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Abstract：
Biliary system cancers are most commonly gallbladder cancers (GBC). Elderly patients (≥ 65) were reported to suffer from an unfavorable prognosis. In this study, we analyzed the RNA-seq and clinical data of elderly GBC patients to derive the genetic characteristics and the survival-related nomograms.
RNA-seq data from 14 GBC cases were collected from the Gene Expression Omnibus (GEO) database, grouped by age, and subjected to gene differential and enrichment analysis. In addition, a Weighted Gene Co-expression Network Analysis (WGCNA) was performed to determine the gene sets associated with age grouping further to characterize the gene profile of elderly GBC patients. The database of Surveillance, Epidemiology, and End Results (SEER) was searched for clinicopathological information regarding elderly GBC patients. Nomograms were constructed to predict the overall survival (OS) and cancer-specific survival (CSS) of elderly GBC patients. The predictive accuracy and capability of nomograms were evaluated through the concordance index (C-index), calibration curves, time-dependent operating characteristic curves (ROC), as well as area under the curve (AUC). Decision curve analysis (DCA) was performed to check out the clinical application value of nomograms.
Among the 14 patients with GBC, four were elderly, while the remaining ten were young. Analysis of gene differential and enrichment indicated that elderly GBC patients exhibited higher expression levels of cell cycle-related genes and lower expression levels of energy metabolism-related genes. Furthermore, the WGCNA analysis indicated that elderly GBC patients demonstrated a decrease in the expression of genes related to mitochondrial respiratory enzymes and an increase in the expression of cell cycle-related genes. 2131 elderly GBC patients were randomly allocated into the training cohort (70%) and validation cohort (30%). Our nomograms showed robust discriminative ability with a C-index of 0.717/0.747 for OS/CSS in the training cohort and 0.708/0.740 in the validation cohort. Additionally, calibration curves, AUCs, and DCA results suggested moderate predictive accuracy and superior clinical application value of our nomograms.
Discrepancies in cell cycle signaling and metabolic disorders, especially energy metabolism, were obviously observed between elderly and young GBC patients. In addition to being predictively accurate, the nomograms of elderly GBC patients also contributed to managing and strategizing clinical care.
RNA-seq data from 14 GBC cases were collected from the Gene Expression Omnibus (GEO) database, grouped by age, and subjected to gene differential and enrichment analysis. In addition, a Weighted Gene Co-expression Network Analysis (WGCNA) was performed to determine the gene sets associated with age grouping further to characterize the gene profile of elderly GBC patients. The database of Surveillance, Epidemiology, and End Results (SEER) was searched for clinicopathological information regarding elderly GBC patients. Nomograms were constructed to predict the overall survival (OS) and cancer-specific survival (CSS) of elderly GBC patients. The predictive accuracy and capability of nomograms were evaluated through the concordance index (C-index), calibration curves, time-dependent operating characteristic curves (ROC), as well as area under the curve (AUC). Decision curve analysis (DCA) was performed to check out the clinical application value of nomograms.
Among the 14 patients with GBC, four were elderly, while the remaining ten were young. Analysis of gene differential and enrichment indicated that elderly GBC patients exhibited higher expression levels of cell cycle-related genes and lower expression levels of energy metabolism-related genes. Furthermore, the WGCNA analysis indicated that elderly GBC patients demonstrated a decrease in the expression of genes related to mitochondrial respiratory enzymes and an increase in the expression of cell cycle-related genes. 2131 elderly GBC patients were randomly allocated into the training cohort (70%) and validation cohort (30%). Our nomograms showed robust discriminative ability with a C-index of 0.717/0.747 for OS/CSS in the training cohort and 0.708/0.740 in the validation cohort. Additionally, calibration curves, AUCs, and DCA results suggested moderate predictive accuracy and superior clinical application value of our nomograms.
Discrepancies in cell cycle signaling and metabolic disorders, especially energy metabolism, were obviously observed between elderly and young GBC patients. In addition to being predictively accurate, the nomograms of elderly GBC patients also contributed to managing and strategizing clinical care.
摘要：
■胆道系统癌症是最常见的胆囊癌（GBC）。据报道，老年患者（≥65岁）预后不良。在这项研究中,我们分析了老年GBC患者的RNA-seq和临床数据,以得出遗传特征和生存相关列线图.
■从基因表达综合（GEO）数据库中收集来自14例GBC病例的RNA-seq数据，按年龄分组，并进行基因差异和富集分析。此外,进行加权基因共表达网络分析(WGCNA)以确定与年龄分组相关的基因集,从而进一步表征老年GBC患者的基因谱.监测数据库,流行病学,和最终结果（SEER）搜索有关老年GBC患者的临床病理信息。构建列线图来预测老年GBC患者的总生存期(OS)和癌症特异性生存期(CSS)。通过一致性指数（C指数）评估列线图的预测准确性和能力，校正曲线,时间依赖性工作特性曲线(ROC)，以及曲线下面积（AUC）。进行决策曲线分析（DCA）以检查列线图的临床应用价值。
■在14例GBC患者中，四个是老人，而剩下的十个都很年轻。基因差异和富集分析表明，老年GBC患者细胞周期相关基因表达水平较高，能量代谢相关基因表达水平较低。此外，WGCNA分析显示,老年GBC患者线粒体呼吸酶相关基因的表达降低,细胞周期相关基因的表达增加.2131例老年GBC患者被随机分配到训练队列（70％）和验证队列（30％）。我们的列线图显示出强大的辨别能力，在训练队列中OS/CSS的C指数为0.717/0.747，在验证队列中为0.708/0.740。此外,校正曲线,AUCs,和DCA结果表明我们的列线图具有中等的预测准确性和较高的临床应用价值。
■细胞周期信号传导和代谢紊乱的差异，尤其是能量代谢，在老年和年轻GBC患者之间明显观察到。除了预测准确之外，老年GBC患者的列线图也有助于管理和制定临床护理策略.
■从基因表达综合（GEO）数据库中收集来自14例GBC病例的RNA-seq数据，按年龄分组，并进行基因差异和富集分析。此外,进行加权基因共表达网络分析(WGCNA)以确定与年龄分组相关的基因集,从而进一步表征老年GBC患者的基因谱.监测数据库,流行病学,和最终结果（SEER）搜索有关老年GBC患者的临床病理信息。构建列线图来预测老年GBC患者的总生存期(OS)和癌症特异性生存期(CSS)。通过一致性指数（C指数）评估列线图的预测准确性和能力，校正曲线,时间依赖性工作特性曲线(ROC)，以及曲线下面积（AUC）。进行决策曲线分析（DCA）以检查列线图的临床应用价值。
■在14例GBC患者中，四个是老人，而剩下的十个都很年轻。基因差异和富集分析表明，老年GBC患者细胞周期相关基因表达水平较高，能量代谢相关基因表达水平较低。此外，WGCNA分析显示,老年GBC患者线粒体呼吸酶相关基因的表达降低,细胞周期相关基因的表达增加.2131例老年GBC患者被随机分配到训练队列（70％）和验证队列（30％）。我们的列线图显示出强大的辨别能力，在训练队列中OS/CSS的C指数为0.717/0.747，在验证队列中为0.708/0.740。此外,校正曲线,AUCs,和DCA结果表明我们的列线图具有中等的预测准确性和较高的临床应用价值。
■细胞周期信号传导和代谢紊乱的差异，尤其是能量代谢，在老年和年轻GBC患者之间明显观察到。除了预测准确之外，老年GBC患者的列线图也有助于管理和制定临床护理策略.
