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

Thejus T Jayakrishnan, Naseer Sangwan, Shimoli V Barot, Nicole Farha, Arshiya Mariam, Shao Xiang, Federico Aucejo, Madison Conces, Kanika G Nair, Smitha S Krishnamurthi, Stephanie L Schmit, David Liska, Daniel M Rotroff, Alok A Khorana, Suneel D Kamath

July 2024

Synopsis of Social media discussions

The group generally supports the study’s innovative multi-omics approach, as evidenced by comments on its potential to uncover biological patterns, with examples like metabolites glycerol and pseudouridine’s correlations with specific bacteria. The tone combines appreciation for the scientific methods with cautious optimism about future therapeutic implications, showcasing a balance of engagement and realistic expectations.

A
Agreement
 Moderate agreement

Most discussions recognize the value of applying multi-omics machine learning methods to understand early-onset colorectal cancer, indicating general support for the research approach.

I
Interest
 High level of interest

The discussions show high interest, especially with mentions of specific metabolites and microbiome interactions, highlighting curiosity about the biological insights gained.

E
Engagement
 Moderate level of engagement

Participants engage by referencing specific findings like correlations between metabolites and microbial communities, demonstrating analytical involvement.

I
Impact
 Moderate level of impact

While the research is viewed as promising, discussions suggest cautious optimism about its immediate clinical application, reflecting moderate perceived impact.

Social Mentions

YouTube

1 Videos

Twitter

12 Posts

Blogs

5 Articles

News

62 Articles

Reddit

2 Posts

Metrics

Total Likes

5

Extended Reach

34,397

Social Features

82

Timeline: Posts about article

Top Social Media Posts

Posts referencing the article

Uncovering Host-Microbiome Interactions in Early-Onset Colorectal Cancer Using Machine Learning

Uncovering Host-Microbiome Interactions in Early-Onset Colorectal Cancer Using Machine Learning

This video explores how multi-omics and machine learning are used to study host-microbiome interactions in early-onset colorectal cancer, revealing distinct biological patterns and potential therapeutic targets.

DrOmics Labs Pvt Ltd

August 24, 2024

0 views

  • サイエンスあれこれ
    @sarekore (Twitter)

    実際にがんリスクにどのように影響するのかについては明らかにされてはいませんが、大腸がんの早期発見用診断マーカーとして有望です。https://t.co/D2AlND2Okj
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    August 31, 2024

  • Wafik S. El-Deiry, MD, PhD, FACP
    @weldeiry (Twitter)

    Multi-omics machine learning to study host-microbiome interactions in early-onset colorectal cancer “Metabolites glycerol and pseudouridine (higher abundance in individuals with aoCRC) had negative correlations with Parasutterella, and Ruminococcaceae (higher abundance in
    view full post

    August 24, 2024

  • drjasonmccloskey
    @drjmccloskey (Twitter)

    Multi-omics machine learning to study host-microbiome interactions in early-onset colorectal cancer | npj Precision Oncology https://t.co/UhkI6YyVvG
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    August 19, 2024

  • Altamiro Bottino
    @BottinoAltamiro (Twitter)

    Multi-omics machine learning to study host-microbiome interactions in early-onset colorectal cancer https://t.co/jgGXK9ie4h https://t.co/bTdZNYqnea
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    August 17, 2024

  • Yasuhisa Nagasaki＠NERON, RIKEN
    @Neron_Ngsk32 (Twitter)

    RT @yasuhisanagasa1: Multi-omics machine learning to study host-microbiome interactions in early-onset colorectal cancer https://t.co/Gqxm4…
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    July 25, 2024

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  • yasuhisa nagasaki
    @yasuhisanagasa1 (Twitter)

    Multi-omics machine learning to study host-microbiome interactions in early-onset colorectal cancer https://t.co/Gqxm4fJyF0 https://t.co/JIcWJj9Ep7
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    July 25, 2024

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  • A.n.n.
    @fireflyann (Twitter)

    RT @josepereag: Multi-omics analysis can be utilized to study host-microbiome correlations in #EOCRC https://t.co/gRbfRcwgFf
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    July 18, 2024

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  • Roberta Horgan
    @HorganRoberta (Twitter)

    RT @josepereag: Multi-omics analysis can be utilized to study host-microbiome correlations in #EOCRC https://t.co/gRbfRcwgFf
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    July 18, 2024

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  • lynch syndrome irl @lsireland.bsky.social
    @Lynchsyndromirl (Twitter)

    RT @josepereag: Multi-omics analysis can be utilized to study host-microbiome correlations in #EOCRC https://t.co/gRbfRcwgFf
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    July 18, 2024

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  • JOSÉ PEREA GARCÍA
    @josepereag (Twitter)

    Multi-omics analysis can be utilized to study host-microbiome correlations in #EOCRC https://t.co/gRbfRcwgFf
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    July 18, 2024

    5

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  • JOSÉ PEREA GARCÍA
    @josepereag (Twitter)

    multi-omics analysis can be utilized to study host-microbiome correlations in #EOCRC https://t.co/gRbfRcwgFf
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    July 18, 2024

  • LUKMAN AFOLABI
    @saintlukman (Twitter)

    Multi-omics machine learning to study host-microbiome interactions in early-onset colorectal cancer https://t.co/64Hf49qG3w https://t.co/nFlk3sr1Bt
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    July 17, 2024

Abstract Synopsis

  • The incidence of early-onset colorectal cancer (eoCRC) is increasing, yet its causes remain unclear, leading researchers to explore machine learning techniques to uncover differences between eoCRC and average-onset CRC (aoCRC).
  • Researchers analyzed data from 64 individuals with colorectal cancer, using advanced technologies like plasma metabolomics and microbiome sequencing, and found that a machine-learning model based on metabolomics outperformed the microbiome-based model in identifying unique features associated with eoCRC.
  • Key findings included specific metabolites that correlated with different microbial communities, suggesting that multi-omics approaches not only reveal distinct biological patterns for eoCRC but also hint at potential therapeutic targets for treatment.