Construction of lung cancer serum markers based on ReliefF feature selection

AbstractSerum 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.Keywords: LUADReliefFmachine learningserum marker Ethical approvalThis study was conducted in accordance with the Helsinki Declaration II (2022KY022) and was approved by the Institutional Review Boards of Fujian Medical University Union Hospital. Participants filled out the written informed consent form.Availability of data and materialsThe data used to support the findings of this study are included within the article.Disclosure statementThe authors declare no conflicts of interest.Authors' contributionsConceptualization: Dr. YL,Formal analysis and investigation: Dr. NDY and Dr. XLY,Writing - original draft preparation: Dr. XQC and Dr. MCJ,Writing - review and editing: Dr. YL, Dr. NDY and Dr. XLY,All authors read and approved the final manuscript.Additional informationFundingThis study was Sponsored by Fujian provincial health technology project (2020GGB027) and Natural Science Foundation of Fujian Province (2021J01747).