Development of a DNAzyme-Driven Fluorescent Light-Up Aptasensor for Label-Free Detection of Multiple lncRNAs.

Long noncoding RNAs (lncRNAs) act as the dynamic regulatory molecules that control the expression of genes and affect numerous biological processes, and their dysregulation is associated with tumor progression. Herein, we develop a fluorescent light-up aptasensor to simultaneously measure multiple lncRNAs in living cells and breast tissue samples based on the DNAzyme-mediated cleavage reaction and transcription-driven synthesis of light-up aptamers. When target lncRNAs are present, they can be recognized by template probes to form the active DNAzyme structures, initiating the T4 PNK-catalyzed dephosphorylation-triggered extension reaction to generate double-strand DNAs with the T7 promoter sequences. The corresponding T7 promoters can initiate the transcription amplification catalyzed by the T7 RNA polymerase to generate abundant Broccoli aptamers and malachite green aptamers, which can bind DFHBI-1T and MG to generate strong fluorescence signals. Taking advantage of the good selectivity of DNAzyme-mediated cleavage of lncRNAs, high amplification efficiency of T7 transcription-driven amplification reaction, and bright fluorescence of the RNA aptamer-fluorophore complex, this method exhibits high sensitivity with a detection limit of 21.4 aM for lncRNA HOTAIR and 18.47 aM for lncRNA MALAT1, and it can accurately measure multiple lncRNAs in both tumor cell lines and breast tissue samples, providing a powerful paradigm for biomedical research and early clinic diagnostics.

Pseudouridine Detection and Quantification Using Bisulfite Incorporation Hindered Ligation.

Pseudouridine (Ψ) is a widespread RNA modification found in various RNA species, including rRNA, tRNA, snRNA, mRNA, and long noncoding RNA (lncRNA). Understanding the function of Ψ in these RNA types requires a robust method for the detection and quantification of the Ψ level at single-nucleotide resolution. A previously used method utilizes Ψ labeling by N-cyclohexyl-N'-ß-(4-methylmorpholinium)ethylcarbodiimide (CMC). The quantification of Ψ is based on the stop ratio after reverse transcription. However, the use of CMC followed by strong alkaline treatment causes severe RNA degradation, often requiring a large amount of RNA. The removal of CMC and recovery of RNA by ethanol precipitation are also time-consuming. Here, we introduce a Bisulfite Incorporation Hindered ligation-based method (BIHIND), which can detect and quantify Ψ sites on rRNA, mRNA, and noncoding RNA. BIHIND can be coupled with quantitative PCR (BIHIND-qPCR) for quantitative detection of Ψ fraction at individual modification sites, as well as with next-generation sequencing (BIHIND-seq) for high-throughput sequencing of Ψ without requiring reverse transcription. We validated the robustness of BIHIND with the elucidation of Ψ dynamics following pseudouridine synthase depletion.

Quantum Dots-Based Protocols for the Detection of RNAs.

RNA (ribonucleic acid) plays a crucial role in various cellular processes and is involved in the development and progression of several diseases. RNA molecules have gained considerable attention as potential biomarkers for various ailments, as they reflect the activity of genes in a particular cell or tissue. By measuring the levels of specific RNA molecules, such as messenger RNA (mRNA), noncoding RNAs, including microRNAs (miRNAs), and long noncoding RNAs (lncRNAs), researchers can infer the expression patterns of genes associated with a particular disease. Aberrant expression of specific miRNAs or lncRNAs has been associated with conditions such as cancer, cardiovascular diseases, neurodegenerative disorders, and more. Detection and quantification of these RNAs in biological samples, such as blood or tissue, can provide valuable diagnostic or prognostic information. Yet their analysis is a challenging endeavor due to their length, sequence similarity across family members, sensitivity to disintegration, and low quantity in total samples. New advances in nanophotonics have provided novel options for fabrication of quantum dots (QDs)-based biosensing devices capable of detecting a variety of disease-specific RNAs. Thus, we proposed and designed a nanophotonic method employing oligonucleotide-conjugated quantum dot nanoconjugates for the rapid and accurate detection of RNAs. Despite the abundance of other molecules in the sample, the approach delivers highly selective, precise identification of the target RNAs. The data also indicated the method's great practicality and simplicity in determining RNAs selectively. Overall, the approach enables the evaluation of RNA expression in relation to the initial onset and progression of a human health disorder.

Value of the HOTAIR expression assay in predicting therapy target in hepatocellular carcinoma: A meta-analysis and bioinformatics analysis.

BACKGROUND: Several studies show that the long non-coding RNA HOX transcript antisense RNA (HOTAIR) was upregulated in human cancer, which was associated with several clinical features and may have the potential to be prognostic markers. However, the significance of HOTAIR in hepatocellular carcinoma remains unclear. We performed a meta-analysis and bioanalysis to further investigate the association between HOTAIR and hepatocellular carcinoma. METHODS: Eligible literature was systematically retrieved from PubMed, Embase, and Web of Science databases. The pooled hazard ratios with 95% confidence intervals were used to evaluate to the effect. Raw data on HOTAIR expression were obtained from The Cancer Genome Atlas data portals. All bioinformatics analyses were performed using R software (version 4.3.1). RESULTS: We identified eight studies in this meta-analysis with a total of 399 patients. High-level HOTAIR expression was found to be significantly related to advanced tumor node metastasis stage, distant metastasis, poor tumor differentiation, and patients with hepatitis. Correspondingly, HOTAIR was also associated with poor overall survival and relapse-free survival. Subsequently, in bioanalysis, HOTAIR expression was higher in hepatocellular carcinoma as well as poor overall survival. High HOTAIR expression was strongly correlated with tumor node metastasis stage. Kyoto Encyclopedia of Genes and Genomes analysis revealed that the differentially expressed genes related to HOTAIR may be involved in the cancer-associated signaling pathway. CONCLUSION: HOTAIR may be a potential biomarker for HCC prediction and is expected to become a new choice for clinical HCC prediction..

AMPFLDAP: Adaptive Message Passing and Feature Fusion on Heterogeneous Network for LncRNA-Disease Associations Prediction.

Exploration of the intricate connections between long noncoding RNA (lncRNA) and diseases, referred to as lncRNA-disease associations (LDAs), plays a pivotal and indispensable role in unraveling the underlying molecular mechanisms of diseases and devising practical treatment approaches. It is imperative to employ computational methods for predicting lncRNA-disease associations to circumvent the need for superfluous experimental endeavors. Graph-based learning models have gained substantial popularity in predicting these associations, primarily because of their capacity to leverage node attributes and relationships within the network. Nevertheless, there remains much room for enhancing the performance of these techniques by incorporating and harmonizing the node attributes more effectively. In this context, we introduce a novel model, i.e., Adaptive Message Passing and Feature Fusion (AMPFLDAP), for forecasting lncRNA-disease associations within a heterogeneous network. Firstly, we constructed a heterogeneous network involving lncRNA, microRNA (miRNA), and diseases based on established associations and employing Gaussian interaction profile kernel similarity as a measure. Then, an adaptive topological message passing mechanism is suggested to address the information aggregation for heterogeneous networks. The topological features of nodes in the heterogeneous network were extracted based on the adaptive topological message passing mechanism. Moreover, an attention mechanism is applied to integrate both topological and semantic information to achieve the multimodal features of biomolecules, which are further used to predict potential LDAs. The experimental results demonstrated that the performance of the proposed AMPFLDAP is superior to seven state-of-the-art methods. Furthermore, to validate its efficacy in practical scenarios, we conducted detailed case studies involving three distinct diseases, which conclusively demonstrated AMPFLDAP's effectiveness in the prediction of LDAs.

The long noncoding RNAs lnc10 and lnc11 regulating flavonoid biosynthesis in Ginkgo biloba.

Although long non-coding RNAs have been recognized to play important roles in plant, their possible functions and potential mechanism in Ginkgo biloba flavonoid biosynthesis are poorly understood. Flavonoids are important secondary metabolites and healthy components of Ginkgo biloba. They have been widely used in food, medicine, and natural health products. Most previous studies have focused on the molecular mechanisms of structural genes and transcription factors that regulate flavonoid biosynthesis. Few reports have examined the biological functions of flavonoid biosynthesis by long non-coding RNAs in G. biloba. Long noncoding RNAs associated with flavonoid biosynthesis in G. biloba have been identified through RNA sequencing, but the function of lncRNAs has not been reported. In this study, the expression levels of lnc10 and lnc11 were identified. Quantitative real-time polymerase chain reaction analysis revealed that lnc10 and lnc11 were expressed in all detected organs, and they showed significantly higher levels in immature and mature leaves than in other organs. In addition, to fully identify the function of lnc10 and lnc11 in flavonoid biosynthesis in G. biloba, lnc10 and lnc11 were cloned from G. biloba, and were transformed into Arabidopsis and overexpressed. Compared with the wild type, the flavonoid content was increased in transgenic plants. Moreover, the RNA-sequencing analysis of wild-type, lnc10-overexpression, and lnc11-overexpression plants screened out 2019 and 2552 differentially expressed genes, and the transcript levels of structural genes and transcription factors associated with flavonoid biosynthesis were higher in transgenic Arabidopsis than in the wild type, indicating that lnc10 and lnc11 activated flavonoid biosynthesis in the transgenic lines. Overall, these results suggest that lnc10 and lnc11 positively regulate flavonoid biosynthesis in G. biloba.

High SNHG expression may predict a poor lung cancer prognosis based on a meta-analysis.

BACKGROUND: An increasing number of small nucleolar RNA host genes (SNHGs) have been revealed to be dysregulated in lung cancer tissues, and abnormal expression of SNHGs is significantly correlated with the prognosis of lung cancer. The purpose of this study was to conduct a meta-analysis to explore the correlation between the expression level of SNHGs and the prognosis of lung cancer. METHODS: A comprehensive search of six related databases was conducted to obtain relevant literature. Relevant information, such as overall survival (OS), progression-free survival (PFS), TNM stage, lymph node metastasis (LNM), and tumor size, was extracted. Hazard ratios (HRs) and 95% confidence intervals (CIs) were pooled to evaluate the relationship between SNHG expression and the survival outcome of lung cancers. Sensitivity and publication bias analyses were performed to explore the stability and reliability of the overall results. RESULTS: Forty publications involving 2205 lung cancer patients were included in this meta-analysis. The pooled HR and 95% CI values indicated a significant positive association between high SNHG expression and poor OS (HR: 1.890, 95% CI: 1.595-2.185), disease-free survival (DFS) (HR: 2.31, 95% CI: 1.57-3.39) and progression-free survival (PFS) (HR: 2.01, 95% CI: 0.66-6.07). The pooled odds ratio (OR) and 95% CI values indicated that increased SNHG expression may be correlated with advanced TNM stage (OR: 1.509, 95% CI: 1.267-1.799), increase risk of distant lymph node metastasis (OR: 1.540, 95% CI: 1.298-1.828), and large tumor size (OR: 1.509, 95% CI: 1.245-1.829). Sensitivity analysis and publication bias results showed that each result had strong reliability and robustness, and there was no significant publication bias or other bias. CONCLUSION: Most SNHGs are upregulated in lung cancer tissues, and high expression of SNHGs predicts poor survival outcomes in lung cancer. SNHGs may be potential prognostic markers and promising therapeutic targets.

NCAPG as a novel prognostic biomarker in numerous cancers: a meta-analysis and bioinformatics analysis.

BACKGROUND: Identification of effective biomarkers for cancer prognosis is a primary research challenge. Recently, several studies have reported the relationship between NCAPG and the occurrence of various tumors. However, none have combined meta-analytical and bioinformatics approaches to systematically assess the role of NCAPG in cancer. METHODS: We searched four databases, namely, PubMed, Web of Science, Embase, and the Cochrane Library, for relevant articles published before April 30, 2022. The overall hazard ratio or odds ratio and 95% confidence intervals were calculated to assess the relationship between NCAPG expression and cancer survival prognosis or clinical characteristics. Furthermore, the aforementioned results were validated using the GEPIA2, Kaplan-Meier plotter, and PrognoScan databases. RESULTS: The meta-analysis included eight studies with 1096 samples. The results showed that upregulation of NCAPG was correlated with poorer overall survival (hazard ratio = 2.90, 95% confidence interval = 2.06-4.10, P < 0.001) in the cancers included in the study. Subgroup analysis showed that in some cancers, upregulation of NCAPG was correlated with age, distant metastasis, lymph node metastasis, TNM stage, relapse, differentiation, clinical stage, and vascular invasion. These results were validated using the GEPIA2, UALCAN, and PrognoScan databases. We also explored the processes of NCAPG methylation and phosphorylation. CONCLUSION: Dysregulated NCAPG expression is associated with the clinical prognostic and pathological features of various cancers. Therefore, NCAPG can serve as a human cancer therapeutic target and a new potential prognostic biomarker.

New Insights into lncRNAs in Aß Cascade Hypothesis of Alzheimer's Disease.

Alzheimer's disease (AD) is the most common type of dementia, but its pathogenesis is not fully understood, and effective drugs to treat or reverse the progression of the disease are lacking. Long noncoding RNAs (lncRNAs) are abnormally expressed and deregulated in AD and are closely related to the occurrence and development of AD. In addition, the high tissue specificity and spatiotemporal specificity make lncRNAs particularly attractive as diagnostic biomarkers and specific therapeutic targets. Therefore, an in-depth understanding of the regulatory mechanisms of lncRNAs in AD is essential for developing new treatment strategies. In this review, we discuss the unique regulatory functions of lncRNAs in AD, ranging from Aß production to clearance, with a focus on their interaction with critical molecules. Additionally, we highlight the advantages and challenges of using lncRNAs as biomarkers for diagnosis or therapeutic targets in AD and present future perspectives in clinical practice.

Identification of an Oxidative Stress-Related LncRNA Signature for Predicting Prognosis and Chemotherapy in Patients With Hepatocellular Carcinoma.

Background: Oxidative stress plays a critical role in oncogenesis and tumor progression. However, the prognostic role of oxidative stress-related lncRNA in hepatocellular carcinomas (HCC) has not been fully explored. Methods: We used the gene expression data and clinical data from The Cancer Genome Atlas (TCGA) database to identify oxidative stress-related differentially expressed lncRNAs (DElncRNAs) by pearson correlation analysis. A four-oxidative stress-related DElncRNA signature was constructed by LASSO regression and Cox regression analyses. The predictive signature was further validated by Kaplan-Meier (K-M) survival analysis, receiver operating characteristic (ROC) curves, nomogram and calibration plots, and principal component analysis (PCA). Single-sample gene set enrichment analysis (ssGSEA) was used to explore the relationship between the signature and immune status. Finally, the correlation between the signature and chemotherapeutic response of HCC patients was analyzed. Results: In our study, the four-DElncRNA signature was not only proved to be a robust independent prognostic factor for overall survival (OS) prediction, but also played a crucial role in the regulation of progression and chemotherapeutic response of HCC. ssGSEA showed that the signature was correlated with the infiltration level of immune cells. HCC patients in high-risk group were more sensitive to the conventional chemotherapeutic drugs including Sorafenib, lapatinib, Nilotinib, Gefitinib, Erlotinib and Dasatinib, which pave the way for targeting DElncRNA-associated treatments for HCC patients. Conclusion: Our study has originated a prognostic signature for HCC based on oxidative stress-related DElncRNAs, deepened the understanding of the biological role of four key DElncRNAs in HCC and laid a theoretical foundation for the choice of chemotherapy.

m6A-Related lncRNAs Predict Overall Survival of Patients and Regulate the Tumor Immune Microenvironment in Osteosarcoma.

Background: m6A-related lncRNAs have demonstrated great potential tumor diagnostic and therapeutic targets. The goal of this work was to find m6A-regulated lncRNAs in osteosarcoma patients. Method: The Cancer Genome Atlas (TCGA) database was used to retrieve RNA sequencing and medical information from osteosarcoma sufferers. The Pearson's correlation test was used to identify the m6A-related lncRNAs. A risk model was built using univariate and multivariable Cox regression analysis. Kaplan-Meier survival analysis and receiver functional requirements were used to assess the risk model's performance (ROC). By using the CIBERSORT method, the associations between the relative risks and different immune cell infiltration were investigated. Lastly, the bioactivities of high-risk and low-risk subgroups were investigated using Gene Set Enrichment Analysis (GSEA). Result: A total of 531 m6A-related lncRNAs were obtained from TCGA. Seven lncRNAs have demonstrated prognostic values. A total of 88 OS patients were separated into cluster 1, cluster 2, and cluster 3. The overall survival rate of OS patients in cluster 3 was more favorable than that of those in cluster 1 and cluster 2. The average Stromal score was much higher in cluster 1 than in cluster 2 and cluster 3 (P < 0.05). The expression levels of lncRNAs used in the construction of the risk prediction model in the high-risk group were generally lower than those in the low-risk group. Analysis of patient survival indicated that the survival of the low-risk group was higher than that of the high-risk group (P < 0.0001) and the area under the curve (AUC) of the ROC curve was 0.719. Using the CIBERSORT algorithm, the results revealed that Macrophages M0, Macrophages M2, and T cells CD4 memory resting accounted for a large proportion of immune cell infiltration. By GSEA analysis, our results implied that the high-risk group was mainly involved in unfolded protein response, DNA repair signaling, and epithelial-mesenchymal transition signaling pathway and glycolysis pathway; meanwhile, the low-risk group was mainly involved in estrogen response early and KRAS signaling pathway. Conclusion: Our investigation showed that m6A-related lncRNAs remained tightly connected to the immunological microenvironment of osteosarcoma tumors, potentially influencing carcinogenesis and development. The immune microenvironment and immune-related biochemical pathways can be changed by regulating the transcription of M6A modulators or lncRNAs. In addition, we looked for risk-related signaling of m6A-related lncRNAs in osteosarcomas and built and validated the risk prediction system. The findings of our current analysis will facilitate the assessment of outcomes and the development of immunotherapies for sufferers of osteosarcomas.

Differential expression profile and in-silico functional analysis of long noncoding RNA and mRNA in duck embryo fibroblasts infected with duck plague virus.

BACKGROUND: Duck plague virus (DPV), belonging to herpesviruses, is a linear double-stranded DNA virus. There are many reports about the outbreak of the duck plague in a variety of countries, which caused huge economic losses. Recently, increasing reports revealed that multiple long non-coding RNAs (lncRNAs) can possess great potential in the regulation of host antiviral immune response. Furthermore, it remains to be determined which specific molecular mechanisms are responsible for the DPV-host interaction in host immunity. Here, lncRNAs and mRNAs in DPV infected duck embryonic fibroblast (DEF) cells were identified by high-throughput RNA-sequencing (RNA-seq). And we predicted target genes of differentially expressed genes (DEGs) and formed a complex regulatory network depending on in-silico analysis and prediction. RESULT: RNA-seq analysis results showed that 2921 lncRNAs were found at 30 h post-infection (hpi). In our study, 218 DE lncRNAs and 2840 DE mRNAs were obtained in DEF after DPV infection. Among these DEGs and target genes, some have been authenticated as immune-related molecules, such as a Macrophage mannose receptor (MR), Anas platyrhynchos toll-like receptor 2 (TLR2), leukocyte differentiation antigen, interleukin family, and their related regulatory factors. Furthermore, according to the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analysis, we found that the target genes may have important effects on biological development, biosynthesis, signal transduction, cell biological regulation, and cell process. Also, we obtained, the potential targeting relationship existing in DEF cells between host lncRNAs and DPV-encoded miRNAs by software. CONCLUSIONS: This study revealed not only expression changes, but also the possible biological regulatory relationship of lncRNAs and mRNAs in DPV infected DEF cells. Together, these data and analyses provide additional insight into the role of lncRNAs and mRNAs in the host's immune response to DPV infection.

Prognosis and Tumour Immune Microenvironment of Patients With Hepatocellular Carcinoma by a Novel Pyroptosis-Related lncRNA Signature.

Worldwide, hepatocellular carcinoma (HCC) is the most common subtype of liver cancer. However, the survival rate of patients with HCC continues to be poor. The recent literature has revealed that long non-coding RNAs (lncRNAs) and the occurrence of pyroptosis can perform a substantial task in predicting the prognosis of the respective condition along with the response to immunotherapy among HCC patients. Thus, screening and identifying lncRNAs corelated with pyroptosis in HCC patients are critical. In the current study, pyroptosis-related lncRNAs (PR-lncRNAs) have been obtained by co-expression analysis. The Least Absolute Shrinkage and Selection Operator (LASSO) and univariate and multivariate Cox regression assessments have been performed to develop a PR-lncRNA prognostic model. The risk model was analysed using Kaplan-Meier analysis, principal component analysis (PCA), functional enrichment annotation, and a nomogram. The risk model composed of five PR-lncRNAs was identified as an independent prognostic factor. The tumour immune microenvironment (TIME) was assessed using model groupings. Finally, we validated the five PR-lncRNAs in vitro using a quantitative real-time polymerase chain reaction (qRT-PCR).

Cross-Talk Between m6A- and m5C-Related lncRNAs to Construct a Novel Signature and Predict the Immune Landscape of Colorectal Cancer Patients.

Background: N6-methyladenosine (m6A) and 5-methylcytosine (m5C) can modify long non-coding RNAs (lncRNAs), thereby affecting tumorigenesis and tumor progression. However, there is a lack of knowledge regarding the potential roles and cross-talk of m6A- and m5C-related lncRNAs in the tumor microenvironment (TME) and their effect on prognosis. Methods: We systematically evaluated the expression patterns of m6A- and m5C-related lncRNAs in 1358 colorectal cancer (CRC) samples from four datasets. Consensus clustering was conducted to identify molecular subtypes of CRC, and the clinical significance, TME, tumor-infiltrating immune cells (TIICs), and immune checkpoints in the different molecular subtypes were analyzed. Finally, we established a m6A- and m5C-related lncRNA signature and a prognostic nomogram. Results: We identified 141 m6A- and m5C-related lncRNAs by co-expression analysis, among which 23 lncRNAs were significantly associated with the overall survival (OS) of CRC patients. Two distinct molecular subtypes (cluster A and cluster B) were identified, and these two distinct molecular subtypes could predict clinicopathological features, prognosis, TME stromal activity, TIICs, immune checkpoints. Next, a m6A- and m5C-related lncRNA signature for predicting OS was constructed, and its predictive capability in CRC patients was validated. We then constructed a highly accurate nomogram for improving the clinical applicability of the signature. Analyses of clinicopathological features, prognosis, TIICs, cancer stem cell (CSC), and drug response revealed significant differences between two risk groups. In addition, we found that patients with a low-risk score exhibited enhanced response to anti-PD-1/L1 immunotherapy. Functional enrichment analysis showed that these lncRNAs related to the high-risk group were involved in the development and progression of CRC. Conclusions: We conducted a comprehensive analysis of m6A- and m5C-related lncRNAs in CRC and revealed their potential functions in predicting tumor-immune-stromal microenvironment, clinicopathological features, and prognosis, and determined their role in immunotherapy. These findings may improve our understanding of the cross-talk between m6A- and m5C-related lncRNAs in CRC and pave a new road for prognosis assessment and more effective immunotherapy strategies.

Identification of Novel Prognostic Signatures for Clear Cell Renal Cell Carcinoma Based on ceRNA Network Construction and Immune Infiltration Analysis.

Objective: Clear cell renal cell carcinoma (ccRCC) carries significant morbidity and mortality globally and is often resistant to conventional radiotherapy and chemotherapy. Immune checkpoint blockade (ICB) has received attention in ccRCC patients as a promising anticancer treatment. Furthermore, competitive endogenous RNA (ceRNA) networks are crucial for the occurrence and progression of various tumors. This study was aimed at identifying reliable prognostic signatures and exploring potential mechanisms between ceRNA regulation and immune cell infiltration in ccRCC patients. Methods and Results: Gene expression profiling and clinical information of ccRCC samples were obtained from The Cancer Genome Atlas (TCGA) database. Through comprehensive bioinformatic analyses, differentially expressed mRNAs (DEmRNAs; n = 131), lncRNAs (DElncRNAs; n = 12), and miRNAs (DEmiRNAs; n = 25) were identified to establish ceRNA networks. The CIBERSORT algorithm was applied to calculate the proportion of 22 types of tumor-infiltrating immune cells (TIICs) in ccRCC tissues. Subsequently, univariate Cox, Lasso, and multivariate Cox regression analyses were employed to construct ceRNA-related and TIIC-related prognostic signatures. In addition, we explored the relationship between the crucial genes and TIICs via coexpression analysis, which revealed that the interactions between MALAT1, miR-1271-5p, KIAA1324, and follicular helper T cells might be closely correlated with the progression of ccRCC. Ultimately, we preliminarily validated that the potential MALAT1/miR-1271-5p/KIAA1324 axis was consistent with the ceRNA theory by qRT-PCR in the ccRCC cell lines. Conclusion: On the basis of the ceRNA networks and TIICs, we constructed two prognostic signatures with excellent predictive value and explored possible molecular regulatory mechanisms, which might contribute to the improvement of prognosis and individualized treatment for ccRCC patients.

A Potential Nine-lncRNAs Signature Identification and Nomogram Diagnostic Model Establishment for Papillary Thyroid Cancer.

The purpose of our current study was to establish a long non-coding RNA(lncRNA) signature and assess its prognostic and diagnostic power in papillary thyroid cancer (PTC). LncRNA expression profiles were obtained from the Cancer Genome Atlas (TCGA). The key module and hub lncRNAs related to PTC were determined by weighted gene co-expression network analysis (WGCNA) and LASSO Cox regression analyses, respectively. Functional enrichment analyses, including Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene set enrichment analysis were implemented to analyze the possible biological processes and signaling pathways of hub lncRNAs. Associations between key lncRNA expressions and tumor-infiltrating immune cells were identified using the TIMER website, and proportions of immune cells in high/low risk score groups were compared. Kaplan-Meier Plotter was used to evaluate the prognostic significance of hub genes in PTC. A diagnostic model was conducted with logistic regression analysis, and its diagnostic performance was assessed by calibration/receiver operating characteristic curves and principal component analysis. A nine-lncRNAs signature (SLC12A5-AS1, LINC02028, KIZ-AS1, LINC02019, LINC01877, LINC01444, LINC01176, LINC01290, and LINC00581) was established in PTC, which has significant diagnostic and prognostic power. Functional enrichment analyses elucidated the regulatory mechanism of the nine-lncRNAs signature in the development of PTC. This signature and expressions of nine hub lncRNAs were correlated with the distributions of tumor infiltrating immune cells. A diagnostic nomogram was also established for PTC. By comparing with the published models with less than or equal to nine lncRNAs, our signature showed a preferable performace for prognosis prediction. In conclusion, our present research established an innovative nine-lncRNAs signature and a six-lncRNAs nomogram that might act as a potential indicator for PTC prognosis and diagnosis, which could be conducive to the PTC treatment.

Clinical significance and mechanism of long noncoding RNA HAGLROS in triple negative breast cancer.

PURPOSE: Triple Negative Breast Cancer (TNBC) is the malignant tumor with the fastest progression rate in breast cancer. LncRNAs are widely involved in various biological characteristics of tumor. The purpose of this study was to mine LncRNAs that can be used to diagnose and evaluate the prognosis of TNBC. METHODS: Base on TCGA dataset, we used three R language packages to analyze the differentially expressed (DE) lncRNAs in TNBC. Survival analysis and ROC curve analysis were conducted to estimate the potential diagnostic and prognostic value of LncRNAs for TNBC. Furthermore, CCK-8 and Transwell assays were used to assess the effects of LncRNA on MDA-MB-231 cells proliferation and migration. Additionally, targets mRNAs of candidate LncRNA were predicted by co-expression analysis and multiple target gene prediction databases, then KEGG pathway and GO analysis were conducted using DAVID online tool. RESULTS: 6165 DERNAs and 1258 DElncRNAs were obtained. 40 LncRNAs were significantly correlated with the survival time of TNBC patients. Among them, HAGLROS has the highest HR value. ROC curve analysis also showed that HAGLROS had high sensitivity and specificity. Further in vitro experiments showed that downregulation of HAGLROS inhibited the proliferation and migration of MDA-MB-231 cells. Moreover, by conducting bioinformatics analysis, we found that these target genes of HAGLROS were involved in regulating five signaling pathways. Mechanistic investigations demonstrated that HAGLROS might regulate the expression of PAX5 through miR-330-5p, the effects of miR-330-5p in MDA-MB-231 cells were also analyzed. CONCLUSION: Our results showed that HAGLROS was significantly overexpressed in TNBC, and high HAGLROS expression predicted poor overall survival. Downregulation of HAGLROS could inhibite the proliferation and migration of MDA-MB-231 cell by regulating PAX5 expression through miR-330-5p.

Radiosynthesis of a novel antisense imaging probe targeting LncRNA HOTAIR in malignant glioma.

BACKGROUND: Long non-coding RNA (LncRNA) HOTAIR was amplified and overexpressed in many human carcinomas, which could serve as a useful target for cancer early detection and treatment. The 99mTc radiolabeled antisense oligonucleotides (ASON) could visualize the expression of HOTAIR and provide a diagnostic value for malignant tumors. The aim of this study was to evaluate whether liposome-coated antisense oligonucleotide probe 99mTc-HYNIC-ASON targeting HOTAIR can be used in in vivo imaging of HOTAIR in malignant glioma xenografts. METHODS: The ASON targeting LncRNA HOTAIR as well as mismatched ASON (ASONM) were designed and modified. The radiolabeling of 99mTc with two probes were via the conjugation of bifunctional chelator HYNIC. Then probes were purified by Sephadex G25 and tested for their radiolabeling efficiency and purity, as well as stability by ITLC (Instant thin-layer chromatography) and gel electrophoresis. Then the radiolabeled probes were transfected with lipofectamine 2000 for cellular uptake test and the next experimental use. Furthermore, biodistribution study and SPECT imaging were performed at different times after liposome-coated 99mTc-HYNIC-ASON/ASONM were intravenously injected in glioma tumor-bearing mice models. All data were analyzed by statistical software. RESULTS: The labeling efficiencies of 99mTc-HYNIC-ASON and 99mTc-HYNIC-ASONM measured by ITLC were (91 ± 1.5) % and (90 ± 0.6) %, respectively, and both radiochemical purities were more than 89%. Two probes showed good stability within 12 h. Gel electrophoresis confirmed that the oligomers were successfully radiolabeled no significant degradation were found. Biodistribution study demonstrated that liposome-coated antisense probes were excreted mainly through the kidney and bladder and has higher uptake in the tumor. Meanwhile, the tumor was clearly shown after injection of liposome coated 99mTc-HYNIC-ASON, and its T/M ratio was higher than that in the non-transfection group and mismatched group. No tumor was seen in mismatched and blocking group. CONCLUSION: The liposome encapsulated 99mTc-HYNIC-ASON probe can be used in the in vivo, real-time imaging of LncRNA HOTAIR expression in malignant glioma.

Advances in the identification of long non-coding RNA binding proteins.

Long non-coding RNAs (lncRNAs) are transcripts longer than 200 nt without evident protein coding function. They play important regulatory roles in many biological processes, e.g., gene regulation, chromatin remodeling, and cell fate determination during development. Dysregulation of lncRNAs has been observed in various diseases including cancer. Interacting with proteins is a crucial way for lncRNAs to play their biological roles. Therefore, the characterization of lncRNA binding proteins is important to understand their functions and to delineate the underlying molecular mechanism. Large-scale studies based on mass spectrometry have characterized over a thousand new RNA binding proteins without known RNA-binding domains, thus revealing the complexity and diversity of RNA-protein interactions. In addition, several methods have been developed to identify the binding proteins for particular RNAs of interest. Here we review the progress of the RNA-centric methods for the identification of RNA-protein interactions, focusing on the studies involving lncRNAs, and discuss their strengths and limitations.

Long Non-Coding RNAs profiling in pathogenesis of Verticillium dahliae: New insights in the host-pathogen interaction.

Verticillium dahliae causes vascular wilt disease on cotton (Gossypium hirsutum), resulting in devastating yield loss worldwide. While little is known about the mechanism of long non-coding RNAs (lncRNAs), several lncRNAs have been implicated in numerous physiological processes and diseases. To better understand V. dahliae pathogenesis, lncRNA was conducted in a V. dahliae virulence model. Potential target genes of significantly regulated lncRNAs were predicted using cis/trans-regulatory algorithms. This study provides evidence for lncRNAs' regulatory role in pathogenesis-related genes. Interestingly, lncRNAs were identified and varying in terms of RNA length and nutrient starvation treatments. Efficient pathogen nutrition during the interaction with the host is a requisite factor during infection. Our observations directly link to mutated V. dahliae invasion, explaining infected cotton have lower pathogenicity and lethality compared to V. dahliae. Remarkably, lncRNAs XLOC_006536 and XLOC_000836 involved in the complex regulation of pathogenesis-related genes in V. dahliae were identified. For the first time the regulatory role of lncRNAs in filamentous fungi was uncovered, and it is our contention that elucidation of lncRNAs will advance our understanding in the development and pathogenesis of V. dahliae and offer alternatives in the control of the diseases caused by fungus V. dahliae attack.