Padullés Bernat, López-Aladid Ruben, Ingelmo-Torres Mercedes, Roldán Fiorella L, Martínez Carmen, Juez Judith, Izquierdo Laura, Mengual Lourdes, Alcaraz Antonio
Urology Department and Laboratory, Hospital Clínic de Barcelona, 08036 Barcelona, Spain.
Genetics and Urological Tumors, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036 Barcelona, Spain.
Cancers (Basel). 2025 Aug 11;17(16):2619. doi: 10.3390/cancers17162619.
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.
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.
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.
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.
上尿路尿路上皮癌（UTUC）是一种罕见且侵袭性强的恶性肿瘤，预测疾病进展的预后工具有限。由于其发病率低，UTUC的分子发病机制仍知之甚少，很少有研究探索这种情况下的转录组谱。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。
在本研究中，我们将机器学习方法应用于来自我们机构连续治疗的17例pT2或pT3期UTUC患者根治性肾输尿管切除术（RNU）标本的基因表达数据。从福尔马林固定石蜡包埋组织中提取RNA，并使用Ion AmpliSeq™转录组人类基因表达试剂盒在Illumina HiSeq 2500平台上进行测序。使用DESeq2评估差异基因表达，结果用火山图可视化。通过逻辑回归和受试者工作特征（ROC）分析评估预测能力。基因本体富集分析用于探索生物学途径。
进展期和非进展期患者之间共有76个基因差异表达。随机森林分类器识别出10个具有预后潜力的关键基因。逻辑回归验证产生的ROC曲线下面积（AUC）为0.88，表明具有高判别能力。这些基因与免疫调节、细胞周期控制和肿瘤进展相关。
这项初步研究证明了将机器学习与转录组分析相结合以识别UTUC预后生物标志物的潜力。需要在更大的独立队列中进一步验证以证实这些发现并支持其临床应用。
