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Abstract：
BACKGROUND: Transactivating DNA-binding protein 43 (TDP-43) is intimately associated with tumorigenesis and progression by regulating mRNA splicing, transport, stability, and non-coding RNA molecules. The exact role of TDP-43 in lung adenocarcinoma (LUAD) has not yet been fully elucidated, despite extensive research on its function in various cancer types. An imperative aspect of comprehending the underlying biological characteristics associated with TDP-43 involves investigating the genes that are co-expressed with this protein. This study assesses the prognostic significance of these co-expressed genes in LUAD and subsequently explores potential therapeutic strategies based on these findings.
METHODS: Transcriptomic and clinical data pertaining to LUAD were retrieved from open-access databases to establish an association between mRNA expression profiles and the presence of TDP-43. A risk-prognosis model was developed to compare patient survival rates across various groups, and its accuracy was also assessed. Additionally, differences in tumor stemness, mutational profiles, tumor microenvironment (TME) characteristics, immune checkpoints, and immune cell infiltration were analyzed in the different groups. Moreover, the study entailed predicting the potential response to immunotherapy as well as the sensitivity to commonly employed chemotherapeutic agents and targeted drugs for each distinct group.
RESULTS: The TDP-43 Co-expressed Gene Risk Score (TCGRS) model was constructed utilizing four genes: Kinesin Family Member 20A (KIF20A), WD Repeat Domain 4 (WDR4), Proline Rich 11 (PRR11), and Glia Maturation Factor Gamma (GMFG). The value of this model in predicting LUAD patient survival is effectively illustrated by both the Kaplan-Meier (K-M) survival curve and the area under the receiver operating characteristic curve (AUC-ROC). The Gene Set Enrichment Analysis (GSEA) revealed that the high TCGRS group was primarily enriched in biological pathways and functions linked to DNA replication and cell cycle; the low TCGRS group showed primary enrichment in immune-related pathways and functions. The high and low TCGRS groups showed differences in tumor stemness, mutational burden, TME, immune infiltration level, and immune checkpoints. The predictions analysis of immunotherapy indicates that the Tumor Immune Dysfunction and Exclusion (TIDE) score (p < 0.001) and non-response rate (74% vs. 51%, p < 0.001) in the high TCGRS group are higher than those in the low TCGRS group. The Immune Phenotype Score (IPS) in the high TCGRS group is lower than in the low TCGRS group (p < 0.001). The drug sensitivity analysis revealed that the half-maximal inhibitory concentration (IC50) values for cisplatin, docetaxel, doxorubicin, etoposide, gemcitabine, paclitaxel, vincristine, erlotinib, and gefitinib (all p < 0.01) in the high TCGRS group are lower than those in the low TCGRS group.
CONCLUSIONS: The TCGRS derived from the model exhibits a reliable biomarker for evaluating both prognosis and treatment effectiveness among patients with LUAD. This study is anticipated to offer valuable insights into developing effective treatment strategies for this patient population. It is believed that this study is anticipated to contribute significantly to clinical diagnostics, the development of therapeutic drugs, and the enhancement of patient care.
METHODS: Transcriptomic and clinical data pertaining to LUAD were retrieved from open-access databases to establish an association between mRNA expression profiles and the presence of TDP-43. A risk-prognosis model was developed to compare patient survival rates across various groups, and its accuracy was also assessed. Additionally, differences in tumor stemness, mutational profiles, tumor microenvironment (TME) characteristics, immune checkpoints, and immune cell infiltration were analyzed in the different groups. Moreover, the study entailed predicting the potential response to immunotherapy as well as the sensitivity to commonly employed chemotherapeutic agents and targeted drugs for each distinct group.
RESULTS: The TDP-43 Co-expressed Gene Risk Score (TCGRS) model was constructed utilizing four genes: Kinesin Family Member 20A (KIF20A), WD Repeat Domain 4 (WDR4), Proline Rich 11 (PRR11), and Glia Maturation Factor Gamma (GMFG). The value of this model in predicting LUAD patient survival is effectively illustrated by both the Kaplan-Meier (K-M) survival curve and the area under the receiver operating characteristic curve (AUC-ROC). The Gene Set Enrichment Analysis (GSEA) revealed that the high TCGRS group was primarily enriched in biological pathways and functions linked to DNA replication and cell cycle; the low TCGRS group showed primary enrichment in immune-related pathways and functions. The high and low TCGRS groups showed differences in tumor stemness, mutational burden, TME, immune infiltration level, and immune checkpoints. The predictions analysis of immunotherapy indicates that the Tumor Immune Dysfunction and Exclusion (TIDE) score (p < 0.001) and non-response rate (74% vs. 51%, p < 0.001) in the high TCGRS group are higher than those in the low TCGRS group. The Immune Phenotype Score (IPS) in the high TCGRS group is lower than in the low TCGRS group (p < 0.001). The drug sensitivity analysis revealed that the half-maximal inhibitory concentration (IC50) values for cisplatin, docetaxel, doxorubicin, etoposide, gemcitabine, paclitaxel, vincristine, erlotinib, and gefitinib (all p < 0.01) in the high TCGRS group are lower than those in the low TCGRS group.
CONCLUSIONS: The TCGRS derived from the model exhibits a reliable biomarker for evaluating both prognosis and treatment effectiveness among patients with LUAD. This study is anticipated to offer valuable insights into developing effective treatment strategies for this patient population. It is believed that this study is anticipated to contribute significantly to clinical diagnostics, the development of therapeutic drugs, and the enhancement of patient care.
摘要：
背景：反式激活DNA结合蛋白43（TDP-43）通过调节mRNA剪接与肿瘤发生和进展密切相关，运输,稳定性,和非编码RNA分子。TDP-43在肺腺癌（LUAD）中的确切作用尚未完全阐明，尽管对其在各种癌症类型中的功能进行了广泛的研究。理解与TDP-43相关的潜在生物学特征的一个必要方面涉及研究与该蛋白共表达的基因。这项研究评估了这些共表达基因在LUAD中的预后意义，并随后基于这些发现探索了潜在的治疗策略。
方法：从开放访问数据库中检索与LUAD相关的转录组学和临床数据，以建立mRNA表达谱与TDP-43存在之间的关联。开发了一个风险-预后模型来比较不同组的患者生存率，并对其准确性进行了评估。此外,肿瘤干性的差异，突变谱,肿瘤微环境（TME）特征，免疫检查点,并对不同组的免疫细胞浸润情况进行分析。此外，该研究需要预测每个不同组患者对免疫治疗的潜在反应以及对常用化疗药物和靶向药物的敏感性.
结果：利用四个基因构建TDP-43共表达基因风险评分（TCGRS）模型：Kinesin家族成员20A（KIF20A），WD重复域4(WDR4)，脯氨酸Rich11(PRR11),和Glia成熟因子Gamma(GMFG)。Kaplan-Meier(K-M)存活曲线和受试者工作特征曲线下面积(AUC-ROC)有效地说明了该模型在预测LUAD患者存活期中的价值。基因集富集分析（GSEA）显示，高TCGRS组主要富集与DNA复制和细胞周期相关的生物学途径和功能；低TCGRS组显示出免疫相关途径和功能的初步富集。高、低TCGRS组的肿瘤干性有差异，突变负担，TME,免疫浸润水平，和免疫检查点。免疫治疗的预测分析表明，肿瘤免疫功能障碍和排斥（TIDE）评分（p<0.001）和无反应率（74％vs.51%,p<0.001)高TCGRS组高于低TCGRS组。高TCGRS组的免疫表型评分(IPS)低于低TCGRS组(p<0.001)。药物敏感性分析显示，顺铂的半数最大抑制浓度（IC50）值，多西他赛,阿霉素,依托泊苷,吉西他滨,紫杉醇,长春新碱,厄洛替尼,高TCGRS组和吉非替尼(均p<0.01)均低于低TCGRS组。
结论：来自该模型的TCGRS显示了评估LUAD患者预后和治疗有效性的可靠生物标志物。这项研究预计将为开发针对该患者人群的有效治疗策略提供有价值的见解。相信这项研究有望为临床诊断做出重大贡献，治疗药物的发展，加强对病人的护理。
方法：从开放访问数据库中检索与LUAD相关的转录组学和临床数据，以建立mRNA表达谱与TDP-43存在之间的关联。开发了一个风险-预后模型来比较不同组的患者生存率，并对其准确性进行了评估。此外,肿瘤干性的差异，突变谱,肿瘤微环境（TME）特征，免疫检查点,并对不同组的免疫细胞浸润情况进行分析。此外，该研究需要预测每个不同组患者对免疫治疗的潜在反应以及对常用化疗药物和靶向药物的敏感性.
结果：利用四个基因构建TDP-43共表达基因风险评分（TCGRS）模型：Kinesin家族成员20A（KIF20A），WD重复域4(WDR4)，脯氨酸Rich11(PRR11),和Glia成熟因子Gamma(GMFG)。Kaplan-Meier(K-M)存活曲线和受试者工作特征曲线下面积(AUC-ROC)有效地说明了该模型在预测LUAD患者存活期中的价值。基因集富集分析（GSEA）显示，高TCGRS组主要富集与DNA复制和细胞周期相关的生物学途径和功能；低TCGRS组显示出免疫相关途径和功能的初步富集。高、低TCGRS组的肿瘤干性有差异，突变负担，TME,免疫浸润水平，和免疫检查点。免疫治疗的预测分析表明，肿瘤免疫功能障碍和排斥（TIDE）评分（p<0.001）和无反应率（74％vs.51%,p<0.001)高TCGRS组高于低TCGRS组。高TCGRS组的免疫表型评分(IPS)低于低TCGRS组(p<0.001)。药物敏感性分析显示，顺铂的半数最大抑制浓度（IC50）值，多西他赛,阿霉素,依托泊苷,吉西他滨,紫杉醇,长春新碱,厄洛替尼,高TCGRS组和吉非替尼(均p<0.01)均低于低TCGRS组。
结论：来自该模型的TCGRS显示了评估LUAD患者预后和治疗有效性的可靠生物标志物。这项研究预计将为开发针对该患者人群的有效治疗策略提供有价值的见解。相信这项研究有望为临床诊断做出重大贡献，治疗药物的发展，加强对病人的护理。
