目的 基于铜死亡相关长链非编码RNA(lncRNA)构建膀胱癌患者预后风险评估模型。 方法 下载癌症基因组图谱数据库中的膀胱癌患者RNA序列数据和临床数据,采用Pearson相关性分析、单因素Cox回归、Lasso回归和多因素Cox回归分析筛选与铜死亡及膀胱癌患者预后相关的lncRNA,并构建铜死亡相关的lncRNA膀胱癌患者预后风险评分方程。根据风险评分方程计算的中位数将患者分为高风险组和低风险组,比较两组免疫细胞丰度差异。应用Kaplan-Meier生存曲线评估风险评分方程的准确性;应用受试者操作特征曲线(ROC曲线)评估风险评分方程预测患者1、3、5年存活率的价值;采用单因素和多因素Cox回归筛选与膀胱癌患者预后相关的影响因素,构建膀胱癌患者预后风险评估列线图,并通过校准曲线评估列线图预测的准确性。 结果 膀胱癌患者预后风险评分方程由9个铜死亡相关的lncRNA构建。免疫浸润分析结果显示,高风险组M0巨噬细胞、M1巨噬细胞、M2巨噬细胞、静息肥大细胞及中性粒细胞丰度明显高于低风险组,而低风险组CD8 + T细胞、辅助性T细胞、调节性T细胞及浆细胞丰度明显高于高风险组(均 P <0.05)。Kaplan-Meier生存曲线分析结果显示,与高风险组比较,低风险组的总生存期和无进展生存期更长(均 P <0.01)。单因素和多因素Cox回归分析结果显示,风险评分、年龄及肿瘤分期为患者预后的独立影响因素。ROC曲线分析结果显示,风险评分预测患者1、3、5年生存曲线下面积(AUC)分别为0.716、0.697、0.717,当联合年龄、肿瘤分期后,其预测患者1年生存的AUC可提高至0.725。基于患者年龄、肿瘤分期和风险评分构建的膀胱癌患者预后风险评估列线图,其预测值与实际值基本一致。 结论 基于铜死亡相关lncRNA构建的膀胱癌患者预后风险评估模型不仅能较准确地预测膀胱癌患者的预后,还可以评估患者的免疫浸润状态,为后续肿瘤免疫治疗提供参考。
BACKGROUND: Breast cancer is the leading cause of death in females around the world. Its occurrence and development has been linked to genetic factors, living habits and health conditions, and socioeconomic factors. Comparisons of incidence and mortality rates of female breast cancer are useful approaches to define cancer-related socioeconomic disparities. METHODS: This was a retrospective observational cohort study on breast cancer of females in several developed countries between 1980 and 2012. The path diagram analysis for five factors, i.e. years, population, gross domestic product, gross domestic product per capita, and unemployment rate, were conducted using Excel database function, and the effects on breast cancer incidence and mortality rates were analyzed. International Agency for Research on Cancer's CANCERMondial clearinghouse was used to determine the incidence and mortality rates of female breast cancer data from several developed countries for 1980–2012. RESULTS: The relationship between socioeconomic factors and the occurrence and development of breast cancer did not follow a monotonic function. We found a positive, significant association of national public wealth on the incidence and mortality of breast cancer. The path coefficients in the structural equations model are -0.51 and -0.39, respectively. In addition to the significant relationship between individual physical and psychological characteristics, social pressure, such as unemployment rate has a significant impact on the incidence and mortality of breast cancer. The path coefficients in the structural equations model are all 0.2. The path coefficients of individual economic wealth to the incidence rate and mortality rate of breast cancer is 0.18 and 0.27, respectively. CONCLUSIONS: A significant statistical relationship between the socioeconomic development and the crude rates of female breast cancer was shown in this study. The incidence and mortality rates of breast cancer can be regulated effectively by a moderate increase in national public wealth, and clearly was affected by the individual’s economic wealth. In addition, the influence of social pressure (e.g., unemployment rate) on the incidence and mortality of breast cancer was not typical monotonous. The survival rate of breast cancer determined by the ratio of mortality rate to incidence rate also showed a similar pattern with socioeconomic factors.
PURPOSE: Breast cancer is the leading cause of death in females around the world. Its occurrence and development has been linked to genetic factors, living habits and health conditions, but also by socioeconomic factors. Comparisons of incidence and mortality rates of female breast cancer are useful approaches to define cancer-related socioeconomic disparities. METHODS: International Agency for Research on Cancer's CANCERMondial clearinghouse was used to determine the incidence and mortality rates of female breast cancer data from several developed countries for 1980–2012. We subsequently investigated the effects of socioeconomic factors on breast cancer incidence and mortality rates by regression methods from univariate analysis to path diagram analysis. RESULTS: The relationship between socioeconomic factors and the occurrence and development of breast cancer did not follow a monotonic function. We found a positive, significant association of national public wealth (GDP) on the incidence and mortality of breast cancer. The path coefficients in the structuralequations model are -0.51 and -0.39, respectively. In addition to the significant relationship between individual physical and psychological characteristics, social pressure, such as unemployment rate (UR) has a significant impact on the incidence and mortality of breast cancer. The path coefficients in the structural equations model are all 0.2. The path coefficients of individual economic wealth to the incidence rate and mortality rate of breast cancer is 0.18 and 0.27, respectively. CONCLUSIONS: A significant statistical relationship between the socioeconomic development and the crude rates of female breast cancer was shown in this study. The incidence and mortality rates of breast cancer can be regulated effectively by a moderate increase in GDP, and clearly was affected by the individual’s economic wealth (GDPPC). In addition, the influence of social pressure (e.g., unemployment rate) on the incidence and mortality of breast cancer was not typical monotonous. The survival rate of breast cancer determined by the ratio of mortality rate to incidence rate also showed a similar pattern with socioeconomic factors.
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