Area of Genetics and Rare Diseases, Unit of Human Microbiome, Bambino Gesù Children's Hospital, IRCCS, 00146 Rome, Italy.
IMT School for Advanced Studies Lucca, Networks Unit, 55100 Lucca, Italy.
Int J Mol Sci. 2020 Nov 19;21(22):8730. doi: 10.3390/ijms21228730.
Several studies in recent times have linked gut microbiome (GM) diversity to the pathogenesis of cancer and its role in disease progression through immune response, inflammation and metabolism modulation. This study focused on the use of network analysis and weighted gene co-expression network analysis (WGCNA) to identify the biological interaction between the gut ecosystem and its metabolites that could impact the immunotherapy response in non-small cell lung cancer (NSCLC) patients undergoing second-line treatment with anti-PD1. Metabolomic data were merged with operational taxonomic units (OTUs) from 16S RNA-targeted metagenomics and classified by chemometric models. The traits considered for the analyses were: (i) condition: disease or control (CTRLs), and (ii) treatment: responder (R) or non-responder (NR). Network analysis indicated that indole and its derivatives, aldehydes and alcohols could play a signaling role in GM functionality. WGCNA generated, instead, strong correlations between short-chain fatty acids (SCFAs) and a healthy GM. Furthermore, commensal bacteria such as , Rikenellaceae, , Peptostreptococcaceae, Mogibacteriaceae and Clostridiaceae were found to be more abundant in CTRLs than in NSCLC patients. Our preliminary study demonstrates that the discovery of microbiota-linked biomarkers could provide an indication on the road towards personalized management of NSCLC patients.
近年来，多项研究将肠道微生物组（GM）多样性与癌症的发病机制及其通过免疫反应、炎症和代谢调节在疾病进展中的作用联系起来。本研究专注于使用网络分析和加权基因共表达网络分析（WGCNA）来识别肠道生态系统与其代谢物之间的生物学相互作用，这些相互作用可能会影响接受二线抗 PD1 治疗的非小细胞肺癌（NSCLC）患者的免疫治疗反应。代谢组学数据与 16S RNA 靶向宏基因组学的操作分类单位（OTUs）合并，并通过化学计量模型进行分类。用于分析的特征包括：（i）条件：疾病或对照（CTRLs），和（ii）治疗：应答者（R）或非应答者（NR）。网络分析表明吲哚及其衍生物、醛和醇可能在 GM 功能中发挥信号作用。相反，WGCNA 生成了短链脂肪酸（SCFAs）与健康 GM 之间的强相关性。此外，发现共生菌如，Rikenellaceae，，Peptostreptococcaceae，Mogibacteriaceae 和 Clostridiaceae 在 CTRLs 中的丰度高于 NSCLC 患者。我们的初步研究表明，发现与微生物组相关的生物标志物可以为 NSCLC 患者的个体化管理提供线索。
