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

Comprehensive transcriptional analysis of pig facial skin development

Background

Skin development is a complex process that is influenced by many factors. Pig skin is used as an ideal material for xenografts because it is more anatomically and physiologically similar to human skin. It has been shown that the skin development of different pig breeds is different, and some Chinese pig breeds have the characteristics of skin thickness and facial skin folds, but the specific regulatory mechanism of this skin development is not yet clear.

Methods

In this study, the facial skin of Chenghua sows in the four developmental stages of postnatal Day 3 (D3) , Day 90 (D90) , Day 180 (D180), and Year 3 (Y3) were used as experimental materials, and RNA sequencing (RNA-seq) analysis was used to explore the changes in RNA expression in skin development at the four developmental stages, determine the differentially expressed messenger RNAs (mRNAs), long noncoding RNAs (lncRNAs), microRNAs (miRNAs), and circular RNAs (circRNAs), and perform functional analysis of related genes by Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses.

Results

A pairwise comparison of the four developmental stages identified several differentially expressed genes (DEGs) and found that the number of differentially expressed RNAs (DE RNAs) increased with increasing developmental time intervals. Elastin (ELN) is an important component of the skin. Its content affects the relaxation of the epidermis and dermal connection, and its expression is continuously downregulated during the four developmental stages. The functions of DEGs at different developmental stages were examined by performing GO and KEGG analyses, and the GO terms and enrichment pathways of mRNAs, lncRNAs, miRNAs, and circRNAs highly overlapped, among which the PPAR signaling pathway, a classical pathway for skin development, was enriched by DEGs of D3 vs. D180, D90 vs. D180 and D180 vs. Y3. In addition, we constructed lncRNA-miRNA-mRNA and circRNA-miRNA interaction networks and found genes that may be associated with skin development, but their interactions need further study.

Conclusions

We identified a number of genes associated with skin development, performed functional analyses on some important DEGs and constructed interaction networks that facilitate further studies of skin development.

A review of literature: role of long noncoding RNA TPT1-AS1 in human diseases

Human diseases are multifactorial processes mainly driven by the intricate interactions of genetic and environmental factors. Long noncoding RNAs (lncRNAs) represent a type of non-coding RNAs with more than 200 nucleotides. Multiple studies have demonstrated that the dysregulation of lncRNAs is associated with complex biological as well as pathological processes through various mechanism, especially the regulation of gene transcription and related signal transduction pathways. Moreover, an increasing number of studies have explored lncRNA-based clinical applications in different diseases. For instance, the lncRNA Tumor Protein Translationally Controlled 1 (TPT1) Antisense RNA 1 (TPT1-AS1) was found to be dysregulated in several types of disease and strongly associated with patient prognosis and diverse clinical features. Recent studies have also documented that TPT1-AS1 modulates numerous biological processes through multiple mechanisms, including cell proliferation, apoptosis, autophagy, invasion, migration, radiosensitivity, chemosensitivity, stemness, and extracellular matrix (ECM) synthesis. Furthermore, TPT1-AS1 was regarded as a promising biomarker for the diagnosis, prognosis and treatment of several human diseases. In this review, we summarize the role of TPT1-AS1 in human diseases with the aspects of its expression, relevant clinical characteristics, molecular mechanisms, biological functions, and subsequent clinical applications.

Analysis of Long Noncoding RNAs-Related Regulatory Mechanisms in Duchenne Muscular Dystrophy Using a Disease-Related lncRNA-mRNA Pathway Network

Objective

This study aimed to investigate the molecular regulatory mechanisms underpinning Duchenne muscular dystrophy (DMD).

Methods

Using microarray data, differentially expressed long noncoding RNAs (DELs) and DMD-related differentially expressed mRNAs (DEMs) were screened based on the comparative toxicogenomics database, using a cutoff of |log₂ fold change| > 1 and false discovery rate (FDR) < 0.05. Then, protein-protein interaction (PPI), coexpression network of lncRNA-mRNA, and DMD-related lncRNA-mRNA pathway networks were constructed, and functional analyses of the genes in the network were performed. Finally, the proportions of immune cells infiltrating the muscle tissues in DMD were analyzed, and the correlation between the immune cells and expression of the DELs/DEMs was studied.

Results

A total of 46 DELs and 313 DMD-related DEMs were identified. The PPI network revealed STAT1, VEGFA, and CCL2 to be the top three hub genes. The DMD-related lncRNA-mRNA pathway network comprising two pathways, nine DELs, and nine DMD-related DEMs showed that PYCARD, RIPK2, and CASP1 were significantly enriched in the NOD-like receptor signaling pathway, whereas MAP2K2, LUM, RPS6, PDCD4, TWIST1, and HIF1A were significantly enriched with proteoglycans in cancers. The nine DELs in this network were DBET, MBNL1-AS1, MIR29B2CHG, CCDC18-AS1, FAM111A-DT, GAS5, LINC01290, ATP2B1-AS1, and PSMB8-AS1.

Conclusion

The nine DMD-related DEMs and DELs identified in this study may play important roles in the occurrence and progression of DMD through the two pathways of the NOD-like receptor signaling pathway and proteoglycans in cancers.

Based on Gene Expression Analysis: Low-Density Neutrophil Expression Is a Characteristic of the Fast Responders Treated With Guselkumab for Psoriasis

Psoriasis is a worldwide chronic inflammatory skin disease. The treatment of disease is usually designed according to its severity. In this research, RNA-seq was performed on 37 patients with psoriasis treated with guselkumab before and after treatment, and the patients were divided into fast responder and slow responder according to PASI score to analyze the differentially expressed genes (DEGs) between them. Moreover, The biological mechanism of psoriasis was explored by Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, Gene Ontology (GO) analysis, and Gene Set Enrichment Analysis (GSEA) analysis. And then, this protein-protein interaction network was constructed and 17 DEGs including IL-1β, CXCL8, S100A12 and MMP9 were analyzed by GSVA. DEGs were detected by GO and KEGG analysis of target genes, which were primarily associated with immune response, neutrophil activation, neutrophil degranulation. GSEA reminded that fast responders were mainly involved in low-density neutrophils and abundant NK cells. And the GSVA showed that the DEGs were down-regulated after the early stage of the fast responder and the reverse in the slow responder by GSVA analysis. On the whole, these results indicated that these DEGs may serve as a psoriasis potential diagnostic and predictive biomarkers after been treated by guselkumab.

A Prognostic Pyroptosis-Related lncRNAs Risk Model Correlates With the Immune Microenvironment in Colon Adenocarcinoma

Recent studies have indicated that long non-coding RNAs (lncRNAs) may participate in the regulation of tumor cell proptosis. However, the connection between lncRNA expression and pyroptosis remains unclear in colon adenocarcinoma (COAD). This study aims to explore and establish a prognostic signature of COAD based on the pyroptosis-related lncRNAs. We identify 15 prognostic pyroptosis-related lncRNAs (ZNF667-AS1, OIP5-AS1, AL118506.1, AF117829.1, POC1B-AS1, CCDC18-AS1, THUMPD3-AS1, FLNB-AS1, SNHG11, HCG18, AL021707.2, UGDH-AS1, LINC00641, FGD5-AS1 and AC245452.1) from the TCGA-COAD dataset and use them to construct the risk model. After then, this pyroptosis-related lncRNA signature is validated in patients from the GSE17536 dataset. The COAD patients are divided into low-risk and high-risk groups by setting the median risk score as the cut-off point and represented differences in the immune microenvironment. Hence, we construct the immune risk model based on the infiltration levels of ssGSEA immune cells. Interestingly, the risk model and immune risk model are both independent prognostic risk factors. Therefore, a nomogram combined risk score, immune risk score with clinical information which is meaningful in univariate and multivariate Cox regression analysis is established to predict the overall survival (OS) of COAD patients. In general, the signature consisted of 15 pyroptosis-related lncRNAs and was proved to be associated with the immune landscape of COAD patients.

A Five Autophagy-Related Long Non-Coding RNA Prognostic Model for Patients with Lung Adenocarcinoma

Purpose

Lung adenocarcinoma is the most common pathological type among non-small cell lung cancer. Although huge progress has been made in terms of early diagnosis and precision treatment in recent years, the overall 5-year survival rate of a patient remains low. In our study, we try to construct an autophagy-related lncRNA prognostic signature that may guide clinical practice.

Methods

The mRNA and lncRNA expression matrix of lung adenocarcinoma patients were retrieved from the TCGA database. Next, we constructed a co-expression network of lncRNAs and autophagy-related genes. Lasso regression and multivariate Cox regression were then applied to establish a prognostic risk model. Subsequently, a risk score was generated to differentiate the high and low risk groups and a ROC curve and nomogram to visualize the predictive ability of the current signature. Finally, gene ontology and pathway enrichment analysis were executed via GSEA.

Results

A total of 1,703 autophagy-related lncRNAs were screened and five autophagy-related lncRNAs (LINC01137, AL691432.2, LINC01116, AL606489.1, and HLA-DQB1-AS1) were finally included in our signature. Judging from univariate (HR=1.075, 95% CI=1.046–1.104) and multivariate (HR=1.088, 95% CI=1.057−1.120) Cox regression analysis, the risk score is an independent factor for LUAD patients. Further, the AUC value based on the risk score for 1-year, 3-years, and 5-years, was 0.735, 0.672, and 0.662, respectively, indicating a reliable model. Drug sensitivity analysis revealed low risk patients were more sensitive to Gemcitabine and Gefitinib, while high risk patients had a better response to Paclitaxel and Erlotinib. Moreover, the lncRNAs included in our signature were primarily enriched in the autophagy process, metabolism, p53 pathway, and JAK/STAT pathway. Finally, a multi-omics analysis of correlated genes showed CFLAR overexpressed in the tumor sample, while GAPDH and MLST8 had a slightly higher expression in the normal sample.

Conclusion

Overall, our study indicated that the prognostic model we generated had certain predictability for LUAD patients’ prognosis and the related genes might be potential biomarkers and therapeutic targets.