Asset Details
Do you wish to reserve the book?
Integrating transcriptomic and epigenomic data to identify potential biomarkers in gestational diabetes mellitus patients
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Integrating transcriptomic and epigenomic data to identify potential biomarkers in gestational diabetes mellitus patients
Do you wish to request the book?
Integrating transcriptomic and epigenomic data to identify potential biomarkers in gestational diabetes mellitus patients
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Integrating transcriptomic and epigenomic data to identify potential biomarkers in gestational diabetes mellitus patients
2025
Overview
Gestational diabetes mellitus (GDM), one of the prevalent pregnancy-related metabolic disorders, have shown immediate or long-term adverse health outcomes for maternal and fetal health. Therefore, it is crucial to understand the ongoing cellular and molecular changes in GDM patients for characterizing novel biomarkers for diagnosis and therapeutic purposes. In the current study, we analyzed 3 transcriptomic datasets, characterized 449 unique upregulated and 785 downregulated DEGs, and performed several analyses. Gene ontology shows enrichment of migration, development, and immune-related processes in GDM patients. KEGG pathway shows enrichment of pathways like “type 1 diabetes mellitus” and “graft versus host disease”. Disease ontology shows enrichment of “female reproductive system disease,” “anemia,” etc. Integration of methylation and transcriptomic data identified 11 genes (
RASSF2
,
WSCD1
,
TNFAIP3
,
TPST1
,
UBASH3B
,
ZFP36
,
CRISPLD2
,
IGFBP7
,
TNS3
,
TPM2
, and
VTRNA1-2
), as potential novel diagnostic biomarkers and therapeutic targets. Furthermore, immune cell-type infiltration analysis shows higher memory B-cells and lower M1 macrophages and CD8 T-cells. Protein-protein interaction analysis followed by ROC analysis in an independent dataset identified 7 hub genes (
POLR2G
,
VWF
,
COL5A1
,
COL6A1
,
CD44
,
COL3A1
, and
COL1A1
) with high diagnostic potential. Overall, we obtained 18 genes that could serve as novel diagnostic biomarkers and therapeutic targets in GDM patients.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
Subject
/ 631/337
/ 631/80
/ Datasets
/ Diabetes mellitus (insulin dependent)
/ Diabetes, Gestational - diagnosis
/ Diabetes, Gestational - genetics
/ Diabetes, Gestational - metabolism
/ Female
/ Fetuses
/ Gestational diabetes mellitus
/ Glucose
/ Humanities and Social Sciences
/ Humans
/ Immune cell-type infiltration
/ Proteins
/ Receiver operating characteristic
/ Science
