Identification of Novel Hub Genes Associated with Psoriasis Using Integrated Bioinformatics Analysis

Identification of biomarkers related to prognosis and diagnosis of periodontitis by bioinformatics based on public database

OBJECTIVES

Periodontitis is a multifactorial disease that has a negative impact on people's life. However, studies on potential key genes with excellent diagnostic value for periodontitis disease have not been systematically explored.

METHODS

GSE10334 data set was downloaded from the Gene Expression Omnibus database. Following the gene expression profiles were normalized by the Robust multi-array average (RMA) algorithm, the differentially expressed genes were screened and incorporated into Weight gene correlation network analysis to obtain hub genes. Receiver-operating characteristic curve analysis was used to verify the validity and agility of the hub genes-based least absolute shrinkage and selection operator model. Furthermore, we validated the expression of these hub genes by real-time polymerase chain reaction and western blotting.

RESULTS

Eight hub genes were identified and had good diagnostic values. Besides, the upregulations of eight hub genes were verified both in protein and mRNA levels in clinical periodontitis gum tissue.

CONCLUSION

We discovered potential biomarkers in periodontitis based on the public database and these biomarkers focused on several immune responses and inflammatory pathways. Thus, this study may provide potential therapeutic targets for early diagnosis and treatment of periodontitis.

Single-cell and bulk RNAseq unveils the immune infiltration landscape and targeted therapeutic biomarkers of psoriasis

Background

Psoriasis represents a multifaceted and debilitating immune-mediated systemic ailment afflicting millions globally. Despite the continuous discovery of biomarkers associated with psoriasis, identifying lysosomal biomarkers, pivotal as cellular metabolic hubs, remains elusive.

Methods

We employed a combination of differential expression analysis and weighted gene co-expression network analysis (WGCNA) to initially identify lysosomal genes. Subsequently, to mitigate overfitting and eliminate collinear genes, we applied 12 machine learning algorithms to screen robust lysosomal genes. These genes underwent further refinement through random forest (RF) and Lasso algorithms to ascertain the final hub lysosomal genes. To assess their predictive efficacy, we conducted receiver operating characteristic (ROC) analysis and verified the expression of diagnostic biomarkers at both bulk and single-cell levels. Furthermore, we utilized single-sample gene set enrichment analysis (ssGSEA), CIBERSORT, and Pearson's correlation analysis to elucidate the association between immune phenotypes and hub lysosomal genes in psoriatic samples. Finally, employing the Cellchat algorithm, we explored potential mechanisms underlying the participation of these hub lysosomal genes in cell-cell communication.

Results

Functional enrichment analyses revealed a close association between psoriasis and lysosomal functions. Subsequent intersection analysis identified 19 key lysosomal genes, derived from DEGs, phenotypic genes of WGCNA, and lysosomal gene sets. Following the exclusion of collinear genes, we identified 11 robust genes, further refined through RF and Lasso, yielding 3 hub lysosomal genes (S100A7, SERPINB13, and PLBD1) closely linked to disease occurrence, with high predictive capability for disease diagnosis. Concurrently, we validated their relative expression in separate bulk datasets and single-cell datasets. A nomogram based on these hub genes may offer clinical advantages for patients. Notably, these three hub genes facilitated patient classification into two subtypes, namely metabolic-immune subtype 1 and signaling subtype 2. CMap analysis suggested butein and arachidonic fasudil as preferred treatment agents for subtype 1 and subtype 2, respectively. Finally, through Cellchat and correlation analysis, we identified PRSS3-F2R as potentially promoting the expression of hub genes in the psoriasis group, thereby enhancing keratinocyte-fibroblast interaction, ultimately driving psoriasis occurrence and progression.

Conclusion

Our study identifies S100A7, SERPINB13, and PLBD1 as potential diagnostic biomarkers, offering promising prospects for more precisely tailored psoriatic immunotherapy designs.

The high expression of TOP2A and MELK induces the occurrence of psoriasis

BACKGROUND

Psoriasis is a chronic inflammatory skin disease. However, the influence of the TOP2A and MELK genes on psoriasis remains unclear.

METHODS

Psoriasis datasets GSE166388 and GSE181318 were downloaded from the Gene Expression Omnibus (GEO) database generated from GPL570 and GPL22120. Differential gene expression (DEGs) was identified. Functional enrichment analysis, gene set enrichment analysis (GSEA), weighted gene co-expression network analysis (WGCNA), and immune infiltration analysis were conducted. The protein-protein interaction (PPI) network was constructed and analyzed. Gene expression heat map was generated. The most relevant diseases associated with core genes were determined through comparison with the Comparative Toxicogenomics Database (CTD) website. TargetScan was used to select miRNAs regulating central DEGs.

RESULTS

A total of 773 DEGs were identified. According to Gene Ontology (GO) analysis, they were mainly enriched in mitochondrial gene expression, oxidative phosphorylation, mitochondrial envelope, mitochondria and ribosome. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that target cells were mainly enriched in metabolic pathways, proteasome, and oxidative phosphorylation. Seven core genes (TOP2A, NUF2, MELK, ASPM, DLGAP5, CCNA2, DEPDC1B) were obtained. The gene expression heatmap showed high expression of core genes (TOP2A, MELK) in psoriasis samples, while DEPDC1B, CCNA2, DLGAP5, NUF2, ASPM were lowly expressed in psoriasis samples. CTD analysis found that TOP2A and MELK were related to skin neoplasms, skin diseases, psoriasis, erythema, dermatitis, and infections.

CONCLUSION

TOP2A and MELK genes are highly expressed in psoriasis, and higher expression of TOP2A and MELK genes is associated with poorer prognosis.

Effects of biological agents on glycogen metabolism in psoriasis patients: A systematic review and meta-analysis

The effectiveness and safety of biological agents for treating psoriasis have been confirmed; however, their effects on glucose metabolism biomarkers in psoriasis patients remain unclear. A systematic review and meta-analysis were performed according to PRISMA guidelines. The final analysis enrolled 12 studies, including eight randomized controlled trial (RCT) (n = 5628 patients) and four observational cohort studies (OBSs) (n = 393 patients). The meta-analysis comprising nine studies (six RCTs and three OBSs) revealed a slight reduction in the levels of HOMA-IR associated with the use of biological therapies in OBS (biological therapies vs. traditional therapies: WMD = -0.2, CI = -0.10 to 0.50, p = 0.02). Although a considerable number of studies were analysed, our review did not show a significant alteration in HOMA-IR levels among patients treated with biological therapies such as IL-17 inhibitors and IL-12/23 inhibitors at weeks 12-16 in RCTs. We also did not observe remarkable alterations in the fasting plasma glucose levels of patients in both OBS and RCT. Additional RCT on a larger scale and duration is required to provide more conclusive evidence regarding the effect of biological agents on glycogen metabolism in psoriasis.

Special Issue "Immunoanalytical and Bioinformatics Methods in Immunology Research"

To effectively control and prevent diseases on a global scale, it is essential to employ precise, sensitive, selective, and rapid immunoanalytical methods [...].

Research on the resistance of isoviolanthin to hydrogen peroxide-triggered injury of skin keratinocytes based on Transcriptome sequencing and molecular docking

Apoptosis of skin keratinocytes is closely associated with skin problems in humans and natural flavonoids have shown excellent biological activity. Hence, the study of flavonoids against human keratinocyte apoptosis has aroused the interest of numerous researchers. In this study, methyl thiazolyl tetrazolium (MTT) assay and Western blots were used to investigate the skin-protective effect of isoviolanthin, a di-C-glycoside derived from Dendrobium officinale, on hydrogen peroxide (H2O2)-triggered apoptosis of skin keratinocytes. Transcriptome sequencing (RNA-Seq) was used to detect the altered expression genes between the model and treatment group and qRT-PCR was used to verify the accuracy of transcriptome sequencing results. Finally, molecular docking was used to observe the binding ability of isoviolanthin to the selected differential genes screened by transcriptome sequencing. Our results found isoviolanthin could probably increase skin keratinocyte viability, by resisting against apoptosis of skin keratinocytes through downregulating the level of p53 for the first time. By comparing transcriptome differences between the model and drug administration groups, a total of 2953 differential expression genes (DEGs) were identified. Enrichment analysis showed that isoviolanthin may regulate these pathways, such as DNA replication, Mismatch repair, RNA polymerase, Fanconi anemia pathway, Cell cycle, p53 signaling pathway. Last, our results found isoviolanthin has a strong affinity for binding to KDM6B, CHAC2, ESCO2, and IPO4, which may be the potential target for treating skin injuries induced by reactive oxide species. The current study confirms isoviolanthin potential as a skin protectant. The findings may serve as a starting point for further research into the mechanism of isoviolanthin protection against skin damage caused by reactive oxide species (e.g., hydrogen peroxide).

Identication and validation of cell senescence biomarkers in idiopathic pulmonary hypertension via integrated transcriptome analyses and machine learning

BACKGROUND

Idiopathic pulmonary hypertension (IPAH) is a rare and severe disease that affects the pulmonary vasculature. As the diagnosis of IPAH requires invasive right heart catheterization surgery, early detection of this condition is notoriously challenging. Therefore, it is of utmost importance to investigate biomarkers present in peripheral blood that could aid physicians in the early identification and management of IPAH.

METHOD

We speculate that cellular senescence may be involved in the occurrence and development of IPAH through various pathways. In this study, we utilized integrated transcriptome analyses and machine learning-based approach to develop a diagnostic model for IPAH cell senescence. To select genetic features, we employed two machine learning algorithms: the Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest (RF). Additionally, we validated our findings through both external data sets and qRT-PCR experiments.

RESULTS

The resulting diagnostic nomogram was able to identify five important biomarkers that can aid in the diagnosis of IPAH, including TNFRSF1B, CCL16, GCLM, IL15, and SOD1. These genes are primarily associated with the immune system, as well as with cell senescence and apoptosis.

CONCLUSION

Our study demonstrates the utility of machine learning algorithms in making accurate diagnoses of IPAH, providing clinicians with a more directed approach to the diagnosis and treatment of this disease.

Topical application of TOPK inhibitor OTS514 suppresses psoriatic progression by inducing keratinocytes cell cycle arrest and apoptosis

T-LAK cell-oriented protein kinase (TOPK) potently promotes malignant proliferation of tumour cells and is considered as a maker of tumour progression. Psoriasis is a common inflammatory skin disease characterized by abnormal proliferation of keratinocytes. However, the role of TOPK in psoriasis has not been well elucidated. This study aims to investigate the expression and role of TOPK in psoriasis, and the role of TOPK inhibitor in psoriasis attenuation. Gene Expression Omnibus datasets derived from psoriasis patients and psoriatic model mice were screened for analysis. Skin specimens from psoriasis patients were collected for TOPK immunohistochemical staining to investigate the expression and localization of TOPK. Next, psoriatic mice model was established to further confirm TOPK expression pattern. Then, TOPK inhibitor was applied to investigate the role of TOPK in psoriasis progression. Finally, cell proliferation assay, apoptosis assay and cell cycle analysis were performed to investigate the potential mechanism involved. Our study showed that TOPK was upregulated in the lesions of both psoriasis patients and psoriatic model mice, and TOPK levels were positively associated with psoriasis progression. TOPK was upregulated in psoriatic lesions and expressed predominantly by epidermal keratinocytes. In addition, TOPK levels in epidermal keratinocytes were positively correlated with epidermal hyperplasia. Furthermore, topical application of TOPK inhibitor OTS514 obviously alleviated disease severity and epidermal hyperplasia. Mechanismly, inhibiting TOPK induces G2/M phase arrest and apoptosis of keratinocytes, thereby attenuating epidermal hyperplasia and disease progression. Collectively, this study identifies that upregulation of TOPK in keratinocytes promotes psoriatic progression, and inhibiting TOPK attenuates epidermal hyperplasia and psoriatic progression.

Multi-Omics Data Analysis Identifies Prognostic Biomarkers across Cancers

Combining omics data from different layers using integrative methods provides a better understanding of the biology of a complex disease such as cancer. The discovery of biomarkers related to cancer development or prognosis helps to find more effective treatment options. This study integrates multi-omics data of different cancer types with a network-based approach to explore common gene modules among different tumors by running community detection methods on the integrated network. The common modules were evaluated by several biological metrics adapted to cancer. Then, a new prognostic scoring method was developed by weighting mRNA expression, methylation, and mutation status of genes. The survival analysis pointed out statistically significant results for GNG11, CBX2, CDKN3, ARHGEF10, CLN8, SEC61G and PTDSS1 genes. The literature search reveals that the identified biomarkers are associated with the same or different types of cancers. Our method does not only identify known cancer-specific biomarker genes, but also proposes new potential biomarkers. Thus, this study provides a rationale for identifying new gene targets and expanding treatment options across cancer types.