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MicroRNAs01:22

MicroRNAs

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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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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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MicroRNAs Expression Patterns Predict Tumor Mutational Burden in Colorectal Cancer.

Jiahao Huang1,2,3, Haizhou Liu4, Yang Zhao5

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A new microRNA (miRNA) signature accurately predicts tumor mutational burden (TMB) in colorectal cancer (CRC) patients. This finding may guide immune checkpoint inhibitor therapy selection for CRC.

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Area of Science:

  • Oncology
  • Genomics
  • Immunology

Background:

  • Tumor mutational burden (TMB) is a potential biomarker for predicting response to immune checkpoint inhibitors (ICIs) in colorectal cancer (CRC).
  • MicroRNAs (miRNAs) play a role in regulating anti-tumor immune responses.
  • Identifying predictive biomarkers for TMB is crucial for optimizing CRC treatment strategies.

Purpose of the Study:

  • To determine miRNA expression patterns in CRC patients.
  • To develop and validate a miRNA-based signature for predicting TMB in CRC.
  • To assess the performance of the miRNA signature compared to microsatellite instability (MSI).

Main Methods:

  • Tumor mutational burden (TMB) was measured using next-generation sequencing (NGS) on formalin-fixed paraffin-embedded samples.
  • MicroRNA expression data from The Cancer Genome Atlas (TCGA) was analyzed to identify differentially expressed miRNAs between high and low TMB groups.
  • A miRNA signature was constructed using the least absolute shrinkage and selection operator (LASSO) method and validated in independent cohorts using RT-PCR.

Main Results:

  • A four-miRNA signature was identified as a robust predictor of TMB in CRC patients.
  • The signature demonstrated high accuracy in predicting TMB: 0.963 (training set), 0.902 (test set), and 0.946 (total set).
  • The miRNA signature outperformed microsatellite instability (MSI) in predicting TMB within the TCGA dataset, although MSI showed a stronger correlation in the validation cohort.

Conclusions:

  • This study presents the first miRNA-based signature classifier validated with high-quality clinical data for accurate TMB prediction in CRC.
  • The developed miRNA signature offers a promising tool for predicting TMB and potentially guiding ICI therapy selection in colorectal cancer.
  • Further validation in larger, diverse patient cohorts is warranted to confirm the clinical utility of this miRNA signature.