Bioinformatics analysis to reveal the potential comorbidity mechanism in psoriasis and nonalcoholic steatohepatitis

Determining IFI44 as a key lupus nephritis's biomarker through bioinformatics and immunohistochemistry

BACKGROUND

Lupus nephritis (LN) emerges as a severe complication of systemic lupus erythematosus (SLE), significantly affecting patient survival. Despite improvements in treatment reducing LN's morbidity and mortality, existing therapies remain suboptimal, emphasizing the necessity for early detection to improve patient outcomes.

METHODS

This study employs bioinformatics and machine learning to identify and validate potential LN biomarkers using immunohistochemistry (IHC). It explores the relationship between these biomarkers and the clinical and pathological characteristics of LN, assessing their prognostic significance. The research provides deeper mechanistic insights by employing Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Additionally, the study characterizes the immune profiles of LN patients through the CIBERSORT algorithm, focusing on the role of interferon-inducible protein 44 (IFI44) as a key biomarker.

RESULTS

IFI44 shows elevated expression in LN-affected kidneys, compared to healthy controls. The levels of IFI44 positively correlate with serum creatinine and the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) and inversely with serum complement C3 and initial estimated glomerular filtration rate (eGFR).

CONCLUSION

IFI44 is identified as a promising biomarker for LN, offering potential to refine the assessment of disease progression and predict clinical outcomes. This facilitates the development of more personalized treatment strategies for LN patients.

Hypoxia-induced RHCG as a key regulator in psoriasis and its modulation by secukinumab

The interaction between keratinocytes (KCs) and immune cells is essential in the pathogenesis of psoriasis. Understanding this crosstalk is crucial for developing effective treatment strategies. Recent studies indicate that Rh family C-type glycoprotein (RHCG) enhances cell proliferation and alters cell differentiation; however, its exact pathogenic mechanisms in psoriasis remain unclear. We employed bioinformatics approaches, including spatial transcriptomics analysis, single-cell transcriptomics analysis, and bulk data analysis, to elucidate the biological functions of RHCG. These predictions were validated through ex vivo experiments and analysis of clinical specimens. In psoriatic skin, RHCG protein levels were significantly upregulated, with an expanded expression area. Notably, RHCG expression was induced under hypoxic conditions. Furthermore, the upregulation of RHCG enhanced the expression of KC markers S100 Calcium Binding Protein A family (S100A) and Keratin 17 (KRT17), while decreasing Keratin 1 (KRT1) expression. Additionally, RHCG overexpression increased the secretion of C-X-C motif chemokine ligand 14 (CXCL14) from KCs, which subsequently activated dendritic cells. Importantly, treatment with secukinumab effectively ameliorated psoriasis by downregulating RHCG expression and inhibiting associated signaling pathways, whereas glucocorticoid and methotrexate treatments resulted in elevated RHCG expression. These findings indicate that RHCG plays a significant role in hypoxia-induced cellular crosstalk and suggest that RHCG-associated signaling may contribute to the superior efficacy of biological agents compared to conventional hormonal and immunosuppressive therapies.

The diagnostic value and associated molecular mechanism study for fibroblast-related mitochondrial genes on keloid

PURPOSE

This study aims to reveal the mechanism of fibroblast-related mitochondrial genes on keloid formation and explore promising signature genes for keloid diagnosis.

METHOD

The distribution of fibroblasts between the keloid sample and control sample based on three keloid datasets, followed by the differentially expressed genes (DEGs) investigation and associated enrichment analysis. Then, hub genes were explored based on DEGs, mitochondrial genes from an online database, as well as fibroblast-related genes that were revealed by WCGNA. Subsequently, signature genes were screened through machine learning, and their diagnostic value was validated by nomogram. Moreover, the targeted drugs and related transcriptional regulation of these genes were analyzed. Finally, the verification analysis was performed on signature genes using qPCR analysis.

RESULT

A total of totally 329 DEGs were revealed based on three datasets, followed by enrichment analysis. WGCNA revealed a total of 258 fibroblast-related genes, which were primarily assembled in functions like muscle tissue development. By using machine learning, we screened four signature genes (ACSF2, ALDH1B1, OCIAD2, and SIRT4) based on eight hub genes (fibroblast-related mitochondrial genes). Nomogram and validation analyses confirmed the well-diagnostic performance of these four genes in keloid. Immune infiltration and drug correlation analyses showed that SIRT4 was significantly associated with immune cell type 2 T helper cells and molecular drug cyclosporin. All these findings provided new perspectives for the clinical diagnosis and therapy of keloid.

CONCLUSION

The fibroblast-related mitochondrial genes including SIRT4, OCIAD2, ALDH1B1, and ACSF2 were novel signature genes for keloid diagnosis, offering novel targets and strategies for diagnosis and therapy of keloid.

Key genes and immune infiltration patterns and the clinical implications in psoriasis patients

BACKGROUND

Psoriasis is an immune-mediated skin disease, closely related to immune regulation. The aim was to understand the pathogenesis of psoriasis further, reveal potential therapeutic targets, and provide new clues for its diagnosis, treatment, and prevention.

MATERIALS AND METHODS

Expression profiling data were obtained from the Gene Expression Omnibus (GEO) database for skin tissues from healthy population and psoriasis patients. Differentially expressed genes (DEGs) were selected for Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) analysis separately. Machine learning algorithms were used to obtain characteristic genes closely associated with psoriasis. Receiver operating characteristic (ROC) curve was used to assess the diagnostic value of the characteristic genes for psoriasis. The Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was used to calculate the proportion of immune cell infiltration. Correlation analysis was used to characterize the connection between gene expression and immune cell, Psoriasis Area and Severity Index (PASI).

RESULTS

A total of 254 DEGs were identified in the psoriasis group, including 185 upregulated and 69 downregulated genes. GO was mainly enriched in cytokine-mediated signaling pathway, response to virus, and cytokine activity. KEGG was mainly focused on cytokine-cytokine receptor interaction and IL-17 signaling pathway. GSEA was mainly in chemokine signaling pathway and cytokine-cytokine receptor interaction. The machine learning algorithm screened nine characteristic genes C10orf99, GDA, FCHSD1, C12orf56, S100A7, INA, CHRNA9, IFI44, and CXCL9. In the validation set, the expressions of these nine genes increased in the psoriasis group, and the AUC values were all > 0.9, consistent with those of the training set. The immune infiltration results showed increased proportions of macrophages, T cells, and neutrophils in the psoriasis group. The characteristic genes were positively or negatively correlated to varying degrees with T cells and macrophages. Nine characteristic genes were highly expressed in the moderate to severe psoriasis group and positively correlated with PASI scores.

CONCLUSION

High levels of nine characteristic genes C10orf99, GDA, FCHSD1, C12orf56, S100A7, INA, CHRNA9, IFI44, and CXCL9 were risk factors for psoriasis, the differential expression of which was related to the regulation of immune system activity and PASI scores, affecting the proportions of different immune cells and promoting the occurrence and development of psoriasis.

Molecular Morbidity Score-Can MicroRNAs Assess the Burden of Disease?

Multimorbidity refers to the presence of two or more chronic diseases and is associated with adverse outcomes for patients. Factors such as an ageing population have contributed to a rise in prevalence of multimorbidity globally; however, multimorbidity is often neglected in clinical guidelines. This is largely because patients with multimorbidity are systematically excluded from clinical trials. Accordingly, there is an urgent need to develop novel biomarkers and methods of prognostication for this cohort of patients. The hallmarks of ageing are now thought to potentiate the pathogenesis of multimorbidity. MicroRNAs are small, regulatory, noncoding RNAs which have been implicated in the pathogenesis and prognostication of numerous chronic diseases; there is a substantial body of evidence now implicating microRNA dysregulation with the different hallmarks of ageing in the aetiology of chronic diseases. This article proposes using the hallmarks of ageing as a framework to develop a panel of microRNAs to assess the prognostic burden of multimorbidity. This putative molecular morbidity score would have many potential applications, including assessing the efficacy of clinical interventions, informing clinical decision making and facilitating wider inclusion of patients with multimorbidity in clinical trials.

Identification of common mechanisms and biomarkers for dermatomyositis and atherosclerosis based on bioinformatics analysis

BACKGROUND

Dermatomyositis (DM) manifests as an autoimmune and inflammatory condition, clinically characterized by subacute progressive proximal muscle weakness, rashes or both along with extramuscular manifestations. Literature indicates that DM shares common risk factors with atherosclerosis (AS), and they often co-occur, yet the etiology and pathogenesis remain to be fully elucidated. This investigation aims to utilize bioinformatics methods to clarify the crucial genes and pathways that influence the pathophysiology of both DM and AS.

METHOD

Microarray datasets for DM (GSE128470, GSE1551, GSE143323) and AS (GSE100927, GSE28829, GSE43292) were retrieved from the Gene Expression Omnibus (GEO) database. The weighted gene co-expression network analysis (WGCNA) was used to reveal their co-expressed modules. Differentially expression genes (DEGs) were identified using the "limma" package in R software, and the functions of common DEGs were determined by functional enrichment analysis. A protein-protein interaction (PPI) network was established using the STRING database, with central genes evaluated by the cytoHubba plugin, and validated through external datasets. Immune infiltration analysis of the hub genes was conducted using the CIBERSORT method, along with Gene Set Enrichment Analysis (GSEA). Finally, the NetworkAnalyst platform was employed to examine the transcription factors (TFs) responsible for regulating pivotal crosstalk genes.

RESULTS

Utilizing WGCNA analysis, a total of 271 overlapping genes were pinpointed. Subsequent DEG analysis revealed 34 genes that are commonly found in both DM and AS, including 31 upregulated genes and 3 downregulated genes. The Degree Centrality algorithm was applied separately to the WGCNA and DEG collections to select the 15 genes with the highest connectivity, and crossing the two gene sets yielded 3 hub genes (PTPRC, TYROBP, CXCR4). Validation with external datasets showed their diagnostic value for DM and AS. Analysis of immune infiltration indicates that lymphocytes and macrophages are significantly associated with the pathogenesis of DM and AS. Moreover, GSEA analysis suggested that the shared genes are enriched in various receptor interactions and multiple cytokines and receptor signaling pathways. We coupled the 3 hub genes with their respective predicted genes, identifying a potential key TF, CBFB, which interacts with all 3 hub genes.

CONCLUSION

This research utilized comprehensive bioinformatics techniques to explore the shared pathogenesis of DM and AS. The three key genes, including PTPRC, TYROBP, and CXCR4, are related to the pathogenesis of DM and AS. The central genes and their correlations with immune cells may serve as potential diagnostic and therapeutic targets.

Identification of key genes and molecular mechanisms of chronic urticaria based on bioinformatics

Chronic urticaria (CU) is characterized by persistent skin hives, redness, and itching, enhanced by immune dysregulation and inflammation. Our main objective is identifying key genes and molecular mechanisms of chronic urticaria based on bioinformatics. We used the Gene Expression Omnibus (GEO) database and retrieved two GEO datasets, GSE57178 and GSE72540. The raw data were extracted, pre-processed, and analyzed using the GEO2R tool to identify the differentially expressed genes (DEGs). The samples were divided into two groups: healthy samples and CU samples. We defined cut-off values of log2 fold change ≥1 and p < .05. Analyses were performed in the Kyoto Encyclopaedia of Genes and Genomes (KEGG), the Database for Annotation, Visualization and Integrated Discovery (DAVID), Metascape, Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and CIBERSOFT databases. We obtained 1613 differentially expressed genes. There were 114 overlapping genes in both datasets, out of which 102 genes were up-regulated while 12 were down-regulated. The biological processes included activation of myeloid leukocytes, response to inflammations, and response to organic substances. Moreover, the KEGG pathways of CU were enriched in the Nuclear Factor-Kappa B (NF-kB) signaling pathway, Tumor Necrosis Factor (TNF) signaling pathway, and Janus kinase/signal transducers and activators of transcription (JAK-STAT) signaling pathway. We identified 27 hub genes that were implicated in the pathogenesis of CU, such as interleukin-6 (IL-6), Prostaglandin-endoperoxide synthase 2 (PTGS2), and intercellular adhesion molecule-1 (ICAM1). The complex interplay between immune responses, inflammatory pathways, cytokine networks, and specific genes enhances CU. Understanding these mechanisms paves the way for potential interventions to mitigate symptoms and improve the quality of life of CU patients.

Exploring the comorbidity mechanisms between psoriasis and obesity based on bioinformatics

BACKGROUND

Psoriasis is a chronic, recurrent, immune-mediated inflammatory skin disease characterized by erythematous scaly lesions. Obesity is currently a major global health concern, increasing the risk of diseases such as cardiovascular diseases and diabetes. Since the correlation between psoriasis and obesity, as well as hypertension, diabetes, and cardiovascular diseases, has been clinically evidenced, it is of certain clinical significance to explore the mechanisms underlying the comorbidity of psoriasis with these conditions.

MATERIALS AND METHODS

Gene targets for both diseases were obtained from the Gene Expression Omnibus (GEO) comprehensive gene expression database. Differential gene analysis, intersection gene analysis, construction and visualization of protein-protein interaction networks (PPI) using R software, Cytoscape 3.8.2 software, online tools such as String, and enrichment analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed, with relevant graphics generated.

RESULTS

Analysis identified 29 intersecting genes between the two diseases, with 10 key targets such as S100A7 and SERPINB4. Enrichment analysis indicated their involvement in regulating biological processes such as leukocyte chemotaxis, migration, and chronic inflammatory responses through cellular structures such as intracellular vesicles and extracellular matrix. Molecular functions, including RAGE receptor binding, Toll-like receptor binding, and fatty acid binding, were found to simultaneously regulate psoriasis and obesity.

CONCLUSION

Psoriasis and obesity may mutually influence each other through multiple targets and pathways, emphasizing the importance of considering comorbidity treatment and daily care in clinical practice.

Identification of potential crucial genes shared in psoriasis and ulcerative colitis by machine learning and integrated bioinformatics

BACKGROUND

Mounting evidence suggest that there are an association between psoriasis and ulcerative colitis (UC), although the common pathogeneses are not fully understood. Our study aimed to find potential crucial genes in psoriasis and UC through machine learning and integrated bioinformatics.

METHODS

The overlapping differentially expressed genes (DEGs) of the datasets GSE13355 and GSE87466 were identified. Then the functional enrichment analysis was performed. The overlapping genes in LASSO, SVM-RFE and key module in WGCNA were considered as potential crucial genes. The receiver operator characteristic (ROC) curve was used to estimate their diagnostic confidence. The CIBERSORT was conducted to evaluate immune cell infiltration. Finally, the datasets GSE30999 and GSE107499 were retrieved to validate.

RESULTS

112 overlapping DEGs were identified in psoriasis and UC and the functional enrichment analysis revealed they were closely related to the inflammatory and immune response. Eight genes, including S100A9, PI3, KYNU, WNT5A, SERPINB3, CHI3L2, ARNTL2, and SLAMF7, were ultimately identified as potential crucial genes. ROC curves showed they all had high confidence in the test and validation datasets. CIBERSORT analysis indicated there was a correlation between infiltrating immune cells and potential crucial genes.

CONCLUSION

In our study, we focused on the comprehensive understanding of pathogeneses in psoriasis and UC. The identification of eight potential crucial genes may contribute to not only understanding the common mechanism, but also identifying occult UC in psoriasis patients, even serving as therapeutic targets in the future.

The causal relationship between pure hypercholesterolemia and psoriasis: A bidirectional, two-sample Mendelian randomization study

BACKGROUND

Several studies have reported the association between pure hypercholesterolemia (PH) and psoriasis, but the causal effect remains unclear.

METHODS

We explored the causal effect between PH and psoriasis using two-sample bidirectional Mendelian randomization (MR) analysis using data from genome-wide association studies. Single nucleotide polymorphisms related with exposures at the genome-wide significance level (p < 5×10-8 ) and less than the linkage disequilibrium level (r2  < 0.001) were chosen as instrumental variables. Subsequently, we used inverse variance weighting (IVW), MR-Egger and weighted median (WM) methods for causal inference. p < 0.05 was considered statistically significant. Heterogeneity was tested using Cochran's Q-test, and horizontal pleiotropy was examined using the MR-Egger intercept. Leave-one-out analyses were performed to assess the robustness and reliability of the results.

RESULTS

MR results showed a positive causal effect of PH on psoriasis [IVW: odds ratios (OR): 1.139, p = 0.032; MR-Egger: OR: 1.434, p = 0.035; WM: OR: 1.170, p = 0.045] and psoriatic arthritis (PsA) (IVW: OR: 1.210, p = 0.049; MR-Egger regression: OR: 1.796, p = 0.033; WM: OR: 1.317, p = 0.028). However, there is no causal relationship between PH and psoriasis vulgaris as well as other unspecified psoriasis. Inverse MR results suggested a negative causal relationship between PsA and PH (IVW: OR: 0.950, p = 0.037). No heterogeneity and horizontal pleiotropy exist, and these results were confirmed to be robust.

CONCLUSION

PH has a positive casual effect on psoriasis and PsA, and PsA may reduce the risk of having PH.