Cargando…

Finding Colon Cancer- and Colorectal Cancer-Related Microbes Based on Microbe–Disease Association Prediction

Microbes are closely associated with the formation and development of diseases. The identification of the potential associations between microbes and diseases can boost the understanding of various complex diseases. Wet experiments applied to microbe–disease association (MDA) identification are cost...

Descripción completa

Detalles Bibliográficos
Autores principales: Chen, Yu, Sun, Hongjian, Sun, Mengzhe, Shi, Changguo, Sun, Hongmei, Shi, Xiaoli, Ji, Binbin, Cui, Jinpeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8007907/
https://www.ncbi.nlm.nih.gov/pubmed/33796094
http://dx.doi.org/10.3389/fmicb.2021.650056
_version_ 1783672588067667968
author Chen, Yu
Sun, Hongjian
Sun, Mengzhe
Shi, Changguo
Sun, Hongmei
Shi, Xiaoli
Ji, Binbin
Cui, Jinpeng
author_facet Chen, Yu
Sun, Hongjian
Sun, Mengzhe
Shi, Changguo
Sun, Hongmei
Shi, Xiaoli
Ji, Binbin
Cui, Jinpeng
author_sort Chen, Yu
collection PubMed
description Microbes are closely associated with the formation and development of diseases. The identification of the potential associations between microbes and diseases can boost the understanding of various complex diseases. Wet experiments applied to microbe–disease association (MDA) identification are costly and time-consuming. In this manuscript, we developed a novel computational model, NLLMDA, to find unobserved MDAs, especially for colon cancer and colorectal carcinoma. NLLMDA integrated negative MDA selection, linear neighborhood similarity, label propagation, information integration, and known biological data. The Gaussian association profile (GAP) similarity of microbes and GAPs similarity and symptom similarity of diseases were firstly computed. Secondly, linear neighborhood method was then applied to the above computed similarity matrices to obtain more stable performance. Thirdly, negative MDA samples were selected, and the label propagation algorithm was used to score for microbe–disease pairs. The final association probabilities can be computed based on the information integration method. NLLMDA was compared with the other five classical MDA methods and obtained the highest area under the curve (AUC) value of 0.9031 and 0.9335 on cross-validations of diseases and microbe–disease pairs. The results suggest that NLLMDA was an effective prediction method. More importantly, we found that Acidobacteriaceae may have a close link with colon cancer and Tannerella may densely associate with colorectal carcinoma.
format Online
Article
Text
id pubmed-8007907
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-80079072021-03-31 Finding Colon Cancer- and Colorectal Cancer-Related Microbes Based on Microbe–Disease Association Prediction Chen, Yu Sun, Hongjian Sun, Mengzhe Shi, Changguo Sun, Hongmei Shi, Xiaoli Ji, Binbin Cui, Jinpeng Front Microbiol Microbiology Microbes are closely associated with the formation and development of diseases. The identification of the potential associations between microbes and diseases can boost the understanding of various complex diseases. Wet experiments applied to microbe–disease association (MDA) identification are costly and time-consuming. In this manuscript, we developed a novel computational model, NLLMDA, to find unobserved MDAs, especially for colon cancer and colorectal carcinoma. NLLMDA integrated negative MDA selection, linear neighborhood similarity, label propagation, information integration, and known biological data. The Gaussian association profile (GAP) similarity of microbes and GAPs similarity and symptom similarity of diseases were firstly computed. Secondly, linear neighborhood method was then applied to the above computed similarity matrices to obtain more stable performance. Thirdly, negative MDA samples were selected, and the label propagation algorithm was used to score for microbe–disease pairs. The final association probabilities can be computed based on the information integration method. NLLMDA was compared with the other five classical MDA methods and obtained the highest area under the curve (AUC) value of 0.9031 and 0.9335 on cross-validations of diseases and microbe–disease pairs. The results suggest that NLLMDA was an effective prediction method. More importantly, we found that Acidobacteriaceae may have a close link with colon cancer and Tannerella may densely associate with colorectal carcinoma. Frontiers Media S.A. 2021-03-16 /pmc/articles/PMC8007907/ /pubmed/33796094 http://dx.doi.org/10.3389/fmicb.2021.650056 Text en Copyright © 2021 Chen, Sun, Sun, Shi, Sun, Shi, Ji and Cui. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Microbiology
Chen, Yu
Sun, Hongjian
Sun, Mengzhe
Shi, Changguo
Sun, Hongmei
Shi, Xiaoli
Ji, Binbin
Cui, Jinpeng
Finding Colon Cancer- and Colorectal Cancer-Related Microbes Based on Microbe–Disease Association Prediction
title Finding Colon Cancer- and Colorectal Cancer-Related Microbes Based on Microbe–Disease Association Prediction
title_full Finding Colon Cancer- and Colorectal Cancer-Related Microbes Based on Microbe–Disease Association Prediction
title_fullStr Finding Colon Cancer- and Colorectal Cancer-Related Microbes Based on Microbe–Disease Association Prediction
title_full_unstemmed Finding Colon Cancer- and Colorectal Cancer-Related Microbes Based on Microbe–Disease Association Prediction
title_short Finding Colon Cancer- and Colorectal Cancer-Related Microbes Based on Microbe–Disease Association Prediction
title_sort finding colon cancer- and colorectal cancer-related microbes based on microbe–disease association prediction
topic Microbiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8007907/
https://www.ncbi.nlm.nih.gov/pubmed/33796094
http://dx.doi.org/10.3389/fmicb.2021.650056
work_keys_str_mv AT chenyu findingcoloncancerandcolorectalcancerrelatedmicrobesbasedonmicrobediseaseassociationprediction
AT sunhongjian findingcoloncancerandcolorectalcancerrelatedmicrobesbasedonmicrobediseaseassociationprediction
AT sunmengzhe findingcoloncancerandcolorectalcancerrelatedmicrobesbasedonmicrobediseaseassociationprediction
AT shichangguo findingcoloncancerandcolorectalcancerrelatedmicrobesbasedonmicrobediseaseassociationprediction
AT sunhongmei findingcoloncancerandcolorectalcancerrelatedmicrobesbasedonmicrobediseaseassociationprediction
AT shixiaoli findingcoloncancerandcolorectalcancerrelatedmicrobesbasedonmicrobediseaseassociationprediction
AT jibinbin findingcoloncancerandcolorectalcancerrelatedmicrobesbasedonmicrobediseaseassociationprediction
AT cuijinpeng findingcoloncancerandcolorectalcancerrelatedmicrobesbasedonmicrobediseaseassociationprediction