A prediction model for distinguishing lung squamous cell carcinoma from adenocarcinoma
Metrics: PDF 731 views | HTML 1018 views | ?
Hui Li1,*, Zhengran Jiang2,3,*, Qixin Leng2, Fan Bai1, Juan Wang4, Xiaosong Ding1, Yuehong Li4, Xianghong Zhang1,4, HongBin Fang5, Harris G Yfantis6, Lingxiao Xing1 and Feng Jiang2
1Department of Pathology, Hebei Medical University, Shijiazhuang, Hebei, China
2Department of Pathology, the University of Maryland School of Medicine, Baltimore, Maryland, USA
3The F. Edward Hébert School of Medicine at the Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA
4Department of Pathology, Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
5Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University Medical Center, Washington, D.C., USA
6Pathology and Laboratory Medicine, Baltimore Veterans Affairs Medical Center, Baltimore, Maryland, USA
*These authors contributed equally to this work
Lingxiao Xing, email: email@example.com
Feng Jiang, email: firstname.lastname@example.org
Keywords: MiRNA, biomarkers, lung cancer, histology, cytology
Received: March 13, 2017 Accepted: April 04, 2017 Published: April 11, 2017
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.
All site content, except where otherwise noted, is licensed under a Creative Commons Attribution 3.0 License.