ArticleOncotarget2017
A prediction model for distinguishing lung squamous cell carcinoma from adenocarcinoma.
Article in Oncotarget, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Review
- Bronchoalveolar lavage fluid assessment facilitates precision medicine for lung cancer.Cancer biology & medicine · 2023Review
- Utilizing MiSeq Sequencing to Detect Circulating microRNAs in Plasma for Improved Lung Cancer Diagnosis.International journal of molecular sciences · 2023Article
- Article
- Bronchoalveolar Lavage Fluid-Isolated Biomarkers for the Diagnostic and Prognostic Assessment of Lung Cancer.Diagnostics (Basel, Switzerland) · 2022Review
- Current and Emerging Applications of Droplet Digital PCR in Oncology: An Updated Review.Molecular diagnosis & therapy · 2022Review
- High-Throughput Detection of Multiple miRNAs and Methylated DNA by Droplet Digital PCR.Journal of personalized medicine · 2021Article
- Microbiota Biomarkers for Lung Cancer.Diagnostics (Basel, Switzerland) · 2021Article
- Article
- Integrated analysis of miRNAs and DNA methylation identifies miR-132-3p as a tumor suppressor in lung adenocarcinoma.Thoracic cancer · 2020Article
- A Strong Decrease inFrontiers in oncology · 2019Article
- A Plasma Long Noncoding RNA Signature for Early Detection of Lung Cancer.Translational oncology · 2018Article
- A Direct Plasma miRNA Assay for Early Detection and Histological Classification of Lung Cancer.Translational oncology · 2018Article
- An integromic signature for lung cancer early detection.Oncotarget · 2018Article
- An epigenetic classifier for early stage lung cancer.Clinical epigenetics · 2018Article
- A plasma miRNA signature for lung cancer early detection.Oncotarget · 2017Article
- Plasma exosome miR-196a and miR-1246 are potential indicators of localized pancreatic cancer.Oncotarget · 2017Article
- A classifier integrating plasma biomarkers and radiological characteristics for distinguishing malignant from benign pulmonary nodules.International journal of cancer · 2017Article
Corrections and comments
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Authors and funding
12 authors.
Funding
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%;
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Registered trials
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