Integrated bioinformatic analysis of differentially expressed genes and signaling pathways in plaque psoriasis

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

PURPOSE

An increasing amount of evidence suggests that psoriasis and nonalcoholic steatohepatitis (NASH) may occur simultaneously, whereas the underlying mechanisms remain unclear. Our research aims to explore the potential comorbidity mechanism in psoriasis and nonalcoholic steatohepatitis.

MATERIALS AND METHODS

The expression profiles of psoriasis (GSE30999, GSE13355) and NASH (GSE24807, GSE17470) were downloaded from GEO datasets. Next, common differently expressed genes (DEGs) of psoriasis and NASH were investigated. Then, GO and KEGG enrichment, protein interaction network (PPI) construction, and hub gene identification for DEGs were performed. Finally, immune cells expression, target genes predicted by common miRNAs, and transcription factors interaction analysis for hub genes were carried out.

RESULTS

Twenty DEGs were identified in totally. GO analysis revealed response to the virus was the most enriched term, and hepatitis C and coronavirus disease-COVID-19 infection-associated pathways were mainly enriched in KEGG. A total of eight hub genes were collected, including IFIT1, IFIT3, OAS1, HPGDS, IFI27, IFI44, CXCL10, IRF9, and 11 TFs were predicted. Then, neutrophils and monocytes were identified as immune cells that express the most hub genes. Moreover, five common miRNAs for psoriasis and NASH and one common miRNAs (hsa-miR-1305)-mRNAs (CHL1, MBNL2) network were presented.

CONCLUSION

CHL1 and MBNL2 may participate in the process of psoriasis and NASH via regulating hsa-miR-1305, and together with eight hub genes may be potential therapeutic targets for future treatment for the co-occurrence of these two diseases. This comprehensive bioinformatic analysis provides new insights on molecular pathogenesis and identification of potential therapeutic targets for the co-occurrence of them.

Cutaneous lesions in psoriatic arthritis are enriched in chemokine transcriptomic pathways

OBJECTIVES

Skin from people with psoriasis has been extensively studied and is assumed to be identical to skin from those with psoriatic arthritis (PsA). Chemokines and the CC chemokine scavenger receptor ACKR2 are upregulated in uninvolved psoriasis. ACKR2 has been proposed as a regulator of cutaneous inflammation in psoriasis. The aim of this study was to compare the transcriptome of PsA skin to healthy control (HC) skin and evaluate ACKR2 expression in PsA skin.

METHODS

Full-thickness skin biopsies from HC, lesional and uninvolved skin from participants with PsA were sequenced on NovaSeq 6000. Findings were validated using qPCR and RNAscope.

RESULTS

Nine HC and nine paired PsA skin samples were sequenced. PsA uninvolved skin was transcriptionally similar to HC skin, and lesional PsA skin was enriched in epidermal and inflammatory genes. Lesional PsA skin was enriched in chemokine-mediated signalling pathways, but uninvolved skin was not. ACKR2 was upregulated in lesional PsA skin but had unchanged expression in uninvolved compared with HC skin. The expression of ACKR2 was confirmed by qPCR, and RNAscope demonstrated strong expression of ACKR2 in the suprabasal layer of the epidermis in PsA lesions.

CONCLUSION

Chemokines and their receptors are upregulated in lesional PsA skin but relatively unchanged in uninvolved PsA skin. In contrast to previous psoriasis studies, ACKR2 was not upregulated in uninvolved PsA skin. Further understanding of the chemokine system in PsA may help to explain why inflammation spreads from the skin to the joints in some people with psoriasis.

LncRNA UCA1 promotes keratinocyte-driven inflammation via suppressing METTL14 and activating the HIF-1α/NF-κB axis in psoriasis

Keratinocytes are closely associated with innate immunity and inflammatory responses, and are dysregulated during the development of psoriasis, but the underlying mechanisms are not yet fully understood. This work aims to reveal the effects of long non-coding RNA (lncRNA) UCA1 in psoriatic keratinocytes. UCA1 was identified as a psoriasis-related lncRNA that highly expressed in psoriatic lesions. The transcriptome and proteome data of keratinocyte cell line HaCaT showed that UCA1 could positively regulate inflammatory functions, such as response to cytokine. Furthermore, UCA1 silencing decreased inflammatory cytokine secretion and innate immunity gene expression in HaCaT, its culture supernatant also decreased the migration and tube formation ability of vascular endothelial cells (HUVECs). Mechanistically, UCA1 activated the NF-κB signaling pathway, which is regulated by HIF-1α and STAT3. We also observed a direct interaction between UCA1 and N6-methyladenosine (m⁶A) methyltransferase METTL14. Knocking down METTL14 counteracted the effects of UCA1 silencing, indicating that it can suppress inflammation. In addition, the levels of m⁶A-modified HIF-1α were decreased in psoriatic lesions, indicating that HIF-1α is a potential target of METTL14. Taken together, this work indicates that UCA1 positively regulates keratinocyte-driven inflammation and psoriasis development by binding to METTL14, and activating HIF-1α and NF-κB signaling pathway. Our findings provide new insights into the molecular mechanisms of keratinocyte-driven inflammation in psoriasis.

Identification of key apoptosis-related genes and immune infiltration in the pathogenesis of psoriasis

Background

Psoriasis is a condition in which skin cells build up and form itchy scales and dry patches. It is also considered a common lifelong disease with an unclear pathogenesis. Furthermore, an effective cure for psoriasis is still unavailable. Reductive apoptosis of keratinocytes and immune infiltration are common in psoriasis. This study aimed to explore underlying functions of key apoptosis-related genes and the characteristics of immune infiltration in psoriasis. We used GSE13355 and GSE30999 to screen differentially expressed apoptosis related genes (DEARGs) in our study. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and gene set enrichment analysis (GSEA) were performed using clusterProfiler package. Protein–protein interaction (PPI) network was constructed to acquire key DEARGs. Transcription factor (TF)–target and miRNA–mRNA network analyses, drug sensitivity prediction, and immune infiltration were applied. Key DEARGs were validated using real-time quantitative PCR (RT-qPCR).

Results

We identified 482 and 32 DEARGs from GSE13355 and GSE30999, respectively. GO analysis showed that DEARGs were commonly enriched in cell chemotaxis, receptor ligand activity, and signaling receptor activator activity. KEGG pathway analysis indicated that viral protein interaction with cytokine and cytokine receptor was maximally enriched pathway. The GSEA analysis of GSE13355 and GSE30999 demonstrated a high consistency degree of enriched pathways. Thirteen key DEARGs with upregulation were obtained in the PPI network. Eleven key DEARGs were confirmed using RT-qPCR. Additionally, 5 TFs and 553 miRNAs were acquired, and three novel drugs were predicted. Moreover, Dendritic.cells.activated exhibited high levels of immune infiltration while Mast.cells.resting showed low levels of immune infiltration in psoriasis groups.

Conclusion

Results of this study may reveal some insights into the underlying molecular mechanism of psoriasis and provide novel targeted drugs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41065-022-00233-0.

Identification of hub biomarkers and immune cell infiltration in polymyositis and dermatomyositis

Objective: Polymyositis (PM) and dermatomyositis (DM) are heterogeneous disorders. However, the etiology of PM/DM development has not been thoroughly clarified.

Methods: Gene expression data of PM/DM were obtained from Gene Expression Omnibus. We used robust rank aggregation (RRA) to identify differentially expressed genes (DEGs). Gene Ontology functional enrichment and pathway analyses were used to investigate potential functions of the DEGs. Weighted gene co-expression network analysis (WGCNA) was used to establish a gene co-expression network. CIBERSORT was utilized to analyze the pattern of immune cell infiltration in PM/DM. Protein–protein interaction (PPI) network, Venn, and association analyses between core genes and muscle injury were performed to identify hub genes. Receiver operating characteristic analyses were executed to investigate the value of hub genes in the diagnosis of PM/DM, and the results were verified using the microarray dataset GSE48280.

Results: Five datasets were included. The RRA integrated analysis identified 82 significant DEGs. Functional enrichment analysis revealed that immune function and the interferon signaling pathway were enriched in PM/DM. WGCNA outcomes identified MEblue and MEturquoise as key target modules in PM/DM. Immune cell infiltration analysis revealed greater macrophage infiltration and lower regulatory T-cell infiltration in PM/DM patients than in healthy controls. PPI network, Venn, and association analyses of muscle injury identified five putative hub genes: TRIM22, IFI6, IFITM1, IFI35, and IRF9.

Conclusions: Our bioinformatics analysis identified new genetic biomarkers of the pathogenesis of PM/DM. We demonstrated that immune cell infiltration plays a pivotal part in the occurrence of PM/DM.

Revealing Potential Bioactive Compounds and Mechanisms of Lithospermum erythrorhizon against COVID-19 via Network Pharmacology Study

Lithospermum erythrorhizon (LE) is known in Korean traditional medicine for its potent therapeutic effect and antiviral activity. Currently, coronavirus (COVID-19) disease is a developing global pandemic that can cause pneumonia. A precise study of the infection and molecular pathway of COVID-19 is therefore obviously important. The compounds of LE were identified from the Natural Product Activity and Species Source (NPASS) database and screened by SwissADME. The targets interacted with the compounds and were selected using the Similarity Ensemble Approach (SEA) and Swiss Target Prediction (STP) methods. PubChem was used to classify targets linked to COVID-19. The protein–protein interaction (PPI) networks and signaling pathways–targets–bioactive compounds (STB) networks were constructed by RPackage. Lastly, we performed the molecular docking test (MDT) to verify the binding affinity between significant complexes through AutoDock 1.5.6. The Natural Product Activity and Species Source (NPASS) revealed a total of 82 compounds from LE, which interacted with 1262 targets (SEA and STP), and 249 overlapping targets were identified. The 19 final overlapping targets from the 249 targets and 356 COVID-19 targets were ultimately selected. A bubble chart exhibited that inhibition of the MAPK signaling pathway could be a key mechanism of LE on COVID-19. The three key targets (RELA, TNF, and VEGFA) directly related to the MAPK signaling pathway, and methyl 4-prenyloxycinnamate, tormentic acid, and eugenol were related to each target and had the most stable binding affinity. The three bioactive effects on the three key targets might be synergistic effects to alleviate symptoms of COVID-19 infection. Overall, this study shows that LE can play a role in alleviating COVID-19 symptoms, revealing that the three components (bioactive compounds, targets, and mechanism) are the most significant elements of LE against COVID-19. However, the promising mechanism of LE on COVID-19 is only predicted on the basis of mining data; the efficacy of the chemical compounds and the affinity between compounds and the targets in experiment was ignored, which should be further substantiated through clinical trials.

Candidate Genes of Allergic Dermatitis Are Associated with Immune Response

Allergic dermatitis (AD) is a common and burdensome inflammatory skin disease, and diagnosis is challenging. This study was conducted to identify candidate genes for AD diagnosis and underlying molecular mechanisms. Gene expression profiles were obtained from datasets GSE121212, GSE130588, and GSE157194. Use differential analysis to identify differentially expressed genes (DEGs) between AD and control. Use enrichment analysis to identify potential molecular dysregulation mechanisms. Comprehensive least absolute shrinkage and selection operator (LASSO) logistic regression, receiver operator characteristic (ROC) curve, and logistic regression analysis are used to identify candidate genes. In addition, ssGSEA and ImmPort database were used to identify AD-related immune response abnormalities. In this study, a total of 60 common genes were identified. Enrichment analysis found that these genes are mainly involved in Th17 cell immune and complement and coagulation cascades. LASSO regression analysis identified 18 feature genes, and screened genes with AUC >0.75 were selected as candidate genes. Finally, PLA2G4D, IFI6, AGR3, IGFL1, SPRR3, ATP13A5, SERPINB13, KRT16, HAS3, and CH25H were recognized as candidate genes and may be able to diagnose AD. PLA2G4D, CH25H, and IFI6 may be risk factors for AD based on logistic analysis. Furthermore, we identified the abnormalities of immune response activation in AD patients. Interestingly, PLA2G4D, CH25H, and IFI6 had positive correlations with immune cells and signaling pathways. PLA2G4D, CH25H, and IFI6 may be candidate diagnostic genes for AD. This may be related to their promotion of abnormal immune activation, especially Th17 cell immune.

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.

Multiomics Identification of Potential Targets for Alzheimer Disease and Antrocin as a Therapeutic Candidate

Alzheimer’s disease (AD) is the most frequent cause of neurodegenerative dementia and affects nearly 50 million people worldwide. Early stage diagnosis of AD is challenging, and there is presently no effective treatment for AD. The specific genetic alterations and pathological mechanisms of the development and progression of dementia remain poorly understood. Therefore, identifying essential genes and molecular pathways that are associated with this disease’s pathogenesis will help uncover potential treatments. In an attempt to achieve a more comprehensive understanding of the molecular pathogenesis of AD, we integrated the differentially expressed genes (DEGs) from six microarray datasets of AD patients and controls. We identified ATPase H+ transporting V1 subunit A (ATP6V1A), BCL2 interacting protein 3 (BNIP3), calmodulin-dependent protein kinase IV (CAMK4), TOR signaling pathway regulator-like (TIPRL), and the translocase of outer mitochondrial membrane 70 (TOMM70) as upregulated DEGs common to the five datasets. Our analyses revealed that these genes exhibited brain-specific gene co-expression clustering with OPA1, ITFG1, OXCT1, ATP2A2, MAPK1, CDK14, MAP2K4, YWHAB, PARK2, CMAS, HSPA12A, and RGS17. Taking the mean relative expression levels of this geneset in different brain regions into account, we found that the frontal cortex (BA9) exhibited significantly (p < 0.05) higher expression levels of these DEGs, while the hippocampus exhibited the lowest levels. These DEGs are associated with mitochondrial dysfunction, inflammation processes, and various pathways involved in the pathogenesis of AD. Finally, our blood–brain barrier (BBB) predictions using the support vector machine (SVM) and LiCABEDS algorithm and molecular docking analysis suggested that antrocin is permeable to the BBB and exhibits robust ligand–receptor interactions with high binding affinities to CAMK4, TOMM70, and T1PRL. Our results also revealed good predictions for ADMET properties, drug-likeness, adherence to Lipinskís rules, and no alerts for pan-assay interference compounds (PAINS) Conclusions: These results suggest a new molecular signature for AD parthenogenesis and antrocin as a potential therapeutic agent. Further investigation is warranted.

Identification of potential hub genes associated with skin wound healing based on time course bioinformatic analyses

Background

The skin is the largest organ of the body and has multiple functions. Wounds remain a significant healthcare problem due to the large number of traumatic and pathophysiological conditions patients suffer.

Methods

Gene expression profiles of 37 biopsies collected from patients undergoing split-thickness skin grafts at five different time points were downloaded from two datasets (GSE28914 and GSE50425) in the Gene Expression Omnibus (GEO) database. Principal component analysis (PCA) was applied to classify samples into different phases. Subsequently, differentially expressed genes (DEGs) analysis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway functional enrichment analyses were performed, and protein–protein interaction (PPI) networks created for each phase. Furthermore, based on the results of the PPI, hub genes in each phase were identified by molecular complex detection combined with the ClueGO algorithm.

Results

Using principal component analysis, the collected samples were divided into four phases, namely intact phase, acute wound phase, inflammatory and proliferation phase, and remodeling phase. Intact samples were used as control group. In the acute wound phase, a total of 1 upregulated and 100 downregulated DEGs were identified. Tyrosinase (TYR), tyrosinase Related Protein 1 (TYRP1) and dopachrome tautomerase (DCT) were considered as hub genes and enriched in tyrosine metabolism which dominate the process of melanogenesis. In the inflammatory and proliferation phase, a total of 85 upregulated and 164 downregulated DEGs were identified. CHEK1, CCNB1 and CDK1 were considered as hub genes and enriched in cell cycle and P53 signaling pathway. In the remodeling phase, a total of 121 upregulated and 49 downregulated DEGs were identified. COL4A1, COL4A2, and COL6A1 were considered as hub genes and enriched in protein digestion and absorption, and ECM-receptor interaction.

Conclusion

This comprehensive bioinformatic re-analysis of GEO data provides new insights into the molecular pathogenesis of wound healing and the potential identification of therapeutic targets for the treatment of wounds.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12893-021-01298-w.

The paeonol target gene autophagy-related 5 has a potential therapeutic value in psoriasis treatment

Background

Paeonol is a potent therapy for psoriasis. This study aimed to screen out paeonol-targeted genes in psoriasis and validate the potential of using paeonol for the management of psoriasis.

Methods

Microarray datasets were obtained from the Gene Expression Omnibus. The differentially expressed genes (DEGs) in the lesional skin samples and the overlapping genes between DEGs and paeonol- and psoriasis-related genes were defined as potential targets for psoriasis. After being treated with si-ATG5 and pc-ATG5, human HaCaT cells were treated with 100 ng/ml IL-22 and 10 ng/ml TNF-α with and without paeonol. Cell proliferation, apoptosis, and expression of interleukin (IL)-6, IL-1β, Beclin 1, ATG5, and p62 in HaCaT cells were determined using ESLIA, PCR, and Western blot analysis.

Results

A total of 779 DEGs were identified in the lesional skin samples compared with the non-lesional tissues. The autophagy-related 5 (ATG5) gene was the only gene that overlapped between the DEGs and genes related to paeonol and psoriasis. Cell proliferation, inflammatory cytokines (IL-6 and IL-1β), and ATG5 expression were increased in IL-22/TNF-α-stimulated HaCaT (model) cells compared with control. Paeonol treatment rescued all changes. si-ATG5 transfection increased inflammation and apoptosis in model cells compared with controls. pc-ATG5 prevented IL-22/TNF-α-induced changes in HaCaT cells. Also, si-ATG5 decreased p62 and Beclin 1 proteins, while pc-ATG5 increased them both.

Conclusions

ATG5-dependent autophagy plays a crucial role in psoriasis. The ATG5 gene might be a therapeutic target for the management of in vitro psoriasis.

Identification of Hub Genes and Immune Infiltration in Psoriasis by Bioinformatics Method

Background

Psoriasis is a chronic, prolonged, and recurrent skin inflammatory disease. However, the pathogenesis of psoriasis is not completely clear, thus we aimed to explore potential molecular basis of it.

Methods

Two datasets were downloaded from the Gene Expression Omnibus database. After identifying the differentially expressed genes of psoriasis skin lesion samples and healthy controls, three kinds of analyses, namely functional annotation, protein-protein interaction (PPI) network, and immune infiltration analyses, were performed.

Results

A total of 152 up-regulated genes and 38 down-regulated genes were selected for subsequent analyses. Evaluation of the PPI network identified the most important module containing 13 hub genes. Gene ontology analysis showed that the hub genes have a significant enrichment effect on positive regulation of cell migration, defense response to the other organism and epithelial cell differentiation. KEGG signaling pathway analysis showed that the hub genes were significantly enriched in chemokine signaling, Toll-like receptor signaling pathway, and IL-17 signaling pathway. Compared with the normal control sample, naive B cells, CD8⁺ T cells, activated memory CD4⁺ T cells, follicular helper T cells, gamma delta T cells, resting NK cells, monocytes, M0 macrophages, M1 macrophages, activated dendritic cells and neutrophils infiltrated more, while memory B cells, naive CD4⁺ T cells, regulatory T cells (Tregs), activated NK cells, resting mast cells, and eosinophils infiltrated less.

Conclusion

To conclude, the hub genes and pathways identified from psoriasis lesions and normal controls along with the immune infiltration profile may provide new insights into the study of psoriasis.

Decoding Psoriasis: Integrated Bioinformatics Approach to Understand Hub Genes and Involved Pathways

BACKGROUND

Psoriasis is a chronic immune mediated skin disorder with global prevalence of 0.2- 11.4%. Despite rare mortality, the severity of the disease could be understood by the accompanying comorbidities, that has even led to psychological problems among several patients. The cause and the disease mechanism still remain elusive.

OBJECTIVE

To identify potential therapeutic targets and affecting pathways for better insight of the disease pathogenesis.

METHOD

The gene expression profile GSE13355 and GSE14905 were retrieved from NCBI, Gene Expression Omnibus database. The GEO profiles were integrated and the DEGs of lesional and non-lesional psoriasis skin were identified using the affy package in R software. The Kyoto Encyclopaedia of Genes and Genomes pathways of the DEGs were analyzed using clusterProfiler. Cytoscape, V3.7.1 was utilized to construct protein interaction network and analyze the interactome map of candidate proteins encoded in DEGs. Functionally relevant clusters were detected through Cytohubba and MCODE.

RESULTS

A total of 1013 genes were differentially expressed in lesional skin of which 557 were upregulated and 456 were downregulated. Seven dysregulated genes were extracted in non-lesional skin. The disease gene network of these DEGs revealed 75 newly identified differentially expressed gene that might have a role in development and progression of the disease. GO analysis revealed keratinocyte differentiation and positive regulation of cytokine production to be the most enriched biological process and molecular function. Cytokines -cytokine receptor was the most enriched pathways. Among 1013 identified DEGs in lesional group, 36 DEGs were found to have altered genetic signature including IL1B and STAT3 which are also reported as hub genes. CCNB1, CCNA2, CDK1, IL1B, CXCL8, MKI 67, ESR1, UBE2C, STAT1 and STAT3 were top 10 hub gene.

CONCLUSION

The hub genes, genomic altered DEGs and other newly identified differentially dysregulated genes would improve our understanding of psoriasis pathogenesis, moreover, the hub genes could be explored as potential therapeutic targets for psoriasis.

Comprehensive analysis and identification of key genes and signaling pathways in the occurrence and metastasis of cutaneous melanoma

Background

Melanoma is a malignant tumor of melanocytes, and the incidence has increased faster than any other cancer over the past half century. Most primary melanoma can be cured by local excision, but metastatic melanoma has a poor prognosis. Cutaneous melanoma (CM) is prone to metastasis, so the research on the mechanism of melanoma occurrence and metastasis will be beneficial to diagnose early, improve treatment, and prolong life survival. In this study, we compared the gene expression of normal skin (N), primary cutaneous melanoma (PM) and metastatic cutaneous melanoma (MM) in the Gene Expression Omnibus (GEO) database. Then we identified the key genes and molecular pathways that may be involved in the development and metastasis of cutaneous melanoma, thus to discover potential markers or therapeutic targets.

Methods

Three gene expression profiles (GSE7553, GSE15605 and GSE46517) were downloaded from the GEO database, which contained 225 tissue samples. R software identified the differentially expressed genes (DEGs) between pairs of N, PM and MM samples in the three sets of data. Subsequently, we analyzed the gene ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway of the DEGs, and constructed a protein-protein interaction (PPI) network. MCODE was used to seek the most important modules in PPI network, and then the GO function and KEGG pathway of them were analyzed. Finally, the hub genes were calculated by the cytoHubba in Cytoscape software. The Cancer Genome Atlas (TCGA) data were analyzed using UALCAN and GEPIA to validate the hub genes and analyze the prognosis of patients.

Results

A total of 134, 317 and 147 DEGs were identified between N, PM and MM in pair. GO functions and KEGG pathways analysis results showed that the upregulated DEGs mainly concentrated in cell division, spindle microtubule, protein kinase activity and the pathway of transcriptional misregulation in cancer. The downregulated DEGs occurred in epidermis development, extracellular exosome, structural molecule activity, metabolic pathways and p53 signaling pathway. The PPI network obtained the most important module, whose GO function and KEGG pathway were enriched in oxidoreductase activity, cell division, cell exosomes, protein binding, structural molecule activity, and metabolic pathways. 14, 18 and 18 DEGs were identified respectively as the hub genes between N, PM and MM, and TCGA data confirmed the expression differences of hub genes. In addition, the overall survival curve of hub genes showed that the differences in these genes may lead to a significant decrease in overall survival of melanoma patients.

Conclusions

In this study, several hub genes were found from normal skin, primary melanoma and metastatic melanoma samples. These hub genes may play an important role in the production, invasion, recurrence or death of CM, and may provide new ideas and potential targets for its diagnosis or treatment.

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.

Predicting Functions of Uncharacterized Human Proteins: From Canonical to Proteoforms

Despite tremendous efforts in genomics, transcriptomics, and proteomics communities, there is still no comprehensive data about the exact number of protein-coding genes, translated proteoforms, and their function. In addition, by now, we lack functional annotation for 1193 genes, where expression was confirmed at the proteomic level (uPE1 proteins). We re-analyzed results of AP-MS experiments from the BioPlex 2.0 database to predict functions of uPE1 proteins and their splice forms. By building a protein-protein interaction network for 12 ths. identified proteins encoded by 11 ths. genes, we were able to predict Gene Ontology categories for a total of 387 uPE1 genes. We predicted different functions for canonical and alternatively spliced forms for four uPE1 genes. In total, functional differences were revealed for 62 proteoforms encoded by 31 genes. Based on these results, it can be carefully concluded that the dynamics and versatility of the interactome is ensured by changing the dominant splice form. Overall, we propose that analysis of large-scale AP-MS experiments performed for various cell lines and under various conditions is a key to understanding the full potential of genes role in cellular processes.

Review-Current Concepts in Inflammatory Skin Diseases Evolved by Transcriptome Analysis: In-Depth Analysis of Atopic Dermatitis and Psoriasis

During the last decades, high-throughput assessment of gene expression in patient tissues using microarray technology or RNA-Seq took center stage in clinical research. Insights into the diversity and frequency of transcripts in healthy and diseased conditions provide valuable information on the cellular status in the respective tissues. Growing with the technique, the bioinformatic analysis toolkit reveals biologically relevant pathways which assist in understanding basic pathophysiological mechanisms. Conventional classification systems of inflammatory skin diseases rely on descriptive assessments by pathologists. In contrast to this, molecular profiling may uncover previously unknown disease classifying features. Thereby, treatments and prognostics of patients may be improved. Furthermore, disease models in basic research in comparison to the human disease can be directly validated. The aim of this article is not only to provide the reader with information on the opportunities of these techniques, but to outline potential pitfalls and technical limitations as well. Major published findings are briefly discussed to provide a broad overview on the current findings in transcriptomics in inflammatory skin diseases.