For Publishers
DiscoveryMetadataPeer reviewHostingPublishing
For Researchers
JoinPublishReviewCollect
Register
Blog
About
Search
Advanced search
My ScienceOpen
Search
For Publishers
For Researchers
Blog
About
3
views
39
references
 Top references
cited by
0
    
0 reviews
 Review
0
comments
 Comment
0
recommends
+1 Recommend
0 collections
    0
    shares
      599
      similar
       All similar
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Prediction and validation of anoikis-related genes in neuropathic pain using machine learning

      research-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          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 collections

          Most cited references39

          • Record: found
          • Abstract: found
          • Article: not found

          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          A. Subramanian, P Tamayo, V K Mootha … (2005)
          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
          Article 0 comments Cited 15489 times – based on 0 reviews     Review now
          Article has an altmetric score of 190
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            limma powers differential expression analyses for RNA-sequencing and microarray studies

            Matthew E. Ritchie, Belinda Phipson, Di Wu … (2015)
            limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
            Article 0 comments Cited 13569 times – based on 0 reviews     Review now
            Article has an altmetric score of 177
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Enrichr: a comprehensive gene set enrichment analysis web server 2016 update

              Maxim Kuleshov, Matthew Jones, Andrew D. Rouillard … (2016)
              Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at: http://amp.pharm.mssm.edu/Enrichr.
              Article 0 comments Cited 3542 times – based on 0 reviews     Review now
              Article has an altmetric score of 67
                Bookmark
                All references

                Author and article information

                Contributors
                Yufeng He: Role: ConceptualizationRole: Writing – original draft
                Ye Wei:
                ORCID: https://orcid.org/0009-0003-6314-9793
                Role: ConceptualizationRole: Data curationRole: Writing – original draft
                Yongxin Wang: Role: Data curationRole: Formal analysis
                Chunyan Ling: Role: Data curationRole: Formal analysis
                Xiang Qi: Role: Data curationRole: Formal analysisRole: Methodology
                Siyu Geng: Role: Data curation
                Yingtong Meng: Role: Data curationRole: Validation
                Hao Deng: Role: Data curationRole: Validation
                Qisong Zhang: Role: Data curationRole: Visualization
                Xiaoling Qin: Role: Data curationRole: Project administrationRole: Writing – review & editing
                Guanghui Chen:
                ORCID: https://orcid.org/0009-0005-2956-4402
                Role: ConceptualizationRole: Project administrationRole: Writing – original draftRole: Writing – review & editing
                Zongjin Li: Role: Editor
                Journal
                Journal ID (nlm-ta): PLoS One
                Journal ID (iso-abbrev): PLoS One
                Journal ID (publisher-id): plos
                Title: PLOS One
                Publisher: Public Library of Science (San Francisco, CA USA )
                ISSN (Electronic): 1932-6203
                Publication date (Electronic): 27 February 2025
                Publication date Collection: 2025
                Volume: 20
                Issue: 2
                Electronic Location Identifier: e0314773
                Affiliations
                [1 ] Department of Massage, The First Affiliated Hospital of Guangxi University of Chinese Medicine, Nanning, China
                [2 ] Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi University of Chinese Medicine, Nanning, China
                [3 ] Graduate School, Guangxi University of Chinese Medicine, Nanning, China
                [4 ] Graduate School, Henan University of Chinese Medicine, Zhengzhou, China
                [5 ] Second Clinical Medical College, Henan University of Chinese Medicine, Zhengzhou, China
                [6 ] Medical College, Guangxi University, Nanning, China
                [7 ] School of Pharmacy, Guangxi University of Chinese Medicine, Nanning, China
                Nankai University, CHINA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                ‡ GC and XQ are also contributed equally to this work and should be considered co-Corresponding authors

                Author information
                https://orcid.org/0009-0003-6314-9793
                https://orcid.org/0009-0005-2956-4402
                Article
                Publisher ID: PONE-D-24-19013
                DOI: 10.1371/journal.pone.0314773
                PMC ID: 11867322
                PubMed ID: 40014587
                SO-VID: 3ea5e6d7-619e-444c-99eb-d1a9aeef5b6f
                Copyright © © 2025 He et al
                License:

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                Date received : 14 May 2024
                Date accepted : 15 November 2024
                Page count
                Figures: 12, Tables: 1, Pages: 15
                Funding
                Funded by: Guangxi Key Research Laboratory of Meridian rehabilitation
                Award ID: 2024019-03
                Award Recipient :
                ORCID: https://orcid.org/0009-0005-2956-4402
                Funded by: Guangxi University of Chinese Medicine High level Talent Cultivation and Innovation Team
                Award ID: 2022A006
                Award Recipient :
                ORCID: https://orcid.org/0009-0003-6314-9793
                Funded by: funder-id http://dx.doi.org/10.13039/100012547, Natural Science Foundation of Guangxi Zhuang Autonomous Region;
                Award ID: 2020GXNSFAA259013
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100012547, Natural Science Foundation of Guangxi Zhuang Autonomous Region;
                Award ID: 2023GXNSFAA026456
                Award Recipient :
                ORCID: https://orcid.org/0009-0005-2956-4402
                Funded by: funder-id http://dx.doi.org/10.13039/501100017691, Guangxi Key Research and Development Program;
                Award ID: Guike AB20159026
                Award Recipient :
                ORCID: https://orcid.org/0009-0005-2956-4402
                Funded by: Guangxi Traditional Chinese Medicine Key Cultivation Discipline Construction Project
                Award ID: GZXK-Z-20-32
                Award Recipient :
                ORCID: https://orcid.org/0009-0005-2956-4402
                Funded by: Guangxi High Level Traditional Chinese Medicine Key Discipline Construction Project
                Award ID: 2024016-02-04
                Award Recipient :
                ORCID: https://orcid.org/0009-0003-6314-9793
                Funded by: Yufeng He Guangxi Famous Traditional Chinese Medicine Inheritance Studio
                Award ID: GZY2024014
                Award Recipient :
                Guangxi Key Research Laboratory of Meridian rehabilitation Award Number: 2024019-03 | Recipient: Guanghui Chen Guangxi University of Chinese Medicine High level Talent Cultivation and Innovation Team Award Number: 2022A006 | Recipient: Ye Wei Natural Science Foundation of Guangxi Zhuang Autonomous Region Award Number: 2020GXNSFAA259013 | Recipient: Yufeng He Natural Science Foundation of Guangxi Zhuang Autonomous Region Award Number: 2023GXNSFAA026456 | Recipient: Guanghui Chen Guangxi Key Research and Development Program Award Number: Guike AB20159026 | Recipient: Guanghui Chen Guangxi Traditional Chinese Medicine Key Cultivation Discipline Construction Project Award Number: GZXK-Z-20-32 | Recipient: Guanghui Chen Guangxi High Level Traditional Chinese Medicine Key Discipline Construction Project Award Number: 2024016-02-04 | Recipient: Ye Wei Yufeng He Guangxi Famous Traditional Chinese Medicine Inheritance Studio Award Number: GZY2024014 | Recipient: Yufeng He.
                Categories
                Subject: Research Article
                Subject: Biology and Life Sciences
                Subject: Cell Biology
                Subject: Cell Processes
                Subject: Cell Death
                Subject: Apoptosis
                Subject: Biology and Life Sciences
                Subject: Cell Biology
                Subject: Cellular Types
                Subject: Animal Cells
                Subject: Immune Cells
                Subject: Biology and Life Sciences
                Subject: Immunology
                Subject: Immune Cells
                Subject: Medicine and Health Sciences
                Subject: Immunology
                Subject: Immune Cells
                Subject: Biology and Life Sciences
                Subject: Genetics
                Subject: Gene Expression
                Subject: Biology and Life Sciences
                Subject: Anatomy
                Subject: Nervous System
                Subject: Neuroanatomy
                Subject: Spinal Cord
                Subject: Medicine and Health Sciences
                Subject: Anatomy
                Subject: Nervous System
                Subject: Neuroanatomy
                Subject: Spinal Cord
                Subject: Biology and Life Sciences
                Subject: Neuroscience
                Subject: Neuroanatomy
                Subject: Spinal Cord
                Subject: Medicine and Health Sciences
                Subject: Diagnostic Medicine
                Subject: Biology and Life Sciences
                Subject: Cell Biology
                Subject: Chromosome Biology
                Subject: Chromosomes
                Subject: Computer and Information Sciences
                Subject: Artificial Intelligence
                Subject: Machine Learning
                Subject: Support Vector Machines
                Subject: Medicine and Health Sciences
                Subject: Pathology and Laboratory Medicine
                Subject: Pathogenesis
                Custom metadata
                Data Availability “All relevant data are within the paper and its Supporting Information files.”

                ScienceOpen disciplines: Uncategorized
                Data availability:
                ScienceOpen disciplines: Uncategorized

                Comments

                Comment on this article

                scite_
                0
                0
                0
                0
                Smart Citations
                0
                0
                0
                0
                Citing PublicationsSupportingMentioningContrasting
                View Citations

                See how this article has been cited at scite.ai

                scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.

                Similar content599

                See all similar

                Most referenced authors1,016

                See all reference authors