Dataset Information

Screening Driving Transcription Factors in the Processing of Gastric Cancer.

ABSTRACT: Background. Construction of the transcriptional regulatory network can provide additional clues on the regulatory mechanisms and therapeutic applications in gastric cancer. Methods. Gene expression profiles of gastric cancer were downloaded from GEO database for integrated analysis. All of DEGs were analyzed by GO enrichment and KEGG pathway enrichment. Transcription factors were further identified and then a global transcriptional regulatory network was constructed. Results. By integrated analysis of the six eligible datasets (340 cases and 43 controls), a bunch of 2327 DEGs were identified, including 2100 upregulated and 227 downregulated DEGs. Functional enrichment analysis of DEGs showed that digestion was a significantly enriched GO term for biological process. Moreover, there were two important enriched KEGG pathways: cell cycle and homologous recombination. Furthermore, a total of 70 differentially expressed TFs were identified and the transcriptional regulatory network was constructed, which consisted of 566 TF-target interactions. The top ten TFs regulating most downstream target genes were BRCA1, ARID3A, EHF, SOX10, ZNF263, FOXL1, FEV, GATA3, FOXC1, and FOXD1. Most of them were involved in the carcinogenesis of gastric cancer. Conclusion. The transcriptional regulatory network can help researchers to further clarify the underlying regulatory mechanisms of gastric cancer tumorigenesis.

SUBMITTER: Xu G

PROVIDER: S-EPMC4925953 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Screening Driving Transcription Factors in the Processing of Gastric Cancer.

Xu Guangzhong G Li Kai K Zhang Nengwei N Zhu Bin B Feng Guosheng G

Gastroenterology research and practice 20160615

Background. Construction of the transcriptional regulatory network can provide additional clues on the regulatory mechanisms and therapeutic applications in gastric cancer. Methods. Gene expression profiles of gastric cancer were downloaded from GEO database for integrated analysis. All of DEGs were analyzed by GO enrichment and KEGG pathway enrichment. Transcription factors were further identified and then a global transcriptional regulatory network was constructed. Results. By integrated analy ...[more]

PMID: 27403158

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Project description:BackgroundTranscription factors (TFs) are involved in important molecular biological processes of tumor cells and play an essential role in the occurrence and development of gastric cancer (GC).MethodsCombined The Cancer Genome Atlas Program and Genotype-Tissue Expression database to extract the expression of TFs in GC, analyzed the differences, and weighted gene co-expression network analysis to extract TFs related to GC. The cohort including the training and validation cohort. Univariate Cox, least absolute contraction and selection operator (LASSO) regression, and multivariate Cox analysis was used for screening hub TFs to construct the prognostic signature in the training cohort. The Kaplan-Meier (K-M) and the receiver operating characteristic curve (ROC) was drawn to evaluate the predictive ability of the prognostic signature. A nomogram combining clinical information and prognostic signatures of TFs was constructed and its prediction accuracy was evaluated through various methods. The target genes of the hub TFs was predicted and enrichment analysis was performed to understand its molecular biological mechanism. Clinical samples and public data of GC was collected to verify its expression and prognosis. 5-Ethynyl-2'-deoxyuridine and Acridine Orange/Ethidium Bromide staining, flow cytometry and Western-Blot detection were used to analyze the effects of hub-TF ELK3 on the proliferation and apoptosis of gastric cancer in vitro.ResultsA total of 511 misaligned TFs were obtained and 200 GC-related TFs were exposed from them. After systematic analysis, a prognostic signature composed of 4 TFs (ZNF300, ELK3, SP6, MEF2B) were constructed. The KM and ROC curves demonstrated the good predictive ability in training, verification, and complete cohort. The areas under the ROC curve are respectively 0.737, 0.705, 0.700. The calibration chart verified that the predictive ability of the nomogram constructed by combining the prognostic signature of TFs and clinical information was accurate, with a C-index of 0.714. Enriching the target genes of hub TFs showed that it plays an vital role in tumor progression, and its expression and prognostic verification were consistent with the previous analysis. Among them, ELK3 was proved in vitro, and downregulation of its expression inhibited the proliferation of gastric cancer cells, induced proliferation, and exerted anti-tumor effects.ConclusionsThe 4-TFs prognostic signature accurately predicted the overall survival of GC, and ELK3 may be potential therapeutic targets for GC.

Dataset Information

Screening Driving Transcription Factors in the Processing of Gastric Cancer.

Publications

Screening Driving Transcription Factors in the Processing of Gastric Cancer.

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