Dataset Information

Identification of Significant Gene Signatures and Prognostic Biomarkers for Patients With Cervical Cancer by Integrated Bioinformatic Methods.

ABSTRACT: Cervical cancer is the leading cause of death with gynecological malignancies. We aimed to explore the molecular mechanism of carcinogenesis and biomarkers for cervical cancer by integrated bioinformatic analysis. We employed RNA-sequencing details of 254 cervical squamous cell carcinomas and 3 normal samples from The Cancer Genome Atlas. To explore the distinct pathways, messenger RNA expression was submitted to a Gene Set Enrichment Analysis. Kyoto Encyclopedia of Genes and Genomes and protein-protein interaction network analysis of differentially expressed genes were performed. Then, we conducted pathway enrichment analysis for modules acquired in protein-protein interaction analysis and obtained a list of pathways in every module. After intersecting the results from the 3 approaches, we evaluated the survival rates of both mutual pathways and genes in the pathway, and 5 survival-related genes were obtained. Finally, Cox hazards ratio analysis of these 5 genes was performed. DNA replication pathway ( P < .001; 12 genes included) was suggested to have the strongest association with the prognosis of cervical squamous cancer. In total, 5 of the 12 genes, namely, minichromosome maintenance 2, minichromosome maintenance 4, minichromosome maintenance 5, proliferating cell nuclear antigen, and ribonuclease H2 subunit A were significantly correlated with survival. Minichromosome maintenance 5 was shown as an independent prognostic biomarker for patients with cervical cancer. This study identified a distinct pathway (DNA replication). Five genes which may be prognostic biomarkers and minichromosome maintenance 5 were identified as independent prognostic biomarkers for patients with cervical cancer.

SUBMITTER: Li X

PROVIDER: S-EPMC5900817 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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Identification of Significant Gene Signatures and Prognostic Biomarkers for Patients With Cervical Cancer by Integrated Bioinformatic Methods.

Li Xiaofang X Tian Run R Gao Hugh H Yan Feng F Ying Le L Yang Yongkang Y Yang Pei P Gao Yan'e Y

Technology in cancer research & treatment 20180101

Cervical cancer is the leading cause of death with gynecological malignancies. We aimed to explore the molecular mechanism of carcinogenesis and biomarkers for cervical cancer by integrated bioinformatic analysis. We employed RNA-sequencing details of 254 cervical squamous cell carcinomas and 3 normal samples from The Cancer Genome Atlas. To explore the distinct pathways, messenger RNA expression was submitted to a Gene Set Enrichment Analysis. Kyoto Encyclopedia of Genes and Genomes and protein ...[more]

PMID: 29642758

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Project description:Oral squamous cell carcinoma (OSCC) is a life?threatening disease with a poor prognosis. Although previous studies have reported that the methylation of certain genes is associated with the pathogenesis of OSCC, the methylation of genes that have relevance to OSCC progression is not clearly documented. The present study aimed to gain insights into the mechanisms underlying DNA methylation regulation associated with OSCC progression and to identify potential prognostic markers for OSCC treatment. DNA methylation dataset GSE41114 and gene expression dataset GSE74530 were downloaded from the Gene Expression Omnibus database. The global methylation status of OSCC tumor samples and normal control samples was determined, and differentially methylated genes (DMGs) in OSCC samples compared with control samples were identified. The mRNA expression data were then integrated to identify differentially expressed genes (DEGs) in OSCC samples compared with control samples. Overlapping genes between DEGs and DMGs were identified, and functional enrichment analysis was performed. In addition, survival analysis of the overlapping genes was performed to screen genes with prognostic significance in OSCC. A total of 40,115 differential methylation CpG sites spanning 3,360 DMGs were identified; CpG sites in the promoter, gene body and intergenic regions were generally highly hypermethylated or hypomethylated. Additionally, 508 DEGs in OSCC samples were identified, including 332 upregulated and 176 downregulated genes. A total of 82 overlapping genes between DEGs and DMGs were found, which were mainly involved in protein metabolism, regulation of the metabolic process and the immune system. Additionally, differential methylation or expression of several genes, including fibroblast activation protein ? (FAP), interferon ? inducible protein 27 (IFI27), laminin subunit ?2 (LAMC2), matrix metallopeptidase 1 (MMP1), serine peptidase inhibitor Kazal?type 5 (SPINK5) and zinc finger protein 662 (ZNF662), was significantly associated with the survival of OSCC patients, and their differential expression in OSCC patients was further confirmed by reverse transcription?quantitative polymerase chain reaction in OSCC and normal oral cell lines. Overall, FAP, IFI27, LAMC2, MMP1, SPINK5 and ZNF662 genes caused by epigenetic changes via DNA methylation may be associated with the development and progression of OSCC, and should be valuable OSCC therapeutic biomarkers.

Project description:BackgroundTo identify prognostic genes which were associated with adrenocortical carcinoma (ACC) tumor microenvironment (TME).Methods and materialsTranscriptome profiles and clinical data of ACC samples were collected from The Cancer Genome Atlas (TCGA) database. We use ESTIMATE (estimation of stromal and Immune cells in malignant tumor tissues using expression data) algorithm to calculate immune scores, stromal scores and estimate scores. Heatmap and volcano plots were applied for differential analysis. Venn plots were used for intersect genes selection. We used protein-protein interaction (PPI) networks and functional analysis to explore underlying pathways. After performing stepwise regression method and multivariate Cox analysis, we finally screened hub genes associated with ACC TME. We calculated risk scores (RS) for ACC cases based on multivariate Cox results and evaluated the prognostic value of RS shown by receiver operating characteristic curve (ROC). We investigated the association between hub genes with immune infiltrates supported by algorithm from online TIMER database.ResultsGene expression profiles and clinical data were downloaded from TCGA. Lower immune scores were observed in disease with distant metastasis (DM) and locoregional recurrence (LR) than other cases (P = .0204). Kaplan-Meier analysis revealed that lower immune scores were significantly associated with poor overall survival (OS) (P = .0495). We screened 1649 differentially expressed genes (DEGs) and 1521 DEGs based on immune scores and stromal scores, respectively. Venn plots helped us find 1122 intersect genes. After analysing by cytoHubba from Cytoscape software, 18 hub genes were found. We calculated RS and ROC showed significantly predictive accuracy (area under curve (AUC) = 0.887). ACC patients with higher RS had worse survival outcomes (P < .0001). Results from TIMER (tumor immune estimation resource) database revealed that HLA-DOA was significantly related with immune cells infiltration.ConclusionWe screened a list of TME-related genes which predict poor survival outcomes in ACC patients from TCGA database.

Dataset Information

Identification of Significant Gene Signatures and Prognostic Biomarkers for Patients With Cervical Cancer by Integrated Bioinformatic Methods.

Publications

Identification of Significant Gene Signatures and Prognostic Biomarkers for Patients With Cervical Cancer by Integrated Bioinformatic Methods.

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