Evidence map›Paper›PMID 40263502›Full record

ArticleScientific reports2025

Identification of microbial species and proteins associated with colorectal cancer by reanalyzing CPTAC proteomic datasets.

Jamie Canderan, Yuzhen Ye

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Jamie CanderanLuddy School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN, 47408, USA.
Yuzhen YeLuddy School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN, 47408, USA. yye@iu.edu.

Funding

Subtractive assembly approaches for inferring disease-associated microbial genes and pathways from microbiome sequencing dataR01AI143254 · NIAID · TRUSTEES OF INDIANA UNIVERSITY · PI YE, YUZHEN · 2019 to 2023
$1.1M
National Science Foundation (NSF) EF-202545NIAID NIH HHS R01 AI143254U.S. Department of Health & Human Services | NIH | Center for Information Technology (Center for Information Technology, National Institutes of Health) R01AI143254
6 · The paper itself

Abstract

Microbiome research has revealed associations between microbial species and colorectal cancer (CRC). Most of the existing research relied on metagenomic data. We leveraged a tool that we recently developed for detecting human and microbial peptides from (meta)proteomics data to reanalyze Clinical Proteomic Tumor Analysis Consortium CRC proteomics datasets. Our analyses revealed potential microbial species and proteins that are associated with CRC, especially when analyzing multiplexed proteomics data consisting of cancerous and healthy tissue taken from the same individuals. Many of the identified proteins are associated with species with known links to CRC, such as the fungi Aspergillus kawachii, but many are unstudied or their specific roles unknown. Proteins from other microbial species, such as Paenibacillus cellulosilyticus, were also identified in the samples. We showed that Aspergillus kawachii and others are depleted overall in cancer samples, which is consistent with a previous genomic-based multi-cohort study. Our analysis also revealed that some proteins belonging to this species are more abundantly detected, while others in this and other species are not. Further, we showed that microbial identifications could be used to build predictive models for tumor detection, but caution needs to be taken when applying such models trained on one dataset to another due to the substantial impacts of different experimental techniques on peptide detection profiles.

Indexed as

Bacterial ProteinsColorectal NeoplasmsProteomicsAspergillusHumansBacterial Proteins

Identifiers

PMID40263502
PMCPMC12015240

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.