Importance of Feature Weighing in Cervical Cancer Subtypes Identification

TLDR: The weighing method proposed in this study is compared with some other methods and proved to be more efficient and can be applied to other types of cancer data where histological subtypes play a key role in designing treatments and therapies.

Abstract: Cancer subtypes identification is very important for the advancement of precision cancer disease diagnosis and therapy. It is one of the important components of the personalized medicine framework. Cervical cancer (CC) is one of the leading gynecological cancers that causes deaths in women worldwide. However, there is a lack of studies to identify histological subtypes among the patients suffering from tumor of the uterine cervix. Hence, sub-typing of cancer can help in analyzing shared molecular profiles between different histological subtypes of solid tumors of uterine cervix. With the advancement in technology, large scale multi-omics data are generated. The integration of genomics data generated from different platforms helps in capturing complementary information about the patients. Several computational approaches have been developed that integrate muti-omics data for cancer sub-typing. In this study, mRNA (messenger RNA) and miRNA (microRNA) expression data are integrated to identify the histological subtypes of CC. In this regard, a method is proposed that ranks the biomarkers (mRNA and miRNA) on the basis of their varying expression across the samples. The ranking method generates a weight for every biomarker which is further used to identify the similarity between the samples. A well-known approach named Similarity Network Fusion (SNF) is then applied, followed by Spectral clustering, to identify groups of related samples. This study focuses on the role of weighing the biomarkers prior to their integration and application of the clustering algorithm. The weighing method proposed in this study is compared with some other methods and proved to be more efficient. The proposed method helps in identifying histological subtypes of CC and can also be applied to other types of cancer data where histological subtypes play a key role in designing treatments and therapies.