Evidence map›Paper›PMID 39069497›Full record

ArticleEndocrine journal2024

Constructing the biomolecular networks associated with diabetic nephropathy and dissecting the effects of biomolecule variation underlying pathogenesis.

Zi-Han Wang, Qi Dong, Qian Yan, Wan-Rong Yu, Dan-Dan Zhang, Ran Yi

Abstract read
In one paragraph

Article in Endocrine journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Zi-Han WangDepartment of Endocrine, the First Affiliated Hospital of Harbin Medical University, Harbin 150001, People's Republic of China.
Qi DongDepartment of Neurology, the First Affiliated Hospital of Harbin Medical University, Harbin 150001, People's Republic of China.
Qian YanDepartment of Endocrine, the First Affiliated Hospital of Harbin Medical University, Harbin 150001, People's Republic of China.
Wan-Rong YuDepartment of Endocrine, the First Affiliated Hospital of Harbin Medical University, Harbin 150001, People's Republic of China.
Dan-Dan ZhangDepartment of Endocrine, the First Affiliated Hospital of Harbin Medical University, Harbin 150001, People's Republic of China.
Ran YiDepartment of Endocrine, the First Affiliated Hospital of Harbin Medical University, Harbin 150001, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Diabetic nephropathy (DN) is a common and serious complication of diabetes, contributing significantly to patient mortality. Complication of DN (CDN) ranks as the second leading cause of end-stage renal disease globally. To address this, understanding the genetic regulation underlying DN is crucial for personalized treatment strategies. In this study, we identified genes and lncRNAs associated with diabetes and diabetic nephropathy constructing a DN-related lncRNA-mRNA network (DNLMN). This network, characterized by scale-free biomolecular properties, generated through the study of topological properties, elucidates key regulatory interactions. Enrichment analysis of important network modules revealed critical biological processes and pathways involved in DN pathogenesis. In the second step, we investigated the differential expression and co-expression of hub nodes in diseased and normal individuals, identifying lncRNA-mRNA relationships implicated in disease regulation. Finally, we gathered DN-related single nucleotide polymorphisms (SNPs) and lncRNAs from the LincSNP 3.0 database. The DNLMN encompasses SNP-associated lncRNAs, and transcription factors (TFs) linked to differentially expressed lncRNAs between diseased and normal samples. These results underscore the significance of biomolecular networks in disease progression and highlighting the role of biomolecular variability contributes to personalized disease phenotyping and treatment.

Indexed as

Diabetic NephropathiesGene Regulatory NetworksPolymorphism, Single NucleotideRNA, Long NoncodingRNA, MessengerGene Expression ProfilingHumansTranscription FactorsRNA, Long NoncodingRNA, MessengerTranscription FactorsBiomolecular networkComplex diseaseDiabetesDiabetic nephropathyOmics data

Identifiers

PMID39069497
PMCPMC11778351

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.