Abstract：
BACKGROUND: Liver cancer is one of the most malignant liver diseases in the world, and the 5-year survival rate of such patients is low. Analgesics are often used to cure pain prevalent in liver cancer. The expression changes and clinical significance of the analgesic targets (ATs) in liver cancer have not been deeply understood.
OBJECTIVE: The purpose of this study is to clarify the expression pattern of ATs gene in liver cancer and its clinical significance. Through the comprehensive analysis of transcriptome data and clinical parameters, the prognosis model related to ATs gene is established, and the drug information sensitive to ATs is mined.
METHODS: The study primarily utilized transcriptomic data and clinical information from liver cancer patients sourced from The Cancer Genome Atlas (TCGA) database. These data were employed to analyze the expression of ATs, conduct survival analysis, gene set variation analysis (GSVA), immune cell infiltration analysis, establish a prognostic model, and perform other bioinformatic analyses. Additionally, data from liver cancer patients in the International Cancer Genome Consortium (ICGC) were utilized to validate the accuracy of the model. Furthermore, the impact of analgesics on key genes in the prognostic model was assessed using data from the Comparative Toxicogenomics Database (CTD).
RESULTS: The study investigated the differential expression of 58 ATs genes in liver cancer compared to normal tissues. Patients were stratified based on ATs expression, revealing varied survival outcomes. Functional enrichment analysis highlighted distinctions in spindle organization, centrosome, and spindle microtubule functions. Prognostic modeling identified low TP53 expression as protective, while elevated CCNA2, NEU1, and HTR2C levels posed risks. Commonly used analgesics, including acetaminophen and others, were found to influence the expression of these genes. These findings provide insights into potential therapeutic strategies for liver cancer and shed light on the molecular mechanisms underlying its progression.
CONCLUSIONS: The collective analysis of gene signatures associated with ATs suggests their potential as prognostic predictors in hepatocellular carcinoma patients. These findings not only offer insights into cancer therapy but also provide novel avenues for the development of indications for analgesics.
OBJECTIVE: The purpose of this study is to clarify the expression pattern of ATs gene in liver cancer and its clinical significance. Through the comprehensive analysis of transcriptome data and clinical parameters, the prognosis model related to ATs gene is established, and the drug information sensitive to ATs is mined.
METHODS: The study primarily utilized transcriptomic data and clinical information from liver cancer patients sourced from The Cancer Genome Atlas (TCGA) database. These data were employed to analyze the expression of ATs, conduct survival analysis, gene set variation analysis (GSVA), immune cell infiltration analysis, establish a prognostic model, and perform other bioinformatic analyses. Additionally, data from liver cancer patients in the International Cancer Genome Consortium (ICGC) were utilized to validate the accuracy of the model. Furthermore, the impact of analgesics on key genes in the prognostic model was assessed using data from the Comparative Toxicogenomics Database (CTD).
RESULTS: The study investigated the differential expression of 58 ATs genes in liver cancer compared to normal tissues. Patients were stratified based on ATs expression, revealing varied survival outcomes. Functional enrichment analysis highlighted distinctions in spindle organization, centrosome, and spindle microtubule functions. Prognostic modeling identified low TP53 expression as protective, while elevated CCNA2, NEU1, and HTR2C levels posed risks. Commonly used analgesics, including acetaminophen and others, were found to influence the expression of these genes. These findings provide insights into potential therapeutic strategies for liver cancer and shed light on the molecular mechanisms underlying its progression.
CONCLUSIONS: The collective analysis of gene signatures associated with ATs suggests their potential as prognostic predictors in hepatocellular carcinoma patients. These findings not only offer insights into cancer therapy but also provide novel avenues for the development of indications for analgesics.
摘要：
背景：肝癌是世界上最恶性的肝脏疾病之一，5年生存率低。镇痛药通常用于治疗肝癌中普遍存在的疼痛。镇痛目标(ATs)在肝癌中的表达变更及临床意义还未被深刻懂得。
目的：本研究的目的是阐明ATs基因在肝癌中的表达模式及其临床意义。通过对转录组数据和临床参数的综合分析,建立与ATs基因相关的预后模型，并且对ATs敏感的药物信息被挖掘。
方法：该研究主要利用来自癌症基因组图谱（TCGA）数据库的肝癌患者的转录组数据和临床信息。这些数据用于分析ATs的表达，进行生存分析，基因集变异分析（GSVA），免疫细胞浸润分析，建立预后模型，并进行其他生物信息学分析。此外,来自国际癌症基因组联盟(ICGC)的肝癌患者的数据被用来验证模型的准确性.此外，使用比较毒理基因组学数据库(CTD)的数据评估了止痛药对预后模型中关键基因的影响.
结果：该研究调查了肝癌中58个ATs基因与正常组织的差异表达。根据ATs表达对患者进行分层，揭示不同的生存结果。功能富集分析突出了纺锤体组织的区别，中心体,和纺锤体微管功能。预后建模确定低TP53表达是保护性的，而升高的CCNA2、NEU1和HTR2C水平构成了风险。常用镇痛药,包括对乙酰氨基酚等,被发现影响这些基因的表达。这些发现为肝癌的潜在治疗策略提供了见解，并阐明了其进展的分子机制。
结论：对与ATs相关的基因特征的综合分析提示其作为肝细胞癌患者预后预测因子的潜力。这些发现不仅为癌症治疗提供了见解，而且为开发镇痛药适应症提供了新的途径。
目的：本研究的目的是阐明ATs基因在肝癌中的表达模式及其临床意义。通过对转录组数据和临床参数的综合分析,建立与ATs基因相关的预后模型，并且对ATs敏感的药物信息被挖掘。
方法：该研究主要利用来自癌症基因组图谱（TCGA）数据库的肝癌患者的转录组数据和临床信息。这些数据用于分析ATs的表达，进行生存分析，基因集变异分析（GSVA），免疫细胞浸润分析，建立预后模型，并进行其他生物信息学分析。此外,来自国际癌症基因组联盟(ICGC)的肝癌患者的数据被用来验证模型的准确性.此外，使用比较毒理基因组学数据库(CTD)的数据评估了止痛药对预后模型中关键基因的影响.
结果：该研究调查了肝癌中58个ATs基因与正常组织的差异表达。根据ATs表达对患者进行分层，揭示不同的生存结果。功能富集分析突出了纺锤体组织的区别，中心体,和纺锤体微管功能。预后建模确定低TP53表达是保护性的，而升高的CCNA2、NEU1和HTR2C水平构成了风险。常用镇痛药,包括对乙酰氨基酚等,被发现影响这些基因的表达。这些发现为肝癌的潜在治疗策略提供了见解，并阐明了其进展的分子机制。
结论：对与ATs相关的基因特征的综合分析提示其作为肝细胞癌患者预后预测因子的潜力。这些发现不仅为癌症治疗提供了见解，而且为开发镇痛药适应症提供了新的途径。
