Sequence-Similarity-Based Approach to SARS-CoV-2 Genome Sequence and Lung Cancer-Related Genes via Multivariate Feature Extraction Method

Abstract

The COVID-19 pandemic has prompted genomic studies linking SARS–CoV-2 and lung cancer- related genes. This study explores sequence similarity and motif patterns to assess disease sus-ceptibility. We applied a data mining approach to compare human and SARS–CoV-2 genomes, revealing high sequence identity (0.74–0.99%) with lung cancer-related genes. Low-entropy motifs were associated with higher genetic risk. We identified shared patterns of lengths 4, 5, and 10, selecting the most significant motifs. These findings support the hypothesis that sequence similarity and conserved motifs provide insights into gene function, evolutionary proc-esses, and the genetic links between cancer and viral infections.