Analysis of Immune-Related Signatures Related to CD4+ T Cell Infiltration With Gene Co-Expression Network in Pancreatic Adenocarcinoma.
Analysis of Immune-Related Signatures Related to CD4+ T Cell Infiltration With Gene Co-Expression Network in Pancreatic Adenocarcinoma.
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胰腺癌中与基因共表达网络 CD4 T 细胞浸润相关的免疫相关特征分析
DOI：
10.3389/fonc.2021.674897
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发表时间：
2021
影响因子：
4.7
通讯作者：
Liang C
中科院分区：
文献类型：
--
作者：
Tan Z;Lei Y;Zhang B;Shi S;Liu J;Yu X;Xu J;Liang C
胰腺导管腺癌是最具侵袭性的实体恶性肿瘤之一。免疫治疗和靶向治疗证实对PDAC有一定疗效。本研究的目的是开发一种免疫相关的分子标记物，以提高预测III期和IV期PDAC患者的能力。在这项研究中，加权基因共表达网络（WGCNA）分析和去卷积算法（CIBERSORT），评估免疫细胞的细胞成分被用来评估PDAC表达数据从GEO（基因表达Omnibus）数据集，并确定与CD 4 + T细胞相关的模块。应用LASSO考克斯回归分析和Kaplan-Meier曲线来选择和构建TCGA III期和IV期PDAC患者（N = 126）中的预后多基因签名。随后在国际癌症基因组联盟（ICGC，N = 62）和复旦大学上海肿瘤中心（FUSCC，N = 42）队列中进行独立的III期和IV期基因签名验证。遗传性生殖细胞突变和肿瘤免疫学研究是PDAC发病的分子机制。采用单因素和多因素考克斯回归分析方法对影响预后的因素进行分析。最后，根据TCGA-PDAC数据集创建预后列线图。建立了包括NAPSB、ZNF 831、CXCL 9和PYHIN 1的四基因签名来预测PDAC的总生存期。该特征还在两个独立的验证队列中稳健地预测了生存率。四基因签名可以将患者分为高风险组和低风险组，通过对数秩检验验证了总生存期的差异。四种基因的表达与免疫抑制活性（PD-L1和PD 1）呈正相关。TCGA-PDAC数据集中免疫相关基因列线图和相应的校准曲线显示了预测3年生存率的显著性能。我们构建了一种新的四基因签名，通过应用WGCNA和CIBERSORT算法对转录组数据进行评分来预测III期和IV期PDAC患者的预后，这与传统的筛选癌症和健康组织中差异基因的方法不同。本研究结果可为预测晚期PDAC患者的生存期提供参考，并有助于对晚期PDAC患者进行个体化治疗。
Pancreatic ductal adenocarcinoma (PDAC) is one of the most invasive solid malignancies. Immunotherapy and targeted therapy confirmed an existing certain curative effect in treating PDAC. The aim of this study was to develop an immune-related molecular marker to enhance the ability to predict Stages III and IV PDAC patients. In this study, weighted gene co-expression network (WGCNA) analysis and a deconvolution algorithm (CIBERSORT) that evaluated the cellular constituent of immune cells were used to evaluate PDAC expression data from the GEO (Gene Expression Omnibus) datasets, and identify modules related to CD4+ T cells. LASSO Cox regression analysis and Kaplan–Meier curve were applied to select and build prognostic multi-gene signature in TCGA Stages III and IV PDAC patients (N = 126). This was followed by independent Stages III and IV validation of the gene signature in the International Cancer Genome Consortium (ICGC, N = 62) and the Fudan University Shanghai Cancer Center (FUSCC, N = 42) cohort. Inherited germline mutations and tumor immunity exploration were applied to elucidate the molecular mechanisms in PDAC. Univariate and Multivariate Cox regression analyses were applied to verify the independent prognostic factors. Finally, a prognostic nomogram was created according to the TCGA-PDAC dataset. A four-gene signature comprising NAPSB, ZNF831, CXCL9 and PYHIN1 was established to predict overall survival of PDAC. This signature also robustly predicted survival in two independent validation cohorts. The four-gene signature could divide patients into high and low-risk groups with disparity overall survival verified by a Log-rank test. Expression of four genes positively correlated with immunosuppression activity (PD-L1 and PD1). Immune-related genes nomogram and corresponding calibration curves showed significant performance for predicting 3-year survival in TCGA-PDAC dataset. We constructed a novel four-gene signature to predict the prognosis of Stages III and IV PDAC patients by applying WGCNA and CIBERSORT algorithm scoring to transcriptome data different from traditional methods of filtrating for differential genes in cancer and healthy tissues. The findings may provide reference to predict survival and was beneficial to individualized management for advanced PDAC patients.
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影响因子：
14.9
作者：
Frost HR;Amos CI
通讯作者：
Amos CI
影响因子：
14.9
作者：
Ritchie ME;Phipson B;Wu D;Hu Y;Law CW;Shi W;Smyth GK
通讯作者：
Smyth GK
影响因子：
3.7
作者：
Raman P;Maddipati R;Lim KH;Tozeren A
通讯作者：
Tozeren A
WGCNA：用于加权相关网络分析的 R 包。
影响因子：
3
作者：
Langfelder P;Horvath S
通讯作者：
Horvath S
影响因子：
5.7
作者：
Zhang, Shichao;Zhang, Erdong;Zeng, Zhu
通讯作者：
Zeng, Zhu