A prediction model for distinguishing lung squamous cell carcinoma from adenocarcinoma.
A prediction model for distinguishing lung squamous cell carcinoma from adenocarcinoma.
复制标题
区分肺鳞癌和腺癌的预测模型
DOI：
10.18632/oncotarget.17038
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发表时间：
2017-08-01
期刊：
影响因子：
--
通讯作者：
Jiang F
中科院分区：
文献类型：
--
作者：
Li H;Jiang Z;Leng Q;Bai F;Wang J;Ding X;Li Y;Zhang X;Fang H;Yfantis HG;Xing L;Jiang F
非小细胞肺癌（NSCLC）的鳞状细胞癌（SCC）和腺癌（AC）的准确分类可以导致肺癌的个性化治疗。我们的目的是建立一个基于mirna的预测模型，用于在手术切除组织和支气管肺泡灌洗（BAL）样本中区分SCC和AC。采用逆转录聚合酶链反应（RT-PCR）技术检测了128个快速冷冻手术肺肿瘤标本中7个组织学亚型相关mirna的表达水平，以建立一个最佳的mirna小组，用于急性区分SCC和AC。这些生物标志物在112个FFPE肺肿瘤组织的独立队列中得到验证。建立了两种mirna （miRs-205-5p和944）的预测模型，该模型用于冷冻组织中SCC与AC的鉴别，曲线下面积（AUC）为0.988，灵敏度为96.55%，特异性为96.43%；用于FFPE标本的鉴别，AUC为0.997，灵敏度为96.43%，特异性为96.43%。预测模型的诊断性能在BAL标本中可重复验证，用于区分SCC和AC，与细胞学相比准确率更高（95.69比68.10%,P < 0.05）。该预测模型对于准确区分外科肺肿瘤组织和液体细胞学标本中的SCC和AC可能具有临床价值。
Accurate classification of squamous cell carcinoma (SCC) from adenocarcinoma (AC) of non–small cell lung cancer (NSCLC) can lead to personalized treatments of lung cancer. We aimed to develop a miRNA-based prediction model for differentiating SCC from AC in surgical resected tissues and bronchoalveolar lavage (BAL) samples. Expression levels of seven histological subtype-associated miRNAs were determined in 128 snap-frozen surgical lung tumor specimens by using reverse transcription-polymerase chain reaction (RT-PCR) to develop an optimal panel of miRNAs for acutely distinguishing SCC from AC. The biomarkers were validated in an independent cohort of 112 FFPE lung tumor tissues, and a cohort of 127 BAL specimens by using droplet digital PCR for differentiating SCC from AC. A prediction model with two miRNAs (miRs-205-5p and 944) was developed that had 0.988 area under the curve (AUC) with 96.55% sensitivity and 96.43% specificity for differentiating SCC from AC in frozen tissues, and 0.997 AUC with 96.43% sensitivity and 96.43% specificity in FFPE specimens. The diagnostic performance of the prediction model was reproducibly validated in BAL specimens for distinguishing SCC from AC with a higher accuracy compared with cytology (95.69 vs. 68.10%; P < 0.05). The prediction model might have a clinical value for accurately discriminating SCC from AC in both surgical lung tumor tissues and liquid cytological specimens.
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DOI：
10.1634/theoncologist.2015-0507
发表时间：
2016-05
期刊：
The oncologist
影响因子：
--
作者：
Kazandjian D;Suzman DL;Blumenthal G;Mushti S;He K;Libeg M;Keegan P;Pazdur R
通讯作者：
Pazdur R
DOI：
10.4137/bic.s37333
发表时间：
2016
期刊：
Biomarkers in cancer
影响因子：
--
作者：
Ma J;Li N;Lin Y;Gupta C;Jiang F
通讯作者：
Jiang F
影响因子：
6.4
作者：
Gao, Lu;Ma, Jie;Jiang, Feng
通讯作者：
Jiang, Feng
影响因子：
10.6
作者：
Anjuman N;Li N;Guarnera M;Stass SA;Jiang F
通讯作者：
Jiang F
DOI：
10.1038/labinvest.2015.88
发表时间：
2015-10
期刊：
Laboratory investigation; a journal of technical methods and pathology
影响因子：
--
作者：
Ma J;Lin Y;Zhan M;Mann DL;Stass SA;Jiang F
通讯作者：
Jiang F