Song Wei, Wang Yatao, Zhou Min, Guo Fengqin, Liu Yanliang
Department of Gastrointestinal Surgery, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei, China.
Department of Obstetrics & Gynecology, Renmin Hospital of Wuhan University, No. 238, Jiefang Road, Wuhan, 430060, Hubei, China.
Hum Genomics. 2025 Aug 13;19(1):92. doi: 10.1186/s40246-025-00805-x.
Recent advancements in transcriptomic analysis, combined with single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics, have deepened our understanding of the tumor microenvironment and cellular heterogeneity, laying the groundwork for personalized therapies. The aim of this research is to explore the heterogeneity of tumor cells in colorectal cancer (CRC) and evaluate their prognostic value in different therapeutic contexts, emphasizing the impact of tumor cell heterogeneity on disease progression.
scRNA-seq alongside spatial transcriptomics was employed to analyze the heterogeneity of tumor cells in CRC, the spatial distribution of tumor cells, and their interactions with the microenvironment.
We identified nine distinct tumor cell subtypes, with MLXIPL + neoplasm prevalent in advanced CRC, while ADH1C + and MUC2 + neoplasms were more common in early-stage CRC. MLXIPL + neoplasm was significantly associated with chemotherapy and targeted therapy efficacy. Analysis of spatial transcriptomics indicated that MLXIPL + neoplasm is located in the core area of the tumor cells. We developed a 13-gene prognostic signature (PS) using machine learning algorithm (StepCox backward), which predicts the prognosis of CRC patients. Furthermore, the patients with low PS score demonstrated higher immune cell infiltration and immune regulatory factors, suggesting enhanced immune surveillance and treatment response.
The findings highlight the critical role of tumor cell heterogeneity in CRC progression and treatment response, suggesting the need for personalized therapeutic strategies targeting different subpopulations. The constructed PS demonstrates significant clinical application value in predicting patient prognosis.
转录组分析的最新进展，结合单细胞RNA测序（scRNA-seq）和空间转录组学，加深了我们对肿瘤微环境和细胞异质性的理解，为个性化治疗奠定了基础。本研究的目的是探索结直肠癌（CRC）中肿瘤细胞的异质性，并评估它们在不同治疗背景下的预后价值，强调肿瘤细胞异质性对疾病进展的影响。
采用scRNA-seq和空间转录组学分析CRC中肿瘤细胞的异质性、肿瘤细胞的空间分布及其与微环境的相互作用。
我们鉴定出九种不同的肿瘤细胞亚型，其中MLXIPL+肿瘤在晚期CRC中普遍存在，而ADH1C+和MUC2+肿瘤在早期CRC中更为常见。MLXIPL+肿瘤与化疗和靶向治疗疗效显著相关。空间转录组学分析表明，MLXIPL+肿瘤位于肿瘤细胞的核心区域。我们使用机器学习算法（逐步Cox向后法）开发了一个13基因的预后特征（PS），用于预测CRC患者的预后。此外，PS评分低的患者表现出更高的免疫细胞浸润和免疫调节因子，提示免疫监视和治疗反应增强。
研究结果突出了肿瘤细胞异质性在CRC进展和治疗反应中的关键作用，表明需要针对不同亚群制定个性化治疗策略。构建的PS在预测患者预后方面具有显著的临床应用价值。
