前列腺癌（PCa）是一种多灶性疾病，其特征是单个腺体内的基因组和表型异质性。在本研究中，Visium 空间转录组学 (ST) 分析应用于具有不同组织学结构的前列腺癌组织，以推断参与格里森评分 (GS) 进展的分子事件。根据转录组数据，使用主成分分析 (PCA) 和 Louvain 聚类分析将组织切片中的斑点分为不同的组。根据 GS 对斑点的注释揭示了转录组图谱和组织学可识别结构之间的显着相似性。通过恶性肿瘤相关特征分析，特别是推断的拷贝数变异（inferCNV）以及发育轨迹分析，例如扩散伪时间（DPT）和基于分区的图抽象（PAGA），从生物信息学角度验证了宏观GS测定的准确性。通过沿着 GS 梯度对组进行两两比较，从差异表达基因 (DEG) 中鉴定出与 GS 进展相关的基因。代表性癌基因 UQCRB 和 LBH 编码的蛋白质被发现在晚期 PCa 组织中高表达。其 mRNA 的敲低显着抑制了 PCa 细胞的增殖和侵袭。这些发现使用癌症基因组图谱前列腺腺癌 (TCGA-PRAD) 数据集以及组织学和细胞学实验进行了验证。这里提出的结果为基于 ST 的 GS 进展评估奠定了基础，并为 GS 进展相关基因 UQCRB 和 LBH 提供了宝贵的见解。© 2024 作者。

Prostate cancer (PCa) is a multifocal disease characterized by genomic and phenotypic heterogeneity within a single gland. In this study, Visium spatial transcriptomics (ST) analysis was applied to PCa tissues with different histological structures to infer the molecular events involved in Gleason score (GS) progression. The spots in tissue sections were classified into various groups using Principal Component Analysis (PCA) and Louvain clustering analysis based on transcriptome data. Anotation of the spots according to GS revealed notable similarities between transcriptomic profiles and histologically identifiable structures. The accuracy of macroscopic GS determination was bioinformatically verified through malignancy-related feature analysis, specifically inferred copy number variation (inferCNV), as well as developmental trajectory analyses, such as diffusion pseudotime (DPT) and partition-based graph abstraction (PAGA). Genes related to GS progression were identified from the differentially expressed genes (DEGs) through pairwise comparisons of groups along a GS gradient. The proteins encoded by the representative oncogenes UQCRB and LBH were found to be highly expressed in advanced-stage PCa tissues. Knockdown of their mRNAs significantly suppressed PCa cell proliferation and invasion. These findings were validated using The Cancer Genome Atlas Prostate Adenocarcinoma (TCGA-PRAD) dataset, as well as through histological and cytological experiments. The results presented here establish a foundation for ST-based evaluation of GS progression and provide valuable insights into the GS progression-related genes UQCRB and LBH.© 2024 The Authors.