Consensus gene modules identification of cervical cancer and head neck cancer Objectives: Squamous cell carcinoma (SCC) is the most common histological type of head neck cancer and cervical cancer. Carcinogenesis in these two types of cancers demonstrate similar multistep progression. The purpose of the study is to identify the significant consensus gene modules of these two cancers. Materials and Methods: We used a publicly available expression dataset for the study. The dataset included head neck cancer (42 cancer samples and 14 normal samples) and cervical cancer (20 cancer samples and 8 normal samples). We used only human papilloma virus 16 positive samples for excluding the bias according to the different types of HPV. We identified consensus modules of two types of cancers and explored the biological functions of each modules by annotation tool. Results: We identified 8 consensus gene modules of head neck cancer and cervical cancer. Each module was well preserved between the two types of cancer. The modules included significant biological functions, including ATP binding and extracellular exosome. Conclusion: Consensus gene module identification is expected to contribute to more personalized management of multiple cancer types. The identified consensus modules could be common targets for multicancer therapy.

Importance of Feature Weighing in Cervical Cancer Subtypes Identification 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, subtyping 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 multiomics 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 mutiomics data for cancer subtyping. 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 wellknown 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.