Construction of lncRNA‐miRNA‐mRNA network based on ceRNA mechanism reveals the function of lncRNA in the pathogenesis of gout

ObjectiveTo identify differentially expressed lncRNA, miRNA, and mRNA during the pathogenesis of gout, explore the ceRNA network regulatory mechanism of gout, and seek potential therapeutic targets.MethodFirst, gout‐related chips were retrieved by GEO database. Then, the analysis of differentially expressed lncRNAs and mRNAs was conducted by R language and other software. Besides, miRNA and its regulated mRNA were predicted based on public databases, the intersection of differentially expressed mRNA and predicated mRNA was taken, and the lncRNA‐miRNA‐mRNA regulatory relationships were obtained to construct the ceRNA regulatory network. Subsequently, hub genes were screened by the STRING database and Cytoscape software. Then the DAVID database was used to illustrate the gene functions and related pathways of hub genes and to mine key ceRNA networks.ResultsThree hundred and eighty‐eight lncRNAs and 758 mRNAs were identified with significant differential expression in gout patient, which regulates hub genes in the ceRNA network, such as JUN, FOS, PTGS2, NR4A2, and TNFAIP3. In the ceRNA network, lncRNA competes with mRNA for miRNA, thus affecting the IL‐17 signaling pathway, TNF signaling pathway, Oxytocin signaling pathway, and NF‐κB signaling pathway through regulating the cell''s response to chemical stress. The research indicates that five miRNAs (miR‐429, miR‐137, miR‐139‐5p, miR‐217, miR‐23b‐3p) and five lncRNAs (SNHG1, FAM182A, SPAG5‐AS1, HNF1A‐AS1, UCA1) play an important role in the formation and development of gout.ConclusionThe interaction in the ceRNA network can affect the formation and development of gout by regulating the body''s inflammatory response as well as proliferation, differentiation, and apoptosis of chondrocytes and osteoclasts. The identification of potential therapeutic targets and signaling pathways through ceRNA network can provide a reference for further research on the pathogenesis of gout.     

Immune‐related eight‐lncRNA signature for improving prognosis prediction of lung adenocarcinoma

BackgroundLung adenocarcinoma (LUAD) is the leading cause of cancer‐related deaths worldwide. Therefore, the identification of a novel prediction signature for predicting the prognosis risk and survival outcomes is urgently demanded.MethodsWe integrated a machine‐learning frame by combing the Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) regression model to identify the LUAD‐related long non‐coding RNA (lncRNA) survival biomarkers. Subsequently, the Spearman correlation test was employed to interrogate the relationships between lncRNA signature and tumor immunity and constructed the competing endogenous RNA (ceRNA) network.ResultsHerein, we identified an eight‐lncRNA signature (PR‐lncRNA signature, NPSR1‐AS1, SATB2‐AS1, LINC01090, FGF12‐AS2, {"type":"entrez-nucleotide","attrs":{"text":"AC005256","term_id":"11993971"}}AC005256.1, MAFA‐AS1, BFSP2‐AS1, and CPC5‐AS1), which contributes to predicting LUAD patient''s prognosis risk and survival outcomes. The PR‐lncRNA signature has also been confirmed as the robust signature in independent datasets. Further parsing of the LUAD tumor immune infiltration showed the PR‐lncRNAs were closely associated with the abundance of multiple immune cells infiltration and the expression of MHC molecules. Furthermore, by constructing the PR‐lncRNA–related ceRNA network, we interrogated more potential anti‐cancer therapy targets.ConclusionlncRNAs, as emerging cancer biomarkers, play an important role in a variety of cancer processes. Identification of PR‐lncRNA signatures allows us to better predict patient''s survival outcomes and disease risk. Finally, the PR‐lncRNA signatures could help us to develop novel LUAD anti‐cancer therapeutic strategies.     

A ten N6‐methyladenosine‐related long non‐coding RNAs signature predicts prognosis of triple‐negative breast cancer

BackgroundPatients with triple‐negative breast cancer (TNBC) face a major challenge of the poor prognosis, and N6‐methyladenosine‐(m6A) mediated regulation in cancer has been proposed. Therefore, this study aimed to explore the prognostic roles of m6A‐related long non‐coding RNAs (LncRNAs) in TNBC.MethodsClinical information and expression data of TNBC samples were collected from TCGA and GEO databases. Pearson correlation, univariate, and multivariate Cox regression analysis were employed to identify independent prognostic m6A‐related LncRNAs to construct the prognostic score (PS) risk model. Receiver operating characteristic (ROC) curve was used to evaluate the performance of PS risk model. A competing endogenous RNA (ceRNA) network was established for the functional analysis on targeted mRNAs.ResultsWe identified 10 independent prognostic m6A‐related LncRNAs (SAMD12‐AS1, BVES‐AS1, LINC00593, MIR205HG, LINC00571, ANKRD10‐IT1, CIRBP‐AS1, SUCLG2‐AS1, BLACAT1, and HOXB‐AS1) and established a PS risk model accordingly. Relevant results suggested that TNBC patients with lower PS had better overall survival status, and ROC curves proved that the PS model had better prognostic abilities with the AUC of 0.997 and 0.864 in TCGA and {"type":"entrez-geo","attrs":{"text":"GSE76250","term_id":"76250"}}GSE76250 datasets, respectively. Recurrence and PS model status were defined as independent prognostic factors of TNBC. These ten LncRNAs were all differentially expressed in high‐risk TNBC compared with controls. The ceRNA network revealed the regulatory axes for nine key LncRNAs, and mRNAs in the network were identified to function in pathways of cell communication, signaling transduction and cancer.ConclusionOur findings proposed a ten‐m6A‐related LncRNAs as potential biomarkers to predict the prognostic risk of TNBC.     

Screening of tumor grade‐related mRNAs and lncRNAs for Esophagus Squamous Cell Carcinoma

BackgroundThe goal of our study was to screen tumor grade‐related lncRNAs and mRNAs to reveal the underlying molecular mechanism of esophagus squamous cell carcinoma (ESCC).MethodsThe lncRNA and mRNA sequencing data were obtained from The Cancer Genome Atlas (TCGA). Tumor grade correlation analysis of lncRNAs and mRNAs was executed, followed by the functional enrichment analysis of all tumor grade‐related mRNAs. The differentially expression mRNAs (DEmRNAs) and differentially expressed lncRNAs (DElncRNAs) were obtained. PPI network and DEmRNA‐DElncRNA interaction analysis were constructed. The functional annotation of the DEmRNAs co‐expressed with DElncRNAs was performed. The expression levels of the candidate genes were validated using qRT‐PCR.ResultsA total of 1864 tumor grade‐related mRNAs (846 positively related and 1018 negatively related) and 552 tumor grade‐related lncRNAs (331 positively related and 221 negatively related) were obtained. The top 10 significantly grade‐related mRNAs and lncRNAs included CA12, FABP4, DECR1, BAIAP2, IL1RAPL2, PPARD, LAD1, TSPAN10, LDOC1, ZNF853, RP11‐25G10.2, RP11‐557H15.3, RP11‐521D12.5, CHKB‐AS1, RP11‐219B4.3, CH17‐335B8.4, RP11‐99 J16‐A.2, CTB‐111H14.1, ADNP‐AS1, and JHDM1D‐AS1. SFN, IL1RAPL2, and RP11‐25G10.2 were overlapped from grade 1, grade 2, and grade 3. PPI network showed that top 10 proteins with higher degrees, including GNAI1, RAP2B, GNAZ, SHH, ADCY1, PRKAR2B, SH3GL1, GNA15, and ARRB1. A DElncRNAs‐nearby DEmRNAs network was constructed to obtain hub lncRNAs including ADAMTS9‐AS2, RP11‐210 M15.2, RP11‐13 K12.1, ZBED3‐AS1, and RP11‐25G10.2. Except for RP11‐25G10.2, ADAMTS9‐AS1, ZBED3‐AS1, SFN, ATP1A2, and GNA15 were consistent with our TCGA analysis.ConclusionsAlterations of DEmRNAs and DElncRNAs may provide key insights into the molecular mechanisms of ESCC.     

N‐6 methylation‐related lncRNA is potential signature in lung adenocarcinoma and influences tumor microenvironment

BackgroundN‐6 methylation (m6A) pushes forward an immense influence on the occurrence and development of lung adenocarcinoma (LUAD). However, the methylation on non‐coding RNA in LUAD, especially long non‐coding RNA (lncRNA), has not been received sufficient attention.MethodsSpearman correlation analysis was used to screen lncRNA correlated with m6A regulators expression from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) repositories, respectively. Then, the least absolute shrinkage and selection operator (LASSO) was applied to build a risk signature consisting m6A‐related lncRNA. Univariate and multivariate independent prognostic analysis were applied to evaluate the performance of signature in predicting patients'' survival. Next, we applied Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) to conduct pathway enrichment analysis of 3344 different expression genes (DEGs). Finally, we set up a competing endogenous RNAs (ceRNA) network to this lncRNA.ResultsA total of 85 common lncRNAs were selected to acquire the components related to prognosis. The final risk signature established by LASSO regression contained 11 lncRNAs: ARHGEF26‐AS1, COLCA1, CRNDE, DLGAP1‐AS2, FENDRR, LINC00968, TMPO‐AS1, TRG‐AS1, MGC32805, RPARP‐AS1, and TBX5‐AS1. M6A‐related lncRNA risk score could predict the prognostic of LUAD and was significantly associated with clinical pathological. And in the evaluation of lung adenocarcinoma tumor microenvironment (TME) by using ESTIMATE algorithm, we found a statistically significant correlation between risk score and stromal/immune cells.ConclusionM6A‐related lncRNA was a potential prognostic and therapy target for lung adenocarcinoma.     

Development of a novel five‐lncRNA prognostic signature for predicting overall survival in elderly patients with breast cancer

BackgroundBreast cancer (BC) is an age‐related disease. Long noncoding RNAs (lncRNAs) have been proven to be crucial contributors in tumorigenesis. This study aims to develop a novel lncRNA‐based signature to predict elderly BC patients’ prognosis.MethodsThe RNA expression profiles and corresponding clinical information of 182 elderly BC patients were retrieved from The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs (DElncRNAs) between BC and adjacent normal samples were used to construct the signature in the training set through univariate Cox regression analysis, LASSO regression analysis, and multivariate Cox regression analysis. Kaplan–Meier analysis and time‐dependent receiver operating characteristic (ROC) analysis were used to evaluate the predictive performance. Besides, we developed the nomogram. Gene set enrichment analysis (GSEA) was performed to reveal the underlying molecular mechanisms.ResultsWe constructed the five‐lncRNA signature (including LEF1‐AS1, MEF2C‐AS1, ST8SIA6‐AS1, LINC01224, and LINC02408) in the training set, which successfully divided the patients into low‐ and high‐risk groups with significantly different prognosis (p = 0.000049), and the AUC at 3 and 5 years of the signature was 0.779 and 0.788, respectively. The predictive performance of this signature was validated in the test and entire set. The 5‐lncRNA signature was an independent prognostic factor of OS (p = 0.007) and the nomogram constructed by independent prognostic factors was an accurate predictor of predicting overall survival probability. Besides, several pathways associated with tumorigenesis have been identified by GSEA.ConclusionsThe 5‐lncRNA signature and nomogram are reliable in predicting elderly BC patients’ prognosis and provide clues for clinical decision‐making.     

LncRNA PVT1 is increased in renal cell carcinoma and affects viability and migration in vitro

BackgroundRenal cell carcinoma is difficult to diagnose and unpredictable in disease course and severity. There are no specific biomarkers for diagnosis and prognosis estimation feasible in clinical practice. Long non‐coding RNAs (lncRNAs) have emerged as potent regulators of gene expression in recent years. Aside from their cellular role, their expression patterns could be used as a biomarker of ongoing pathology.MethodsIn this work, we used next‐generation sequencing for global lncRNA expression profiling in tumor and non‐tumor tissue of RCC patients. The four candidate lncRNAs have been further validated on an independent cohort. PVT1, as the most promising lncRNA, has also been studied using functional in vitro tests.ResultsNext‐generation sequencing showed significant dysregulation of 1163 lncRNAs; among them top 20 dysregulated lncRNAs were AC061975.7, {"type":"entrez-nucleotide","attrs":{"text":"AC124017.1","term_id":"21327652","term_text":"AC124017.1"}}AC124017.1, {"type":"entrez-nucleotide","attrs":{"text":"AP000696.1","term_id":"6705905","term_text":"AP000696.1"}}AP000696.1, AC148477.4, LINC02437, GATA3‐AS, LINC01762, LINC01230, LINC01271, LINC01187, LINC00472, {"type":"entrez-nucleotide","attrs":{"text":"AC007849.1","term_id":"5091570","term_text":"AC007849.1"}}AC007849.1, LINC00982, LINC01543, {"type":"entrez-nucleotide","attrs":{"text":"AL031710.1","term_id":"3821014","term_text":"AL031710.1"}}AL031710.1, and {"type":"entrez-nucleotide","attrs":{"text":"AC019197.1","term_id":"6648325","term_text":"AC019197.1"}}AC019197.1 as down‐regulated lncRNAs; and SLC16A1‐AS1, PVT1, LINC0887, and LUCAT1 as up‐regulated lncRNAs. We observed statistically significant dysregulation of PVT1, LUCAT1, and LINC00982. Moreover, we studied the effect of artificial PVT1 decrease in renal cell line 786–0 and observed an effect on cell viability and migration.ConclusionOur results show not only the diagnostic but also the therapeutic potential of PVT1 in renal cell carcinoma.     

LncRNA PITPNA‐AS1/miR‐223‐3p/PTN axis regulates malignant progression and stemness in lung squamous cell carcinoma

BackgroundLong noncoding RNAs (lncRNAs) are a kind of molecule that cannot code proteins, and their expression is dysregulated in diversified cancers. LncRNA PITPNA‐AS1 has been shown to act as a tumor promoter in a variety of malignancies, but its function and regulatory mechanisms in lung squamous cell carcinoma (LUSC) are yet unknown.MethodsThe mRNA and protein expression of genes were examined by RT‐qPCR, western blot, and IHC assay. The cell proliferation, migration, invasion, and stemness were detected through CCK‐8, colony formation, Transwell and spheroid formation assays. The CD44⁺ and CD166⁺‐positive cells were detected through flow cytometry. The binding ability among genes through luciferase reporter and RNA pull‐down assays. The tumor growth was detected through in vivo nude mice assay.ResultsThe lncRNA PITPNA‐AS1 had increased expression in LUSC and was linked to a poor prognosis. In LUSC, PITPNA‐AS1 also enhanced cell proliferation, migration, invasion, and stemness. This mechanistic investigation showed that PITPNA‐AS1 absorbed miR‐223‐3p and that miR‐223‐3p targeted PTN. MiR‐223‐3p inhibition or PTN overexpression might reverse the inhibitory effects of PITPNA‐AS1 suppression on LUSC progression, as demonstrated by rescue experiments. In addition, the PITPNA‐AS1/miR‐223‐3p/PTN axis accelerated tumor development in vivo.ConclusionsIt is the first time we investigated the potential role and ceRNA regulatory mechanism of PITPNA‐AS1 in LUSC. The data disclosed that PITPNA‐AS1 upregulated PTN through sponging miR‐223‐3p to enhance the onset and progression of LUSC. These findings suggested the ceRNA axis may serve as a promising therapeutic biomarker for LUSC patients.     

Comprehensive analysis of N6‐methyladenosine‐related long non‐coding RNAs for prognosis prediction in liver hepatocellular carcinoma

BackgroundLiver hepatocellular carcinoma (LIHC) is a lethal cancer. This study aimed to identify the N⁶‐methyladenosine (m⁶A)‐targeted long non‐coding RNA (lncRNA) related to LIHC prognosis and to develop an m⁶A‐targeted lncRNA model for prognosis prediction in LIHC.MethodsThe expression matrix of mRNA and lncRNA was obtained, and differentially expressed (DE) mRNAs and lncRNAs between tumor and normal samples were identified. Univariate Cox and pathway enrichment analyses were performed on the m⁶A‐targeted lncRNAs and the LIHC prognosis‐related m⁶A‐targeted lncRNAs. Prognostic analysis, immune infiltration, and gene DE analyses were performed on LIHC subgroups, which were obtained from unsupervised clustering analysis. Additionally, a multi‐factor Cox analysis was used to construct a prognostic risk model based on the lncRNAs from the LASSO Cox model. Univariate and multivariate Cox analyses were used to assess prognostic independence.ResultsA total of 5031 significant DEmRNAs and 292 significant DElncRNAs were screened, and 72 LIHC‐specific m⁶A‐targeted binding lncRNAs were screened. Moreover, a total of 29 LIHC prognosis‐related m⁶A‐targeted lncRNAs were obtained and enriched in cytoskeletal, spliceosome, and cell cycle pathways. An 11‐m⁶A‐lncRNA prognostic model was constructed and verified; the top 10 lncRNAs included LINC00152, RP6‐65G23.3, RP11‐620J15.3, RP11‐290F5.1, RP11‐147L13.13, RP11‐923I11.6, {"type":"entrez-nucleotide","attrs":{"text":"AC092171.4","term_id":"19526159"}}AC092171.4, KB‐1460A1.5, LINC00339, and RP11‐119D9.1. Additionally, the two LIHC subgroups, Cluster 1 and Cluster 2, showed significant differences in the immune microenvironment, m⁶A enzyme genes, and prognosis of LIHC.ConclusionThe m⁶A‐lncRNA prognostic model accurately and effectively predicted the prognostic survival of LIHC. Immune cells, immune checkpoints (ICs), and m⁶A enzyme genes could act as novel therapeutic targets for LIHC.     

Necroptosis‐related lncRNA to establish novel prognostic signature and predict the immunotherapy response in breast cancer

BackgroundNecroptosis is a type of programmed cell death, and recent researches have showed that lncRNAs could regulate the process of necroptosis in multiple cancers. We tried to screen necroptosis‐related lncRNAs and investigate the immune landscape in breast cancer (BC).MethodsThe samples of breast normal and cancer tissue were acquired from TCGA and GTEx databases. A risk prognostic model was constructed based on the identified necroptosis‐related lncRNAs by Cox regression and least absolute shrinkage and selection operator (LASSO) method. Moreover, the forecast performance of this model was verified and accredited by synthetic approach. Subsequently, an accurate nomogram was constructed to predict the prognosis of BC patients. The biological differences were investigated through GO, GSEA, and immune analysis. The immunotherapy response was estimated through tumor mutation burden (TMB) and tumor immune dysfunction and exclusion (TIDE) score.ResultsA total of 251 necroptosis‐related lncRNAs were identified by differential coexpression analysis, and SH3BP5‐AS1, AC012073.1, AC120114.1, LINC00377, AL133467.1, AC036108.3, and AC020663.2 were involved in the risk model, which had an excellent concordance with the prediction. The pathway analyses showed that immune‐related pathways were relevant to the necroptosis‐related lncRNAs risk model. And the risk score was significantly correlated with immune cell infiltration, as well as the ESTIMATE score. Most notably, the patients of higher risk score were characterized with increased TMB and decreased TIDE score, indicating that these patients showed better immune checkpoint blockade response.ConclusionThese findings were conducive to understand the function of necroptosis‐related lncRNAs in BC and provide a potential promising therapeutic strategy for BC.     

Characterization of LncRNA expression profile and identification of functional LncRNAs associated with unstable angina

BackgroundIncreasing evidences suggest that long noncoding RNAs (lncRNAs) play critical roles in the pathogenesis of coronary artery disease (CAD). However, the association between lncRNAs expression profiles and unstable angina (UA) remained poorly known. Thus, the present study aims to investigate expression patterns, biological functions, and diagnostic value of lncRNAs in UA.MethodsThe present study explored the lncRNA and mRNA expression profiles in peripheral blood mononuclear cells (PBMCs) of UA patients and normal coronary artery (NCA) controls using RNA‐seq. The biological function of differentially expressed lncRNAs was analyzed using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The expression of the selected lncRNAs was validated in another 44 UA patients and 46 NCA controls. Receiver operating characteristic curve (ROC) was performed to evaluate the diagnostic value of lncRNAs for UA.ResultsA total of 98 lncRNAs and 615 mRNAs were observed differentially expressed in PBMCs of UA patients as compared to NCA controls. The 10 most upregulated lncRNAs were LNC_000226, DANCR, RP1‐167A14.2, LNC_002091, LNC_001526, LNC_001165, LNC_002772, LNC_000088, LNC_001226, and FAM157C, and the 10 most downregulated lncRNAs were RP11‐734I18.1, RP11‐185E8.1, RP11‐360I2.1, LNC_001302, LNC_001287, RN7SL471P, LNC_000914, LINC01506, RP11‐160E2.6, and LNC_000995. LNC_000226 and MALAT1 have high area under the curve values (AUC) for distinguishing UA from NCA patients (0.810 and 0.799, respectively), and the combination of MALAT1 and LNC_000226 increased the AUC value to 0.878.ConclusionsThe present study added our understanding about the lncRNA expression profile in UA patients and provided potential biomarkers for the diagnosis of UA.     

LncRNA TUG1 functions as a ceRNA for miR‐1‐3p to promote cell proliferation in hepatic carcinogenesis

BackgroundHepatocellular carcinoma (HCC) is characterised by high malignancy, metastasis and recurrence, but the specific mechanism that drives these outcomes is unclear. Recent studies have shown that long noncoding RNAs (lncRNAs) can regulate the proliferation and apoptosis of hepatic cells.MethodsWe searched for lncRNAs and microRNAs (miRNAs), which can regulate IGF1 expression, through a bioinformatics website, and predicted that lncRNA taurine‐upregulated gene 1 (TUG1) would have multiple targets for miR‐1‐3p binding, meaning that lncRNA TUG1 played an adsorption role. A double luciferase assay was used to verify the targeting relationship between lncRNA TUG1 and miR‐1‐3p. Western blotting and qPCR were used to verify the targeting relationship between miR‐1‐3p and IGF1, and qPCR was used to verify the regulatory relationship between the lncRNA TUG1‐miR‐1‐3p‐IGF1 axis. CCK‐8 was used to detect the growth activity of miRNA‐transfected L‐O2 cells, and flow cytometry was used to detect cell cycle changes and apoptosis.ResultThe proliferation cycle of L‐O2 cells transfected with miR‐1‐3p mimics was significantly slowed. Flow cytometry showed that the proliferation of L‐O2 cells was slowed, and the apoptosis rate was increased. In contrast, when L‐O2 cells were transfected with miR‐1‐3p inhibitor, the expression of IGF1 was significantly upregulated, and the cell proliferation cycle was significantly accelerated. Flow cytometry showed that the cell proliferation rate was accelerated, and the apoptosis rate was reduced.ConclusionLncRNA TUG1 can adsorb miR‐1‐3p as a competitive endogenous RNA (ceRNA) to promote the expression of IGF1 and promote cell proliferation in hepatic carcinogenesis.     

Potential roles of hsa_circ_000839 and hsa_circ_0005986 in breast cancer

BackgroundBreast cancer (BC) is one of the leading causes of death among women around the world. Circular RNAs (circRNAs) are a newly discovered group of non‐coding RNAs that their roles are being investigated in BC and other cancer types. In this study, we evaluated the association of hsa_circ_0005986 and hsa_circ_000839 in tumor and adjacent normal tissues of BC patients with their clinicopathological characteristics.Materials and methodsTotal RNA was extracted from tumors and adjacent non‐tumor tissues by the Trizol isolation reagent, and cDNA was synthesized using First Strand cDNA Synthesis Kit (Thermo Scientific). The expression level of hsa_circ_0005986 and hsa_circ_000839 was quantified using RT‐qPCR. Online in silico tools were used for identifying potentially important competing endogenous RNA (ceRNA) networks of these two circRNAs.ResultsThe expression level of hsa_circ_0005986 and hsa_circ_000839 was lower in the tumor as compared to adjacent tissues. The expression level of hsa_circ_0005986 in the patients who had used hair dye in the last 5 years was significantly lower. Moreover, a statistically significant negative correlation between body mass index (BMI) and hsa_circ_000839 expression was observed. In silico analysis of the ceRNA network of these circRNAs revealed mRNAs and miRNAs with crucial roles in BC.ConclusionDownregulation of hsa_circ_000839 and hsa_circ_0005986 in BC tumors suggests a tumor‐suppressive role for these circRNAs in BC, meriting the need for more experimentations to delineate the exact mechanism of their involvement in BC pathogenesis.     

Expression changes of serum LINC00941 and LINC00514 in HBV infection‐related liver diseases and their potential application values

BackgroundLong non‐coding RNAs (LncRNAs) are considered as potential diagnostic markers for a variety of tumors. Here, we aimed to explore the changes of LINC00941 and LINC00514 expression in hepatitis B virus (HBV) infection‐related liver disease and evaluate their application value in disease diagnosis.MethodsSerum levels of LINC00941 and LINC00514 were detected by qRT‐PCR. Potential diagnostic values were evaluated by receiver operating characteristic curve (ROC) analysis.ResultsSerum LINC00941 and LINC00514 levels were elevated in patients with chronic hepatitis B (CHB), liver cirrhosis (LC), and hepatocellular carcinoma (HCC) compared with controls. When distinguishing HCC from controls, serum LINC00941 and LINC00514 had diagnostic parameters of an AUC of 0.919 and 0.808, sensitivity of 85% and 90%, and specificity of 86.67% and 56.67%, which were higher than parameters for alpha fetal protein (AFP) (all p < 0.0001). When distinguishing HCC from LC, CHB, or LC from controls, the combined detection of LINC00941 or LINC00514 can significantly improve the accuracy of AFP test alone (all p < 0.05).ConclusionsLINC00941 and LINC00514 were increased in the serum of HBV infection‐associated liver diseases and might be independent markers for the detection of liver diseases.     

A novel prognostic model associated with the overall survival in patients with breast cancer based on lipid metabolism‐related long noncoding RNAs

BackgroundLipid metabolism is closely related to the occurrence and development of breast cancer. Our purpose was to establish a novel model based on lipid metabolism‐related long noncoding RNAs (lncRNAs) and evaluate the potential clinical value in predicting prognosis for patients suffering from breast cancer.MethodsRNA data and clinical information for breast cancer were obtained from the cancer genome atlas (TCGA) database. Lipid metabolism‐related lncRNAs were identified via the criteria of correlation coefficient |R ²| > 0.4 and p < 0.001, and prognostic lncRNAs were identified to establish model through Cox regression analysis. The training set and validation set were established to certify the feasibility, and all samples were separated into high‐risk group or low‐risk group. Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA) were conducted to evaluate the potential biological functions, and the immune infiltration levels were explored through Cibersortx database.ResultsA total of 14 lncRNAs were identified as protective genes ({"type":"entrez-nucleotide","attrs":{"text":"AC022150.4","term_id":"7711716","term_text":"AC022150.4"}}AC022150.4, {"type":"entrez-nucleotide","attrs":{"text":"AC061992.1","term_id":"7630731","term_text":"AC061992.1"}}AC061992.1, AC090948.3, {"type":"entrez-nucleotide","attrs":{"text":"AC092794.1","term_id":"15027722","term_text":"AC092794.1"}}AC092794.1, {"type":"entrez-nucleotide","attrs":{"text":"AC107464.3","term_id":"19339290","term_text":"AC107464.3"}}AC107464.3, AL021707.8, {"type":"entrez-nucleotide","attrs":{"text":"AL451085.2","term_id":"13561296","term_text":"AL451085.2"}}AL451085.2, AL606834.2, FLJ42351, LINC00926, LINC01871, TNFRSF14−AS1, {"type":"entrez-nucleotide","attrs":{"text":"U73166.1","term_id":"1613889","term_text":"U73166.1"}}U73166.1 and USP30−AS1) with HRs < 1 while 10 lncRNAs ({"type":"entrez-nucleotide","attrs":{"text":"AC022150.2","term_id":"7021694","term_text":"AC022150.2"}}AC022150.2, {"type":"entrez-nucleotide","attrs":{"text":"AC090948.1","term_id":"13384352","term_text":"AC090948.1"}}AC090948.1, {"type":"entrez-nucleotide","attrs":{"text":"AC243960.1","term_id":"325121040","term_text":"AC243960.1"}}AC243960.1, AL021707.6, ITGB2−AS1, OTUD6B−AS1, SP2−AS1, TOLLIP−AS1, {"type":"entrez-nucleotide","attrs":{"text":"Z68871.1","term_id":"1164911","term_text":"Z68871.1"}}Z68871.1 and ZNF337−AS1) were associated with increased risk with HRs >1. A total of 24 prognostic lncRNAs were selected to construct the model. The patients in low‐risk group were associated with better prognosis in both training set (p < 0.001) and validation set (p < 0.001). The univariate and multivariate Cox regression analyses revealed that risk score was an independent prognostic factors in both training set (p < 0.001) and validation set (p < 0.001). GO and GSEA analyses revealed that these lncRNAs were related to metabolism‐related signal pathway and immune cells signal pathway. Risk score was negatively correlated with B cells (r = −0.097, p = 0.002), NK cells (r = −0.097, p = 0.002), Plasma cells (r = −0.111, p = 3.329e‐04), T‐cells CD4 (r = −0.064, p = 0.039) and T‐cells CD8 (r = −0.322, p = 2.357e‐26) and positively correlated with Dendritic cells (r = 0.077, p = 0.013) and Monocytes (r = 0.228, p = 1.107e‐13).ConclusionThe prognostic model based on lipid metabolism lncRNAs possessed an important value in survival prediction of breast cancer patients.