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Background: The purpose of this study was to explore the potential molecular targets of hyperlipidaemia and the related molecular mechanisms.
Methods: The microarray data set of GSE66676 obtained from patients with hyperlipidaemia was downloaded. The weighted gene co‑expression network (WGCNA) analysis was used to analyze the gene expression profile and royalblue module was considered as the highest correlation. Gene Ontology (GO) functional and Kyoto Encyclopedia of Genes and genomes (KEGG) pathway enrichment analyses were implemented for the identification of genes in the royalblue module using the Database for Annotation, Visualization and Integrated Discovery (DAVID) online tool (version 6.8; http://david.abcc.ncifcrf.gov). A protein-protein interaction (PPI) network was established by using the online STRING tool. Then, several hub genes were identified by the MCODE and cytoHubba plug-ins in Cytoscape software.
Results: The significant module (royalblue) identified was associated with TC, TG and Non-HDL-C. GO and KEGG enrichment analyses revealed that the genes in the royalblue module were associated with carbon metabolism, steroid biosynthesis, fatty acid metabolism and biosynthesis of unsaturated fatty acids pathways. SQLE (degree = 17) was revealed as key molecules that associated with hypercholesterolemia (HCH) and SCD was revealed as key molecules that associated with hypertriglyceridemia (HTG). Meanwhile, RT-qPCR analysis also confirmed the above results based on our HCH/HTG samples.
Conclusions: SQLE and SCD are related to hyperlipidaemia, SQLE/SCD may be new targets for cholesterol-lowering or triglyceride-lowering therapy, respectively.
Keywords
Weighted gene co-expression network analysis, Hyperlipidaemia, Significant modules, Hub genes
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CURRENT STATUS: Published
View the published article at Nutrition & Metabolism.
No community comments so far
Editorial decision: Minor revision
On 18 Feb, 2021
Editor assigned
On 20 Jan, 2021
Reviewers invited
Invitations sent on 20 Jan, 2021
Submission checks complete
On 20 Jan, 2021
Editor invited
On 20 Jan, 2021
Version 1
Posted 07 Aug, 2020
No comments provided
Editorial decision: Major revision
On 01 Dec, 2020
Review #1 received
Received 16 Sep, 2020
Reviewer #1 agreed
On 09 Sep, 2020
Reviewers invited
Invitations sent on 25 Aug, 2020
Editor assigned
On 06 Aug, 2020
Submission checks complete
On 05 Aug, 2020
Editor invited
On 05 Aug, 2020
First submitted
On 04 Aug, 2020
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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 Nutrition & Metabolism.
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
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 Nutrition & Metabolism.
No community comments so far
Editorial decision: Minor revision
On 18 Feb, 2021
Editor assigned
On 20 Jan, 2021
Reviewers invited
Invitations sent on 20 Jan, 2021
Submission checks complete
On 20 Jan, 2021
Editor invited
On 20 Jan, 2021
Version 1
Posted 07 Aug, 2020
No comments provided
Editorial decision: Major revision
On 01 Dec, 2020
Review #1 received
Received 16 Sep, 2020
Reviewer #1 agreed
On 09 Sep, 2020
Reviewers invited
Invitations sent on 25 Aug, 2020
Editor assigned
On 06 Aug, 2020
Submission checks complete
On 05 Aug, 2020
Editor invited
On 05 Aug, 2020
First submitted
On 04 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 Nutrition & Metabolism.
Background: The purpose of this study was to explore the potential molecular targets of hyperlipidaemia and the related molecular mechanisms.
Methods: The microarray data set of GSE66676 obtained from patients with hyperlipidaemia was downloaded. The weighted gene co‑expression network (WGCNA) analysis was used to analyze the gene expression profile and royalblue module was considered as the highest correlation. Gene Ontology (GO) functional and Kyoto Encyclopedia of Genes and genomes (KEGG) pathway enrichment analyses were implemented for the identification of genes in the royalblue module using the Database for Annotation, Visualization and Integrated Discovery (DAVID) online tool (version 6.8; http://david.abcc.ncifcrf.gov). A protein-protein interaction (PPI) network was established by using the online STRING tool. Then, several hub genes were identified by the MCODE and cytoHubba plug-ins in Cytoscape software.
Results: The significant module (royalblue) identified was associated with TC, TG and Non-HDL-C. GO and KEGG enrichment analyses revealed that the genes in the royalblue module were associated with carbon metabolism, steroid biosynthesis, fatty acid metabolism and biosynthesis of unsaturated fatty acids pathways. SQLE (degree = 17) was revealed as key molecules that associated with hypercholesterolemia (HCH) and SCD was revealed as key molecules that associated with hypertriglyceridemia (HTG). Meanwhile, RT-qPCR analysis also confirmed the above results based on our HCH/HTG samples.
Conclusions: SQLE and SCD are related to hyperlipidaemia, SQLE/SCD may be new targets for cholesterol-lowering or triglyceride-lowering therapy, respectively.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
This is a list of supplementary files associated with this preprint. Click to download.
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