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Significance of platelet adhesion-related genes in colon cancer based on non-negative matrix factorization-based clustering algorithm

BACKGROUND: Although surgical methods are the most effective treatments for colon adenocarcinoma (COAD), the cure rates remain low, and recurrence rates remain high. Furthermore, platelet adhesion-related genes are gaining attention as potential regulators of tumorigenesis. Therefore, identifying th...

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Autores principales: Chi, Xiao-jv, Song, Yi-bei, Liu, Deng-he, Wei, Li-qiang, An, Xin, Feng, Zi-zhen, Lan, Xiao-hua, Lan, Dong, Huang, Chao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10521306/
https://www.ncbi.nlm.nih.gov/pubmed/37766908
http://dx.doi.org/10.1177/20552076231203902
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author Chi, Xiao-jv
Song, Yi-bei
Liu, Deng-he
Wei, Li-qiang
An, Xin
Feng, Zi-zhen
Lan, Xiao-hua
Lan, Dong
Huang, Chao
author_facet Chi, Xiao-jv
Song, Yi-bei
Liu, Deng-he
Wei, Li-qiang
An, Xin
Feng, Zi-zhen
Lan, Xiao-hua
Lan, Dong
Huang, Chao
author_sort Chi, Xiao-jv
collection PubMed
description BACKGROUND: Although surgical methods are the most effective treatments for colon adenocarcinoma (COAD), the cure rates remain low, and recurrence rates remain high. Furthermore, platelet adhesion-related genes are gaining attention as potential regulators of tumorigenesis. Therefore, identifying the mechanisms responsible for the regulation of these genes in patients with COAD has become important. The present study aims to investigate the underlying mechanisms of platelet adhesion-related genes in COAD patients. METHODS: The present study was an experimental study. Initially, the effects of platelet number and related genomic alteration on survival were explored using real-world data and the cBioPortal database, respectively. Then, the differentially expressed platelet adhesion-related genes of COAD were analyzed using the TCGA database, and patients were further classified by employing the non-negative matrix factorization (NMF) analysis method. Afterward, some of the clinical and expression characteristics were analyzed between clusters. Finally, least absolute shrinkage and selection operator regression analysis was used to establish the prognostic nomogram. All data analyses were performed using the R package. RESULTS: High platelet counts are associated with worse survival in real-world patients, and alternations to platelet adhesion-related genes have resulted in poorer prognoses, based on online data. Based on platelet adhesion-related genes, patients with COAD were classified into two clusters by NMF-based clustering analysis. Cluster2 had a better overall survival, when compared to Cluster1. The gene copy number and enrichment analysis results revealed that two pathways were differentially enriched. In addition, the differentially expressed genes between these two clusters were enriched for POU6F1 in the transcription factor signaling pathway, and for MATN3 in the ceRNA network. Finally, a prognostic nomogram, which included the ALOX12 and ACTG1 genes, was established based on the platelet adhesion-related genes, with a concordance (C) index of 0.879 (0.848–0.910). CONCLUSION: The mRNA expression-based NMF was used to reveal the potential role of platelet adhesion-related genes in COAD. The series of experiments revealed the feasibility of targeting platelet adhesion-associated gene therapy.
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spelling pubmed-105213062023-09-27 Significance of platelet adhesion-related genes in colon cancer based on non-negative matrix factorization-based clustering algorithm Chi, Xiao-jv Song, Yi-bei Liu, Deng-he Wei, Li-qiang An, Xin Feng, Zi-zhen Lan, Xiao-hua Lan, Dong Huang, Chao Digit Health Original Research BACKGROUND: Although surgical methods are the most effective treatments for colon adenocarcinoma (COAD), the cure rates remain low, and recurrence rates remain high. Furthermore, platelet adhesion-related genes are gaining attention as potential regulators of tumorigenesis. Therefore, identifying the mechanisms responsible for the regulation of these genes in patients with COAD has become important. The present study aims to investigate the underlying mechanisms of platelet adhesion-related genes in COAD patients. METHODS: The present study was an experimental study. Initially, the effects of platelet number and related genomic alteration on survival were explored using real-world data and the cBioPortal database, respectively. Then, the differentially expressed platelet adhesion-related genes of COAD were analyzed using the TCGA database, and patients were further classified by employing the non-negative matrix factorization (NMF) analysis method. Afterward, some of the clinical and expression characteristics were analyzed between clusters. Finally, least absolute shrinkage and selection operator regression analysis was used to establish the prognostic nomogram. All data analyses were performed using the R package. RESULTS: High platelet counts are associated with worse survival in real-world patients, and alternations to platelet adhesion-related genes have resulted in poorer prognoses, based on online data. Based on platelet adhesion-related genes, patients with COAD were classified into two clusters by NMF-based clustering analysis. Cluster2 had a better overall survival, when compared to Cluster1. The gene copy number and enrichment analysis results revealed that two pathways were differentially enriched. In addition, the differentially expressed genes between these two clusters were enriched for POU6F1 in the transcription factor signaling pathway, and for MATN3 in the ceRNA network. Finally, a prognostic nomogram, which included the ALOX12 and ACTG1 genes, was established based on the platelet adhesion-related genes, with a concordance (C) index of 0.879 (0.848–0.910). CONCLUSION: The mRNA expression-based NMF was used to reveal the potential role of platelet adhesion-related genes in COAD. The series of experiments revealed the feasibility of targeting platelet adhesion-associated gene therapy. SAGE Publications 2023-09-26 /pmc/articles/PMC10521306/ /pubmed/37766908 http://dx.doi.org/10.1177/20552076231203902 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by-nc-nd/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 License (https://creativecommons.org/licenses/by-nc-nd/4.0/) which permits non-commercial use, reproduction and distribution of the work as published without adaptation or alteration, without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Original Research
Chi, Xiao-jv
Song, Yi-bei
Liu, Deng-he
Wei, Li-qiang
An, Xin
Feng, Zi-zhen
Lan, Xiao-hua
Lan, Dong
Huang, Chao
Significance of platelet adhesion-related genes in colon cancer based on non-negative matrix factorization-based clustering algorithm
title Significance of platelet adhesion-related genes in colon cancer based on non-negative matrix factorization-based clustering algorithm
title_full Significance of platelet adhesion-related genes in colon cancer based on non-negative matrix factorization-based clustering algorithm
title_fullStr Significance of platelet adhesion-related genes in colon cancer based on non-negative matrix factorization-based clustering algorithm
title_full_unstemmed Significance of platelet adhesion-related genes in colon cancer based on non-negative matrix factorization-based clustering algorithm
title_short Significance of platelet adhesion-related genes in colon cancer based on non-negative matrix factorization-based clustering algorithm
title_sort significance of platelet adhesion-related genes in colon cancer based on non-negative matrix factorization-based clustering algorithm
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10521306/
https://www.ncbi.nlm.nih.gov/pubmed/37766908
http://dx.doi.org/10.1177/20552076231203902
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