Investigation of risk signatures associated with anoikis in thyroid cancer through integrated transcriptome and Mendelian randomization analysis.

IF 3.9 2区 医学 Q2 ENDOCRINOLOGY & METABOLISM Frontiers in Endocrinology Pub Date : 2024-11-06 eCollection Date: 2024-01-01 DOI:10.3389/fendo.2024.1458956
Xiang-Yi Chen, Jia-Ying Lai, Wen-Jun Shen, Dawei Wang, Zhi-Xiao Wei
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Abstract

Background: Anoikis is intricately associated with the malignant progression of cancer. Thyroid cancer (THCA) is the most common endocrine tumor, metastasis is closely related to treatment response and prognosis of THCA. Hence, it is imperative to comprehensively identify predictive prognostic genes and novel molecular targets for effective THCA therapy.

Methods: Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were utilized to mine differentially expressed anoikis-related (DE-ARGs). Then, the prognostic genes were identified and a risk signature was constructed for THCA using univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) method. Furthermore, the associations between risk signature and immune infiltration, immunotherapy, as well as potential mechanisms of action were determined using multiple R packages and Wilcoxon test. Finally, Mendelian randomized (MR) analysis was conducted to investigate the causal relationship between the prognostic genes and THCA.

Results: In total, six prognostic genes (LRRC75A, METTL7B, ADRA1B, TPD52L1, TNFRSF10C, and CXCL8) related to anoikis were identified, and the corresponding risk signature were constructed to assess the survival time of THCA patients. Immunocorrelation analysis demonstrated the anoikis-relevant risk signature could be used to evaluate immunotherapy effects in THCA patients, and the infiltration of immune cells was correlated with the degree of risk in THCA patients. According to two-sample MR analysis, there was the significant causal relationship between CXCL8 and THCA (odds ratio [OR] > 1 & p< 0.05), and the increase of its gene expression would lead to an increased risk of THCA. Furthermore, real-time quantitative polymerase chain reaction (RT-qPCR) confirmed the upregulated expression patterns of these prognostic genes in THCA tissues.

Conclusion: In conclusion, we constructed the risk signature related to anoikis for THCA, which might have important clinical significance for improving the quality of life and treatment effect of THCA patients.

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通过综合转录组和孟德尔随机分析研究甲状腺癌中与anoikis相关的风险特征。
背景:无丝分裂与癌症的恶性进展密切相关。甲状腺癌(THCA)是最常见的内分泌肿瘤,其转移与治疗反应和预后密切相关。因此,全面鉴定预测预后的基因和新的分子靶点以有效治疗甲状腺癌势在必行:方法:利用差异表达分析和加权基因共表达网络分析(WGCNA)挖掘差异表达的肿瘤相关基因(DE-ARGs)。然后,利用单变量考克斯分析和最小绝对收缩和选择算子(LASSO)方法确定了预后基因并构建了THCA的风险特征。此外,还使用多个 R 软件包和 Wilcoxon 检验确定了风险特征与免疫浸润、免疫疗法以及潜在作用机制之间的关联。最后,进行了孟德尔随机(MR)分析,以研究预后基因与THCA之间的因果关系:结果:共发现了6个与厌氧症相关的预后基因(LRRC75A、METTL7B、ADRA1B、TPD52L1、TNFRSF10C和CXCL8),并构建了相应的风险特征来评估THCA患者的生存时间。免疫相关性分析表明,免疫相关风险特征可用于评估THCA患者的免疫治疗效果,免疫细胞的浸润与THCA患者的风险程度相关。根据双样本 MR 分析,CXCL8 与 THCA 之间存在显著的因果关系(几率比 [OR] > 1,P< 0.05),其基因表达的增加会导致 THCA 风险的增加。此外,实时定量聚合酶链反应(RT-qPCR)证实了这些预后基因在THCA组织中的上调表达模式:总之,我们构建了与THCA厌氧相关的风险特征,这可能对提高THCA患者的生活质量和治疗效果具有重要的临床意义。
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来源期刊
Frontiers in Endocrinology
Frontiers in Endocrinology Medicine-Endocrinology, Diabetes and Metabolism
CiteScore
5.70
自引率
9.60%
发文量
3023
审稿时长
14 weeks
期刊介绍: Frontiers in Endocrinology is a field journal of the "Frontiers in" journal series. In today’s world, endocrinology is becoming increasingly important as it underlies many of the challenges societies face - from obesity and diabetes to reproduction, population control and aging. Endocrinology covers a broad field from basic molecular and cellular communication through to clinical care and some of the most crucial public health issues. The journal, thus, welcomes outstanding contributions in any domain of endocrinology. Frontiers in Endocrinology publishes articles on the most outstanding discoveries across a wide research spectrum of Endocrinology. The mission of Frontiers in Endocrinology is to bring all relevant Endocrinology areas together on a single platform.
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