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Myeloid cell differentiation-related gene signature for predicting clinical outcome, immune microenvironment, and treatment response in lung adenocarcinoma

  • 0Experimental Research Center, Qingpu Branch of Zhongshan Hospital Affiliated to Fudan University, Shanghai, 201700, China.
Scientific Reports +

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July 29, 2024

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July 29, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

Researchers developed a prognostic model for lung adenocarcinoma (LUAD) using myeloid cell differentiation genes. This model accurately predicts patient outcomes and can guide LUAD treatment strategies.

Area Of Science

  • Oncology
  • Genomics
  • Immunology

Background

  • Myeloid cell differentiation genes play a crucial role in the tumor microenvironment (TME).
  • Lung adenocarcinoma (LUAD) requires effective prognostic and therapeutic strategies.

Purpose Of The Study

  • To construct a prognostic risk model for LUAD utilizing myeloid cell differentiation-related genes.
  • To evaluate the model's performance and its association with immune infiltration and treatment response.

Main Methods

  • Downloaded LUAD mRNA gene expression data from TCGA and GEO databases for training and validation.
  • Applied "edgeR" R package for differential gene expression analysis and univariate Cox regression with backward stepwise selection to build the prognostic model.
  • Utilized multiple algorithms (ESTIMATE, TIMER, XCELL, etc.) to assess the correlation between risk levels and immune/stromal cell infiltration.

Main Results

  • A six-gene signature (F2RL1, PRKDC, TNFSF11, INHA, PLA2G3, TUBB1) was identified and used to construct the prognostic model.
  • The model demonstrated excellent prognostic performance in both TCGA and GEO datasets.
  • High-risk patients exhibited increased expression of immune checkpoint molecules and lower IC50 values for chemotherapy agents.

Conclusions

  • The developed myeloid cell differentiation-related gene signature effectively predicts LUAD prognosis.
  • This gene signature holds potential for guiding personalized treatment strategies in LUAD patients.

Keywords: