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LncRNAs and EGFRvIII sequestered in TEPs enable blood-based NSCLC diagnosis.

ABSTRACT: Background:Tissue biopsy-based cancer diagnosis has limitations because of the fact that tumor tissues are in constant evolution and extremely heterogeneous. The current study was aimed to examine whether tumor-educated blood platelets (TEPs) might be a potential all-in-one source for blood-based cancer diagnostics to overcome the limitations of conventional cancer biopsy. Methods:In the present study, we evaluated the expression pattern of MAGI2 antisense RNA 3 (MAGI2-AS3) and ZNFX1 antisense RNA 1 (ZFAS1) in both plasma and platelets of 101 non-small-cell lung cancer (NSCLC) patients. Receiver operating characteristic (ROC) curve was generated to evaluate their diagnostic potential. In addition, epidermal growth factor receptor (EGFR) mutations were detected in DNA and RNA samples of platelets for companion diagnostics. Results:Our results showed that the levels of MAGI2-AS3 and ZFAS1 in both plasma and platelets of NSCLC patients were significantly downregulated than those in healthy controls. A positive correlation of long noncoding RNA expression was observed between platelets and plasma (r=0.738 for MAGI2-AS3, r=0.751 for ZFAS1, respectively). By ROC analysis, we found that molecular interrogation of MAGI2-AS3 and ZFAS1 in TEPs and plasma can offer valuable diagnostic performance for NSCLC patients (area under the ROC curve [AUC] MAGI2-AS3 = 0.853/0.892, and AUC ZFAS1 =0.780/0.744 for diagnosing adenocarcinoma and squamous cell carcinoma cases from controls, respectively). Clinicopathologic characteristic analysis further revealed that MAGI2-AS3 level significantly correlated with tumor-node-metastasis (TNM) stage (p=0.001 in TEPs, p=0.003 in plasma), lymph-node metastasis (p=0.016 in TEPs, p=0.023 in plasma), and distant metastasis (p=0.045 in TEPs, p=0.045 in plasma), while ZFAS1 level was only correlated with TNM stage (p=0.005 in TEPs, p=0.044 in plasma). Furthermore, EGFRvIII RNA existed in both TEPs and plasma, but EGFR intracellular mutations cannot be detected in DNA of TEPs isolated from NSCLC. Conclusion:Our data suggested that TEP is a promising source for NSCLC diagnosis and companion diagnostics.

SUBMITTER: Luo CL

PROVIDER: S-EPMC5997181 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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LncRNAs and <i>EGFRvIII</i> sequestered in TEPs enable blood-based NSCLC diagnosis.

Luo Chang-Liang CL Xu Zhi-Gao ZG Chen Hao H Ji Jia J Wang Yu-Hui YH Hu Wei W Wang Kun K Zhang Wu-Wen WW Yuan Chun-Hui CH Wang Fu-Bing FB

Cancer management and research 20180608

<h4>Background</h4>Tissue biopsy-based cancer diagnosis has limitations because of the fact that tumor tissues are in constant evolution and extremely heterogeneous. The current study was aimed to examine whether tumor-educated blood platelets (TEPs) might be a potential all-in-one source for blood-based cancer diagnostics to overcome the limitations of conventional cancer biopsy.<h4>Methods</h4>In the present study, we evaluated the expression pattern of MAGI2 antisense RNA 3 (<i>MAGI2-AS3</i>) ...[more]

PMID: 29922089

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Project description:BackgroundEarly diagnosis is crucial for effective medical management of cancer patients. Tissue biopsy has been widely used for cancer diagnosis, but its invasive nature limits its application, especially when repeated biopsies are needed. Over the past few years, genomic explorations have led to the discovery of various blood-based biomarkers. Tumor Educated Platelets (TEPs) have, of late, generated considerable interest due to their ability to infer tumor existence and subtype accurately. So far, a majority of the studies involving TEPs have offered marker-panels consisting of several hundreds of genes. Profiling large numbers of genes incur a significant cost, impeding its diagnostic adoption. As such, it is important to construct minimalistic molecular signatures comprising a small number of genes.ResultsTo address the aforesaid challenges, we analyzed publicly available TEP expression profiles and identified a panel of 11 platelet-genes that reliably discriminates between cancer and healthy samples. To validate its efficacy, we chose non-small cell lung cancer (NSCLC), the most prevalent type of lung malignancy. When applied to platelet-gene expression data from a published study, our machine learning model could accurately discriminate between non-metastatic NSCLC cases and healthy samples. We further experimentally validated the panel on an in-house cohort of metastatic NSCLC patients and healthy controls via real-time quantitative Polymerase Chain Reaction (RT-qPCR) (AUC = 0.97). Model performance was boosted significantly after artificial data-augmentation using the EigenSample method (AUC = 0.99). Lastly, we demonstrated the cancer-specificity of the proposed gene-panel by benchmarking it on platelet transcriptomes from patients with Myocardial Infarction (MI).ConclusionWe demonstrated an end-to-end bioinformatic plus experimental workflow for identifying a minimal set of TEP associated marker-genes that are predictive of the existence of cancers. We also discussed a strategy for boosting the predictive model performance by artificial augmentation of gene expression data.

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LncRNAs and EGFRvIII sequestered in TEPs enable blood-based NSCLC diagnosis.

Publications

LncRNAs and <i>EGFRvIII</i> sequestered in TEPs enable blood-based NSCLC diagnosis.

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