Uterine Cervix and Corpus Cancers Characterization Through Gene Expression Analysis Using the KnowSeq Tool

The characterization of cancer through gene expression quantification data analysis is a powerful and widely used approach in cancer research. This paper describes two experiments that demonstrate its potential in identifying differentially expressed genes (DEGs) and accurately predicting cancer subtypes. To achieve this, RNA-seq data was obtained from TCGA database and subsequently preprocessed and analyzed using the KnowSeq package from Bioconductor. In the first experiment, the study focuses on identifying DEGs in healthy, cervical cancerous, and uterine corpus cancerous tissues. The kNN classifier was employed to evaluate the utility of these genes in predicting a sample belonging to one of these three classes. A gene signature consisting of only three genes produced remarkable results on a 5-fold cross-validation assessment process, with overall test accuracy and F1 values of 99.33% and 96.73%, respectively. The paper provides ontological enrichment, associated diseases, and pathways of the gene signature to shed light on the molecular mechanisms involved in both cancers. The second experiment extends the work by classifying cervical cancer samples into their two most common histological types: adenocarcinoma and squamous cell carcinoma. By using a single gene, the study was able to achieve 100% of test accuracy in a 5-fold cross-validation process. Additionally, the classification of an adenosquamous sample into one of these two categories based on the number of genes used was also examined. Overall, these experiments demonstrate the potential of these techniques to improve cancer diagnosis and treatment. Moreover, the study provides valuable insights into the underlying molecular mechanisms of cervix and uterine corpus cancers, laying the groundwork for further research in this field.