GENOMİK BİYOBELİRTEÇLER KULLANILARAK HBV VE HCV İLE İLİŞKİLİ HEPATOSELLÜLER KARSİNOMUN MAKİNE ÖĞRENİMİ TABANLI SINIFLANDIRILMASI

Year 2022, Volume: 85 Issue: 4, 532 - 540, 28.10.2022

Sami Akbulut Zeynep Küçükakçalı Cemil Çolak

https://doi.org/10.26650/IUITFD.1130442

Abstract

Amaç: Hepatoselüler karsinomun (HCC) optimal yönetimi için altında yatan nedenleri bilmek çok önemlidir. Bu çalışma, HBV veya HCV enfeksiyonu olan HCC hastalarının açık erişim gen ekspresyon verilerini XGboost yöntemini kullanarak sınıflandırmayı amaçlamaktadır. Gereç ve Yöntem: Bu vaka-kontrol çalışmasında, HBV ve HCV ile ilişkili HCC’li hastaların açık erişimli gen ekspresyonu verileri dikkate alınmıştır. Bu amaçla, 17 HBV+HCC ve 17 HCV+HCC hastadan elde edilen veriler çalışmaya dahil edildi. Sınıflandırma için on katlı çapraz geçerlilik kullanılarak XGboost modeli oluşturuldu. Model performansı için doğruluk, dengeli doğruluk, duyarlılık, özgüllük, pozitif tahmin değeri ve negatif tahmin değeri ve F1 skor performans metrikleri değerlendirildi. Bulgular: Özellik seçimi yaklaşımı ile 17 gen seçilmiş ve bu girdi değişkenleri kullanılarak modelleme yapılmıştır. XGboost modelinden elde edilen doğruluk, dengeli doğruluk, duyarlılık, özgüllük, pozitif tahmin değeri, negatif tahmin değeri ve F1 skor sırasıyla %97,1, %97,1, %94,1, %100, %100, %94,4 ve %97 idi. XGboost’tan elde edilen değişken önemliliği bulgularına dayanarak, ALDOC, GLUD2, TRAPPC10, FLJ12998, RPL39, KDELR2 ve KIAA0446 genleri, HBV ile ilişkili HCC için potansiyel biyobelirteçler olarak kullanılabilir. Sonuç: Çalışma sonucunda, HCC’ye neden olan iki farklı etiyolojik faktör (HBV ve HCV), makine öğrenimi tabanlı bir tahmin yaklaşımı kullanılarak sınıflandırıldı ve HBV ile ilişkili HCC için biyobelirteç olabilecek genler tanımlandı. Ortaya çıkan genler sonraki araştırmalarda klinik olarak doğrulandıktan sonra, bu genlere dayalı terapötik prosedürler oluşturulabilir ve klinik uygulamada kullanımları belgelenebilir. Anahtar Kelimeler: 

Keywords

Hepatosellüler kanser, Hepatit B enfeksiyonu, Hepatit C enfeksiyonu, makine öğrenimi, sınıflandırma

MACHINE LEARNING-BASED CLASSIFICATION OF HBV AND HCV-RELATED HEPATOCELLULAR CARCINOMA USING GENOMIC BIOMARKERS

Abstract

Objective: It is crucial to know the underlying causes of hepatocellular carcinoma (HCC) for optimal management. This study aims to classify open access gene expression data of HCC patients who have an HBV or HCV infection using the XGboost method. Material and Methods: This case-control study considered the open-access gene expression data of patients with HBV-related HCC and HCV-related HCC. For this purpose, data from 17 patients with HBV+HCC and 17 patients with HCV+HCC were included. XGboost was constructed for the classification via tenfold cross-validation. Accuracy, balanced accuracy, sensitivity, specificity, the positive predictive value, the negative predictive value, and F1 score performance metrics were evaluated for a model performance. Results: With the feature selection approach, 17 genes were chosen, and modeling was done using these input variables. Accuracy, balanced accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and the F1 score obtained from the XGboost model were 97.1%, 97.1%, 94.1%, 100%, 100%, 94.4%, and 97%, respectively. Based on the variable importance findings from the XGboost, the ALDOC, GLUD2, TRAPPC10, FLJ12998, RPL39, KDELR2, and KIAA0446 genes can be employed as potential biomarkers for HBV-related HCC. Conclusion: As a result of the study, two different etiological factors (HBV and HCV) causing HCC were classified using a machine learning-based prediction approach, and genes that could be biomarkers for HBV-related HCC were identified. After the resulting genes have been clinically validated in subsequent research, therapeutic procedures based on these genes can be established and their utility in clinical practice documented. 

Keywords

Hepatocellular carcinoma, Hepatitis B infection, Hepatitis C infection, machine learning, classification

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