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Integrated analysis of single‑cell and bulk RNA sequencing data to construct a risk assessment model based on plasma cell immune‑related genes for predicting patient prognosis and therapeutic response in lung adenocarcinoma

  • 0Department of Thoracic Surgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi 330006, P.R. China.
Oncology Letters +

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April 16, 2025

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April 16, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

This study developed a risk model using nine plasma cell immune-related genes (PCIGs) to predict lung adenocarcinoma (LUAD) patient prognosis. The model identifies high-risk patients with worse outcomes and aids in potential drug selection.

Area Of Science

  • Immunology
  • Oncology
  • Genomics

Background

  • Plasma cells are key immune players involved in tumor progression.
  • The specific role of plasma cell immune-related genes (PCIGs) in lung adenocarcinoma (LUAD) is not fully understood.
  • Accurate prognostic markers are needed for LUAD patient management.

Purpose Of The Study

  • To establish a prognostic risk assessment model for LUAD patients based on PCIGs.
  • To explore the molecular mechanisms and clinical significance of PCIGs in LUAD.
  • To identify potential therapeutic targets and guide drug selection for LUAD.

Main Methods

  • Utilized The Cancer Genome Atlas and Gene Expression Omnibus databases for gene identification.
  • Constructed a risk model and nomogram using nine identified PCIGs.
  • Performed Gene Set Enrichment Analysis (GSEA), tumor mutational burden (TMB) analysis, tumor microenvironment (TME) analysis, and drug sensitivity prediction.
  • Validated the model using reverse transcription-quantitative PCR (RT-qPCR).

Main Results

  • A nine-PCIG risk model was established, with a higher risk score correlating with a worse prognosis.
  • The nomogram demonstrated predictive value for survival (AUC=0.727).
  • High-risk group showed enrichment in focal adhesion and extracellular matrix-receptor interaction pathways, higher TMB, and lower ESTIMATE scores.
  • RT-qPCR validated differential gene expression in LUAD cell lines.

Conclusions

  • The nine-PCIG risk model can predict prognosis in LUAD patients.
  • The model may assist in personalized drug selection for LUAD treatment.
  • PCIGs represent potential biomarkers for LUAD progression and therapeutic intervention.

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