Dataset Information

Analysis of differential gene expression caused by cervical intraepithelial neoplasia based on GEO database.

ABSTRACT: The aim of the present study was to identify the differentially expressed genes between cervical intraepithelial neoplasias (CIN) and adjacent normal tissue, and to construct a protein-protein interaction (PPI) network. A CIN dataset was obtained from Gene Expression Omnibus, and data of gene expression in CIN and adjacent normal tissue were extracted from GSE64217. The differentially expressed genes were selected using software package and heat map was drawn using the 'pheatmap' package. The selected differentially expressed genes were subjected to PPI, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis using Cytoscape, Database for Annotation, Visualization and Integrated Discovery, STRING and KOBAS. In the present study, 287 genes were differentially expressed between CIN and adjacent normal tissue, of which 170 were significantly upregulated and 118 genes were significantly downregulated (P<0.00001, fold-change >6). A differential gene expression network map was constructed to show the interactions of 30 protein products encoded by differentially expressed genes using STRING software. In particular, the key gene, EGR1, was identified using Cytoscape software. The KEGG pathway analysis revealed that the differential genes were mainly involved in several pathways, including 'glutathione metabolism', 'arachidonic acid metabolism', and 'pentose phosphate pathway'. Results of the GO analysis showed that differential genes were enriched in different subsets. Specifically, small proline-rich protein 2E and 3, distal-less homeobox 5, epithelial membrane protein 1, cornifelin, periplakin, homeobox protein Hox-A13, estrogen receptor ?, transglutaminase 1, small proline-rich protein 2A, Rh C glycoprotein, tumor protein p63, TGM3, homeobox B5 and small proline-rich protein 2D were enriched in 'epithelial cell differentiation', which affected the differentiation of epithelial cells. In conclusion, 287 differentially expressed genes were identified successfully. The key gene was identified based on the results of PPI, GO and KEGG analyses, and functional annotation and pathway analysis were also performed. Our study provides the basis for further studies on the interaction among differentially expressed genes.

SUBMITTER: Yao S

PROVIDER: S-EPMC5950031 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Analysis of differential gene expression caused by cervical intraepithelial neoplasia based on GEO database.

Yao Shenghui S Liu Taifeng T

Oncology letters 20180330 6

The aim of the present study was to identify the differentially expressed genes between cervical intraepithelial neoplasias (CIN) and adjacent normal tissue, and to construct a protein-protein interaction (PPI) network. A CIN dataset was obtained from Gene Expression Omnibus, and data of gene expression in CIN and adjacent normal tissue were extracted from GSE64217. The differentially expressed genes were selected using software package and heat map was drawn using the 'pheatmap' package. The se ...[more]

PMID: 29805564

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Project description:The highest rates of cervical cancer are found in developing countries. Frontline monitoring has reduced these rates in developed countries and present day screening programs primarily identify precancerous lesions termed cervical intraepithelial neoplasias (CIN). CIN lesions described as mild dysplasia (CIN I) are likely to spontaneously regress while CIN III lesions (severe dysplasia) are likely to progress if untreated. Thoughtful consideration of gene expression changes paralleling the progressive pre invasive neoplastic development will yield insight into the key casual events involved in cervical cancer development.In this study, we have identified gene expression changes across 16 cervical cases (CIN I, CIN II, CIN III and normal cervical epithelium) using the unbiased long serial analysis of gene expression (L-SAGE) method. The 16 L-SAGE libraries were sequenced to the level of 2,481,387 tags, creating the largest SAGE data collection for cervical tissue worldwide. We have identified 222 genes differentially expressed between normal cervical tissue and CIN III. Many of these genes influence biological functions characteristic of cancer, such as cell death, cell growth/proliferation and cellular movement. Evaluation of these genes through network interactions identified multiple candidates that influence regulation of cellular transcription through chromatin remodelling (SMARCC1, NCOR1, MRFAP1 and MORF4L2). Further, these expression events are focused at the critical junction in disease development of moderate dysplasia (CIN II) indicating a role for chromatin remodelling as part of cervical cancer development.We have created a valuable publically available resource for the study of gene expression in precancerous cervical lesions. Our results indicate deregulation of the chromatin remodelling complex components and its influencing factors occur in the development of CIN lesions. The increase in SWI/SNF stabilizing molecule SMARCC1 and other novel genes has not been previously illustrated as events in the early stages of dysplasia development and thus not only provides novel candidate markers for screening but a biological function for targeting treatment.

Project description:To investigate the differentiated whole genome expression profiling of gastric high- and low-grade intraepithelial neoplasia and early-stage adenocarcinoma.Gastric specimens from an upper magnifying chromoendoscopic targeted biopsy were collected from March 2010 to May 2013. Whole genome expression profiling was performed on 19 low-grade intraepithelial neoplasia (LGIN), 20 high-grade intraepithelial neoplasia (HGIN), 19 early-stage adenocarcinoma (EGC), and 19 chronic gastritis tissue samples using Agilent 4 × 44K Whole Human Genome microarrays. Differentially expressed genes between different types of lesions were identified using an unpaired t-test and corrected with the Benjamini and Hochberg false discovery rate algorithm. A gene ontology (GO) enrichment analysis was performed using the GeneSpring software GX 12.6. The differentially expressed gene was verified using a real-time TaqMan® PCR assay with independent tissue samples, including 26 LGIN, 15 HGIN, 14 EGC, and 20 chronic gastritis. The expression of G0S2 were further validated by immunohistochemical staining (IHC) in 24 LGIN, 40 HGIN, 30 EGC and 61 chronic gastritis specimens.The gene expression patterns of LGIN and HGIN tissues were distinct. There were 2521 significantly differentially expressed transcripts in HGIN, with 951 upregulated and 1570 downregulated. A GO enrichment analysis demonstrated that the most striking overexpressed transcripts in HGIN compared with LGIN were in the category of metabolism, defense response, and nuclear factor ?B (NF-?B) cascade. While the vast majority of transcripts had barely altered expression in HGIN and EGC tissues, only 38 transcripts were upregulated in EGC. A GO enrichment analysis revealed that the alterations of the immune response were most prominent in the progression from HGIN to EGC. It is worth noting that, compared with LGIN, 289 transcripts were expressed at higher levels both in HGIN and EGC. A characteristic gene, G0/G1 switch 2 (G0S2) was one of the 289 transcripts and related to metabolism, the immune response, and the NF-?B cascade, and its expression was validated in independent samples through real-time TaqMan® PCR and immunohistochemical staining. In real-time PCR analysis, the expression of G0S2 was elevated both in HGIN and EGC compared with that in LGIN (P < 0.01 and P < 0.001, respectively). In IHC analysis, G0S2 immunoreactivity was detected in the cytoplasmic of neoplastic cells, but was undetectable in chronic gastritis cells. The G0S2 expression in HGIN was higher than that of LGIN (P = 0.012, ? (2) = 6.28) and EGC (P = 0.008, ? (2) = 6.94).A clear biological distinction between gastric high- and low-grade intraepithelial neoplasia was identified, and provides molecular evidence for clinical application.

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Dataset Information

Analysis of differential gene expression caused by cervical intraepithelial neoplasia based on GEO database.

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Analysis of differential gene expression caused by cervical intraepithelial neoplasia based on GEO database.

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