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Project description:This study focuses on platform comparison to assess performance variability in circulating microRNA (ct-miR) detection, agreement in assignment of a miR signature classifier (MSC) and concordance for the identification of cancer-associated miRs in plasma samples from non‐small cell lung cancer (NSCLC) patients. A plasma cohort of 10 NSCLC patients and 10 healthy donors matched for clinical features and MSC risk level was profiled for miRs expression using two sequencing- and three quantitative PCR (qPCR)-based platforms. Intra- and inter-platform variations were examined by correlation and concordance analysis. MSC risk levels were compared to those estimated using a reference method. Differentially expressed ct-miRs were identified among NSCLC patients and donors and the diagnostic value of those dysregulated in patients was assessed by receiver operating characteristic curve analysis. Downregulation of miR-150-5p was verified by qPCR. The Cancer Genome Atlas (TCGA) lung carcinoma dataset was used for validation at tissue level. Intra-platform reproducibility was consistent whereas the highest values of inter-platform correlations were among qPCR-based platforms. MSC classification concordance was >80% for four platforms. Dysregulation and discriminatory power of miR-150-5p and -210-3p were documented. Both were significantly dysregulated also on TCGA tissue-originated profiles from lung cell carcinoma in comparison to normal samples. Overall, our studies provide a large performance analysis between five different platforms for miRs quantification, indicate the solidity of MSC classifier and identify two noninvasive biomarkers for NSCLC
2022-08-10 | GSE204950 | GEO
Project description:This study focuses on platform comparison to assess performance variability in circulating microRNA (ct-miR) detection, agreement in assignment of a miR signature classifier (MSC) and concordance for the identification of cancer-associated miRs in plasma samples from non‐small cell lung cancer (NSCLC) patients. A plasma cohort of 10 NSCLC patients and 10 healthy donors matched for clinical features and MSC risk level was profiled for miRs expression using two sequencing- and three quantitative PCR (qPCR)-based platforms. Intra- and inter-platform variations were examined by correlation and concordance analysis. MSC risk levels were compared to those estimated using a reference method. Differentially expressed ct-miRs were identified among NSCLC patients and donors and the diagnostic value of those dysregulated in patients was assessed by receiver operating characteristic curve analysis. Downregulation of miR-150-5p was verified by qPCR. The Cancer Genome Atlas (TCGA) lung carcinoma dataset was used for validation at tissue level. Intra-platform reproducibility was consistent whereas the highest values of inter-platform correlations were among qPCR-based platforms. MSC classification concordance was >80% for four platforms. Dysregulation and discriminatory power of miR-150-5p and -210-3p were documented. Both were significantly dysregulated also on TCGA tissue-originated profiles from lung cell carcinoma in comparison to normal samples. Overall, our studies provide a large performance analysis between five different platforms for miRs quantification, indicate the solidity of MSC classifier and identify two noninvasive biomarkers for NSCLC
2022-08-10 | GSE204945 | GEO
Project description:This study focuses on platform comparison to assess performance variability in circulating microRNA (ct-miR) detection, agreement in assignment of a miR signature classifier (MSC) and concordance for the identification of cancer-associated miRs in plasma samples from non‐small cell lung cancer (NSCLC) patients. A plasma cohort of 10 NSCLC patients and 10 healthy donors matched for clinical features and MSC risk level was profiled for miRs expression using two sequencing- and three quantitative PCR (qPCR)-based platforms. Intra- and inter-platform variations were examined by correlation and concordance analysis. MSC risk levels were compared to those estimated using a reference method. Differentially expressed ct-miRs were identified among NSCLC patients and donors and the diagnostic value of those dysregulated in patients was assessed by receiver operating characteristic curve analysis. Downregulation of miR-150-5p was verified by qPCR. The Cancer Genome Atlas (TCGA) lung carcinoma dataset was used for validation at tissue level. Intra-platform reproducibility was consistent whereas the highest values of inter-platform correlations were among qPCR-based platforms. MSC classification concordance was >80% for four platforms. Dysregulation and discriminatory power of miR-150-5p and -210-3p were documented. Both were significantly dysregulated also on TCGA tissue-originated profiles from lung cell carcinoma in comparison to normal samples. Overall, our studies provide a large performance analysis between five different platforms for miRs quantification, indicate the solidity of MSC classifier and identify two noninvasive biomarkers for NSCLC
2022-08-10 | GSE203514 | GEO
Project description:This study focuses on platform comparison to assess performance variability in circulating microRNA (ct-miR) detection, agreement in assignment of a miR signature classifier (MSC) and concordance for the identification of cancer-associated miRs in plasma samples from non‐small cell lung cancer (NSCLC) patients. A plasma cohort of 10 NSCLC patients and 10 healthy donors matched for clinical features and MSC risk level was profiled for miRs expression using two sequencing- and three quantitative PCR (qPCR)-based platforms. Intra- and inter-platform variations were examined by correlation and concordance analysis. MSC risk levels were compared to those estimated using a reference method. Differentially expressed ct-miRs were identified among NSCLC patients and donors and the diagnostic value of those dysregulated in patients was assessed by receiver operating characteristic curve analysis. Downregulation of miR-150-5p was verified by qPCR. The Cancer Genome Atlas (TCGA) lung carcinoma dataset was used for validation at tissue level. Intra-platform reproducibility was consistent whereas the highest values of inter-platform correlations were among qPCR-based platforms. MSC classification concordance was >80% for four platforms. Dysregulation and discriminatory power of miR-150-5p and -210-3p were documented. Both were significantly dysregulated also on TCGA tissue-originated profiles from lung cell carcinoma in comparison to normal samples. Overall, our studies provide a large performance analysis between five different platforms for miRs quantification, indicate the solidity of MSC classifier and identify two noninvasive biomarkers for NSCLC
2022-08-10 | GSE203510 | GEO
Project description:This study focuses on platform comparison to assess performance variability in circulating microRNA (ct-miR) detection, agreement in assignment of a miR signature classifier (MSC) and concordance for the identification of cancer-associated miRs in plasma samples from non‐small cell lung cancer (NSCLC) patients. A plasma cohort of 10 NSCLC patients and 10 healthy donors matched for clinical features and MSC risk level was profiled for miRs expression using two sequencing- and three quantitative PCR (qPCR)-based platforms. Intra- and inter-platform variations were examined by correlation and concordance analysis. MSC risk levels were compared to those estimated using a reference method. Differentially expressed ct-miRs were identified among NSCLC patients and donors and the diagnostic value of those dysregulated in patients was assessed by receiver operating characteristic curve analysis. Downregulation of miR-150-5p was verified by qPCR. The Cancer Genome Atlas (TCGA) lung carcinoma dataset was used for validation at tissue level. Intra-platform reproducibility was consistent whereas the highest values of inter-platform correlations were among qPCR-based platforms. MSC classification concordance was >80% for four platforms. Dysregulation and discriminatory power of miR-150-5p and -210-3p were documented. Both were significantly dysregulated also on TCGA tissue-originated profiles from lung cell carcinoma in comparison to normal samples. Overall, our studies provide a large performance analysis between five different platforms for miRs quantification, indicate the solidity of MSC classifier and identify two noninvasive biomarkers for NSCLC
2022-08-10 | GSE204942 | GEO
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