First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China.
Affiliated Jiangmen Hospital, Sun Yat-sen University, Jiangmen, Guangdong, China.
Brief Bioinform. 2021 Nov 5;22(6). doi: 10.1093/bib/bbab173.
Increasing evidences show the clinical significance of the interaction between hypoxia and immune in clear cell renal cell carcinoma (ccRCC) microenvironment. However, reliable prognostic signatures based on a combination of hypoxia and immune have not been well established. Moreover, many studies have only used RNA-seq profiles to screen the prognosis feature of ccRCC. Presently, there is no comprehensive analysis of multiomics data to mine a better one. Thus, we try and get it. First, t-SNE and ssGSEA analysis were used to establish tumor subtypes related to hypoxia-immune, and we investigated the hypoxia-immune-related differences in three types of genetic or epigenetic characteristics (gene expression profiles, somatic mutation, and DNA methylation) by analyzing the multiomics data from The Cancer Genome Atlas (TCGA) portal. Additionally, a four-step strategy based on lasso regression and Cox regression was used to construct a satisfying prognostic model, with average 1-year, 3-year and 5-year areas under the curve (AUCs) equal to 0.806, 0.776 and 0.837. Comparing it with other nine known prognostic biomarkers and clinical prognostic scoring algorithms, the multiomics-based signature performs better. Then, we verified the gene expression differences in two external databases (ICGC and SYSU cohorts). Next, eight hub genes were singled out and seven hub genes were validated as prognostic genes in SYSU cohort. Furthermore, it was indicated high-risk patients have a better response for immunotherapy in immunophenoscore (IPS) analysis and TIDE algorithm. Meanwhile, estimated by GDSC and cMAP database, the high-risk patients showed sensitive responses to six chemotherapy drugs and six candidate small-molecule drugs. In summary, the signature can accurately predict the prognosis of ccRCC and may shed light on the development of novel hypoxia-immune biomarkers and target therapy of ccRCC.
越来越多的证据表明,缺氧与免疫在透明细胞肾细胞癌(ccRCC)微环境中的相互作用具有临床意义。然而,基于缺氧和免疫相结合的可靠预后标志物尚未得到很好的建立。此外,许多研究仅使用 RNA-seq 图谱筛选 ccRCC 的预后特征。目前,尚无综合分析多组学数据以挖掘更好的预后特征的研究。因此,我们尝试进行了此项研究。首先,t-SNE 和 ssGSEA 分析用于建立与缺氧免疫相关的肿瘤亚型,我们通过分析来自癌症基因组图谱(TCGA)门户的多组学数据,研究了三种遗传或表观遗传特征(基因表达谱、体细胞突变和 DNA 甲基化)中的缺氧免疫相关差异。此外,我们使用基于lasso 回归和 Cox 回归的四步策略构建了一个令人满意的预后模型,平均 1 年、3 年和 5 年的曲线下面积(AUC)分别为 0.806、0.776 和 0.837。与其他九个已知的预后生物标志物和临床预后评分算法相比,该多组学标志物的性能更好。然后,我们在两个外部数据库(ICGC 和 SYSU 队列)中验证了基因表达差异。接下来,我们筛选出 8 个关键基因,并在 SYSU 队列中验证了其中 7 个基因作为预后基因。此外,在免疫表型评分(IPS)分析和 TIDE 算法中,高风险患者对免疫治疗的反应更好。同时,根据 GDSC 和 cMAP 数据库的评估,高风险患者对六种化疗药物和六种候选小分子药物表现出敏感反应。总之,该标志物可以准确预测 ccRCC 的预后,并可能为开发新的缺氧免疫生物标志物和 ccRCC 的靶向治疗提供思路。