Searching the Resource Information Network

Our searching services are busy right now. Please try again later

  • Register
X
Forgot Password

If you have forgotten your password you can enter your email here and get a temporary password sent to your email.

X

Leaving Community

Are you sure you want to leave this community? Leaving the community will revoke any permissions you have been granted in this community.

No
Yes

Multi-block data integration analysis for identifying and validating targeted N-glycans as biomarkers for type II diabetes mellitus.

Eric Adua | Ebenezer Afrifa-Yamoah | Emmanuel Peprah-Yamoah | Enoch Odame Anto | Emmanuel Acheampong | Kwaafo Akoto Awuah-Mensah | Wei Wang
Scientific reports | 2022

Plasma N-glycan profiles have been shown to be defective in type II diabetes Mellitus (T2DM) and holds a promise to discovering biomarkers. The study comprised 232 T2DM patients and 219 healthy individuals. N-glycans were analysed by high-performance liquid chromatography. The multivariate integrative framework, DIABLO was employed for the statistical analysis. N-glycan groups (GPs 34, 32, 26, 31, 36 and 30) were significantly expressed in T2DM in component 1 and GPs 38 and 20 were related to T2DM in component 2. Four clusters were observed based on the correlation of the expressive signatures of the 39 N-glycans across T2DM and controls. Cluster A, B, C and D had 16, 16, 4 and 3 N-glycans respectively, of which 11, 8, 1 and 1 were found to express differently between controls and T2DM in a univariate analysis [Formula: see text]. Multi-block analysis revealed that trigalactosylated (G3), triantennary (TRIA), high branching (HB) and trisialylated (S3) expressed significantly highly in T2DM than healthy controls. A bipartite relevance network revealed that HB, monogalactosylated (G1) and G3 were central in the network and observed more connections, highlighting their importance in discriminating between T2DM and healthy controls. Investigation of these N-glycans can enhance the understanding of T2DM.

Pubmed ID: 35768493

Research resources used in this publication

None found

Additional research tools detected in this publication

Antibodies used in this publication

None found

Associated grants

None

Publication data is provided by the National Library of Medicine ® and PubMed ®. Data is retrieved from PubMed ® on a weekly schedule. For terms and conditions see the National Library of Medicine Terms and Conditions.

This is a list of tools and resources that we have found mentioned in this publication.


Bioconductor (tool)

RRID:SCR_006442

Software repository for R packages related to analysis and comprehension of high throughput genomic data. Uses separate set of commands for installation of packages. Software project based on R programming language that provides tools for analysis and comprehension of high throughput genomic data.

View all literature mentions