Exosomal miRNAs as novel potential biomarkers for endometriosis

Zhang, Lei; Líu, Hao; Zhu, Liqiong; Luo, Huang-jin; Chen, Xiaolin; Yu, Gui-yuan; Li, Qiuxia; Jin, Ping

doi:10.21203/rs.3.rs-56647/v1

Cited by 3 publications

(1 citation statement)

References 24 publications

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“…The first rule for the identification of Benign Ovarian Disease is rule 58, involving 14 different microRNAs. Among these microRNAs, a specific microRNA named as miR-5100 (MIMAT0022259) has been detected in the plasma of benign ovarian cysts, which can be classified into benign ovarian disease, corresponding with our prediction ( Zhang L. et al, 2020 ). As for Borderline Ovarian Tumor, rule 72 has been confirmed to contribute to the identification of patients with such disease.…”

Section: Resultsmentioning

confidence: 83%

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

Yuan

Chen

et al. 2021

Front. Genet.

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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.

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Section: Resultsmentioning

confidence: 83%