Establishment of a nomogram model based on immune-related genes using machine learning for aortic dissection diagnosis and immunomodulation assessment Article Swipe
Yanjun Hou
,
Yangyang Zhao
,
Zhensu Shi
,
Yipeng Pan
,
Kaijia Shi
,
Chaoyang Zhao
,
Shengnan Liu
,
Yongkun Chen
,
Lini Zhao
,
Jizhen Wu
,
Guangquan Ge
,
Jie Wei
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.7150/ijms.100572
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.7150/ijms.100572
The clinical manifestation of aortic dissection (AD) is complex and varied, making early diagnosis crucial for patient survival. This study aimed to identify immune-related markers to establish a nomogram model for AD diagnosis. Three datasets from GEO-GSE52093, GSE147026 and GSE153434-were combined and used for identification of immune-related causative genes using weighted gene co-expression network analysis, and 136 immune-related genes were obtained. Then, 15 pivotal genes were screened by the protein-protein interaction network. Through machine learning including the Least Absolute Shrinkage and Selection Operator algorithm, random forest algorithm, and multivariate logistic regression, four key feature genes were obtained-CXCL1, ITGA5, PTX3, and TIMP1-and the diagnostic scores based on these four genes were proved to be effective in distinguishing between AD patients and healthy donors. External dataset (GSE98770 and GSE190635) validation revealed this nomogram displayed strong predictive significance. Further analysis revealed that these genes are related with neutrophils, resting NK cells, resting mast cells, activated mast cells, activated dendritic cells, central memory CD4 T cells, γδ T cells, natural killer T cells, and myeloid-derived suppressor cells in AD. Finally, these four genes were validated to be upregulated in AD patients' tissue and serum samples compared with controls. These results suggest that this nomogram model, using machine learning identified four immune-related genes CXCL1, ITGA5, PTX3, and TIMP1, displays superior diagnostic ability in distinguishing AD and healthy individuals, and immune cells commonly associated with these hub genes may be therapeutic targets for AD.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.7150/ijms.100572
- OA Status
- gold
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4406960843
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Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4406960843Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.7150/ijms.100572Digital Object Identifier
- Title
-
Establishment of a nomogram model based on immune-related genes using machine learning for aortic dissection diagnosis and immunomodulation assessmentWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-01-21Full publication date if available
- Authors
-
Yanjun Hou, Yangyang Zhao, Zhensu Shi, Yipeng Pan, Kaijia Shi, Chaoyang Zhao, Shengnan Liu, Yongkun Chen, Lini Zhao, Jizhen Wu, Guangquan Ge, Jie WeiList of authors in order
- Landing page
-
https://doi.org/10.7150/ijms.100572Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.7150/ijms.100572Direct OA link when available
- Concepts
-
Nomogram, Aortic dissection, Immune system, Gene, Medicine, Dissection (medical), Computer science, Machine learning, Artificial intelligence, Computational biology, Radiology, Surgery, Immunology, Internal medicine, Biology, Aorta, GeneticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.(AD) | 6 |
| abstract_inverted_index.This | 18 |
| abstract_inverted_index.four | 91, 107, 177, 205 |
| abstract_inverted_index.from | 35 |
| abstract_inverted_index.gene | 51 |
| abstract_inverted_index.mast | 149, 152 |
| abstract_inverted_index.that | 138, 197 |
| abstract_inverted_index.this | 129, 198 |
| abstract_inverted_index.used | 42 |
| abstract_inverted_index.were | 59, 65, 95, 109, 179 |
| abstract_inverted_index.with | 143, 192, 228 |
| abstract_inverted_index.γδ | 162 |
| abstract_inverted_index.Least | 77 |
| abstract_inverted_index.Then, | 61 |
| abstract_inverted_index.These | 194 |
| abstract_inverted_index.Three | 33 |
| abstract_inverted_index.aimed | 20 |
| abstract_inverted_index.based | 104 |
| abstract_inverted_index.cells | 172, 225 |
| abstract_inverted_index.early | 12 |
| abstract_inverted_index.genes | 48, 58, 64, 94, 108, 140, 178, 207, 231 |
| abstract_inverted_index.model | 29 |
| abstract_inverted_index.serum | 189 |
| abstract_inverted_index.study | 19 |
| abstract_inverted_index.these | 106, 139, 176, 229 |
| abstract_inverted_index.using | 49, 201 |
| abstract_inverted_index.aortic | 4 |
| abstract_inverted_index.cells, | 147, 150, 153, 156, 161, 164, 168 |
| abstract_inverted_index.forest | 85 |
| abstract_inverted_index.immune | 224 |
| abstract_inverted_index.killer | 166 |
| abstract_inverted_index.making | 11 |
| abstract_inverted_index.memory | 158 |
| abstract_inverted_index.model, | 200 |
| abstract_inverted_index.proved | 110 |
| abstract_inverted_index.random | 84 |
| abstract_inverted_index.scores | 103 |
| abstract_inverted_index.strong | 132 |
| abstract_inverted_index.tissue | 187 |
| abstract_inverted_index.Further | 135 |
| abstract_inverted_index.Through | 72 |
| abstract_inverted_index.ability | 216 |
| abstract_inverted_index.between | 116 |
| abstract_inverted_index.central | 157 |
| abstract_inverted_index.complex | 8 |
| abstract_inverted_index.crucial | 14 |
| abstract_inverted_index.dataset | 123 |
| abstract_inverted_index.donors. | 121 |
| abstract_inverted_index.feature | 93 |
| abstract_inverted_index.healthy | 120, 221 |
| abstract_inverted_index.machine | 73, 202 |
| abstract_inverted_index.markers | 24 |
| abstract_inverted_index.natural | 165 |
| abstract_inverted_index.network | 53 |
| abstract_inverted_index.patient | 16 |
| abstract_inverted_index.pivotal | 63 |
| abstract_inverted_index.related | 142 |
| abstract_inverted_index.resting | 145, 148 |
| abstract_inverted_index.results | 195 |
| abstract_inverted_index.samples | 190 |
| abstract_inverted_index.suggest | 196 |
| abstract_inverted_index.targets | 235 |
| abstract_inverted_index.varied, | 10 |
| abstract_inverted_index.Absolute | 78 |
| abstract_inverted_index.External | 122 |
| abstract_inverted_index.Finally, | 175 |
| abstract_inverted_index.Operator | 82 |
| abstract_inverted_index.analysis | 136 |
| abstract_inverted_index.clinical | 1 |
| abstract_inverted_index.combined | 40 |
| abstract_inverted_index.commonly | 226 |
| abstract_inverted_index.compared | 191 |
| abstract_inverted_index.datasets | 34 |
| abstract_inverted_index.displays | 213 |
| abstract_inverted_index.identify | 22 |
| abstract_inverted_index.learning | 74, 203 |
| abstract_inverted_index.logistic | 89 |
| abstract_inverted_index.network. | 71 |
| abstract_inverted_index.nomogram | 28, 130, 199 |
| abstract_inverted_index.patients | 118 |
| abstract_inverted_index.revealed | 128, 137 |
| abstract_inverted_index.screened | 66 |
| abstract_inverted_index.superior | 214 |
| abstract_inverted_index.weighted | 50 |
| abstract_inverted_index.(GSE98770 | 124 |
| abstract_inverted_index.GSE147026 | 37 |
| abstract_inverted_index.Selection | 81 |
| abstract_inverted_index.Shrinkage | 79 |
| abstract_inverted_index.activated | 151, 154 |
| abstract_inverted_index.analysis, | 54 |
| abstract_inverted_index.causative | 47 |
| abstract_inverted_index.controls. | 193 |
| abstract_inverted_index.dendritic | 155 |
| abstract_inverted_index.diagnosis | 13 |
| abstract_inverted_index.displayed | 131 |
| abstract_inverted_index.effective | 113 |
| abstract_inverted_index.establish | 26 |
| abstract_inverted_index.including | 75 |
| abstract_inverted_index.obtained. | 60 |
| abstract_inverted_index.patients' | 186 |
| abstract_inverted_index.survival. | 17 |
| abstract_inverted_index.validated | 180 |
| abstract_inverted_index.GSE190635) | 126 |
| abstract_inverted_index.algorithm, | 83, 86 |
| abstract_inverted_index.associated | 227 |
| abstract_inverted_index.diagnosis. | 32 |
| abstract_inverted_index.diagnostic | 102, 215 |
| abstract_inverted_index.dissection | 5 |
| abstract_inverted_index.identified | 204 |
| abstract_inverted_index.predictive | 133 |
| abstract_inverted_index.suppressor | 171 |
| abstract_inverted_index.validation | 127 |
| abstract_inverted_index.interaction | 70 |
| abstract_inverted_index.regression, | 90 |
| abstract_inverted_index.therapeutic | 234 |
| abstract_inverted_index.upregulated | 183 |
| abstract_inverted_index.<i>PTX3</i>, | 98, 210 |
| abstract_inverted_index.individuals, | 222 |
| abstract_inverted_index.multivariate | 88 |
| abstract_inverted_index.neutrophils, | 144 |
| abstract_inverted_index.<i>CXCL1</i>, | 208 |
| abstract_inverted_index.<i>ITGA5</i>, | 97, 209 |
| abstract_inverted_index.<i>TIMP1</i>, | 212 |
| abstract_inverted_index.GEO-GSE52093, | 36 |
| abstract_inverted_index.co-expression | 52 |
| abstract_inverted_index.manifestation | 2 |
| abstract_inverted_index.significance. | 134 |
| abstract_inverted_index.GSE153434-were | 39 |
| abstract_inverted_index.distinguishing | 115, 218 |
| abstract_inverted_index.identification | 44 |
| abstract_inverted_index.immune-related | 23, 46, 57, 206 |
| abstract_inverted_index.myeloid-derived | 170 |
| abstract_inverted_index.protein-protein | 69 |
| abstract_inverted_index.<i>TIMP1</i>-and | 100 |
| abstract_inverted_index.obtained-<i>CXCL1</i>, | 96 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 12 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/2 |
| sustainable_development_goals[0].score | 0.5 |
| sustainable_development_goals[0].display_name | Zero hunger |
| citation_normalized_percentile.value | 0.8269401 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |