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Related Concept Videos

lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA (lncRNA)...

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Updated: May 28, 2026

MicroRNA Based Liquid Biopsy: The Experience of the Plasma miRNA Signature Classifier (MSC) for Lung Cancer Screening
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Published on: October 26, 2017

Machine Learning-Based Identification of Candidate Serum miRNA Features for Pan-Cancer and Cancer Type

Kaiyan Feng1, Yusheng Bao2, Jingxin Ren2

  • 1Department of Computer Science, Guangdong AIB Polytechnic College, Guangzhou 510507, China.

Life (Basel, Switzerland)
|May 27, 2026
PubMed
Summary
This summary is machine-generated.

Serum microRNA (miRNA) profiles can classify cancer. This study identified specific miRNAs that distinguish between cancer and non-cancer patients and differentiate among 13 solid cancer types, aiding in diagnostic biomarker discovery.

Keywords:
candidate classificatory featuremachine learningmiRNApan-cancer

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Area of Science:

  • Biochemistry
  • Molecular Biology
  • Genomics

Background:

  • MicroRNA (miRNA) regulation is crucial for gene expression and offers insights into disease states.
  • miRNAs are implicated as cancer-associated molecules and potential biomarkers for cancer classification.
  • Serum miRNA profiling is a non-invasive method for disease state analysis.

Purpose of the Study:

  • To identify serum miRNAs that can differentiate between cancer patients and healthy individuals.
  • To discover specific miRNAs capable of distinguishing among 13 distinct solid cancer types.
  • To develop robust classification models for cancer detection and subtyping using miRNA expression data.

Main Methods:

  • Analysis of serum miRNA expression data from 13 solid cancer types and non-cancer controls.
  • Application of seven feature-ranking algorithms to identify significant miRNAs.
  • Utilized incremental feature selection to refine miRNA feature lists and build classification models.

Main Results:

  • Identified candidate miRNAs (e.g., miR-4783-3p, miR-663a) for distinguishing pan-cancer from non-cancer samples.
  • Discovered specific miRNAs (e.g., miR-629-3p, miR-6087) that differentiate among various solid cancer types.
  • Developed effective classification models based on selected serum miRNA features.

Conclusions:

  • Serum miRNA profiling is a promising approach for cancer detection and classification.
  • Specific miRNAs hold potential as diagnostic biomarkers for early cancer detection and subtype identification.
  • Further research can validate these candidate miRNAs for clinical application in oncology.