MicroRNA-based Assay Proposed for Early Detection of Cancer
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By LabMedica International staff writers Posted on 13 Nov 2017 |
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Image: A scanning electron micrograph (SEM) of an ovarian cancer cell (Photo courtesy of Steve Gschmeissner / SPL).
Cancer researchers have proposed using a network of circulating microRNAs to diagnose ovarian carcinoma at a stage earlier than currently possible.
MicroRNAs (miRNAs) are a family of noncoding 19- to 25-nucleotide RNAs that regulate gene expression by targeting messenger RNAs (mRNAs) in a sequence specific manner, inducing translational repression or mRNA degradation, depending on the degree of complementarity between miRNAs and their targets. Many miRNAs are conserved in sequence between distantly related organisms, suggesting that these molecules participate in essential processes. In fact, miRNAs have been shown to be involved in the regulation of gene expression during development, cell proliferation, apoptosis, glucose metabolism, stress resistance, and cancer.
Screening techniques are currently not available for early stage ovarian cancer, making it challenging to diagnose the disease. As recent studies have suggested a role for non-coding RNAs in epithelial ovarian cancer (EOC), investigators at Brigham and Women's Hospital (Boston, MA, USA) and Dana-Farber Cancer Institute (Boston, MA, USA) evaluated the diagnostic potential for a serum miRNA neural network for detection of ovarian cancer.
The investigators combined small RNA sequencing from 179 human serum samples with neural network analysis to produce a miRNA algorithm for diagnosis of EOC. The model significantly outperformed CA125 testing and functioned well regardless of patient age, histology, or stage. Among 454 patients with various diagnoses, the miRNA neural network had 100% specificity for ovarian cancer. After using 325 samples to adapt the neural network to qPCR measurements, the model was validated using 51 independent clinical samples, with a positive predictive value of 91.3% and negative predictive value of 78.6%. Biologic relevance was tested using in situ hybridization on 30 pre-metastatic lesions, showing intratumoral concentration of relevant miRNAs.
"The key is that this test is very unlikely to misdiagnose ovarian cancer and give a positive signal when there is no malignant tumor. This is the hallmark of an effective diagnostic test," said senior author Dr. Dipanjan Chowdhury, chief of the division of radiation and genomic stability at Dana-Farber Cancer Institute.
The miRNA test for early detection of ovarian cancer was described in the October 31, 2017, online edition of the journal eLife.
Related Links:
Brigham and Women's Hospital
Dana-Farber Cancer Institute
MicroRNAs (miRNAs) are a family of noncoding 19- to 25-nucleotide RNAs that regulate gene expression by targeting messenger RNAs (mRNAs) in a sequence specific manner, inducing translational repression or mRNA degradation, depending on the degree of complementarity between miRNAs and their targets. Many miRNAs are conserved in sequence between distantly related organisms, suggesting that these molecules participate in essential processes. In fact, miRNAs have been shown to be involved in the regulation of gene expression during development, cell proliferation, apoptosis, glucose metabolism, stress resistance, and cancer.
Screening techniques are currently not available for early stage ovarian cancer, making it challenging to diagnose the disease. As recent studies have suggested a role for non-coding RNAs in epithelial ovarian cancer (EOC), investigators at Brigham and Women's Hospital (Boston, MA, USA) and Dana-Farber Cancer Institute (Boston, MA, USA) evaluated the diagnostic potential for a serum miRNA neural network for detection of ovarian cancer.
The investigators combined small RNA sequencing from 179 human serum samples with neural network analysis to produce a miRNA algorithm for diagnosis of EOC. The model significantly outperformed CA125 testing and functioned well regardless of patient age, histology, or stage. Among 454 patients with various diagnoses, the miRNA neural network had 100% specificity for ovarian cancer. After using 325 samples to adapt the neural network to qPCR measurements, the model was validated using 51 independent clinical samples, with a positive predictive value of 91.3% and negative predictive value of 78.6%. Biologic relevance was tested using in situ hybridization on 30 pre-metastatic lesions, showing intratumoral concentration of relevant miRNAs.
"The key is that this test is very unlikely to misdiagnose ovarian cancer and give a positive signal when there is no malignant tumor. This is the hallmark of an effective diagnostic test," said senior author Dr. Dipanjan Chowdhury, chief of the division of radiation and genomic stability at Dana-Farber Cancer Institute.
The miRNA test for early detection of ovarian cancer was described in the October 31, 2017, online edition of the journal eLife.
Related Links:
Brigham and Women's Hospital
Dana-Farber Cancer Institute
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