Prediction and validation of anoikis-related genes in neuropathic pain using machine learning Article Swipe
Yufeng He
,
Wei Ye
,
Yongxin Wang
,
Chunyan Ling
,
Qi Xiang
,
Suxia Geng
,
Ying-Tong Meng
,
Hao Deng
,
Qisong Zhang
,
Xiaoling Qin
,
Guanghui Chen
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0314773
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0314773
Background Neuropathic pain (NP) can be induced by a variety of clinical conditions, such as spinal cord injury, lumbar disc herniation (LDH), lumbar spinal stenosis, diabetes, herpes zoster, and spinal cord tumors, and inflammatory stimuli. The pathogenesis of NP is extremely complex. Specifically, in LDH, the herniated nucleus pulposus exerts mechanical pressure on nerve roots, triggering local inflammation and consequent NP. Anoikis, a special form of programmed cell death, is closely related to the progression of NP. In this study, we sought to clarify the molecular characteristics of anoikis-related genes in NP, providing novel insights for the diagnosis and treatment of NP. Methods We screened NP-related genes based on the GSE124272 dataset and obtained 439 anoikis-related genes from the GeneCards database. Through Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Machine (SVM) machine learning algorithms, six key hub genes were identified: hepatocyte growth factor ( HGF ), matrix metalloproteinase 13 ( MMP13 ), c-abl oncogene 1, non-receptor tyrosine kinase ( ABL1 ), elastase neutrophil expressed ( ELANE ), fatty acid synthase ( FASN ), and long non-coding RNA ( Linc00324 ). Functional enrichment analyses, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), alongside Gene Set Enrichment Analysis (GSEA) and immune infiltration analysis, were performed on these hub genes. Additionally, transcription factors and potential therapeutic drugs were predicted. We also used rats to construct an NP model and validated the analyzed hub genes using hematoxylin and eosin (H&E) staining, real-time polymerase chain reaction (PCR), and Western blotting assays. Results Our data indicated that anoikis-related genes have diagnostic value in NP patients, as confirmed by experimental results. Moreover, this study elucidated the role of these genes in immune infiltration during the pathogenesis of NP and identified potential therapeutic drugs targeting these key genes. Conclusion This study further explores the pathogenesis of NP and provides certain reference value for developing targeted therapeutic strategies, thereby improving NP management.
Related Topics
Concepts
Anoikis
KEGG
Biology
Bioinformatics
Cancer research
Medicine
Gene expression
Gene
Cancer
Cancer cell
Genetics
Transcriptome
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1371/journal.pone.0314773
- https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0314773&type=printable
- OA Status
- gold
- Cited By
- 1
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408019015
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4408019015Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1371/journal.pone.0314773Digital Object Identifier
- Title
-
Prediction and validation of anoikis-related genes in neuropathic pain using machine learningWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-02-27Full publication date if available
- Authors
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Yufeng He, Wei Ye, Yongxin Wang, Chunyan Ling, Qi Xiang, Suxia Geng, Ying-Tong Meng, Hao Deng, Qisong Zhang, Xiaoling Qin, Guanghui ChenList of authors in order
- Landing page
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https://doi.org/10.1371/journal.pone.0314773Publisher landing page
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https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0314773&type=printableDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0314773&type=printableDirect OA link when available
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Anoikis, KEGG, Biology, Bioinformatics, Cancer research, Medicine, Gene expression, Gene, Cancer, Cancer cell, Genetics, TranscriptomeTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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39Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.LDH, | 44 |
| abstract_inverted_index.This | 298 |
| abstract_inverted_index.acid | 171 |
| abstract_inverted_index.also | 224 |
| abstract_inverted_index.cell | 67 |
| abstract_inverted_index.cord | 16, 30 |
| abstract_inverted_index.data | 255 |
| abstract_inverted_index.disc | 19 |
| abstract_inverted_index.form | 64 |
| abstract_inverted_index.from | 117 |
| abstract_inverted_index.have | 260 |
| abstract_inverted_index.long | 177 |
| abstract_inverted_index.pain | 2 |
| abstract_inverted_index.rats | 226 |
| abstract_inverted_index.role | 276 |
| abstract_inverted_index.such | 13 |
| abstract_inverted_index.that | 257 |
| abstract_inverted_index.this | 78, 272 |
| abstract_inverted_index.used | 225 |
| abstract_inverted_index.were | 141, 208, 221 |
| abstract_inverted_index.(SVM) | 133 |
| abstract_inverted_index.ELANE | 168 |
| abstract_inverted_index.Genes | 194 |
| abstract_inverted_index.Kyoto | 191 |
| abstract_inverted_index.Least | 122 |
| abstract_inverted_index.MMP13 | 153 |
| abstract_inverted_index.based | 107 |
| abstract_inverted_index.c-abl | 155 |
| abstract_inverted_index.chain | 246 |
| abstract_inverted_index.drugs | 220, 292 |
| abstract_inverted_index.eosin | 241 |
| abstract_inverted_index.fatty | 170 |
| abstract_inverted_index.genes | 89, 106, 116, 140, 237, 259, 279 |
| abstract_inverted_index.local | 56 |
| abstract_inverted_index.model | 231 |
| abstract_inverted_index.nerve | 53 |
| abstract_inverted_index.novel | 93 |
| abstract_inverted_index.study | 273, 299 |
| abstract_inverted_index.these | 211, 278, 294 |
| abstract_inverted_index.using | 238 |
| abstract_inverted_index.value | 262, 310 |
| abstract_inverted_index.(GSEA) | 203 |
| abstract_inverted_index.(LDH), | 21 |
| abstract_inverted_index.(PCR), | 248 |
| abstract_inverted_index.Vector | 131 |
| abstract_inverted_index.death, | 68 |
| abstract_inverted_index.during | 283 |
| abstract_inverted_index.exerts | 49 |
| abstract_inverted_index.factor | 145 |
| abstract_inverted_index.genes. | 213, 296 |
| abstract_inverted_index.growth | 144 |
| abstract_inverted_index.herpes | 26 |
| abstract_inverted_index.immune | 205, 281 |
| abstract_inverted_index.kinase | 160 |
| abstract_inverted_index.lumbar | 18, 22 |
| abstract_inverted_index.matrix | 149 |
| abstract_inverted_index.roots, | 54 |
| abstract_inverted_index.sought | 81 |
| abstract_inverted_index.spinal | 15, 23, 29 |
| abstract_inverted_index.study, | 79 |
| abstract_inverted_index.(KEGG), | 197 |
| abstract_inverted_index.(LASSO) | 128 |
| abstract_inverted_index.Genomes | 196 |
| abstract_inverted_index.Machine | 132 |
| abstract_inverted_index.Methods | 102 |
| abstract_inverted_index.Results | 253 |
| abstract_inverted_index.Support | 130 |
| abstract_inverted_index.Through | 121 |
| abstract_inverted_index.Western | 250 |
| abstract_inverted_index.assays. | 252 |
| abstract_inverted_index.certain | 308 |
| abstract_inverted_index.clarify | 83 |
| abstract_inverted_index.closely | 70 |
| abstract_inverted_index.dataset | 111 |
| abstract_inverted_index.factors | 216 |
| abstract_inverted_index.further | 300 |
| abstract_inverted_index.induced | 6 |
| abstract_inverted_index.injury, | 17 |
| abstract_inverted_index.machine | 134 |
| abstract_inverted_index.nucleus | 47 |
| abstract_inverted_index.related | 71 |
| abstract_inverted_index.special | 63 |
| abstract_inverted_index.thereby | 316 |
| abstract_inverted_index.tumors, | 31 |
| abstract_inverted_index.variety | 9 |
| abstract_inverted_index.zoster, | 27 |
| abstract_inverted_index.Absolute | 123 |
| abstract_inverted_index.Analysis | 202 |
| abstract_inverted_index.Anoikis, | 61 |
| abstract_inverted_index.Ontology | 188 |
| abstract_inverted_index.Operator | 127 |
| abstract_inverted_index.analyzed | 235 |
| abstract_inverted_index.blotting | 251 |
| abstract_inverted_index.clinical | 11 |
| abstract_inverted_index.complex. | 41 |
| abstract_inverted_index.elastase | 164 |
| abstract_inverted_index.explores | 301 |
| abstract_inverted_index.insights | 94 |
| abstract_inverted_index.learning | 135 |
| abstract_inverted_index.obtained | 113 |
| abstract_inverted_index.oncogene | 156 |
| abstract_inverted_index.pressure | 51 |
| abstract_inverted_index.provides | 307 |
| abstract_inverted_index.pulposus | 48 |
| abstract_inverted_index.reaction | 247 |
| abstract_inverted_index.results. | 270 |
| abstract_inverted_index.screened | 104 |
| abstract_inverted_index.stimuli. | 34 |
| abstract_inverted_index.synthase | 172 |
| abstract_inverted_index.targeted | 313 |
| abstract_inverted_index.tyrosine | 159 |
| abstract_inverted_index.(H&E) | 242 |
| abstract_inverted_index.GSE124272 | 110 |
| abstract_inverted_index.GeneCards | 119 |
| abstract_inverted_index.Linc00324 | 181 |
| abstract_inverted_index.Moreover, | 271 |
| abstract_inverted_index.Selection | 126 |
| abstract_inverted_index.Shrinkage | 124 |
| abstract_inverted_index.alongside | 198 |
| abstract_inverted_index.analyses, | 185 |
| abstract_inverted_index.analysis, | 207 |
| abstract_inverted_index.confirmed | 267 |
| abstract_inverted_index.construct | 228 |
| abstract_inverted_index.database. | 120 |
| abstract_inverted_index.diabetes, | 25 |
| abstract_inverted_index.diagnosis | 97 |
| abstract_inverted_index.expressed | 166 |
| abstract_inverted_index.extremely | 40 |
| abstract_inverted_index.herniated | 46 |
| abstract_inverted_index.improving | 317 |
| abstract_inverted_index.including | 186 |
| abstract_inverted_index.indicated | 256 |
| abstract_inverted_index.molecular | 85 |
| abstract_inverted_index.patients, | 265 |
| abstract_inverted_index.performed | 209 |
| abstract_inverted_index.potential | 218, 290 |
| abstract_inverted_index.providing | 92 |
| abstract_inverted_index.real-time | 244 |
| abstract_inverted_index.reference | 309 |
| abstract_inverted_index.staining, | 243 |
| abstract_inverted_index.stenosis, | 24 |
| abstract_inverted_index.targeting | 293 |
| abstract_inverted_index.treatment | 99 |
| abstract_inverted_index.validated | 233 |
| abstract_inverted_index.Background | 0 |
| abstract_inverted_index.Conclusion | 297 |
| abstract_inverted_index.Enrichment | 201 |
| abstract_inverted_index.Functional | 183 |
| abstract_inverted_index.NP-related | 105 |
| abstract_inverted_index.consequent | 59 |
| abstract_inverted_index.developing | 312 |
| abstract_inverted_index.diagnostic | 261 |
| abstract_inverted_index.elucidated | 274 |
| abstract_inverted_index.enrichment | 184 |
| abstract_inverted_index.hepatocyte | 143 |
| abstract_inverted_index.herniation | 20 |
| abstract_inverted_index.identified | 289 |
| abstract_inverted_index.mechanical | 50 |
| abstract_inverted_index.neutrophil | 165 |
| abstract_inverted_index.non-coding | 178 |
| abstract_inverted_index.polymerase | 245 |
| abstract_inverted_index.predicted. | 222 |
| abstract_inverted_index.programmed | 66 |
| abstract_inverted_index.triggering | 55 |
| abstract_inverted_index.Neuropathic | 1 |
| abstract_inverted_index.algorithms, | 136 |
| abstract_inverted_index.conditions, | 12 |
| abstract_inverted_index.hematoxylin | 239 |
| abstract_inverted_index.identified: | 142 |
| abstract_inverted_index.management. | 319 |
| abstract_inverted_index.progression | 74 |
| abstract_inverted_index.strategies, | 315 |
| abstract_inverted_index.therapeutic | 219, 291, 314 |
| abstract_inverted_index.Encyclopedia | 192 |
| abstract_inverted_index.experimental | 269 |
| abstract_inverted_index.infiltration | 206, 282 |
| abstract_inverted_index.inflammation | 57 |
| abstract_inverted_index.inflammatory | 33 |
| abstract_inverted_index.non-receptor | 158 |
| abstract_inverted_index.pathogenesis | 36, 285, 303 |
| abstract_inverted_index.Additionally, | 214 |
| abstract_inverted_index.Specifically, | 42 |
| abstract_inverted_index.transcription | 215 |
| abstract_inverted_index.anoikis-related | 88, 115, 258 |
| abstract_inverted_index.characteristics | 86 |
| abstract_inverted_index.metalloproteinase | 150 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| corresponding_author_ids | https://openalex.org/A5003772057, https://openalex.org/A5110981349, https://openalex.org/A5040556753, https://openalex.org/A5111934800, https://openalex.org/A5101669935, https://openalex.org/A5010201925, https://openalex.org/A5119247306, https://openalex.org/A5002302179, https://openalex.org/A5029427154, https://openalex.org/A5115595284, https://openalex.org/A5052162948 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 11 |
| corresponding_institution_ids | https://openalex.org/I150807315, https://openalex.org/I160354086, https://openalex.org/I4210113348, https://openalex.org/I4210155511 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.8100000023841858 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.79943946 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |