MULTILAYER ANALYSIS OF NICOTINE-INDUCED GENE EXPRESSION ALTERATIONS IN BREAST CANCER CELLS USING CLUSTERING AND SUPERVISED LEARNING METHODS

Taybe Alabed; Sema Servi

doi:10.17780/ksujes.1730962

EN TR

MULTILAYER ANALYSIS OF NICOTINE-INDUCED GENE EXPRESSION ALTERATIONS IN BREAST CANCER CELLS USING CLUSTERING AND SUPERVISED LEARNING METHODS

Abstract

Nicotine is known not only for its addictive properties but also for its potential to alter the genetic structure of cancer cells. This study investigates the genetic alterations caused by chronic nicotine exposure in HCC38 breast cancer cells through a multi-layered computational analysis. Using K-means clustering and seven supervised machine learning algorithms, differentially expressed genes were grouped into three clusters and used as class labels in classification models. Logistic Regression achieved the highest performance with 98.76% accuracy and an F1 score of 0.9869. The Friedman test was applied to evaluate the statistical significance of performance differences among classifiers, and multi-class ROC curves were used to demonstrate their discriminative power. The findings indicate that nicotine exposure leads to genetic reprogramming and activates inflammation-related gene pathways in specific cellular subpopulations. These results highlight the utility of machine learning methods in uncovering biologically meaningful gene expression patterns in cancer research.

Keywords

KÜMELEME VE GÖZETİMLİ ÖĞRENME YÖNTEMLERİ KULLANILARAK MEME KANSERİ HÜCRELERİNDE NİKOTİNLE TETİKLENEN GEN EKSPRESYON DEĞİŞİKLİKLERİNİN ÇOK KATMANLI ANALİZİ

Öz

Nikotin, yalnızca bağımlılık yapıcı etkisiyle değil, aynı zamanda kanser hücrelerinin genetik yapısını değiştirme potansiyeliyle de bilinmektedir. Bu çalışma, HCC38 meme kanseri hücrelerinde kronik nikotin maruziyetinin yol açtığı genetik değişiklikleri çok katmanlı hesaplamalı analizle incelemiştir. K-means kümeleme ve yedi denetimli makine öğrenimi algoritması kullanılarak, değişen genler üç kümeye ayrılmış ve bu kümeler sınıf etiketi olarak sınıflandırma modellerinde kullanılmıştır. Lojistik Regresyon algoritması %98,76 doğruluk ve 0,9869 F1 skoru ile en yüksek performansı göstermiştir. Friedman testiyle sınıflandırıcılar arasındaki farkların anlamlılığı değerlendirilmiş, çok sınıflı ROC eğrileri ile sınıfların ayırt edici gücü gösterilmiştir. Bulgular, nikotin maruziyetinin genetik düzeyde yeniden yapılanma ve inflamasyonla ilişkili gen yollarının aktivasyonuna yol açtığını ortaya koymuştur. Sonuçlar, makine öğrenimi yöntemlerinin biyolojik olarak anlamlı gen ekspresyon desenlerini ortaya çıkarmadaki rolünü vurgulamaktadır.

Anahtar Kelimeler

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Yapay Zeka (Diğer)

Bölüm

Araştırma Makalesi

Yazarlar

Taybe Alabed
0009-0009-3722-2911
Türkiye

Sema Servi ^*
0000-0003-2069-9085
Türkiye

Yayımlanma Tarihi

3 Eylül 2025

Gönderilme Tarihi

1 Temmuz 2025

Kabul Tarihi

15 Ağustos 2025

Yayımlandığı Sayı

Yıl 2025 Cilt: 28 Sayı: 3

DOI

https://doi.org/10.17780/ksujes.1730962

IZ

https://izlik.org/JA49KM99AT

Kaynak Göster

RIS / Bibtex

APA

Alabed, T., & Servi, S. (2025). MULTILAYER ANALYSIS OF NICOTINE-INDUCED GENE EXPRESSION ALTERATIONS IN BREAST CANCER CELLS USING CLUSTERING AND SUPERVISED LEARNING METHODS. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(3), 1558-1573. https://doi.org/10.17780/ksujes.1730962

MULTILAYER ANALYSIS OF NICOTINE-INDUCED GENE EXPRESSION ALTERATIONS IN BREAST CANCER CELLS USING CLUSTERING AND SUPERVISED LEARNING METHODS

MULTILAYER ANALYSIS OF NICOTINE-INDUCED GENE EXPRESSION ALTERATIONS IN BREAST CANCER CELLS USING CLUSTERING AND SUPERVISED LEARNING METHODS

Abstract

Keywords

KÜMELEME VE GÖZETİMLİ ÖĞRENME YÖNTEMLERİ KULLANILARAK MEME KANSERİ HÜCRELERİNDE NİKOTİNLE TETİKLENEN GEN EKSPRESYON DEĞİŞİKLİKLERİNİN ÇOK KATMANLI ANALİZİ

Öz

Anahtar Kelimeler

Kaynakça

Ayrıntılar

Birincil Dil

Konular

Bölüm

Yazarlar

Yayımlanma Tarihi

Gönderilme Tarihi

Kabul Tarihi

Yayımlandığı Sayı

DOI

IZ

Kaynak Göster

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