Cell-Type-Specific Profibrotic Scores across Multi-Organ Systems Predict Cancer Prognosis.
Cell-Type-Specific Profibrotic Scores across Multi-Organ Systems Predict Cancer Prognosis.
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DOI：
10.3390/cancers13236024
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发表时间：
2021-11-30
期刊：
影响因子：
5.2
通讯作者：
Zhao Z
中科院分区：
文献类型：
--
作者：
Fan H;Jia P;Zhao Z
纤维化是肿瘤微环境的主要参与者和贡献者。纤维化的变化先于各种人类疾病的早期发展和确立，如纤维化和癌症。能够在单细胞水平上测量这种早期信号对于确定新的机制和针对广泛疾病的潜在药物靶点至关重要。这项研究的目的是利用单细胞转录数据通过计算机识别促纤维细胞群，并识别可以预测癌症预后的基因特征。纤维化是导致死亡的主要原因。关键的促纤维化机制是参与肿瘤发生的常见途径。确定纤维原表型的特征将有助于揭示各种人类疾病早期发展和进展的潜在机制，如纤维化和癌症。成纤维细胞是病毒感染等各种刺激反应的中心细胞。然而，目前还缺乏这一过程中所涉及的细胞类型的全面目录。在这里，我们部署了跨多器官系统(即心、肾、肝和肺)的单细胞转录组数据，以基于单细胞分辨率下的ECM途径活性来识别新的促纤维化细胞群。除了成纤维细胞，我们还报道了肾上皮细胞、内皮细胞、髓样细胞、自然杀伤T细胞和分泌细胞以及肾单位近端曲管细胞的显著参与。在病毒感染途径、糖酵解增强和致癌等方面，细胞类型特异性基因特征得到了丰富；在这项研究中，他们使用独立的数据集进行了验证。通过将这些信号投影到大量的TCGA肿瘤样本中，我们可以使用促纤维化评分来预测患者的预后。我们的促纤维化细胞表型有助于在细胞类型水平上确定新的机制和潜在的药物靶点，用于治疗与ECM途径激活有关的一系列疾病。
Fibrosis is a major player and contributor in the tumor microenvironment. Profibrotic changes precede the early development and establishment of a variety of human diseases, such as fibrosis and cancer. Being able to measure such early signals at the single cell level is critically useful for identifying new mechanisms and potential drug targets for a wide range of diseases. This study was designed to computationally identify profibrotic cell populations using single-cell transcriptomic data and to identify gene signatures that could predict cancer prognosis. Fibrosis is a major cause of mortality. Key profibrotic mechanisms are common pathways involved in tumorigenesis. Characterizing the profibrotic phenotype will help reveal the underlying mechanisms of early development and progression of a variety of human diseases, such as fibrosis and cancer. Fibroblasts have been center stage in response to various stimuli, such as viral infections. However, a comprehensive catalog of cell types involved in this process is currently lacking. Here, we deployed single-cell transcriptomic data across multi-organ systems (i.e., heart, kidney, liver, and lung) to identify novel profibrotic cell populations based on ECM pathway activity at single-cell resolution. In addition to fibroblasts, we also reported that epithelial, endothelial, myeloid, natural killer T, and secretory cells, as well as proximal convoluted tubule cells of the nephron, were significantly actively involved. Cell-type-specific gene signatures were enriched in viral infection pathways, enhanced glycolysis, and carcinogenesis, among others; they were validated using independent datasets in this study. By projecting the signatures into bulk TCGA tumor samples, we could predict prognosis in the patients using profibrotic scores. Our profibrotic cellular phenotype is useful for identifying new mechanisms and potential drug targets at the cell-type level for a wide range of diseases involved in ECM pathway activation.
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影响因子：
5.8
作者：
Kato S;Inui N;Hakamata A;Suzuki Y;Enomoto N;Fujisawa T;Nakamura Y;Watanabe H;Suda T
通讯作者：
Suda T
影响因子：
64.8
作者：
Delorey TM;Ziegler CGK;Heimberg G;Normand R;Yang Y;Segerstolpe Å;Abbondanza D;Fleming SJ;Subramanian A;Montoro DT;Jagadeesh KA;Dey KK;Sen P;Slyper M;Pita-Juárez YH;Phillips D;Biermann J;Bloom-Ackermann Z;Barkas N;Ganna A;Gomez J;Melms JC;Katsyv I;Normandin E;Naderi P;Popov YV;Raju SS;Niezen S;Tsai LT;Siddle KJ;Sud M;Tran VM;Vellarikkal SK;Wang Y;Amir-Zilberstein L;Atri DS;Beechem J;Brook OR;Chen J;Divakar P;Dorceus P;Engreitz JM;Essene A;Fitzgerald DM;Fropf R;Gazal S;Gould J;Grzyb J;Harvey T;Hecht J;Hether T;Jané-Valbuena J;Leney-Greene M;Ma H;McCabe C;McLoughlin DE;Miller EM;Muus C;Niemi M;Padera R;Pan L;Pant D;Pe'er C;Pfiffner-Borges J;Pinto CJ;Plaisted J;Reeves J;Ross M;Rudy M;Rueckert EH;Siciliano M;Sturm A;Todres E;Waghray A;Warren S;Zhang S;Zollinger DR;Cosimi L;Gupta RM;Hacohen N;Hibshoosh H;Hide W;Price AL;Rajagopal J;Tata PR;Riedel S;Szabo G;Tickle TL;Ellinor PT;Hung D;Sabeti PC;Novak R;Rogers R;Ingber DE;Jiang ZG;Juric D;Babadi M;Farhi SL;Izar B;Stone JR;Vlachos IS;Solomon IH;Ashenberg O;Porter CBM;Li B;Shalek AK;Villani AC;Rozenblatt-Rosen O;Regev A
通讯作者：
Regev A
影响因子：
82.9
作者：
通讯作者：
--
影响因子：
13.5
作者：
Jeong, Won-Il;Park, Ogyi;Suh, Yang-Gun;Byun, Jin-Seok;Park, So-Young;Choi, Earl;Kim, Ja-Kyung;Ko, Hyojin;Wang, Hua;Miller, Andrew M.;Gao, Bin
通讯作者：
Gao, Bin
影响因子：
4.7
作者：
Barh D;Aljabali AA;Tambuwala MM;Tiwari S;Serrano-Aroca Á;Alzahrani KJ;Silva Andrade B;Azevedo V;Ganguly NK;Lundstrom K
通讯作者：
Lundstrom K