Integrative analysis of RNA expression data unveils distinct cancer types through machine learning techniques

Saad Awadh Alanazi; Nasser Alshammari; Maddalah Alruwaili; Kashaf Junaid; Muhammad Rizwan Abid; Fahad Ahmad

doi:10.1016/j.sjbs.2023.103918

Integrative analysis of RNA expression data unveils distinct cancer types through machine learning techniques

Saudi J Biol Sci. 2024 Mar;31(3):103918. doi: 10.1016/j.sjbs.2023.103918. Epub 2023 Dec 30.

Authors

Saad Awadh Alanazi¹, Nasser Alshammari¹, Maddalah Alruwaili², Kashaf Junaid³, Muhammad Rizwan Abid⁴, Fahad Ahmad⁵

Affiliations

¹ Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka, Aljouf 72341, Saudi Arabia.
² Department of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf University, Sakaka 72341, Saudi Arabia.
³ School of Biological and Behavioural Sciences, Queen Mary University of London, London E1 4NS, United Kingdom.
⁴ Department of Computer Science, Florida Polytechnic University, Lakeland, FL 33805, United States.
⁵ Department of Basic Sciences, Common First Year, Jouf University, Sakaka 72341, Saudi Arabia.

Abstract

Cancer is a highly complex and heterogeneous disease. Traditional methods of cancer classification based on histopathology have limitations in guiding personalized prognosis and therapy. Gene expression profiling provides a powerful approach to unraveling molecular intricacies and better-stratifying cancer subtypes. In this study, we performed an integrative analysis of RNA sequencing data from five cancer types - BRCA, KIRC, COAD, LUAD, and PRAD. A machine learning workflow consisting of dataset identification, normalization, feature selection, dimensionality reduction, clustering, and classification was implemented. The k-means algorithm was applied to categorize samples into distinct clusters based solely on gene expression patterns. Five unique clusters emerged from the unsupervised machine learning based analysis, significantly correlating with the known cancer types. BRCA aligned predominantly with one cluster, while COAD spanned three clusters. KIRC was represented within two main clusters. LUAD is associated strongly with a single cluster and PRAD with another cluster. This demonstrates the ability of machine learning approaches to unravel complex signatures within transcriptomic profiles that can delineate cancer subtypes. The proposed study highlights the potential of integrative analytics to derive meaningful biological insights from high-dimensional omics datasets. Molecular subtyping through machine learning clustering enhances our understanding of the intrinsic heterogeneities and pathways dysregulated in different cancers. Overall, this study exemplifies a powerful computational framework to classify gene expressions of patients having different types of cancers and guide personalized therapeutic decisions. Finally, Wide Neural Network demonstrates a significantly higher accuracy, achieving 99.834% on the validation set and an even more impressive 99.995% on the test set.

Keywords: Classification; Clustering; Gene; Machine learning; RNA expression, cancer; Tumour, diagnosis.