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Identification of Candidate Therapeutic Target Genes and Profiling of Tumor-Infiltrating Immune Cells in Pancreatic Cancer via Integrated Transcriptomic Analysis

Pancreatic cancer (PC) has a dismal prognosis despite advancing scientific and technological knowledge. The exploration of novel genes is critical to improving current therapeutic measures. This research is aimed at selecting hub genes that can act as candidate therapeutic target genes and as progno...

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Autores principales: Ding, Wei, Wang, Yuxu, Ma, Yongbiao, Lin, Li, Li, Manjiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9428685/
https://www.ncbi.nlm.nih.gov/pubmed/36061357
http://dx.doi.org/10.1155/2022/3839480
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author Ding, Wei
Wang, Yuxu
Ma, Yongbiao
Lin, Li
Li, Manjiang
author_facet Ding, Wei
Wang, Yuxu
Ma, Yongbiao
Lin, Li
Li, Manjiang
author_sort Ding, Wei
collection PubMed
description Pancreatic cancer (PC) has a dismal prognosis despite advancing scientific and technological knowledge. The exploration of novel genes is critical to improving current therapeutic measures. This research is aimed at selecting hub genes that can act as candidate therapeutic target genes and as prognostic biomarkers in PC. Gene expression profiles of datasets GSE101448, GSE15471, and GSE62452 were extracted from the GEO database. The “limma” package was performed to select differentially expressed genes (DEGs) between PC and normal tissue samples in each dataset. Robust rank aggregation (RRA) algorithm was conducted to integrate multiple expression profiles and identify robust DEGs. GO analysis and KEGG analysis were conducted to identify the functional correlation of the DEGs. The CIBERSORT algorithm was conducted to estimate the immune cell composition of each tissue sample. STRING and Cytoscape were used to establish the protein-protein interaction (PPI) network. The cytoHubba plugin in Cytoscape was performed to identify hub genes. Survival analysis based on hub gene expression was performed with clinical information from TCGA database. 566 robust DEGs (338 upregulated genes and 226 downregulated genes) were identified. Tumor tissue had a higher infiltration of resting dendritic cells and tumor-associated macrophages (TAM), including M0, M1, and M2 macrophages, while infiltration levels of B memory cells, plasma cells, T cells CD8, T follicular helper cells, and NK cells in normal tissue were relatively higher. GO terms and KEGG pathway analysis results revealed enrichment in tumor-associated pathways, including the extracellular matrix organization, cell−substrate adhesion cytokine−cytokine receptor interaction, calcium signaling pathway, and glycine, serine, and threonine metabolism, to name a few. Finally, FN1, MSLN, PLAU, and VCAN were selected as hub genes. High expression of FN1, MSLN, PLAU, and VCAN in PC significantly correlated with poor prognosis. Integrated transcriptomic analysis was used to provide new insights into PC pathogenesis. FN1, MSLN, PLAU, and VCAN may be considered as novel biomarkers of PC.
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spelling pubmed-94286852022-09-01 Identification of Candidate Therapeutic Target Genes and Profiling of Tumor-Infiltrating Immune Cells in Pancreatic Cancer via Integrated Transcriptomic Analysis Ding, Wei Wang, Yuxu Ma, Yongbiao Lin, Li Li, Manjiang Dis Markers Research Article Pancreatic cancer (PC) has a dismal prognosis despite advancing scientific and technological knowledge. The exploration of novel genes is critical to improving current therapeutic measures. This research is aimed at selecting hub genes that can act as candidate therapeutic target genes and as prognostic biomarkers in PC. Gene expression profiles of datasets GSE101448, GSE15471, and GSE62452 were extracted from the GEO database. The “limma” package was performed to select differentially expressed genes (DEGs) between PC and normal tissue samples in each dataset. Robust rank aggregation (RRA) algorithm was conducted to integrate multiple expression profiles and identify robust DEGs. GO analysis and KEGG analysis were conducted to identify the functional correlation of the DEGs. The CIBERSORT algorithm was conducted to estimate the immune cell composition of each tissue sample. STRING and Cytoscape were used to establish the protein-protein interaction (PPI) network. The cytoHubba plugin in Cytoscape was performed to identify hub genes. Survival analysis based on hub gene expression was performed with clinical information from TCGA database. 566 robust DEGs (338 upregulated genes and 226 downregulated genes) were identified. Tumor tissue had a higher infiltration of resting dendritic cells and tumor-associated macrophages (TAM), including M0, M1, and M2 macrophages, while infiltration levels of B memory cells, plasma cells, T cells CD8, T follicular helper cells, and NK cells in normal tissue were relatively higher. GO terms and KEGG pathway analysis results revealed enrichment in tumor-associated pathways, including the extracellular matrix organization, cell−substrate adhesion cytokine−cytokine receptor interaction, calcium signaling pathway, and glycine, serine, and threonine metabolism, to name a few. Finally, FN1, MSLN, PLAU, and VCAN were selected as hub genes. High expression of FN1, MSLN, PLAU, and VCAN in PC significantly correlated with poor prognosis. Integrated transcriptomic analysis was used to provide new insights into PC pathogenesis. FN1, MSLN, PLAU, and VCAN may be considered as novel biomarkers of PC. Hindawi 2022-08-23 /pmc/articles/PMC9428685/ /pubmed/36061357 http://dx.doi.org/10.1155/2022/3839480 Text en Copyright © 2022 Wei Ding et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Ding, Wei
Wang, Yuxu
Ma, Yongbiao
Lin, Li
Li, Manjiang
Identification of Candidate Therapeutic Target Genes and Profiling of Tumor-Infiltrating Immune Cells in Pancreatic Cancer via Integrated Transcriptomic Analysis
title Identification of Candidate Therapeutic Target Genes and Profiling of Tumor-Infiltrating Immune Cells in Pancreatic Cancer via Integrated Transcriptomic Analysis
title_full Identification of Candidate Therapeutic Target Genes and Profiling of Tumor-Infiltrating Immune Cells in Pancreatic Cancer via Integrated Transcriptomic Analysis
title_fullStr Identification of Candidate Therapeutic Target Genes and Profiling of Tumor-Infiltrating Immune Cells in Pancreatic Cancer via Integrated Transcriptomic Analysis
title_full_unstemmed Identification of Candidate Therapeutic Target Genes and Profiling of Tumor-Infiltrating Immune Cells in Pancreatic Cancer via Integrated Transcriptomic Analysis
title_short Identification of Candidate Therapeutic Target Genes and Profiling of Tumor-Infiltrating Immune Cells in Pancreatic Cancer via Integrated Transcriptomic Analysis
title_sort identification of candidate therapeutic target genes and profiling of tumor-infiltrating immune cells in pancreatic cancer via integrated transcriptomic analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9428685/
https://www.ncbi.nlm.nih.gov/pubmed/36061357
http://dx.doi.org/10.1155/2022/3839480
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