A plasma 9-microRNA signature for lung cancer early detection: a multicenter validation
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ABSTRACT:
PROVIDER: PRJNA1171393 | ENA |
REPOSITORIES: ENA
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Project description:Lung cancer is the most critical concern in oncology worldwide, due to its substantial morbidity and mortality rates. Low-dose computed tomography (LD-CT) screening is considered the best promising strategy to alter the fatal prognosis of this disease, in combination with effective minimally invasive molecular testing. Although, several approaches based on blood-borne biomarkers (e.g. ctDNA, CTC, proteins, antibodies and microRNAs) have been proposed to better identify at-risk populations, and characterize the nature of screening-detected nodules, their use has not been successfully implemented in clinical settings. To this end, we propose a signature of 9 circulating miRNAs derived from a robust multi-platform workflow with a multi-center design, for a total of 276 lung cancer and 451 non-cancer controls, based on the data from two European LD-CT screening cohorts (Poland and Italy).
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Project description:Purpose: Oncologists for pancreatobiliary cancer (PBca) demand a new circulating biomarker superior to carbohydrate antigen 19-9 (CA19-9). This study aimed to identify the serum microRNA signature composed of reproducible and disease-related microRNAs with clinical bioinformatics. Methods: This multicenter study enrolled patients with treatment-naïve PBca and healthy participants. Optimized serum processing condition was evaluated with t-distributed stochastic neighbor embedding (t-SNE) visualization. The serum microRNA candidates were selected in disease association with Weighted Gene Coexpression Network Analysis (WGCNA). A microRNA signature, combination of multiple serum microRNAs, was examined in exploratory, validation and independent validation set. Biology of the diagnostic miRNAs was evaluated using human pancreatic cancer cells. Results: The 284 (healthy 150, PBca 134) of 827 serum samples were processed within 2hr of time-to serum collection, distributed at the same area of t-SNE map, assigned to exploratory set. The 193 optimized samples were assigned to validation (healthy 50, PBca 47) or independent validation set (healthy 50, PBca 46). Index-1 was a combination of the 5 serum microRNAs having disease-association on WGCNA, showed a sensitivity/specificity >80% and an AUC outperforming CA19-9 in exploratory, validation and independent validation set. The AUC of Index-1 for detecting T1 tumor was superior to CA19-9 (0.856 vs. 0.649, p = 0.038). Human pancreatic cancer cells had intra- and extracellular expression of miR-665, a component of Index-1. Transfection of miR-665 to a human pancreatic cancer cell inhibited cell growth. Conclusions: A serum microRNA signature, Index-1, was useful for detecting PBca, which would improve the early diagnosis of PBca.
2025-01-22 | GSE268524 | GEO
Project description:OBJECTIVES:Specific microRNA (miRNA) signatures in biological fluids can facilitate earlier detection of the tumors being then minimally invasive diagnostic biomarkers. Circulating miRNAs have also emerged as promising diagnostic biomarkers for colorectal cancer (CRC) screening. In this study, we investigated the performance of a specific signature of miRNA in plasma samples to design a robust predictive model that can distinguish healthy individuals from those with CRC or advanced adenomas (AA) diseases. METHODS:Case control study of 297 patients from 8 Spanish centers including 100 healthy individuals, 101 diagnosed with AA, and 96 CRC cases. Quantitative real-time reverse transcription was used to quantify a signature of miRNA (miRNA19a, miRNA19b, miRNA15b, miRNA29a, miRNA335, and miRNA18a) in plasma samples. Binary classifiers (Support Vector Machine [SVM] linear, SVM radial, and SVM polynomial) were built for the best predictive model. RESULTS:Area under receiving operating characteristic curve of 0.92 (95% confidence interval 0.871-0.962) was obtained retrieving a model with a sensitivity of 0.85 and specificity of 0.90, positive predictive value of 0.94, and negative predictive value of 0.76 when advanced neoplasms (CRC and AA) were compared with healthy individuals. CONCLUSIONS:We identified and validated a signature of 6 miRNAs (miRNA19a, miRNA19b, miRNA15b, miRNA29a, miRNA335, and miRNA18a) as predictors that can differentiate significantly patients with CRC and AA from those who are healthy. However, large-scale validation studies in asymptomatic screening participants should be conducted.
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