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lncRNA - Long Non-coding RNAs02:39

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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...
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MicroRNA (miRNA) are short, regulatory RNA transcribed from introns (non-coding regions of a gene) or intergenic regions (stretches of DNA present between genes). Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself, forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After the pre-miRNA...
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MicroRNA (miRNA) are short, regulatory RNA transcribed from introns—non-coding regions of a gene—or intergenic regions—stretches of DNA present between genes. Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After...
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Classifying Lung Neuroendocrine Neoplasms through MicroRNA Sequence Data Mining.

Justin J M Wong1, Paula S Ginter2, Kathrin Tyryshkin1

  • 1Laboratory of Translational RNA Biology, Department of Pathology and Molecular Medicine, Queen's University, Kingston, ON K7L 3N6, Canada.

Cancers
|September 22, 2020
PubMed
Summary
This summary is machine-generated.

MicroRNAs (miRNAs) can help classify lung neuroendocrine neoplasms (NENs). Specific miRNA expression patterns, like miR-18a and miR-155, accurately distinguish between carcinoids and neuroendocrine carcinomas.

Keywords:
classificationlung neuroendocrine neoplasmsmarkersmicroRNAsmall RNA sequencing

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Area of Science:

  • Oncology
  • Molecular Biology
  • Genomics

Background:

  • Lung neuroendocrine neoplasms (NENs) present classification challenges due to subtle histologic variations.
  • MicroRNAs (miRNAs) are recognized as significant biomarkers in various neoplastic conditions.

Purpose of the Study:

  • To investigate the utility of miRNA expression profiles as diagnostic markers for lung NENs.
  • To develop a miRNA-based classification system to differentiate between lung NEN subtypes.

Main Methods:

  • Comprehensive miRNA expression profiling was performed on lung NEN samples using barcoded small RNA sequencing.
  • Samples included typical carcinoid (TC), atypical carcinoid (AC), small cell lung carcinoma (SCLC), and large cell neuroendocrine carcinoma (LCNEC).
  • Expression data was analyzed to identify differential miRNA signatures and build classification models.

Main Results:

  • miR-21 and miR-375 were found to be abundant across all lung NENs.
  • The ratio of miR-21/miR-375 expression was significantly lower in carcinoids (TC, AC) compared to neuroendocrine carcinomas (NECs; SCLC, LCNEC).
  • A classifier using miR-18a and miR-155 achieved 93% and 100% accuracy in discriminating carcinoids from NECs in discovery and validation sets, respectively. Candidate markers for subtype discrimination were also identified.

Conclusions:

  • miRNA expression profiling offers a promising approach for classifying lung NENs.
  • Specific miRNAs, such as miR-18a and miR-155, demonstrate high accuracy in distinguishing major NEN categories.
  • Further external validation is necessary to confirm these findings due to the study's sample size.