Distinct intratumoral microbiome of young-onset and average-onset colorectal cancer

Barot, Shimoli V.; Sangwan, Naseer; Nair, Kanika G.; Schmit, Stephanie L.; Xiang, Shao; Kamath, Suneel; Liska, David; Khorana, Alok A.

doi:10.1016/j.ebiom.2024.104980

Cited by 14 publications

(7 citation statements)

References 77 publications

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“…These alterations may serve as markers for cancer development and thus be utilized to identify individuals at increased risk for developing cancer 10 . We and others have previously demonstrated the significance of microbiome and metabolome in carcinogenesis and have shown unique associations of these features with eoCRC 11 , 12 . By using advanced multi-omic analytic techniques, new insights into their relationships can be gained.…”

Section: Introductionmentioning

confidence: 79%

Multi-omics machine learning to study host-microbiome interactions in early-onset colorectal cancer

Jayakrishnan,

Sangwan,

Barot

et al. 2024

npj Precis. Onc.

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The incidence of early-onset colorectal cancer (eoCRC) is rising, and its pathogenesis is not completely understood. We hypothesized that machine learning utilizing paired tissue microbiome and plasma metabolome features could uncover distinct host-microbiome associations between eoCRC and average-onset CRC (aoCRC). Individuals with stages I–IV CRC (n = 64) were categorized as eoCRC (age ≤ 50, n = 20) or aoCRC (age ≥ 60, n = 44). Untargeted plasma metabolomics and 16S rRNA amplicon sequencing (microbiome analysis) of tumor tissue were performed. We fit DIABLO (Data Integration Analysis for Biomarker Discovery using Latent variable approaches for Omics studies) to construct a supervised machine-learning classifier using paired multi-omics (microbiome and metabolomics) data and identify associations unique to eoCRC. A differential association network analysis was also performed. Distinct clustering patterns emerged in multi-omic dimension reduction analysis. The metabolomics classifier achieved an AUC of 0.98, compared to AUC 0.61 for microbiome-based classifier. Circular correlation technique highlighted several key associations. Metabolites glycerol and pseudouridine (higher abundance in individuals with aoCRC) had negative correlations with Parasutterella, and Ruminococcaceae (higher abundance in individuals with eoCRC). Cholesterol and xylitol correlated negatively with Erysipelatoclostridium and Eubacterium, and showed a positive correlation with Acidovorax with higher abundance in individuals with eoCRC. Network analysis revealed different clustering patterns and associations for several metabolites e.g.: urea cycle metabolites and microbes such as Akkermansia. We show that multi-omics analysis can be utilized to study host-microbiome correlations in eoCRC and demonstrates promising biomarker potential of a metabolomics classifier. The distinct host-microbiome correlations for urea cycle in eoCRC may offer opportunities for therapeutic interventions.

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Section: Introductionmentioning

confidence: 79%

Multi-omics machine learning to study host-microbiome interactions in early-onset colorectal cancer

Jayakrishnan,

Sangwan,

Barot

et al. 2024

npj Precis. Onc.

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“…It is known to have beneficial roles in autoimmune and chronic inflammatory diseases, with other findings showing that lower levels of Akkermansia are present in the gut of humans and mice with metabolic diseases [73][74][75][76]. When accounting for BMI as well, the tumor microbiome for those with a BMI > 25 also demonstrated a dysregulation of Limosilactobacillus, Listeria, Akkermansia, Enterococcus, and Escherichia/Shigella [72]. This increased level of diversity, as well as distinction within EOCRC microbiome pathogenesis, demonstrates a likely correlation between environmental/lifestyle factors and the upregulation of microbiota in response to inflammatory processes to protect the gut barrier integrity.…”

Section: Microbiomementioning

confidence: 93%

“…Both the older adult cohort (median age 73) and younger cohort exhibited a core microbial profile consisting of Staphylococcus, Lactobacillus, Bacillus, Listeria, Akkermansia, Pseudomonas, Enterococcus, Alistipes, Escherichia/Shigella, and Fusobacterium compared to non-malignant tissue samples. Those within the EOCRC cohort, however, demonstrated a tumor microbiota with dysbiosis of both Bacteroides and Akkermansia compared with the older CRC patients [72]. Akkermansia is a mucin-degrading bacterium that promotes maintenance of the gut integrity barrier.…”

Section: Microbiomementioning

confidence: 99%

“…This increased level of diversity, as well as distinction within EOCRC microbiome pathogenesis, demonstrates a likely correlation between environmental/lifestyle factors and the upregulation of microbiota in response to inflammatory processes to protect the gut barrier integrity. The novelty of this dysbiosis could also signify the role of the microbiome in aiding the tumor microenvironment to evade the host defense systems [72]. In either scenario, continued understanding of the role of the microbiome in the development of EOCRC is an important area for future therapeutic interventions and possible preventions.…”

Section: Microbiomementioning

confidence: 99%

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Obesity and Inflammatory Factors in the Progression of Early-Onset Colorectal Cancer

Jones,

Scheurlen,

Macleod

et al. 2024

Cancers

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Metabolic dysfunction associated with obesity leads to a chronic pro-inflammatory state with systemic effects, including the alteration of macrophage metabolism. Tumor-associated macrophages have been linked to the formation of cancer through the production of metabolites such as itaconate. Itaconate downregulates peroxisome proliferator-activated receptor gamma as a tumor-suppressing factor and upregulates anti-inflammatory cytokines in M2-like macrophages. Similarly, leptin and adiponectin also influence macrophage cytokine expression and contribute to the progression of colorectal cancer via changes in gene expression within the PI3K/AKT pathway. This pathway influences cell proliferation, differentiation, and tumorigenesis. This work provides a review of obesity-related hormones and inflammatory mechanisms leading to the development and progression of early-onset colorectal cancer (EOCRC). A literature search was performed using the PubMed and Cochrane databases to identify studies related to obesity and EOCRC, with keywords including ‘EOCRC’, ‘obesity’, ‘obesity-related hormones’, ‘itaconate’, ‘adiponectin’, ‘leptin’, ‘M2a macrophage’, and ‘microbiome’. With this concept of pro-inflammatory markers contributing to EOCRC, increased use of chemo-preventative agents such as aspirin may have a protective effect. Elucidating this association between obesity-related, hormone/cytokine-driven inflammatory effects with EOCRC may help lead to new therapeutic targets in preventing and treating EOCRC.

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“…The role of microbiome has also been explored in the context of increasing incidence of young or early onset CRC. It appears that certain bacterial genera such as Akkermansia and Bacteriodes are differentially abundant in young individuals who develop CRC as opposed to several other genera ( Bacillus , Staphylococcus , Listeria , Enterococcus , Pseudomonas , Fusobacterium , and Escherichia/Shigella) that are more abundant in CRC arising at the usual age [ 72 ]. Approaches such as plasma metabolomics analysis and machine learning are being used to further define the relationship between the altered microbiome in young individuals and CRC to identify microbiome-derived signatures for screening and therapy [ 73 , 74 ].…”

Section: Abnormal Gut Microbiome In Patients With Colorectal Cancermentioning

confidence: 99%

Gut Microbiome–Colorectal Cancer Relationship

Yadav,

Sainatham,

Filippov

et al. 2024

Microorganisms

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Traditionally, the role of gut dysbiosis was thought to be limited to pathologies like Clostridioides difficile infection, but studies have shown its role in other intestinal and extraintestinal pathologies. Similarly, recent studies have surfaced showing the strong potential role of the gut microbiome in colorectal cancer, which was traditionally attributed mainly to sporadic or germline mutations. Given that it is the third most common cancer and the second most common cause of cancer-related mortality, 78 grants totaling more than USD 28 million have been granted to improve colon cancer management since 2019. Concerted efforts by several of these studies have identified specific bacterial consortia inducing a proinflammatory environment and promoting genotoxin production, causing the induction or progression of colorectal cancer. In addition, changes in the gut microbiome have also been shown to alter the response to cancer chemotherapy and immunotherapy, thus changing cancer prognosis. Certain bacteria have been identified as biomarkers to predict the efficacy of antineoplastic medications. Given these discoveries, efforts have been made to alter the gut microbiome to promote a favorable diversity to improve cancer progression and the response to therapy. In this review, we expand on the gut microbiome, its association with colorectal cancer, and antineoplastic medications. We also discuss the evolving paradigm of fecal microbiota transplantation in the context of colorectal cancer management.

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Distinct intratumoral microbiome of young-onset and average-onset colorectal cancer

Cited by 14 publications

References 77 publications

Multi-omics machine learning to study host-microbiome interactions in early-onset colorectal cancer

Multi-omics machine learning to study host-microbiome interactions in early-onset colorectal cancer

Obesity and Inflammatory Factors in the Progression of Early-Onset Colorectal Cancer

Gut Microbiome–Colorectal Cancer Relationship

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