A lncRNA signature associated with tumor immune heterogeneity predicts distant metastasis in locoregionally advanced nasopharyngeal carcinoma

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作者
Ye-Lin Liang
Yuan Zhang
Xi-Rong Tan
Han Qiao
Song-Ran Liu
Ling-Long Tang
Yan-Ping Mao
Lei Chen
Wen-Fei Li
Guan-Qun Zhou
Yin Zhao
Jun-Yan Li
Qian Li
Sheng-Yan Huang
Sha Gong
Zi-Qi Zheng
Zhi-Xuan Li
Ying Sun
Wei Jiang
Jun Ma
Ying-Qin Li
Na Liu
机构
[1] Sun Yat-sen University Cancer Center,State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy
[2] Sun Yat-sen University Cancer Center,Department of Radiation Oncology
[3] Sun Yat-sen University Cancer Center,Department of Experimental Research
[4] Sun Yat-sen University Cancer Center,Department of Pathology
[5] Affiliated Hospital of Guilin Medical University,Department of Radiation Oncology
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摘要
Increasing evidence has revealed the roles of long noncoding RNAs (lncRNAs) as tumor biomarkers. Here, we introduce an immune-associated nine-lncRNA signature for predicting distant metastasis in locoregionally advanced nasopharyngeal carcinoma (LA-NPC). The nine lncRNAs are identified through microarray profiling, followed by RT–qPCR validation and selection using a machine learning method in the training cohort (n = 177). This nine-lncRNA signature classifies patients into high and low risk groups, which have significantly different distant metastasis-free survival. Validations in the Guangzhou internal (n = 177) and Guilin external (n = 150) cohorts yield similar results, confirming that the signature is an independent risk factor for distant metastasis and outperforms anatomy-based metrics in identifying patients with high metastatic risk. Integrative analyses show that this nine-lncRNA signature correlates with immune activity and lymphocyte infiltration, which is validated by digital pathology. Our results suggest that the immune-associated nine-lncRNA signature can serve as a promising biomarker for metastasis prediction in LA-NPC.
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