A Prediction Model for Distinguishing Lung Squamous Cell Carcinoma from Adenocarcinoma

Overview

Journal Oncotarget

Specialty Oncology

Date 2017 Sep 9

PMID 28881596

Citations 17

Authors

Hui Li

Zhengran Jiang

Qixin Leng

Fan Bai

Juan Wang

Xiaosong Ding

Yuehong Li

Xianghong Zhang

Hongbin Fang

Harris G Yfantis

Lingxiao Xing

Feng Jiang

Affiliations

Soon will be listed here.

Abstract

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.

Citing Articles

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