Identification and validation of a glycolysis-related taxonomy for improving outcomes in glioma.

Background: Reprogramming of glucose metabolism is a prominent abnormal energy metabolism in glioma. However, the efficacy of treatments targeting glycolysis varies among patients. The present study aimed to classify distinct glycolysis subtypes (GS) of glioma, which may help to improve the therapy response.

Retraction note: Risk miRNA screening of ovarian cancer based on miRNA functional synergistic network.

The Publisher and Editor regretfully retract this article (Ying H, Lyu J, Ying T, Li J, Jin S, Shao J, et al. Risk miRNA screening of ovarian cancer based on miRNA functional synergistic network. J Ovarian Res.

Retraction note: Gene-gene interaction network analysis of ovarian cancer using TCGA data.

The Publisher and Editor regretfully retract this article (Ying H, Lv J, Ying T, Jin S, Shao J, Wang L, et al. Gene-gene interaction network analysis of ovarian cancer using TCGA data. J Ovarian Res.

Chemoresistance is associated with Beclin-1 and PTEN expression in epithelial ovarian cancers.

The aim of the present study was to investigate the protein expression of the autophagy-related genes, and , and the association with drug resistance in epithelial ovarian cancers. In total, 40 patients with pathologically diagnosed epithelial ovarian cancer were divided into a chemotherapy-sensitive group (n=20) and a chemotherapy-resistant group (n=20), according to the results of the pre- or post-operative normative chemotherapy and the post-operative follow-up. The protein expression of the phosphatase and tensin homolog (PTEN) and the gene product, Beclin-1, was analyzed using immunohistochemistry in the 40 patients with ovarian carcinoma.

Risk miRNA screening of ovarian cancer based on miRNA functional synergistic network.

Background: miRNAs are proved to have causal roles in tumorgenesis involving various types of human cancers, but the mechanism is not clear. We aimed to explore the effect of miRNAs on the development of ovarian cancer and the underlying mechanism.

Methods: The miRNA expression profile GSE31801 was downloaded from GEO (Gene Expression Omnibus) database.

Gene-gene interaction network analysis of ovarian cancer using TCGA data.

Background: The Cancer Genome Atlas (TCGA) Data portal provides a platform for researchers to search, download, and analysis data generated by TCGA. The objective of this study was to explore the molecular mechanism of ovarian cancer pathogenesis.

Methods: Microarray data of ovarian cancer were downloaded from TCGA database, and Limma package in R language was used to identify the differentially expressed genes (DEGs) between ovarian cancer and normal samples, followed by the function and pathway annotations of the DEGs.

MicroRNA and transcription factor mediated regulatory network for ovarian cancer: regulatory network of ovarian cancer.

A better understanding on the regulatory interactions of microRNA (miRNA) target genes and transcription factor (TF) target genes in ovarian cancer may be conducive for developing early diagnosis strategy. Thus, gene expression data and miRNA expression data were downloaded from The Cancer Genome Atlas in this study. Differentially expressed genes and miRNAs were selected out with t test, and Gene Ontology enrichment analysis was performed with DAVID tools.

Screening of feature genes of the ovarian cancer epithelia with DNA microarray.

Objective: We aimed to screen differentially expressed genes (DEGs) of ovarian surface epithelia in order to provide beneficial help for early diagnosis and treatment of ovarian cancer with DNA microarrays.

Methods: We extracted the microarray expression profile GSE14407 from Gene Expression Omnibus database which conducted gene expression profiling analysis of 12 ovarian surface epithelia (OSE) and 12 laser capture microdissected serous ovarian cancer epithelia (CEPI) samples. The DEGs between OSE and CEPI were identified by Limma package of R language.