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Integrative Proteo-Genomic Analysis for Recurrent Survival Prognosis in Colon Adenocarcinoma

BACKGROUND: The survival prognosis is the hallmark of cancer progression. Here, we aimed to develop a recurrence-related gene signature to predict the prognosis of colon adenocarcinoma (COAD). METHODS: The proteomic data from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and genomic data...

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Autores principales: Ai, FeiYan, Wang, Wenhao, Liu, Shaojun, Zhang, Decai, Yang, Zhenyu, Liu, Fen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9281446/
https://www.ncbi.nlm.nih.gov/pubmed/35847888
http://dx.doi.org/10.3389/fonc.2022.871568
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author Ai, FeiYan
Wang, Wenhao
Liu, Shaojun
Zhang, Decai
Yang, Zhenyu
Liu, Fen
author_facet Ai, FeiYan
Wang, Wenhao
Liu, Shaojun
Zhang, Decai
Yang, Zhenyu
Liu, Fen
author_sort Ai, FeiYan
collection PubMed
description BACKGROUND: The survival prognosis is the hallmark of cancer progression. Here, we aimed to develop a recurrence-related gene signature to predict the prognosis of colon adenocarcinoma (COAD). METHODS: The proteomic data from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and genomic data from the cancer genomic maps [The Cancer Genome Atlas (TCGA)] dataset were analyzed to identify co-differentially expressed genes (cDEGs) between recurrence samples and non-recurrence samples in COAD using limma package. Functional enrichment analysis, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway was conducted. Univariate and multivariate Cox regressions were applied to identify the independent prognostic feature cDEGs and establish the signature whose performance was evaluated by Kaplan–Meier curve, receiver operating characteristic (ROC), Harrell’s concordance index (C-index), and calibration curve. The area under the receiver operating characteristic (ROC) curve (AUROC) and a nomogram were calculated to assess the predictive accuracy. GSE17538 and GSE39582 were used for external validation. Quantitative real-time PCR and Western blot analysis were carried out to validate our findings. RESULTS: We identified 86 cDEGs in recurrence samples compared with non-recurrence samples. These genes were primarily enriched in the regulation of carbon metabolic process, fructose and mannose metabolism, and extracellular exosome. Then, an eight-gene-based signature (CA12, HBB, NCF1, KBTBD11, MMAA, DMBT1, AHNAK2, and FBLN2) was developed to separate patients into high- and low-risk groups. Patients in the low-risk group had significantly better prognosis than those in the high-risk group. Four prognostic clinical features, including pathological M, N, T, and RS model status, were screened for building the nomogram survival model. The PCR and Western blot analysis results suggested that CA12 and AHNAK2 were significantly upregulated, while MMAA and DMBT1 were downregulated in the tumor sample compared with adjacent tissues, and in non-recurrent samples compared with non-recurrent samples in COAD. CONCLUSION: These identified recurrence-related gene signatures might provide an effective prognostic predictor and promising therapeutic targets for COAD patients.
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spelling pubmed-92814462022-07-15 Integrative Proteo-Genomic Analysis for Recurrent Survival Prognosis in Colon Adenocarcinoma Ai, FeiYan Wang, Wenhao Liu, Shaojun Zhang, Decai Yang, Zhenyu Liu, Fen Front Oncol Oncology BACKGROUND: The survival prognosis is the hallmark of cancer progression. Here, we aimed to develop a recurrence-related gene signature to predict the prognosis of colon adenocarcinoma (COAD). METHODS: The proteomic data from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and genomic data from the cancer genomic maps [The Cancer Genome Atlas (TCGA)] dataset were analyzed to identify co-differentially expressed genes (cDEGs) between recurrence samples and non-recurrence samples in COAD using limma package. Functional enrichment analysis, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway was conducted. Univariate and multivariate Cox regressions were applied to identify the independent prognostic feature cDEGs and establish the signature whose performance was evaluated by Kaplan–Meier curve, receiver operating characteristic (ROC), Harrell’s concordance index (C-index), and calibration curve. The area under the receiver operating characteristic (ROC) curve (AUROC) and a nomogram were calculated to assess the predictive accuracy. GSE17538 and GSE39582 were used for external validation. Quantitative real-time PCR and Western blot analysis were carried out to validate our findings. RESULTS: We identified 86 cDEGs in recurrence samples compared with non-recurrence samples. These genes were primarily enriched in the regulation of carbon metabolic process, fructose and mannose metabolism, and extracellular exosome. Then, an eight-gene-based signature (CA12, HBB, NCF1, KBTBD11, MMAA, DMBT1, AHNAK2, and FBLN2) was developed to separate patients into high- and low-risk groups. Patients in the low-risk group had significantly better prognosis than those in the high-risk group. Four prognostic clinical features, including pathological M, N, T, and RS model status, were screened for building the nomogram survival model. The PCR and Western blot analysis results suggested that CA12 and AHNAK2 were significantly upregulated, while MMAA and DMBT1 were downregulated in the tumor sample compared with adjacent tissues, and in non-recurrent samples compared with non-recurrent samples in COAD. CONCLUSION: These identified recurrence-related gene signatures might provide an effective prognostic predictor and promising therapeutic targets for COAD patients. Frontiers Media S.A. 2022-06-30 /pmc/articles/PMC9281446/ /pubmed/35847888 http://dx.doi.org/10.3389/fonc.2022.871568 Text en Copyright © 2022 Ai, Wang, Liu, Zhang, Yang and Liu 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
Ai, FeiYan
Wang, Wenhao
Liu, Shaojun
Zhang, Decai
Yang, Zhenyu
Liu, Fen
Integrative Proteo-Genomic Analysis for Recurrent Survival Prognosis in Colon Adenocarcinoma
title Integrative Proteo-Genomic Analysis for Recurrent Survival Prognosis in Colon Adenocarcinoma
title_full Integrative Proteo-Genomic Analysis for Recurrent Survival Prognosis in Colon Adenocarcinoma
title_fullStr Integrative Proteo-Genomic Analysis for Recurrent Survival Prognosis in Colon Adenocarcinoma
title_full_unstemmed Integrative Proteo-Genomic Analysis for Recurrent Survival Prognosis in Colon Adenocarcinoma
title_short Integrative Proteo-Genomic Analysis for Recurrent Survival Prognosis in Colon Adenocarcinoma
title_sort integrative proteo-genomic analysis for recurrent survival prognosis in colon adenocarcinoma
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9281446/
https://www.ncbi.nlm.nih.gov/pubmed/35847888
http://dx.doi.org/10.3389/fonc.2022.871568
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