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

Oct 5, 2024Scientific reports

Gene activity analysis of cancer-associated fibroblasts in lung adenocarcinoma to build a risk prediction model

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Abstract

A risk signature based on 8 genes related to (CAFs) may predict prognosis in lung adenocarcinoma (LUAD).

Five clusters of CAFs were identified in LUAD, with four clusters showing a correlation to patient prognosis.
Out of 2811 differentially expressed genes (DEGs), 1002 were significantly associated with CAF clusters.
The CAF-based risk signature was found to be an independent prognostic factor for LUAD.
The signature demonstrated significant associations with stromal and immune scores, along with various immune cells.
A integrating clinical stage and the CAF-based risk signature showed favorable predictability and reliability for prognosis.

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Angiogenesis, metastasis, and resistance to therapy are all facilitated by (CAFs). A CAF-based risk signature can be used to predict patients' prognoses for Lung adenocarcinoma (LUAD) based on CAF characteristics. The Gene Expression Omnibus (GEO) database was used to gather signal-cell RNA sequencing (scRNA-seq) data for this investigation. The GEO and TCGA databases were used to gather bulk RNA-seq and microarray data for LUAD. The scRNA-seq data were analyzed using the Seurat R program based on the CAF markers. Our goal was to use differential expression analysis to discover differentially expressed genes (DEGs) across normal and tumor samples in the TCGA dataset. Pearson correlation analysis was utilized to discover prognostic genes related with CAF, followed by univariate Cox regression analysis. Using Lasso regression, a risk signature based on CAF-related prognostic genes was created. A model was created based on the clinical and pathological aspects. 5 CAF clusters were identified in LUAD, 4 of which were associated with prognosis. From 2811 DEGs, 1002 genes were found to be significantly correlated with CAF clusters, which led to the creation of a risk signature with 8 genes. These 8 genes were primarily connected with 41 pathways, such as antigen paocessing and presentation, apoptosis, and cell cycle. Meanwhile, the risk signature was significantly associated with stromal and immune scores, as well as some immune cells. Multivariate analysis revealed that risk signature was an independent prognostic factor for LUAD, and its value in predicting immunotherapeutic outcomes was confirmed. A novel nomogram integrating the stage and CAF-based risk signature was constructed, which exhibited favorable predictability and reliability in the prognosis prediction of LUAD. CAF-based risk signatures can be effective in predicting the prognosis of LUAD, and they may provide new strategies for cancer treatments by interpreting the response of LUAD to immunotherapy.

Key numbers

3.258

Independent Prognostic Factor Hazard Ratio

Hazard ratio for the CAF-based risk signature in multivariate analysis.

Number of Genes in Risk Signature

Eight genes were included in the CAF-based risk signature.

2811

Total Differentially Expressed Genes

Total DEGs identified in the study.

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

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