Spatial transcriptomics (ST) examines gene expression within its spatial context on tissue, linking morphology and function. Advances in ST resolution and throughput have led to an increase in scientific interest, notably in cancer research. This scoping study reviews the challenges and practical applications of ST, summarizing current methods, trends, and data analysis techniques for ST in neoplasm research. We analyzed 41 articles published by the end of 2023 alongside public data repositories. The findings indicate cancer biology is an important focus of ST research, with a rising number of studies each year. Visium (10x Genomics, Pleasanton, CA, USA) is the leading ST platform, and SCTransform from Seurat R library is the preferred method for data normalization and integration. Many studies incorporate additional data types like single-cell sequencing and immunohistochemistry. Common ST applications include discovering the composition and function of tumor tissues in the context of their heterogeneity, characterizing the tumor microenvironment, or identifying interactions between cells, including spatial patterns of expression and co-occurrence. However, nearly half of the studies lacked comprehensive data processing protocols, hindering their reproducibility. By recommending greater transparency in sharing analysis methods and adapting single-cell analysis techniques with caution, this review aims to improve the reproducibility and reliability of future studies in cancer research.
空间转录组学（ST）在组织原位检测基因表达，将形态与功能联系起来。ST分辨率和通量的提升引发了科学界日益浓厚的兴趣，尤其在癌症研究领域。本范围综述探讨了ST面临的挑战与实际应用，总结了肿瘤研究中ST的现有方法、发展趋势及数据分析技术。我们分析了截至2023年底发表的41篇文献及公共数据库资料。研究结果表明癌症生物学是ST研究的重要方向，相关文献数量逐年增长。Visium（10x Genomics，美国普莱森顿）是目前主流的ST平台，而Seurat R程序包中的SCTransform是数据标准化与整合的首选方法。许多研究还整合了单细胞测序和免疫组化等其他数据类型。ST的常见应用包括：在肿瘤异质性背景下解析组织构成与功能、表征肿瘤微环境、识别细胞间相互作用（包括表达空间模式与共现关系）。然而，近半数研究缺乏完整的数据处理流程说明，影响了结果的可重复性。本文通过建议提高分析方法共享的透明度，并审慎借鉴单细胞分析技术，旨在提升未来癌症研究的可重复性与可靠性。
Scoping Review: Methods and Applications of Spatial Transcriptomics in Tumor Research
肿瘤(癌症)患者之家