背景:头颈部鳞状细胞癌(HNSCC)是一种恶性程度高的恶性肿瘤,侵入性,和转移率。放射治疗,作为HNSCC的重要辅助治疗,可以降低术后复发率,提高生存率。鉴定与HNSCC放疗抵抗(HNSCC-RR)相关的基因有助于寻找潜在的治疗靶点。然而,从数以万计的基因中鉴定出放疗抵抗相关基因是一项具有挑战性的任务。虽然基因之间的相互作用对于阐明复杂的生物过程很重要,大量的基因使得基因相互作用的计算不可行。
方法:我们提出了一种基因选择算法,RGIE,基于ReliefF,树木集合的基因网络推断(GENIE3)和特征消除。ReliefF用于选择对HNSCC-RR具有区别性的特征子集,GENIE3基于该子集构建了基因调控网络,分析了基因间的调控关系,特征消除用于消除冗余和嘈杂的特征。
结果:9个基因(SPAG1,FIGN,NUBPL,CHMP5,TCF7L2,COQ10B,BSDC1,ZFPM1,GRPEL1)被鉴定并用于鉴定HNSCC-RR,在精度方面达到0.9730、0.9679、0.9767和0.9885的性能,精度,召回,AUC,分别。最后,qRT-PCR验证了9个标记基因在细胞系(SCC9、SCC9-RR)中的差异表达。
结论:RGIE可有效筛选HNSCC-RR相关基因。这种方法可能有助于指导患者的临床治疗方式并开发潜在的治疗方法。
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).
CONCLUSIONS: RGIE is effective in screening genes related to HNSCC-RR. This approach may help guide clinical treatment modalities for patients and develop potential treatments.