Zhang Ze, Wei Yan-Yan, Guo Qiong-Mei, Zhou Chang-Hao, Li Nan, Wu Jin-Fang, Li Ya-Ting, Gao Wei-Wei, Li Hui-Li
Department of Anesthesiology, The First Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
Department of Operating Room, The First Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
J Oncol. 2022 Jan 31;2022:2559258. doi: 10.1155/2022/2559258. eCollection 2022.
There is much evidence that confirms the inextricable link between inflammation and malignancy. Inflammation-related regulators were involved in the progression of kidney renal clear cell carcinoma (KIRC). However, the predictive role of single gene biomarkers is inadequate, and more accurate prognostic models are necessary. We undertook the current research to construct a robust inflammation-related gene signature that could stratify patients with KIRC.
The transcriptome sequencing data along with clinicopathologic information of KIRC were obtained from TCGA. A list of inflammation-related genes was acquired from the Molecular Signatures Database. Using the RNA-seq and survival time data from the TCGA training cohort, an inflammation-related gene signature was built using bioinformatic methods, and its performance in predicting patient prognosis was assessed by Kaplan-Meier and ROC curve analyses. Furthermore, we explored the association of risk score with immune score, stromal score, tumor immune-infiltrating cells (TIICs), immunosuppressive molecules, m6A regulators, and autophagy-related biomarkers.
Herein, nine inflammation-related hub genes (ROS1, PLAUR, ACVR2A, KLF6, GABBR1, APLNR, SPHK1, PDPN, and ADORA2B) were determined and used to build a predictive model. All sets, including training set, four testing sets, and the entire TCGA group, were divided into two groups (low and high risk), and Kaplan-Meier curves all showed an adverse prognosis for patients in the high-risk group. ESTIMATE algorithm revealed a higher immune score in the high-risk subgroup. CIBERSORT algorithm illustrated that the high-risk group showed higher-level immune infiltrates. Furthermore, LAG3, TIGIT, and CTLA4 were overexpressed in the high-risk subgroup and positively associated with risk scores. Moreover, except for METTL3 and ALKBH5, the other m6A regulators decreased in the high-risk subgroup.
In conclusion, a novel inflammation-related gene signature comprehensively constructed in the current study may help stratify patients with KIRC.
有大量证据证实炎症与恶性肿瘤之间存在千丝万缕的联系。炎症相关调节因子参与了肾透明细胞癌（KIRC）的进展。然而，单基因生物标志物的预测作用并不充分，需要更准确的预后模型。我们开展了当前的研究，以构建一个能够对KIRC患者进行分层的强大的炎症相关基因特征。
从TCGA获取KIRC的转录组测序数据以及临床病理信息。从分子特征数据库中获取炎症相关基因列表。利用TCGA训练队列的RNA-seq和生存时间数据，采用生物信息学方法构建炎症相关基因特征，并通过Kaplan-Meier和ROC曲线分析评估其预测患者预后的性能。此外，我们探讨了风险评分与免疫评分、基质评分、肿瘤免疫浸润细胞（TIICs）、免疫抑制分子、m6A调节因子和自噬相关生物标志物之间的关联。
在此，确定了9个炎症相关的枢纽基因（ROS1、PLAUR、ACVR2A、KLF6、GABBR1、APLNR、SPHK1、PDPN和ADORA2B）并用于构建预测模型。所有数据集，包括训练集、四个测试集和整个TCGA组，均分为两组（低风险和高风险），Kaplan-Meier曲线均显示高风险组患者预后不良。ESTIMATE算法显示高风险亚组的免疫评分更高。CIBERSORT算法表明高风险组显示出更高水平的免疫浸润。此外，LAG3、TIGIT和CTLA4在高风险亚组中过表达且与风险评分呈正相关。此外，除METTL3和ALKBH5外，其他m6A调节因子在高风险亚组中减少。
总之，本研究全面构建的一种新型炎症相关基因特征可能有助于对KIRC患者进行分层。
