Department of Stomatology, Weihai Central Hospital, Weihai, China.
Department of Stomatology, Feicheng Hospital Affiliated to Shandong First Medical University, Taian, China.
World J Surg Oncol. 2022 Jul 8;20(1):227. doi: 10.1186/s12957-022-02630-1.
With low response to present immunotherapy, it is imperative to identify new immune-related biomarkers for more effective immunotherapies for oral cancer.
RNA profiles for 390 oral cancer patients and 32 normal samples were downloaded from The Cancer Genome Atlas (TCGA) database and differentially expressed genes (DEGs) were analyzed. Immune genesets from ImmPort repository were overlapped with DEGs. After implementing univariate Cox analysis and the least absolute shrinkage and selection operator (LASSO) Cox regression analysis, key immune-related gene pairs (IRGPs) among the overlapped DEGs for predicting the survival risk were obtained. Then, the cutoff of risk score was calculated by the receiver operating characteristic (ROC) curve to stratify oral cancer patients into high and low-risk groups. Multivariate Cox analysis was used to analyze independent prognostic indicators for oral cancer. Besides, infiltration of immune cells, functional annotation, and mutation analysis of IRGPs were conducted. Biological functions correlated with IRGPs were enriched by Gene Set Enrichment Analysis (GSEA) method.
We identified 698 differentially expressed genes (DEGs) in response to oral cancer. 17 IRGPs among the DEGs were identified and integrated into a risk score model. Patients in the high-risk group have a significantly worse prognosis than those in the low-risk group in both training (P<0.001) and test (P=0.019) cohorts. Meanwhile, the IRGP model was identified as an independent prognostic factor for oral cancer. Different infiltration patterns of immune cells were found between the high- and low-risk groups that more types of T and B cells were enriched in the low-risk group. More immune-related signaling pathways were highly enriched in the low-risk group and Tenascin C (TNC) was the most frequently mutated gene. We have developed a novel 17-IRGPs signature for risk stratification and prognostic prediction of oral cancer.
Our study provides a foundation for improved immunotherapy and prognosis and is beneficial to the individualized management of oral cancer patients.
由于目前免疫疗法的反应率较低，因此迫切需要确定新的免疫相关生物标志物，以实现更有效的口腔癌免疫治疗。
从癌症基因组图谱（TCGA）数据库中下载了 390 名口腔癌患者和 32 名正常样本的 RNA 图谱，并分析了差异表达基因（DEGs）。将 ImmPort 存储库中的免疫基因集与 DEGs 进行重叠。在实施单变量 Cox 分析和最小绝对收缩和选择算子（LASSO）Cox 回归分析后，获得了重叠 DEGs 中用于预测生存风险的关键免疫相关基因对（IRGPs）。然后，通过接收者操作特征（ROC）曲线计算风险评分的截断值，将口腔癌患者分为高风险和低风险组。使用多变量 Cox 分析分析口腔癌的独立预后指标。此外，还进行了 IRGPs 的免疫细胞浸润、功能注释和突变分析。通过基因集富集分析（GSEA）方法富集与 IRGPs 相关的生物学功能。
我们鉴定出了 698 个在口腔癌中差异表达的基因（DEGs）。在 DEGs 中鉴定出 17 个 IRGPs，并将其整合到一个风险评分模型中。在训练（P<0.001）和测试（P=0.019）队列中，高风险组的患者预后明显差于低风险组。同时，IRGP 模型被鉴定为口腔癌的独立预后因素。在高风险组和低风险组之间发现了不同的免疫细胞浸润模式，低风险组中更多类型的 T 细胞和 B 细胞被富集。低风险组中富集了更多的免疫相关信号通路，Tenascin C（TNC）是最常突变的基因。我们已经开发了一种新的 17-IRGPs 签名，用于口腔癌的风险分层和预后预测。
我们的研究为改善免疫疗法和预后提供了基础，有利于口腔癌患者的个体化管理。
