RNA-seq and bulk RNA-seq data analysis of cancer-related fibroblasts (CAF) in LUAD to construct a CAF-based risk signature

Youjiao Si ¹, Zhonghua Zhao ², Xiangjiao Meng ³

⁰Department of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, Shandong, China.

Scientific Reports +

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October 5, 2024

View abstract on PubMed

Summary

Cancer-associated fibroblasts (CAFs) drive tumor progression. A new 8-gene risk signature based on CAF characteristics accurately predicts prognosis and immunotherapy response in lung adenocarcinoma (LUAD).

Area Of Science

Oncology
Bioinformatics
Cancer Genomics

Background

Cancer-associated fibroblasts (CAFs) play a crucial role in promoting tumor growth, metastasis, and therapeutic resistance.
Understanding CAF heterogeneity and its impact on patient outcomes is essential for developing effective lung adenocarcinoma (LUAD) treatments.
Predictive biomarkers are needed to guide therapeutic strategies and improve patient prognoses in LUAD.

Purpose Of The Study

To identify CAF-related prognostic genes and develop a risk signature for predicting patient outcomes in LUAD.
To investigate the association between CAF characteristics and LUAD prognosis using multi-omics data.
To evaluate the potential of a CAF-based risk signature in predicting response to immunotherapy.

Main Methods

Utilized single-cell and bulk RNA sequencing data from public databases (GEO, TCGA) for LUAD.
Performed differential gene expression analysis, Pearson correlation, and univariate Cox regression to identify prognostic genes.
Developed an 8-gene CAF-based risk signature using Lasso regression and constructed a nomogram integrating clinical factors.

Main Results

Identified 5 CAF clusters in LUAD, with 4 significantly associated with prognosis.
Developed an 8-gene risk signature significantly correlated with CAF clusters, stromal/immune scores, and immune cells.
The risk signature independently predicted LUAD prognosis and immunotherapy outcomes, demonstrating high predictability in the nomogram.

Conclusions

A novel 8-gene CAF-based risk signature effectively predicts prognosis and immunotherapy response in LUAD patients.
This signature offers potential new therapeutic strategies by interpreting LUAD's response to immunotherapy.
The developed nomogram provides a reliable tool for predicting LUAD prognosis.

Keywords:

Cancer-associated fibroblasts Immunotherapy Lung adenocarcinoma Nomogram

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