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Abstract：
The primary objective of this study is to conduct a comprehensive screening and analysis of differentially expressed genes related to disulfidoptosis (DEDRGs) in thyroid carcinoma (THCA). This entails delving into the intricate characterization of immune cell infiltration within the THCA context and subsequently formulating and validating a novel prognostic model.
To achieve our objectives, we first delineated two distinct subtypes of disulfidoptosis-related genes (DRGs) via consensus clustering methodology. Subsequently, employing the limma R package, we identified the DEDRGs critical for our investigation. These DEDRGs underwent meticulous validation across various databases, alongside an in-depth analysis of gene regulation. Employing functional enrichment techniques, we explored the potential molecular mechanisms underlying disulfidoptosis in THCA. Furthermore, we scrutinized the immune landscape within the two identified subtypes utilizing CIBERSORT and ESTIMATE algorithms. The construction of the prognostic model for THCA entailed intricate methodologies including univariate, multivariate Cox regression, and LASSO regression algorithms. The validity and efficacy of our prognostic model were corroborated through Kaplan-Meier survival curves and ROC curves. Additionally, a nomogram was meticulously formulated to facilitate the prediction of patient prognosis. To fortify our findings, we conducted a comprehensive Bayesian co-localization analysis coupled with rigorous in vitro experimentation, aimed at unequivocally establishing the validity of the identified DEDRGs.
Our analyses unveiled Cluster C1, characterized by elevated expression levels of DEDRGs, as harboring a favorable prognosis accompanied by abundant immune cell infiltration. Correlation analyses underscored predominantly positive associations among the DEDRGs, further affirming their significance in THCA. Differential expression patterns of DEDRGs between tumor samples and normal tissues were evident across the GEPIA and HPA databases. Insights from the TIMER database underscored a robust correlation between DEDRGs and immune cell infiltration. KEGG analysis elucidated the enrichment of DEDRGs primarily in pivotal pathways including MAPK, PPAR signaling pathway, and Proteoglycans in cancer. Furthermore, analyses using CIBERSORT and ESTIMATE algorithms shed light on the crucial role played by DEDRGs in shaping the immune microenvironment. The prognostic model, anchored by five genes intricately associated with THCA prognosis, exhibited commendable predictive accuracy and was intricately linked to the tumor immune microenvironment. Notably, patients categorized with low-risk scores stood to potentially benefit more from immunotherapy. The validation of DEDRGs unequivocally underscores the protective role of INF2 in THCA.
In summary, our study delineates two discernible subtypes intricately associated with DRGs, revealing profound disparities in immune infiltration and survival prognosis within the THCA milieu. The implications of our findings extend to potential treatment strategies for THCA patients, which could entail targeted interventions directed towards DEDRGs and prognostic genes, thereby influencing disulfidptosis and the immune microenvironment. Moreover, the robust predictive capability demonstrated by our prognostic model, based on the five genes (ANGPTL7, FIRRE, ODAPH, PROKR1, SFRP5), underscores its potential clinical utility in guiding personalized therapeutic approaches for THCA patients.
To achieve our objectives, we first delineated two distinct subtypes of disulfidoptosis-related genes (DRGs) via consensus clustering methodology. Subsequently, employing the limma R package, we identified the DEDRGs critical for our investigation. These DEDRGs underwent meticulous validation across various databases, alongside an in-depth analysis of gene regulation. Employing functional enrichment techniques, we explored the potential molecular mechanisms underlying disulfidoptosis in THCA. Furthermore, we scrutinized the immune landscape within the two identified subtypes utilizing CIBERSORT and ESTIMATE algorithms. The construction of the prognostic model for THCA entailed intricate methodologies including univariate, multivariate Cox regression, and LASSO regression algorithms. The validity and efficacy of our prognostic model were corroborated through Kaplan-Meier survival curves and ROC curves. Additionally, a nomogram was meticulously formulated to facilitate the prediction of patient prognosis. To fortify our findings, we conducted a comprehensive Bayesian co-localization analysis coupled with rigorous in vitro experimentation, aimed at unequivocally establishing the validity of the identified DEDRGs.
Our analyses unveiled Cluster C1, characterized by elevated expression levels of DEDRGs, as harboring a favorable prognosis accompanied by abundant immune cell infiltration. Correlation analyses underscored predominantly positive associations among the DEDRGs, further affirming their significance in THCA. Differential expression patterns of DEDRGs between tumor samples and normal tissues were evident across the GEPIA and HPA databases. Insights from the TIMER database underscored a robust correlation between DEDRGs and immune cell infiltration. KEGG analysis elucidated the enrichment of DEDRGs primarily in pivotal pathways including MAPK, PPAR signaling pathway, and Proteoglycans in cancer. Furthermore, analyses using CIBERSORT and ESTIMATE algorithms shed light on the crucial role played by DEDRGs in shaping the immune microenvironment. The prognostic model, anchored by five genes intricately associated with THCA prognosis, exhibited commendable predictive accuracy and was intricately linked to the tumor immune microenvironment. Notably, patients categorized with low-risk scores stood to potentially benefit more from immunotherapy. The validation of DEDRGs unequivocally underscores the protective role of INF2 in THCA.
In summary, our study delineates two discernible subtypes intricately associated with DRGs, revealing profound disparities in immune infiltration and survival prognosis within the THCA milieu. The implications of our findings extend to potential treatment strategies for THCA patients, which could entail targeted interventions directed towards DEDRGs and prognostic genes, thereby influencing disulfidptosis and the immune microenvironment. Moreover, the robust predictive capability demonstrated by our prognostic model, based on the five genes (ANGPTL7, FIRRE, ODAPH, PROKR1, SFRP5), underscores its potential clinical utility in guiding personalized therapeutic approaches for THCA patients.
摘要：
目的：本研究的主要目的是对甲状腺癌（THCA）中与二硫掺杂剂（DEDRGs）相关的差异表达基因进行全面的筛选和分析。这需要深入研究THCA背景下免疫细胞浸润的复杂表征，并随后制定和验证新的预后模型。
方法：为了实现我们的目标，我们首先通过共识聚类方法划分了二硫化物掺杂相关基因(DRGs)的两种不同亚型.随后，使用limmaR包，我们确定了对我们的调查至关重要的DEDRG。这些DEDRG在各种数据库中进行了细致的验证，以及对基因调控的深入分析。采用功能丰富技术，我们探索了THCA中二硫化物形成的潜在分子机制.此外，我们利用CIBERSORT和ESTIMATE算法仔细检查了两种已识别亚型中的免疫状况.THCA预后模型的构建需要复杂的方法，包括单变量，多元Cox回归，和LASSO回归算法。通过Kaplan-Meier存活曲线和ROC曲线证实了我们的预后模型的有效性和有效性。此外,我们精心制定了列线图，以帮助预测患者的预后。为了加强我们的发现，我们进行了全面的贝叶斯共定位分析，并进行了严格的体外实验，旨在明确确定已识别的DEDRG的有效性。
结果：我们的分析揭示了簇C1，其特征是DEDRGs表达水平升高，具有良好的预后，伴有丰富的免疫细胞浸润。相关分析强调了DEDRG之间的主要正相关，进一步肯定了它们在THCA中的意义。肿瘤样品和正常组织之间的DEDRG的差异表达模式在GEPIA和HPA数据库中是明显的。来自TIMER数据库的见解强调了DEDRG与免疫细胞浸润之间的牢固相关性。KEGG分析阐明了DEDRG的富集主要在关键途径，包括MAPK，PPAR信号通路,和蛋白聚糖在癌症中。此外，使用CIBERSORT和ESTIMATE算法的分析揭示了DEDRG在塑造免疫微环境中的关键作用。预后模型，锚定与THCA预后密切相关的五个基因，表现出良好的预测准确性，并且与肿瘤免疫微环境密切相关。值得注意的是,低危评分分类的患者有可能从免疫治疗中获益更多.DEDRGs的验证明确强调了INF2在THCA中的保护作用。
结论：总之，我们的研究描绘了与DRGs复杂相关的两种可识别的亚型，揭示了THCA环境中免疫浸润和生存预后的巨大差异。我们的发现的意义延伸到THCA患者的潜在治疗策略，这可能需要针对DEDRG和预后基因的针对性干预措施，从而影响二硫键下垂和免疫微环境。此外，我们的预后模型证明了强大的预测能力，基于这五个基因(ANGPTL7，FIRRE，ODAPH,PROKR1、SFRP5)、强调了其在指导THCA患者个性化治疗方法方面的潜在临床实用性。
方法：为了实现我们的目标，我们首先通过共识聚类方法划分了二硫化物掺杂相关基因(DRGs)的两种不同亚型.随后，使用limmaR包，我们确定了对我们的调查至关重要的DEDRG。这些DEDRG在各种数据库中进行了细致的验证，以及对基因调控的深入分析。采用功能丰富技术，我们探索了THCA中二硫化物形成的潜在分子机制.此外，我们利用CIBERSORT和ESTIMATE算法仔细检查了两种已识别亚型中的免疫状况.THCA预后模型的构建需要复杂的方法，包括单变量，多元Cox回归，和LASSO回归算法。通过Kaplan-Meier存活曲线和ROC曲线证实了我们的预后模型的有效性和有效性。此外,我们精心制定了列线图，以帮助预测患者的预后。为了加强我们的发现，我们进行了全面的贝叶斯共定位分析，并进行了严格的体外实验，旨在明确确定已识别的DEDRG的有效性。
结果：我们的分析揭示了簇C1，其特征是DEDRGs表达水平升高，具有良好的预后，伴有丰富的免疫细胞浸润。相关分析强调了DEDRG之间的主要正相关，进一步肯定了它们在THCA中的意义。肿瘤样品和正常组织之间的DEDRG的差异表达模式在GEPIA和HPA数据库中是明显的。来自TIMER数据库的见解强调了DEDRG与免疫细胞浸润之间的牢固相关性。KEGG分析阐明了DEDRG的富集主要在关键途径，包括MAPK，PPAR信号通路,和蛋白聚糖在癌症中。此外，使用CIBERSORT和ESTIMATE算法的分析揭示了DEDRG在塑造免疫微环境中的关键作用。预后模型，锚定与THCA预后密切相关的五个基因，表现出良好的预测准确性，并且与肿瘤免疫微环境密切相关。值得注意的是,低危评分分类的患者有可能从免疫治疗中获益更多.DEDRGs的验证明确强调了INF2在THCA中的保护作用。
结论：总之，我们的研究描绘了与DRGs复杂相关的两种可识别的亚型，揭示了THCA环境中免疫浸润和生存预后的巨大差异。我们的发现的意义延伸到THCA患者的潜在治疗策略，这可能需要针对DEDRG和预后基因的针对性干预措施，从而影响二硫键下垂和免疫微环境。此外，我们的预后模型证明了强大的预测能力，基于这五个基因(ANGPTL7，FIRRE，ODAPH,PROKR1、SFRP5)、强调了其在指导THCA患者个性化治疗方法方面的潜在临床实用性。
