A prediction model for distinguishing lung squamous cell carcinoma from adenocarcinoma

• Published in: Oncotarget Vol. 8; no. 31; pp. 50704 - 50714
• Main Authors: Li, Hui, Jiang, Zhengran, Leng, Qixin, Bai, Fan, Wang, Juan, Ding, Xiaosong, Li, Yuehong, Zhang, Xianghong, Fang, HongBin, Yfantis, Harris G, Xing, Lingxiao, Jiang, Feng
• Format: Journal Article
• Language: English
• Published: United States Impact Journals LLC 01.08.2017
• Subjects: Research Paper; lung cancer; cytology; histology; biomarkers; MiRNA
• ISSN: 1949-2553; 1949-2553
• DOI: 10.18632/oncotarget.17038

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%; < 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.