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Comprehensive analysis to identify a novel diagnostic marker of lung adenocarcinoma and its immune infiltration landscape
BACKGROUND: Lung cancer continues to be a problem faced by all of humanity. It is the cancer with the highest morbidity and mortality in the world, and the most common histological type of lung cancer is lung adenocarcinoma (LUAD), accounting for about 40% of lung malignant tumors. This study was co...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10319060/ https://www.ncbi.nlm.nih.gov/pubmed/37409245 http://dx.doi.org/10.3389/fonc.2023.1199608 |
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author | Zhu, Ankang Pei, Dongchen Zong, Yan Fan, Yan Wei, Shuai Xing, Zhisong Song, Shuailin Wang, Xin Gao, Xingcai |
author_facet | Zhu, Ankang Pei, Dongchen Zong, Yan Fan, Yan Wei, Shuai Xing, Zhisong Song, Shuailin Wang, Xin Gao, Xingcai |
author_sort | Zhu, Ankang |
collection | PubMed |
description | BACKGROUND: Lung cancer continues to be a problem faced by all of humanity. It is the cancer with the highest morbidity and mortality in the world, and the most common histological type of lung cancer is lung adenocarcinoma (LUAD), accounting for about 40% of lung malignant tumors. This study was conducted to discuss and explore the immune-related biomarkers and pathways during the development and progression of LUAD and their relationship with immunocyte infiltration. METHODS: The cohorts of data used in this study were downloaded from the Gene Expression Complex (GEO) database and the Cancer Genome Atlas Program (TCGA) database. Through the analysis of differential expression analysis, weighted gene co-expression network analysis (WGCNA), and least absolute shrinkage and selection operator(LASSO), selecting the module with the highest correlation with LUAD progression, and then the HUB gene was further determined. The Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) were then used to study the function of these genes. Single-sample GSEA (ssGSEA) analysis was used to investigate the penetration of 28 immunocytes and their relationship with HUB genes. Finally, the receiver operating characteristic curve (ROC) was used to evaluate these HUB genes accurately to diagnose LUAD. In addition, additional cohorts were used for external validation. Based on the TCGA database, the effect of the HUB genes on the prognosis of LUAD patients was assessed using the Kaplan-Meier curve. The mRNA levels of some HUB genes in cancer cells and normal cells were analyzed by reverse transcription-quantitative polymerase chain reaction (RT-qPCR). RESULTS: The turquoise module with the highest correlation with LUAD was identified among the seven modules obtained with WGCNA. Three hundred fifty-four differential genes were chosen. After LASSO analysis, 12 HUB genes were chosen as candidate biomarkers for LUAD expression. According to the immune infiltration results, CD4 + T cells, B cells, and NK cells were high in LUAD sample tissue. The ROC curve showed that all 12 HUB genes had a high diagnostic value. Finally, the functional enrichment analysis suggested that the HUB gene is mainly related to inflammatory and immune responses. According to the RT-qPCR study, we found that the expression of DPYSL2, OCIAD2, and FABP4 in A549 was higher than BEAS-2B. The expression content of DPYSL2 was lower in H1299 than in BEAS-2B. However, the expression difference of FABP4 and OCIAD2 genes in H1299 lung cancer cells was insignificant, but both showed a trend of increase. CONCLUSIONS: The mechanism of LUAD pathogenesis and progression is closely linked to T cells, B cells, and monocytes. 12 HUB genes(ADAMTS8, CD36, DPYSL2, FABP4, FGFR4, HBA2, OCIAD2, PARP1, PLEKHH2, STX11, TCF21, TNNC1) may participate in the progression of LUAD via immune-related signaling pathways. |
format | Online Article Text |
id | pubmed-10319060 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-103190602023-07-05 Comprehensive analysis to identify a novel diagnostic marker of lung adenocarcinoma and its immune infiltration landscape Zhu, Ankang Pei, Dongchen Zong, Yan Fan, Yan Wei, Shuai Xing, Zhisong Song, Shuailin Wang, Xin Gao, Xingcai Front Oncol Oncology BACKGROUND: Lung cancer continues to be a problem faced by all of humanity. It is the cancer with the highest morbidity and mortality in the world, and the most common histological type of lung cancer is lung adenocarcinoma (LUAD), accounting for about 40% of lung malignant tumors. This study was conducted to discuss and explore the immune-related biomarkers and pathways during the development and progression of LUAD and their relationship with immunocyte infiltration. METHODS: The cohorts of data used in this study were downloaded from the Gene Expression Complex (GEO) database and the Cancer Genome Atlas Program (TCGA) database. Through the analysis of differential expression analysis, weighted gene co-expression network analysis (WGCNA), and least absolute shrinkage and selection operator(LASSO), selecting the module with the highest correlation with LUAD progression, and then the HUB gene was further determined. The Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) were then used to study the function of these genes. Single-sample GSEA (ssGSEA) analysis was used to investigate the penetration of 28 immunocytes and their relationship with HUB genes. Finally, the receiver operating characteristic curve (ROC) was used to evaluate these HUB genes accurately to diagnose LUAD. In addition, additional cohorts were used for external validation. Based on the TCGA database, the effect of the HUB genes on the prognosis of LUAD patients was assessed using the Kaplan-Meier curve. The mRNA levels of some HUB genes in cancer cells and normal cells were analyzed by reverse transcription-quantitative polymerase chain reaction (RT-qPCR). RESULTS: The turquoise module with the highest correlation with LUAD was identified among the seven modules obtained with WGCNA. Three hundred fifty-four differential genes were chosen. After LASSO analysis, 12 HUB genes were chosen as candidate biomarkers for LUAD expression. According to the immune infiltration results, CD4 + T cells, B cells, and NK cells were high in LUAD sample tissue. The ROC curve showed that all 12 HUB genes had a high diagnostic value. Finally, the functional enrichment analysis suggested that the HUB gene is mainly related to inflammatory and immune responses. According to the RT-qPCR study, we found that the expression of DPYSL2, OCIAD2, and FABP4 in A549 was higher than BEAS-2B. The expression content of DPYSL2 was lower in H1299 than in BEAS-2B. However, the expression difference of FABP4 and OCIAD2 genes in H1299 lung cancer cells was insignificant, but both showed a trend of increase. CONCLUSIONS: The mechanism of LUAD pathogenesis and progression is closely linked to T cells, B cells, and monocytes. 12 HUB genes(ADAMTS8, CD36, DPYSL2, FABP4, FGFR4, HBA2, OCIAD2, PARP1, PLEKHH2, STX11, TCF21, TNNC1) may participate in the progression of LUAD via immune-related signaling pathways. Frontiers Media S.A. 2023-06-20 /pmc/articles/PMC10319060/ /pubmed/37409245 http://dx.doi.org/10.3389/fonc.2023.1199608 Text en Copyright © 2023 Zhu, Pei, Zong, Fan, Wei, Xing, Song, Wang and Gao 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 Zhu, Ankang Pei, Dongchen Zong, Yan Fan, Yan Wei, Shuai Xing, Zhisong Song, Shuailin Wang, Xin Gao, Xingcai Comprehensive analysis to identify a novel diagnostic marker of lung adenocarcinoma and its immune infiltration landscape |
title | Comprehensive analysis to identify a novel diagnostic marker of lung adenocarcinoma and its immune infiltration landscape |
title_full | Comprehensive analysis to identify a novel diagnostic marker of lung adenocarcinoma and its immune infiltration landscape |
title_fullStr | Comprehensive analysis to identify a novel diagnostic marker of lung adenocarcinoma and its immune infiltration landscape |
title_full_unstemmed | Comprehensive analysis to identify a novel diagnostic marker of lung adenocarcinoma and its immune infiltration landscape |
title_short | Comprehensive analysis to identify a novel diagnostic marker of lung adenocarcinoma and its immune infiltration landscape |
title_sort | comprehensive analysis to identify a novel diagnostic marker of lung adenocarcinoma and its immune infiltration landscape |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10319060/ https://www.ncbi.nlm.nih.gov/pubmed/37409245 http://dx.doi.org/10.3389/fonc.2023.1199608 |
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