Novel cellular senescence-related risk model identified as the prognostic biomarkers for lung squamous cell carcinoma
X Hu, L Guo, G Liu, Z Dai, L Wang, J Zhang… - Frontiers in …, 2022 - frontiersin.org
X Hu, L Guo, G Liu, Z Dai, L Wang, J Zhang, J Wang
Frontiers in Oncology, 2022•frontiersin.orgBackground Lung cancer is one of the top causes of cancer-related death worldwide.
Cellular senescence is a characteristic of cell cycle arrest that plays a role in carcinogenesis
and immune microenvironment modulation. Despite this, the clinical and immune cell
infiltration features of senescence in lung squamous cell carcinoma (LUSC) are unknown.
Methods The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were
used to get RNA-seq data and clinical information for LUSC. The least absolute shrinkage …
Cellular senescence is a characteristic of cell cycle arrest that plays a role in carcinogenesis
and immune microenvironment modulation. Despite this, the clinical and immune cell
infiltration features of senescence in lung squamous cell carcinoma (LUSC) are unknown.
Methods The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were
used to get RNA-seq data and clinical information for LUSC. The least absolute shrinkage …
Background
Lung cancer is one of the top causes of cancer-related death worldwide. Cellular senescence is a characteristic of cell cycle arrest that plays a role in carcinogenesis and immune microenvironment modulation. Despite this, the clinical and immune cell infiltration features of senescence in lung squamous cell carcinoma (LUSC) are unknown.
Methods
The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were used to get RNA-seq data and clinical information for LUSC. The least absolute shrinkage and selection operator (LASSO)-Cox regression, receiver operating characteristic (ROC), and Kaplan-Meier analysis were used to evaluate a risk model for predicting overall survival based on six differentially expressed genes. The tumor microenvironment (TME) and immunotherapy response were also studied.
Results
To discriminate LUSC into high- and low-risk subgroups, a risk model comprised of six cellular senescence-related genes (CDKN1A, CEBPB, MDH1, SIX1, SNAI1, and SOX5) was developed. The model could stratify patients into high-risk and low-risk groups, according to ROC and Kaplan-Meier analysis. In the TCGA-LUSC and GSE73403 cohorts, the high-risk group had a worse prognosis (P<0.05), and was associated with immune cell inactivation and being insensitive to immunotherapy in IMvigor210.
Conclusions
We discovered a new LUSC classification based on six cellular senescence-related genes, which will aid in identifying patients who will benefit from anti-PD-1 treatment. Targeting senescence-related genes appears to be another option for improving clinical therapy for LUSC.
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