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Identification of prognostic stemness biomarkers in colon adenocarcinoma drug resistance

BACKGROUND: Colon adenocarcinoma (COAD) is one of the leading causes of death worldwide. Cancer stem cells (CSCs) are vital for COAD chemoresistance and recurrence, however little is known about stem cell-related biomarkers in drug resistance and COAD prognosis prediction. METHODS: To uncover the ro...

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Autores principales: Li, Ziyue, Chen, Jierong, Zhu, Dandan, Wang, Xiaoxiao, Chen, Jace, Zhang, Yu, Lian, Qizhou, Gu, Bing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9261069/
https://www.ncbi.nlm.nih.gov/pubmed/35794546
http://dx.doi.org/10.1186/s12863-022-01063-9
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author Li, Ziyue
Chen, Jierong
Zhu, Dandan
Wang, Xiaoxiao
Chen, Jace
Zhang, Yu
Lian, Qizhou
Gu, Bing
author_facet Li, Ziyue
Chen, Jierong
Zhu, Dandan
Wang, Xiaoxiao
Chen, Jace
Zhang, Yu
Lian, Qizhou
Gu, Bing
author_sort Li, Ziyue
collection PubMed
description BACKGROUND: Colon adenocarcinoma (COAD) is one of the leading causes of death worldwide. Cancer stem cells (CSCs) are vital for COAD chemoresistance and recurrence, however little is known about stem cell-related biomarkers in drug resistance and COAD prognosis prediction. METHODS: To uncover the roles of CSC in COAD tumorigenesis, chemoresistance, and prognosis, we retrieved COAD patients’ RNAseq data from TCGA (The Cancer Genome Atlas). We further performed analysis of differentially expressed genes (DEGs) and mRNA expression-based stemness index (mRNAsi) to identify stemness-related COAD biomarkers. We then evaluated the roles of mRNAsi in tumorigenesis, clinical-stage, overall survival (OS), and chemoresistance. Afterward, we used identified prognostic stemness-related genes (PSRGs) to construct a prediction model. After constructing the prediction model, we used elastic Net regression and area under the curve (AUC) to explore the prediction value of PSRGs based on risk scores and the receiver operator characteristic (ROC) curve. To elucidate the underlying interconnected systems, we examined relationships between the levels of TFs, PSRGs, and 50 cancer hallmarks by a Pearson correlation analysis. RESULTS: Twelve thousand one hundred eight DEGs were identified by comparing 456 primary COADs and 41 normal solid tissue samples. Furthermore, we identified 4351 clinical stage-related DEGs, 16,516 stemness-associated DEGs, and 54 chemoresistance-related DEGs from cancer stages: mRNAsi, and COAD chemoresistance. Compared to normal tissue samples, mRNAsi in COAD patients were marked on an elevation and involved in prognosis (p = 0.027), stemness-related DEGs based on chemoresistance (OR = 3.28, p ≤ 0.001) and AJCC clinical stage relating (OR = 4.02, p ≤ 0.001) to COAD patients. The prediction model of prognosis were constructed using the 6 PSRGs with high accuracy (AUC: 0.659). The model identified universal correlation between NRIP2 and FDFT1 (key PRSGs), and some cancer related transcription factors (TFs) and trademarks of cancer gene were in the regulatory network. CONCLUSION: We found that mRNAsi is a reliable predictive biomarker of tumorigenesis and COAD prognosis. Our established prediction model of COAD chemoresistance, which includes the six PSRGs, is effective, as the model provides promising therapeutic targets in the COAD. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12863-022-01063-9.
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spelling pubmed-92610692022-07-08 Identification of prognostic stemness biomarkers in colon adenocarcinoma drug resistance Li, Ziyue Chen, Jierong Zhu, Dandan Wang, Xiaoxiao Chen, Jace Zhang, Yu Lian, Qizhou Gu, Bing BMC Genom Data Research BACKGROUND: Colon adenocarcinoma (COAD) is one of the leading causes of death worldwide. Cancer stem cells (CSCs) are vital for COAD chemoresistance and recurrence, however little is known about stem cell-related biomarkers in drug resistance and COAD prognosis prediction. METHODS: To uncover the roles of CSC in COAD tumorigenesis, chemoresistance, and prognosis, we retrieved COAD patients’ RNAseq data from TCGA (The Cancer Genome Atlas). We further performed analysis of differentially expressed genes (DEGs) and mRNA expression-based stemness index (mRNAsi) to identify stemness-related COAD biomarkers. We then evaluated the roles of mRNAsi in tumorigenesis, clinical-stage, overall survival (OS), and chemoresistance. Afterward, we used identified prognostic stemness-related genes (PSRGs) to construct a prediction model. After constructing the prediction model, we used elastic Net regression and area under the curve (AUC) to explore the prediction value of PSRGs based on risk scores and the receiver operator characteristic (ROC) curve. To elucidate the underlying interconnected systems, we examined relationships between the levels of TFs, PSRGs, and 50 cancer hallmarks by a Pearson correlation analysis. RESULTS: Twelve thousand one hundred eight DEGs were identified by comparing 456 primary COADs and 41 normal solid tissue samples. Furthermore, we identified 4351 clinical stage-related DEGs, 16,516 stemness-associated DEGs, and 54 chemoresistance-related DEGs from cancer stages: mRNAsi, and COAD chemoresistance. Compared to normal tissue samples, mRNAsi in COAD patients were marked on an elevation and involved in prognosis (p = 0.027), stemness-related DEGs based on chemoresistance (OR = 3.28, p ≤ 0.001) and AJCC clinical stage relating (OR = 4.02, p ≤ 0.001) to COAD patients. The prediction model of prognosis were constructed using the 6 PSRGs with high accuracy (AUC: 0.659). The model identified universal correlation between NRIP2 and FDFT1 (key PRSGs), and some cancer related transcription factors (TFs) and trademarks of cancer gene were in the regulatory network. CONCLUSION: We found that mRNAsi is a reliable predictive biomarker of tumorigenesis and COAD prognosis. Our established prediction model of COAD chemoresistance, which includes the six PSRGs, is effective, as the model provides promising therapeutic targets in the COAD. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12863-022-01063-9. BioMed Central 2022-07-06 /pmc/articles/PMC9261069/ /pubmed/35794546 http://dx.doi.org/10.1186/s12863-022-01063-9 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Li, Ziyue
Chen, Jierong
Zhu, Dandan
Wang, Xiaoxiao
Chen, Jace
Zhang, Yu
Lian, Qizhou
Gu, Bing
Identification of prognostic stemness biomarkers in colon adenocarcinoma drug resistance
title Identification of prognostic stemness biomarkers in colon adenocarcinoma drug resistance
title_full Identification of prognostic stemness biomarkers in colon adenocarcinoma drug resistance
title_fullStr Identification of prognostic stemness biomarkers in colon adenocarcinoma drug resistance
title_full_unstemmed Identification of prognostic stemness biomarkers in colon adenocarcinoma drug resistance
title_short Identification of prognostic stemness biomarkers in colon adenocarcinoma drug resistance
title_sort identification of prognostic stemness biomarkers in colon adenocarcinoma drug resistance
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9261069/
https://www.ncbi.nlm.nih.gov/pubmed/35794546
http://dx.doi.org/10.1186/s12863-022-01063-9
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