背景:结直肠癌(CRC)特异性肿瘤细胞内核苷酸代谢的复杂性仍未得到充分表征。对特定肿瘤簇及其与肿瘤微环境(TME)的动态相互作用进行细微差别的检查可能会对这些治疗吉祥的交流网络产生深刻的见解。
方法:通过整合十种类型的单细胞富集评分方法,我们对CRC细胞类型进行了富集分析,通过四个额外的单细胞队列进行了验证。使用评分的平均值确定肿瘤细胞组。使用cellphonedb,monocle,地狱CNV,场景,和细胞色素,进行了功能分析。利用RCTD方法,单细胞分组被映射到空间转录组学上,分析细胞依赖性和通路活性以区分肿瘤细胞亚型。差异表达分析确定了核苷酸代谢中的核心基因,通过单细胞和空间转录组学分析阐明这些基因在肿瘤细胞和免疫微环境中的功能。预后模型是从批量转录组队列开发的,以预测对免疫治疗的反应。进行实验室实验以验证核心基因的生物学功能。
结果:肿瘤细胞中核苷酸代谢显著升高,将它们分为两组:NUhighepi和NUlowepi。表型NUhighepi被识别为表现出明显的恶性属性。利用分析工具stlearn进行细胞间通信评估,确定NUhighepi与成纤维细胞有密切的相互作用。证实了这一观察,通过MISTy进行的空间转录组细胞相互作用评估揭示了NUhighepi对成纤维细胞的特殊依赖。随后,我们确定了核苷酸代谢的关键基因NME1,通过体外检查证实其在阻止转移中的作用。利用多种机器学习算法,已经建立了一个稳定的预后模型(NRS),能够预测生存率和对免疫治疗的反应。此外,靶向药物已被确定为高和低评分组。实验室实验表明,NME1可抑制CRC肿瘤细胞的增殖和侵袭。
结论:我们的研究阐明了NUhighepi的潜在肿瘤机制和NME1在抑制转移中的作用,进一步加深对核苷酸代谢在结直肠癌中作用的认识,并为破坏其属性提供有价值的目标。
BACKGROUND: The intricacies of nucleotide metabolism within tumor cells specific to colorectal cancer (CRC) remain insufficiently characterized. A nuanced examination of particular tumor clusters and their dynamic interplay with the tumor microenvironment (TME) may yield profound insights into these therapeutically auspicious communicative networks.
METHODS: By integrating ten types of single-cell enrichment scoring methods, we carried out enrichment analysis on CRC cell types, which was validated through four additional single-cell cohorts. Groups of tumor cells were determined using the average values of the scores. Using cellphonedb, monocle, inferCNV, SCENIC, and Cytotrace, functional analyses were performed. Utilizing the RCTD approach, single-cell groupings were mapped onto spatial transcriptomics, analyzing cell dependency and pathway activity to distinguish between tumor cell subtypes. Differential expression analysis identified core genes in nucleotide metabolism, with single-cell and spatial transcriptomics analyses elucidating the function of these genes in tumor cells and the immune microenvironment. Prognostic models were developed from bulk transcriptome cohorts to forecast responses to immune therapy. Laboratory experiments were conducted to verify the biological function of the core gene.
RESULTS: Nucleotide metabolism is significantly elevated in tumor cells, dividing them into two groups: NUhighepi and NUlowepi. The phenotype NUhighepi was discerned to exhibit pronounced malignant attributes. Utilizing the analytical tool stlearn for cell-to-cell communication assessment, it was ascertained that NUhighepi engages in intimate interactions with fibroblasts. Corroborating this observation, spatial transcriptome cell interaction assessment through MISTy unveiled a particular reliance of NUhighepi on fibroblasts. Subsequently, we pinpointed NME1, a key gene in nucleotide metabolism, affirming its role in thwarting metastasis via in vitro examination. Utilizing multiple machine learning algorithms, a stable prognostic model (NRS) has been developed, capable of predicting survival and responses to immune therapy. In addition, targeted drugs have been identified for both high and low scoring groups. Laboratory experiments have revealed that NME1 can inhibit the proliferation and invasion of CRC tumor cells.
CONCLUSIONS: Our study elucidates the potential pro-tumor mechanism of NUhighepi and the role of NME1 in inhibiting metastasis, further deepening the understanding of the role of nucleotide metabolism in colorectal cancer, and providing valuable targets for disrupting its properties.