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Related Concept Videos

lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

8.6K
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...
8.6K
  1. Home
  3. Research Domains
  5. Biomedical And Clinical Sciences
  7. Oncology And Carcinogenesis
  9. Predictive And Prognostic Markers
  11. Machine Learning Combined With Single-cell Analysis Reveals Predictive Capacity And Immunotherapy Response Of T Cell Exhaustion-associated Lncrnas In Uterine Corpus Endometrial Carcinoma

Machine learning combined with single-cell analysis reveals predictive capacity and immunotherapy response of T cell exhaustion-associated lncRNAs in uterine corpus endometrial carcinoma

Feng Jiang1, Ziyu Tao2, Yun Zhang3

  • 1Department of Neonatology, Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China.

Cellular Signalling
|February 4, 2024

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Author Spotlight: Advancing Reproductive Immunology with a Protocol for the Quantitative Evaluation of Endometrial Immune Cells
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Author Spotlight: Advancing Reproductive Immunology with a Protocol for the Quantitative Evaluation of Endometrial Immune Cells

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Author Spotlight: Multiplex Immunohistochemistry for Understanding Immune Regulation by Uterine NK Cells in Pregnancy
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View abstract on PubMed

Summary
This summary is machine-generated.

This study identifies long non-coding RNAs (lncRNAs) linked to T-cell exhaustion (TEXLs) in Uterine Corpus Endometrial Carcinoma (UCEC). Predictive models for TEXLs in UCEC were developed, aiding in understanding immune dysfunction and treatment response.

Area of Science:

  • Oncology
  • Immunology
  • Genomics

Background:

  • T-cell exhaustion is a key factor in cancer immune dysfunction.
  • Long non-coding RNAs (lncRNAs) are implicated in Uterine Corpus Endometrial Carcinoma (UCEC) progression and treatment.
  • The role of lncRNAs in T-cell exhaustion (TEXLs) within UCEC remains unexplored.

Purpose of the Study:

  • To identify TEXLs associated with UCEC.
  • To develop predictive models for TEXLs in UCEC.
  • To investigate the immune features and molecular mechanisms related to TEXLs in UCEC.

Main Methods:

  • Utilized transcriptome and single-cell sequencing data from TCGA and GEO.
  • Employed co-expression analysis and univariate Cox regression to identify prognostic-associated TEXLs (pTEXLs).
Keywords:
Immune infiltrationMachine learningT cell exhaustionlncRNA

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  • Developed prognostic and diagnostic models using LASSO regression and NMF, validated with in vitro and in vivo experiments.

    • Main Results:

    • Established valid predictive models for TEXLs in UCEC.
    • Identified a lower-risk subgroup with improved response to immune checkpoint blockade.
    • MIEN1 was identified as a key diagnostic gene, and its suppression reduced UCEC cell proliferation and invasion, potentially via CD8+ T cell exhaustion.

    Conclusions:

    • Elucidated the association between TEXLs and UCEC.
    • Developed a stable pTEXLs risk prediction model and a diagnostic model for UCEC.