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Prognosis and therapy in thyroid cancer by gene signatures related to natural killer cells.
Jin, Zhen; Han, Yadong; Zhang, Jiaxin; Liu, Zhao; Li, Ran; Liu, Zhao.
Afiliação
  • Jin Z; Department of Thyroid and Breast Surgery, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China.
  • Han Y; Department of General Surgery, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China.
  • Zhang J; Department of Thyroid and Breast Surgery, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China.
  • Liu Z; Department of Thyroid and Breast Surgery, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China.
  • Li R; Department of Thyroid and Breast Surgery, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China.
  • Liu Z; Department of Nuclear Medicine, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China.
J Gene Med ; 26(1): e3657, 2024 Jan.
Article em En | MEDLINE | ID: mdl-38282150
ABSTRACT

BACKGROUND:

Natural killer (NK) cells are crucial to cancer development and prognosis. However, the role of NK cell-related genes in immunotherapy and the tumor immune microenvironment (TIME) is not well understood. This study aimed to develop reliable risk signatures associated with NK cell-related genes for predicting thyroid cancer (THCA).

METHODS:

The single-cell RNA sequencing (scRNA-seq) data from seven THCA samples (GSE184362) and bulk-RNA-seq data of 502 THCA patients (TCGA-THCA) were included. The scRNA-seq data was analyzed using the "Seurat" R package to identify differentially expressed genes in NK cells. The clustering analysis was carried out using the R package "ConsensusClusterPlus". The gene set variation analysis (GSVA) algorithm was applied to assess the variations in biological pathways among subtypes. The ESTIMATE algorithm was utilized to calculate the scores for stromal, immune and estimate variables. In addition, we used the single sample Gene Set Enrichment Analysis and CIBERSORT algorithms to assess the degree to which immune cells and pathways related to immunity were enriched based on the meta-cohort. In the TCGA-THCA cohort, the "glmnet" R package was used for the gene selection, and LASSO Cox analysis was used to construct prognostic features. The "maftools" R package was used to examine the somatic mutation landscape of THCA in both low- and high-risk groups.

RESULTS:

One-hundred and eighty-five NK cell marker genes were screened, and nine genes were associated with the THCA prognosis. KLF2, OSTF1 and TAPBP were finally identified and constructed a risk signature with significant prognostic value. KLF2 and OSTF1 were protective genes, and TAPBP was a risk gene. Patients at high risk had a considerably lower overall survival compared with those at low risk. Mutations in the TCGA-THCA cohort were predominantly C > T. Increased tumor mutation burden (TMB) levels were linked to overall survival. The low-risk H-TMB+ group had a better prognosis, while the high-risk L-TMB+ group had the worst prognosis.

CONCLUSION:

Natural killer cell-related genes KLF2, OSTF1 and TAPBP were used to develop a novel prognostic risk signature, offering a new perspective on the prognosis and treatment of THCA.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias da Glândula Tireoide Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias da Glândula Tireoide Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2024 Tipo de documento: Article