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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. Analysis Of Disulfidptosis- And Cuproptosis-related Lncrnas In Modulating The Immune Microenvironment And Chemosensitivity In Colon Adenocarcinoma.
  1. Home
  3. Research Domains
  5. Biomedical And Clinical Sciences
  7. Oncology And Carcinogenesis
  9. Predictive And Prognostic Markers
  11. Analysis Of Disulfidptosis- And Cuproptosis-related Lncrnas In Modulating The Immune Microenvironment And Chemosensitivity In Colon Adenocarcinoma.

Related Experiment Video

Molecular Profiling of the Invasive Tumor Microenvironment in a 3-Dimensional Model of Colorectal Cancer Cells and Ex vivo Fibroblasts
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Analysis of disulfidptosis- and cuproptosis-related LncRNAs in modulating the immune microenvironment and chemosensitivity in colon adenocarcinoma.

Qiang Fan1, Guang-Bo Wu1, Min Chen1

  • 1Department of General Surgery, Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.

IET Systems Biology
|March 8, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

This study developed a novel prognostic model using long non-coding RNAs (lncRNAs) for colon adenocarcinoma (COAD). The model accurately predicts patient outcomes, immune cell infiltration, and chemotherapy response, offering new insights into COAD progression.

Keywords:
bioinformaticscancer

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Area of Science:

  • Oncology
  • Molecular Biology
  • Bioinformatics

Background:

  • Colon adenocarcinoma (COAD) poses a significant health challenge.
  • Identifying reliable prognostic markers is crucial for improving patient outcomes.
  • Disulfidptosis and cuproptosis are emerging cell death pathways with potential roles in cancer.

Purpose of the Study:

  • To establish a prognostic model for COAD utilizing long non-coding RNAs (lncRNAs) associated with disulfidptosis and cuproptosis.
  • To evaluate the model's predictive capability for patient survival, immune cell infiltration, and chemosensitivity.
  • To validate the expression and function of key lncRNAs in colorectal cancer cells.

Main Methods:

  • Acquired RNA-Sequence and clinicopathological data for COAD from The Cancer Genome Atlas (TCGA).
  • Constructed a prognostic model using Cox regression and the Least Absolute Shrinkage and Selection Operator (LASSO) method.
  • Assessed model performance via principal component analysis, Kaplan-Meier analysis, and nomogram; validated lncRNA function in vitro.

    • Main Results:

    • A novel prognostic model based on disulfidptosis and cuproptosis-related lncRNAs was successfully constructed.
    • The model demonstrated excellent independent predictive capability for patient outcomes.
    • High-risk patients showed higher tumor mutation burden and worse survival, with significant differences in immune infiltration and chemosensitivity based on risk scores.
    • In vitro experiments validated aberrant expressions of lncRNAs, with FENDRR and SNHG7 impacting cancer cell proliferation and migration.

    Conclusions:

    • The developed prognostic signature provides valuable insights into prognosis, immune infiltration, and chemosensitivity in COAD patients.
    • This novel model can aid in personalized treatment strategies and risk stratification for COAD.
    • Further research into the roles of specific lncRNAs in COAD pathogenesis is warranted.