Department of Endocrinology and Metabolism, Institute of Endocrinology, National Health Commission Key Laboratory of Diagnosis and Treatment of Thyroid Diseases, The First Hospital of China Medical University, Shenyang, 110001, Liaoning Province, People's Republic of China.
BMC Med Genomics. 2022 Aug 22;15(1):183. doi: 10.1186/s12920-022-01332-7.
Thyroid cancer (TC) is the most common endocrine malignancy worldwide. The incidence of TC is high and increasing worldwide due to continuous improvements in diagnostic technology. Therefore, identifying accurate prognostic predictions to stratify TC patients is important.
Raw data were downloaded from the TCGA database, and pairwise comparisons were applied to identify differentially expressed immune-related lncRNA (DEirlncRNA) pairs. Then, we used univariate Cox regression analysis and a modified Lasso algorithm on these pairs to construct a risk assessment model for TC. We further used qRT‒PCR analysis to validate the expression levels of irlncRNAs in the model. Next, TC patients were assigned to high- and low-risk groups based on the optimal cutoff score of the model for the 1-year ROC curve. We evaluated the signature in terms of prognostic independence, predictive value, immune cell infiltration, immune status, ICI-related molecules, and small-molecule inhibitor efficacy.
We identified 14 DEirlncRNA pairs as the novel predictive signature. In addition, the qRT‒PCR results were consistent with the bioinformatics results obtained from the TCGA dataset. The high-risk group had a significantly poorer prognosis than the low-risk group. Cox regression analysis revealed that this immune-related signature could predict prognosis independently and reliably for TC. With the CIBERSORT algorithm, we found an association between the signature and immune cell infiltration. Additionally, immune status was significantly higher in low-risk groups. Several immune checkpoint inhibitor (ICI)-related molecules, such as PD-1 and PD-L1, showed a negative correlation with the high-risk group. We further discovered that our new signature was correlated with the clinical response to small-molecule inhibitors, such as sunitinib.
We have constructed a prognostic immune-related lncRNA signature that can predict TC patient survival without considering the technical bias of different platforms, and this signature also sheds light on TC's overall prognosis and novel clinical treatments, such as ICB therapy and small molecular inhibitors.
甲状腺癌（TC）是全球最常见的内分泌恶性肿瘤。由于诊断技术的不断提高，全球 TC 的发病率很高且呈上升趋势。因此，确定准确的预后预测以对 TC 患者进行分层非常重要。
从 TCGA 数据库下载原始数据，并进行配对比较以鉴定差异表达的免疫相关长链非编码 RNA（DEirlncRNA）对。然后，我们使用单变量 Cox 回归分析和修正的 Lasso 算法对这些对构建 TC 的风险评估模型。我们进一步使用 qRT-PCR 分析验证模型中 irlncRNA 的表达水平。接下来，根据模型的 1 年 ROC 曲线的最佳截止评分，将 TC 患者分为高风险组和低风险组。我们根据该特征在预后独立性、预测价值、免疫细胞浸润、免疫状态、ICI 相关分子和小分子抑制剂疗效方面进行了评估。
我们确定了 14 个 DEirlncRNA 对作为新的预测特征。此外，qRT-PCR 结果与从 TCGA 数据集获得的生物信息学结果一致。高危组的预后明显比低危组差。Cox 回归分析表明，该免疫相关特征可独立且可靠地预测 TC 的预后。使用 CIBERSORT 算法，我们发现特征与免疫细胞浸润之间存在关联。此外，低危组的免疫状态明显较高。几种免疫检查点抑制剂（ICI）相关分子，如 PD-1 和 PD-L1，与高危组呈负相关。我们进一步发现，我们的新特征与小分子抑制剂（如舒尼替尼）的临床反应相关。
我们构建了一个预后免疫相关 lncRNA 特征，可以预测 TC 患者的生存，而无需考虑不同平台的技术偏差，并且该特征还为 TC 的总体预后和新的临床治疗方法（如 ICB 治疗和小分子抑制剂）提供了新的见解。
