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 The Interplay between Long Noncoding RNAs and Proteins of the Epigenetic Machinery in Ovarian Cancer

Cancers ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2701
Author(s):  
Naiade Calanca ◽  
Cecilie Abildgaard ◽  
Cláudia Aparecida Rainho ◽  
Silvia Regina Rogatto
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Noncoding Rnas ◽  
Response To Therapy ◽  
Long Noncoding Rnas ◽  
Cancer Type ◽  
Aberrant Expression ◽  
Term Survival ◽  
Control Of Gene Expression ◽  
Epigenetic Machinery

Comprehensive large-scale sequencing and bioinformatics analyses have uncovered a myriad of cancer-associated long noncoding RNAs (lncRNAs). Aberrant expression of lncRNAs is associated with epigenetic reprogramming during tumor development and progression, mainly due to their ability to interact with DNA, RNA, or proteins to regulate gene expression. LncRNAs participate in the control of gene expression patterns during development and cell differentiation and can be cell and cancer type specific. In this review, we described the potential of lncRNAs for clinical applications in ovarian cancer (OC). OC is a complex and heterogeneous disease characterized by relapse, chemoresistance, and high mortality rates. Despite advances in diagnosis and treatment, no significant improvements in long-term survival were observed in OC patients. A set of lncRNAs was associated with survival and response to therapy in this malignancy. We manually curated databases and used bioinformatics tools to identify lncRNAs implicated in the epigenetic regulation, along with examples of direct interactions between the lncRNAs and proteins of the epigenetic machinery in OC. The resources and mechanisms presented herein can improve the understanding of OC biology and provide the basis for further investigations regarding the selection of novel biomarkers and therapeutic targets.

scholarly journals A Nomogram Based on a Three-Gene Signature Derived from AATF Coexpressed Genes Predicts Overall Survival of Hepatocellular Carcinoma Patients

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Jun Liu ◽  
Jianjun Lu ◽  
Zhanzhong Ma ◽  
Wenli Li
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Clinical Practice ◽  
Correlation Coefficient ◽  
Cox Regression ◽  
Prognostic Significance ◽  
Gene Signature ◽  
Cancer Genome ◽  
Survival Prediction

Background. Hepatocellular carcinoma (HCC) is a common cancer with an extremely high mortality rate. Therefore, there is an urgent need in screening key biomarkers of HCC to predict the prognosis and develop more individual treatments. Recently, AATF is reported to be an important factor contributing to HCC. Methods. We aimed to establish a gene signature to predict overall survival of HCC patients. Firstly, we examined the expression level of AATF in the Gene Expression Omnibus (GEO), the Cancer Genome Atlas (TCGA), and the International Union of Cancer Genome (ICGC) databases. Genes coexpressed with AATF were identified in the TCGA dataset by the Poisson correlation coefficient and used to establish a gene signature for survival prediction. The prognostic significance of this gene signature was then validated in the ICGC dataset and used to build a combined prognostic model for clinical practice. Results. Gene expression data and clinical information of 2521 HCC patients were downloaded from three public databases. AATF expression in HCC tissue was higher than that in matched normal liver tissues. 644 genes coexpressed with AATF were identified by the Poisson correlation coefficient and used to establish a three-gene signature (KIF20A, UCK2, and SLC41A3) by the univariate and multivariate least absolute shrinkage and selection operator Cox regression analyses. This three-gene signature was then used to build a combined nomogram for clinical practice. Conclusion. This integrated nomogram based on the three-gene signature can predict overall survival for HCC patients well. The three-gene signature may be a potential therapeutic target in HCC.

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 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.

Aberrant expression of long noncoding RNAs in colorectal cancer with liver metastasis

Tumor Biology ◽  
2015 ◽  
Vol 36 (11) ◽  
pp. 8747-8754 ◽  
Author(s):  
Le-chi Ye ◽  
Li Ren ◽  
Jun-jun Qiu ◽  
De-xiang Zhu ◽  
Tao Chen ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Liver Metastasis ◽  
Noncoding Rnas ◽  
Long Noncoding Rnas ◽  
Aberrant Expression

Long Noncoding RNAs in Plants

2021 ◽  
Vol 72 (1) ◽  
Author(s):  
Andrzej T. Wierzbicki ◽  
Todd Blevins ◽  
Szymon Swiezewski
Keyword(s):  
Gene Expression ◽  
Nuclear Dna ◽  
Noncoding Rnas ◽  
Chromatin Accessibility ◽  
Long Noncoding Rnas ◽  
Annual Review ◽  
Publication Date ◽  
Protein Coding ◽  
Ribonucleic Acids ◽  
Coding Potential

Plants have an extraordinary diversity of transcription machineries, including five nuclear DNA-dependent RNA polymerases. Four of these enzymes are dedicated to the production of long noncoding RNAs (lncRNAs), which are ribonucleic acids with functions independent of their protein-coding potential. lncRNAs display a broad range of lengths and structures, but they are distinct from the small RNA guides of RNA interference (RNAi) pathways. lncRNAs frequently serve as structural, catalytic, or regulatory molecules for gene expression. They can affect all elements of genes, including promoters, untranslated regions, exons, introns, and terminators, controlling gene expression at various levels, including modifying chromatin accessibility, transcription, splicing, and translation. Certain lncRNAs protect genome integrity, while others respond to environmental cues like temperature, drought, nutrients, and pathogens. In this review, we explain the challenge of defining lncRNAs, introduce the machineries responsible for their production, and organize this knowledge by viewing the functions of lncRNAs throughout the structure of a typical plant gene. Expected final online publication date for the Annual Review of Plant Biology, Volume 72 is May 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

THU0078 EXPRESSION PROFILE ANALYSIS OF LONG NONCODING RNAS INDUCED BY IL-1ß IN RHEUMATOID ARTHRITIS FIBROBLAST-LIKE SYNOVIOCYTES

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 251.1-251
Author(s):  
J. M. Kim ◽  
H. J. Kang ◽  
S. J. Jung ◽  
B. W. Song ◽  
H. J. Jeong ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Expression Profile ◽  
Long Noncoding Rna ◽  
Noncoding Rna ◽  
Noncoding Rnas ◽  
Long Noncoding Rnas ◽  
Aberrant Expression ◽  
Iκb Kinase ◽  
Ngs Data ◽  
Fibroblast Like Synoviocytes

Background:Long noncoding RNAs (lncRNAs) have recently emerged as important biological regulators and the aberrant expression of lncRNAs has been reported in various diseases including cancer, cardiovascular disease, and diabetes mellitus. However, the role of lncRNAs in the pathogenesis of rheumatoid arthritis (RA) remains unknown.Objectives:Thus, we studied lncRNAs influenced by IL-1, which is one of the key mediators in the pathogenesis of RA, and also investigated whether regulation of NF-κB activation, which is known to be induced by IL-1, could lead to the changes of expression of those lncRNAs.Methods:Fibroblast-like synoviocytes (FLS) were obtained from the knee joints of the patients with RA. The next-generation sequencing (NGS) data were analyzed to identify differentially expressed lncRNAs between unstimulated RA FLS and IL-1-stimulated RA FLS. The expression levels of the top 5 candidates in NGS data were validated by RT-qPCR using extended number of unstimulated RA FLS and IL-1-stimulated RA FLS. IMD-0560, an inhibitor of IκB kinase (IKK) was used for the regulation of NF-κB activation. Activation and inhibition of NF-κB were confirmed by Western blotting. Changed expressions of the lncRNAs were identified by RT-qPCR.Results:NGS analysis revealed up-regulated 30 lncRNAs and down-regulated 15 lncRNAs in IL-1-treated RA FLS compared with unstimulated RA FLS. Top 5 lncRNAs were selected among 30 lncRNAs up-regulated by IL-1 in RA FLS based on fold-change with P-value cutoff. The up-regulated lncRNAs including NR_046035, NR_027783, NR_033422, NR_003133, and NR_049759 were validated by RT-qPCR. IMD-0560 inhibited phosphorylation of IκBα induced by IL-1 in RA FLS. Overexpression of lncRNAs induced by IL-1 was also inhibited by IMD-0560 in RA FLS.Conclusion:Our study revealed that IL-1 increased the expression of NR_046035, NR_027783, NR_033422, NR_003133, and NR_049759 in RA FLS. In addition, the expression of these lncRNAs was regulated by inhibition of NF-κB activation. Thus, our data suggest that the lncRNAs might be involved in the pathogenesis of RA through NF-κB signaling pathway.References:[1]Long noncoding RNAs and human disease. Trends Cell Biol. 2011 Jun;21(6):354-61.[2]A long noncoding RNA mediates both activation and repression of immune response genes. Science. 2013 Aug 16;341(6147):789-92.[3]Long noncoding RNA expression profile in fibroblast-like synoviocytes from patients with rheumatoid arthritis. Arthritis Res Ther. 2016 Oct 6;18(1):227.Disclosure of Interests:None declared

scholarly journals Identification of an extracellular vesicle-related gene signature in the prediction of pancreatic cancer clinical prognosis

2020 ◽  
Vol 40 (12) ◽  
Author(s):  
Dafeng Xu ◽  
Yu Wang ◽  
Kailun Zhou ◽  
Jincai Wu ◽  
Zhensheng Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Immune Cells ◽  
Prognostic Value ◽  
Tumor Tissue ◽  
Clinical Stage ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Sequencing Data ◽  
Clinical Prognosis

Abstract Although extracellular vesicles (EVs) in body fluid have been considered to be ideal biomarkers for cancer diagnosis and prognosis, it is still difficult to distinguish EVs derived from tumor tissue and normal tissue. Therefore, the prognostic value of tumor-specific EVs was evaluated through related molecules in pancreatic tumor tissue. NA sequencing data of pancreatic adenocarcinoma (PAAD) were acquired from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC). EV-related genes in pancreatic cancer were obtained from exoRBase. Protein–protein interaction (PPI) network analysis was used to identify modules related to clinical stage. CIBERSORT was used to assess the abundance of immune and non-immune cells in the tumor microenvironment. A total of 12 PPI modules were identified, and the 3-PPI-MOD was identified based on the randomForest package. The genes of this model are involved in DNA damage and repair and cell membrane-related pathways. The independent external verification cohorts showed that the 3-PPI-MOD can significantly classify patient prognosis. Moreover, compared with the model constructed by pure gene expression, the 3-PPI-MOD showed better prognostic value. The expression of genes in the 3-PPI-MOD had a significant positive correlation with immune cells. Genes related to the hypoxia pathway were significantly enriched in the high-risk tumors predicted by the 3-PPI-MOD. External databases were used to verify the gene expression in the 3-PPI-MOD. The 3-PPI-MOD had satisfactory predictive performance and could be used as a prognostic predictive biomarker for pancreatic cancer.

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