Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Transcription profiling of human cervical cancer, 33 primary tumors, 9 cell lines, and 24 normal cervical epithelium

ABSTRACT: This study is aimed in identification of gene expression profiles in cervical cancer and the role of specific genes in cervical carcinogenesis. Experiment Overall Design: A total of 66 samples were included in this study, which include 33 primary tumors, 9 cell lines, and 24 normal cervical epithelium.

ORGANISM(S): Homo sapiens

SUBMITTER: Murty Vundavalli

PROVIDER: E-GEOD-9750 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Identification of copy number gain and overexpressed genes on chromosome arm 20q by an integrative genomic approach in cervical cancer: potential role in progression.

Scotto Luigi L Narayan Gopeshwar G Nandula Subhadra V SV Arias-Pulido Hugo H Subramaniyam Shivakumar S Schneider Achim A Kaufmann Andreas M AM Wright Jason D JD Pothuri Bhavana B Mansukhani Mahesh M Murty Vundavalli V VV

Genes, chromosomes & cancer 20080901 9

Recurrent karyotypic abnormalities are a characteristic feature of cervical cancer (CC) cells, which may result in deregulated expression of important genes that contribute to tumor initiation and progression. To examine the role of gain of the long arm of chromosome 20 (20q), one of the common chromosomal gains in CC, we evaluated CC at various stages of progression using single nucleotide polymorphism (SNP) array, gene expression profiling, and fluorescence in situ hybridization (FISH) analyse ...[more]

PMID: 18506748

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Project description:Analysis of various of up-regulated and down-regulated genes in Normal Cervical mucosa, Cervical intraepithelial neoplasia and Cervical squamous cell carcinoma. The report provides a data analysis methodology for identification of co-expressed gene patterns, as emerging clusters, in global transcriptome of cervical mucosal pre-malignant and malignant conditions in comparison to their normal counterparts. Microarray based study of global gene expression is often used to extract molecular signatures underlying cancer progression. Such endeavors endorse self organizing map, a type of artificial neural network to analyze high dimensional pre-processed transcriptome data to segregate hotspot genes in component plane for disease subtypes. This report provides a data analysis methodology for identification of coexpressed gene patterns, as emerging clusters, in global transcriptome of oral and cervical mucosal premalignant and malignant conditions in comparison to their normal counterparts. Four exclusive cluster patterns, each involving 100 − 300 genes, were identified from component planes for oral study groups. Gene expression associated with each pattern belonged to 32 biological processes. Analysis on cervical biopsies, where cancer was compared to cervical interepithelial neoplasia and normal counterpart, it revealed three non-overlapping patterns for each condition. In cervical interepithelial neoplasia an intermediate pattern with nine different dominant functional processes was identified, whereas, in cervical squamous cell carcinoma pattern showed dominance for seven different functions. This analysis demonstrated utility of self organizing map to capture dominant enriched patterns as visual plots and revealed six common biological processes like transcription and RNA processing, cytoskeleton reorganization, angiogenesis, immunity, neuron signalling, and connective tissue remodelling in the pathogenesis of oral and cervical cancers. In fact it could provide an intuitive understanding of molecular course in carcinogenesis and may contribute for combinatorial biomarker discovery.

Dataset Information

Transcription profiling of human cervical cancer, 33 primary tumors, 9 cell lines, and 24 normal cervical epithelium

Publications

Identification of copy number gain and overexpressed genes on chromosome arm 20q by an integrative genomic approach in cervical cancer: potential role in progression.

Similar Datasets

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