Identification of potential key mRNAs and LncRNAs for psoriasis by bioinformatic analysis using weighted gene co-expression network analysis

Biological functions and affected signaling pathways by Long Non-Coding RNAs in the immune system

Recently, the various regulative functions of long non-coding RNAs (LncRNAs) have been well determined. Recently, the vital role of LncRNAs as gene regulators has been identified in the immune system, especially in the inflammatory response. All cells of the immune system are governed by a complex and ever-changing gene expression program that is regulated through both transcriptional and post-transcriptional processes. LncRNAs regulate gene expression within the cell nucleus by influencing transcription or through post-transcriptional processes that affect the splicing, stability, or translation of messenger RNAs (mRNAs). Recent studies in immunology have revealed substantial alterations in the expression of lncRNAs during the activation of the innate immune system as well as the development, differentiation, and activation of T cells. These lncRNAs regulate key aspects of immune function, including the manufacturing of inflammatory molecules, cellular distinction, and cell movement. They do this by modulating protein-protein interactions or through base pairing with RNA and DNA. Here we review the current understanding of the mechanism of action of lncRNAs as novel immune-related regulators and their impact on physiological and pathological processes related to the immune system, including autoimmune diseases. We also highlight the emerging pattern of gene expression control in important research areas at the intersection between immunology and lncRNA biology.

Psoriasis and Seasonality: Exploring the Genetic and Epigenetic Interactions

Psoriasis is a multifactorial, chronic, and inflammatory disease that severely impacts patients' quality of life. The disease is caused by genetic irregularities affected by epigenetic and environmental factors. Some of these factors may include seasonal changes, such as solar radiation, air pollution, and humidity, and changes in circadian rhythm, especially in the temporal and polar zones. Thus, some psoriasis patients report seasonal variability of symptoms. Through a comprehensive review, we aim to delve deeper into the intricate interplay between seasonality, environmental factors, and the genetic and epigenetic landscape of psoriasis. By elucidating these complex relationships, we strive to provide insights that may inform targeted interventions and personalized management strategies for individuals living with psoriasis.

Neural stem cell-derived extracellular vesicles: The light of central nervous system diseases

Central nervous system (CNS) diseases are the leading cause of death worldwide. By performing compensatory functions and improving the inflammatory microenvironment, the transplantation of neural stem cells (NSCs) can promote functional recovery from brain injury, aging, brain tumours, and other diseases. However, the ability of NSCs to differentiate into neurons is limited, and they are associated with a risk of tumourigenicity. NSC-derived extracellular vesicles (NSC-EVs) can modulate the local microenvironment of the nervous system as well as distant neuronal functions. Thus, cell-free therapy may be a novel remedy for CNS disorders. This article reviews the characteristics, contents, and mechanisms of action of NSC-EVs as well as their roles and application prospects in various CNS diseases.

Integrative bioinformatic analysis identified IFIT3 as a novel regulatory factor in psoriasis

Psoriasis is an autoimmune skin disease with poor prognosis. Currently, there is no cure for psoriasis and the pathogenic mechanism of psoriasis remains unclear. Our study aims to explore key regulators underlying psoriasis and potential targets for psoriasis treatment. RNA-seq data of psoriasis and normal tissues were extracted from Gene Expression Omnibus database to screen differentially expressed genes (DEGs). Weighted correlation network analysis (WGCNA) was conducted to identify key gene modules correlated with psoriasis. Enrichment analysis was used to characterize identified genes. The expression of identified genes was verified in a data set with various types of psoriasis lesion tissues and six psoriasis and healthy control tissues by quantitative polymerase chain reaction and immunohistochemistry assays. And the biological functions of IFIT3 in keratinocytes were determined by colony formation assays, Cell Counting Kit-8, and enzyme-linked immunosorbent assays. A total of 594 overlapped genes (370 upregulated and 224 downregulated) were selected as DEGs between psoriasis and normal tissues in three independent data sets. These genes were enriched in interferon-related pathway and cytokine-related pathway. Weighted correlation network analysis identified several gene modules that were associated with psoriasis. Overlapped genes between gene modules and DEGs were associated with interferon-related pathway and T cell activities. Among these genes, OAS1, USP18, and IFIT3 had higher expression levels in psoriasis vulgaris (PV) and nonpustular palmoplantar psoriasis (NPPP) tissues but not Palmoplantar Pustular Psoriasis (PPPP). Meanwhile, these results were confirmed in our independent psoriasis tissue cohort. And results of in vitro experiments showed that inhibition of IFIT3 significantly impaired the proliferation capacity and CXCL1, CCL20, IL-1β, and IL-6 secretion of keratinocytes. Our study identified key genes and pathways underlying the pathogenesis of psoriasis through the conduct of integrated analysis. OAS1, USP18, and IFIT3 could be potential targets for the treatment of psoriasis. IFIT3 can promote the proliferation and immune activation of keratinocytes and facilitates the development of psoriasis.

Construction of the coexpression network involved in the pathogenesis of thyroid eye disease via bioinformatics analysis

Background

Thyroid eye disease (TED) is the most common orbital pathology that occurs in up to 50% of patients with Graves’ disease. Herein, we aimed at discovering the possible hub genes and pathways involved in TED based on bioinformatical approaches.

Results

The GSE105149 and GSE58331 datasets were downloaded from the Gene Expression Omnibus (GEO) database and merged for identifying TED-associated modules by weighted gene coexpression network analysis (WGCNA) and local maximal quasi-clique merger (lmQCM) analysis. EdgeR was run to screen differentially expressed genes (DEGs). Transcription factor (TF), microRNA (miR) and drug prediction analyses were performed using ToppGene suite. Function enrichment analysis was used to investigate the biological function of genes. Protein–protein interaction (PPI) analysis was performed based on the intersection between the list of genes obtained by WGCNA, lmQCM and DEGs, and hub genes were identified using the MCODE plugin. Based on the overlap of 497 genes retrieved from the different approaches, a robust TED coexpression network was constructed and 11 genes (ATP6V1A, PTGES3, PSMD12, PSMA4, METAP2, DNAJA1, PSMA1, UBQLN1, CCT2, VBP1 and NAA50) were identified as hub genes. Key TFs regulating genes in the TED-associated coexpression network, including NFRKB, ZNF711, ZNF407 and MORC2, and miRs including hsa-miR-144, hsa-miR-3662, hsa-miR-12136 and hsa-miR-3646, were identified. Genes in the coexpression network were enriched in the biological processes including proteasomal protein catabolic process and proteasome-mediated ubiquitin-dependent protein catabolic process and the pathways of endocytosis and ubiquitin-mediated proteolysis. Drugs perturbing genes in the coexpression network were also predicted and included enzyme inhibitors, chlorodiphenyl and finasteride.

Conclusions

For the first time, TED-associated coexpression network was constructed and key genes and their functions, as well as TFs, miRs and drugs, were predicted. The results of the present work may be relevant in the treatment and diagnosis of TED and may boost molecular studies regarding TED.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40246-022-00412-0.

An integrative analysis to reveal that CLEC2B and ferroptosis may bridge the gap between psoriatic arthritis and cancer development

Patients with cutaneous psoriasis (PsC) and psoriatic arthritis (PsA) are reported with increased cancer risk, but the underlying mechanism is less clear, especially the association between the presence of PsA and cancer risk. Motivated by the role of ferroptosis in the progression of cancers as well as inflammation response in psoriasis, this experiment attempts to investigate the relationship between ferroptosis regulators and hub genes in PsA by bioinformatic analysis. The findings revealed an exclusive correlation between CISD1 (ferroptosis regulator) and CLEC2B (hub gene) in PsA group as well as multiple cancer types. Furthermore, CLEC2B was discovered differentially expressed in a variety of cancers and is closely associated with immune cell infiltration as well as immune checkpoints. These results indicate that ferroptosis may act as a bridge between psoriatic arthritis and the onset of certain malignancies.

Identification of TNFAIP6 as a hub gene associated with the progression of glioblastoma by weighted gene co-expression network analysis

This study aims to discover the genetic modules that distinguish glioblastoma multiforme (GBM) from low‐grade glioma (LGG) and identify hub genes. A co‐expression network is constructed using the expression profiles of 28 GBM and LGG patients from the Gene Expression Omnibus database. The authors performed gene ontology (GO) and Kyoto encyclopaedia of genes and genomes (KEGG) analysis on these genes. The maximal clique centrality method was used to identify hub genes. Online tools were employed to confirm the link between hub gene expression and overall patient survival rate. The top 5000 genes with major variance were classified into 18 co‐expression gene modules. GO analysis indicated that abnormal changes in ‘cell migration’ and ‘collagen metabolic process’ were involved in the development of GBM. KEGG analysis suggested that ‘focal adhesion’ and ‘p53 signalling pathway’ regulate the tumour progression. TNFAIP6 was identified as a hub gene, and the expression of TNFAIP6 was increased with the elevation of pathological grade. Survival analysis indicated that the higher the expression of TNFAIP6, the shorter the survival time of patients. The authors identified TNFAIP6 as the hub gene in the progression of GBM, and its high expression indicates the poor prognosis of the patients.

Competing Endogenous RNA Network in Non-Keloid-Prone Individuals During Wound Healing

ABSTRACT

To study the interaction between differentially expressed long non-coding RNAs (lncRNAs), microRNAs, and messenger RNAs during wound healing in normal individuals. The GSE113621 dataset was downloaded from gene expression matrix, specimens regarding non-keloid-prone individuals were selected, including items before and 6 weeks after injury. A Pearson correlation coefficient of > 0.95 was selected as the index to screen targeting relationships among different RNAs. Cytoscape was used to construct a network diagram. The expression of 2547 lncRNAs was changed during the wound healing process-1479 were upregulated and 1068 were downregulated. After analyzing competitive endogenous RNA network, 4 upregulated (MEG8, MEG3, MIR181A1HG, MIR4435-2HG) lncRNAs were found expressed during wound healing. MEG8/MEG3 may regulate fibroblast proliferation, differentiation, and apoptosis through hsa-miR-296-3p/miR-6763-5p. In-depth mining of gene expression matrix data indicated that lncRNAs and a competitive endogenous RNA regulatory network participate in the wound healing process, possibly providing novel intervention targets and treatment options for delayed wound healing.

Maximizing the Utility of Transcriptomics Data in Inflammatory Skin Diseases

Inflammatory skin diseases are induced by disorders of the host defense system of the skin, which is composed of a barrier, innate and acquired immunity, as well as the cutaneous microbiome. These disorders are characterized by recurrent cutaneous lesions and intense itch, which seriously affecting life quality of people across all ages and ethnicities. To elucidate molecular factors for typical inflammatory skin diseases (such as psoriasis and atopic dermatitis), transcriptomic profiling assays have been largely performed. Additionally, single-cell RNA sequencing (scRNA-seq) as well as spatial transcriptomic profiling have revealed multiple potential translational targets and offered guides to improve diagnosis and treatment strategies for inflammatory skin diseases. High-throughput transcriptomics data has shown unprecedented power to disclose the complex pathophysiology of inflammatory skin diseases. Here, we will summarize discoveries from transcriptomics data and discuss how to maximize the transcriptomics data to propel the development of diagnostic biomarkers and therapeutic targets in inflammatory skin diseases.

Integrated analysis of immune-related long noncoding RNAs as diagnostic biomarkers in psoriasis

Background

Psoriasis is a chronic immune-mediated inflammatory dermatosis. Long noncoding RNAs (lncRNAs) play an important role in immune-related diseases. This study aimed to identify potential immune-related lncRNA biomarkers for psoriasis.

Methods

We screened differentially expressed immune-related lncRNAs biomarkers using GSE13355 (skin biopsy samples of 180 cases) from Gene Expression Omnibus (GEO). Moreover, Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and Gene Set Enrichment Analysis (GSEA) were performed to explore biological mechanisms in psoriasis. In addition, we performed LASSO logistic regression to identify potential diagnostic lncRNAs and further verify the diagnostic value and relationship with drug response using two validation sets: GSE30999 (skin biopsy samples of 170 cases) and GSE106992 (skin biopsy samples of 192 cases). Furthermore, we estimated the degree of infiltrated immune cells and investigated the correlation between infiltrated immune cells and diagnostic lncRNA biomarkers.

Results

A total of 394 differentially expressed genes (DEGs) were extracted from gene expression profile. GO and KEGG analysis of target genes found that immune-related lncRNAs were primarily associated with epidermis development, skin development, collagen-containing extracellular matrix, and glycosaminoglycan binding and mainly enriched in cytokine-cytokine receptor interaction and influenza A and chemokine signaling pathway. We found that LINC01137, LINC01215, MAPKAPK5-AS1, TPT1-AS1, CARMN, CCDC18-AS1, EPB41L4A-AS, and LINC01214 exhibited well diagnostic efficacy. The ROC and ROC CI were 0.944 (0.907–0.982), 0.953 (0.919–0.987), 0.822 (0.758–0.887), 0.854 (0.797–0.911), 0.957(0.929–0.985), 0.894 (0.846–0.942), and 0.964 (0.937–0.991) for LINC01137, LINC01215, MAPKAPK5-AS1, TPT1-AS1,CARMN, CCDC18-AS1, EPB41L4A-AS1, and LINC01214. LINC01137, LINC01215, and LINC01214 were correlated with drug response. LINC01137, CCDC18-AS1, and CARMN were positively correlated with activated memory CD4 T cell, activated myeloid dendritic cell (DC), neutrophils, macrophage M1, and T follicular helper (Tfh) cells, while negatively correlated with T regulatory cell (Treg). LINC01215, MAPKAPK5-AS1, TPT1-AS1, EPB41L4A-AS, and LINC01214 were negatively correlated with activated memory CD4 T cell, activated myeloid DC, neutrophils, macrophage M1, and Tfh, while positively correlated with Treg.

Conclusions

These findings indicated that these immune-related lncRNAs may be used as potential diagnostic and predictive biomarkers for psoriasis.

Omics-Driven Biomarkers of Psoriasis: Recent Insights, Current Challenges, and Future Prospects

Advances in omics technologies have made it possible to unravel biomarkers from different biological levels. Intensive studies have been carried out to uncover the dysregulations in psoriasis and to identify molecular signatures associated with the pathogenesis of psoriasis. In this review, we presented an overview of the current status of the omics-driven biomarker research and emphasized the transcriptomic, epigenomic, proteomic, metabolomic, and glycomic signatures proposed as psoriasis biomarkers. Furthermore, insights on the limitations and future directions of the current biomarker discovery strategies were discussed, which will continue to comprehend broader visions of psoriasis research, diagnosis, and therapy especially in the context of personalized medicine.

Using Micro- and Macro-Level Network Metrics Unveils Top Communicative Gene Modules in Psoriasis

(1) Background: Psoriasis is a multifactorial chronic inflammatory disorder of the skin, with significant morbidity, characterized by hyperproliferation of the epidermis. Even though psoriasis’ etiology is not fully understood, it is believed to be multifactorial, with numerous key components. (2) Methods: In order to cast light on the complex molecular interactions in psoriasis vulgaris at both protein–protein interactions and transcriptomics levels, we studied a set of microarray gene expression analyses consisting of 170 paired lesional and non-lesional samples. Afterwards, a network analysis was conducted on the protein–protein interaction network of differentially expressed genes based on micro- and macro-level network metrics at a systemic level standpoint. (3) Results: We found 17 top communicative genes, all of which were experimentally proven to be pivotal in psoriasis, which were identified in two modules, namely the cell cycle and immune system. Intra- and inter-gene interaction subnetworks from the top communicative genes might provide further insight into the corresponding characteristic interactions. (4) Conclusions: Potential gene combinations for therapeutic/diagnostics purposes were identified. Moreover, our proposed workflow could be of interest to a broader range of future biological network analysis studies.

The eminent roles of ncRNAs in the pathogenesis of psoriasis

Psoriasis is a chronic immune-related disorder in which both genetic and environmental parameters are involved. Recent studies have demonstrated dysregulation of long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) in the peripheral blood or skin lesions of patients with psoriasis. While a number of lncRNAs such as MEG3, AL162231.4 and NONHSAT044111 have been down-regulated in the course of psoriasis, others including PRINS, MIR31HG, RP6‐65G23.1, MSX2P1, SLC6A14-1:1, NR_003062 have been up-regulated. Moreover, expressions of several miRNAs have been dysregulated in this disorder. Among dysregulated miRNAs are miR-126, miR-143, miR-19a and miR-155 whose diagnostic roles in the psoriasis have also been assessed. Dysregulated non-coding RNAs in this disorder participate in the regulation of chemokine signaling pathway and immune response, control of epidermal development and skin barrier as well as modulation of function of certain subsets of T cells. Besides, these transcripts possibly regulate activity of NF-κΒ, mTOR, MAPK and JAK-STAT signaling pathways. Besides, expression levels of circRNAs have been decreased in the psoriasis lesions. Massive alterations in the levels of lncRNAs and miRNAs in the psoriasis lesions or peripheral blood of affected individuals show participation of these transcripts in the pathogenesis of this disorder.

Transcriptomes from German shepherd dogs reveal differences in immune activity between atopic dermatitis affected and control skin

Canine atopic dermatitis (CAD) is an inflammatory and pruritic allergic skin disease with both genetic and environmental risk factors described. We performed mRNA sequencing of non-lesional axillary skin biopsies from nine German shepherd dogs. Obtained RNA sequences were mapped to the dog genome (CanFam3.1) and a high-quality skin transcriptome was generated with 23,510 expressed gene transcripts. Differentially expressed genes (DEGs) were defined by comparing three controls to five treated CAD cases. Using a leave-one-out analysis, we identified seven DEGs: five known to encode proteins with functions related to an activated immune system (CD209, CLEC4G, LOC102156842 (lipopolysaccharide-binding protein-like), LOC480601 (regakine-1-like), LOC479668 (haptoglobin-like)), one (OBP) encoding an odorant-binding protein potentially connected to rhinitis, and the last (LOC607095) encoding a novel long non-coding RNA. Furthermore, high mRNA expression of inflammatory genes was found in axillary skin from an untreated mild CAD case compared with healthy skin. In conclusion, we define genes with different expression patterns in CAD case skin helping us understand post-treatment atopic skin. Further studies in larger sample sets are warranted to confirm and to transfer these results into clinical practice.