Development and validation of a transcriptomics-based gene signature to predict distant metastasis and guide induction chemotherapy in locoregionally advanced nasopharyngeal carcinoma

Metastasis is the primary cause of treatment failure in nasopharyngeal carcinoma (NPC); however, the current tumour-node-metastasis staging system has limitations in predicting distant metastasis and guiding induction chemotherapy (IC) application. Here, we established a transcriptomics-based gene s...

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Bibliographic Details
Published in:European journal of cancer (1990) 2022-03, Vol.163, p.26-34
Main Authors: Liu, Sai-Lan, Sun, Xue-Song, Chen, Qiu-Yan, Liu, Ze-Xian, Bian, Li-Juan, Yuan, Li, Xiao, Bei-Bei, Lu, Zi-Jian, Li, Xiao-Yun, Yan, Jin-Jie, Yan, Shu-Mei, Li, Jian-Ming, Bei, Jin-Xin, Mai, Hai-Qiang, Tang, Lin-Quan
Format: Article
Language:English
Subjects:
Bioinformatics
Biopsy
Chemoradiotherapy
Chemotherapy
Dimethylargininase
Gene signature
Humans
Induction Chemotherapy
Medical prognosis
Metastases
Metastasis
Nasopharyngeal carcinoma
Nasopharyngeal Carcinoma - drug therapy
Nasopharyngeal Neoplasms - drug therapy
Nasopharyngeal Neoplasms - genetics
Nomogram
Nomograms
Patients
Risk groups
Throat cancer
Training
Transcriptome
Transcriptomes
Transcriptomics
Treatment
Tumors
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Summary:Metastasis is the primary cause of treatment failure in nasopharyngeal carcinoma (NPC); however, the current tumour-node-metastasis staging system has limitations in predicting distant metastasis and guiding induction chemotherapy (IC) application. Here, we established a transcriptomics-based gene signature to assess the risk of distant metastasis and guide IC in locoregionally advanced NPC. Transcriptome sequencing was performed on NPC biopsy samples from 12 pairs of patients with different metastasis risks. Bioinformatics and qPCR were used to identify differentially expressed genes (DEGs), while univariate and multivariate analyses were used to select prognostic indicators for the gene signature. A signature-based nomogram was established in a training cohort (n = 191) and validated in an external cohort (n = 263). Eleven DEGs were identified between metastatic and non-metastatic NPC. Four of these (AK4, CPAMD8, DDAH1 and CRTR1) were used to create a gene signature that effectively categorised patients into low- and high-risk metastasis groups (training: 91.1 versus 70.4%, p 
ISSN:0959-8049
1879-0852
DOI:10.1016/j.ejca.2021.12.017