See the published version of this preprint at Journal of Orthopaedic Surgery and Research.
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Research article
Tao Lan
Shenzhen Second People's Hospital
Corresponding Author
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Intervertebral disc degeneration (IDD) is widely known as a main contributor to low back pain which has a negative socioeconomic impact worldwide. However, the underlying mechanism remains unclear. This study aims to analyze the dataset GSE23130 using bioinformatics methods to identify the pivotal genes and pathways associated with IDD.
Material/Methods: The gene expression data of GSE23130 was downloaded and differentially expressed genes (DEGs) were extracted from 8 samples and 15 controls. GO and KEGG pathway enrichment analyses were performed. Also, Protein–protein interaction (PPI) network was constructed and visualized, followed by identification of hub genes and key module.
Results: A total of 30 downregulated and 79 upregulated genes were identified. The DEGs mainly enriched in regulation of protein catabolic process, extracellular matrix organization, collagen fibril organization, and extracellular structure organization. Meanwhile, we found that most of DEGs were primarily enriched in PI3K-Akt signaling pathway. The top 10 hub genes were FN1, COL1A2, SPARC, COL3A1, CTGF, LUM, TIMP1, THBS2, COL5A2, and TGFB1.
Conclusions: In summary, key candidate genes and pathway were identified by using integrated bioinformatics analysis, which may provide insights into underlying mechanisms and offer potential target genes for the treatment of IDD.
Keywords
intervertebral disc degeneration, gene, bioinformatics
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CURRENT STATUS: Published
View the published article at Journal of Orthopaedic Surgery and Research.
Version 2
Posted 09 Jul, 2020
No community comments so far
Editorial decision: Accept
On 17 Aug, 2020
Review #1 received
Received 13 Jul, 2020
Reviewer #1 agreed
On 07 Jul, 2020
Reviewers invited
Invitations sent on 06 Jul, 2020
Editor assigned
On 06 Jul, 2020
Submission checks complete
On 05 Jul, 2020
Editor invited
On 05 Jul, 2020
No comments provided
Editorial decision: Major revision
On 29 Jun, 2020
Review #1 received
Received 29 Jun, 2020
Review #3 received
Received 28 Jun, 2020
Review #4 received
Received 28 Jun, 2020
Reviewer #4 agreed
On 26 Jun, 2020
Review #2 received
Received 22 Jun, 2020
Reviewer #3 agreed
On 19 Jun, 2020
Reviewer #2 agreed
On 17 Jun, 2020
Reviewer #1 agreed
On 16 Jun, 2020
Reviewers invited
Invitations sent on 15 Jun, 2020
Editor assigned
On 11 Jun, 2020
Editor invited
On 10 Jun, 2020
Submission checks complete
On 03 Jun, 2020
First submitted
On 02 Jun, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Orthopedic Surgery
Learn more about our company.
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
Tao Lan
Shenzhen Second People's Hospital
Corresponding Author
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.
Version 2
Posted 09 Jul, 2020
No community comments so far
Editorial decision: Accept
On 17 Aug, 2020
Review #1 received
Received 13 Jul, 2020
Reviewer #1 agreed
On 07 Jul, 2020
Reviewers invited
Invitations sent on 06 Jul, 2020
Editor assigned
On 06 Jul, 2020
Submission checks complete
On 05 Jul, 2020
Editor invited
On 05 Jul, 2020
No comments provided
Editorial decision: Major revision
On 29 Jun, 2020
Review #1 received
Received 29 Jun, 2020
Review #3 received
Received 28 Jun, 2020
Review #4 received
Received 28 Jun, 2020
Reviewer #4 agreed
On 26 Jun, 2020
Review #2 received
Received 22 Jun, 2020
Reviewer #3 agreed
On 19 Jun, 2020
Reviewer #2 agreed
On 17 Jun, 2020
Reviewer #1 agreed
On 16 Jun, 2020
Reviewers invited
Invitations sent on 15 Jun, 2020
Editor assigned
On 11 Jun, 2020
Editor invited
On 10 Jun, 2020
Submission checks complete
On 03 Jun, 2020
First submitted
On 02 Jun, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Orthopedic Surgery
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: Intervertebral disc degeneration (IDD) is widely known as a main contributor to low back pain which has a negative socioeconomic impact worldwide. However, the underlying mechanism remains unclear. This study aims to analyze the dataset GSE23130 using bioinformatics methods to identify the pivotal genes and pathways associated with IDD.
Material/Methods: The gene expression data of GSE23130 was downloaded and differentially expressed genes (DEGs) were extracted from 8 samples and 15 controls. GO and KEGG pathway enrichment analyses were performed. Also, Protein–protein interaction (PPI) network was constructed and visualized, followed by identification of hub genes and key module.
Results: A total of 30 downregulated and 79 upregulated genes were identified. The DEGs mainly enriched in regulation of protein catabolic process, extracellular matrix organization, collagen fibril organization, and extracellular structure organization. Meanwhile, we found that most of DEGs were primarily enriched in PI3K-Akt signaling pathway. The top 10 hub genes were FN1, COL1A2, SPARC, COL3A1, CTGF, LUM, TIMP1, THBS2, COL5A2, and TGFB1.
Conclusions: In summary, key candidate genes and pathway were identified by using integrated bioinformatics analysis, which may provide insights into underlying mechanisms and offer potential target genes for the treatment of IDD.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
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