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Identification of lysosomal genes associated with prognosis in lung adenocarcinoma
BACKGROUND: Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer, representing 40% of all cases of this tumor. Despite immense improvements in understanding the molecular basis, diagnosis, and treatment of LUAD, its recurrence rate is still high. METHODS: RNA-seq data from The Cancer...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AME Publishing Company
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10413022/ https://www.ncbi.nlm.nih.gov/pubmed/37577321 http://dx.doi.org/10.21037/tlcr-23-14 |
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author | Li, Houqiang Sha, Xinyu Wang, Wenmiao Huang, Zhanghao Zhang, Peng Liu, Lei Wang, Silin Zhou, Youlang He, Shuai Shi, Jiahai |
author_facet | Li, Houqiang Sha, Xinyu Wang, Wenmiao Huang, Zhanghao Zhang, Peng Liu, Lei Wang, Silin Zhou, Youlang He, Shuai Shi, Jiahai |
author_sort | Li, Houqiang |
collection | PubMed |
description | BACKGROUND: Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer, representing 40% of all cases of this tumor. Despite immense improvements in understanding the molecular basis, diagnosis, and treatment of LUAD, its recurrence rate is still high. METHODS: RNA-seq data from The Cancer Genome Atlas (TCGA) LUAD cohort were download from Genomic Data Commons Portal. The GSE13213 dataset from Gene Expression Omnibus (GEO) was used for external validation. Differential prognostic lysosome-related genes (LRGs) were identified by overlapping survival-related genes obtained via univariate Cox regression analysis with differentially expressed genes (DEGs). The prognostic model was built using Kaplan-Meier curves and least absolute shrinkage and selection operator (LASSO) analyses. In addition, univariate and multivariate Cox analyses were employed to identify independent prognostic factors. The responses of patients to immune checkpoint inhibitors (ICIs) were further predicted. The pRRophetic package and rank-sum test were used to compute the half maximal inhibitory concentrations (IC(50)) of 56 chemotherapeutic drugs and their differential effects in the low- and high-risk groups. Moreover, quantitative real-time polymerase chain reaction, Western blot, and human protein atlas (HPA) database were used to verify the expression of the four prognostic biomarkers in LUAD. RESULTS: Of the nine candidate differential prognostic LRGs, GATA2, TFAP2A, LMBRD1, and KRT8 were selected as prognostic biomarkers. The prediction of the risk model was validated to be reliable. Cox independent prognostic analysis revealed that risk score and stage were independent prognostic factors in LUAD. Furthermore, the nomogram and calibration curves of the independent prognostic factors performed well. Differential analysis of ICIs revealed CD276, ICOS, PDCD1LG2, CD27, TNFRSF18, TNFSF9, ENTPD1, and NT5E to be expressed differently in the low- and high-risk groups. The IC(50) values of 12 chemotherapeutic drugs, including epothilone.B, JNK.inhibitor.VIII, and AKT.inhibitor.VIII, significantly differed between the two risk groups. KRT8 and TFAP2A were highly expressed, while GATA2 and LMBRD1 were poorly expressed in LUAD cell lines. In addition, KRT8 and TFAP2A were highly expressed, while GATA2 and LMBRD1 were poorly expressed in tumor tissues. CONCLUSIONS: Four key prognostic biomarkers—GATA2, TFAP2A, LMBRD1, and KRT8—were used to construct a significant prognostic model for LUAD patients. |
format | Online Article Text |
id | pubmed-10413022 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | AME Publishing Company |
record_format | MEDLINE/PubMed |
spelling | pubmed-104130222023-08-11 Identification of lysosomal genes associated with prognosis in lung adenocarcinoma Li, Houqiang Sha, Xinyu Wang, Wenmiao Huang, Zhanghao Zhang, Peng Liu, Lei Wang, Silin Zhou, Youlang He, Shuai Shi, Jiahai Transl Lung Cancer Res Original Article BACKGROUND: Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer, representing 40% of all cases of this tumor. Despite immense improvements in understanding the molecular basis, diagnosis, and treatment of LUAD, its recurrence rate is still high. METHODS: RNA-seq data from The Cancer Genome Atlas (TCGA) LUAD cohort were download from Genomic Data Commons Portal. The GSE13213 dataset from Gene Expression Omnibus (GEO) was used for external validation. Differential prognostic lysosome-related genes (LRGs) were identified by overlapping survival-related genes obtained via univariate Cox regression analysis with differentially expressed genes (DEGs). The prognostic model was built using Kaplan-Meier curves and least absolute shrinkage and selection operator (LASSO) analyses. In addition, univariate and multivariate Cox analyses were employed to identify independent prognostic factors. The responses of patients to immune checkpoint inhibitors (ICIs) were further predicted. The pRRophetic package and rank-sum test were used to compute the half maximal inhibitory concentrations (IC(50)) of 56 chemotherapeutic drugs and their differential effects in the low- and high-risk groups. Moreover, quantitative real-time polymerase chain reaction, Western blot, and human protein atlas (HPA) database were used to verify the expression of the four prognostic biomarkers in LUAD. RESULTS: Of the nine candidate differential prognostic LRGs, GATA2, TFAP2A, LMBRD1, and KRT8 were selected as prognostic biomarkers. The prediction of the risk model was validated to be reliable. Cox independent prognostic analysis revealed that risk score and stage were independent prognostic factors in LUAD. Furthermore, the nomogram and calibration curves of the independent prognostic factors performed well. Differential analysis of ICIs revealed CD276, ICOS, PDCD1LG2, CD27, TNFRSF18, TNFSF9, ENTPD1, and NT5E to be expressed differently in the low- and high-risk groups. The IC(50) values of 12 chemotherapeutic drugs, including epothilone.B, JNK.inhibitor.VIII, and AKT.inhibitor.VIII, significantly differed between the two risk groups. KRT8 and TFAP2A were highly expressed, while GATA2 and LMBRD1 were poorly expressed in LUAD cell lines. In addition, KRT8 and TFAP2A were highly expressed, while GATA2 and LMBRD1 were poorly expressed in tumor tissues. CONCLUSIONS: Four key prognostic biomarkers—GATA2, TFAP2A, LMBRD1, and KRT8—were used to construct a significant prognostic model for LUAD patients. AME Publishing Company 2023-07-18 2023-07-31 /pmc/articles/PMC10413022/ /pubmed/37577321 http://dx.doi.org/10.21037/tlcr-23-14 Text en 2023 Translational Lung Cancer Research. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Original Article Li, Houqiang Sha, Xinyu Wang, Wenmiao Huang, Zhanghao Zhang, Peng Liu, Lei Wang, Silin Zhou, Youlang He, Shuai Shi, Jiahai Identification of lysosomal genes associated with prognosis in lung adenocarcinoma |
title | Identification of lysosomal genes associated with prognosis in lung adenocarcinoma |
title_full | Identification of lysosomal genes associated with prognosis in lung adenocarcinoma |
title_fullStr | Identification of lysosomal genes associated with prognosis in lung adenocarcinoma |
title_full_unstemmed | Identification of lysosomal genes associated with prognosis in lung adenocarcinoma |
title_short | Identification of lysosomal genes associated with prognosis in lung adenocarcinoma |
title_sort | identification of lysosomal genes associated with prognosis in lung adenocarcinoma |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10413022/ https://www.ncbi.nlm.nih.gov/pubmed/37577321 http://dx.doi.org/10.21037/tlcr-23-14 |
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