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Identification and prognostic analysis of biomarkers to predict the progression of pancreatic cancer patients

BACKGROUND: Pancreatic cancer (PC) is a malignancy with a poor prognosis and high mortality. Surgical resection is the only “curative” treatment. However, only a minority of patients with PC can obtain surgery. Improving the overall survival (OS) rate of patients with PC is still a major challenge....

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Autores principales: Li, Wei, Li, Tiandong, Sun, Chenguang, Du, Yimeng, Chen, Linna, Du, Chunyan, Shi, Jianxiang, Wang, Weijie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9013045/
https://www.ncbi.nlm.nih.gov/pubmed/35428170
http://dx.doi.org/10.1186/s10020-022-00467-8
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author Li, Wei
Li, Tiandong
Sun, Chenguang
Du, Yimeng
Chen, Linna
Du, Chunyan
Shi, Jianxiang
Wang, Weijie
author_facet Li, Wei
Li, Tiandong
Sun, Chenguang
Du, Yimeng
Chen, Linna
Du, Chunyan
Shi, Jianxiang
Wang, Weijie
author_sort Li, Wei
collection PubMed
description BACKGROUND: Pancreatic cancer (PC) is a malignancy with a poor prognosis and high mortality. Surgical resection is the only “curative” treatment. However, only a minority of patients with PC can obtain surgery. Improving the overall survival (OS) rate of patients with PC is still a major challenge. Molecular biomarkers are a significant approach for diagnostic and predictive use in PCs. Several prediction models have been developed for patients newly diagnosed with PC that is operable or patients with advanced and metastatic PC; however, these models require further validation. Therefore, precise biomarkers are urgently required to increase the efficiency of predicting a disease-free survival (DFS), OS, and sensitivity to immunotherapy in PC patients and to improve the prognosis of PC. METHODS: In the present study, we first evaluated the highly and selectively expressed targets in PC, using the GeoMxTM Digital Spatial Profiler (DSP) and then, we analyzed the roles of these targets in PCs using TCGA database. RESULTS: LAMB3, FN1, KRT17, KRT19, and ANXA1 were defined as the top five upregulated targets in PC compared with paracancer. The TCGA database results confirmed the expression pattern of LAMB3, FN1, KRT17, KRT19, and ANXA1 in PCs. Significantly, LAMB3, FN1, KRT19, and ANXA1 but not KRT17 can be considered as biomarkers for survival analysis, univariate and multivariate Cox proportional hazards model, and risk model analysis. Furthermore, in combination, LAMB3, FN1, KRT19, and ANXA1 predict the DFS and, in combination, LAMB3, KRT19, and ANXA1 predict the OS. Immunotherapy is significant for PCs that are inoperable. The immune checkpoint blockade (ICB) analysis indicated that higher expressions of FN1 or ANXA1 are correlated with lower ICB response. In contrast, there are no significant differences in the ICB response between high and low expression of LAMB3 and KRT19. CONCLUSIONS: In conclusion, LAMB3, FN1, KRT19, and ANXA1 are good predictors of PC prognosis. Furthermore, FN1 and ANXA1 can be predictors of immunotherapy in PCs. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s10020-022-00467-8.
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spelling pubmed-90130452022-04-17 Identification and prognostic analysis of biomarkers to predict the progression of pancreatic cancer patients Li, Wei Li, Tiandong Sun, Chenguang Du, Yimeng Chen, Linna Du, Chunyan Shi, Jianxiang Wang, Weijie Mol Med Research Article BACKGROUND: Pancreatic cancer (PC) is a malignancy with a poor prognosis and high mortality. Surgical resection is the only “curative” treatment. However, only a minority of patients with PC can obtain surgery. Improving the overall survival (OS) rate of patients with PC is still a major challenge. Molecular biomarkers are a significant approach for diagnostic and predictive use in PCs. Several prediction models have been developed for patients newly diagnosed with PC that is operable or patients with advanced and metastatic PC; however, these models require further validation. Therefore, precise biomarkers are urgently required to increase the efficiency of predicting a disease-free survival (DFS), OS, and sensitivity to immunotherapy in PC patients and to improve the prognosis of PC. METHODS: In the present study, we first evaluated the highly and selectively expressed targets in PC, using the GeoMxTM Digital Spatial Profiler (DSP) and then, we analyzed the roles of these targets in PCs using TCGA database. RESULTS: LAMB3, FN1, KRT17, KRT19, and ANXA1 were defined as the top five upregulated targets in PC compared with paracancer. The TCGA database results confirmed the expression pattern of LAMB3, FN1, KRT17, KRT19, and ANXA1 in PCs. Significantly, LAMB3, FN1, KRT19, and ANXA1 but not KRT17 can be considered as biomarkers for survival analysis, univariate and multivariate Cox proportional hazards model, and risk model analysis. Furthermore, in combination, LAMB3, FN1, KRT19, and ANXA1 predict the DFS and, in combination, LAMB3, KRT19, and ANXA1 predict the OS. Immunotherapy is significant for PCs that are inoperable. The immune checkpoint blockade (ICB) analysis indicated that higher expressions of FN1 or ANXA1 are correlated with lower ICB response. In contrast, there are no significant differences in the ICB response between high and low expression of LAMB3 and KRT19. CONCLUSIONS: In conclusion, LAMB3, FN1, KRT19, and ANXA1 are good predictors of PC prognosis. Furthermore, FN1 and ANXA1 can be predictors of immunotherapy in PCs. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s10020-022-00467-8. BioMed Central 2022-04-15 /pmc/articles/PMC9013045/ /pubmed/35428170 http://dx.doi.org/10.1186/s10020-022-00467-8 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/) .
spellingShingle Research Article
Li, Wei
Li, Tiandong
Sun, Chenguang
Du, Yimeng
Chen, Linna
Du, Chunyan
Shi, Jianxiang
Wang, Weijie
Identification and prognostic analysis of biomarkers to predict the progression of pancreatic cancer patients
title Identification and prognostic analysis of biomarkers to predict the progression of pancreatic cancer patients
title_full Identification and prognostic analysis of biomarkers to predict the progression of pancreatic cancer patients
title_fullStr Identification and prognostic analysis of biomarkers to predict the progression of pancreatic cancer patients
title_full_unstemmed Identification and prognostic analysis of biomarkers to predict the progression of pancreatic cancer patients
title_short Identification and prognostic analysis of biomarkers to predict the progression of pancreatic cancer patients
title_sort identification and prognostic analysis of biomarkers to predict the progression of pancreatic cancer patients
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9013045/
https://www.ncbi.nlm.nih.gov/pubmed/35428170
http://dx.doi.org/10.1186/s10020-022-00467-8
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