Integrated proteomic, transcriptomic, and epigenomic profiling identifies SRA1 as a novel therapeutic target for postpartum depression

Cross-tissue Mendelian randomization prioritizes RAB27B as a brain-derived candidate protein for postpartum depression

Identifying miR-431-5p as a promising candidate biomarker for early-onset severe preeclampsia through integrated bioinformatic and clinical analyses: a retrospective study

Background Early-onset severe preeclampsia (EOSPE) is a serious pregnancy complication associated with adverse maternal and neonatal outcomes. This study aimed to identify potential diagnostic biomarkers for EOSPE and to investigate their clinical significance. Methods MicroRNAs (miRNAs) expression datasets from the Gene Expression Omnibus (GEO) database, specifically from placental tissue (GSE103542) and plasma (GSE234611), were screened to identify miRNAs consistently dysregulated in early-onset preeclampsia (EOPE) and to discover robust biomarker candidates. Weighted gene co-expression network analysis (WGCNA) was employed to identify key modules associated with preeclampsia (PE). The differentially expressed miRNAs (DEmiRNAs) were intersected with miRNAs from WGCNA-identified modules to prioritize candidates. Functional enrichment analysis predicted target genes and involved pathways. Clinical samples (serum and placental tissues from EOSPE patients and normal pregnant women) and a lipopolysaccharide (LPS)-induced preeclampsia rat model were utilized to validate the expression of DEmiRNAs via reverse transcription quantitative polymerase chain reaction (RT-qPCR). The diagnostic value of these miRNAs was evaluated using receiver operating characteristic (ROC) curve analysis, and their correlations with clinical parameters were assessed. Results Bioinformatics analysis identified miR-431-5p as a key molecule, with its expression was significantly upregulated in both serum and placental tissues of patients with EOSPE. Circulating miR-431-5p demonstrated promising diagnostic potential for EOSPE (AUC = 0.803). Its expression level positively correlated with systolic blood pressure (r = 0.395, P = 0.017) and diastolic blood pressure (r = 0.41, P = 0.013), uric acid (r = 0.389, P = 0.02), lactate dehydrogenase (r = 0.399, P = 0.018), and the umbilical artery S/D ratio (r = 0.457, P = 0.01). Conversely, it negatively correlated with gestational age at delivery (r = −0.562, P < 0.001) and neonatal birth weight (r = −0.503, P = 0.02). Multivariable regression analysis showed that after adjusting for confounding factors, miR-431-5p remained significantly positively associated with the occurrence of EOSPE. Animal experiments confirmed similar upregulation of miR-431-5p in the LPS-induced preeclampsia rat model. Conclusions In this study, miR-431-5p was significantly upregulated in EOSPE and demonstrated preliminary diagnostic potential. Its expression level was potentially associated with disease severity and adverse perinatal outcomes. These findings suggest that miR-431-5p may serve as a promising candidate biomarker for EOSPE.

Using human proteomic and lipidomic to exploration drug target of bladder cancer

Identification of EIF4A1 as a candidate molecular indicator of autism spectrum disorder using integrative bioinformatic and biological methods.

This study integrated mRNA expression profiles from five post-mortem brain tissue GEO datasets to identify ASD-associated genes and found that EIF4A1 mRNA expression was significantly elevated in ASD subjects and rescued by treatment with the antipsychotics olanzapine or risperidone.

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.

Integrative Transcriptomic and Genetic Analysis Nominally Prioritizes SLC51A and TPMT as Candidate Diagnostic Genes for Thoracic Aortic Aneurysm

Thoracic aortic aneurysm (TAA) is a life‑threatening disease with limited blood‑based biomarkers for early detection and risk stratification. Integrating transcriptomic and genetic data may help identify potentially relevant genes and develop suggestive diagnostic tools. Peripheral blood expression data from GSE9106 (comprising 59 TAA patients and 34 controls) were analyzed to identify differentially expressed genes (DEGs) between TAA patients and controls. Whole‑blood cis‑expression quantitative trait loci (cis‑eQTLs) from the eQTLGen consortium (31,684 individuals) were integrated with genome‑wide association study (GWAS) summary statistics (1,351 cases and 18,295 controls) using summary data–based Mendelian randomization (SMR) with Heterogeneity in Dependent Instruments (HEIDI) test to prioritize genes with suggestive causal effects. Overlapping DEGs and SMR genes were subjected to feature selection using least absolute shrinkage and selection operator (LASSO) logistic regression and random forest (RF). A multivariable logistic model based on core genes was visualized as a nomogram and evaluated by receiver operating characteristic analysis, calibration, and decision curve analysis. Single‑gene gene set enrichment analysis (GSEA) was performed to explore pathways associated with core‑gene expression. Transcriptomic analysis identified 1,108 DEGs, which, when integrated with SMR results, yielded 14 genes supported by both differential expression and genetic evidence at the nominal significance level. Forest plots demonstrated that all 14 genes had nominally significant SMR associations with TAA and passed the HEIDI test, providing limited evidence against heterogeneity, although this does not definitively rule out linkage or pleiotropy. Machine‑learning feature selection converged on two core genes, SLC51A and TPMT, which individually showed moderate diagnostic performance. A two‑gene nomogram constructed from these markers achieved good discrimination and calibration and provided net clinical benefit across a wide range of decision thresholds. Single‑gene GSEA indicated that variation in SLC51A and TPMT expression is associated with coordinated changes in RNA metabolism, macromolecular catabolism, energy utilization, and cell‑cycle–related processes. By integrating blood transcriptomics, eQTL data, and GWAS summary statistics, this study identifies SLC51A and TPMT as candidate diagnostic genes for TAA and proposes a simple two‑gene nomogram. Given the modest sample size, the lack of external validation, and the nominal nature of the genetic evidence, these findings should be considered exploratory and hypothesis‑generating rather than clinically actionable. Independent replication in larger cohorts and functional validation are required before any translational application.