Integrative analysis of long extracellular RNAs reveals a detection panel of noncoding RNAs for liver cancer.
Integrative analysis of long extracellular RNAs reveals a detection panel of noncoding RNAs for liver cancer.
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长细胞外 RNA 的综合分析揭示了肝癌非编码 RNA 的检测组合
作者：
Zhu Y;Wang S;Xi X;Zhang M;Liu X;Tang W;Cai P;Xing S;Bao P;Jin Y;Zhao W;Chen Y;Zhao H;Jia X;Lu S;Lu Y;Chen L;Yin J;Lu ZJ
原理：血浆中的长胞外RNA（exRNA）可以通过新的测序技术进行分析，即使丰度很低。然而，与癌症相关的exRNA及其变异仍然研究不足。研究方法：我们使用多种癌症类型的79个血浆外泌体RNA-seq（exoRNA-seq）数据集研究了不同长exRNA种类（例如长非编码RNA和环状RNA）中的不同变化（即差异表达、选择性剪接、选择性聚腺苷酸化和差异编辑）。然后，我们整合了53个exoRNA-seq数据集和65个自我分析的无细胞RNA-seq（cfRNA-seq）数据集，以识别肝癌患者的复发变异。我们进一步组合了TCGA组织RNA-seq数据集，并通过RT-qPCR在超过100个血浆样本的单个队列中验证了生物标志物候选物。最后，我们使用机器学习模型来识别3种非编码RNA的特征，以检测肝癌。结果如下：我们发现，从exoRNA-seq数据中鉴定出的不同类型的RNA变异在与肿瘤发生和转移、免疫和代谢相关的途径中富集，这表明可以从长exRNA中检测到癌症信号。随后，我们通过整合exoRNA-seq和cfRNA-seq数据集，从肝癌患者的血浆中鉴定出100多种复发性变异。从这些数据集中，5个显著上调的长exRNA通过TCGA数据确认，并在独立组群中通过RT-qPCR验证。当使用机器学习模型联合收割机将这些验证的环状和结构化RNA（SNORD 3B-1，circ-0080695）中的两种与miRNA（miR-122）组合作为一个组来对来自健康供体的肝癌患者进行分类时，交叉验证的平均AUROC为89.4%。所选的3-RNA组在测试和验证集中成功检测到79.2%的AFP阴性样本和77.1%的早期肝癌样本。结论：我们的研究表明，可以在血浆中检测到与癌症相关的不同类型的RNA变异，并确定了肝癌的3-RNA检测面板，特别是对于AFP阴性和早期患者。
Rationale: Long extracellular RNAs (exRNAs) in plasma can be profiled by new sequencing technologies, even with low abundance. However, cancer-related exRNAs and their variations remain understudied. Methods: We investigated different variations (i.e. differential expression, alternative splicing, alternative polyadenylation, and differential editing) in diverse long exRNA species (e.g. long noncoding RNAs and circular RNAs) using 79 plasma exosomal RNA-seq (exoRNA-seq) datasets of multiple cancer types. We then integrated 53 exoRNA-seq datasets and 65 self-profiled cell-free RNA-seq (cfRNA-seq) datasets to identify recurrent variations in liver cancer patients. We further combined TCGA tissue RNA-seq datasets and validated biomarker candidates by RT-qPCR in an individual cohort of more than 100 plasma samples. Finally, we used machine learning models to identify a signature of 3 noncoding RNAs for the detection of liver cancer. Results: We found that different types of RNA variations identified from exoRNA-seq data were enriched in pathways related to tumorigenesis and metastasis, immune, and metabolism, suggesting that cancer signals can be detected from long exRNAs. Subsequently, we identified more than 100 recurrent variations in plasma from liver cancer patients by integrating exoRNA-seq and cfRNA-seq datasets. From these datasets, 5 significantly up-regulated long exRNAs were confirmed by TCGA data and validated by RT-qPCR in an independent cohort. When using machine learning models to combine two of these validated circular and structured RNAs (SNORD3B-1, circ-0080695) with a miRNA (miR-122) as a panel to classify liver cancer patients from healthy donors, the average AUROC of the cross-validation was 89.4%. The selected 3-RNA panel successfully detected 79.2% AFP-negative samples and 77.1% early-stage liver cancer samples in the testing and validation sets. Conclusions: Our study revealed that different types of RNA variations related to cancer can be detected in plasma and identified a 3-RNA detection panel for liver cancer, especially for AFP-negative and early-stage patients.
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影响因子：
14.9
作者：
An O;Dall'Olio GM;Mourikis TP;Ciccarelli FD
通讯作者：
Ciccarelli FD
DOI：
10.1073/pnas.1714397115
发表时间：
2018-06-05
影响因子：
11.1
作者：
Max KEA;Bertram K;Akat KM;Bogardus KA;Li J;Morozov P;Ben-Dov IZ;Li X;Weiss ZR;Azizian A;Sopeyin A;Diacovo TG;Adamidi C;Williams Z;Tuschl T
通讯作者：
Tuschl T
影响因子：
64.5
作者：
Davoli T;Xu AW;Mengwasser KE;Sack LM;Yoon JC;Park PJ;Elledge SJ
通讯作者：
Elledge SJ
影响因子：
14.9
作者：
Kalvari I;Argasinska J;Quinones-Olvera N;Nawrocki EP;Rivas E;Eddy SR;Bateman A;Finn RD;Petrov AI
通讯作者：
Petrov AI
exoRBase：人类血液外泌体中circRNA、lncRNA和mRNA的数据库
影响因子：
14.9
作者：
Li S;Li Y;Chen B;Zhao J;Yu S;Tang Y;Zheng Q;Li Y;Wang P;He X;Huang S
通讯作者：
Huang S