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Development and Validation of a Novel Metabolic-Related Signature Predicting Overall Survival for Pancreatic Cancer

Recently, growing evidence has revealed the significant effect of reprogrammed metabolism on pancreatic cancer in relation to carcinogenesis, progression, and treatment. However, the prognostic value of metabolism-related genes in pancreatic cancer has not been fully revealed. We identified 379 diff...

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Autores principales: Huo, Junyu, Wu, Liqun, Zang, Yunjin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8194314/
https://www.ncbi.nlm.nih.gov/pubmed/34122496
http://dx.doi.org/10.3389/fgene.2021.561254
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author Huo, Junyu
Wu, Liqun
Zang, Yunjin
author_facet Huo, Junyu
Wu, Liqun
Zang, Yunjin
author_sort Huo, Junyu
collection PubMed
description Recently, growing evidence has revealed the significant effect of reprogrammed metabolism on pancreatic cancer in relation to carcinogenesis, progression, and treatment. However, the prognostic value of metabolism-related genes in pancreatic cancer has not been fully revealed. We identified 379 differentially expressed metabolic-related genes (DEMRGs) by comparing 178 pancreatic cancer tissues with 171 normal pancreatic tissues in The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression project (GTEx) databases. Then, we used univariate Cox regression analysis together with Lasso regression for constructing a prognostic model consisting of 15 metabolic genes. The unified risk score formula and cutoff value were taken into account to divide patients into two groups: high risk and low risk, with the former exhibiting a worse prognosis compared with the latter. The external validation results of the International Cancer Genome Consortium (IGCC) cohort and the Gene Expression Omnibus (GEO) cohort further confirm the effectiveness of this prognostic model. As shown in the receiver operating characteristic (ROC) curve, the area under curve (AUC) values of the risk score for overall survival (OS), disease-specific survival (DSS), and progression-free survival (PFS) were 0.871, 0.885, and 0.886, respectively. Based on the Gene Set Enrichment Analysis (GSEA), the 15-gene signature can affect some important biological processes and pathways of pancreatic cancer. In addition, the prognostic model was significantly correlated with the tumor immune microenvironment (immune cell infiltration, and immune checkpoint expression, etc.) and clinicopathological features (pathological stage, lymph node, and metastasis, etc.). We also built a nomogram based on three independent prognostic predictors (including individual neoplasm status, lymph node metastasis, and risk score) for the prediction of 1-, 3-, and 5-year OS of pancreatic cancer, which may help to further improve the treatment strategy of pancreatic cancer.
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spelling pubmed-81943142021-06-12 Development and Validation of a Novel Metabolic-Related Signature Predicting Overall Survival for Pancreatic Cancer Huo, Junyu Wu, Liqun Zang, Yunjin Front Genet Genetics Recently, growing evidence has revealed the significant effect of reprogrammed metabolism on pancreatic cancer in relation to carcinogenesis, progression, and treatment. However, the prognostic value of metabolism-related genes in pancreatic cancer has not been fully revealed. We identified 379 differentially expressed metabolic-related genes (DEMRGs) by comparing 178 pancreatic cancer tissues with 171 normal pancreatic tissues in The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression project (GTEx) databases. Then, we used univariate Cox regression analysis together with Lasso regression for constructing a prognostic model consisting of 15 metabolic genes. The unified risk score formula and cutoff value were taken into account to divide patients into two groups: high risk and low risk, with the former exhibiting a worse prognosis compared with the latter. The external validation results of the International Cancer Genome Consortium (IGCC) cohort and the Gene Expression Omnibus (GEO) cohort further confirm the effectiveness of this prognostic model. As shown in the receiver operating characteristic (ROC) curve, the area under curve (AUC) values of the risk score for overall survival (OS), disease-specific survival (DSS), and progression-free survival (PFS) were 0.871, 0.885, and 0.886, respectively. Based on the Gene Set Enrichment Analysis (GSEA), the 15-gene signature can affect some important biological processes and pathways of pancreatic cancer. In addition, the prognostic model was significantly correlated with the tumor immune microenvironment (immune cell infiltration, and immune checkpoint expression, etc.) and clinicopathological features (pathological stage, lymph node, and metastasis, etc.). We also built a nomogram based on three independent prognostic predictors (including individual neoplasm status, lymph node metastasis, and risk score) for the prediction of 1-, 3-, and 5-year OS of pancreatic cancer, which may help to further improve the treatment strategy of pancreatic cancer. Frontiers Media S.A. 2021-05-28 /pmc/articles/PMC8194314/ /pubmed/34122496 http://dx.doi.org/10.3389/fgene.2021.561254 Text en Copyright © 2021 Huo, Wu and Zang. 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 Genetics
Huo, Junyu
Wu, Liqun
Zang, Yunjin
Development and Validation of a Novel Metabolic-Related Signature Predicting Overall Survival for Pancreatic Cancer
title Development and Validation of a Novel Metabolic-Related Signature Predicting Overall Survival for Pancreatic Cancer
title_full Development and Validation of a Novel Metabolic-Related Signature Predicting Overall Survival for Pancreatic Cancer
title_fullStr Development and Validation of a Novel Metabolic-Related Signature Predicting Overall Survival for Pancreatic Cancer
title_full_unstemmed Development and Validation of a Novel Metabolic-Related Signature Predicting Overall Survival for Pancreatic Cancer
title_short Development and Validation of a Novel Metabolic-Related Signature Predicting Overall Survival for Pancreatic Cancer
title_sort development and validation of a novel metabolic-related signature predicting overall survival for pancreatic cancer
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8194314/
https://www.ncbi.nlm.nih.gov/pubmed/34122496
http://dx.doi.org/10.3389/fgene.2021.561254
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