Sequence-similarity-based approach to SARS–CoV-2 genome sequence and lung cancer-related genes via multivariate feature extraction method

Nazife Çevik; Taner Çevik; Jawad Rasheed; Sachi Nandan Mohanty; Halil Ibrahim Cakar; Shtwai Alsubai

doi:10.1080/10255842.2025.2530645

Sequence-similarity-based approach to SARS–CoV-2 genome sequence and lung cancer-related genes via multivariate feature extraction method

Nazife Çevik
, Taner Çevik
, Jawad Rasheed
, Sachi Nandan Mohanty
, Halil Ibrahim Cakar
, Shtwai Alsubai

Computer Sciences

Research output: Contribution to journal › Article › peer-review

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 susceptibility. 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 processes, and the genetic links between cancer and viral infections.

Original language	English
Journal	Computer Methods in Biomechanics and Biomedical Engineering
DOIs	https://doi.org/10.1080/10255842.2025.2530645
State	Accepted/In press - 2025

Keywords

Data mining
sequence motif
sequence similarity

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1080/10255842.2025.2530645

Cite this

@article{0958f10969e24233a4b5bea05111fa7e,

title = "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 susceptibility. 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 processes, and the genetic links between cancer and viral infections.",

keywords = "Data mining, sequence motif, sequence similarity",

author = "Nazife {\c C}evik and Taner {\c C}evik and Jawad Rasheed and Mohanty, \{Sachi Nandan\} and Cakar, \{Halil Ibrahim\} and Shtwai Alsubai",

note = "Publisher Copyright: {\textcopyright} 2025 Informa UK Limited, trading as Taylor \& Francis Group.",

year = "2025",

doi = "10.1080/10255842.2025.2530645",

language = "English",

journal = "Computer Methods in Biomechanics and Biomedical Engineering",

issn = "1025-5842",

publisher = "Taylor and Francis Ltd.",

}

TY - JOUR

T1 - Sequence-similarity-based approach to SARS–CoV-2 genome sequence and lung cancer-related genes via multivariate feature extraction method

AU - Çevik, Nazife

AU - Çevik, Taner

AU - Rasheed, Jawad

AU - Mohanty, Sachi Nandan

AU - Cakar, Halil Ibrahim

AU - Alsubai, Shtwai

PY - 2025

Y1 - 2025

N2 - 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 susceptibility. 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 processes, and the genetic links between cancer and viral infections.

AB - 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 susceptibility. 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 processes, and the genetic links between cancer and viral infections.

KW - Data mining

KW - sequence motif

KW - sequence similarity

UR - https://www.scopus.com/pages/publications/105010523062

U2 - 10.1080/10255842.2025.2530645

DO - 10.1080/10255842.2025.2530645

M3 - Article

AN - SCOPUS:105010523062

SN - 1025-5842

JO - Computer Methods in Biomechanics and Biomedical Engineering

JF - Computer Methods in Biomechanics and Biomedical Engineering

ER -

Sequence-similarity-based approach to SARS–CoV-2 genome sequence and lung cancer-related genes via multivariate feature extraction method

Abstract

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this