Tumor microbiome metabolism: A game changer in cancer development and therapy

Zhou, Xiaozhuang; Kandalai, Shruthi; Hossain, Farzana; Zheng, Qingfei

doi:10.3389/fonc.2022.933407

Cited by 20 publications

(22 citation statements)

References 200 publications

(200 reference statements)

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“…All of these small molecule metabolites produced by the tumor microbiome can easily diffuse into tissues via transporters or free diffusion and then alter the signaling pathways of cancer and immune cells. Hence, targeting the oral tumor microbiome metabolism could offer a novel method of developing a new treatment for oral cancer [ 62 ]. The negative association of bacterial biosynthesis of essential amino acids and nucleotides as well as folate suggests abundant availability—potentially provided by foods or degradation of necrotic tumor tissue—of these molecules in the tumor microenvironment that allows bacteria deficient in these pathways to grow.…”

Section: Discussionmentioning

confidence: 99%

Oral Microbiome in Nonsmoker Patients with Oral Cavity Squamous Cell Carcinoma, Defined by Metagenomic Shotgun Sequencing

Ganly

Hao

Rosenthal

et al. 2022

Cancers

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Objectives: Smoking is the commonest cause of oral cavity squamous cell carcinoma (OC-SCC), but the etiology of OC-SCC in nonsmokers is unknown. Our primary goal was to use metagenomic shotgun sequencing (MSS) to define the taxonomic composition and functional potential of oral metagenome in nonsmokers with OC-SCC. Methods: We conducted a case–control study with 42 OC-SCC case and 45 control nonsmokers. MSS was performed on DNA extracted from mouthwash samples. Taxonomic analysis and pathway analysis were done using MetaPhlAn2 and HUMAnN2, respectively. Statistical difference was determined using the Mann–Whitney test controlling false discovery rate. Results: There was no significant difference in age, sex, race, or alcohol consumption between OC-SCC and control patients. There was a significant difference in beta diversity between OC-SCC and controls. At the phylum level, Bacteroidetes and Synergistetes were overly represented in OC-SCC while Actinobacteria and Firmicutes were overly represented in controls. At the genus level, Fusobacterium was overly represented in OC-SCC compared with controls, while Corynebacterium, Streptococcus, Actinomyces, Cryptobacterium, and Selenomonas were overly represented in controls. Bacterial pathway analysis identified overrepresentation in OC-SCC of pathways related to metabolism of flavin, biotin, thiamin, heme, sugars, fatty acids, peptidoglycans, and tRNA and overrepresentation of nucleotides and essential amino acids in controls. Conclusions: The oral microbiome in nonsmoker patients with OC-SCC is significantly different from that of nonsmoker control patients in taxonomic compositions and functional potentials. Our study’s MSS findings matched with previous 16S-based methods in taxonomic differentiation but varied greatly in functional differentiation of microbiomes in OC-SCC and controls.

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Section: Discussionmentioning

confidence: 99%

Oral Microbiome in Nonsmoker Patients with Oral Cavity Squamous Cell Carcinoma, Defined by Metagenomic Shotgun Sequencing

Ganly

Hao

Rosenthal

et al. 2022

Cancers

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“…Microbiome‐metabolism interactions: Examining the relationship between the gastrointestinal microbiome, the host's metabolism, and immune responses 113 . The microbiome of the intestine affects systemic metabolism and immune function.…”

Section: Challenges and Future Directionsmentioning

confidence: 99%

Discovering the strength of immunometabolism in cancer therapy: Employing metabolic pathways to enhance immune responses

Huldani,

Malviya,

Rodrigues

et al. 2024

Cell Biochemistry & Function

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Immunometabolism, which studies cellular metabolism and immune cell function, is a possible cancer treatment. Metabolic pathways regulate immune cell activation, differentiation, and effector functions, crucial to tumor identification and elimination. Immune evasion and tumor growth can result from tumor microenvironment metabolic dysregulation. These metabolic pathways can boost antitumor immunity. This overview discusses immune cell metabolism, including glycolysis, oxidative phosphorylation, amino acid, and lipid metabolism. Amino acid and lipid metabolic manipulations may improve immune cell activity and antitumor immunity. Combination therapy using immunometabolism‐based strategies may enhance therapeutic efficacy. The complexity of the metabolic network, biomarker development, challenges, and future approaches are all covered, along with a summary of case studies demonstrating the effectiveness of immunometabolism‐based therapy. Metabolomics, stable isotope tracing, single‐cell analysis, and computational modeling are also reviewed for immunometabolism research. Personalized and combination treatments are considered. This review adds to immunometabolism expertise and sheds light on metabolic treatments' ability to boost cancer treatment immunological response. Also, in this review, we discussed the immune response in cancer treatment and altering metabolic pathways to increase the immune response against malignancies.

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“…While this framework was demonstrated here on metabolic interactions between the human gut epithelium and the microbiota, its application is generic enough to be relevant for various ecosystems and contexts. Tumors are accompanied by bacteria, which promote or suppress cancer based on the situation (42). Applied in this tumor-microenvironment ecosystem, a positive score would highlight pathogenic interaction, opening the way for adapted treatments, like targeted antibiotics.…”

Section: R a F Tmentioning

confidence: 99%

Community metabolic modeling of host-microbiota interactions through multi-objective optimization

Lambert,

Budinich,

Mahé

et al. 2023

Preprint

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The human gut microbiota comprises various microorganisms engaged in intricate interactions among themselves and with the host, affecting its health. While advancements in omics technologies have led to the inference of clear associations between microbiome composition and health conditions, we usually lack a causal and mechanistic understanding of these associations. We simulated the organism's metabolism using in silico Genome-Scale Metabolic Models (GEMs) to model mechanisms driving the interactions. We used multi-objective optimization to predict and explain metabolic interactions among gut microbes and an intestinal epithelial cell. We developed a score integrating model simulation results to predict the type (competition, neutralism, mutualism) and quantify the interaction between several organisms. This framework uncovered a potential cross-feeding for choline, explaining the predicted mutualism between Lactobacillus rhamnosus GG and the epithelial cell. Finally, we analyzed a five-organism ecosystem, revealing that a minimal microbiota can favor the epithelial cell's maintenance.

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Tumor microbiome metabolism: A game changer in cancer development and therapy

Cited by 20 publications

References 200 publications

Oral Microbiome in Nonsmoker Patients with Oral Cavity Squamous Cell Carcinoma, Defined by Metagenomic Shotgun Sequencing

Oral Microbiome in Nonsmoker Patients with Oral Cavity Squamous Cell Carcinoma, Defined by Metagenomic Shotgun Sequencing

Discovering the strength of immunometabolism in cancer therapy: Employing metabolic pathways to enhance immune responses

Community metabolic modeling of host-microbiota interactions through multi-objective optimization

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