Construction of lung cancer serum markers based on ReliefF feature selection

• Published in: Computer methods in biomechanics and biomedical engineering 2024-08, Vol.27 (10), p.1215-1223
• Main Authors: Li, Yong, Yu, Nan-Ding, Ye, Xiang-Li, Jiang, Mei-Chen, Chen, Xiang-Qi
• Format: Article
• Language: English
• Subjects: Adenocarcinoma; Adenocarcinoma of Lung - blood; Adenocarcinoma of Lung - diagnosis; Adenocarcinoma of Lung - genetics; Bayes Theorem; Biomarkers, Tumor - blood; Biomarkers, Tumor - genetics; Cancer screening; Diagnostic systems; Feature selection; Gene expression; Gene Expression Regulation, Neoplastic; Humans; LUAD; Lung cancer; Lung Neoplasms - blood; Lung Neoplasms - diagnosis; Lung Neoplasms - genetics; machine learning; Medical screening; MicroRNAs; MicroRNAs - blood; MicroRNAs - genetics; miRNA; Patients; Performance prediction; Principal Component Analysis; Principal components analysis; ReliefF; serum marker; Support Vector Machine; Support vector machines
• ISSN: 1025-5842; 1476-8259; 1476-8259
• DOI: 10.1080/10255842.2023.2235045

Serum miRNAs are available clinical samples for cancer screening. Identifying early serum markers in lung cancer (LC) is essential for patients' early diagnosis and clinical treatment. Expression data of serum miRNAs of lung adenocarcinoma (LUAD) patients and healthy individuals were downloaded from the Gene Expression Omnibus (GEO). These data were normalized and subjected to differential expression analysis to obtain differentially expressed miRNAs (DEmiRNAs). The DEmiRNAs were subsequently subjected to ReliefF feature selection, and subsets closely related to cancer were screened as candidate feature miRNAs. Thereafter, a Gaussian Naive Bayes (NB), Support Vector Machine (SVM), and Random Forest (RF) classifier were constructed based on these candidate feature miRNAs. Then the best diagnostic signature was constructed through NB combined with incremental feature selection (IFS). Thereafter, these samples were subjected to principal component analysis (PCA) based on miRNAs with optimal predictive performance. Finally, the peripheral serum miRNAs of 64 LUAD patients and 59 normal individuals were extracted for qRT-PCR analysis to validate the performance of the diagnostic model in respect of clinical detection. Finally, according to area under the curve (AUC) and accuracy values, the NB classifier composed of miR-5100 and miR-663a manifested the most outstanding diagnostic performance. The PCA results also revealed that the 2-miRNA diagnostic signature could effectively distinguish cancer patients from healthy individuals. Finally, qRT-PCR results of clinical serum samples revealed that miR-5100 and miR-663a expression in tumor samples was remarkably higher than that in normal samples. The AUC of the 2-miRNA diagnostic signature was 0.968. In summary, we identified markers (miR-5100 and miR-663a) in serum for early LUAD screening, providing ideas for developing early LUAD diagnostic models.