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Transcritome for Identification of Biomarkers for Diagnosis and Targeted Therapy of Type 1 Diabetes

Submitted:

29 October 2025

Posted:

03 November 2025

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Abstract

A Transcritptome dataset of whole blood RNA-seq data from type 1 diabetic (T1D) patients and healthy volunteers was deposited on GEO dataset in 2020 (GSE123658). Because of recurrent citations to the transimmunom dataset, read counts at gene level are revised and putative up-regulated biomarkers for T1D diganosis and targeted therapy are presented. We presente TRHDE, GOLGA8I, HBZ, BOLA2B, SPATA22, SEMA6B and FOXQ1 genes as putative biomakers for diagnosis (log2FC ≥1.5 and a padj ≤ 0.05). All together, our result points to GSE123658 dataset as source of biomarkers and therapy target genes.

Keywords: 
transcriptome
;  
type 1 diabetes
;  
biomarkers
;  
target genes
Subject: 
Biology and Life Sciences  -   Immunology and Microbiology

Introduction

A Transcritptome dataset of whole blood RNA-seq data from type 1 diabetic (T1D) patients and healthy volunteers was deposited on GEO dataset in 2020 (GSE123658). Starting with a protocol for recruitment of participants followed by sample acquisition [1], whole blood RNA from 43 healthy donors and 39 T1D patients were sequenced and the corresponding gene expressions were estimated. Since the upload of the database, GSE123658 was used for in computational experiments in the identification of hub genes related to type 1 diabetes progression [2], and the discovery of gene signatures associated with T Cells and Natural Killer Cells [3]. Moreover, TSPO was identified as the only gene differentially expressed in both conditions T1D (log2FC = 0.504, p = 0.0319) and epilepsy (log2FC = 0.562, p < 0.001) [4]. Finally. three independent diabetes-related GEO datasets - including transimmunom - and found 117 commom T1D genes [5]. Because of recurrent citations to the transimmunom dataset, read counts at gene level are revised and putative up-regulated biomarkers for T1D diganosis and targeted therapy are presented.
Type 1 diabetes (T1D) is an autoimmune disease resultant from β-cell destruction in the pancreascell destruction in the pancreas [6,7,8]. The molecular mechanisms underlying T1D are known to be related to antigen presentation [9,10], beta cell autoimmunity [11,12] immune tolerance [13,14], T cell response [15,16,17], chemokine production [18], among other [19,20,21]. Candidate genes for diagnostic biomarkers can be identified when their expression is high in patients compared to healthy controls. Meanwhile, treatment targets will be found among genes with high interactivity rather than high expression.

Methods

The reads of each sample were then aligned against the Human genome using default parameters of hisat2 for paired reads of forward-cell destruction in the pancreasstranded library. The genome index database was built over the Ensembl Human genome [22] version GRCh37. Next, featureCounts [23] parameters was also adjusted for analysis of paired reads of forward-cell destruction in the pancreasstranded library at gene level. The gene annotation used here was also obtained from Ensembl version GRCh37. The read counts for each sample was merged in a final table and analyzed with R [25] scripts developed specifically for this dataset. Only protein coding genes located on autosomal chromosomes were further considered.

Results

Only seven up-regulated genes are found to be differently expressed (DE) between T1D and healthy samlples when log2FC ≥ 1.5 and padj ≤ 0.05.
Table 1. Up-regulated biomarkers whose fold change (log2FC) are ≥1.5 and a padj ≤ 0.05 between T1D and healthy samlples.
Table 1. Up-regulated biomarkers whose fold change (log2FC) are ≥1.5 and a padj ≤ 0.05 between T1D and healthy samlples.
healthy T1D
Gene name log2FC pvalue padj mean sd mean sd
TRHDE 1.66 1.29E-03 1.35E-02 0.96 1.38 2.97 7.19
GOLGA8I 1.77 8.25E-06 4.95E-04 1.20 1.15 4.26 9.54
HBZ 1.54 1.09E-03 1.21E-02 2.23 3.21 6.64 11.97
BOLA2B 1.95 8.05E-05 2.06E-03 4.67 9.35 17.43 39.83
SPATA22 1.89 3.56E-04 5.58E-03 1.35 2.12 4.60 9.90
SEMA6B 1.65 3.74E-08 1.52E-05 9.73 6.02 29.94 45.24
FOXQ1 2.53 1.97E-05 8.29E-04 1.01 1.89 5.88 17.54

Conclusions

Up-regulated genes whose fold change (log2FC) are ≥1.5 and a padj ≤ 0.05 between T1D and healthy samlples are good candidate for diagnosis biomarkers. Additional molecules can also be found among the down-regulated genes. Furthermore, genes for therapy can be mined by applying criteris to filter out biomarkers with high interactome conectivity.

Declarations

Felipe Leal Valentim FLV submitted the dataset to the GEO database in 2020 (GSE2020123658). FLV was with Unité Immunologie-Immunopathologie-Immunothérapie, INSERM/UPMC for the submission of the dataset. FLV is currently with the Department of Mechatronics and Mechanical Systems Engineering of the Polytechnic School of the University of São Paulo. Scripts to analyse the read counts are found in the github depository : https://github.com/datasciencebioinformatics/TranscriptomeFingerprintAnalysis.

Acknowledgment

I thanks to Adrien SIX and David KLATZMANN for the post-doc oportunity with the data in the Laboratory of Excellence (LabEx) Transimmunom.

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