Network Analysis of Gut Microbiome and Metabolome to Discover Microbiota-Linked Biomarkers in Patients Affected by Non-Small Cell Lung Cancer.
Akkermansia
/ classification
Alcohols
/ metabolism
Aldehydes
/ metabolism
Antineoplastic Agents, Immunological
/ therapeutic use
Bacteroides
/ classification
Carcinoma, Non-Small-Cell Lung
/ drug therapy
Clostridiaceae
/ classification
Databases, Genetic
Disease Progression
Drug Monitoring
/ methods
Fatty Acids, Volatile
/ metabolism
Gastrointestinal Microbiome
/ genetics
Gene Expression Regulation, Neoplastic
Gene Regulatory Networks
Humans
Immunotherapy
/ methods
Indoles
/ metabolism
Lung Neoplasms
/ drug therapy
Metabolome
/ genetics
Metagenomics
/ methods
Peptostreptococcus
/ classification
Precision Medicine
/ methods
Programmed Cell Death 1 Receptor
/ antagonists & inhibitors
RNA, Ribosomal, 16S
/ genetics
Signal Transduction
anti-PD1 immune checkpoint inhibitor
betweenness centrality
clustering coefficient
communities
gut microbiome
metabolite
network analysis
non-small cell lung cancer (NSCLC)
operational taxonomic unit (OTU)
weighted gene co-expression network analysis (WGCNA)
Journal
International journal of molecular sciences
ISSN: 1422-0067
Titre abrégé: Int J Mol Sci
Pays: Switzerland
ID NLM: 101092791
Informations de publication
Date de publication:
19 Nov 2020
19 Nov 2020
Historique:
received:
23
10
2020
revised:
13
11
2020
accepted:
16
11
2020
entrez:
24
11
2020
pubmed:
25
11
2020
medline:
5
3
2021
Statut:
epublish
Résumé
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
Identifiants
pubmed: 33227982
pii: ijms21228730
doi: 10.3390/ijms21228730
pmc: PMC7699235
pii:
doi:
Substances chimiques
Alcohols
0
Aldehydes
0
Antineoplastic Agents, Immunological
0
Fatty Acids, Volatile
0
Indoles
0
PDCD1 protein, human
0
Programmed Cell Death 1 Receptor
0
RNA, Ribosomal, 16S
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
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