TNF Family-Based Signature Predicts Prognosis, Tumor Microenvironment, and Molecular Subtypes in Bladder Carcinoma.
TNF Family-Based Signature Predicts Prognosis, Tumor Microenvironment, and Molecular Subtypes in Bladder Carcinoma.
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DOI：
10.3389/fcell.2021.800967
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发表时间：
2021
影响因子：
5.5
通讯作者：
Zhao C
中科院分区：
文献类型：
--
作者：
Li H;Liu S;Li C;Xiao Z;Hu J;Zhao C
背景：肿瘤坏死因子（TNF）家族成员在肿瘤的发生发展和抗肿瘤免疫应答中起重要作用。然而，膀胱癌（BLCA）中TNF成员的表达模式、预后价值和免疫学特征仍不清楚。 研究方法：训练队列TCGA-BLCA从The Cancer Genome Atlas下载;另外两个Gene Expression Omnibus数据集（GSE 13507和GSE 32894）和湘雅队列（从我院收集的RNA测序队列）用作外部验证队列。采用最小绝对收缩和选择算子（LASSO）算法和交叉验证筛选变量。分别采用考克斯回归模型和随机生存森林（RSF）模型进行风险评分。然后，我们系统地将TNF风险评分与肿瘤微环境（TME）细胞浸润、BLCA分子亚型以及预测免疫治疗疗效的潜在价值相关联。 结果：我们开发了两种基于TNF的模式，命名为TNF簇1和TNF簇2。与TNF簇2相比，TNF簇1表现出较差的生存结局和发炎的TME特征。然后，我们筛选出196个差异表达的基因之间的两个TNF集群和应用LASSO算法和交叉验证筛选出22个基因，建立风险评分。对于风险评分，我们发现RSF表现出比考克斯回归模型更高的疗效，我们选择RSF开发的风险评分进行以下分析。高风险评分组的BLCA患者的生存结局明显较差。此外，这些结果可以在外部验证队列（包括GSE 13507、GSE 32894和Xiangya队列）中进行验证。然后，我们系统地将风险评分与TME细胞浸润相关联，发现它与大多数免疫细胞的浸润呈正相关。此外，较高的风险评分表明BLCA的基础亚型。值得注意的是，风险评分、TME细胞浸润和分子亚型之间的关系可以在湘雅队列中得到验证。 结论：我们开发并验证了一个强大的基于TNF的风险评分，它可以预测预后结果，TME和BLCA的分子亚型。然而，风险评分预测免疫治疗疗效的价值还需要进一步研究。
Background: Tumor necrosis factor (TNF) family members play vital roles in cancer development and antitumor immune responses. However, the expression patterns, prognostic values, and immunological characteristics of TNF members in bladder carcinoma (BLCA) remain unclear. Methods: The training cohort, TCGA-BLCA, was downloaded from The Cancer Genome Atlas; another two Gene Expression Omnibus datasets (GSE13507 and GSE32894) and the Xiangya cohort (RNA-sequencing cohort collected from our hospital) were used as the external validation cohort. The least absolute shrinkage and selection operator (LASSO) algorithm and cross-validation were used to screen variables. Cox regression model and random survival forest (RSF) were used to develop the risk score, respectively. Then, we systematically correlated the TNF risk score with the tumor microenvironment (TME) cell infiltration, molecular subtypes of BLCA, and the potential value for predicting the efficacy of immunotherapy. Results: We developed two TNF-based patterns, named TNF cluster 1 and TNF cluster 2. TNF cluster 1 exhibited poorer survival outcome and an inflamed TME characteristic compared with TNF cluster 2. We then filtered out 196 differentially expressed genes between the two TNF clusters and applied the LASSO algorithm and cross-validation to screen out 22 genes to build the risk score. For risk score, we found that RSF exhibited higher efficacy than the Cox regression model, and we chose the risk score developed by RSF for the following analysis. BLCA patients in the higher risk score group showed significantly poorer survival outcomes. Moreover, these results could be validated in the external validation cohorts, including the GSE13507, GSE32894, and Xiangya cohorts. Then, we systematically correlated the risk score with TME cell infiltration and found that it was positively correlated with the infiltration of a majority of immune cells. Also, a higher risk score indicated a basal subtype of BLCA. Notably, the relationship between risk score, TME cell infiltration, and molecular subtypes could be validated in the Xiangya cohort. Conclusion: We developed and validated a robust TNF-based risk score, which could predict prognostic outcomes, TME, and molecular subtypes of BLCA. However, the value of risk score predicting the efficacy of immunotherapy needs further research.
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DOI：
10.1016/j.jid.2020.03.943
发表时间：
2021-01
期刊：
The Journal of investigative dermatology
影响因子：
--
作者：
Jenkins RW;Fisher DE
通讯作者：
Fisher DE
影响因子：
14.9
作者：
Colaprico A;Silva TC;Olsen C;Garofano L;Cava C;Garolini D;Sabedot TS;Malta TM;Pagnotta SM;Castiglioni I;Ceccarelli M;Bontempi G;Noushmehr H
通讯作者：
Noushmehr H
影响因子：
56.9
作者：
Hahne, M;Rimoldi, D;Tschopp, J
通讯作者：
Tschopp, J
影响因子：
10.3
作者：
Mo, Qianxing;Nikolos, Fotis;Chan, Keith Syson
通讯作者：
Chan, Keith Syson
影响因子：
64.8
作者：
Mariathasan S;Turley SJ;Nickles D;Castiglioni A;Yuen K;Wang Y;Kadel EE III;Koeppen H;Astarita JL;Cubas R;Jhunjhunwala S;Banchereau R;Yang Y;Guan Y;Chalouni C;Ziai J;Şenbabaoğlu Y;Santoro S;Sheinson D;Hung J;Giltnane JM;Pierce AA;Mesh K;Lianoglou S;Riegler J;Carano RAD;Eriksson P;Höglund M;Somarriba L;Halligan DL;van der Heijden MS;Loriot Y;Rosenberg JE;Fong L;Mellman I;Chen DS;Green M;Derleth C;Fine GD;Hegde PS;Bourgon R;Powles T
通讯作者：
Powles T