Ye-Lin Liang, M.D.,1,2† Yuan Zhang, M.D.,1,2† Xi-Rong Tan, M.D.,1,3† Han Qiao, Ph.D.,1,3† Song-Ran Liu, M.D.,5† Ling-Long Tang, M.D.,1,2, Yan-Ping Mao, M.D.,1,2, Lei Chen, M.D.,1,2, Wen-Fei Li, M.D.,1,2, Guan-Qun Zhou, M.D.,1,2, Yin Zhao, Ph.D.,1,3, Jun-Yan Li, M.D.,1,2, Qian Li, M.D.,1,2, Sheng-Yan Huang, M.D.,1,3, Sha Gong, M.D.,1,3, Zi-Qi Zheng, M.D.,1,2, Zhi-Xuan Li, M.D.,1,2, Ying Sun, M.D.,1,2, Wei Jiang, M.D.,4 Jun Ma, M.D.1,2 Ying-Qin Li, Ph.D.,1,3 Na Liu, Ph.D.,1,3
1State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, 510060, P.R. China 2Department of Radiation Oncology, Sun Yat-sen University Cancer Center, Guangzhou, 510060, P.R. China 3Department of Experimental Research, Sun Yat-sen University Cancer Center, Guangzhou, China 4Department of Radiation Oncology, Affiliated Hospital of Guilin Medical University, Guilin, China 5Department of Pathology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
Abstract: Distant metastasis is the primary reason for locoregionally advanced nasopharyngeal carcinoma (LA-NPC) associated deaths, but existing anatomy-based metrics are insufficient to predict distant metastasis. Long noncoding RNAs (lncRNAs), newly characterized as metastasis regulators, may reflect tumour heterogeneity in the molecular perspective. Here, we aimed to assess the clinical utility of lncRNA biomarkers for metastasis prediction in LA-NPC.
Microarrays were initially employed to detect lncRNA profiles in LA-NPC samples with or without metastasis, as well as healthy controls from the discovery cohort (n = 56). Differential analysis identified LA-NPC metastasis-related lncRNAs, which are further measured using qRT-PCR assay in the Guangzhou training cohort (n = 177). A penalized Cox regression method was adopted to select 9 lncRNAs and develop a lncRNA signature. The nine-lncRNA signature was then validated in Guangzhou internal (n = 177) and Guilin external cohorts (n = 150). Furthermore, the lncRNA signature was assessed for associations with tumour heterogeneity using bioinformatic analysis, the finding of which was confirmed with immunohistochemistry.
Differential analysis distinguished 149 lncRNAs related to LA-NPC metastasis. In the training cohort, a nine-lncRNA signature was constructed to classify patients into high-risk and low-risk groups. Patients in the high-risk group had a significantly higher risk of distant metastasis (HR 6.05, 95% CI 2.82–12.94, P < 0.001), disease progression (HR 2.34, 95% CI 1.33–4.10, P = 0.003) and death (HR 3.79, 95% CI 1.95–7.35, P < 0.001). Validation on the Guangzhou internal and Guilin external cohorts yielded similar results and verified that the nine-lncRNA signature was an independent and consistent factor of LA-NPC metastasis. Integrative analyses showed that the nine-lncRNA signature correlated with immune activity and tumour lymphocyte infiltration, which was validated using a digital pathology method. Patients in the low-risk group had significant activation of the immune microenvironment and had more B cell and CD8+ T cell infiltration. Providing insightful information on tumour immune heterogeneity, the lncRNA signature outperforms anatomy-based metrics in identifying LA-NPC patients with high metastatic risk (the lncRNA signature: AUC = 0.78; N stage: AUC = 0.66, P = 0.035; pretreatment EBV-DNA load: AUC = 0.63, P = 0.025).
In this multicenter, retrospective cohort study, we identified an immune-associated signature in LA-NPC patients based on nine lncRNAs, which can serve as a promising biomarker for metastasis prediction in LA-NPC.
• Describe funding body that supported the work (if any): This work was supported by grants from the National Natural Science Foundation of China (81930072), Key-Area Research and Development Program of Guangdong Province (2019B020230002), Natural Science Foundation of Guangdong Province (2017A030312003), Overseas Expertise Introduction Project for Discipline Innovation (111 Project, B14035).