Chip-Seq Data Analysis Identifies New Ovarian Cancer Risk Genes

Two NU students along with their faculty identified novel dysregulated genes linked to ovarian cancer.

A team from NIIT University, Neemrana, Rajasthan, comprising of two B.Tech Biotechnology students (Ms. Sumili Dey and Ms. Riddhi Modi) along with the faculty mentor (Dr. Utkarsh Raj), have identified two novel genes that are associated with an increased risk for developing earliest-stage ovarian cancer. The findings, presented at the‘7th International Conference on Research in Life-Sciences & Healthcare (ICRLSH)’ held at Kuala Lumpur, Malaysia, will both help identify women who are at highest risk of developing ovarian cancer, and pave the way for identifying new therapies that can target these specific genes. 

Ms. Sumili Dey receiving the Best Paper Presenter Award for the work.

Ms. Sumili Dey received the Best Paper Presenter Award for this work and she also got financial support from NU-RAP scheme at NIIT University for presenting the work in the prestigious conference at International level.


Ovarian cancer is ranked as seventh major killer, a malignancy that affects women worldwide [1]. Most of the times we choose to ignore the symptoms, due to their subtlety. Research shows that screening tests for ovarian cancer are present, but they have very low sensitivity and tend to give false negative results [2]. The early stage symptoms are- bloating, abdominal or pelvic pain and loss of appetite [3]. Ring a bell somewhere? Yes, the symptoms are very similar to what a woman faces during her menstrual cycles, which is why it gets overlooked. By the time, the diagnosis is done; ovarian cancer has already reached its later stages [4]. Currently, there are no effective screening tests for ovarian cancer and the disease is notorious for being detected in later stages, when survival rates are poor [5]. However, if ovarian cancer is caught early, survival rates increase dramatically, underscoring the need to identify those who may be at risk for developing the disease.


Statistics show that by 2035, the number of reported cases for ovarian cancer will increase by 55% and related deaths will rise by 67% [6]. Only 39% of stage III ovarian cancer patients have a survival possibility of 5 years [6]. During all this, a woman has to endure heavily loaded chemotherapy sessions and redundant surgeries.


The study by this team builds on previous research of large-scale ChIP-Seq data deposited at SRA database of National Center for Biotechnology Information (NCBI) by the research group (Tomar S et al., “ETS1 induction by the microenvironment promotes ovarian cancer metastasis through focal adhesion kinase.”, Cancer Letters, 2018 Feb 1;414:190-204) from Indiana University, Bloomington, US. Those researchers demonstrated that the interaction of the Ovarian Cancer cells with the mesothelial cells activated MAPK signaling, which led to the upregulation of ETS, a transcription factor, as the most upregulated member of the ETS family as the causal factor involved in the metastasis of ovarian cancer. Since ChIP-Seq has been identified as an effective method that analyses genome-wide DNA binding sites for transcription factors and other proteins (Johnson et al. 2007; “Genome-wide mapping of in vivo protein-DNA interactions.”, Science 316:1497– 1502). Therefore, in this current study, the analysis was done on the ChIP-Seq data obtained from two OC Cell lines, OVCAR 8 and HEYA 8 that contains transcriptional targets of ETS1, in order to mine novel dysregulated genes involved in OC metastasis and the team has come up with two genes TNFSF9 and TPM, common to both the cell lines. Differential expression analysis of TNFSF9 and TPM1 revealed that overexpression of these two genes may be related to the up regulation of metastasis of ovarian cancer. The researchers also documented the three dysregulated genes (ATF4, DUX4 MIR5188 in HEYA9 cell line and SNORD13C, PPIA, MIR6728 in OVCAR8 cell line) specific to the respective cell lines of OC.

The research commenced with selection of raw data from SRA database of NCBI, followed by quality checking of the reads based on Phred score. The good quality ChIP-Seq reads were aligned to the human genome 38 (hg38) for mapping purpose. After that the differential expression analysis studies on this data revealed differential peaks corresponding to the genes (both upregulated/down-regulated) based on fold change, q-value and peak annotation. Network and Pathway Analysis helped us in getting a grip for further identification of these novel dysregulated genes which may be related to Ovarian Cancer. Combing a large amount of biological data to establish which particular genes going to drive the development of OC may appear basic & simple, however, there can be many conceivable gene targets which can be influenced by various mechanisms, so placing the pieces together requires a lot of computational and statistical analysis.


Giving an insight on what the future holds for us. This identification of these genes may help in understanding the ovarian cancer better and become a foundation for drug designing field and development of therapeutics. However, further research and studies are still a requisite for obtaining a more vivid understanding of the genes.


  1. Reid, B. M., Permuth, J. B., & Sellers, T. A. (2017). Epidemiology of ovarian cancer: a review. Cancer biology & medicine, 14(1), 9.
  2. Rauh-Hain, J. A., Krivak, T. C., del Carmen, M. G., & Olawaiye, A. B. (2011). Ovarian cancer screening and early detection in the general population. Reviews in obstetrics and gynecology, 4(1), 15.
  3. (Accessed on 4 May 2019)
  4. (Accessed on 4 May 2019)
  5. Devaja, O., & Papadopoulos, A. (Eds.). (2018). Ovarian Cancer: From Pathogenesis to Treatment. BoD–Books on Demand.
  6. (Accessed on 4 May 2019)