Identification and validation of a novel senescence-related biomarker for thyroid cancer to predict the prognosis and immunotherapy

Kai Hong; Kenan Cen; Qiaoqiao Chen; Ying Dai; Yifeng Mai; Yangyang Guo

doi:10.3389/fimmu.2023.1128390

Identification and validation of a novel senescence-related biomarker for thyroid cancer to predict the prognosis and immunotherapy

Front Immunol. 2023 Jan 24:14:1128390. doi: 10.3389/fimmu.2023.1128390. eCollection 2023.

Authors

Kai Hong^{1

2}, Kenan Cen³, Qiaoqiao Chen^{4

5}, Ying Dai³, Yifeng Mai³, Yangyang Guo^{1

2}

Affiliations

¹ Department of Thyroid and Breast Surgery, Ningbo First Hospital, Ningbo, Zhejiang, China.
² Department of Thyroid and Breast Surgery, Ningbo Hospital of Zhejiang University, Ningbo, Zhejiang, China.
³ Department of Geriatrics Medicine, The Affiliated Hospital of Medical School of Ningbo University, Ningbo, Zhejiang, China.
⁴ Reproductive Medicine Center, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China.
⁵ Key Laboratory of Reproductive Dysfunction Management of Zhejiang Province Assisted Reproduction Unit, Department of Obstetrics and Gynecology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.

Abstract

Introduction: Cellular senescence is a hallmark of tumors and has potential for cancer therapy. Cellular senescence of tumor cells plays a role in tumor progression, and patient prognosis is related to the tumor microenvironment (TME). This study aimed to explore the predictive value of senescence-related genes in thyroid cancer (THCA) and their relationship with the TME.

Methods: Senescence-related genes were identified from the Molecular Signatures Database and used to conduct consensus clustering across TCGA-THCA. Differentially expressed genes (DEGs) were identified between the clusters used to perform multivariate Cox regression and least absolute shrinkage and selection operator regression (LASSO) analyses to construct a senescence-related signature. TCGA dataset was randomly divided into training and test datasets to verify the prognostic ability of the signature. Subsequently, the immune cell infiltration pattern, immunotherapy response, and drug sensitivity of the two subtypes were analyzed. Finally, the expression of signature genes was detected across TCGA-THCA and GSE33630 datasets, and further validated by RT-qPCR.

Results: Three senescence clusters were identified based on the expression of 432 senescence-related genes. Then, 23 prognostic DEGs were identified in TCGA dataset. The signature, composed of six genes, showed a significant relationship with survival, immune cell infiltration, clinical characteristics, immune checkpoints, immunotherapy response, and drug sensitivity. Low-risk THCA shows a better prognosis and higher immunotherapy response than high-risk THCA. A nomogram with perfect stability constructed using signature and clinical characteristics can predict the survival of each patient. The validation part demonstrated that ADAMTSL4, DOCK6, FAM111B, and SEMA6B were expressed at higher levels in the tumor tissue, whereas lower expression of MRPS10 and PSMB7 was observed.

Discussion: In conclusion, the senescence-related signature is a promising biomarker for predicting the outcome of THCA and has the potential to guide immunotherapy.

Keywords: cellular senescence; immunotherapy; prognosis; signature; thyroid cancer; tumor microenvironment.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Biomarkers, Tumor
Humans
Immunotherapy
Nomograms
Prognosis
Thyroid Neoplasms* / genetics
Thyroid Neoplasms* / therapy
Tumor Microenvironment / genetics

Substances

Biomarkers, Tumor

Grants and funding

This study was supported by the Ningbo University Institute of Geriatrics (LNBYJS-2021).