胰腺癌的预后很差，因为它通常在疾病的第四阶段被诊断出来，并且以转移性扩散为特征。肠道微生物群及其代谢物被认为可以通过调节宿主免疫系统或促进血管生成来影响转移扩散。迄今为止，需要探索转移性和非转移性患者的肠道微生物特征。利用 16S 宏基因组测序和 PEnalized LOgistic 回归分析 (PELORA)，我们确定了转移性和非转移性患者之间丰度存在差异的细菌簇。使用这种方法确定了与非转移性患者相比，转移性患者的革兰氏阴性菌总体增加。此外，为了更深入地了解肠道微生物如何预测转移，采用了机器学习方法（迭代随机森林）。迭代随机森林分析揭示了哪些微生物在转移性和非转移性患者之间具有不同水平的相对丰度，并在相对丰度和发生转移的概率之间建立了函数关系。在物种层面，以下细菌被发现具有最高的辨别能力：Anaerostipes hadrus、Coprobacter secundus、Clostridium sp。 619，属水平的 Roseburia inulinivorans、Porphyromonas 和 Odoribacter，以及科水平的红螺菌科、梭菌科和消化球菌科。最后，将这些数据与对有或没有转移的患者粪便样本进行代谢组学分析的数据交织在一起，以更好地了解肠道微生物群在转移过程中的作用。人工智能已在医疗领域的不同领域得到应用。将其在肠道微生物群分析领域的应用可能有助于充分利用如此大量数据中包含的潜在信息，旨在开辟癌症治疗干预的新支持领域。

Pancreatic cancer has a dismal prognosis, as it is often diagnosed at stage IV of the disease and is characterized by metastatic spread. Gut microbiota and its metabolites have been suggested to influence the metastatic spread by modulating the host immune system or by promoting angiogenesis. To date, the gut microbial profiles of metastatic and non-metastatic patients need to be explored. Taking advantage of the 16S metagenomic sequencing and the PEnalized LOgistic Regression Analysis (PELORA) we identified clusters of bacteria with differential abundances between metastatic and non-metastatic patients. An overall increase in Gram-negative bacteria in metastatic patients compared to non-metastatic ones was identified using this method. Furthermore, to gain more insight into how gut microbes can predict metastases, a machine learning approach (iterative Random Forest) was performed. Iterative Random Forest analysis revealed which microorganisms were characterized by a different level of relative abundance between metastatic and non-metastatic patients and established a functional relationship between the relative abundance and the probability of having metastases. At the species level, the following bacteria were found to have the highest discriminatory power: Anaerostipes hadrus, Coprobacter secundus, Clostridium sp. 619, Roseburia inulinivorans, Porphyromonas and Odoribacter at the genus level, and Rhodospirillaceae, Clostridiaceae and Peptococcaceae at the family level. Finally, these data were intertwined with those from a metabolomics analysis on fecal samples of patients with or without metastasis to better understand the role of gut microbiota in the metastatic process. Artificial intelligence has been applied in different areas of the medical field. Translating its application in the field of gut microbiota analysis may help fully exploit the potential information contained in such a large amount of data aiming to open up new supportive areas of intervention in the management of cancer.