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

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11018
Author(s):  
Feixiang Fan ◽  
Zhen Huang ◽  
Yongfeng Chen
Keyword(s):  
Gene Expression ◽  
T Cell ◽  
Skin Biopsy ◽  
Immune Cells ◽  
Drug Response ◽  
Noncoding Rnas ◽  
Long Noncoding Rnas ◽  
Differentially Expressed ◽  
Cd4 T Cell ◽  
Kegg Analysis

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.

scholarly journals Profiling and Functional Analysis of Long Noncoding RNAs and mRNAs during Porcine Skeletal Muscle Development

2021 ◽  
Vol 22 (2) ◽  
pp. 503
Author(s):  
Ya Tan ◽  
Mailin Gan ◽  
Linyuan Shen ◽  
Liang Li ◽  
Yuan Fan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Skeletal Muscle ◽  
Inflection Point ◽  
Muscle Development ◽  
Noncoding Rnas ◽  
Long Noncoding Rnas ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Luciferase Assay ◽  
Skeletal Muscle Development

Gene transcripts or mRNAs and long noncoding RNAs (lncRNAs) are differentially expressed during porcine skeletal muscle development. However, only a few studies have been conducted on skeletal muscle transcriptome in pigs based on timepoints according to the growth curve for porcine. Here, we investigated gene expression in Qingyu pigs at three different growth stages: the inflection point with the maximum growth rate (MGI), the inflection point of the gradually increasing stage to the rapidly increasing stage (GRI), and the inflection point of the rapidly increasing stage to the slowly increasing stage (RSI). Subsequently, we explored gene expression profiles during muscle development at the MGI, GRI and RSI stages by Ribo-Zero RNA sequencing. Qingyu pigs reached the MGI, GRI and RSI stages at 156.40, 23.82 and 288.97 days of age with 51.73, 3.14 and 107.03 kg body weight, respectively. A total of 14,530 mRNAs and 11,970 lncRNAs were identified at the three stages, and 645, 323 differentially expressed genes (DEGs) and 696, 760 differentially expressed lncRNAs (DELs) were identified in the GRI vs. MGI, and RSI vs. MGI, comparisons. Functional enrichment analysis revealed that genes involved in immune system development and energy metabolism (mainly relate to amino acid, carbohydrate and lipid) were enriched at the GRI and MGI stages, respectively, whereas genes involved in lipid metabolism were enriched at the RSI stage. We further characterized G1430, an abundant lncRNA. The full-length sequence (316 nt) of lncRNA G1430 was determined by rapid amplification of cDNA ends (RACE). Subcellular distribution analysis by quantitative real-time PCR (qRT-PCR) revealed that G1430 is a cytoplasmic lncRNA. Binding site prediction and dual luciferase assay showed that lncRNA G1430 directly binds to microRNA 133a (miR-133a). Our findings provide the basis for further investigation of the regulatory mechanisms and molecular genetics of muscle development in pigs.

scholarly journals Identifying Prognostic Significance of RCL1 and Four-Gene Signature as Novel Potential Biomarkers in HCC Patients

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Jun Liu ◽  
Shan-Qiang Zhang ◽  
Jing Chen ◽  
Zhi-Bin Li ◽  
Jia-Xi Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Clinical Practice ◽  
Clinical Outcome ◽  
Immune Cells ◽  
Noncoding Rnas ◽  
Gene Signature ◽  
Cancer Genome ◽  
Long Noncoding Rnas ◽  
Rapid Progression ◽  
Aberrant Expression

Background. Hepatocellular carcinoma (HCC) is a highly malignant disease, and it is characterized by rapid progression and low five-year survival rate. At present, there are no effective methods for monitoring the treatment and prognosis of HCC. Methods. The transcriptome and gene expression profiles of HCC were obtained from the Cancer Genome Atlas (TCGA) program, International Cancer Genome Consortium (ICGC), and Gene Expression Omnibus (GEO) databases. The random forest method was applied to construct a four-gene prognostic model based on RNA terminal phosphate cyclase like 1 (RCL1) expression. The Kaplan-Meier method was performed to evaluate the prognostic value of RCL1, long noncoding RNAs (AC079061, AL354872, and LINC01093), and four-gene signature (SPP1, MYBL2, TRNP1, and FTCD). We examined the relationship between RCL1 expression and immune cells infiltration, tumor mutation burden (TMB), and microsatellite instability (MSI). Results. The results of multiple databases indicated that the aberrant expression of RCL1 was associated with clinical outcome, immune cells infiltration, TMB, and MSI in HCC patients. Meanwhile, we found that long noncoding RNAs (AC079061, AL354872, and LINC01093) and RCL1 were significantly coexpressed in HCC patients. We also confirmed that the four-gene signature was an independent prognostic factor for HCC patients. Ferroptosis potential index, immune checkpoint molecules, and clinical feature were found to have obvious correlations with risk score. The area under the receiver operating characteristic curve values for the model were 0.7–0.8 in the training set and the validation set, suggesting high robustness of the four-gene signature. We then built a nomogram for facilitating the use in clinical practice. Conclusion. Our study demonstrated that RCL1 and a novel four-gene signature can be used as prognostic biomarkers for predicting clinical outcome in HCC patients; and this model may assist in individualized treatment monitoring of HCC patients in clinical practice.

scholarly journals Profiling and Functional Analysis of Long Noncoding RNAs and mRNAs during Porcine Skeletal Muscle Development

2020 ◽  
Author(s):  
Ya Tan ◽  
Mailin Gan ◽  
Linyuan Shen ◽  
Liang Li ◽  
Yuan Fan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Skeletal Muscle ◽  
Inflection Point ◽  
Muscle Development ◽  
Noncoding Rnas ◽  
Long Noncoding Rnas ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Luciferase Assay ◽  
Skeletal Muscle Development

Abstract BackgroundGene transcripts or mRNAs and long noncoding RNAs (lncRNAs) are differentially expressed during porcine skeletal muscle development. However, only a few studies have been conducted on skeletal muscle transcriptome in pigs based on timepoints according to the growth curve for porcine. Here, we investigated gene expression in Qingyu pigs at three different growth stages: of the inflection point with the maximum growth rate (MGI), inflection point of gradual increase stage to rapid increasing stage (GRI) and inflection point of rapid increasing stage to slowly increasing stage (RSI). Subsequently, we explored gene expression profiles during muscle development at the MGI, GRI and RSI stages by Ribo-Zero RNA sequencing. ResultsQingyu pigs reached the MGI, GRI and RSI stages at 156.40, 23.82 and 288.97 days of age with 51.73, 3.14 and 107.03 kg body weight, respectively. A total of 14,530 mRNAs and 11,970 lncRNAs were identified at the three stages, and 645, 323 differentially expressed genes (DEGs) and 696, 760 differentially expressed lncRNAs (DELs) were identified in the GRI vs. MGI, RSI vs. MGI comparisons. Functional enrichment analysis revealed that genes involved in immune system development and energy metabolism (mainly relate to amino acid, carbohydrate and lipid) were enriched at the GRI and MGI stages, respectively, whereas genes involved in energy and lipid metabolism were enriched at the RSI stage. We further characterized G1430, an abundant lncRNA. The full-length sequence (316 nt) of lncRNA G1430 was determined by rapid amplification of cDNA ends (RACE). Subcellular distribution analysis by quantitative real-time PCR (qRT-PCR) revealed that G1430 is a cytoplasmic lncRNA. Binding site prediction and dual luciferase assay showed that lncRNA G1430 directly binds to microRNA 133a (miR-133a). ConclusionThese findings indicate lncRNAs and a certain lncRNA G1430 involved in the regulatory mechanism during pig muscle development. Our findings provide the basis for further investigation of the regulatory mechanisms and molecular genetics of muscle development in pigs.

scholarly journals Genome-Wide Identification and Characterization of Long Noncoding RNAs Involved in Chinese Wheat Mosaic Virus Infection of Nicotiana benthamiana

Biology ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 232
Author(s):  
Weiran Zheng ◽  
Haichao Hu ◽  
Qisen Lu ◽  
Peng Jin ◽  
Linna Cai ◽  
...  
Keyword(s):  
Virus Infection ◽  
Mosaic Virus ◽  
Nicotiana Benthamiana ◽  
Noncoding Rnas ◽  
Long Noncoding Rnas ◽  
Differentially Expressed ◽  
Genome Wide ◽  
Chinese Wheat

Recent studies have shown that a large number of long noncoding RNAs (lncRNAs) can regulate various biological processes in animals and plants. Although lncRNAs have been identified in many plants, they have not been reported in the model plant Nicotiana benthamiana. Particularly, the role of lncRNAs in plant virus infection remains unknown. In this study, we identified lncRNAs in N. benthamiana response to Chinese wheat mosaic virus (CWMV) infection by RNA sequencing. A total of 1175 lncRNAs, including 65 differentially expressed lncRNAs, were identified during CWMV infection. We then analyzed the functions of some of these differentially expressed lncRNAs. Interestingly, one differentially expressed lncRNA, XLOC_006393, was found to participate in CWMV infection as a precursor to microRNAs in N. benthamiana. These results suggest that lncRNAs play an important role in the regulatory network of N. benthamiana in response to CWMV infection.

scholarly journals Publisher Correction: Exogenously overexpressed intronic long noncoding RNAs activate host gene expression by affecting histone modification in Arabidopsis

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Zhang-Wei Liu ◽  
Nan Zhao ◽  
Yin-Na Su ◽  
Shan-Shan Chen ◽  
Xin-Jian He
Keyword(s):  
Gene Expression ◽  
Histone Modification ◽  
Noncoding Rnas ◽  
Long Noncoding Rnas ◽  
Host Gene ◽  
Host Gene Expression

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

scholarly journals Comprehensive high-throughput meta-analysis of differentially expressed microRNAs in transcriptomic datasets reveals significant disruption of MAPK/JNK signal transduction pathway in Adult T-cell leukemia/lymphoma

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Shahrzad Shadabi ◽  
Nargess Delrish ◽  
Mehdi Norouzi ◽  
Maryam Ehteshami ◽  
Fariba Habibian-Sezavar ◽  
...  
Keyword(s):  
Gene Expression ◽  
Signal Transduction ◽  
T Cell ◽  
Network Analysis ◽  
High Throughput ◽  
Target Gene ◽  
Differentially Expressed ◽  
Cell Leukemia ◽  
Adult T Cell Leukemia ◽  
Healthy Donors

Abstract Background Human T-lymphotropic virus 1 (HTLV-1) infection may lead to the development of Adult T-cell leukemia/lymphoma (ATLL). To further elucidate the pathophysiology of this aggressive CD4+ T-cell malignancy, we have performed an integrated systems biology approach to analyze previous transcriptome datasets focusing on differentially expressed miRNAs (DEMs) in peripheral blood of ATLL patients. Methods Datasets GSE28626, GSE31629, GSE11577 were used to identify ATLL-specific DEM signatures. The target genes of each identified miRNA were obtained to construct a protein-protein interactions network using STRING database. The target gene hubs were subjected to further analysis to demonstrate significantly enriched gene ontology terms and signaling pathways. Quantitative reverse transcription Polymerase Chain Reaction (RTqPCR) was performed on major genes in certain pathways identified by network analysis to highlight gene expression alterations. Results High-throughput in silico analysis revealed 9 DEMs hsa-let-7a, hsa-let-7g, hsa-mir-181b, hsa-mir-26b, hsa-mir-30c, hsa-mir-186, hsa-mir-10a, hsa-mir-30b, and hsa-let-7f between ATLL patients and healthy donors. Further analysis revealed the first 5 of DEMs were directly associated with previously identified pathways in the pathogenesis of HTLV-1. Network analysis demonstrated the involvement of target gene hubs in several signaling cascades, mainly in the MAPK pathway. RT-qPCR on human ATLL samples showed significant upregulation of EVI1, MKP1, PTPRR, and JNK gene vs healthy donors in MAPK/JNK pathway. Discussion The results highlighted the functional impact of a subset dysregulated microRNAs in ATLL on cellular gene expression and signal transduction pathways. Further studies are needed to identify novel biomarkers to obtain a comprehensive mapping of deregulated biological pathways in ATLL.

scholarly journals Gene expression and DNA methylation analyses suggest that two immune related genes are prognostic factors of colorectal cancer

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Xiao-Liang Xing ◽  
Zhi-Yong Yao ◽  
Chaoqun Xing ◽  
Zhi Huang ◽  
Jing Peng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Risk Assessment ◽  
Dna Methylation ◽  
Differentially Expressed Genes ◽  
Risk Score ◽  
Immune Cells ◽  
Assessment Model ◽  
Differentially Expressed ◽  
Risk Assessment Model

Abstract Background Colorectal cancer (CRC) is the second most prevalent cancer, as it accounts for approximately 10% of all annually diagnosed cancers. Studies have indicated that DNA methylation is involved in cancer genesis. The purpose of this study was to investigate the relationships among DNA methylation, gene expression and the tumor-immune microenvironment of CRC, and finally, to identify potential key genes related to immune cell infiltration in CRC. Methods In the present study, we used the ChAMP and DESeq2 packages, correlation analyses, and Cox regression analyses to identify immune-related differentially expressed genes (IR-DEGs) that were correlated with aberrant methylation and to construct a risk assessment model. Results Finally, we found that HSPA1A expression and CCRL2 expression were positively and negatively associated with the risk score of CRC, respectively. Patients in the high-risk group were more positively correlated with some types of tumor-infiltrating immune cells, whereas they were negatively correlated with other tumor-infiltrating immune cells. After the patients were regrouped according to the median risk score, we could more effectively distinguish them based on survival outcome, clinicopathological characteristics, specific tumor-immune infiltration status and highly expressed immune-related biomarkers. Conclusion This study suggested that the risk assessment model constructed by pairing immune-related differentially expressed genes correlated with aberrant DNA methylation could predict the outcome of CRC patients and might help to identify those patients who could benefit from antitumor immunotherapy.

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