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Identification of a prognostic signature and ENTR1 as a prognostic biomarker for colorectal mucinous adenocarcinoma

BACKGROUND: Mucinous adenocarcinoma (MAC) is a unique clinicopathological colorectal cancer (CRC) type that has been recognized as a separate entity from non-mucinous adenocarcinoma (NMAC), with distinct clinical, pathologic, and molecular characteristics. We aimed to construct prognostic signatures...

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Autores principales: Huang, An, Shi, Jingyi, Sun, Zhuang, Yang, Yong, Gao, Zhaoya, Gu, Jin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10172661/
https://www.ncbi.nlm.nih.gov/pubmed/37182178
http://dx.doi.org/10.3389/fonc.2023.1061785
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author Huang, An
Shi, Jingyi
Sun, Zhuang
Yang, Yong
Gao, Zhaoya
Gu, Jin
author_facet Huang, An
Shi, Jingyi
Sun, Zhuang
Yang, Yong
Gao, Zhaoya
Gu, Jin
author_sort Huang, An
collection PubMed
description BACKGROUND: Mucinous adenocarcinoma (MAC) is a unique clinicopathological colorectal cancer (CRC) type that has been recognized as a separate entity from non-mucinous adenocarcinoma (NMAC), with distinct clinical, pathologic, and molecular characteristics. We aimed to construct prognostic signatures and identifying candidate biomarkers for patients with MAC. METHODS: Differential expression analysis, weighted correlation network analysis (WGCNA), and least absolute shrinkage and selection operator (LASSO)-Cox regression model were used to identify hub genes and construct a prognostic signature based on RNA sequencing data from TCGA datasets. The Kaplan-Meier survival curve, gene set enrichment analysis (GSEA), cell stemness, and immune infiltration were analyzed. Biomarker expression in MAC and corresponding normal tissues from patients operated in 2020 was validated using immunohistochemistry. RESULTS: We constructed a prognostic signature based on ten hub genes. Patients in the high-risk group had significantly worse overall survival (OS) than patients in the low-risk group (p < 0.0001). We also found that ENTR1 was closely associated with OS (p = 0.016). ENTR1 expression was significantly positively correlated with cell stemness of MAC (p < 0.0001) and CD8+ T cell infiltration (p = 0.01), whereas it was negatively associated with stromal scores (p = 0.03). Finally, the higher expression of ENTR1 in MAC tissues than in normal tissues was validated. CONCLUSION: We established the first MAC prognostic signature, and determined that ENTR1 could serve as a prognostic marker for MAC.
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spelling pubmed-101726612023-05-12 Identification of a prognostic signature and ENTR1 as a prognostic biomarker for colorectal mucinous adenocarcinoma Huang, An Shi, Jingyi Sun, Zhuang Yang, Yong Gao, Zhaoya Gu, Jin Front Oncol Oncology BACKGROUND: Mucinous adenocarcinoma (MAC) is a unique clinicopathological colorectal cancer (CRC) type that has been recognized as a separate entity from non-mucinous adenocarcinoma (NMAC), with distinct clinical, pathologic, and molecular characteristics. We aimed to construct prognostic signatures and identifying candidate biomarkers for patients with MAC. METHODS: Differential expression analysis, weighted correlation network analysis (WGCNA), and least absolute shrinkage and selection operator (LASSO)-Cox regression model were used to identify hub genes and construct a prognostic signature based on RNA sequencing data from TCGA datasets. The Kaplan-Meier survival curve, gene set enrichment analysis (GSEA), cell stemness, and immune infiltration were analyzed. Biomarker expression in MAC and corresponding normal tissues from patients operated in 2020 was validated using immunohistochemistry. RESULTS: We constructed a prognostic signature based on ten hub genes. Patients in the high-risk group had significantly worse overall survival (OS) than patients in the low-risk group (p < 0.0001). We also found that ENTR1 was closely associated with OS (p = 0.016). ENTR1 expression was significantly positively correlated with cell stemness of MAC (p < 0.0001) and CD8+ T cell infiltration (p = 0.01), whereas it was negatively associated with stromal scores (p = 0.03). Finally, the higher expression of ENTR1 in MAC tissues than in normal tissues was validated. CONCLUSION: We established the first MAC prognostic signature, and determined that ENTR1 could serve as a prognostic marker for MAC. Frontiers Media S.A. 2023-04-27 /pmc/articles/PMC10172661/ /pubmed/37182178 http://dx.doi.org/10.3389/fonc.2023.1061785 Text en Copyright © 2023 Huang, Shi, Sun, Yang, Gao and Gu https://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 Oncology
Huang, An
Shi, Jingyi
Sun, Zhuang
Yang, Yong
Gao, Zhaoya
Gu, Jin
Identification of a prognostic signature and ENTR1 as a prognostic biomarker for colorectal mucinous adenocarcinoma
title Identification of a prognostic signature and ENTR1 as a prognostic biomarker for colorectal mucinous adenocarcinoma
title_full Identification of a prognostic signature and ENTR1 as a prognostic biomarker for colorectal mucinous adenocarcinoma
title_fullStr Identification of a prognostic signature and ENTR1 as a prognostic biomarker for colorectal mucinous adenocarcinoma
title_full_unstemmed Identification of a prognostic signature and ENTR1 as a prognostic biomarker for colorectal mucinous adenocarcinoma
title_short Identification of a prognostic signature and ENTR1 as a prognostic biomarker for colorectal mucinous adenocarcinoma
title_sort identification of a prognostic signature and entr1 as a prognostic biomarker for colorectal mucinous adenocarcinoma
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10172661/
https://www.ncbi.nlm.nih.gov/pubmed/37182178
http://dx.doi.org/10.3389/fonc.2023.1061785
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