Cargando…
Identification of Key mRNAs as Prediction Models for Early Metastasis of Pancreatic Cancer Based on LASSO
Pancreatic cancer is a highly malignant and metastatic tumor of the digestive system. Even after surgical removal of the tumor, most patients are still at risk of metastasis. Therefore, screening for metastatic biomarkers can identify precise therapeutic intervention targets. In this study, we analy...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8415976/ https://www.ncbi.nlm.nih.gov/pubmed/34485257 http://dx.doi.org/10.3389/fbioe.2021.701039 |
_version_ | 1783748078507917312 |
---|---|
author | Xue, Ke Zheng, Huilin Qian, Xiaowen Chen, Zheng Gu, Yangjun Hu, Zhenhua Zhang, Lei Wan, Jian |
author_facet | Xue, Ke Zheng, Huilin Qian, Xiaowen Chen, Zheng Gu, Yangjun Hu, Zhenhua Zhang, Lei Wan, Jian |
author_sort | Xue, Ke |
collection | PubMed |
description | Pancreatic cancer is a highly malignant and metastatic tumor of the digestive system. Even after surgical removal of the tumor, most patients are still at risk of metastasis. Therefore, screening for metastatic biomarkers can identify precise therapeutic intervention targets. In this study, we analyzed 96 pancreatic cancer samples from The Cancer Genome Atlas (TCGA) without metastasis or with metastasis after R0 resection. We also retrieved data from metastatic pancreatic cancer cell lines from Gene Expression Omnibus (GEO), as well as collected sequencing data from our own cell lines, BxPC-3 and BxPC-3-M8. Finally, we analyzed the expression of metastasis-related genes in different datasets by the Limma and edgeR packages in R software, and enrichment analysis of differential gene expression was used to gain insight into the mechanism of pancreatic cancer metastasis. Our analysis identified six genes as risk factors for predicting metastatic status by LASSO regression, including zinc finger BED-Type Containing 2 (ZBED2), S100 calcium-binding protein A2 (S100A2), Jagged canonical Notch ligand 1 (JAG1), laminin subunit gamma 2 (LAMC2), transglutaminase 2 (TGM2), and the transcription factor hepatic leukemia factor (HLF). We used these six EMT-related genes to construct a risk-scoring model. The receiver operating characteristic (ROC) curve showed that the risk score could better predict the risk of metastasis. Univariate and multivariate Cox regression analyses revealed that the risk score was also an important predictor of pancreatic cancer. In conclusion, 6-mRNA expression is a potentially valuable method for predicting pancreatic cancer metastasis, assessing clinical outcomes, and facilitating future personalized treatment for patients with ductal adenocarcinoma of the pancreas (PDAC). |
format | Online Article Text |
id | pubmed-8415976 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-84159762021-09-04 Identification of Key mRNAs as Prediction Models for Early Metastasis of Pancreatic Cancer Based on LASSO Xue, Ke Zheng, Huilin Qian, Xiaowen Chen, Zheng Gu, Yangjun Hu, Zhenhua Zhang, Lei Wan, Jian Front Bioeng Biotechnol Bioengineering and Biotechnology Pancreatic cancer is a highly malignant and metastatic tumor of the digestive system. Even after surgical removal of the tumor, most patients are still at risk of metastasis. Therefore, screening for metastatic biomarkers can identify precise therapeutic intervention targets. In this study, we analyzed 96 pancreatic cancer samples from The Cancer Genome Atlas (TCGA) without metastasis or with metastasis after R0 resection. We also retrieved data from metastatic pancreatic cancer cell lines from Gene Expression Omnibus (GEO), as well as collected sequencing data from our own cell lines, BxPC-3 and BxPC-3-M8. Finally, we analyzed the expression of metastasis-related genes in different datasets by the Limma and edgeR packages in R software, and enrichment analysis of differential gene expression was used to gain insight into the mechanism of pancreatic cancer metastasis. Our analysis identified six genes as risk factors for predicting metastatic status by LASSO regression, including zinc finger BED-Type Containing 2 (ZBED2), S100 calcium-binding protein A2 (S100A2), Jagged canonical Notch ligand 1 (JAG1), laminin subunit gamma 2 (LAMC2), transglutaminase 2 (TGM2), and the transcription factor hepatic leukemia factor (HLF). We used these six EMT-related genes to construct a risk-scoring model. The receiver operating characteristic (ROC) curve showed that the risk score could better predict the risk of metastasis. Univariate and multivariate Cox regression analyses revealed that the risk score was also an important predictor of pancreatic cancer. In conclusion, 6-mRNA expression is a potentially valuable method for predicting pancreatic cancer metastasis, assessing clinical outcomes, and facilitating future personalized treatment for patients with ductal adenocarcinoma of the pancreas (PDAC). Frontiers Media S.A. 2021-08-17 /pmc/articles/PMC8415976/ /pubmed/34485257 http://dx.doi.org/10.3389/fbioe.2021.701039 Text en Copyright © 2021 Xue, Zheng, Qian, Chen, Gu, Hu, Zhang and Wan. 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 | Bioengineering and Biotechnology Xue, Ke Zheng, Huilin Qian, Xiaowen Chen, Zheng Gu, Yangjun Hu, Zhenhua Zhang, Lei Wan, Jian Identification of Key mRNAs as Prediction Models for Early Metastasis of Pancreatic Cancer Based on LASSO |
title | Identification of Key mRNAs as Prediction Models for Early Metastasis of Pancreatic Cancer Based on LASSO |
title_full | Identification of Key mRNAs as Prediction Models for Early Metastasis of Pancreatic Cancer Based on LASSO |
title_fullStr | Identification of Key mRNAs as Prediction Models for Early Metastasis of Pancreatic Cancer Based on LASSO |
title_full_unstemmed | Identification of Key mRNAs as Prediction Models for Early Metastasis of Pancreatic Cancer Based on LASSO |
title_short | Identification of Key mRNAs as Prediction Models for Early Metastasis of Pancreatic Cancer Based on LASSO |
title_sort | identification of key mrnas as prediction models for early metastasis of pancreatic cancer based on lasso |
topic | Bioengineering and Biotechnology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8415976/ https://www.ncbi.nlm.nih.gov/pubmed/34485257 http://dx.doi.org/10.3389/fbioe.2021.701039 |
work_keys_str_mv | AT xueke identificationofkeymrnasaspredictionmodelsforearlymetastasisofpancreaticcancerbasedonlasso AT zhenghuilin identificationofkeymrnasaspredictionmodelsforearlymetastasisofpancreaticcancerbasedonlasso AT qianxiaowen identificationofkeymrnasaspredictionmodelsforearlymetastasisofpancreaticcancerbasedonlasso AT chenzheng identificationofkeymrnasaspredictionmodelsforearlymetastasisofpancreaticcancerbasedonlasso AT guyangjun identificationofkeymrnasaspredictionmodelsforearlymetastasisofpancreaticcancerbasedonlasso AT huzhenhua identificationofkeymrnasaspredictionmodelsforearlymetastasisofpancreaticcancerbasedonlasso AT zhanglei identificationofkeymrnasaspredictionmodelsforearlymetastasisofpancreaticcancerbasedonlasso AT wanjian identificationofkeymrnasaspredictionmodelsforearlymetastasisofpancreaticcancerbasedonlasso |