Clustering reveals common check-point and growth factor receptor genes expressed in six different cancer types - Pawar, Shrikant; Stanam, Aditya; Lahiri, Chandrajit * (Springer, 2020)
Cancer diagnosis and prognosis has been significantly impacted by understandings of gene expression data analysis. Several groups have utilized supervised and unsupervised machine learning tools for classification and predictions on gene expression data sets. Clustering, principal component analysis, regression are some important and promising tools for analyzing gene expression data. The complex and multi-dimensions of this data with limited samples makes it challenging to understand common patterns. Several features of high dimensional data contributing to a cluster generated by a finite mixture of underlying probability distributions can be implemented with a model-based clustering method. While some groups have shown that projective clustering and ensemble techniques can be effective to combat these challenges, we have employed clustering on 6 different cancer types to address the problem of multi-dimensionality and extracting common gene expression patterns. Our analysis has provided an expression pattern of 42 genes common throughout all cancer types with most of the genes involved in important check-point and growth factor receptor functions associated with cancer pathophysiology.