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Related Concept Videos

lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

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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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As the name suggests, non-LTR retrotransposons lack the long terminal repeats characteristic of the LTR retrotransposons. Additionally, both LTR and non-LTR retrotransposons use distinct mechanisms of mobilization. Non-LTR retrotransposons are further divided into two classes - Long interspersed nuclear elements (LINEs) and short interspersed nuclear elements (SINEs), both of which occur abundantly in most mammals, including humans. Some of the active non-LTR retrotransposons in humans are L1...
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Related Experiment Video

Updated: Dec 27, 2025

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Identification of Potential Prognostic Long Non-Coding RNA Biomarkers for Predicting Recurrence in Patients with

Yan Zhang1, Xing Zhang1, Haixia Zhu2

  • 1Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, People's Republic of China.

Cancer Management and Research
|February 27, 2020
PubMed
Summary

This study identified long non-coding RNAs (lncRNAs) linked to cervical cancer (CC) recurrence. MIR22HG was found to suppress CC recurrence and may serve as a prognostic biomarker for cervical cancer.

Keywords:
TCGAbiomarkercervical cancerlncRNAsrecurrence

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Area of Science:

  • Oncology
  • Molecular Biology
  • Genomics

Background:

  • Cervical cancer (CC) is a prevalent malignancy in women, frequently associated with high recurrence rates post-treatment.
  • Identifying molecular markers for CC recurrence is crucial for improving patient outcomes.

Purpose of the Study:

  • To identify long non-coding RNAs (lncRNAs) associated with cervical cancer recurrence.
  • To develop a recurrence risk score (RRS) model based on identified lncRNAs.
  • To investigate the functional role of key lncRNAs in cervical cancer progression.

Main Methods:

  • Utilized The Cancer Genome Atlas (TCGA) dataset for lncRNA expression data analysis.
  • Employed Cox regression models to identify lncRNAs correlated with recurrence-free survival (RFS).
  • Constructed a recurrence risk score (RRS) model and validated findings through bioinformatics and in vitro experiments.

Main Results:

  • Four lncRNAs (HCG11, CASC15, LINC00189, LINC00905) were associated with worse RFS, while three (HULC, LINC00173, MIR22HG) showed the opposite effect.
  • The RRS model effectively predicted increased recurrence risk in high-risk patients.
  • MIR22HG downregulation was observed in multiple tumor types, including CC, and its increased expression correlated with reduced recurrence risk across various patient subgroups, suppressing CC cell proliferation, migration, and invasion.

Conclusions:

  • MIR22HG plays a significant role in regulating cervical cancer recurrence.
  • MIR22HG demonstrates potential as a prognostic biomarker for cervical cancer.