Value Of Long Non-coding RNAs Research Articles

Identification and evaluation of a six-lncRNA prognostic signature for multiple myeloma

PurposeMultiple myeloma (MM) is the second most common hematologic malignancy, and there is no cure for this disease. This study aimed to explore the prognostic value of long noncoding RNAs (lncRNAs) in MM and to reveal related immune and chemotherapy resistance mechanisms.MethodsIn this study, lncRNA profiles from the Multiple Myeloma Research Foundation (MMRF) and Gene Expression Omnibus (GEO) databases were analyzed to identify lncRNAs linked to MM patient survival. A risk assessment model stratified patients into high- and low-risk groups, and survival was evaluated. Additionally, a triple-ceRNA (lncRNA–miRNA–mRNA) network was constructed, and functional analysis was performed. The research also involved immune function analysis and chemotherapy drug sensitivity assessment using oncoPredict and the GDSC dataset.ResultsWe identified 422 lncRNAs significantly associated with overall survival in MM patients and ultimately focused on the 6 with the highest prognostic value. These lncRNAs were used to develop a risk score formula that stratified patients into high- and low-risk groups. Kaplan–Meier analysis revealed shorter survival in high-risk patients. We integrated this lncRNA signature with clinical parameters to construct a nomogram for predicting MM prognosis. Additionally, a triple-ceRNA network was constructed to reveal potential miRNA targets, coding genes related to these lncRNAs and significantly enriched pathways. Immune checkpoint gene expression and immune cell composition were also analyzed in relation to the lncRNA risk score. Finally, using the oncoPredict tool, we observed that high-risk patients exhibited decreased sensitivity to key MM chemotherapeutics, suggesting that lncRNA profiles are linked to chemotherapy resistance.

Emerging potential value of long non-coding RNAs as biomarkers and therapeutic targets in pancreatic cancer

Emerging potential value of long non-coding RNAs as biomarkers and therapeutic targets in pancreatic cancer

Expression and diagnostic value of lncRNA MALAT1 and NLRP3 in lower limb atherosclerosis in diabetes

ObjectiveThis study aimed to examine the diagnostic predictive value of long non-coding RNA (lncRNA) metastasis-associated lung adenocarcinoma transcript 1(MALAT1) and NOD-like receptor protein 3(NLRP3) expression in patients with type 2 diabetes mellitus(T2DM) and lower extremity atherosclerosis disease (LEAD).MethodsA total of 162 T2DM patients were divided into T2DM with LEAD group (T2DM + LEAD group) and T2DM alone group (T2DM group). The lncRNA MALAT1 and NLRP3 expression levels were measured in peripheral blood, and their correlation was examined. Least absolute shrinkage and selection operator (LASSO) regression model was used to screen for the best predictors of LEAD, and multivariate logistic regression was used to establish a predictive model and construct the nomogram. The effectiveness of the nomogram was assessed using the receiver operating characteristic (ROC) curve, area under the curve (AUC), calibration curve, and decision curve analysis (DCA).ResultsThe levels of the lncRNA MALAT1 and NLRP3 in the T2DM + LEAD group were significantly greater than those in the T2DM group (P <0.001), and the level of the lncRNA MALAT1 was positively correlated with that of NLRP3 (r = 0.453, P<0.001). The results of the LASSO combined with the logistic regression analysis showed that age, smoking, systolic blood pressure (SBP), NLRP3, and MALAT1 were the influencing factors of T2DM with LEAD(P<0.05). ROC curve analysis comparison: The discriminatory ability of the model (AUC = 0.898), MALAT1 (AUC = 0.804), and NLRP3 (AUC = 0.794) was greater than that of the other indicators, and the predictive value of the model was the greatest. Calibration curve: The nomogram model was consistent in predicting the occurrence of LEAD in patients with T2DM (Cindex = 0.898). Decision curve: The net benefit rates obtained from using the predictive models for clinical intervention decision-making were greater than those obtained from using the individual factors within the model.ConclusionMALAT1 and NLRP3 expression increased significantly in T2DM patients with LEAD, while revealing the correlation between MALAT1 and NLRP3. The lncRNA MALAT1 was found as a potential biomarker for T2DM with LEAD.

Long noncoding nuclear enriched abundant transcript 1_2 is a promising biomarker for childhood-onset systemic lupus erythematosus.

Systemic lupus erythematosus (SLE) is a diffuse connective tissue disease with complex clinical manifestations and prolonged course. The early diagnosis and condition monitoring of SLE are crucial to disease prognosis. To assess the diagnostic value of long noncoding RNA (lncRNA) nuclear enriched abundant transcript 1 (NEAT1) in childhood-onset SLE (cSLE). Fifty-seven children diagnosed with SLE, 40 children diagnosed with juvenile idiopathic arthritis (JIA), and 40 healthy children were included. Peripheral blood samples from each patient were collected. A quantitative polymerase chain reaction was used to confirm the expression of lncNEAT1_1 and lncNEAT1_2 in peripheral blood. Associations among parameters were analyzed using the Mann-Whitney U test or independent sample t-test. The expression of both lncNEAT1_1 and lncNEAT1_2 in patients with cSLE were significantly higher than that of healthy controland patients with JIA. Receiver operating characteristic curves revealed an area under the curve (AUC) of 0.633 (95% confidence interval [CI], 0.524-0.742; P = 0.024) for lncNEAT1_1. The AUC of lncNEAT1_2 was 0.812 (95% CI, 0.727-0.897; P < 0.0001) to discriminate individuals with cSLE from health controland children with JIA with a sensitivity of 0.622 and a specificity of 0.925. Moreover, lncNEAT1_2 expression was higher in patients with cSLE presenting with fever, lupus nephritis, elevated erythrocyte sedimentation rate, active disease activity, and decreased C3 level, compared with those without these conditions. However, no similar correlation was observed for lncNEAT1_1. The expression of lncNEAT1_2 was significantly elevated in children with SLE, especially those with fever, renal involvement, and low C3 levels. These findings suggest that lncNEAT1_2 may represent a potential biomarker for cSLE.

Predictive value of prenatal ultrasound combined with long non-coding RNA CRNDE of women for their postpartum lower extremity deep venous thrombosis

Objective Deep vein thrombosis (DVT) is a common complication in obstetrics that needs early interaction. The study examined the expression change and clinical value of long non-coding RNA (lncRNA) colorectal neoplasia differentially expressed (CRNDE) in DVT early diagnosis. Methods One hundred patients with DVT after delivery and 100 healthy parturients without DVT were enrolled. Serum samples were collected one day before delivery and received qRT-PCR for mRNA detection. Prenatal coagulation markers including prothrombin time (PT), activated partial prothrombin time (APTT), fibrinogen (FIB) and thrombin time (TT), D-dimer (D-D), thrombomodulin (TM), and peroxidase anti-peroxidase soluble complex (PAP) were tested. The receiver operating characteristic (ROC) curve was drawn for the diagnostic value assessment. Results LncRNA CRNDE levels increased remarkably in the serum of DVT patients compared with the healthy controls, which were negatively correlated with serum concentration of PT, APTT, and TT while positively correlated with FIB, D-D, TM, and PAP. Serum CRNDE (HR = 5.973, 95% CI = 2.990–11.933, p < .001) was independently related to the occurrence of DVT after delivery. Then, ROC curve using serum CRNDE showed a good diagnostic value for DVT with the AUC of 0.899. ROC curve of ultrasonography combined with CRNDE produced an AUC of 0.968, and both sensitivity and specificity were enhanced compared to a single indicator. Conclusions The increase of CRNDE level was an independent risk factor for postpartum DVT. Prenatal ultrasonography combined with CRNDE can improve the predictive efficacy for DVT.

LINC00460 knockdown sensitizes cervical cancer to cisplatin by downregulating TGFBI.

The acquired resistance of cancer to cisplatin (DDP) limits the efficacy of chemotherapy. The prognostic value of long noncoding RNA (lncRNA) LINC00460 has been reported in cervical cancer. However, its effect on DDP sensitivity in cervical cancer remains poorly understood. In present study, LINC00460 was screened out through bioinformatics analysis. The expression levels of mRNAs and proteins were measured by reverse transcription-quantitative PCR (RT-qPCR) or western blot analysis. The sensitivity to DDP was investigated using an CCK8 assay. Cell apoptosis was determined by flow cytometry. The differentially expressed genes that were associated with the poor prognosis of cervical cancer were screened, and their correlations with LINC00460 expression were explored using Pearson's correlation analysis. Tumor xenograft model was used to assess the effect of LINC00460 knockdown on DDP sensitivity invivo. The interaction between miR-338-3p and LINC00460 or transforming growth factor β-induced protein (TGFBI) was confirmed by RNA immunoprecipitation (RIP) and luciferase reporter assays. LINC00460 expression was increased in cervical cancer tissues and cells. High expression of LINC00460 was associated with dismal prognosis in cervical cancer patients. Silencing of LINC00460 increased drug sensitivity and induced apoptosis in DDP-resistant-cervical cancer cells. LINC00460 knockdown enhanced DDP sensitivity in cervical cancer cells largely by downregulating TGFBI expression. LINC00460 knockdown enhanced the sensitivity of cervical cancer to DDP invivo, and this effect was partly mediated by the downregulation of TGFBI. LINC00460 positively regulated TGFBI expression, possibly by acting as a sponge of miR-338-3p. LINC00460 knockdown contributed to DDP sensitivity of cervical cancer by downregulating TGFBI, providing a novel mechanism underlying the acquisition of DDP sensitivity.

Multi-omics approach for identifying CNV-associated lncRNA signatures with prognostic value in prostate cancer

BackgroundProstate cancer, the second most prevalent malignancy among men, poses a significant threat to affected patients’ well-being due to its poor prognosis. Novel biomarkers are required to enhance clinical outcomes and tailor personalized treatments. Herein, we describe our research to explore the prognostic value of long non-coding RNAs (lncRNAs) deregulated by copy number variations (CNVs) in prostate cancer. MethodsThe study employed an integrative multi-omics data analysis of the prostate cancer transcriptomic, CNV and methylation datasets to identify prognosis-related subtypes. Subtype-specific expression profiles of protein-coding genes (PCGs) and lncRNAs were determined. We analysed CNV patterns of lncRNAs across the genome to identify subtype-specific lncRNAs with CNV changes. LncRNAs exhibiting significant amplification or deletion and a positive correlation were designated CNV-deregulated lncRNAs. A prognostic risk score model was subsequently developed using these CNV-driven lncRNAs. ResultsSix molecular subtypes of prostate cancer were identified, demonstrating significant differences in prognosis (P = 0.034). The CNV profiles of subtype-specific lncRNAs were examined, revealing their correlation with CNV amplification or deletion. Six lncRNAs (CCAT2, LINC01593, LINC00276, GACAT2, LINC00457, LINC01343) were selected based on significant CNV amplifications or deletions using a rigorous univariate Cox proportional risk regression model. A robust risk score model was developed, stratifying patients into high-risk and low-risk categories. Notably, our prognostic model based on these six lncRNAs exhibited exceptional predictive capabilities for recurrence-free survival (RFS) in prostate cancer patients (P = 0.024). ConclusionsOur study successfully identified a prognostic risk score model comprising six CNV-driven lncRNAs that could potentially be prognostic biomarkers for prostate cancer. These lncRNA signatures are closely associated with RFS, providing promising prospects for improved patient prognostication and personalized therapeutic strategies for novel prostate cancer treatment.

Diagnostic and prognostic value of long non-coding RNA PROX1‑AS1 and miR-647 expression in gastric cancer

Introduction. Gastric cancer remains one of the most common cancers and has a high mortality rate worldwide. Epigenetic alternations of non-coding RNAs (ncRNAs), including microRNAs and long ncRNAs can contribute to its pathogenesis and progression, and could be potent diagnostic and prognostic biomarkers.Aim. Estimation of PROX1‑AS1 and miR-647 expression in gastric cancer and investigation of its clinical significance. Materials and methods. Tumor and adjacent normal tissues (n = 62), and sectional normal tissue samples (n = 5) were included in the study. The expression of the ncRNAs was quantified by reverse transcription-polymerase chain reaction assay.Results. We have reviled the significant difference in the PROX1‑AS1 expression in tumor (p = 0.002) and non-tumor tissues (p &lt;0.001) obtained from gastric cancer patients in comparison with sectional gastric tissues without pathology. Pearson correlation analysis confirmed a negative correlation between PROX1‑AS1 and miR-647 in gastric cancer both in tumor (р &lt;0,001) and adjacent normal tissues (р &lt;0.001). Besides, expression of PROX1‑AS1 and miR-647 was associated with the size and extent of the primary tumor.Conclusion. The obtained results allow to suggest a potential prognostic value of PROX1‑AS1 and miR-647 in gastric cancer.

LncRNA DIRC1 is a novel prognostic biomarker and correlated with immune infiltrates in stomach adenocarcinoma.

The potential application value of Long non-coding RNA disrupted in renal carcinoma 1 (DIRC1) has not yet been explored, the purpose of this study was to explore the relationship between DIRC1 and stomach adenocarcinoma (STAD) based on the cancer genome atlas database. Wilcoxon rank sum test, Chi-square test, Fisher test and logistic regression were used to evaluate relationships between clinical-pathologic features and DIRC1 expression. Receiver operating characteristic (ROC) curves were used to describe binary classifier value of DIRC1 using area under curve (AUC) score. Kaplan-Meier method was used to assess the impact of DIRC1 on prognosis and the impact of DIRC1-related hub genes on prognosis. Gene oncology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were used to predict the function of differentially expressed genes associated with DIRC1. Gene set enrichment analysis (GSEA) was used to predict biological states or processes associated with DIRC1. Immune infiltration analysis was performed to identify the significantly involved functions of DIRC1. Protein-protein interaction (PPI) networks were established and 10 hub genes identified with Cytoscape software. Real time-polymerase chain reaction (RT-PCR) was used to detect the expression of DIRC1 in Gastric Cancer patients and healthy people. Increased DIRC1 expression in STAD was associated with T stage (P = .004), race (P = .045), histologic grade (P = .029) and anatomic neoplasm subdivision (P = .034). ROC curve suggested the significant diagnostic ability of DIRC1 (AUC = 0.779). High DIRC1 expression predicted a poorer Overall survival (P = .004, hazard ratio: 1.63; 95% confidence interval: 1.17-2.27; P = .034). GO and KEGG analysis demonstrated that DIRC1 is related to epidermis, collagen-containing extracellular matrix, receptor-ligand activity, protein digestion and absorption, etc. GSEA demonstrated that E2F target, G2M checkpoint, Myc target, interferon γ reaction were differentially enriched in the high DIRC1 expression phenotype. SsGSEA and Spearman correlation revealed the relationships between DIRC1 and macrophages, dendritic cells, and Th1 cells were the strongest. Coregulatory proteins were included in the PPI network, higher expressions of 4 hub genes were associated with worse prognosis in STAD. RT-PCR showed that the expression of DIRC1 in the serum of Gastric Cancer patients was higher than healthy people (P = .027). DIRC1 expression was significantly correlated with poor survival and immune infiltrations in STAD, and it may be a promising prognostic biomarker in STAD.

Long non-coding RNA SNHG16 silencing inhibits proliferation and inflammation in Mycobacterium tuberculosis-infected macrophages by targeting miR-140-5p expression.

The study investigated the clinical diagnostic value of long non-coding RNA (LncRNA) small nucleolar RNA host gene 16 (SNHG16) and explored its underlying molecular mechanism through Mycobacterium tuberculosis (M. tuberculosiinfection of macrophages. RT-qPCR analysis of the serum SNHG16 levels of the 66 healthy individuals, 67 latent TB (LTB) patients, and 67 active TB (ATB) patients. The receiver-operating characteristic (ROC) curve to detect the clinical diagnostic value of SNHG16 in TB patients. In vitro, M. tuberculosis-infected macrophages, CCK-8 and ELISA to detect cell proliferation and inflammatory factor levels. Luciferase reported assay was performed to analyze the targeting relationship between SNHG16 and miR-140-5p. SNHG16 was significantly elevated in TB patients, and among them, ATB patients were higher than LTB patients. ROC confirmed that SNHG16 could distinguish LTB patients from healthy controls, and ATB patients from LTB patients, and can be used as a good diagnostic biomarker for TB. M. tuberculosis infection increased SNHG16 levels and promoted the proliferation and inflammation in macrophages. However, SNHG16 silencing significantly reversed the effect of infection. miR-140-5p, a direct target miRNA of SNHG16, was down-regulated in TB patients and was negatively correlated with SNHG16. When miR-140-5p was inhibited, the alleviating effect of SNHG16 silencing on M. tuberculosis infection proliferation and inflammation was significantly reversed. The present results suggested that SNHG16 may be a new diagnostic biomarker for TB patients and SNHG16 silencing may alleviate TB by inhibiting the proliferation of macrophages in TB by regulation miR-140-5p.

An Updated Review on the Therapeutic, Diagnostic, and Prognostic Value of Long Non-Coding RNAs in Gastric Cancer.

As a novel group of non-coding RNAs, long non-coding RNA (lncRNAs) can substantially regulate various biological processes. Downregulated tumor-suppressive lncRNAs and upregulated oncogenic lncRNAs (onco-lncRNAs) have been implicated in gastric cancer (GC) development. These dysregulations have been associated with decreased chemosensitivity, inhibited apoptosis, and increased tumor migration in GC. Besides, growing evidence indicates that lncRNAs can be a valuable diagnostic and prognostic biomarker, and their expression levels are substantially associated with the clinicopathological features of affected patients. The current study aims to review the recent findings of the tumor-suppressive lncRNAs and onco-lncRNAs in GC development and highlight their therapeutic, diagnostic, and prognostic values in treating GC cells. Besides, it intends to highlight the future direction of lncRNAs in treating GC.

Preliminary study on the diagnostic value of serum-derived exosomal lncRNA in epithelial ovarian cancer

Objective: To investigate the diagnostic value of long noncoding RNA (lncRNA) extracted from serum exosomes in epithelial ovarian cancer (EOC). Methods: (1) Patients with ovarian tumors who were hospitalized in the Affiliated Tumor Hospital of Guangxi Medical University from August 2018 to December 2019, including 35 cases of EOC patients (malignant group) and 20 cases of benign ovarian tumor patients (benign group) were collected; during the same period, 15 healthy women (normal group) who underwent physical examination in the Affiliated Tumor Hospital of Guangxi Medical University were used as controls. Fasting venous blood serum was collected from the above three groups of women, and serum exosomes were isolated and purified using commercial kits. The morphology of exosomal particles was observed with transmission electron microscope, and the particle size distribution of the exosomes was detected by NanoSight technology. The expression of specific proteins cluster of differentiation (CD)63, CD81, and tumor susceptibility gene 101 (TSG101) of exosomes were analyzed by western blot. (2) Four cases of EOC patients and three cases of healthy women were randomly selected. High-throughput sequencing technology was used to analyze the differentially expressed lncRNA in serum exosomes of these four EOC patients and three healthy women, and screen out the significantly differentially expressed lncRNA. The screened lncRNA with different expression levels was verified by quantitative reverse transcription-polymerase chain reaction (QRT-PCR) in these seven original clinical samples, furtherly confirmed and tested with QRT-PCR in larger clinical samples (a total of 70 serum samples). (3) The receiver operating characteristic (ROC) curve of the target lncRNA was drawn and its diagnostic indicators such as sensitivity and specificity were evaluated. By using logistic binary regression model, multi-factor joint diagnostic models were constructed and evaluated. Results: (1) Under transmission electron microscope, clear lipid bilayer structure was observed in serum exosomes, and one side presented a concave hemispheric or cup like structure; the peak diameter of the exosomal particles detected with NanoSight technology was 127.6 nm, and the particles between 30 and 150 nm accounted for 58.9%; western blot confirmed that the obtained (exosomal) particles could detect the expression of the marker proteins CD63, CD81, and TSG101. (2) Analysis of high-throughput sequencing technology showed that compared with the women in the normal sequencing group (3 cases), 425 differentially expressed lncRNAs (including 23 up-regulated and 402 down-regulated) were screened in the serum exosomes of the malignant sequencing group (4 cases). Six types of lncRNA with significantly abnormal expression levels (including FER1L6-AS2, LINC00470, LINC01811, CXXC4-AS1, LINC02343, and LINC02428) were randomly selected for original sample verification, and the results were consistent with the sequencing results. Subsequently, these six lncRNAs were used for larger samples QRT-PCR verification. Compared with the benign and normal groups, the expression of FER1L6-AS2, LINC00470 and LINC01811 in malignant group increased by 1.66 and 1.84-fold, 2.05 and 2.46-fold, 2.94 and 2.35-fold, respectively; the expressions of CXXC4-AS1, LINC02343 and LINC02428 were down-regulated to 29% and 34%, 40% and 46%, 42% and 42%, respectively. For the same lncRNA, there were statistical differences between the malignant group and the benign group, between the malignant group and the normal group (all P<0.05), and there were no statistical differences between the benign group and the normal group (all P>0.05). (3) The results showed that the area under curve (AUC) of these six lncRNAs ranged from 0.722 to 0.805, which had moderate diagnostic efficiency. To use logistic binary regression model to establish multi-indicator joint diagnostic models and establish different joint factor ROC curves. The results showed that the AUC of the joint factor prediction model 1 (composed of FER1L6-AS2 and LINC01811), the joint factor prediction model 2 (composed of CXXC4-AS1, LINC02343, and LINC02428), and the joint factor prediction model 3 (composed of FER1L6-AS2, CXXC4-AS1, LINC02343, and LINC02428) were 0.865, 0.934, and 0.962, respectively. The diagnostic efficacy of the combined factor prediction models was higher than that of the single lncRNA (all P<0.05). Conclusions: High-throughput sequencing technology is an effective method for screening out the different expression levels of lncRNA extracted from serum exosomes. The combined detection of multiple serum exosomal lncRNA indicators has a certain diagnostic efficacy for patients with EOC. Detection of serum exosomal lncRNA indicators will provide new ideas for the diagnosis of EOC.

High Expression of lncRNA HEIH is Helpful in the Diagnosis of Non-Small Cell Lung Cancer and Predicts Poor Prognosis.

BackgroundThis study aims to investigate the expression and clinical value of long non-coding RNA (lncRNA) HEIH in peripheral blood of patients with non-small cell lung cancer (NSCLC).MethodsHealthy subjects (N=70), patients with lung squamous cell carcinoma (LUSC, N=70) and patients with lung adenocarcinoma (LUAD, N=80) were included. LncRNA HEIH expression in peripheral blood of included subjects was detected using RT-qPCR. According to the median expression of lncRNA HEIH, LUSC and LUAD patients were allocated into lncRNA HEIH high/low expression groups. The correlation between lncRNA HEIH and clinical indicators of patients was analyzed; Logistic multifactor regression was used to analyze the independent risk factors influencing lncRNA HEIH level. Receiver-operating characteristic (ROC) curve was used to evaluate the diagnostic efficacy of lncRNA HEIH and carcinoembryonic antigen (CEA) in LUSC/LUAD patients. MedCalc-Comparison of ROC curves was used to compare the area under ROC curve. The cumulative survival rates of lncRNA HEIH high/low expression group were analyzed by Kaplan–Meier curve. COX multivariate analysis was used to assess the independent factors affecting prognosis of NSCLC.ResultsLncRNA HEIH in peripheral blood of LUSC/LUAD patients was higher than that in healthy controls, with no evident difference between LUSC and LUAD groups. In LUSC/LUAD patients, TNM stage, lymph node metastasis, distal metastasis, and CEA were independent risk factors affecting lncRNA HEIH; patients with high lncRNA HEIH expression had larger pack-years and tumor size, higher CEA level and tumor stage, and higher risk of lymph node metastasis and distal metastasis. LncRNA HEIH had higher diagnostic efficiency than CEA in NSCLC patients. High expression of lncRNA HEIH predicted poor prognosis in patients with NSCLC and was an independent risk factor for prognosis of NSCLC.ConclusionHigh expression of lncRNA HEIH is helpful in the diagnosis of NSCLC and predicts poor prognosis.

Evaluation of the Prognostic Value of Long Noncoding RNAs in Lung Squamous Cell Carcinoma.

Lung squamous cell carcinoma (LUSC) is the most common type of lung cancer accounting for 40% to 51%. Long noncoding RNAs (lncRNAs) have been reported to play a significant role in the invasion, migration, and proliferation of lung cancer tissue cells. However, systematic identification of lncRNA signatures and evaluation of the prognostic value for LUSC are still an urgent problem. In this work, LUSC RNA-seq data were collected from TCGA database, and the limma R package was used to screen differentially expressed lncRNAs (DElncRNAs). In total, 216 DElncRNAs were identified between the LUSC and normal samples. lncRNAs associated with prognosis were calculated using univariate Cox regression analysis. The overall survival (OS) prognostic model containing 10 lncRNAs and the disease-free survival (DFS) prognostic model consisting of 11 lncRNAs were constructed using a machine learning-based algorithm, systematic LASSO-Cox regression analysis. We found that the survival rate of samples in the high-risk group was lower than that in the low-risk group. Results of ROC curves showed that both the OS and DFS risk score had better prognostic effects than the clinical characteristics, including age, stage, gender, and TNM. Two lncRNAs (LINC00519 and FAM83A-AS1) that were commonly identified as prognostic factors in both models could be further investigated for their clinical significance and therapeutic value. In conclusion, we constructed lncRNA prognostic models with considerable prognostic effect for both OS and DFS of LUSC.

Prognostic significance of long non-coding RNA five prime to XIST in various cancers

BackgroundTo observe the clinicopathological and prognostic value of long non-coding RNA five prime to X inactive specific transcript (lncFTX) in multiple tumors.MethodsEligible studies for lncFTX were identified by searching PubMed, Embase, Web of Science and Cochrane Library databases from inception to December 01, 2020. Stata 12.0 software was used to calculate the odds ratio (OR)/hazard ratio (HR) and 95% confidence interval (95% CI). We used The Cancer Genome Atlas (TCGA) dataset to further investigate the differential expression and prognostic value of lncFTX.ResultsWe included 11 studies involving a total of 1633 patients. The results showed that the expression of lncFTX was positively associated with advanced TNM stage (III-IV versus I-II) (OR = 2.30, 95% CI: 1.74–3.03, P < 0.05), lymph nodes metastasis (OR = 3.01, 95% CI: 2.00–4.52, P < 0.05), distant metastasis (OR = 3.68, 95% CI: 2.13–6.34, P < 0.05), and cancer mortality (HR = 1.83, 95% CI: 1.20–2.81, P < 0.05). However, the expression of lncFTX was not associated with tumor differentiation (poor differentiation versus well or moderate differentiation) and vessel invasion of cancer. Subgroup analysis showed that the higher lncFTX expression was associated with shorter overall survival in cancer patients, regardless of the sample size and cancer type. No publication bias was found, and the sensitivity analysis results suggested that the main findings were robust. Elevated expression and prognostic significance of FTX were confirmed using TCGA dataset.ConclusionsThis study found that the expression of lncFTX was positively associated with advanced tumor node metastasis (TNM) stage, lymph nodes, distant metastasis and, cancer mortality, suggesting that lncFTX might be a potential prognostic biomarker for tumors.

Diagnostic value of exosome derived long noncoding RNA in gastric cancer in Chinese population: A PRISMA-compliant systematic review and meta-analysis.

Background:Objective to systematically evaluate the diagnostic value of long noncoding RNA (lncRNA) in gastric cancer (GC) in the Chinese population.Methods:PubMed, Web of Science, EMBASE, Cochrane Library, CNKI, and Wanfang Database were searched. According to the search strategy and inclusion and exclusion criteria, 2 staff members screened the relevant kinds of literature from January 2010 to December 2020 and extracted the relevant data. Revman5.3, Meta-Disc1.4, and Stata15.1 software were used to analyze the relationship between lncRNA from exosomes and the diagnosis of GC. The combined values of sensitivity (SEN), specificity (SPE), positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio (DOR) and their corresponding 95% confidence intervals (CIs) were calculated. The summary receiver operating characteristic curve was drawn and the area under the ROC curve (AUC) value was calculated.Results:In 9 studies, 1314 samples were included, including 792 cases in the case group and 522 cases in the control group. The combined SEN was 0.82 (95% CI: 0.77–0.86), the combined SPE was 0.78 (95% CI: 0.72–0.83), the combined positive likelihood ratio was 3.7 (95% CI: 2.9–4.6), the negative likelihood ratio was 0.23 (95% CI: 0.18–0.29), and the DOR was 16 (95% CI: 12–23), AUC was 0.87 (95% CI: 0.84–0.90). Subgroup analysis showed that the SEN, SPE, likelihood ratio, DOR, and AUC of plasma-derived lncRNA in the diagnosis of GC were better than those of serum.Conclusions:Exosome-derived lncRNA may be a new potential biomarker for the clinical diagnosis of GC.

Prognostic Value of Long Non-coding RNAs in Urological Malignancies: A Systematic Review, and Meta-Analysis

Background: Many studies have explored the potential roles of long non-coding RNAs (lncRNAs) in urological cancer (UC) progression. The clinical outcome and prognosis of UCs remain weak. Therefore, finding clinical prognostic markers is needed to improve therapeutic potential. The aim of this study was to consider the possible association between the lncRNAs expression with the survival time and clinical outcomes in patients with UC. Methods: A literature search was performed in several related databases to find eligible English papers published before 9 February 2021. Hazard ratios (HRs) with 95% CI were calculated to investigate the association between lncRNAs expression and overall survival in patients with UC. Results: A total of 46 studies, including 39 lncRNAs were identified. Results indicated that lncRNAs expression was significantly correlated with poor overall survival (OS) outcome in patients with UCs (HR: 1.923, 95% CI: 1.448-2.554, P&lt;0.001). Also, we divided included studies into up-regulated and down-regulated subgroups according to lncRNAs expression. The results indicated a significant association with poor OS outcomes in both up-regulated (HR=2.546, 95% CI: 1.896-3.41, P&lt;0.001) and down-regulated (HR=0.33, 95% CI: 0.22-0.49, P&lt;0.001). Moreover, expression of lncRNAs was significantly associated with lymph node metastasis (LNM) (OR=0.25, 95% CI: 0.13-0.47, P&lt;0.001) Conclusion: Abnormal expression of various lncRNAs is a potential novel marker for predicting the clinical outcomes of urological tumors.

Prognostic value and immune infiltration of novel signatures in colon cancer microenvironment

BackgroundGrowing evidence has shown that the prognosis for colon cancer depends on changes in microenvironment. The purpose of this study was to elucidate the prognostic value of long noncoding RNAs (lncRNAs) related to immune microenvironment (IM) in colon cancer.MethodsSingle sample gene set enrichment analysis (ssGSEA) was used to identify the subtypes of colon cancer based on the immune genomes of 29 immune signatures. Cox regression analysis identified a lncRNA signatures associated with immune infiltration. The Tumor Immune Estimation Resource database was used to analyze immune cell content.ResultsColon cancer samples were divided into three subtypes by unsupervised cluster analysis. Cox regression analysis identified an immune infiltration-related 5-lncRNA signature. This signature combined with clinical factors can effectively improve the predictive ability for the overall survival (OS) of colon cancer. At the same time, we found that the expression of H19 affects the content of B cells and macrophages in the microenvironment of colon cancer and affects the prognosis of colon cancer. Finally, we constructed the H19 regulatory network and further analyzed the possible mechanisms. We found that knocking down the expression of H19 can significantly inhibit the expression of CCND1 and VEGFA. At the same time, the immunohistochemical assay found that the expression of CCND1 and VEGFA protein was significantly positively correlated with the infiltration of M2 type macrophages.ConclusionThe findings may help to formulate clinical strategies and understand the underlying mechanisms of H19 regulation. H19 may be a biomarker for targeted treatment of colon cancer.

Identification of a nomogram based on an 8-lncRNA signature as a novel diagnostic biomarker for childhood acute lymphoblastic leukemia.

Childhood acute lymphoblastic leukemia (cALL) still represents a major cause of disease-related death in children. This study aimed to explore the prognostic value of long non-coding RNAs (lncRNAs) in cALL. We downloaded lncRNA expression profiles from the TARGET and GEO databases. Univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses were applied to identify lncRNA-based signatures. We identified an eight-lncRNA signature (LINC00630, HDAC2-AS2, LINC01278, AL356599.1, AC114490.1, AL132639.3, FUT8.AS1, and TTC28.AS1), which separated the patients into two groups with significantly different overall survival rates. A nomogram based on the signature, BCR ABL1 status and white blood cell count at diagnosis was developed and showed good accuracy for predicting the 3-, 5- and 7-year survival probability of cALL patients. The C-index values of the nomogram in the training and internal validation set reached 0.8 (95% CI, 0.757 to 0.843) and 0.806 (95% CI, 0.728 to 0.884), respectively. The nomogram proposed in this study objectively and accurately predicted the prognosis of cALL. In vitro experiments suggested that LINC01278 promoted the proliferation of leukemic cells and inhibited leukemic cell apoptosis by targeting the inhibition of miR-500b-3p in cALL, and LINC01278 may be a biological target for the treatment of cALL in the future.

Expression and Clinical Significance of Long Non-Coding RNA RP4-669H2.1 in Glioblastoma

Neuroglioma is the most common malignant tumor in the central nervous system and still has a poor prognosis. Here, we investigated the prognostic value of long non-coding RNAs(lncRNAs) in glioblastoma. We first analyzed the lncRNA expression profiles of glioblastoma (GBM) in The Cancer Genome Atlas database and selected the most differential survival genes (lncRNA RP4-669H2.1) for further validation. We then performed qRT-PCR using samples of 88 glioblastoma patients treated in our department between January 2011 and December 2017 that were retrospectively selected to validate the prognostic value of RP4-669H2.1expression in glioblastoma. Using a Cox multivariate analysis, we explored the prognostic value of RP4-669H2.1 and analyzed whether it was an independent prognostic factor. This analysis confirmed that the RP4-669H2.1 expression was significantly associated with glioblastoma-associated mortality in this patient cohort (P =1.80E–05) in TCGA database. In fact, the overall survival (OS) of the RP4-669H2.1 high-expression group (78 cases) was lower than that of the low-expression group (49 cases) (P = 4.6E–06) in TCGA database. Moreover, the TNM stage of the RP4-669H2.1 high-expression group was higher than that of the RP4-669H2.1 low-expression group (P = 0.001). A multivariate analysis further showed that a higher TNM stage (OR = 2.167, 95% CI: 1.349–3.479) and a higher RP4-669H2.1 expression (OR = 2.933, 95% CI: 1.122–7.663) were independent risk factors for the OS of glioblastoma patients. Finally, we predicted the target genes of RP4-669H2.1 using a Multi Experiment Matrix and annotated their biological functions. We observed that the target genes of RP4-669H2.1 were mainly enriched in biological functions related to DNA-binding transcription factor activity. Among these, we selected SMAD6 because the expression of RP4-669H2.1 was positively correlated with that of SMAD6 in glioblastoma. Overall, we conclude that the upregulation of RP4-669H2.1 is an independent poor prognosis factor for glioblastoma and that it can regulate the DNA-binding activity of transcription factors.