Related Experiment Video
Updated: Feb 1, 2026

Integration of Bioinformatics Approaches and Experimental Validations to Understand the Role of Notch Signaling in Ovarian Cancer
Published on: January 12, 2020
Th17-related genes PGAP1 and TMBIM1 serve as potential diagnostic and predictive biomarkers in systemic sclerosis:
Qian Li1, Hanchao Li1, Bomiao Ju1
1Department of Rheumatology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi Province, China.
Background:
T helper 17 (Th17) cells participate in all pathological stages of systemic sclerosis (SSc). Molecular markers associated with Th17 cells hold promise as therapeutic targets for SSc. This study aims to screen and validate key genes related to Th17 cells that have diagnostic and predictive value in SSc and to identify potential therapeutic targets for SSc.
Methods:
Candidate genes were identified by intersecting Th17 cell-dysregulated genes (TCDRGs) from single-cell RNA sequencing data (GSE292979) with bulk RNA sequencing data (GSE95065). Machine learning algorithms, receiver operating characteristic (ROC) curve analysis, and expression level validation were employed to further identify key Th17-related genes. Subsequently, the diagnostic and predictive capabilities of these Th17-related genes were validated using a nomogram model. Then, gene set enrichment analysis (GSEA), immune infiltration analysis, drug prediction, and molecular docking were conducted on the key genes. Finally, the expression of genes was confirmed in bleomycin (BLM)-induced SSc mice.
Results:
We identified 48 candidate genes from 472 TCDRGs and 1653 bulk DEGs. Using the Boruta feature selection algorithm and random forest (RF) model, two genes related to Th17 cells, PGAP1 and TMBIM1, were identified. PGAP1 and TMBIM1 exhibited exceptional discriminative efficiency with an AUC of 0.9852 in the nomogram model. Moreover, GSEA analysis revealed that both genes were co-enriched in pathways such as oxidative phosphorylation and the proteasome. Immune infiltration analysis indicated that PGAP1 and TMBIM1 were closely associated with five types of immune cells, exhibiting opposing correlation trends, such as with M1-type macrophages. Drug prediction analysis identified 45 potential drugs targeting PGAP1 and 36 targeting TMBIM1. Notably, Bafilomycin A1 was associated with PGAP1, while Ciglitazone was associated with TMBIM1, and both displayed stable binding conformations. Finally, in a BLM-induced mouse model, quantitative real-time PCR analysis and immunohistochemical experiments revealed upregulation of TMBIM1, whereas PGAP1 expression was downregulated in fibrotic skin and lung tissues. These results suggest the relevance of PGAP1 and TMBIM1 in fibrotic processes. However, further studies using human SSc samples are warranted to confirm their diagnostic and therapeutic potential.
Conclusion:
PGAP1 and TMBIM1 have been identified as key molecules associated with Th17 cells. They exhibit significant diagnostic and predictive value in SSc and show promise as therapeutic targets for SSc. Key Points • SSc skin tissues exhibit significant changes in the composition of immune and non-immune cells. • PGAP1 and TMBIM1 have been identified as key molecules in SSc that are associated with Th17 cells, showing remarkable diagnostic accuracy. • PGAP1 and TMBIM1 exhibit a negative correlation in their expression patterns in SSc.
More Related Videos
09:07Prediction and Validation of Gene Regulatory Elements Activated During Retinoic Acid Induced Embryonic Stem Cell Differentiation
Published on: June 21, 2016
08:29Biomarkers in an Animal Model for Revealing Neural, Hematologic, and Behavioral Correlates of PTSD
Published on: October 10, 2012
Related Concept Videos
Reliability and Validity
Self-Serving Bias
Predicting Molecular Geometry
Standard Electrode Potentials
Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
Data Validation
Key parameters for method validation include: