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PubMed2026

Exploring shared biomarkers and their mechanisms in thyroid cancer and systemic lupus erythematosus via bioinformatics analysis.

BACKGROUND: Systemic lupus erythematosus (SLE), an autoimmune disorder, is linked to a heightened risk of multiple malignancies, including thyroid cancer. Thyroid cancer is the most prevalent malignancy of the endocrine system, and its autoimmune-related pathological features render it an optimal subject for investigating the mechanisms of their comorbidity. The molecular mechanisms underlying this comorbidity are still ambiguous. The accurate diagnosis and treatment of thyroid cancer urgently necessitate innovative molecular targets that extend beyond conventional pathological characteristics. This study seeks to employ integrated bioinformatics approaches to elucidate potential shared molecular mechanisms and immunological features between thyroid cancer and systemic lupus erythematosus (SLE), aiming to enhance understanding of their comorbidity and identify novel intervention targets. METHODS: This study initially acquired gene expression data for TC and SLE from the GEO database and subsequently screened and identified differentially expressed genes (DEGs) shared by both diseases. Subsequently, we conducted Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Reactome functional enrichment analyses on these 46 shared differentially expressed genes (DEGs) and further assessed the activation status of pertinent pathways using Gene Set Enrichment Analysis (GSEA). Subsequently, we employed CIBERSORTx to examine immune infiltration patterns and developed protein-protein interaction networks utilising the STRING database. We identified hub genes utilising the MCODE and cytoHubba plugins and visualised the findings with Cytoscape software. We additionally assessed the diagnostic efficacy of these core hub genes in an independent dataset utilising ROC curves and investigated their prognostic relevance in thyroid cancer through Kaplan-Meier survival analysis and multivariate Cox proportional hazards regression. Ultimately, we employed the Network Analyst platform to forecast transcription factor-gene and miRNA-gene regulatory networks and identified potential targeted therapeutic compounds utilising the DSigDB database. RESULTS: This study identified 46 differentially expressed genes (DEGs) commonly linked to thyroid cancer and systemic lupus erythematosus (SLE), which were significantly enriched in signalling pathways associated with immune-inflammatory activation, type I interferon responses, and complement pathway activation. Moreover, GSEA findings validated that immune-inflammatory and autoimmune-related pathways are markedly activated in both conditions. Twelve hub genes were discerned through protein-protein interaction networks. Analysis of immune infiltration indicated that thyroid cancer and systemic lupus erythematosus exhibit a shared characteristic of innate immune dysregulation, marked by the infiltration of myeloid cells (neutrophils, M0/M2 macrophages). Receiver operating characteristic (ROC) curve analysis identified six significant core hub genes with substantial diagnostic value: C1QB, LCN2, C1QC, LTF, VSIG4, and C3AR1. Univariate survival analysis indicated that elevated expression of C1QC and C3AR1 significantly enhances overall survival in thyroid cancer patients; however, multivariate COX regression analysis revealed that their independent prognostic significance necessitates further validation. This study predicted the interaction networks of transcription factors and miRNAs regulating key genes, with LCN2 demonstrating the highest connectivity to miRNAs, and identified candidate therapeutic compounds linked to it. CONCLUSION: This study employed bioinformatics analysis to identify critical shared hub genes and molecular pathways connecting thyroid cancer and systemic lupus erythematosus, offering novel insights into their shared pathogenesis and the advancement of targeted biomarkers and therapeutic strategies.

Bioinformatics analysis↗

PubMed2026

Methylation profiling of normal tissue adjacent to breast tumors reveals two distinct groups with divergent tumor microenvironment features.

We previously identified diverse genetic evolutionary patterns in whole-genome sequencing of paired normal tissue adjacent to tumor (NAT) and tumor tissues from Hong Kong breast cancer (HKBC) patients. Here, we investigated whether DNA methylation (DNAm) contributes to NAT heterogeneity and shapes the tumor microenvironment (TME). Genome-wide DNAm profiling was performed on paired NAT and tumor tissues from 188 HKBC patients using the Infinium 850 K array. RNA-seq data were available for 76 NATs and 177 tumors. Cellular composition was inferred using MethylCIBERSORT, CIBERSORTx, and EpiDISH, and histopathologic features were assessed on 115 H&E-stained sections. Unsupervised clustering identified two distinct NAT subtypes with divergent TME characteristics. Cluster 1 (N = 139) showed higher epithelial and fibroblast content and enrichment of estrogen response pathways. Cluster 2 (N = 49) exhibited an immune-metabolic phenotype characterized by increased fat and immune cells, stromal disruption, inflammatory pathway activation, and greater macrophage infiltration. Cluster 2 patients also demonstrated significantly younger epigenetic age estimated using multiple epigenetic clocks. These DNAm-defined NAT subtypes and associated TME features were validated in 97 NAT samples from TCGA breast cancer patients. Overall, our findings identify DNAm-driven NAT heterogeneity with distinct TME landscapes, providing new insights into field cancerization and tumor evolution in breast cancer.

Journal Article↗

PubMed2026

POU2F3 expression in lung squamous cell carcinoma: transcriptomic and immunohistochemical profiling with prognosis.

BACKGROUND: Lung squamous cell carcinoma (LUSC) lacks well-defined molecular targets. This study investigated the clinical and biological relevance of POU class 2 homeobox 3 (POU2F3), a tuft cell-associated transcription factor, in LUSC. METHODS: RNA sequencing data of patients with LUSC from The Cancer Genome Atlas (TCGA cohort, n = 190) was analysed and compared to a cohort of surgically resected cases analyzed via immunohistochemistry (IHC cohort, n = 137). Prognostic impact was assessed via survival analyses. Transcriptomic features, pathway enrichment, and immune profiles were evaluated via differentially expressed gene analysis, Gene Set Enrichment Analysis, and CIBERSORTx. RESULTS: High POU2F3 expression independently predicted poor overall survival in the TCGA cohort (HR = 2.06, 95% CI: 1.04-4.08, P = 0.039). In contrast, POU2F3 expression was not prognostic in the IHC cohort (P = 0.995). Morphologically, POU2F3-positive tumours were enriched for non-keratinizing and poorly differentiated subtypes. Transcriptomic analysis showed suppression of proliferation and immune-related pathways (FDR < 0.001), with suggestive enrichment of the TGF-β (FDR = 0.143) and p53 (FDR = 0.229) signaling pathways. On immune deconvolution, POU2F3-high tumours showed a nominal increase in activated dendritic cells, which did not withstand multiple testing correction. POU2F3 protein was detected in 12.4% of tumours and was significantly associated with p53 or RB1 abnormalities (single or double) (P = 0.028). CONCLUSIONS: POU2F3 marks a transcriptionally distinct, early-stage subtype of LUSC with keratinization-related features. Its prognostic relevance appears context-dependent and requires prospective validation in uniformly treated cohorts.

Humans↗

PubMed2026

Admission whole-blood transcriptomic characterization of a neutrophil-predominant systemic immune response in patients with acute traumatic brain injury.

BACKGROUND: Acute traumatic brain injury (TBI) is accompanied by systemic immune responses, but their whole-blood transcriptomic features at hospital arrival remain incompletely characterized. We aimed to characterize these features in patients with acute TBI compared with healthy controls. METHODS: In this single-center prospective observational study, we performed whole-blood RNA sequencing on hospital-arrival samples from 42 patients with acute TBI and 21 healthy controls. Analyses included differential expression (limma-voom; FDR < 0.05, |log2FC| > 0.7), functional enrichment, Ingenuity Pathway Analysis, CIBERSORTx LM22 deconvolution, and per-sample neutrophil degranulation signature scoring. RESULTS: Differential expression analysis identified 996 upregulated and 863 downregulated genes, with marked upregulation of inflammation-, innate immunity-, and neutrophil-related genes including DUSP1, HMGB2, MMP9, and S100A8. Canonical pathways with positive IPA z-scores included Neutrophil degranulation, Neutrophil Extracellular Trap Signaling Pathway, and Toll-like Receptor Signaling; upstream regulators included TNF, IL1B, IFNG, and STAT3. Deconvolution identified 7 of 22 differing subsets (q < 0.05), with relatively higher myeloid and lower lymphoid fractions in TBI. The Neutrophil degranulation signature score correlated with Injury Severity Score within TBI (Spearman ρ = +0.55; q < 0.001). CONCLUSIONS: Admission whole-blood transcriptomics characterized a neutrophil-predominant systemic transcriptional response in patients with acute TBI. This response was also evident among patients without major extracranial injury and was associated with total ISS. However, because the study lacked an appropriately matched non-TBI trauma comparator, the findings should be interpreted as a descriptive characterization of a systemic injury response accompanying TBI and do not establish a TBI-specific molecular signature or mechanism.

gene expression↗

PubMed2026

Early Transcriptional Changes in Neutrophil-Mediated Processes Following Recanalization After Ischemic Stroke.

BACKGROUND: Ischemic stroke is a leading cause of death and long-term disability worldwide. Recanalization therapies, including thrombolysis and mechanical thrombectomy, restore blood flow, yet many patients experience poor outcomes, a phenomenon known as futile recanalization. Given the short therapeutic window for ischemic stroke, identifying early biomarkers to guide targeted interventions and improve outcomes is critical. METHODS: Using a murine middle cerebral occlusion model that mimics a large vessel occlusion with recanalization, a comprehensive microarray analysis from blood samples collected immediately and 3 hours after recanalization (N=44) was performed. Differentially expressed genes, enrichment pathways, immune cell proportions, enriched cell markers, predicted micro-RNAs, and transcription factors were identified using RStudio. Findings in mice were validated with rat middle cerebral artery occlusion (GSE21136) and patients with stroke (GSE16561) data sets to confirm transcriptional changes in peripheral blood postrecanalization. RESULTS: Il1r2, Cd55, Mmp8, Cd14, and Cd69 were early biomarkers poststroke and postrecanalization. Cross-validation revealed Vcan as a differentially expressed gene conserved across species, making it a novel ischemic marker detected as early as 3 hours postrecanalization (4 hours after middle cerebral artery occlusion) in mice, 24 hours after recanalization in rats (middle cerebral artery occlusion-thrombectomy), and within 24 hours from onset in humans receiving recombinant tissue plasminogen activator-thrombolysis. CIBERSORTx and ImmuCellAI-mouse deconvolution showed neutrophil elevation postrecanalization. Leukocyte and neutrophil activation pathways were enriched early after stroke in mice and humans, with stronger upregulation in the female sex. Several regulatory micro-RNAs were identified, and Nuclear Factor Erythroid 4 (NFE4) and Metal Regulatory Transcription Factor 1 (MTF1) emerged as key transcription factors. A coregulatory network underlying neutrophil activity was constructed, highlighting its central role in early responses to ischemia and recanalization, which was enriched in the female sex. CONCLUSIONS: We identified novel early genomic markers for ischemia and recanalization, including the conserved marker Vcan, and highlighted age- and sex-specific immune responses. Mapping a neutrophil-centered coregulatory network provides mechanistic insight into futile recanalization and supports the development of targeted therapies to improve clinical outcomes.

Animals↗

PubMed2025

Penalised regression improves imputation of cell-type specific expression using RNA-seq data from mixed cell populations compared to domain-specific methods.

Gene expression studies often use bulk RNA sequencing of mixed cell populations because single cell or sorted cell sequencing may be prohibitively expensive. However, mixed cell studies may miss expression patterns that are restricted to specific cell populations. Computational deconvolution can be used to estimate cell fractions from bulk expression data and infer average cell-type expression in a set of samples (e.g., cases or controls), but imputing sample-level cell-type expression is required for more detailed analyses, such as relating expression to quantitative traits, and is less commonly addressed. Here, we assessed the accuracy of imputing sample-level cell-type expression using a real dataset where mixed peripheral blood mononuclear cells (PBMC) and sorted (CD4, CD8, CD14, CD19) RNA sequencing data were generated from the same subjects (N=158), and pseudobulk datasets synthesised from eQTLgen single cell RNA-seq data. We compared three domain-specific methods, CIBERSORTx, bMIND and debCAM/swCAM, and two cross-domain machine learning methods, multiple response LASSO and ridge, that had not been used for this task before. We also assessed the methods according to their ability to recover differential gene expression (DGE) results. LASSO/ridge showed higher sensitivity but lower specificity for recovering DGE signals seen in observed data compared to deconvolution methods, although LASSO/ridge had higher area under curves than deconvolution methods. Machine learning methods have the potential to outperform domain-specific methods when suitable training data are available.

Humans↗

PubMed2025

Unravelling the transcriptomic characteristics of bronchoalveolar lavage in post-covid pulmonary fibrosis.

BACKGROUND: Post-Covid Pulmonary Fibrosis (PCPF) has emerged as a significant global issue associated with a poor quality of life and significant morbidity. Currently, our understanding of the molecular pathways of PCPF is limited. Hence, in this study, we performed whole transcriptome sequencing of the RNA isolated from the bronchoalveolar lavage (BAL) samples of PCPF and compared it with idiopathic pulmonary fibrosis (IPF) and non-ILD (Interstitial Lung Disease) control to understand the gene expression profile and associated pathways. METHODS: BAL samples from PCPF (n = 3), IPF (n = 3), and non-ILD Control (n = 3) (individuals with apparent healthy lung without interstitial lung disease) groups were obtained and RNA were isolated for whole transcriptomic sequencing. Differentially Expressed Genes (DEGs) were determined followed by functional enrichment analysis and qPCR validation. RESULTS: A panel of differentially expressed genes were identified in bronchoalveolar lavage fluid cells (BALF) of PCPF as compare to control and IPF. Our analysis revealed dysregulated pathways associated with cell cycle regulation, immune responses, and neuroinflammatory processes. Real-time validation further supported these findings. The PPI network and module analysis shed light on potential biomarkers and underscore the complex interplay of molecular mechanisms in PCPF. The comparison of PCPF and IPF identified a significant downregulation of pathways that were more prominent in IPF. CONCLUSION: This investigation provides crucial insights into the molecular mechanism of PCPF and also outlines avenues for prospective research and the development of therapeutic approaches.

Humans↗