Gene Expression Profiling of Cervical Cancer Using Statistical Method
Cervical cancer is accountable for numerous cancer-related deaths in women worldwide. Cancer causes molecular alterations in two types of genes with opposing functions, proto-oncogenes and tumor suppressor genes (TSG), respectively. Proto-oncogenes stimul
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Abstract Cervical cancer is accountable for numerous cancer-related deaths in women worldwide. Cancer causes molecular alterations in two types of genes with opposing functions, proto-oncogenes and tumor suppressor genes (TSG), respectively. Proto-oncogenes stimulate cell growth and hinder apoptosis, whereas TSGs inhibit growth and maintain the cell integrity. Deregulation of both types of genes may change the growth and division of cells, leading to a tumorigenic transformation. Thus we aim to study the gene expression of cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC). The data were selected and retrieved from TCGA data portal. A list of 12 driver genes responsible for causing cervical cancer was found. A code in R was written to find the correlation of these driver genes with the cancer genes by Spearman’s method. Different statistical methods were applied to calculate the significantly co-expressed genes. Co-express pathways were also identified by DAVID.
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Keywords Correlation Multiple testing correction
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Spearman’s method DAVID R
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Bonferroni correction
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1 Introduction Every day the body discards the old cells by replacing the new ones. But sometimes this process becomes hampered [1] in the genome level leading to uncontrolled cell growth and metastasis [2]. In this situation the old cells are also not discarded when they should be. These additional cells can form tumor which is the accumulation of new and old ones. Occasionally these accumulations of cells can be malignant in its nature. Among several cancer types, cervical cancer is accountable for 10–15 % of deaths in women worldwide [3]. As per the Human Papillomavirus (www. D. Kapse ⋅ K. Mukherjee (✉) ⋅ D. Banerjee Bioinformatics Lab, Department of Bio-Engineering, Birla Institute of Technology, Mesra, Ranchi 835215, Jharkhand, India e-mail: [email protected] © Springer Science+Business Media Singapore 2017 S.K. Sahana and S.K. Saha (eds.), Advances in Computational Intelligence, Advances in Intelligent Systems and Computing 509, DOI 10.1007/978-981-10-2525-9_16
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hpvcentre.net) and Related Diseases Report, 1,22,844 women diagnosed every year and 67,477 die among them. Cervical cancer holds the rank of second position among all other cancers in Indian women where 432.20 million women are at the threat of developing cervical cancer [4]. According to GLOBOCAN statistics [5], by 2030, approximately, 2.14 million new cases will be diagnosed and 13.2 million deaths from cancer will occur. Cervical cancer occurs in the cervix which is the lower part of the uterus [3]. Cervical cancer can often be successfully treated if it is detected at an early stage. Most cervical cancers are caused by a virus called human papillomavirus, or HPV [6]. Much importance has been given to RNA-Seq data to characterize numerous tumor samples and their cell lines, respectively, after the encyclopedia of the regulatory elements (ENCODE) and The Cancer Genome Atlas (TCGA) projects have submitted their projects. The id
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