Identification of four serum miRNAs as potential markers to screen for thirteen cancer types

Abstract

Introduction Cancer 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. Method To 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. Result The 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.

Identifying potential circulating miRNA biomarkers for the diagnosis and prediction of ovarian cancer using machine-learning approach: application of Boruta

“…Figure 5 presents the common miRNAs between two related studies ( 18 , 21 ) and miRNAs that were obtained in our study. There is some evidence in the literature for the biomarkers included in our study.…”

“…Jeon et al ( 17 ) reported that the serum and tissue miR-1290 was significantly elevated in patients with epithelial ovarian cancer compared with patients with benign ovarian neoplasm. Chen et al ( 18 ) reported a total of 19 miRNAs, which were identified by random forest models, that were important in cancer diagnosis. In this study, the top five miRNAs with the highest frequency were chosen to be the biomarker candidates for cancer screening, which has-miR-3184-5p achieved a high rank.…”

Identification of hub genes associated with spermatogenesis by bioinformatics analysis

“…The miRNA of has-miR-3127-5p regulating the target key genes of tumor mutation burden (TMB), has a potential effect on the therapeutic responses in cancers 28 . In addition, serum has-miR-3184-5p was predicted as a diagnosis biomarker for multiple cancer types 29 . Both miRNAs have not been identified with spermatogenesis or testis development yet.…”

Predict ovarian cancer by pairing serum miRNAs: Construct of single sample classifiers

“…Notably, a pre-printed article trains serum samples of 13 cancers (including OVC) and non-cancer serum samples and identifies a set of serum pan-cancer biomarkers consisting of five miRNAs ( 26 ). We found that one of the selected miRNAs, miR-6784-5p, is also included in our 13-miRPairs classifier, indicating the pan-cancer predictive ability of this miRNA.…”