构建并验证免疫相关长链非编码 RNA 分类器，以预测喉鳞状细胞癌的预后和免疫治疗反应。
Construction and validation of immune-related LncRNAs classifier to predict prognosis and immunotherapy response in laryngeal squamous cell carcinoma.
机构信息
Department of Otolaryngology-Head and Neck Surgery, the First Affiliated Hospital of Hainan Medical University, Haikou, 570102, Hainan Province, People's Republic of China.
Department of Oncology, Huizhou Third People's Hospital, Guangzhou Medical University, Huizhou, 516000, Guangdong Province, People's Republic of China.
出版信息
World J Surg Oncol. 2022 May 24;20(1):164. doi: 10.1186/s12957-022-02608-z.
BACKGROUND
Rapid advances in transcriptomic profiles have resulted in recognizing IRLs (immune-related long noncoding RNAs), as modulators of the expression of genes related to immune cells that mediate immune inhibition as well as immune stimulatory, indicating LncRNAs play fundamental roles in immune modulation. Hence, we establish an IRL classifier to precisely predict prognosis and immunotherapeutic efficiency in laryngeal squamous cell carcinoma (LSCC).
METHODS
LSCC RNA-seq (RNA sequencing) datasets, somatic mutation data, and corresponding clinicopathologic information were acquired from TCGA (the Cancer Genome Atlas) and Gene Expression Omnibus (GEO) databases. Spearman correlation analysis identified LncRNAs associated with immune-related genes (IRG). Based on Lasso penalized regression and random forest (RF), we constructed an IRL classifier associated with prognosis. GEO database was utilized to validate the IRL classifier. The predictive precision and clinical application of the IRL classifier were assessed and compared to clinicopathologic features. The immune cell infiltration of LSCC was calculated via CIBERSORTx tools and ssGSEA (single-sample gene set enrichment analysis). Then, we systematically correlated the IRL classifier with immunological characteristics from multiple perspectives, such as immune-related cells infiltrating, tumor microenvironment (TME) scoring, microsatellite instability (MSI), tumor mutation burden (TMB), and chemokines. Finally, the TIDE (tumor immune dysfunction and exclusion) algorithm was used to predict response to immunotherapy.
RESULTS
Based on machine learning approach, three prognosis-related IRLs (BARX1-DT, KLHL7-DT, and LINC02154) were selected to build an IRL classifier. The IRL classifier could availably classify patients into the low-risk and high-risk groups based on the different endpoints, including recurrence-free survival (RFS) and overall survival (OS). In terms of predictive ability and clinical utility, the IRL classifier was superior to other clinical characteristics. Encouragingly, similar results were observed in the GEO databases. Immune infiltration analysis displayed immune cells that are significantly richer in low-risk group, CD8 T cells and activated NK cells via CIBERSORTx algorithm as well as activated CD8 T cell via ssGSEA. Additionally, compared with the high-risk group, immune score, CD8 T effector was higher in the low-risk group, yet stromal score, score of p53 signaling pathway and TGFher in the Tx algorithm, was lower in the low-risk group. Corresponding results were confirmed in GEO dataset. Finally, TIDE analysis uncovered that the IRL classifier may be effectually predict the clinical response of immunotherapy in LSCC.
CONCLUSION
Based on BARX1-DT, KLHL7-DT, and LINC02154, the IRL classifier was established, which can be used to predict the prognosis, immune infiltration status, and immunotherapy response in LSCC patients and might facilitate personalized counseling for immunotherapy.
背景
转录组谱的快速进展导致了免疫相关长非编码 RNA（IRL）的识别，作为介导免疫细胞表达基因的调节剂，这些基因既可以介导免疫抑制，也可以介导免疫刺激，表明 LncRNAs 在免疫调节中发挥着重要作用。因此，我们建立了一个 IRL 分类器，以精确预测喉鳞状细胞癌（LSCC）的预后和免疫治疗效果。
方法
从 TCGA（癌症基因组图谱）和 GEO（基因表达综合数据库）数据库中获取了 LSCC RNA-seq（RNA 测序）数据集、体细胞突变数据以及相应的临床病理信息。Spearman 相关性分析确定了与免疫相关基因（IRG）相关的 LncRNAs。基于 Lasso 惩罚回归和随机森林（RF），我们构建了一个与预后相关的 IRL 分类器。使用 GEO 数据库验证了 IRL 分类器。评估并比较了 IRL 分类器的预测精度和临床应用与临床病理特征。通过 CIBERSORTx 工具和 ssGSEA（单样本基因集富集分析）计算 LSCC 的免疫细胞浸润。然后，我们从多个角度系统地将 IRL 分类器与免疫特征相关联，例如免疫相关细胞浸润、肿瘤微环境（TME）评分、微卫星不稳定性（MSI）、肿瘤突变负担（TMB）和趋化因子。最后，使用 TIDE（肿瘤免疫功能障碍和排除）算法预测免疫治疗的反应。
结果
基于机器学习方法，选择了三个与预后相关的 IRL（BARX1-DT、KLHL7-DT 和 LINC02154）来构建 IRL 分类器。IRL 分类器可以根据不同的终点（包括无复发生存期（RFS）和总生存期（OS））将患者有效地分为低风险组和高风险组。在预测能力和临床实用性方面，IRL 分类器优于其他临床特征。令人鼓舞的是，在 GEO 数据库中也观察到了类似的结果。免疫浸润分析显示，通过 CIBERSORTx 算法，低风险组中富含的免疫细胞包括 CD8 T 细胞和活化的 NK 细胞，通过 ssGSEA 分析则为活化的 CD8 T 细胞。此外，与高风险组相比，低风险组的免疫评分、CD8 T 效应细胞更高，而基质评分、p53 信号通路评分和 TGF-β信号通路评分则更低。在 GEO 数据集也得到了相应的证实。最后，TIDE 分析表明，IRL 分类器可以有效地预测 LSCC 免疫治疗的临床反应。
结论
基于 BARX1-DT、KLHL7-DT 和 LINC02154，建立了 IRL 分类器，可用于预测 LSCC 患者的预后、免疫浸润状态和免疫治疗反应，并可能为免疫治疗的个体化咨询提供帮助。
Zotero 插件