Ovarian Cancer – Identification of Novel Dysregulated Genes using NGS Data Analysis
Dr. Utkarsh Raj (Biotechnology & Bioinformatics) & Ms. Sumili Dey (Final Year Student, B.Tech – Biotech)
Ovarian Cancer ranks seventh as a major killer. It is one of the most common malignancies affecting women, worldwide. Research shows that screen tests for ovarian cancer are present, but they have very low sensitivity and tends to give false negative results. Moreover, diagnosis of Ovarian Cancer, in itself is a challenge, as the symptoms are very subtle. In early stages, the symptoms include bloating, abdominal or pelvic pains and loss of appetite. By the time, the patient has been diagnosed; the cancer has already reached higher stages. Diagnosis typically involves surgery and chemotherapy, and most patients with Stage III ovarian cancer have a 5 year survival rate of 39% (approx.). Statistics show that by 2035, the total reported cases for ovarian cancer will rise by 55% and related deaths will increase by 67%. In order to enhance its evolving therapy, research and knowledge at the transcriptional level is required.
Studies identify ETS1, a transcription factor, as the most up-regulated member of the ETS family that metastasize Ovarian Cancer cells. Human ovarian cancer cells show an increased expression of ETS1. Chip Seq is a method to analyse genome-wide DNA binding sites for transcription factors and other proteins.
In our study, with the use of the ChIP Seq data, we applied Next Generation Sequencing or NGS Data Analysis which, over the last decade, has emerged as the most powerful tool of sequencing strategies. We used two OC Cell lines, OVCAR 8 and HEYA 8 that contain transcriptional targets of ETS1 and applied Chip Seq Data Analysis. Raw data was collected from the National Centre of Biotechnology Information (NCBI) and the quality of the data was tested on the basis of Phred score. Differential analysis on these cell lines revealed differential peaks, based on fold change and peak annotation. Network and Pathway Analysis helped in the identification of novel dysregulated genes which are related to Ovarian Cancer.
The identified genes may help in the better understanding of ovarian cancer and may lay the platform for future research and development. It may also provide insights into drug designing. Effective therapies are yet to be developed and in order to do so, further research needs to be conducted, so that, more detailed information about the genes is revealed.