ArticleFrontiers in oncology2022
Novel cellular senescence-related risk model identified as the prognostic biomarkers for lung squamous cell carcinoma.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 8 citations in OpenAlex.
- Identification of Hub Gene Characteristics and Immune Landscapes Across Aging Subtypes in Atherosclerosis: A Machine Learning-Based Multi-Omics Study With Experimental Verification.Chemical biology & drug design · 2026Article
- Cellular senescence in lung cancer immune microenvironment and senotherapeutic opportunities.iScience · 2026Review
- PAQR4: A Critical Senescence-Related Gene Influencing Immune Evasion and Metastasis in Bladder Urothelial Carcinoma.Human mutation · 2025Article
- Transcriptome-Based Traits of Radioresistant Sublines of Non-Small Cell Lung Cancer Cells.International journal of molecular sciences · 2023Article
- Long-Term Cultured Human Glioblastoma Multiforme Cells Demonstrate Increased Radiosensitivity and Senescence-Associated Secretory Phenotype in Response to Irradiation.International journal of molecular sciences · 2023Article
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Authors and funding
7 authors at 4 institutions in 1 country.
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Abstract
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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