Identification of targeted molecules in cervical cancer by computational approaches

Abstract Cervical cancer is the second leading cause of cancer death in adult women. The three most widely employed techniques for the treatment of cervical cancer are radiotherapy, surgery, and hormone chemotherapy. Recently several biomarkers have also been identified using classical and high-throughput technologies. High-throughput technologies generate huge data, which in turn demand development of robust computational approaches for analysis of this big data in a more comprehensive way. This, in turn, will enable us to better understand mechanisms associated with many diseases, including cervical cancer. Considering this, in the present chapter, we present information about different computational approaches that have been employed to detect target molecules associated with cervical cancer. Information obtained revealed that to date limited computational studies have identified several cervical cancer-associated key hub genes (e.g., BTD, PEG3, RPLP2, and SPON1), long noncoding RNA (e.g., GOLGA2P5, EMX2OS, FLJ10038, FAM66C, ACVR2B-AS1, AMZ2P1, LINC00341, ZNF876P, MIR9-3HG, and ILF3-AS1), and miRNAs (e.g., Hsa-mir-1273g, Hsa-mir-5095, Hsa-mir-5096, and Hsa-mir-1273f) that play a key role in cervical cancer development. However, as there are only a few number of computational studies performed on cervical cancer datasets, there is still scope for developing more robust software/algorithms and analyzing cervical cancer datasets. In the near future, the information in this chapter will be highly valuable for cancer biologists and immunologists toward cervical cancer treatment.