A new thinking: extended application of genomic selection to screen multiomics data for development of novel hypoxia-immune biomarkers and target therapy of clear cell renal cell carcinoma
A new thinking: extended application of genomic selection to screen multiomics data for development of novel hypoxia-immune biomarkers and target therapy of clear cell renal cell carcinoma
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新思维：扩展应用基因组选择来筛选多组学数据，以开发新型缺氧免疫生物标志物和透明细胞肾细胞癌的靶向治疗
DOI：
10.1093/bib/bbab173
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发表时间：
2021-05-11
影响因子：
9.5
通讯作者：
Luo, Jun-Hang
中科院分区：
文献类型：
--
作者：
Gui, Cheng-Peng;Wei, Jin-Huan;Luo, Jun-Hang
越来越多的证据表明，肾透明细胞癌（ccRCC）微环境中缺氧与免疫相互作用具有临床意义。然而，基于缺氧和免疫的组合的可靠的预后特征还没有很好地建立。此外，许多研究仅使用RNA-seq图谱来筛选ccRCC的预后特征。目前，还没有对多组学数据进行全面分析，以挖掘出更好的数据。首先，我们利用t-SNE和ssGSEA分析来建立与缺氧免疫相关的肿瘤亚型，并通过分析来自The Cancer Genome Atlas（TCGA）portal的多组学数据，研究了缺氧免疫相关的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）的差异。此外，基于套索回归和考克斯回归的四步策略用于构建令人满意的预后模型，平均1年、3年和5年曲线下面积（AUC）分别为0.806、0.776和0.837。与其他九种已知的预后生物标志物和临床预后评分算法相比，基于多组学的签名表现更好。然后，我们验证了两个外部数据库（ICGC和SYSU队列）中的基因表达差异。接下来，挑选出8个枢纽基因，并在SYSU队列中验证7个枢纽基因作为预后基因。此外，免疫表型评分（IPS）分析和TIDE算法表明，高危患者对免疫治疗有更好的反应。同时，通过GDSC和cMAP数据库估计，高危患者对6种化疗药物和6种候选小分子药物表现出敏感反应。综上所述，该特征可以准确地预测ccRCC的预后，并可能为开发新的缺氧免疫生物标志物和ccRCC的靶向治疗提供启示。
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.