Xu Zhihong, Li Xiaodong, Pan Lanlan, Tan Ruolan, Ji Ping, Tang Han
Chongqing Key Laboratory of Oral Diseases and Biomedical Sciences, Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, Stomatological Hospital of Chongqing Medical University, Chongqing, China.
J Oral Pathol Med. 2022 Apr;51(4):358-368. doi: 10.1111/jop.13281. Epub 2022 Feb 8.
We aimed to establish a long noncoding RNA (lncRNA)-based signature for accurately predicting prognosis and guiding the personalized clinical management of oral squamous cell carcinoma (OSCC).
OSCC RNA sequencing profiles were acquired from The Cancer Genome Atlas and Gene Expression Omnibus. Univariate Cox regression, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses were performed to construct a lncRNA-based prognostic signature. Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curves and calibration curves were used to assess the effectiveness and accuracy of the signature. Additionally, we conducted single-sample gene-set enrichment analysis to infer the different degrees of immunocyte infiltration. Weighted correlation network analysis, enrichment analysis and Spearman's correlation analysis were implemented to screen immune-related genes that interact with the lncRNA signature.
In total, 14 lncRNAs were defined as potential prognostic biomarkers. Based on these lncRNAs, patients were divided into low- and high-risk subgroups with different survival times (p < 0.001). In addition, the reliability of the prognostic signature was verified by Kaplan-Meier analysis, ROC analysis and calibration curves. Patients in the low-risk group exhibited more significant immune cell infiltration. Simultaneously, a potential regulatory network consisting of eight lncRNAs and 159 protein-coding genes in the top 10 immune-related biological process terms was constructed.
Our findings suggested that the 14-lncRNA signature has satisfactory performance in predicting the prognosis of OSCC, thereby providing new insights to the pathogenesis, clinical patient management and therapeutic intervention. The different immune cell infiltration statuses of OSCC patients may encourage immunotherapy.
我们旨在建立一种基于长链非编码RNA（lncRNA）的特征，用于准确预测口腔鳞状细胞癌（OSCC）的预后并指导个性化临床管理。
从癌症基因组图谱和基因表达综合数据库获取OSCC的RNA测序数据。进行单因素Cox回归、最小绝对收缩和选择算子（LASSO）以及多因素Cox回归分析，以构建基于lncRNA的预后特征。采用Kaplan-Meier生存分析、受试者工作特征（ROC）曲线和校准曲线来评估该特征的有效性和准确性。此外，我们进行单样本基因集富集分析，以推断免疫细胞浸润的不同程度。实施加权相关网络分析、富集分析和Spearman相关分析，以筛选与lncRNA特征相互作用的免疫相关基因。
总共14种lncRNA被确定为潜在的预后生物标志物。基于这些lncRNA，患者被分为具有不同生存时间的低风险和高风险亚组（p < 0.001）。此外，通过Kaplan-Meier分析、ROC分析和校准曲线验证了预后特征的可靠性。低风险组患者表现出更显著的免疫细胞浸润。同时，构建了一个潜在的调控网络，该网络由前10个免疫相关生物学过程术语中的8种lncRNA和159个蛋白质编码基因组成。
我们的研究结果表明，14-lncRNA特征在预测OSCC预后方面具有令人满意的性能，从而为发病机制、临床患者管理和治疗干预提供了新的见解。OSCC患者不同的免疫细胞浸润状态可能会促进免疫治疗。
