A novel exosome‐related prognostic risk model for thyroid cancer

IF 1.4 4区医学 Q4 ONCOLOGY

Asia-Pacific journal of clinical oncology Pub Date : 2024-04-05 DOI:10.1111/ajco.14063

Junfeng Qi, Hanshan Cheng, Long Su, Jun Li, Fei Cheng

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Abstract

AimThe aim was to build an exosome‐related gene (ERG) risk model for thyroid cancer (TC) patients.MethodsNote that, 510 TC samples from The Cancer Genome Atlas database and 121 ERGs from the ExoBCD database were obtained. Differential gene expression analysis was performed to get ERGs in TC (TERGs). Functional enrichment analyses including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were conducted on the TERGs. Then we constructed a model based on LASSO Cox regression analysis. Kaplan‐Meier survival analysis was applied and a Nomogram model was also built. The immune landscape was evaluated by CIBERSORT.ResultsThirty‐eight TERGs were identified and their functions were enriched on 591 GO terms and 30 KEGG pathways. We built a Risk Score model based on FGFR3, ADRA1B, and POSTN. Risk Scores were significantly higher in T4 than in other stages, meanwhile, it didn't significantly differ in genders and TNM N or M classifications. The nomogram model could reliably predict the overall survival of TC patients. The mutation rate of BRAF and expression of cytotoxic T‐lymphocyte‐associated protein 4 were significantly higher in the high‐risk group than in the low‐risk group. The risk score was significantly correlated to the immune landscape.ConclusionWe built a Risk Score model using FGFR3, ADRA1B, and POSTN which could reliably predict the prognosis of TC patients.

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与外泌体相关的新型甲状腺癌预后风险模型

目的建立甲状腺癌（TC）患者外泌体相关基因（ERG）风险模型。方法从癌症基因组图谱（The Cancer Genome Atlas）数据库中获得510个TC样本，从ExoBCD数据库中获得121个ERG。通过差异基因表达分析，得出甲状腺癌中的 ERGs（TERGs）。对TERGs进行了功能富集分析，包括基因本体（GO）和京都基因组百科全书（KEGG）。然后，我们构建了一个基于 LASSO Cox 回归分析的模型。我们还应用了卡普兰-迈尔生存分析法，并建立了一个Nomogram模型。通过 CIBERSORT 对免疫图谱进行了评估。结果确定了 38 个 TERGs，并在 591 个 GO 术语和 30 个 KEGG 通路上丰富了它们的功能。我们根据 FGFR3、ADRA1B 和 POSTN 建立了一个风险评分模型。T4期的风险评分明显高于其他分期，而在性别、TNM N或M分级上没有明显差异。提名图模型可以可靠地预测TC患者的总生存期。高危组的BRAF突变率和细胞毒性T淋巴细胞相关蛋白4的表达明显高于低危组。结论我们利用FGFR3、ADRA1B和POSTN建立了一个风险评分模型，可以可靠地预测TC患者的预后。

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来源期刊

Asia-Pacific journal of clinical oncology 医学-肿瘤学

CiteScore

3.40

自引率

0.00%

发文量

175

审稿时长

6-12 weeks

期刊介绍： Asia–Pacific Journal of Clinical Oncology is a multidisciplinary journal of oncology that aims to be a forum for facilitating collaboration and exchanging information on what is happening in different countries of the Asia–Pacific region in relation to cancer treatment and care. The Journal is ideally positioned to receive publications that deal with diversity in cancer behavior, management and outcome related to ethnic, cultural, economic and other differences between populations. In addition to original articles, the Journal publishes reviews, editorials, letters to the Editor and short communications. Case reports are generally not considered for publication, only exceptional papers in which Editors find extraordinary oncological value may be considered for review. The Journal encourages clinical studies, particularly prospectively designed clinical trials.