Gene Coexpression Network Characterizing Microenvironmental Heterogeneity and Intercellular Communication in Pancreatic Ductal Adenocarcinoma: Implications of Prognostic Significance and Therapeutic Target.
Gene Coexpression Network Characterizing Microenvironmental Heterogeneity and Intercellular Communication in Pancreatic Ductal Adenocarcinoma: Implications of Prognostic Significance and Therapeutic Target.
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DOI：
10.3389/fonc.2022.840474
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发表时间：
2022
影响因子：
4.7
通讯作者：
Wang, Boshi
中科院分区：
文献类型：
--
作者：
Wu, Chengsi;Liu, Yizhen;Wei, Dianhui;Tao, Li;Yuan, Lili;Jing, Tiantian;Wang, Boshi
胰腺导管腺癌（PDAC）的特点是强烈的间质参与和异质性。胰腺癌细胞与周围的肿瘤微环境（TME）相互作用，导致肿瘤发展，预后不良和治疗抵抗。在这里，我们的目标是澄清一个基因网络指示TME功能，并找到一个打击胰腺癌的漏洞。通过Seurat软件包处理的单细胞RNA测序数据用于检索细胞组分标记基因（CCMG）。通过WGCNA确定了CCMG的相关网络/模块。建立神经网络模型和风险评分模型进行预后预测。利用NATMI软件进行细胞间通讯分析。通过使用KrasG 12 D驱动的鼠胰腺癌模型在体内评价ITGA 2抑制剂的作用。WGCNA将CCMG分为八个基因共表达网络。来自显著网络的TME基因能够将PDAC样品分层为具有不同突变的两个主要TME亚类。此外，我们生成了一个神经网络模型和风险评分模型，可以稳健地预测预后和治疗结果。一个功能富集分析枢纽基因管理基因网络揭示了细胞连接分子介导的细胞间通讯在PDAC恶性肿瘤的关键作用。ITGA 2的药理学抑制抵消了促癌微环境并改善了体内胰腺病变。通过利用单细胞数据和WGCNA对大量转录组进行去卷积，我们开发了新的PDAC凋亡预测策略。靶向中枢基因ITGA 2在PDAC小鼠模型中减弱肿瘤发展。这些发现可能为PDAC治疗提供新的见解。
Pancreatic ductal adenocarcinoma (PDAC) is characterized by intensive stromal involvement and heterogeneity. Pancreatic cancer cells interact with the surrounding tumor microenvironment (TME), leading to tumor development, unfavorable prognosis, and therapy resistance. Herein, we aim to clarify a gene network indicative of TME features and find a vulnerability for combating pancreatic cancer. Single-cell RNA sequencing data processed by the Seurat package were used to retrieve cell component marker genes (CCMGs). The correlation networks/modules of CCMGs were determined by WGCNA. Neural network and risk score models were constructed for prognosis prediction. Cell–cell communication analysis was achieved by NATMI software. The effect of the ITGA2 inhibitor was evaluated in vivo by using a KrasG12D -driven murine pancreatic cancer model. WGCNA categorized CCMGs into eight gene coexpression networks. TME genes derived from the significant networks were able to stratify PDAC samples into two main TME subclasses with diverse prognoses. Furthermore, we generated a neural network model and risk score model that robustly predicted the prognosis and therapeutic outcomes. A functional enrichment analysis of hub genes governing gene networks revealed a crucial role of cell junction molecule–mediated intercellular communication in PDAC malignancy. The pharmacological inhibition of ITGA2 counteracts the cancer-promoting microenvironment and ameliorates pancreatic lesions in vivo. By utilizing single-cell data and WGCNA to deconvolute the bulk transcriptome, we exploited novel PDAC prognosis–predicting strategies. Targeting the hub gene ITGA2 attenuated tumor development in a PDAC mouse model. These findings may provide novel insights into PDAC therapy.
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影响因子：
5.5
作者：
Liu X;Luo Z;Ren X;Chen Z;Bao X;Zheng J;Zuo Z
通讯作者：
Zuo Z
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：
28.2
作者：
Elyada, Ela;Bolisetty, Mohan;Tuveson, David A.
通讯作者：
Tuveson, David A.
单细胞染色质可及性识别胰岛细胞类型和特定州糖尿病风险的调节计划。
影响因子：
30.8
作者：
Chiou, Joshua;Zeng, Chun;Cheng, Zhang;Han, Jee Yun;Schlichting, Michael;Miller, Michael;Mendez, Robert;Huang, Serina;Wang, Jinzhao;Sui, Yinghui;Deogaygay, Allison;Okino, Mei-Lin;Qiu, Yunjiang;Sun, Ying;Kudtarkar, Parul;Fang, Rongxin;Preissl, Sebastian;Sander, Maike;Gorkin, David U.;Gaulton, Kyle J.
通讯作者：
Gaulton, Kyle J.
DOI：
10.3322/caac.21626
发表时间：
2020-09
期刊：
CA: a cancer journal for clinicians
影响因子：
--
作者：
Grossberg AJ;Chu LC;Deig CR;Fishman EK;Hwang WL;Maitra A;Marks DL;Mehta A;Nabavizadeh N;Simeone DM;Weekes CD;Thomas CR Jr
通讯作者：
Thomas CR Jr