Bladder cancer (BLCA) is the fifth most common type of cancer worldwide, with high recurrence and progression rates. Although considerable progress has been made in the treatment of BLCA through accurate typing of molecular characteristics, little is known regarding the various genetic and epigenetic changes that have evolved in stem and progenitor cells. To address this issue, we have developed a novel stem cell typing method. Based on six published genomic datasets, we used 26 stem cell gene sets to classify each dataset. Unsupervised and supervised machine learning methods were used to perform the classification. We classified BLCA into three subtypes—high stem cell enrichment (SCE_H), medium stem cell enrichment (SCE_M), and low stem cell enrichment (SCE_L)—based on multiple cross-platform datasets. The stability and reliability of the classification were verified. Compared with the other subtypes, SCE_H had the highest degree of cancer stem cell concentration, highest level of immune cell infiltration, and highest sensitivity not only to predicted anti-PD-1 immunosuppressive therapy but also to conventional chemotherapeutic agents such as cisplatin, sunitinib, and vinblastine; however, this group had the worst prognosis. Comparison of gene set enrichment analysis results for pathway enrichment of various subtypes reveals that the SCE_H subtype activates the important pathways regulating cancer occurrence, development, and even poor prognosis, including epithelial-mesenchymal transition, hypoxia, angiogenesis, KRAS signal upregulation, interleukin 6-mediated JAK-STAT signaling pathway, and inflammatory response. Two identified pairs of transcription factors, GRHL2 and GATA6 and IRF5 and GATA3, possibly have opposite regulatory effects on SCE_H and SCE_L, respectively. The identification of BLCA subtypes based on cancer stem cell gene sets revealed the complex mechanism of carcinogenesis of BLCA and provides a new direction for the diagnosis and treatment of BLCA.

中文翻译：

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基于干基因组分析的膀胱癌新型干细胞亚型的开发和验证

膀胱癌 (BLCA) 是全球第五大最常见的癌症类型，具有高复发率和进展率。尽管通过分子特征的准确分型在治疗 BLCA 方面取得了相当大的进展，但人们对干细胞和祖细胞中发生的各种遗传和表观遗传变化知之甚少。为了解决这个问题，我们开发了一种新的干细胞分型方法。基于六个已发表的基因组数据集，我们使用 26 个干细胞基因集对每个数据集进行分类。无监督和有监督机器学习方法用于执行分类。我们基于多个跨平台数据集将 BLCA 分为三个亚型——高干细胞富集 (SCE_H)、中等干细胞富集 (SCE_M) 和低干细胞富集 (SCE_L)。验证了分类的稳定性和可靠性。与其他亚型相比，SCE_H 的癌症干细胞浓度最高，免疫细胞浸润水平最高，不仅对预测的抗 PD-1 免疫抑制治疗，而且对顺铂、舒尼替尼等常规化疗药物的敏感性最高，和长春碱；然而，这一组的预后最差。不同亚型通路富集的基因集富集分析结果比较表明，SCE_H亚型激活调节癌症发生、发展甚至不良预后的重要通路，包括上皮间质转化、缺氧、血管生成、KRAS信号上调、白细胞介素6-介导的 JAK-STAT 信号通路和炎症反应。两对已鉴定的转录因子 GRHL2 和 GATA6 以及 IRF5 和 GATA3 可能分别对 SCE_H 和 SCE_L 具有相反的调节作用。基于肿瘤干细胞基因组的BLCA亚型鉴定揭示了BLCA复杂的致癌机制，为BLCA的诊断和治疗提供了新的方向。