Developing a novel immune infiltration-associated mitophagy prediction model for amyotrophic lateral sclerosis using bioinformatics strategies
BackgroundAmyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease, which leads to muscle weakness and eventual paralysis. Numerous studies have indicated that mitophagy and immune inflammation have a significant impact on the onset and advancement of ALS. Nevertheless, the possible...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-03-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2024.1360527/full |
| _version_ | 1797242605650575360 |
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| author | Rongrong Du Rongrong Du Peng Chen Peng Chen Mao Li Yahui Zhu Yahui Zhu Zhengqing He Xusheng Huang Xusheng Huang Xusheng Huang |
| author_facet | Rongrong Du Rongrong Du Peng Chen Peng Chen Mao Li Yahui Zhu Yahui Zhu Zhengqing He Xusheng Huang Xusheng Huang Xusheng Huang |
| author_sort | Rongrong Du |
| collection | DOAJ |
| description | BackgroundAmyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease, which leads to muscle weakness and eventual paralysis. Numerous studies have indicated that mitophagy and immune inflammation have a significant impact on the onset and advancement of ALS. Nevertheless, the possible diagnostic and prognostic significance of mitophagy-related genes associated with immune infiltration in ALS is uncertain. The purpose of this study is to create a predictive model for ALS using genes linked with mitophagy-associated immune infiltration.MethodsALS gene expression profiles were downloaded from the Gene Expression Omnibus (GEO) database. Univariate Cox analysis and machine learning methods were applied to analyze mitophagy-associated genes and develop a prognostic risk score model. Subsequently, functional and immune infiltration analyses were conducted to study the biological attributes and immune cell enrichment in individuals with ALS. Additionally, validation of identified feature genes in the prediction model was performed using ALS mouse models and ALS patients.ResultsIn this study, a comprehensive analysis revealed the identification of 22 mitophagy-related differential expression genes and 40 prognostic genes. Additionally, an 18-gene prognostic signature was identified with machine learning, which was utilized to construct a prognostic risk score model. Functional enrichment analysis demonstrated the enrichment of various pathways, including oxidative phosphorylation, unfolded proteins, KRAS, and mTOR signaling pathways, as well as other immune-related pathways. The analysis of immune infiltration revealed notable distinctions in certain congenital immune cells and adaptive immune cells between the low-risk and high-risk groups, particularly concerning the T lymphocyte subgroup. ALS mouse models and ALS clinical samples demonstrated consistent expression levels of four mitophagy-related immune infiltration genes (BCKDHA, JTB, KYNU, and GTF2H5) with the results of bioinformatics analysis.ConclusionThis study has successfully devised and verified a pioneering prognostic predictive risk score for ALS, utilizing eighteen mitophagy-related genes. Furthermore, the findings indicate that four of these genes exhibit promising roles in the context of ALS prognostic. |
| first_indexed | 2024-04-24T18:41:53Z |
| format | Article |
| id | doaj.art-49cb293a65884bf6b792d9369bcb1897 |
| institution | Directory Open Access Journal |
| issn | 1664-3224 |
| language | English |
| last_indexed | 2024-04-24T18:41:53Z |
| publishDate | 2024-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj.art-49cb293a65884bf6b792d9369bcb18972024-03-27T10:36:14ZengFrontiers Media S.A.Frontiers in Immunology1664-32242024-03-011510.3389/fimmu.2024.13605271360527Developing a novel immune infiltration-associated mitophagy prediction model for amyotrophic lateral sclerosis using bioinformatics strategiesRongrong Du0Rongrong Du1Peng Chen2Peng Chen3Mao Li4Yahui Zhu5Yahui Zhu6Zhengqing He7Xusheng Huang8Xusheng Huang9Xusheng Huang10School of Medicine, Nankai University, Tianjin, ChinaDepartment of Neurology, The First Medical Center, Chinese People's Liberation Army (PLA) General Hospital, Beijing, ChinaMedical School of Chinese People's Liberation Army (PLA), Beijing, ChinaDepartment of General Surgery & Institute of General Surgery, The First Medical Center of Chinese People's Liberation Army (PLA) General Hospital, Beijing, ChinaDepartment of Neurology, The First Medical Center, Chinese People's Liberation Army (PLA) General Hospital, Beijing, ChinaDepartment of Neurology, The First Medical Center, Chinese People's Liberation Army (PLA) General Hospital, Beijing, ChinaMedical School of Chinese People's Liberation Army (PLA), Beijing, ChinaDepartment of Neurology, Beijing Friendship Hospital, Capital Medical University, Beijing, ChinaSchool of Medicine, Nankai University, Tianjin, ChinaDepartment of Neurology, The First Medical Center, Chinese People's Liberation Army (PLA) General Hospital, Beijing, ChinaMedical School of Chinese People's Liberation Army (PLA), Beijing, ChinaBackgroundAmyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease, which leads to muscle weakness and eventual paralysis. Numerous studies have indicated that mitophagy and immune inflammation have a significant impact on the onset and advancement of ALS. Nevertheless, the possible diagnostic and prognostic significance of mitophagy-related genes associated with immune infiltration in ALS is uncertain. The purpose of this study is to create a predictive model for ALS using genes linked with mitophagy-associated immune infiltration.MethodsALS gene expression profiles were downloaded from the Gene Expression Omnibus (GEO) database. Univariate Cox analysis and machine learning methods were applied to analyze mitophagy-associated genes and develop a prognostic risk score model. Subsequently, functional and immune infiltration analyses were conducted to study the biological attributes and immune cell enrichment in individuals with ALS. Additionally, validation of identified feature genes in the prediction model was performed using ALS mouse models and ALS patients.ResultsIn this study, a comprehensive analysis revealed the identification of 22 mitophagy-related differential expression genes and 40 prognostic genes. Additionally, an 18-gene prognostic signature was identified with machine learning, which was utilized to construct a prognostic risk score model. Functional enrichment analysis demonstrated the enrichment of various pathways, including oxidative phosphorylation, unfolded proteins, KRAS, and mTOR signaling pathways, as well as other immune-related pathways. The analysis of immune infiltration revealed notable distinctions in certain congenital immune cells and adaptive immune cells between the low-risk and high-risk groups, particularly concerning the T lymphocyte subgroup. ALS mouse models and ALS clinical samples demonstrated consistent expression levels of four mitophagy-related immune infiltration genes (BCKDHA, JTB, KYNU, and GTF2H5) with the results of bioinformatics analysis.ConclusionThis study has successfully devised and verified a pioneering prognostic predictive risk score for ALS, utilizing eighteen mitophagy-related genes. Furthermore, the findings indicate that four of these genes exhibit promising roles in the context of ALS prognostic.https://www.frontiersin.org/articles/10.3389/fimmu.2024.1360527/fullamyotrophic lateral sclerosismitophagyimmune infiltrationgeneprediction modelprognosis |
| spellingShingle | Rongrong Du Rongrong Du Peng Chen Peng Chen Mao Li Yahui Zhu Yahui Zhu Zhengqing He Xusheng Huang Xusheng Huang Xusheng Huang Developing a novel immune infiltration-associated mitophagy prediction model for amyotrophic lateral sclerosis using bioinformatics strategies Frontiers in Immunology amyotrophic lateral sclerosis mitophagy immune infiltration gene prediction model prognosis |
| title | Developing a novel immune infiltration-associated mitophagy prediction model for amyotrophic lateral sclerosis using bioinformatics strategies |
| title_full | Developing a novel immune infiltration-associated mitophagy prediction model for amyotrophic lateral sclerosis using bioinformatics strategies |
| title_fullStr | Developing a novel immune infiltration-associated mitophagy prediction model for amyotrophic lateral sclerosis using bioinformatics strategies |
| title_full_unstemmed | Developing a novel immune infiltration-associated mitophagy prediction model for amyotrophic lateral sclerosis using bioinformatics strategies |
| title_short | Developing a novel immune infiltration-associated mitophagy prediction model for amyotrophic lateral sclerosis using bioinformatics strategies |
| title_sort | developing a novel immune infiltration associated mitophagy prediction model for amyotrophic lateral sclerosis using bioinformatics strategies |
| topic | amyotrophic lateral sclerosis mitophagy immune infiltration gene prediction model prognosis |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2024.1360527/full |
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