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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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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
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Related Experiment Video

Updated: Jul 4, 2025

From a 2DE-Gel Spot to Protein Function: Lesson Learned From HS1 in Chronic Lymphocytic Leukemia
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Decoding the genetic symphony: Profiling protein-coding and long noncoding RNA expression in T-acute lymphoblastic

Deepak Verma1, Shruti Kapoor2, Sarita Kumari1

  • 1Laboratory Oncology, Dr BRAIRCH, All India Institute of Medical Sciences, New Delhi-110029, India.

PNAS Nexus
|February 8, 2024
PubMed
Summary

This study reveals key gene expression patterns in T-acute lymphoblastic leukemia (T-ALL). Specific gene and lncRNA levels can predict patient survival outcomes, offering new prognostic markers for T-ALL.

Keywords:
ETP-ALLT-ALLgene expressionimmunophenotypeleukemiatranscriptomics

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Area of Science:

  • Hematology
  • Molecular Biology
  • Oncology

Background:

  • T-acute lymphoblastic leukemia (T-ALL) is a complex blood cancer with diverse molecular features.
  • Current understanding of T-ALL's molecular landscape and its prognostic implications is limited.
  • Identifying novel biomarkers is crucial for improving patient outcomes.

Purpose of the Study:

  • To comprehensively analyze the transcriptomic profile of T-ALL.
  • To identify and validate gene expression signatures associated with T-ALL prognosis.
  • To uncover novel molecular markers for predicting patient outcomes.

Main Methods:

  • RNA sequencing was performed on 35 T-ALL patients.
  • Prognostic relevance of 23 molecular targets was validated in a cohort of 99 T-ALL patients.
  • Principal component analysis and survival analyses were employed.

Main Results:

  • Distinct transcriptomic clusters correlated with T-ALL immunophenotypic subtypes.
  • High expression of MEF2C, BAALC, HHEX, and LYL1 indicated poor survival (OS, EFS, RFS).
  • Increased ST20 lncRNA and RAG1 expression showed favorable prognostic impact.

Conclusions:

  • Novel associations between gene expression patterns, clinicopathologic features, and prognosis in T-ALL were identified.
  • Specific genes (MEF2C, BAALC, HHEX, LYL1, LMO2) and lncRNA (ST20) alongside RAG1 serve as potential prognostic markers.
  • These findings contribute to understanding T-ALL molecular pathogenesis and may guide therapeutic strategies.