INTEGRATIVE ANALYSIS OF CERVICAL CANCER MULTI-OMICS DATA: TOWARDS NOVEL BIOMARKERS DISCOVERY
Somia Mohammed1, Mohssin H. Abdalla2, Alia Benkahla3, Faisal M. Fadlelmola1 1Centre for Bioinformatics & Systems Biology, Faculty of Science, University of Khartoum, Khartoum, Bayan College for science and Technology, Sudan 2Faculty of Mathematical Sciences, University of Khartoum, Khartoum, Sudan 3Laboratory of BioInformatics, bioMathematics and bioStatistics (BIMS), Institute Pasteur of Tunis, Tunis
Cervical cancer is the second most common type of cancer in women worldwide. Due to poor access to screening and treatment services, more than 90% of deaths occur in women living in low- and middle-income countries. Although a diagnostic tool (PapTest) is widely available, cervical cancer incidence still remains high worldwide, and especially in developing countries, attributed to a large extent to sensitivities of the Pap test and unavailability of test in developing countries.
In this study, multi-omics data analysis was evaluated by integrating array-CGH,gene expression datasets in cervical cancer. meta-analysis approach was adopted whereby cervical cancer multi-omics datasets from Gene Expression Omnibus (GEO) to increase insurance of result and extract all passible in formation by reusing data sets, aCGH and gene expression in early stages of cancer, were selected to set up the analysis. Each data set was analyzed separately by implementing R scripts. The dataset were subject to statistical tests (e.g. T-test and Other statistical tests).
We get result in multi-layer of biological genomic transciptomics. Aberration chromosomal regions showed significant amplification or deletion in most of samples in chromosomes 1,3 and 19 whereas deletion found in 11, 4 and 13. The aCGH data is searched for genes with a known regulatory role whose copy number is altered in the samples.
In genes level get gene list in We find that list of more significant genes its high expressed in cancer stage. we tracing that gene in matched transcriptomics data is then examined to see if a gene’s altered with a concurrent change in the gene’s expression, thus adding weight to the argument that the gene may be contributing to cervical cancer. The list of genes that show strong aCGH/expression is subjected to various public databases (e.g. OMIM) to perform further in silico analysis and validation.
The last 2 authors contributed equally to this work.