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Related Concept Videos

lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

8.5K
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...
8.5K
  1. Home
  3. Research Domains
  5. Biomedical And Clinical Sciences
  7. Oncology And Carcinogenesis
  9. Predictive And Prognostic Markers
  11. Prognosis Prediction Of Head And Neck Squamous Cell Carcinoma Through The Basement Membrane-related Lncrna Risk Model

Prognosis prediction of head and neck squamous cell carcinoma through the basement membrane-related lncRNA risk model

Wenchao Bu1, Mingguo Cao1, Xinru Wu1

  • 1School of Medicine, Lishui University, Lishui, Zhejiang, China.

Frontiers in Molecular Biosciences
|November 7, 2024

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Development of Compendium for Esophageal Squamous Cell Carcinoma
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Development of Compendium for Esophageal Squamous Cell Carcinoma

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View abstract on PubMed

Summary
This summary is machine-generated.

We developed a prognostic model using 14 basement membrane-associated long non-coding RNA pairs for head and neck squamous cell carcinoma (HNSCC). This model predicts patient prognosis, identifies immunotherapy targets, and reveals chemotherapy sensitivities in HNSCC.

Area of Science:

  • Oncology
  • Genomics
  • Molecular Biology

Background:

  • Head and neck squamous cell carcinoma (HNSCC) is a globally prevalent and heterogeneous malignancy.
  • The basement membrane (BM) and its associated long non-coding RNAs (lncRNAs) are increasingly implicated in HNSCC development and prognosis.
  • Identifying reliable prognostic markers is crucial for improving HNSCC patient outcomes.

Purpose of the Study:

  • To develop a prognostic marker based on basement membrane-associated lncRNAs (BMlncRNAs) for HNSCC.
  • To predict the prognosis of HNSCC patients accurately.
  • To identify potential novel immunotherapy targets in HNSCC.

Main Methods:

  • Utilized The Cancer Genome Atlas (TCGA) database for HNSCC transcriptome, mutation, and clinical data.
  • Identified BMlncRNAs via co-expression analysis and filtered differentially expressed BMlncRNAs (DEBMlncRNAs) using FDR<0.05 and |log2FC|≥1.
Keywords:
basement membrane-related genesdata mininghead and neck squamous cell carcinomaimmune infiltration

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  • Constructed a prognostic risk model using univariate, LASSO, and multivariable Cox regression analyses, followed by evaluation of tumor immune infiltration, TMB, and chemotherapy sensitivity.

    • Main Results:

    • A prognostic model comprising 14 BMlncRNA pairs was established, demonstrating the risk score's reliability.
    • High-risk patients showed significantly poorer prognosis, altered tumor mutation burden (TMB), and distinct tumor immune infiltration patterns compared to low-risk patients.
    • Patients in the high-risk group exhibited increased sensitivity to paclitaxel and docetaxel.

    Conclusions:

    • A novel BMlncRNA-based prognostic model for HNSCC has been successfully established.
    • This model offers valuable clinical guidance for future research and therapeutic strategies in HNSCC.
    • The findings highlight the potential of BMlncRNAs as biomarkers and therapeutic targets in HNSCC.
    lncRNA