ArticleFrontiers in immunology2025
Identification of crosstalk genes and diagnostic biomarkers in systemic sclerosis associated sarcopenia through integrative analysis and machine learning.
Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Sarcopenia associated with systemic sclerosis (SSc) significantly compromises patient prognosis and quality of life. However, reliable diagnostic biomarkers remain lacking. This study aimed to identify molecular markers for early detection using integrative computational approaches. Methods: An integrated analysis based on the Gene Expression Omnibus (GEO) database was performed. Crosstalk genes (CGs) were identified using least absolute shrinkage and selection operator (LASSO) regularization, ensemble decision trees, and support vector machine-based feature selection. Machine learning algorithms were employed to construct a predictive scoring model and to assess the diagnostic value of key biomarkers. Hub mRNAs were validated using quantitative polymerase chain reaction (qPCR). Immune cell infiltration profiles and functional correlations were also examined. Results: Five key CGs-NOX4, STC2, NEK6, IGSF10, and EMX2-were identified as molecular links between SSc and sarcopenia. A predictive model incorporating NOX4 and NEK6 was developed, and a diagnostic threshold was established. PCR validation confirmed the differential expression of NOX4 and NEK6 in both SSc and SSc-associated sarcopenia, demonstrating high predictive accuracy. Furthermore, the combined NOX4-NEK6 model exhibited a superior area under the curve (AUC) compared to either gene alone. Immune infiltration analysis revealed significant correlations between CGs and multiple immune cell populations. Conclusion: This study proposes NOX4 and NEK6 as novel biomarkers, offering a non-invasive strategy for the early detection of SSc-associated sarcopenia. This study also reveals a shared immune-dysregulation node linking SSc and sarcopenia, positions these crosstalk genes as multi-disease prevention targets, and paves the way for personalized immunotherapy and rapid bench-to-bedside translation.
Indexed as
Identifiers
What Socratic holds
Registered trials
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 Socratic graph.