Antiphospholipid syndrome, a disorder in which the immune system mistakenly produces antibodies that drive dangerous blood clots and pregnancy complications, has long been studied through the lens of its telltale antibodies. Now a new computational study has shifted attention to a shadowy layer of the genome: long non-coding RNAs, the regulatory molecules that do not code for proteins but exert powerful control over gene expression. In a cross-dataset transcriptomic analysis published in Discover Informatics, researchers at the All India Institute of Medical Sciences in New Delhi report twenty-four candidate dysregulated long non-coding RNAs in neutrophils from patients with antiphospholipid syndrome, along with a predicted interaction network linking these RNAs to hundreds of microRNAs. The work does not prove causation, but it opens a window onto molecular mechanisms that have remained largely invisible in a disease affecting roughly one in two thousand people worldwide.
The research team, led by Yash Bisht and Ashok Kumar Ahirwar, drew on three publicly available datasets from the Gene Expression Omnibus repository. The discovery dataset, GSE102215, contained RNA sequencing data from neutrophils of nine seropositive antiphospholipid syndrome patients and nine healthy controls. Two additional datasets served as exploratory replication cohorts: GSE50395, a microarray study of monocytes from three patients and three controls, and GSE312344, which profiled plasma exosomal RNA from three women with obstetric antiphospholipid syndrome and three healthy donors. Because these datasets differed in cell type, platform, and sample size, the researchers describe their approach as a directional comparison rather than formal meta-analysis, and they are careful to frame all cross-dataset findings as preliminary.
Using the DESeq2 statistical framework, the team identified 2,425 significantly differentially expressed genes in antiphospholipid syndrome neutrophils, with 1,277 genes upregulated and 1,148 downregulated. Among the most striking signals was a robust interferon signature. The genes IFIT1, with a log2 fold change of plus 3.13, MX1 at plus 2.32, STAT1 at plus 1.12, and IRF7 at plus 1.14 were all strongly activated, echoing earlier transcriptomic studies of the syndrome. Additional dysregulated genes included LILRA5, ALPL, THBD, and the downregulated transcripts HEMK1, DYNC2H1, and AUTS2. A hierarchical clustering heatmap of the fifty most altered genes separated patients and controls completely, a sign that the transcriptional differences were consistent across all samples rather than driven by outliers.
Pathway enrichment analysis added biological texture to these numbers. Gene Set Enrichment Analysis revealed significant activation of the proteasome pathway, with a normalized enrichment score of 2.15 and immunoproteasome subunits such as PSMB9 among the leading-edge genes, and of neutrophil extracellular trap formation, with a score of 1.87. These DNA-histone-protein webs, expelled by activated neutrophils, are increasingly recognized as central drivers of thrombosis in antiphospholipid syndrome, where antiphospholipid antibodies can trigger their release through TLR4 and reactive oxygen species signaling. The authors caution, however, that enrichment of trap-associated transcripts does not confirm actual trap formation; functional assays measuring citrullinated histone H3, cell-free DNA, or PAD4 activity would be needed to demonstrate NETosis directly. KEGG analysis also flagged cytokine-cytokine receptor interactions, MAPK signaling, and Th17 cell differentiation as significantly enriched pathways.
The centerpiece of the study is its long non-coding RNA discovery. Of 171 candidate lncRNA-annotated genes identified by name-based pattern matching in the discovery dataset, twenty-four were significantly dysregulated in patient neutrophils. The most strongly downregulated was LINC00515, with a log2 fold change of minus 3.38. MIR155HG, the host gene from which the inflammatory microRNA miR-155 is processed, was downregulated at minus 2.25, a finding that dovetails with prior work showing reduced miR-155 in monocytes of antiphospholipid syndrome patients. MIR210HG, MIAT, and SNHG7 were also significantly reduced, while LINC00493 and WDFY3-AS2 stood out among the upregulated candidates. Five of the significant candidates showed very low expression abundance, and the authors flag them as requiring cautious interpretation.
To explore how these non-coding RNAs might interact with the microRNA landscape, the researchers submitted the dysregulated candidates to miRNet 2.0, a network analysis platform drawing on predicted interactions from the starBase database. Thirteen of the twenty-four candidates mapped into a network of 294 predicted lncRNA-microRNA pairs spanning 389 edges. SNHG7 emerged as the top hub, with a degree of 70 and a betweenness centrality of 19,264, suggesting it could theoretically connect to and regulate an unusually large share of the microRNA pool. In other disease contexts, SNHG7 has been shown to modulate inflammatory pathways, including the miR-425-5p/TRAF5/NF-kB axis in neuroinflammation and the miR-485-5p/FSP1 axis in osteoarthritis. Whether SNHG7 acts as a competing endogenous RNA sponge in antiphospholipid syndrome neutrophils cannot be determined from transcriptomic data alone, and the authors emphasize that their network captures predicted interactions only.
A protein-protein interaction network built from the full set of differentially expressed genes placed STAT1 at the center of the molecular conversation, with a degree of 160, ahead of UBC, GRB2, FYN, JUN, IL6, and TLR4. This finding reinforces the established role of interferon-driven STAT1 signaling in the syndrome and validates the technical quality of the underlying dataset. An exploratory machine learning analysis, reported in the supplementary material as a proof of concept, combined LASSO feature selection with a random forest classifier and produced a perfect leave-one-out cross-validation area under the curve on eighteen samples. The authors are refreshingly candid about this result: with nine features selected from eighteen samples, and feature selection performed outside the cross-validation folds, the classifier almost certainly overfits the data and must not be interpreted as diagnostic performance. They note only that two long non-coding RNAs appeared among the top features, a hypothesis-generating observation for future larger studies.
Cross-dataset comparison offered modest but informative support. Against the monocyte microarray dataset, six of nine prespecified genes showed concordant direction of change, a 67 percent directional agreement rate, with the interferon genes STAT1, MX1, IRF7, and IFIT1 all upregulated in patient monocytes as in neutrophils. Three genes, LILRA5, PSMB9, and JUN, were discordant, likely reflecting genuine cell-type-specific expression patterns. SNHG7 showed nominal downregulation in the obstetric exosomal dataset as well. The authors stress that these comparisons are qualitative and limited by tiny sample sizes of three per group and by cross-platform differences that make direct fold-change comparisons unreliable.
The study arrives at a moment when the field has explicitly called for this kind of work. A 2025 systematic review identified long non-coding RNA research in antiphospholipid syndrome as a major gap, and the only prior lncRNA study in the disease, by Guzman-Martin and colleagues, had reported dysregulation of FGD5-AS1, OIP5-AS1, and GAS5 in monocytes. The new findings extend the non-coding RNA map into neutrophils, the very cells whose extracellular traps drive much of the thrombotic burden. They also raise an intriguing, if entirely speculative, possibility relevant to seronegative antiphospholipid syndrome, the perplexing subset of patients who display all clinical hallmarks of the disease yet test persistently negative on standard antibody assays. No such patients were included in this study, and the authors are unambiguous that any implications for seronegative disease are untested. Still, the hypothesis that RNA-level dysregulation might operate independently of antibody production offers a testable framework for future cohorts.
The limitations of the work are numerous and openly acknowledged: small samples, datasets originally generated for other purposes, absent clinical metadata such as age, sex, and medication status, and a lupus NETosis origin for the discovery dataset that complicates attribution of disease-specific signals. The lncRNA identification strategy relied on gene symbol pattern matching rather than formal transcript biotype annotation, and the interaction network rests entirely on predictions without accompanying microRNA expression data. Yet the researchers lay out a clear path forward, proposing quantitative PCR profiling of candidate microRNAs, luciferase assays to validate SNHG7-microRNA interactions, knockdown and overexpression experiments in neutrophil models, and replication in large, prospectively recruited cohorts including seronegative patients. Until those experiments are done, the study stands as a carefully hedged but genuinely novel cartography of the non-coding RNA landscape in antiphospholipid syndrome, pointing investigators toward SNHG7 and its neighbors as candidate regulators worth pursuing in the hunt for the missing molecular layers of a thrombotic autoimmune disease.
The competing endogenous RNA framework underlying this work rests on a simple idea: because microRNAs silence messenger RNAs by binding complementary sequences, transcripts that share microRNA response elements can titrate one another’s repressors. Long non-coding RNAs, with lengths exceeding two hundred nucleotides and often multiple binding sites, are well suited to act as such molecular sponges. In autoimmune disease, this mechanism has already been implicated in inflammatory regulation, and the SNHG7-centered network identified here echoes a previously characterized miR-425-5p/TRAF5/NF-kB axis, lending biological plausibility to the computational predictions.
The interferon signature observed in the discovery dataset also fits a broader pattern. Type I interferon signaling is a recognized transcriptomic hallmark of antiphospholipid syndrome, and the prominence of STAT1 as the top protein interaction hub, with a degree of 160, aligns with the known biology of interferon-driven neutrophil priming. Because antiphospholipid antibodies can provoke neutrophil extracellular trap release through TLR4 and reactive oxygen species, the concurrent enrichment of trap formation pathways and dysregulated non-coding RNAs in the same cells suggests a regulatory layer worth testing experimentally. The 2023 ACR/EULAR classification criteria, meanwhile, continue to define the disease by antibodies alone, underscoring why transcriptomic approaches that operate independently of antibody status may prove valuable for patients who fall outside conventional serological detection.
Subject of Research: Identification of dysregulated long non-coding RNAs and predicted lncRNA-microRNA interactions in antiphospholipid syndrome through cross-dataset transcriptomic analysis
Article Title: Identification of candidate dysregulated lncRNAs and predicted lncRNA–miRNA interactions in antiphospholipid syndrome via cross−dataset transcriptomic analysis
Article References: Bisht, Y., Joshi, S. S., Yadav, K., Tiwari, H., Tyagi, S., Sangwan, H., & Ahirwar, A. K. (2026). Identification of candidate dysregulated lncRNAs and predicted lncRNA–miRNA interactions in antiphospholipid syndrome via cross−dataset transcriptomic analysis. Discover Informatics, 1(1), Article 14. https://doi.org/10.1007/s44564-026-00014-1
Image Credits: AI Generated
DOI: 10.1007/s44564-026-00014-1
Keywords: antiphospholipid syndrome, long non-coding RNAs, lncRNA-miRNA interactions, neutrophils, interferon signaling, neutrophil extracellular traps, SNHG7, MIR155HG, transcriptomics, seronegative APS, competing endogenous RNA, autoimmune thrombosis
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Juliet Wilcox. (September 10, 2026). Hidden RNA Regulators Uncovered in Antiphospholipid Syndrome Blood Cells. Scienmag. https://scienmag.com/hidden-rna-regulators-uncovered-in-antiphospholipid-syndrome-blood-cells/
Juliet Wilcox. “Hidden RNA Regulators Uncovered in Antiphospholipid Syndrome Blood Cells.” Scienmag, 10 September 2026, https://scienmag.com/hidden-rna-regulators-uncovered-in-antiphospholipid-syndrome-blood-cells/. Accessed 10 September 2026.
Juliet Wilcox. “Hidden RNA Regulators Uncovered in Antiphospholipid Syndrome Blood Cells.” Scienmag. September 10, 2026. https://scienmag.com/hidden-rna-regulators-uncovered-in-antiphospholipid-syndrome-blood-cells/
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Tags: Antiphospholipid syndromeautoimmune thrombosisbioinformatics in autoimmune researchcompeting endogenous RNAcomputational genomics of antiphospholipid syndromegene expression profiling in blood immune cellsimmune cell gene regulation ininterferon signalinglncRNA miRNA interactionslong non-coding RNA dysregulation in autoimmune diseaseslong non-coding RNAslong non-coding RNAs in autoimmune diseasesmicroRNA interactions in autoimmune disordersMIR155HGmolecular mechanisms of blood clotting disordersneutrophil extracellular trapsneutrophilsRNA regulation in blood cellsRNA-based biomarkers for antiphospholipid syndromeseronegative APSSNHG7transcriptomic analysis of neutrophilsTranscriptomics
