Identifying the Signatures and Rules of Circulating Extracellular MicroRNA for Distinguishing Cancer Subtypes

Abstract

Cancer is one of the most threatening diseases to humans. It can invade multiple significant organs, including lung, liver, stomach, pancreas, and even brain. The identification of cancer biomarkers is one of the most significant components of cancer studies as the foundation of clinical cancer diagnosis and related drug development. During the large-scale screening for cancer prevention and early diagnosis, obtaining cancer-related tissues is impossible. Thus, the identification of cancer-associated circulating biomarkers from liquid biopsy targeting has been proposed and has become the most important direction for research on clinical cancer diagnosis. Here, we analyzed pan-cancer extracellular microRNA profiles by using multiple machine-learning models. The extracellular microRNA profiles on 11 cancer types and non-cancer were first analyzed by Boruta to extract important microRNAs. Selected microRNAs were then evaluated by the Max-Relevance and Min-Redundancy feature selection method, resulting in a feature list, which were fed into the incremental feature selection method to identify candidate circulating extracellular microRNA for cancer recognition and classification. A series of quantitative classification rules was also established for such cancer classification, thereby providing a solid research foundation for further biomarker exploration and functional analyses of tumorigenesis at the level of circulating extracellular microRNA.

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

“…Chung et al (15) reported let-7b, miR-26a, miR-132, and miR-145 as potential biomarkers in ovarian cancer patients. Among the results of Yuan et al's (16) study, has-miR-6784-5p, has-miR-6800-5p, and has-miR-5100 are indicating ovarian-associated cancer signature. 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.…”

Investigating Spatial Correlations Between Land Use and Pedestrian Injury Severity in Crashes Occurring Away From Intersections in Northwest Florida

“…A review of the literature suggests that the Boruta method is one of the most effective feature selection methods that allows us to automatically filter out unnecessary features (noise) ( 34 ). This variable selection algorithm has been widely used in previous studies and outperforms the other variable selection methods in various fields, namely, power system transient stability assessment ( 35 ), modeling soil moisture ( 36 ), and medical science ( 37 ). As a result of Boruta’s variable selection method, we obtain a more reliable list of effective features, bearing greater predictive value on the response variable, that can be used in conjunction with the Boruta method.…”

Multicancer Early Detection Tests for Cancer Diagnosis

“…Lead-time bias occurs when a screeningdetected tumor would have been clinically diagnosed later, after metastasis had occurred, or when the tumor was already incurable at the time of screening detection. 99 In both cases, there is no actual improvement in long-term outcomes. Length-time bias refers to the tendency of screening to preferentially detect slower-growing tumors, which may not significantly impact survival rates.…”