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Machine learning and BP neural network revealed abnormal B cell infiltration predicts the survival of lung cancer patients
FAM83A gene is related to the invasion and metastasis of various tumors. However, the abnormal immune cell infiltration associated with the gene is poorly understood in the pathogenesis and prognosis of NSCLC. Based on the TCGA and GEO databases, we used COX regression and machine learning algorithm...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9592816/ https://www.ncbi.nlm.nih.gov/pubmed/36303835 http://dx.doi.org/10.3389/fonc.2022.882018 |
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author | Tu, Pinghua Li, Xinjun Cao, Lingli Zhong, Minghua Xie, Zhibin Wu, Zhanling |
author_facet | Tu, Pinghua Li, Xinjun Cao, Lingli Zhong, Minghua Xie, Zhibin Wu, Zhanling |
author_sort | Tu, Pinghua |
collection | PubMed |
description | FAM83A gene is related to the invasion and metastasis of various tumors. However, the abnormal immune cell infiltration associated with the gene is poorly understood in the pathogenesis and prognosis of NSCLC. Based on the TCGA and GEO databases, we used COX regression and machine learning algorithms (CIBERSORT, random forest, and back propagation neural network) to study the prognostic value of FAM83A and immune infiltration characteristics in NSCLC. High FAM83A expression was significantly associated with poor prognosis of NSCLC patients (p = 0.00016), and had excellent prognostic independence. At the same time, the expression level of FAM83A is significantly related to the T, N, and Stage. Subsequently, based on machine learing strategies, we found that the infiltration level of naive B cells was negatively correlated with the expression of FAM83A. The low infiltration of naive B cells was significantly related to the poor overall survival rate of NSCLC (p = 0.0072). In addition, Cox regression confirmed that FAM83A and naive B cells are risk factors for the prognosis of NSCLC patients. The nomogram combining FAM83A and naive B cells (C-index = 0.748) has a more accurate prognostic ability than the Stage (C-index = 0.651) system. Our analysis shows that abnormal infiltration of naive B cells associated with FAM83A is a key factor in the prognostic prediction of NSCLC patients. |
format | Online Article Text |
id | pubmed-9592816 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95928162022-10-26 Machine learning and BP neural network revealed abnormal B cell infiltration predicts the survival of lung cancer patients Tu, Pinghua Li, Xinjun Cao, Lingli Zhong, Minghua Xie, Zhibin Wu, Zhanling Front Oncol Oncology FAM83A gene is related to the invasion and metastasis of various tumors. However, the abnormal immune cell infiltration associated with the gene is poorly understood in the pathogenesis and prognosis of NSCLC. Based on the TCGA and GEO databases, we used COX regression and machine learning algorithms (CIBERSORT, random forest, and back propagation neural network) to study the prognostic value of FAM83A and immune infiltration characteristics in NSCLC. High FAM83A expression was significantly associated with poor prognosis of NSCLC patients (p = 0.00016), and had excellent prognostic independence. At the same time, the expression level of FAM83A is significantly related to the T, N, and Stage. Subsequently, based on machine learing strategies, we found that the infiltration level of naive B cells was negatively correlated with the expression of FAM83A. The low infiltration of naive B cells was significantly related to the poor overall survival rate of NSCLC (p = 0.0072). In addition, Cox regression confirmed that FAM83A and naive B cells are risk factors for the prognosis of NSCLC patients. The nomogram combining FAM83A and naive B cells (C-index = 0.748) has a more accurate prognostic ability than the Stage (C-index = 0.651) system. Our analysis shows that abnormal infiltration of naive B cells associated with FAM83A is a key factor in the prognostic prediction of NSCLC patients. Frontiers Media S.A. 2022-10-11 /pmc/articles/PMC9592816/ /pubmed/36303835 http://dx.doi.org/10.3389/fonc.2022.882018 Text en Copyright © 2022 Tu, Li, Cao, Zhong, Xie and Wu 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 Tu, Pinghua Li, Xinjun Cao, Lingli Zhong, Minghua Xie, Zhibin Wu, Zhanling Machine learning and BP neural network revealed abnormal B cell infiltration predicts the survival of lung cancer patients |
title | Machine learning and BP neural network revealed abnormal B cell infiltration predicts the survival of lung cancer patients |
title_full | Machine learning and BP neural network revealed abnormal B cell infiltration predicts the survival of lung cancer patients |
title_fullStr | Machine learning and BP neural network revealed abnormal B cell infiltration predicts the survival of lung cancer patients |
title_full_unstemmed | Machine learning and BP neural network revealed abnormal B cell infiltration predicts the survival of lung cancer patients |
title_short | Machine learning and BP neural network revealed abnormal B cell infiltration predicts the survival of lung cancer patients |
title_sort | machine learning and bp neural network revealed abnormal b cell infiltration predicts the survival of lung cancer patients |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9592816/ https://www.ncbi.nlm.nih.gov/pubmed/36303835 http://dx.doi.org/10.3389/fonc.2022.882018 |
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