Identification of candidate dysregulated lncRNAs and predicted lncRNA–miRNA interactions in antiphospholipid syndrome via cross−dataset transcriptomic analysis

Antiphospholipid syndrome (APS) is a systemic autoimmune thrombotic disorder characterised by persistent antiphospholipid antibodies (aPL) and recurrent thrombosis or pregnancy morbidity. Seronegative APS (SNAPS)—clinically indistinguishable from seropositive APS but persistently antibody-negative—represents a major unresolved diagnostic challenge. Non-coding RNAs (ncRNAs), particularly long non-coding RNAs (lncRNAs) acting through competing endogenous RNA (ceRNA) mechanisms, may constitute a missing mechanistic layer in APS pathogenesis. A cross-dataset comparative bioinformatic analysis was performed using three publicly available GEO datasets: GSE102215 (9 APS vs 9 HC, neutrophils, RNA-seq, discovery), GSE50395 (3 APS vs 3 HC, monocytes, microarray, exploratory replication), and GSE312344 (3 obstetric APS vs 3 HC, plasma exosomal RNA, lncRNA replication). DESeq2 and limma were used for differential expression; clusterProfiler for pathway enrichment; STRINGdb for PPI network; miRNet 2.0 for predicted lncRNA–miRNA interaction network. A pilot machine learning analysis is reported in supplementary material. DESeq2 identified 2,425 significant DEGs including a prominent IFN signature (IFIT1 log2FC=+3.13, MX1 log2FC=+2.32, STAT1 log2FC = + 1.12). GSEA revealed transcriptomic enrichment of NET formation (NES=1.87) and Proteasome (NES=2.15) pathways. PPI analysis identified STAT1 as the top hub gene (degree=160). Twenty-four candidate dysregulated lncRNAs were identified in APS neutrophils, including MIR155HG (log2FC = − 2.25), LINC00515 (log2FC = − 3.38), and SNHG7 (log2FC = − 1.22). Predicted lncRNA–miRNA interaction network analysis identified SNHG7 as the top hub lncRNA (degree=70, betweenness=19,264). Exploratory cross-dataset comparison showed 67% directional concordance in GSE50395; SNHG7 showed nominal dysregulation in GSE312344. This purely computational study identifies candidate dysregulated lncRNAs and predicted lncRNA–miRNA interactions in APS neutrophils, providing a hypothesis-generating framework for future experimental validation. No seronegative APS patients were studied; seronegative APS implications are speculative.

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Journal
Discover Informatics
Published
2026-09-10
DOI
https://doi.org/10.1007/s44564-026-00014-1
Primary Topic
Systemic Lupus Erythematosus Research
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article
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article

Identification of candidate dysregulated lncRNAs and predicted lncRNA–miRNA interactions in antiphospholipid syndrome via cross−dataset transcriptomic analysis

Kajal Yadav, Himanshu Sangwan, Ashok Kumar Ahirwar, Yash Bisht et al.
Discover Informatics
Systemic Lupus Erythematosus Research
article

Identification of candidate dysregulated lncRNAs and predicted lncRNA–miRNA interactions in antiphospholipid syndrome via cross−dataset transcriptomic analysis

Kajal Yadav, Himanshu Sangwan, Ashok Kumar Ahirwar, Yash Bisht, Sumedh Joshi, Shally Tyagi, Himanshu Tiwari
article en

Abstract

Antiphospholipid syndrome (APS) is a systemic autoimmune thrombotic disorder characterised by persistent antiphospholipid antibodies (aPL) and recurrent thrombosis or pregnancy morbidity. Seronegative APS (SNAPS)—clinically indistinguishable from seropositive APS but persistently antibody-negative—represents a major unresolved diagnostic challenge. Non-coding RNAs (ncRNAs), particularly long non-coding RNAs (lncRNAs) acting through competing endogenous RNA (ceRNA) mechanisms, may constitute a missing mechanistic layer in APS pathogenesis. A cross-dataset comparative bioinformatic analysis was performed using three publicly available GEO datasets: GSE102215 (9 APS vs 9 HC, neutrophils, RNA-seq, discovery), GSE50395 (3 APS vs 3 HC, monocytes, microarray, exploratory replication), and GSE312344 (3 obstetric APS vs 3 HC, plasma exosomal RNA, lncRNA replication). DESeq2 and limma were used for differential expression; clusterProfiler for pathway enrichment; STRINGdb for PPI network; miRNet 2.0 for predicted lncRNA–miRNA interaction network. A pilot machine learning analysis is reported in supplementary material. DESeq2 identified 2,425 significant DEGs including a prominent IFN signature (IFIT1 log2FC=+3.13, MX1 log2FC=+2.32, STAT1 log2FC = + 1.12). GSEA revealed transcriptomic enrichment of NET formation (NES=1.87) and Proteasome (NES=2.15) pathways. PPI analysis identified STAT1 as the top hub gene (degree=160). Twenty-four candidate dysregulated lncRNAs were identified in APS neutrophils, including MIR155HG (log2FC = − 2.25), LINC00515 (log2FC = − 3.38), and SNHG7 (log2FC = − 1.22). Predicted lncRNA–miRNA interaction network analysis identified SNHG7 as the top hub lncRNA (degree=70, betweenness=19,264). Exploratory cross-dataset comparison showed 67% directional concordance in GSE50395; SNHG7 showed nominal dysregulation in GSE312344. This purely computational study identifies candidate dysregulated lncRNAs and predicted lncRNA–miRNA interactions in APS neutrophils, providing a hypothesis-generating framework for future experimental validation. No seronegative APS patients were studied; seronegative APS implications are speculative.

Discover InformaticsVol. 1(1)
All India Institute of Medical Sciences (IN)
Good health and well-being
Openalex Percentile: Top 10%
Systemic Lupus Erythematosus Research
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