Genome Consortium, Genotype-Tissue Expression, the Human Protein Atlas, cBioPortal for Cancer Genomics, and Cancer Cell Line Encyclopedia.ResultsThe expression levels of ATP1A1 and ATP1A3 were higher in OSC tissues than in normal ovarian tissues, whereas the expression levels of ATP1A2 and ATP1A4 were lower in OSC tissues than in normal ovarian tissues. Overexpression of ATP1A2 was significantly associated with a higher Federation of Gynecology and Obstetrics (FIGO) stage and histological grade. Increased mRNA expression of ATP1A3 was significantly associated with shorter overall survival (OS) and disease-specific survival (DSS) in patients with OSC, whereas higher expression of ATP1A4 was associated with favorable OS and DSS. Multivariate analysis showed that primary therapy outcome, residual tumor, and mRNA expressions of ATP1A3 and ATP1A4 were independent prognostic factors for both OS and DSS in patients with OSC. Moreover, ATP1A1 staining was abundant in tumor tissues. A high expression of ATP1A3 was significantly correlated with poor OS and DSS in the subgroup of patients aged ≥ 60 years and with FIGO stage III, histological grade G3, and TP53 mutation. Mutation frequencies of the ATP1As were 3-5%.ConclusionsThese results indicate that the ATP1A gene family could be potential diagnostic or prognostic markers of OSC. In addition, ATP1As may be effective therapeutic targets in the treatment of OSC.
| S-EPMC7362554 | biostudies-literature
Project description:Epithelial ovarian cancer is morphologically and clinically heterogeneous. Transcriptional profiling has revealed molecular subtypes (referred to as “C-signatures”) that correlate to biological as well as clinical features. We aimed to determine gene expression differences between malignant, benign and borderline serous ovarian tumors, and to investigate similarities to the intrinsic molecular subtypes of breast cancer. Global gene expression profiling was performed using Illumina's HT12 Bead Arrays and applied to 59 fresh-frozen ovarian tumors. SAM analysis revealed enrichment of cell cycel processes among the malignant tumors, in line with malignant tumors being highly proliferative. The borderline tumors were split between the malignant and benign tumor clusters, indicating that borderline tumors have both malignant and benign features. Furthermore, nearest centroid classification was performed applying previously published gene profiles for the ovarian cancer C-signatures and the intrinsic breast cancer subtypes, respectively, and showed significant correlations between the malignant serous tumors and the highly aggressive C1, C2 and C4 ovarian cancer signatures, and the basal-like breast cancer subtype. The benign and borderline serous tumors together were significantly correlated to the normal-like breast cancer subtype and the ovarian cancer C3 signature. The borderline tumors, on the other hand, correlated significantly to the Luminal A breast cancer subtype. These findings remained when analyzed in a large, independent dataset. The data in this study link the transcriptional profiles of serous ovarian cancer to the intrinsic molecular subtypes of breast cancer, in line with the shared clinical and molecular features between high-grade serous ovarian cancer and basal-like breast cancer, including an aggressive phenotype, frequent TP53 mutations and a high degree of genomic instability, and suggest that biomarkers and targeted therapies may overlap between these subsets of ovarian and breast cancers. Finally, the link between benign and borderline ovarian cancer and luminal breast cancer may indicate endocrine responsiveness in a subset of ovarian cancers. Total RNA obtained from serous ovarian adenocarcinomas, adenomas and borderline tumors. Gene expression profiling using Illumina's HT12 v4 bead arrays. Application of ovarian cancer molecular subtypes and intrinsic breast cancer subtypes using nearest centroid classification. KRAS and BRAF mutation analyses in the malignant and borderline tumors.
| S-ECPF-GEOD-57477 | biostudies-other
Project description:BackgroundOvarian serous cystadenocarcinoma is one of the most serious gynecological malignancies. Circular RNA (circRNA) is a type of noncoding RNA with a covalently closed continuous loop structure. Abnormal circRNA expression might be associated with tumorigenesis because of its complex biological mechanisms by, for example, functioning as a microRNA (miRNA) sponge. However, the circRNA expression profile in ovarian serous cystadenocarcinoma and their associations with other RNAs have not yet been characterized. The main purpose of this study was to reveal the circRNA expression profile in ovarian serous cystadenocarcinoma.MethodsWe collected six specimens from three patients with ovarian serous cystadenocarcinoma and adjacent normal tissues. After RNA sequencing, we analyzed the expression of circRNAs with relevant mRNAs and miRNAs to characterize potential function.Results15,092 unique circRNAs were identified in six specimens. Approximately 46% of these circRNAs were not recorded in public databases. We then reported 353 differentially expressed circRNAs with oncogenes and tumor-suppressor genes. Furthermore, a conjoint analysis with relevant mRNAs revealed consistent changes between circRNAs and their homologous mRNAs. Overall, construction of a circRNA-miRNA network suggested that 4 special circRNAs could be used as potential biomarkers.ConclusionsOur study revealed the circRNA expression profile in the tissues of patients with ovarian serous cystadenocarcinoma. The differential expression of circRNAs was thought to be associated with ovarian serous cystadenocarcinoma in the enrichment analysis, and co-expression analysis with relevant mRNAs and miRNAs illustrated the latent regulatory network. We also constructed a complex circRNA-miRNA interaction network and then demonstrated the potential function of certain circRNAs to aid future diagnosis and treatment.
| S-EPMC8638095 | biostudies-literature
Project description:Ovarian serous cystadenocarcinoma is a common malignant tumor of female genital organs. Treatment is generally less effective as patients are usually diagnosed in the late stage. Therefore, a well-designed prognostic marker provides valuable data for optimizing therapy. In this study, we analyzed 303 samples of ovarian serous cystadenocarcinoma and the corresponding RNA-seq data. We observed the correlation between gene expression and patients' survival and eventually established a risk assessment model of five factors using Cox proportional hazards regression analysis. We found that the survival time in high-risk patients was significantly shorter than in low-risk patients in both training and testing sets after Kaplan-Meier analysis. The AUROC value was 0.67 when predicting the survival time in testing set, which indicates a relatively high specificity and sensitivity. The results suggest diagnostic and therapeutic applications of our five-gene model for ovarian serous cystadenocarcinoma.
| S-EPMC4949334 | biostudies-literature
Project description:ObjectiveTranscriptional profiling of epithelial ovarian cancer has revealed molecular subtypes correlating to biological and clinical features. We aimed to determine gene expression differences between malignant, benign and borderline serous ovarian tumors, and investigate similarities with the well-established intrinsic molecular subtypes of breast cancer.MethodsGlobal gene expression profiling using Illumina's HT12 Bead Arrays was applied to 59 fresh-frozen serous ovarian malignant, benign and borderline tumors. Nearest centroid classification was performed applying previously published gene profiles for the ovarian and breast cancer subtypes. Correlations to gene expression modules representing key biological breast cancer features were also sought. Validation was performed using an independent, publicly available dataset.Results5,944 genes were significantly differentially expressed between benign and malignant serous ovarian tumors, with cell cycle processes enriched in the malignant subgroup. Borderline tumors were split between the two clusters. Significant correlations between the malignant serous tumors and the highly aggressive ovarian cancer signatures, and the basal-like breast cancer subtype were found. The benign and borderline serous tumors together were significantly correlated to the normal-like breast cancer subtype and the ovarian cancer signature derived from borderline tumors. The borderline tumors in the study dataset, in addition, also correlated significantly to the luminal A breast cancer subtype. These findings remained when analyzed in an independent dataset, supporting links between the molecular subtypes of ovarian cancer and breast cancer beyond those recently acknowledged.ConclusionsThese data link the transcriptional profiles of serous ovarian cancer to the intrinsic molecular subtypes of breast cancer, in line with the shared clinical and molecular features between high-grade serous ovarian cancer and basal-like breast cancer, and suggest that biomarkers and targeted therapies may overlap between these tumor subsets. The link between benign and borderline ovarian cancer and luminal breast cancer may indicate endocrine responsiveness in a subset of ovarian cancers.
| S-EPMC4166462 | biostudies-literature