Genomic analyses based on pulmonary adenocarcinoma in situ reveal early lung cancer signature.
Genomic analyses based on pulmonary adenocarcinoma in situ reveal early lung cancer signature.
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DOI：
10.1186/s12920-018-0413-3
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发表时间：
2018-11-20
影响因子：
2.7
通讯作者：
Yang MQ
中科院分区：
文献类型：
--
作者：
Li D;Yang W;Zhang Y;Yang JY;Guan R;Xu D;Yang MQ
非小细胞肺癌（NSCLC）占肺癌的约80%以上。早期非小细胞肺癌可以完全切除，预后良好。然而，大多数病例是在疾病的晚期发现的。浸润性肺癌患者的平均生存率仅为4%左右。原位腺癌（AIS）是肺腺癌的一种中间亚型，表现出早期生长模式，但可发展为侵袭性。在这项研究中，我们使用了来自正常、AIS和浸润性肺癌组织的RNA-seq数据来鉴定一个基因模块，该基因模块代表了AIS作为AIS特异性基因的区别特征。采用两种差异表达分析算法来鉴定AIS特异性基因。然后，通过随机森林选择用于早期肺癌预测的表现最好的AIS特异性基因的子集。最后，使用随机森林，支持向量机（SVM）和人工神经网络（ANN）对四个独立的早期肺癌数据集，包括一个肿瘤教育的血小板（TEPs）数据集的早期肺癌预测的性能进行了评估。在差异表达分析的基础上，鉴定出107个AIS特异性基因，包括93个蛋白质编码基因和14个长链非编码RNA（lncRNA）。与这些基因相关的重要功能包括血管生成和ECM-受体相互作用，这些功能与癌症的发展高度相关，并有助于无烟肺癌。此外，12种AIS特异性lncRNA通过潜在地调节ECM-受体相互作用途径参与肺癌进展。通过随机森林进行的特征选择使用从癌症基因组图谱（TCGA）肺腺癌样本获得的数据集将20个AIS特异性基因鉴定为早期肺癌特征。在这20个特征中，有两个是lncRNA，BLACAT 1和CTD-2527I21.15，据报道它们与膀胱癌、结肠直肠癌和乳腺癌相关。在对三个独立组织样本数据集的盲分类中，这些特征基因一致地产生了约98%的区分早期肺癌与正常病例的准确率。然而，对血小板样本的预测准确率仅为64.35%（敏感性78.1%，特异性50.59%，AUROC 0.747）。AIS与正常和侵袭性肿瘤的比较揭示了疾病特异性基因，并为AIS进展为侵袭性肿瘤的机制提供了新的见解。这些基因也可以作为肺癌早期诊断的标志，具有较高的准确性。从组织癌样品中鉴定的基因标签的表达谱产生了对组织样品的显著的早期癌症预测，然而，对血小板样品的准确性相对较低。本文的在线版本（10.1186/s12920-018-0413-3）包含补充材料，可供授权用户使用。
Non-small cell lung cancer (NSCLC) represents more than about 80% of the lung cancer. The early stages of NSCLC can be treated with complete resection with a good prognosis. However, most cases are detected at late stage of the disease. The average survival rate of the patients with invasive lung cancer is only about 4%. Adenocarcinoma in situ (AIS) is an intermediate subtype of lung adenocarcinoma that exhibits early stage growth patterns but can develop into invasion. In this study, we used RNA-seq data from normal, AIS, and invasive lung cancer tissues to identify a gene module that represents the distinguishing characteristics of AIS as AIS-specific genes. Two differential expression analysis algorithms were employed to identify the AIS-specific genes. Then, the subset of the best performed AIS-specific genes for the early lung cancer prediction were selected by random forest. Finally, the performances of the early lung cancer prediction were assessed using random forest, support vector machine (SVM) and artificial neural networks (ANNs) on four independent early lung cancer datasets including one tumor-educated blood platelets (TEPs) dataset. Based on the differential expression analysis, 107 AIS-specific genes that consisted of 93 protein-coding genes and 14 long non-coding RNAs (lncRNAs) were identified. The significant functions associated with these genes include angiogenesis and ECM-receptor interaction, which are highly related to cancer development and contribute to the smoking-free lung cancers. Moreover, 12 of the AIS-specific lncRNAs are involved in lung cancer progression by potentially regulating the ECM-receptor interaction pathway. The feature selection by random forest identified 20 of the AIS-specific genes as early stage lung cancer signatures using the dataset obtained from The Cancer Genome Atlas (TCGA) lung adenocarcinoma samples. Of the 20 signatures, two were lncRNAs, BLACAT1 and CTD-2527I21.15 which have been reported to be associated with bladder cancer, colorectal cancer and breast cancer. In blind classification for three independent tissue sample datasets, these signature genes consistently yielded about 98% accuracy for distinguishing early stage lung cancer from normal cases. However, the prediction accuracy for the blood platelets samples was only 64.35% (sensitivity 78.1%, specificity 50.59%, and AUROC 0.747). The comparison of AIS with normal and invasive tumor revealed diseases-specific genes and offered new insights into the mechanism underlying AIS progression into an invasive tumor. These genes can also serve as the signatures for early diagnosis of lung cancer with high accuracy. The expression profile of gene signatures identified from tissue cancer samples yielded remarkable early cancer prediction for tissues samples, however, relatively lower accuracy for boold platelets samples. The online version of this article (10.1186/s12920-018-0413-3) contains supplementary material, which is available to authorized users.
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DOI：
10.1093/bioinformatics/btu638
发表时间：
2015-01-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Anders S;Pyl PT;Huber W
通讯作者：
Huber W
影响因子：
8
作者：
Bikkavilli RK;Avasarala S;Van Scoyk M;Arcaroli J;Brzezinski C;Zhang W;Edwards MG;Rathinam MK;Zhou T;Tauler J;Borowicz S;Lussier YA;Parr BA;Cool CD;Winn RA
通讯作者：
Winn RA
影响因子：
37.3
作者：
Jen J;Tang YA;Lu YH;Lin CC;Lai WW;Wang YC
通讯作者：
Wang YC
影响因子：
6
作者：
Lin, Juan;Marquardt, Gabrielle;Spivack, Simon D.
通讯作者：
Spivack, Simon D.
DOI：
10.1097/jto.0b013e31825aba47
发表时间：
2012-09
期刊：
Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer
影响因子：
--
作者：
Bracci PM;Sison J;Hansen H;Walsh KM;Quesenberry CP;Raz DJ;Wrensch M;Wiencke JK
通讯作者：
Wiencke JK