Background: Upper tract urothelial carcinoma (UTUC) is a rare and aggressive malignancy with limited prognostic tools to predict disease progression. Due to its low incidence, the molecular pathogenesis of UTUC remains poorly understood, and few studies have explored transcriptomic profiling in this setting. Identifying gene expression biomarkers associated with progression may help improve risk stratification and guide postoperative management. Methods: In this study, we applied a machine learning approach to gene expression data from radical nephroureterectomy (RNU) specimens of 17 consecutive patients with pT2 or pT3 UTUC treated at our institution. RNA was extracted from formalin-fixed paraffin-embedded tissues and sequenced using the Ion AmpliSeq™ Transcriptome Human Gene Expression Kit on an Illumina HiSeq 2500 platform. Differential gene expression was assessed using DESeq2, and results were visualized with volcano plots. Predictive power was evaluated through logistic regression and receiver operating characteristic (ROC) analysis. Gene Ontology enrichment analysis was used to explore biological pathways. Results: A total of 76 genes were differentially expressed between progressive and non-progressive patients. A random forest classifier identified ten key genes with prognostic potential. Validation with logistic regression yielded an area under the ROC curve (AUC) of 0.88, indicating high discriminative ability. These genes were associated with immune regulation, cell cycle control, and tumor progression. Conclusions: This pilot study demonstrates the potential of integrating machine learning with transcriptomic analysis to identify prognostic biomarkers in UTUC. Further validation in larger, independent cohorts is needed to confirm these findings and support their clinical application.
背景：上尿路尿路上皮癌（UTUC）是一种罕见且侵袭性强的恶性肿瘤，目前预测疾病进展的预后工具有限。由于其发病率较低，UTUC的分子发病机制仍知之甚少，且少有研究探索其转录组学特征。识别与疾病进展相关的基因表达生物标志物，可能有助于改善风险分层并指导术后管理。 方法：本研究对来自本机构连续收治的17例pT2或pT3期UTUC患者行根治性肾输尿管切除术（RNU）标本的基因表达数据，应用机器学习方法进行分析。从福尔马林固定石蜡包埋组织中提取RNA，并在Illumina HiSeq 2500平台上使用Ion AmpliSeq™转录组人类基因表达试剂盒进行测序。采用DESeq2评估差异基因表达，并通过火山图可视化结果。通过逻辑回归和受试者工作特征（ROC）分析评估预测效能，并利用基因本体富集分析探索相关生物学通路。 结果：在进展与非进展患者之间共鉴定出76个差异表达基因。随机森林分类器筛选出十个具有预后潜力的关键基因。经逻辑回归验证，其ROC曲线下面积（AUC）达0.88，显示出较高的判别能力。这些基因主要与免疫调节、细胞周期控制和肿瘤进展相关。 结论：这项初步研究表明，将机器学习与转录组学分析相结合，在识别UTUC预后生物标志物方面具有潜力。未来需要在更大规模的独立队列中进行进一步验证，以确认这些发现并支持其临床应用。
肿瘤(癌症)患者之家