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Gene Expression Signature: An Influential Access to Drug Discovery in Ovarian Cancer

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Computational Intelligence in Oncology

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1016))

Abstract

Ovarian cancer is the worst gynecological malignancy around the globe because of its asymptomatic existence. Due to the lack of diagnostic strategies in ovarian cancer, it is being detected at an advanced stage. It will continue to rise in the future as well because of the rapidly aging population and lifestyle. At the individual level, the financial burden is relatively high. Currently, there is no remedy for disease treatment and the perfect way to reduce its morbidity and incidence is to detect it early. In clinical trials, the advancement in molecular biology, bioinformatics, and laboratory technology have broadened the use and feasibility of applying biomarkers in disease treatment. Amidst all these, the applications of Gene expression signatures (GES) have shown to be a powerful tool. GES are dominant tools that can unfold a spectrum of biologically and clinically relevant aspects of biological samples. Over the past few years differentiation has been made in different subtypes of tumors with the help of gene expression signatures. It also helps in predicting clinical outcomes in cancer and model the signaling pathways activation. During the past two decades, gene signatures detection from genomic data has always been an important topic of discussion in the medical field. This chapter deals with gene expression signature and its role in complex diseases. Further, we also describe the prognostic relevance of gene expression signature in ovarian cancer.

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Acknowledgements

The authors are thankful for the high computing infrastructure in the Department of Computer Science, Jamia Millia Islamia, New Delhi. Anam Beg is also thankful for the award of Senior Research Fellowship from the Indian Council of Medical Research (ICMR). This work is supported by the Indian Council of Medical Research.

Funding

This work was supported by the Indian Council of Medical Research (ICMR); PROJECT ID: 2019-4178.

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Authors and Affiliations

  1. Department of Computer Science, Jamia Millia Islamia, New Delhi, 110025, India

    Anam Beg & Rafat Parveen

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  1. Anam Beg
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  2. Rafat Parveen
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Editors and Affiliations

  1. Department of Computer Science, Jamia Millia Islamia, New Delhi, India

    Khalid Raza

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Beg, A., Parveen, R. (2022). Gene Expression Signature: An Influential Access to Drug Discovery in Ovarian Cancer. In: Raza, K. (eds) Computational Intelligence in Oncology. Studies in Computational Intelligence, vol 1016. Springer, Singapore. https://doi.org/10.1007/978-981-16-9221-5_16

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