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Related Concept Videos

Cancer Survival Analysis01:21

Cancer Survival Analysis

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Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
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  11. Deciphering Lung Adenocarcinoma Prognosis And Immunotherapy Response Through An Ai-driven Stemness-related Gene Signature.
  1. Home
  3. Research Domains
  5. Biomedical And Clinical Sciences
  7. Oncology And Carcinogenesis
  9. Predictive And Prognostic Markers
  11. Deciphering Lung Adenocarcinoma Prognosis And Immunotherapy Response Through An Ai-driven Stemness-related Gene Signature.

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Deciphering lung adenocarcinoma prognosis and immunotherapy response through an AI-driven stemness-related gene signature.

Bicheng Ye1, Ge Hongting2, Wen Zhuang3

  • 1School of Clinical Medicine, Yangzhou Polytechnic College, Yangzhou, China.

Journal of Cellular and Molecular Medicine
|July 24, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

An artificial intelligence (AI)-driven stemness-related gene signature (SRS) accurately predicts lung adenocarcinoma (LUAD) prognosis and immunotherapy response. This AI tool identifies patients likely to benefit from immunotherapy by analyzing stemness and immune interactions.

Keywords:
artificial intelligenceimmunotherapymachine learningprognosis

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Area of Science:

  • Oncology
  • Computational Biology
  • Immunology

Background:

  • Lung adenocarcinoma (LUAD) is a major cause of cancer mortality, necessitating improved prognostic tools for personalized treatment, particularly for immunotherapy.
  • Accurate prediction of patient response to immunotherapy remains a significant challenge in LUAD management.

Purpose of the Study:

  • To develop an artificial intelligence (AI)-driven stemness-related gene signature (SRS) for predicting LUAD prognosis and immunotherapy response.
  • To investigate the association between the SRS, stemness, and the tumor immune microenvironment in LUAD.

Main Methods:

  • Utilized CytoTRACE analysis on single-cell RNA sequencing data to identify stemness-associated genes in LUAD.
  • Developed an AI network integrating regression, machine learning, and deep learning to construct the SRS.
single‐cell RNA sequencing
stemness
  • Performed multi-omics data analysis to explore the link between SRS and immune environments.
  • Conducted in vitro experiments with siRNA knockdown of CKS1B to validate functional roles.

    • Main Results:

    • The AI-driven SRS accurately predicted LUAD prognosis and immunotherapy response.
    • High-risk groups identified by SRS showed reduced immunogenicity and immune cell infiltration.
    • Knockdown of CKS1B, a key SRS gene, significantly inhibited LUAD cell proliferation, migration, and invasion.
    • Established a strong correlation between stemness, tumor immunity, and immunotherapy outcomes in LUAD.

    Conclusions:

    • The developed SRS is a promising tool for predicting LUAD prognosis and immunotherapy efficacy.
    • Stemness plays a crucial role in shaping the tumor immune landscape and influencing immunotherapy response in LUAD.
    • The SRS can aid in stratifying LUAD patients for personalized treatment strategies, especially for immunotherapy selection.