EA15, MIR22, LINC00472 as diagnostic markers for diabetic kidney disease

This study aimed to investigate the molecular mechanisms of diabetic kidney disease (DKD) and to explore new potential therapeutic strategies and biomarkers for DKD. First we analyzed the differentially expressed changes between patients with DKD and the control group using the chip data in Gene Expression Omnibus (GEO) database. Then the gene chip was subjected to be annotated again, so as to screen long noncoding RNAs (lncRNAs) and study expression differences of these lncRNAs in DKD and controlled samples. At last, the function of the differential lncRNAs was analyzed. A total of 252 lncRNAs were identified, and 14 were differentially expressed. In addition, there were 1,629 differentially expressed messenger RNAs (mRNAs) genes, and proliferation and apoptosis adapter protein 15 (PEA15), MIR22, and long intergenic nonprotein coding RNA 472 ( LINC00472) were significantly differentially expressed in DKD samples. Through functional analysis of the encoding genes coexpressed by the three lncRNAs, we found these genes were mainly enriched in type 1 diabetes and autoimmune thyroid disease pathways, whereas in Gene Ontology (GO) function classification, they were also mainly enriched in the immune response, type I interferon signaling pathways, interferon-γ mediated signaling pathways, and so forth. To summary, we identified EA15, MIR22, and LINC00472 may serve as the potential diagnostic markers of DKD.     

Novel biomarkers for post-contrast acute kidney injury identified from long non-coding RNA expression profiles

Background: Post-contrast acute kidney injury (PC-AKI) is a severe complication of cardiac catheterization. Emerging evidence indicated that long non-coding RNAs (lncRNAs) could serve as biomarkers for various diseases. However, the lncRNA expression profile and potential biomarkers in PC-AKI remain unclear. This study aimed to investigate novel lncRNA biomarkers for the early detection of PC-AKI.Methods: lncRNA profile in the kidney tissues of PC-AKI rats was evaluated through RNA sequencing. Potential lncRNA biomarkers were identified through human-rat homology analysis, kidney and blood filtering in rats and verified in 112 clinical samples. The expression patterns of the candidate lncRNAs were detected in HK-2 cells and rat models to evaluate their potential for early detection.Results: In total, 357 lncRNAs were found to be differentially expressed in PC-AKI. We identified lnc-HILPDA and lnc-PRND were conservative and remarkably upregulated in both kidneys and blood from rats and the blood of PC-AKI patients; these lncRNAs can precisely distinguish PC-AKI patients (area under the curve (AUC) values of 0.885 and 0.875, respectively). The combination of these two lncRNAs exhibited improved accuracy for predicting PC-AKI, with 100% sensitivity and 83.93% specificity. Time-course experiments showed that the significant difference was first noted in the blood of PC-AKI rats at 12 h for lnc-HILPDA and 24 h for lnc-PRND.Conclusion: Our study revealed that lnc-HILPDA and lnc-PRND may serve as the novel biomarkers for early detection and profoundly affect the clinical stratification and strategy guidance of PC-AKI.     

Construction of lncRNA-associated ceRNA networks to identify prognostic lncRNA biomarkers for glioblastoma

Long noncoding RNAs (lncRNAs) serve as competitive endogenous RNAs (ceRNAs) that play significant regulatory roles in the pathogenesis of tumors. However, the role of lncRNAs, especially the lncRNA-related ceRNA regulatory network, in glioblastoma (GBM) has not been fully elucidated. The goal of the current study was to construct lncRNA-microRNA-mRNA-related ceRNA networks for further investigation of their mechanism of action in GBM. We downloaded data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases and identified differential lncRNAs, microRNAs (miRNAs), and messenger RNAs (mRNAs) associated with GBM. A ceRNA network was constructed and analyzed to examine the relationship between lncRNAs and patients’ overall survival. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGGs) were used to analyze the related mRNAs to indirectly explain the mechanism of action of lncRNAs. The potential effective drugs for the treatment of GBM were identified using the connectivity map (CMap). After integrated analysis, we obtained a total of 210 differentially expressed lncRNAs, 90 differentially expressed miRNAs, and 2508 differentially expressed mRNAs (DEmRNAs) from the TCGA and GEO databases. Using these differential genes, we constructed a lncRNA-associated ceRNA network. Six lncRNAs in the ceRNA network were associated with the overall survival of patients with GBM. Through KEGG analysis, it was found that the DEmRNAs involved in the network are related to cancer-associated pathways, for instance, mitogen-activated protein kinase and Ras signaling pathways. CMap analysis revealed four small-molecule compounds that could be used as drugs for the treatment of GBM. In this study, a multi-database joint analysis was used to construct a lncRNA-related ceRNA network to help identify the regulatory functions of lncRNAs in the pathogenesis of GBM.     

Identification and development of long non‐coding RNA‐associated regulatory network in colorectal cancer

Colorectal cancer (CRC) is one of the leading causes of cancer‐associated death globally. Long non‐coding RNAs (lncRNAs) have been identified as micro RNA (miRNA) sponges in a competing endogenous RNA (ceRNA) network and are involved in the regulation of mRNA expression. This study aims to construct a lncRNA‐associated ceRNA network and investigate the prognostic biomarkers in CRC. A total of 38 differentially expressed (DE) lncRNAs, 23 DEmiRNAs and 27 DEmRNAs were identified by analysing the expression profiles of CRC obtained from The Cancer Genome Atlas (TCGA). These RNAs were chosen to develop a ceRNA regulatory network of CRC, which comprised 125 edges. Survival analysis showed that four lncRNAs, six miRNAs and five mRNAs were significantly associated with overall survival. A potential regulatory axis of ADAMTS9‐AS2/miR‐32/PHLPP2 was identified from the network. Experimental validation was performed using clinical samples by quantitative real‐time PCR (qRT‐PCR), which showed that expression of the genes in the axis was associated with clinicopathological features and the correlation among them perfectly conformed to the ‘ceRNA theory’. Overexpression of ADAMTS9‐AS2 in colon cancer cell lines significantly inhibited the miR‐32 expression and promoted PHLPP2 expression, while ADAMTS9‐AS2 knockdown had the opposite effects. The constructed novel ceRNA network may provide a comprehensive understanding of the mechanisms of CRC carcinogenesis. The ADAMTS9‐AS2/miR‐32/PHLPP2 regulatory axis may serve as a potential therapeutic target for CRC.     

Comprehensive analysis of a ceRNA network reveals potential prognostic cytoplasmic lncRNAs involved in HCC progression

The aberrant expression of long noncoding RNAs (lncRNAs) has drawn increasing attention in the field of hepatocellular carcinoma (HCC) biology. In the present study, we obtained the expression profiles of lncRNAs, microRNAs (miRNAs), and messenger RNAs (mRNAs) in 371 HCC tissues and 50 normal tissues from The Cancer Genome Atlas (TCGA) and identified hepatocarcinogenesis-specific differentially expressed genes (DEGs, log fold change ≥ 2, FDR < 0.01), including 753 lncRNAs, 97 miRNAs, and 1,535 mRNAs. Because the specific functions of lncRNAs are closely related to their intracellular localizations and because the cytoplasm is the main location for competitive endogenous RNA (ceRNA) action, we analyzed not only the interactions among these DEGs but also the distributions of lncRNAs (cytoplasmic, nuclear or both). Then, an HCC-associated deregulated ceRNA network consisting of 37 lncRNAs, 10 miRNAs, and 26 mRNAs was constructed after excluding those lncRNAs located only in the nucleus. Survival analysis of this network demonstrated that 15 lncRNAs, 3 miRNAs, and 16 mRNAs were significantly correlated with the overall survival of HCC patients (p < 0.01). Through multivariate Cox regression and lasso analysis, a risk score system based on 13 lncRNAs was constructed, which showed good discrimination and predictive ability for HCC patient survival time. This ceRNA network-construction approach, based on lncRNA distribution, not only narrowed the scope of target lncRNAs but also provided specific candidate molecular biomarkers for evaluating the prognosis of HCC, which will help expand our understanding of the ceRNA mechanisms involved in the early development of HCC.     

Downregulation of AC061961.2, LING01-AS1, and RP11–13E1.5 is associated with dilated cardiomyopathy progression

This study aimed to explore long noncoding RNAs (lncRNAs) implicated in dilated cardiomyopathy (DCM). Ten samples of failing hearts collected from the left ventricles of patients with DCM undergoing heart transplants, and ten control samples obtained from normal heart donors were included in this study. After sequencing, differentially expressed genes (DEGs) and lncRNAs between DCM and controls were screened, followed with functional enrichment analysis and weighted gene coexpression network analysis (WGCNA). Five key lncNRAs were validated through real-time polymerase chain reaction (PCR). Total 1,398 DEGs were identified, including 267 lncRNAs. WGCNA identified seven modules that were significantly correlated with DCM. The top 50 genes in the three modules (black, dark-green, and green–yellow) were significantly correlated with DCM disease state. Four core enrichment lncRNAs, such as AC061961.2, LING01-AS1, and RP11–557H15.4, in the green–yellow module were associated with neurotransmitter secretion. Five core enrichment lncRNAs, such as KB-1299A7.2 and RP11–13E1.5, in the black module were associated with the functions of blood circulation and heart contraction. AC061961.2, LING01-AS1, and RP11–13E1.5 were confirmed to be downregulated in DCM tissues by real-time PCR. The current study suggests that downregulation of AC061961.2, LING01-AS1, and RP11–13E1.5 may be associated with DCM progression, which may serve as key diagnostic biomarkers and therapeutic targets for DCM.     

Identification of key biomarkers for thyroid cancer by integrative gene expression profiles

Thyroid cancer is a frequently diagnosed malignancy and the incidence has been increased rapidly in recent years. Despite the favorable prognosis of most thyroid cancer patients, advanced patients with metastasis and recurrence still have poor prognosis. Therefore, the molecular mechanisms of progression and targeted biomarkers were investigated for developing effective targets for treating thyroid cancer. Eight chip datasets from the gene expression omnibus database were selected and the inSilicoDb and inSilicoMerging R/Bioconductor packages were used to integrate and normalize them across platforms. After merging the eight gene expression omnibus datasets, we obtained one dataset that contained the expression profiles of 319 samples (188 tumor samples plus 131 normal thyroid tissue samples). After screening, we identified 594 significantly differentially expressed genes (277 up-regulated genes plus 317 down-regulated genes) between the tumor and normal tissue samples. The differentially expressed genes exhibited enrichment in multiple signaling pathways, such as p53 signaling. By building a protein–protein interaction network and module analysis, we confirmed seven hub genes, and they were all differentially expressed at all the clinical stages of thyroid cancer. A diagnostic seven-gene signature was established using a logistic regression model with the area under the receiver operating characteristic curve (AUC) of 0.967. Seven robust candidate biomarkers predictive of thyroid cancer were identified, and the obtained seven-gene signature may serve as a useful marker for thyroid cancer diagnosis and prognosis.     

High throughput deep sequencing elucidates the important role of lncRNAs in Foxtail millet response to herbicides

Long non-coding RNAs (lncRNAs) play an important function in plant growth and development as well as response to stresses. However, little information was known in foxtail millet; no study was reported on lncRNAs in plant response to herbicide treatment. In this study, by using deep sequencing and advanced bioinformatic analysis, a total of 2547 lncRNAs were identified, including 787 known and 1760 novel lncRNAs. These lncRNAs are distributed across all 9 chromosomes, and the majority were located in the intergenic region with 1–2 exons. These lncRNAs were differentially expressed between different genotypes under different herbicide treatments. lncRNAs regulate plant growth and development as well as response to herbicide treatments through targeting protein-coding genes that directly relate to chemical metabolism and defense system. Multiple potential target genes and lncRNA-mRNA-miRNA gene networks were discovered. These results elucidate the potential roles of lncRNAs in plant response to herbicides.     

Integrated analysis of a competing endogenous RNA network reveals key long noncoding RNAs as potential prognostic biomarkers for hepatocellular carcinoma

Growing evidence has revealed that long noncoding RNAs (lncRNAs) have an important impact on tumorigenesis and tumor progression via a mechanism involving competing endogenous RNAs (ceRNAs). However, their use in predicting the survival of a patient with hepatocellular carcinoma (HCC) remains unclear. The aim of this study was to develop a novel lncRNA expression–based risk score system to accurately predict the survival of patients with HCC. In our study, using expression profiles downloaded from The Cancer Genome Atlas database, the differentially expressed messenger RNAs (mRNAs), lncRNAs, and microRNAs (miRNAs) were explored in patients with HCC and normal liver tissues, and then a ceRNA network constructed. A risk score system was established between lncRNA expression of the ceRNA network and overall survival (OS) or recurrence-free survival (RFS); it was further analyzed for associations with the clinical features of patients with HCC. In HCC, 473 differentially expressed lncRNAs, 63 differentially expressed miRNAs, and 1417 differentially expressed mRNAs were detected. The ceRNA network comprised 41 lncRNA nodes, 12 miRNA nodes, 24 mRNA nodes, and 172 edges. The lncRNA expression–based risk score system for OS was constructed based on six lncRNAs (MYLK-AS1, AL359878.1, PART1, TSPEAR-AS1, C10orf91, and LINC00501), while the risk score system for RFS was based on four lncRNAs (WARS2-IT1, AL359878.1, AL357060.1, and PART1). Univariate and multivariate Cox analyses showed the risk score systems for OS or RFS were significant independent factors adjusted for clinical factors. Receiver operating characteristic curve analysis showed the area under the curve for the risk score system was 0.704 for OS, and 0.71 for RFS. Our result revealed a lncRNA expression–based risk score system for OS or RFS can effectively predict the survival of patients with HCC and aid in good clinical decision-making.     

Prognostic genes of hepatocellular carcinoma based on gene coexpression network analysis

Hepatocellular carcinoma (HCC) is the most common subtype in liver cancer whose prognosis is affected by malignant progression associated with complex gene interactions. However, there is currently no available biomarkers associated with HCC progression in clinical application. In our study, RNA sequencing expression data of 50 normal samples and 374 tumor samples was analyzed and 9225 differentially expressed genes were screened. Weighted gene coexpression network analysis was then conducted and the blue module we were interested was identified by calculating the correlations between 17 gene modules and clinical features. In the blue module, the calculation of topological overlap was applied to select the top 30 genes and these 30 genes were divided into the green group (11 genes) and the yellow group (19 genes) through searching whether these genes were validated by in vitro or in vivo experiments. The genes in the green group which had never been validated by any experiments were recognized as hub genes. These hub genes were subsequently validated by a new data set GSE76427 and KM Plotter Online Tool, and the results indicated that 10 genes (FBXO43, ARHGEF39, MXD3, VIPR1, DNASE1L3, PHLDA1, CSRNP1, ADR2B, C1RL, and CDC37L1) could act as prognosis and progression biomarkers of HCC. In summary, 10 genes who have never been mentioned in HCC were identified to be associated with malignant progression and prognosis of patients. These findings may contribute to the improvement of the therapeutic decision, risk stratification, and prognosis prediction for HCC patients.     

Nine hub genes as the potential indicator for the clinical outcome of diabetic nephropathy

Diabetic nephropathy (DN) is one of the most serious and dangerous chronic complications caused by diabetes mellitus, and the identification and development of novel biomarkers could be beneficial for the diagnosis and prognosis of DN patients. This study focused on researching the differently expressed pattern of the DN samples from glomeruli and tubulointerstitium. Significance analysis of microarrays (SAM) was used to identify differentially regulated genes in 44 microdissected human kidney samples. Functional enrichment analysis was used to analyze the functions these genes are mostly enriched in. Besides, protein–protein interaction (PPI) network was used to select the hub genes that were associated with DN. The gene expression pattern of DN samples from glomeruli and tubulointerstitium was found to be quite different. It showed that the recurrence rate of DN originating from glomeruli and control samples was lower than that from tubulointerstitium and control samples. A total of 332 differentially expressed genes were identified between glomeruli tissues and tubulointerstitium tissues. PPI network analysis was performed on these 332 genes. Finally, nine hub genes were selected as the most potential biomarkers in the occurrence of DN. In conclusion, a total of 332 genes were found to be related to DN, and these genes were found to be mostly enriched in pathways such as immunity, inflammatory, and vascular pathways. Three genes VAV1, LCK, and Plk had the potential to serve as indicators for the occurrence and development of DN in clinical management.     

Transcriptional profiling of exosomes derived from plasma of canine with mammary tumor by RNA-seq analysis

Canine mammary tumor (CMT) are the second most common tumor in dogs. Exosomes can act as biomarkers for the early diagnosis of tumors, and also be involved in the pathogenesis and metastasis mechanism of tumors. The expression profile of exosomal RNA revealed that there were a total of 5547 differentially expressed mRNAs, and 196 differentially expressed lncRNAs. GO and KEGG enrichment analysis found that the differentially expressed mRNAs and lncRNA target genes were associated with metabolic processes, DNA replication, cell proliferation, cell junction, and cell adhesion. In conclusion, this study revealed lncRNA and mRNA expression profiles in exosomes derived from plasma of CMT and further annotated their potential functions. The data obtained in this study will also provide valuable resources for understanding lncRNA information in plasma exosomes of dogs with CMT, and contribute to the study of early diagnostic markers and pathogenesis of CMT.     

Identification of a novel tumour microenvironment-based prognostic biomarker in skin cutaneous melanoma

Skin cutaneous melanoma (SKCM) is one of the most destructive skin malignancies and has attracted worldwide attention. However, there is a lack of prognostic biomarkers, especially tumour microenvironment (TME)-based prognostic biomarkers. Therefore, there is an urgent need to investigate the TME in SKCM, as well as to identify efficient biomarkers for the diagnosis and treatment of SKCM patients. A comprehensive analysis was performed using SKCM samples from The Cancer Genome Atlas and normal samples from Genotype-Tissue Expression. TME scores were calculated using the ESTIMATE algorithm, and differential TME scores and differentially expressed prognostic genes were successively identified. We further identified more reliable prognostic genes via least absolute shrinkage and selection operator regression analysis and constructed a prognostic prediction model to predict overall survival. Receiver operating characteristic analysis was used to evaluate the diagnostic efficacy, and Cox regression analysis was applied to explore the relationship with clinicopathological characteristics. Finally, we identified a novel prognostic biomarker and conducted a functional enrichment analysis. After considering ESTIMATEScore and tumour purity as differential TME scores, we identified 34 differentially expressed prognostic genes. Using least absolute shrinkage and selection operator regression, we identified seven potential prognostic biomarkers (SLC13A5, RBM24, IGHV3OR16-15, PRSS35, SLC7A10, IGHV1-69D and IGHV2-26). Combined with receiver operating characteristic and regression analyses, we determined PRSS35 as a novel TME-based prognostic biomarker in SKCM, and functional analysis enriched immune-related cells, functions and signalling pathways. Our study indicated that PRSS35 could act as a potential prognostic biomarker in SKCM by investigating the TME, so as to provide new ideas and insights for the clinical diagnosis and treatment of SKCM.