Canderan Jamie, Ye Yuzhen
Luddy School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN, 47408, USA.
Sci Rep. 2025 Apr 22;15(1):13926. doi: 10.1038/s41598-025-97984-3.
Microbiome research has revealed associations between microbial species and colorectal cancer (CRC). Most of the existing research relied on metagenomic data. We leveraged a tool that we recently developed for detecting human and microbial peptides from (meta)proteomics data to reanalyze Clinical Proteomic Tumor Analysis Consortium CRC proteomics datasets. Our analyses revealed potential microbial species and proteins that are associated with CRC, especially when analyzing multiplexed proteomics data consisting of cancerous and healthy tissue taken from the same individuals. Many of the identified proteins are associated with species with known links to CRC, such as the fungi Aspergillus kawachii, but many are unstudied or their specific roles unknown. Proteins from other microbial species, such as Paenibacillus cellulosilyticus, were also identified in the samples. We showed that Aspergillus kawachii and others are depleted overall in cancer samples, which is consistent with a previous genomic-based multi-cohort study. Our analysis also revealed that some proteins belonging to this species are more abundantly detected, while others in this and other species are not. Further, we showed that microbial identifications could be used to build predictive models for tumor detection, but caution needs to be taken when applying such models trained on one dataset to another due to the substantial impacts of different experimental techniques on peptide detection profiles.
微生物组研究揭示了微生物物种与结直肠癌（CRC）之间的关联。现有的大多数研究都依赖于宏基因组数据。我们利用了一种最近开发的工具，用于从（元）蛋白质组学数据中检测人类和微生物肽，以重新分析临床蛋白质组肿瘤分析联盟的CRC蛋白质组学数据集。我们的分析揭示了与CRC相关的潜在微生物物种和蛋白质，特别是在分析来自同一患者的癌组织和健康组织的多重蛋白质组学数据时。许多已鉴定的蛋白质与已知与CRC有关联的物种相关，如真菌河合曲霉，但也有许多未被研究过，或者其具体作用尚不清楚。在样本中还鉴定出来自其他微生物物种的蛋白质，如解纤维素类芽孢杆菌。我们发现，癌症样本中河合曲霉等真菌的总体丰度较低，这与之前基于基因组的多队列研究结果一致。我们的分析还表明，该物种的一些蛋白质检测到的丰度更高，而该物种及其他物种中的其他蛋白质则不然。此外，我们表明微生物鉴定可用于建立肿瘤检测的预测模型，但由于不同实验技术对肽检测谱有重大影响，因此在将基于一个数据集训练的模型应用于另一个数据集时需要谨慎。
