ArticlePloS one2022
Identification of four serum miRNAs as potential markers to screen for thirteen cancer types.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Machine Learning-Based Identification of Candidate Serum miRNA Features for Pan-Cancer and Cancer Type Classification.Life (Basel, Switzerland) · 2026Article
- Urinary microRNAs for the non-invasive diagnosis of endometriosis identified by next-generation sequencing and machine learning.Reproductive biology and endocrinology : RB&E · 2026Article
- Glioma identification from microRNA biomarkers using machine learning.Frontiers in systems biology · 2026Article
- Decision tree-based machine learning methods for identifying colorectal cancer-associated microRNA signatures and their regulatory networks.Scientific reports · 2025Article
- Identification of hub genes associated with spermatogenesis by bioinformatics analysis.Scientific reports · 2023Article
- Increased levels of microRNA-320 in blood serum and plasma is associated with imminent and advanced lung cancer.Molecular oncology · 2023Article
- Zip Nucleic Acid-Based Genomagnetic Assay for Electrochemical Detection of microRNA-34a.Biosensors · 2023Article
- Identifying potential circulating miRNA biomarkers for the diagnosis and prediction of ovarian cancer using machine-learning approach: application of Boruta.Frontiers in digital health · 2023Article
- Predict ovarian cancer by pairing serum miRNAs: Construct of single sample classifiers.Frontiers in medicine · 2022Article
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2 authors.
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Abstract
introductionCancer consistently remains one of the top causes of death in the United States every year, with many cancer deaths preventable if detected early. Circulating serum miRNAs are a promising, minimally invasive supplement or even an alternative to many current screening procedures. Many studies have shown that different serum miRNAs can discriminate healthy individuals from those with certain types of cancer. Although many of those miRNAs are often reported to be significant in one cancer type, they are also altered in other cancer types. Currently, very few studies have investigated serum miRNA biomarkers for multiple cancer types for general cancer screening purposes.
methodTo identify serum miRNAs that would be useful in screening multiple types of cancers, microarray cancer datasets were curated, yielding 13 different types of cancer with a total of 3352 cancer samples and 2809 non-cancer samples. The samples were divided into training and validation sets. One hundred random forest models were built using the training set to select candidate miRNAs. The selected miRNAs were then used in the validation set to see how well they differentiate cancer from normal samples in an independent dataset. Furthermore, the interactions between these miRNAs and their target mRNAs were investigated.
resultThe random forest models achieved an average of 97% accuracy in the training set with 95% bootstrap confidence interval of 0.9544 to 0.9778. The selected miRNAs were hsa-miR-663a, hsa-miR-6802-5p, hsa-miR-6784-5p, hsa-miR-3184-5p, and hsa-miR-8073. Each miRNA exhibited high area under the curve (AUC) value using receiver operating characteristic analysis. Moreover, the combination of four out of five miRNAs achieved the highest AUC value of 0.9815 with high sensitivity of 0.9773, indicating that these miRNAs have a high potential for cancer screening. miRNA-mRNA and protein-protein interaction analysis provided insights into how these miRNAs play a role in cancer.
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