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Research article
Yan Zhao
Inner Mongolia Medical University
Corresponding Author
ORCiD: https://orcid.org/0000-0002-1494-9917
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background
Lumbar disc degeneration (LDD) is a major pathological process implicated in low back pain. At present, the research in the fields of spinal surgery has highlighted the complex mechanisms underlying LDD, with autophagy being considered as one of the important processes involved.
Objectives
We aimed to identify the genes and molecular pathways associated with LDD and autophagy using computational tools and publicly available data, and to identify drugs targeting the relevant genes associated with LDD and autophagy.
Materials and Methods
We used text mining to detect the LDD and autophagy-associated genes, and the intersection of the two gene sets was selected for gene ontology analysis using the DAVID program. We then constructed protein–protein interaction networks, followed by a functional enrichment analysis, from which we obtained three significant gene modules. Finally, the final list of genes was queried against the Drug Gene Interaction database to find drug candidates targeting relevant genes associated with LDD and autophagy.
Results
Our analysis identified 72 genes common to both the “LDD” and “Autophagy” text mining concepts. Gene enrichment analysis yielded three significant gene modules (22 genes), which represent four significant pathways and could be targeted by 32 Food and Drug Administration (FDA)-approved drug molecules, and identified the drug–gene interactions.
Conclusion
Using text mining, pathway analysis tools, and drug–gene interaction analysis for gene screening, signal pathway analysis and drug discovery can provide new ideas for the scientific research and clinical treatment.
Keywords
Lumbar disc degeneration, Autophagy, Gene, Pathway, Targeting Drug, Bioinformatic-Based Analyses
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CURRENT STATUS: Published
View the published article at Journal of Orthopaedic Surgery and Research.
No community comments so far
Editorial decision: Major revision
On 16 Jan, 2021
Editor assigned
On 02 Jan, 2021
Reviewers invited
Invitations sent on 02 Jan, 2021
Reviewer #1 agreed
On 02 Jan, 2021
Review #1 received
Received 02 Jan, 2021
Submission checks complete
On 02 Jan, 2021
Editor invited
On 02 Jan, 2021
Version 1
Posted 02 Sep, 2020
No comments provided
Editorial decision: Major revision
On 03 Dec, 2020
Reviewer #4 agreed
On 01 Dec, 2020
Review #2 received
Received 01 Dec, 2020
Review #1 received
Received 01 Dec, 2020
Reviewer #3 agreed
On 30 Nov, 2020
Reviewer #2 agreed
On 30 Nov, 2020
Reviewer #1 agreed
On 21 Sep, 2020
Reviewers invited
Invitations sent on 03 Sep, 2020
Editor assigned
On 02 Sep, 2020
Submission checks complete
On 01 Sep, 2020
Editor invited
On 01 Sep, 2020
First submitted
On 31 Aug, 2020
Metrics
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PDF Downloads: ...
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A new preprint design is coming!
We have improved readability, clarity, accessibility, and opportunities for engagement.
See the published version of this preprint at Journal of Orthopaedic Surgery and Research.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research article
Yan Zhao
Inner Mongolia Medical University
Corresponding Author
ORCiD: https://orcid.org/0000-0002-1494-9917
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at Journal of Orthopaedic Surgery and Research.
No community comments so far
Editorial decision: Major revision
On 16 Jan, 2021
Editor assigned
On 02 Jan, 2021
Reviewers invited
Invitations sent on 02 Jan, 2021
Reviewer #1 agreed
On 02 Jan, 2021
Review #1 received
Received 02 Jan, 2021
Submission checks complete
On 02 Jan, 2021
Editor invited
On 02 Jan, 2021
Version 1
Posted 02 Sep, 2020
No comments provided
Editorial decision: Major revision
On 03 Dec, 2020
Reviewer #4 agreed
On 01 Dec, 2020
Review #2 received
Received 01 Dec, 2020
Review #1 received
Received 01 Dec, 2020
Reviewer #3 agreed
On 30 Nov, 2020
Reviewer #2 agreed
On 30 Nov, 2020
Reviewer #1 agreed
On 21 Sep, 2020
Reviewers invited
Invitations sent on 03 Sep, 2020
Editor assigned
On 02 Sep, 2020
Submission checks complete
On 01 Sep, 2020
Editor invited
On 01 Sep, 2020
First submitted
On 31 Aug, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at Journal of Orthopaedic Surgery and Research.
Background
Lumbar disc degeneration (LDD) is a major pathological process implicated in low back pain. At present, the research in the fields of spinal surgery has highlighted the complex mechanisms underlying LDD, with autophagy being considered as one of the important processes involved.
Objectives
We aimed to identify the genes and molecular pathways associated with LDD and autophagy using computational tools and publicly available data, and to identify drugs targeting the relevant genes associated with LDD and autophagy.
Materials and Methods
We used text mining to detect the LDD and autophagy-associated genes, and the intersection of the two gene sets was selected for gene ontology analysis using the DAVID program. We then constructed protein–protein interaction networks, followed by a functional enrichment analysis, from which we obtained three significant gene modules. Finally, the final list of genes was queried against the Drug Gene Interaction database to find drug candidates targeting relevant genes associated with LDD and autophagy.
Results
Our analysis identified 72 genes common to both the “LDD” and “Autophagy” text mining concepts. Gene enrichment analysis yielded three significant gene modules (22 genes), which represent four significant pathways and could be targeted by 32 Food and Drug Administration (FDA)-approved drug molecules, and identified the drug–gene interactions.
Conclusion
Using text mining, pathway analysis tools, and drug–gene interaction analysis for gene screening, signal pathway analysis and drug discovery can provide new ideas for the scientific research and clinical treatment.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Loading...