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Related Concept Videos

T Cell Activation and Clonal Selection01:22

T Cell Activation and Clonal Selection

838
T cells are integral to our adaptive immune system, recognizing and effectively responding to foreign antigens. T cell activation and clonal selection are pivotal in orchestrating this immune response. This article elucidates these mechanisms, detailing the roles of cluster of differentiation (CD) markers, major histocompatibility complex (MHC) molecules, costimulatory signals, and the process of clonal selection.
Naive T cells that have not yet encountered an antigen express two primary CD...
838

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Updated: Jul 27, 2025

Monitoring the Cancer-Immunity Cycle and Exploring Tumor Microenvironment Dynamics
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Comprehensive analyses of a CD8

Liang Chen1, Yiming Weng1, Xue Cui1

  • 1Cancer Center, Renmin Hospital of Wuhan University, Wuhan, 430060, China.

BMC Bioinformatics
|June 6, 2023
PubMed
Summary
This summary is machine-generated.

This study identifies a CD8+ T cell infiltration-related gene signature in lung squamous cell carcinoma (LUSC). A higher density of CD8+ T cells correlates with better immunotherapy response, aiding prognosis prediction.

Keywords:
CD8+ T cellImmunotherapyLung squamous cell carcinomaPrognosis

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Area of Science:

  • Immunology
  • Oncology
  • Genomics

Background:

  • Lung squamous cell carcinoma (LUSC) has a poorer prognosis compared to other non-small cell lung cancer subtypes.
  • CD8+ T cells are crucial for anti-tumor immunity, making their infiltration patterns significant in LUSC.
  • Understanding CD8+ T cell infiltration is vital for improving LUSC patient outcomes and treatment strategies.

Purpose of the Study:

  • To characterize the CD8+ T cell infiltration-related (CTLIR) gene signature in LUSC.
  • To explore the correlation between CD8+ T cell density and immunotherapy response in LUSC patients.
  • To develop a prognostic gene signature and risk model for LUSC patients.

Main Methods:

  • Multiplex immunohistochemistry was used to assess CD8+ T cell density in LUSC tumor tissues.
  • Bulk RNA-sequencing data from TCGA and GEO databases were analyzed.
  • CIBERSORT algorithm and weighted gene co-expression network analysis (WGCNA) were employed to identify immune cell abundance and gene modules.

Main Results:

  • Higher CD8+ T cell infiltration density was associated with a better response to immunotherapy.
  • A novel CTLIR gene signature was developed, stratifying patients into high-risk and low-risk groups.
  • The high-risk group exhibited shorter overall survival, fewer CD8+ T cells, more regulatory T cells, and an immunosuppressive tumor microenvironment.
  • The high-risk group showed a predicted better response to PD-1 and CTLA4 inhibitors.

Conclusions:

  • The CTLIR gene signature serves as an independent prognostic factor for LUSC.
  • The developed risk model can predict both prognosis and immunotherapy response in LUSC patients.
  • This research provides valuable insights into the immune landscape of LUSC and its implications for treatment.