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

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.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2025-04-22
DOI
https://doi.org/10.1038/s41598-025-97984-3
Citations
1
Primary Topic
Metabolomics and Mass Spectrometry Studies
Type
article
Field-Weighted Citation Impact
0.81

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Yuzhen Ye, Jamie Canderan
1 citations
Scientific Reports
Metabolomics and Mass Spectrometry Studies
0.81
article

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

Yuzhen Ye, Jamie Canderan
article en
1 citations

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.

Scientific ReportsVol. 15(1)
Indiana University Bloomington (US)
National Science Foundation, National Institutes of Health, Center for Information Technology
Zero hunger
Openalex Percentile: Top 29%
Metabolomics and Mass Spectrometry Studies
0.81
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Identification of microbial species and proteins associated with colorectal cancer by reanalyzing CPTAC proteomic datasets — Yuzhen Ye, Jamie Canderan · Scientific Reports (2025) | TGRS Research Map | TGRS