Background: Head and Neck Squamous Cell Carcinoma (HNSCC) is a malignant tumor with a high degree of malignancy, invasiveness, and metastasis rate. Radiotherapy, as an important adjuvant therapy for HNSCC, can reduce the postoperative recurrence rate and improve the survival rate. Identifying the genes related to HNSCC radiotherapy resistance (HNSCC-RR) is helpful in the search for potential therapeutic targets. However, identifying radiotherapy resistance-related genes from tens of thousands of genes is a challenging task. While interactions between genes are important for elucidating complex biological processes, the large number of genes makes the computation of gene interactions infeasible.
Methods: We propose a gene selection algorithm, RGIE, which is based on ReliefF, Gene Network Inference with Ensemble of Trees (GENIE3) and Feature Elimination. ReliefF was used to select a feature subset that is discriminative for HNSCC-RR, GENIE3 constructed a gene regulatory network based on this subset to analyze the regulatory relationship among genes, and feature elimination was used to remove redundant and noisy features.
Results: Nine genes (SPAG1, FIGN, NUBPL, CHMP5, TCF7L2, COQ10B, BSDC1, ZFPM1, GRPEL1) were identified and used to identify HNSCC-RR, which achieved performances of 0.9730, 0.9679, 0.9767, and 0.9885 in terms of accuracy, precision, recall, and AUC, respectively. Finally, qRT-PCR validated the differential expression of the nine signature genes in cell lines (SCC9, SCC9-RR).
Conclusion: RGIE is effective in screening genes related to HNSCC-RR. This approach may help guide clinical treatment modalities for patients and develop potential treatments.
Keywords: Head and Neck Squamous Cell Carcinoma; ReliefF; feature selection; genetic interaction.; machine learning; radioresistance.
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