A multi-gene predictive model for the radiation sensitivity of nasopharyngeal carcinoma based on machine learning
Radiotherapy resistance in nasopharyngeal carcinoma is a major cause of recurrence and metastasis. Identifying radiotherapy-related biomarkers is crucial for improving patient survival outcomes. This study developed the nasopharyngeal carcinoma radiotherapy sensitivity score (NPC-RSS) to predict radiotherapy response. By evaluating 113 machine learning algorithm combinations, the glmBoost+NaiveBayes model was selected to construct the NPC-RSS based on 18 key genes, which demonstrated good predictive performance in both public and in-house datasets. The study found that NPC-RSS is closely associated with immune features, including chemokine factors and their receptor families and the major histocompatibility complex (MHC). Gene functional analysis revealed that NPC-RSS influences key signaling pathways such as Wnt/ß-catenin, JAK-STAT, NF-?B, and T cell receptors. Cell line validation confirmed that SMARCA2 and CD9 gene expression is consistent with NPC-RSS. Single-cell analysis revealed that the radiotherapy-sensitive group exhibited richer immune infiltration and activation states. NPC-RSS can serve as a predictive tool for radiotherapy sensitivity in nasopharyngeal carcinoma, offering new insights for precise screening of patients who may benefit from radiotherapy. Overall design: This study was analyzed using two cohorts, including 34 nasopharyngeal cancer patients from Pearl River Hospital (March 2017 to July 2021) as the training set and the GSE32389 dataset (20 patients) from the GEO database as the validation set. Informed consent was obtained for all patient samples and approved by the hospital ethics committee. Fresh nasopharyngeal carcinoma tissue samples were subjected to RNA extraction and transcriptome sequencing using high-throughput sequencing technology. The raw data were quality controlled and pre-processed by FastQC and Trimmomatic, then genomic comparison was performed using HISAT2, gene expression was quantified by featureCounts, and FPKM values were calculated for normalization. Differential expression analysis was accomplished using the limma R package with screening criteria of P < 0.05 and |LogFC| > 0.585. Subsequently, 12 machine learning algorithms were integrated to construct predictive models and experimentally validated by the CNE2 nasopharyngeal carcinoma cell line, including the construction of a radioresistance model (cumulative 50 Gy) and RT-qPCR validation. Meanwhile,