Bioinformatics-based analysis of the prognostic value of CAF-related genes in lung squamous cell carcinoma
Authors: He Siming , Gao Yizhou , Wu Zhihong 
Published: October 2024

The survival rate for lung squamous cell carcinoma (LUSC) is significantly lower compared to other types of tumors, although some effective immunotherapies have been applied in the clinic. The prognosis of LUSC is largely dependent on the individual patients cancer assessment. Current clinical assessments based on clinical indicators and staging systems have limitations in accuracy. Therefore, prognostic prediction and assessment require precise and individualized assessment using genetic tools. Cancer-associated fibroblasts (CAFs) in the tumor microenvironment have been reported to impact the survival of LUSC by expressing specific proteins regulated by CAF-related genes (CAFRGs). Building upon this, the study aimed to identify CAFRGs in the gene expression data of LUSC using weighted gene co-expression network analysis (WGCNA), and one-way Cox and lasso regression screened for prognostically relevant CAFRGs in LUSC, incorporating six independent CAFRGs related to prognosis, to establish a risk score model for LUSC patients, and to further investigate potential CAF biomarkers related to LUSC prognosis. The TCGA database served as the training set, while the external dataset GSE30219 from the GEO database was employed for validating the accuracy and reliability of the model. Univariate Cox and multivariate Cox regression analyses demonstrated the significance of this risk score as a crucial independent prognostic factor for LUSC. According to immune infiltration and differences in immunotherapy response, personalized treatment strategies suitable for people with different risk scores were derived. We posit that the findings from this study offer robust evidence regarding the association between CAFRGs and LUSC prognosis. This can aid in establishing a dependable prognostic risk model, facilitating more precise prognostic predictions to guide personalized treatment decisions.The survival rate for lung squamous cell carcinoma (LUSC) is significantly lower compared to other types of tumors, although some effective immunotherapies have been applied in the clinic. The prognosis of LUSC is largely dependent on the individual patients cancer assessment. Current clinical assessments based on clinical indicators and staging systems have limitations in accuracy. Therefore, prognostic prediction and assessment require precise and individualized assessment using genetic tools. Cancer-associated fibroblasts (CAFs) in the tumor microenvironment have been reported to impact the survival of LUSC by expressing specific proteins regulated by CAF-related genes (CAFRGs). Building upon this, the study aimed to identify CAFRGs in the gene expression data of LUSC using weighted gene co-expression network analysis (WGCNA), and one-way Cox and lasso regression screened for prognostically relevant CAFRGs in LUSC, incorporating six independent CAFRGs related to prognosis, to establish a risk score model for LUSC patients, and to further investigate potential CAF biomarkers related to LUSC prognosis. The TCGA database served as the training set, while the external dataset GSE30219 from the GEO database was employed for validating the accuracy and reliability of the model. Univariate Cox and multivariate Cox regression analyses demonstrated the significance of this risk score as a crucial independent prognostic factor for LUSC. According to immune infiltration and differences in immunotherapy response, personalized treatment strategies suitable for people with different risk scores were derived. We posit that the findings from this study offer robust evidence regarding the association between CAFRGs and LUSC prognosis. This can aid in establishing a dependable prognostic risk model, facilitating more precise prognostic predictions to guide personalized treatment decisions.