Discovery And Validation Of MiRNA Biomarkers In Tissue And Plasma Of The Patients With Different Types Of Lung Cancer

Lung cancer remains the most common cause of cancer-related deaths among man and woman worldwide. Despite some improvements in surgical techniques and combined therapies over the last several decades, the five-year survival rate for all stages combined is about 15% in the United States and Europe. Lung cancers are classified as either small cell lung cancer (SCLC) or non-small cell lung cancer (NSCLC). The predominant (>80%) histological form of lung cancer is NSCLC including adenocarcinoma and squamous-cell carcinoma.Treatment for lung cancer differs according to the subtype of cancer. The treatment of choice for early stage NSCLC is surgery with a 5 year overall survival of 40%. However, a majority of patients are at an advanced disease stage at the time of diagnosis, which limits first-line therapy to multi-agent chemotherapy and an expected survival is less than 8 months. Recent advances in targeted therapies require greater accuracy in the subclassification of NSCLC.Besides the different treatments on the different subtypes and etiologies of lung cancer, the inter-observer variability and the lack of specific, standardized assays also limit the current abilities to adequately stratify patients for suitable treatments. Treatment decisions for an individual patient with lung cancer will soon be based on detailed tumor and host characteristics. Specific molecular biomarkers to differentiate subtypes of lung cancers are definitely needed.One approach to address this issue might be based on small regulatory RNA molecules, microRNAs (miRNAs), constitute an evolutionary conserved class of endogenously expressed small non-coding RNAs of 20-25 nucleotides (nt) in size that can mediate the expression of target mRNAs and thus-since their discovery about ten years ago-have been implicated with critical functions in cellular development, differentiation, proliferation, and apoptosis. Furthermore, miRNAs have advantages over mRNAs as cancer biomarkers, since they are very stable in vitro and long-lived in vivo. Molecular profiling of clinical tissue specimens is frequently complicated by their cellular heterogeneity. Laser capture microdissection (LCM) has successfully been used to tackle this problem by isolating pure cell populations from tissue sections. So far, the large majority of published miRNA expression studies utilized whole tumor tissues without separating the truly transformed cancerous cells from those other cell types commonly present within a solid tumor. Analysis of such complex tissues could conceal the specific signature of the particular cell type of interest.In this study, we combined LCM with genome-wide miRNA analysis to discovery the potential miRNA biomarkers in the different subtype of lung cancer using frozen surgical specimens. We evaluated the miRNA expression profiles to study their potential role in the tumor formation and molecular classification in lung carcinoma. We then validated the candidate miRNA biomarkers in the lung adenocarcinoma, squamous-cell carcinoma and small cell lung cancer using FFPE surgical specimens.To date, the specificity of tumor markers for a specific tumor is poor. A large number of individuals with an abnormal level of serum cancer marker have a non-malignant condition (false-positive). Also, the sensitivity of many of the serum tests for the presence of malignancy is often poor, resulting in false-negative tests. Therefore, frequent false-positive and false-negative tests result in a limited use of tumor markers as a screening test for malignancy. Therefore, the identification of a new class of blood-based cancer biomarkers, and the development of sensitive and specific clinical assays, need to expand the current clinical capabilities for early cancer detection and reduce cancer mortality.Very recent studies have demonstrated that the tumor-derived circulating miRNAs at levels sufficient to be measurable as biomarkers for cancer detection. The measurement of tumor derived miRNAs in blood could be an important approach for detection of human cancer. Researches have begun to identify the potential miRNA biomarkers in serum or plasma. However, there is no comprehensive study on miRNA expression profiles in serum/plasma of lung cancer patients.In this study, we performed genome-wide miRNA analysis on 59 plasma samples collected from 19 patients with adenocarcinoma lung cancer,10 patients with squamous-cell carcinoma lung cancer,7 patients with small cell lung cancer and 23 healthy individuals. We discovered 64 differentially expressed miRNAs in plasma of the patients with different types of lung cancer. Objective:Discover miRNA biomarkers in LCM-selected epithelial cells from the different types of lung cancer.Methods:An optimized LCM protocol was applied to isolate pure epithelial cells from 136 frozen surgical specimens of lung cancer patients. Genome-wide miRNA analysis was performed to determine the expression profiles in the LCM-selected epithelial cells. GeneSpring GX10 software was used for quantile normalization. Unpaired t-test with Bonferroni correction and one-way analysis of variance (ANOVA) were applied for the differential expression analysis. Hierarchical clustering was performed with Pearson correlation using the differentially expressed miRNAs. Prediction analysis of microarray (PAM predictor and WEAK) was employed to analyze the data acquired on the microarrays. Subsequently, quantitative RT-PCR was used to verfy the miRNA expression profiles.Results:161 differentially expressed miRNAs at the different types of lung cancer tissues were discovered. Four classifiers of miRNA signatures were identified for predicting different types of lung cancer. A minimal set of 7 miRNAs could distinct lung cancer from normal tissue with 98% accuracy; a minimal set of 10 miRNAs could distinct SCLC from NSCLC with 95% accuracy; a minimal set of 2 miRNAs could distinct adenocarcinoma from squamous-cell carcinoma with 93.5% accuracy; a minimal set of 13 miRNAs could distinct amongst adenocarcinoma, squamous-cell carcinoma and small cell lung carcinoma with 95.5% accuracy and a minimal set of 20 miRNAs could distinct non-neoplasm polyps and neoplasm polyps with the highest accuracy of 96.9%. The average quantitative correlation (R) of fold changes between Agilent miRNA microarrays and quantitative RT-PCR was 0.980.Conclusions:The candidate miRNA biomarkers were discovered at the different types of lung cancer tissues. Such biomarkers could accurately discriminate adenocarcinoma, squamous-cell carcinoma and small cell lung carcinoma. The predicted outcomes using the miRNA classifiers were well consistent with the pathological diagnosis. Objective:Validate the candidate miRNA biomarkers in discriminating the different types of lung cancer tissuesMethods:Quantitative RT-PCR was performed on 7 candidate miRNA biomarkers that could discriminate amongst adenocarcinoma, squamous-cell carcinoma and small cell lung carcinoma using 192 FFPE lung tissue specimens. Unpaired t-test was performed for significance analysis. Receiver operating characteristic (ROC) curve analysis and logistic regression analysis were performed to determine the specificity and sensitivity of individual miRNA as diagnostic biomarkers.Results:The different types of lung carcinoma tissues could be accurately discriminated by 7 validated miRNA biomarkers. Of them, the combination of hsa-miR-25 with hsa-miR-375 yielded the accuracy of 89%(AUC=0.919) in discriminating adenocarcinoma from squamous-cell carcinoma; the combination of hsa-miR-25, hsa-miR-27a, hsa-miR-29a, hsa-miR-29b and hsa-miR-34a yielded the accuracy of 98%(AUC=0.990)in discriminating adenocarcinoma from small cell lung cancer, and the combination of hsa-miR-29b with hsa-miR-375 yielded the accuracy of 93%(AUC=0.976) in discriminating squamous-cell carcinoma from small cell lung cancer.Conclusions:The different types of lung carcinoma tissues could be accurately discriminated by 7 validated miRNA biomarkers.Objective:Discover miRNA biomarkers in plasma of patients with the different types of lung cancer.Methods:Genome-wide miRNA analysis using Agilent miRNA microarray was performed on 59 plasma samples collected from 19 patients with adenocarcinoma lung cancer,10 patients with squamous-cell carcinoma lung cancer,7 patients with small cell lung cancer and 23 healthy individuals. Unpaired t-test was performed for significance analysis. Receiver operating characteristic (ROC) curve analysis was performed to determine the specificity and sensitivity of individual miRNA as diagnostic biomarkers.Results:64 differentially expressed miRNAs in plasma of the patients with the different types of lung cancer were discovered. Of the 64 miRNAs,11 miRNAs could distinct the patients with adenocarcinoma lung cancer from healthy individuals, the combination of hsa-miR-383 with hsa-miR-1233 yielded the accuracy of 85% (AUC=0.898); 18 miRNAs could distinct the patients with squamous-cell carcinoma lung cancer from healthy individuals, the combination of hsa-miR-623 with hsa-miR-654-5p yielded the accuracy of 82%(AUC=0.843), and 35 miRNAs could distinct the patients with small cell lung cancer from healthy individuals, the combination of hsa-miR-520b with hsa-miR-139-3p yielded the accuracy of 96% (AUC=0.975).Conclusions:The candidate miRNA biomarkers were discovered in plasma of the patients with the different types of lung cancer. Such biomarkers could accurately discriminate the patients with adenocarcinoma, squamous-cell carcinoma and small cell lung cancer from healthy individuals.