Novel cellular senescence-related risk model identified as the prognostic biomarkers for lung squamous cell carcinoma

Xiaoshan Hu; Liyi Guo; Guihong Liu; Zili Dai; Li Wang; Jian Zhang; Jun Wang

doi:10.3389/fonc.2022.997702

Novel cellular senescence-related risk model identified as the prognostic biomarkers for lung squamous cell carcinoma

Front Oncol. 2022 Nov 17:12:997702. doi: 10.3389/fonc.2022.997702. eCollection 2022.

Authors

Xiaoshan Hu¹, Liyi Guo², Guihong Liu³, Zili Dai⁴, Li Wang⁴, Jian Zhang^{4

5}, Jun Wang⁶

Affiliations

¹ Department of Internal Medicine of Oncology, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China.
² Department of Oncology and Hematology, The Sixth People's Hospital of Huizhou City, Huiyang Hospital Affiliated to Southern Medical University, Huizhou, China.
³ Department of Radiation Oncology, Donguan Tungwah Hospital, Dongguan, China.
⁴ Department of Radiation Oncology, State Key Laboratory of Respiratory Diseases, Guangzhou Institute of Respiratory Disease, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China.
⁵ Guangzhou Medical University, Guangzhou, China.
⁶ Department of Interventional Radiology, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China.

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.

Keywords: immune microenvironment; immunotherapy; lung squamous cell carcinoma; overall survival; senescence.