Gene expression profiling of ovarian carcinomas and prognostic analysis of outcome

Cai, Shengyun; Yang, Tian; Chen, Yu; Wang, Jingwen; Li, L i; Xu, Man

doi:10.1186/s13048-015-0176-9

Cited by 10 publications

(16 citation statements)

References 56 publications

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“…cyclin‐dependent kinase inhibitor 2A ( CDKN2A , also known as ARF ) is frequently mutated or deleted in various tumors and is known to be an important tumor suppressor gene related to different cancer pathways . Interestingly, leucine rich repeat containing eight family ( LRRC8D , member D) was also shown to be one of six prognosis‐related genes by COX regression analysis in ovarian carcinomas . The roles of LRRC8D in cisplatin and taurine transport were reported .…”

Section: Discussionmentioning

confidence: 99%

Identification of a six‐gene signature with prognostic value for patients with endometrial carcinoma

et al. 2018

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Uterine corpus endometrial carcinoma (UCEC) is frequently diagnosed among women worldwide. However, there are different prognostic outcomes because of heterogeneity. Thus, the aim of the current study was to identify a gene signature that can predict the prognosis of patients with UCEC. UCEC gene expression profiles were first downloaded from the The Cancer Genome Atlas (TCGA) database. After data processing and forward screening, 11 390 key genes were selected. The UCEC samples were randomly divided into training and testing sets. In total, 996 genes with prognostic value were then examined by univariate Cox survival analysis with a P‐value <0.01 in the training set. Next, using robust likelihood‐based survival modeling, we developed a six‐gene signature (CTSW, PCSK4, LRRC8D, TNFRSF18, IHH, and CDKN2A) with a prognostic function in UCEC. A prognostic risk score system was developed by multivariate Cox proportional hazard regression based on this six‐gene signature. According to the Kaplan‐Meier curve, patients in the high‐risk group had significantly poorer overall survival (OS) outcomes than those in the low‐risk group (log‐rank test P‐value <0.0001). This signature was further validated in the testing dataset and the entire TCGA dataset. In conclusion, we conducted an integrated study to develop a six‐gene signature for the prognostic prediction of patients with UCEC. Our findings may provide novel biomarkers for prognosis and have significant implications in the understanding of therapeutic targets for UCEC.

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Section: Discussionmentioning

confidence: 99%

Identification of a six‐gene signature with prognostic value for patients with endometrial carcinoma

et al. 2018

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“…In total, 569 samples were collected from TCGA database screened within the module described in our previous study [16]. SPINK13 gene was found to be associated with the prognosis of OC patients by univariate Cox regression analysis.…”

Section: Resultsmentioning

confidence: 99%

“…Samples were collected from TCGA database and screened with the module described in our previous study [16]. The survival information of the patients was screened, and then the samples were divided into two groups according to the expression level of SPINK13.…”

Section: Methodsmentioning

confidence: 99%

“…In our previous study, we established a model to screen differentially expressed genes and identify a set of genes related to the prognosis of OC patients from the Gene Expression Omnibus (GEO) database [15, 16]. In this study, bioinformatics analysis was used to identify whether the SPINK13 gene is associated with the prognosis of OC using TCGA database.…”

Section: Introductionmentioning

confidence: 99%

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Downregulation of SPINK13 Promotes Metastasis by Regulating uPA in Ovarian Cancer Cells

Cai

Zhang

Dong

et al. 2018

Cell Physiol Biochem

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Background/Aims: Ovarian cancer (OC) is the fifth leading cause of cancer-related death in women, and it is difficult to diagnose at an early stage. The purpose of this study was to explore the prognostic biological markers of OC. Methods: Univariate Cox regression analysis was used to identify genes related to OC prognosis from the Cancer Genome Atlas(TCGA) database. Immunohistochemistry was used to analyse the level of SPINK13 in OC and normal tissues. Cell proliferation, apoptosis and invasion were performed using MTT assay, flow cytometric analysis and Transwell assay, respectively. Results: We identified the Kazal-type serine protease inhibitor-13 (SPINK13) gene related to OC prognosis from the Cancer Genome Atlas (TCGA) database by univariate Cox regression analysis. Overexpression of SPINK13 was associated with higher overall survival rate in OC patients. Immunohistochemistry showed that the level of SPINK13 protein was significantly lower in OC tissues than in normal tissues (P < 0.05).In vitro experiments showed that the overexpression of SPINK13 inhibited cellular proliferation and promoted apoptosis. Moreover, SPINK13 inhibited cell migration and epithelial to mesenchymal transition (EMT). SPINK13 was found to inhibit the expression of urokinase-type plasminogen activator (uPA), while recombinant uPA protein could reverse the inhibitory effect of SPINK13 on OC metastasis. Conclusion: These results indicate that SPINK13 functions as a tumour suppressor. The role of SPINK13 in cellular proliferation, apoptosis and migration is uPA dependent, and SPINK13 may be used as a potential biomarker for diagnosis and targeted therapy in OC.

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“…In another study, through weighted correlation network analysis, 3095 differentially expressed genes were identified from genome expression profiles of ovarian cancer patients in early and advanced cancer stages, and 6 prognosis related genes were selected out as novel candidates for clinical biomarkers 23 .…”

Section: Introductionmentioning

confidence: 99%

Differential co-expression analysis reveals a novel prognostic gene module in ovarian cancer

Göv

Arğa

2017

Sci Rep

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Ovarian cancer is one of the most significant disease among gynecological disorders that women suffered from over the centuries. However, disease-specific and effective biomarkers were still not available, since studies have focused on individual genes associated with ovarian cancer, ignoring the interactions and associations among the gene products. Here, ovarian cancer differential co-expression networks were reconstructed via meta-analysis of gene expression data and co-expressed gene modules were identified in epithelial cells from ovarian tumor and healthy ovarian surface epithelial samples to propose ovarian cancer associated genes and their interactions. We propose a novel, highly interconnected, differentially co-expressed, and co-regulated gene module in ovarian cancer consisting of 84 prognostic genes. Furthermore, the specificity of the module to ovarian cancer was shown through analyses of datasets in nine other cancers. These observations underscore the importance of transcriptome based systems biomarkers research in deciphering the elusive pathophysiology of ovarian cancer, and here, we present reciprocal interplay between candidate ovarian cancer genes and their transcriptional regulatory dynamics. The corresponding gene module might provide new insights on ovarian cancer prognosis and treatment strategies that continue to place a significant burden on global health.

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Gene expression profiling of ovarian carcinomas and prognostic analysis of outcome

Cited by 10 publications

References 56 publications

Identification of a six‐gene signature with prognostic value for patients with endometrial carcinoma

Identification of a six‐gene signature with prognostic value for patients with endometrial carcinoma

Downregulation of SPINK13 Promotes Metastasis by Regulating uPA in Ovarian Cancer Cells

Differential co-expression analysis reveals a novel prognostic gene module in ovarian cancer

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