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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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Non-LTR Retrotransposons03:18

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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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Correction: Luca et al. Global and Regional Diagnostic Results of Progress Toward Cervical Cancer Elimination, According to the WHO Strategy: A Systematic Literature Review with Narrative Synthesis. <i>Diagnostics</i> 2026, <i>16</i>, 1224.

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Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
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Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

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Integrative Long Non-Coding RNA Analysis and Recurrence Prediction in Cervical Cancer Using a Recurrent Neural

Geeitha Senthilkumar1, Renuka Pitchaimuthu1, Prabu Sankar Panneerselvam2

  • 1Department of Information Technology, M. Kumarasamy College of Engineering, Thalavapalayam, Karur 639113, Tamil Nadu, India.

Diagnostics (Basel, Switzerland)
|November 27, 2025
PubMed
Summary
This summary is machine-generated.

A new nine long non-coding RNA (lncRNA) signature, combined with deep learning, effectively stratifies patients by their risk of recurrent cervical cancer, improving prognostic accuracy.

Keywords:
biomarkerlong non-coding RNAprognosisrecurrent cervical cancerrecurrent neural network

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Area of Science:

  • Oncology
  • Genomics
  • Bioinformatics

Background:

  • Recurrent cervical cancer poses a significant threat to patient survival.
  • Accurate prognostic models are crucial for identifying high-risk individuals.
  • Current models require enhancement with integrated clinical and molecular data.

Purpose of the Study:

  • To integrate clinical data with the GSE44001 dataset to identify cervical cancer recurrence risk factors.
  • To stratify patients into high-, moderate-, and low-risk groups.
  • To identify a long non-coding RNA (lncRNA) gene signature for recurrent cervical cancer.

Main Methods:

  • Utilized the GSE44001 dataset from the NCBI GEO Database, focusing on 138 recurrent cervical cancer patients.
  • Filtered and analyzed long non-coding RNAs (lncRNAs) using the GENCODE Annotation tool.
  • Employed the Least Absolute Shrinkage Selection Operator (LASSO) for feature selection and risk value assignment based on lncRNA expression and coefficients.

Main Results:

  • A Recurrent Neural Network (RNN) Long Short-Term Memory model showed prognostic value, with high-risk patients having shorter recurrence-free survival (p < 0.05).
  • Specific lncRNA markers (ATXN8OS, C5orf60, INE1) were associated with recurrent disease.
  • Other markers (KCNQ1DN, LOH12CR2, RFPL1S, KCNQ1OT1, EMX2OS) correlated with earlier or moderate stage diagnoses.

Conclusions:

  • A nine-lncRNA signature demonstrates significant potential for predicting cervical cancer recurrence.
  • The integration of this lncRNA signature with deep learning provides a robust method for risk stratification.
  • This approach can aid in identifying patients who may benefit from intensified monitoring or treatment.