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.

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.

Long Non-Coding RNA H19 Expression Correlates with Autophagy Process in Adrenocortical Carcinoma.

Adrenocortical carcinoma (ACC) is characterized by poor prognosis and high mortality. The suppression of the long-non-coding RNA H19, counterbalanced by IGF2 over-expression, leads to down-regulation of the autophagy markers, high proliferation rate and metastatic potential in patients affected by ACC. The administration of the deacetylase inhibitors (DACi) panobinostat, trichostatin A (TSA) and SAHA affected the cell viability of H295R monolayer and spheroids and induced the over-expression of H19 and autophagy transcripts. H19 knock down in H295R cells was not able to modulate the expression level of autophagy transcripts. Instead, H19 knock down was able to impede the ability of DACi to modulate the protein level of the autophagy markers. Furthermore, the administration of higher concentration of DACi was able to down-regulate the protein level of Beclin1 and p62 and to induce the conversion of LC3B-I into the active LC3B-II form, thus confirming an active autophagic process. Neither the active protein level nor the activity of caspases 8 and 3 was prompted by the DACi, thus excluding the involvement of the executioners of apoptosis in H295R decay. The DACi restore H19, the autophagy markers and trigger cell death in ACC cells. The re-activation of autophagy would represent a novel strategy for the treatment of patients affected by this severe malignancy.

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 Noncoding RNAs: Recent Insights into Their Role in Male Infertility and Their Potential as Biomarkers and Therapeutic Targets.

Long noncoding RNAs (lncRNAs) are composed of nucleotides located in the nucleus and cytoplasm; these are transcribed by RNA polymerase II and are greater than 200 nt in length. LncRNAs fulfill important functions in a variety of biological processes, including genome imprinting, cell differentiation, apoptosis, stem cell pluripotency, X chromosome inactivation and nuclear transport. As high throughput sequencing technology develops, a substantial number of lncRNAs have been found to be related to a variety of biological processes, such as development of the testes, maintaining the self-renewal and differentiation of spermatogonial stem cells, and regulating spermatocyte meiosis. These indicate that lncRNAs can be used as biomarkers and potential therapeutic targets for male infertility. However, only a few comprehensive reviews have described the role of lncRNAs in male reproduction. In this paper, we summarize recent findings relating to the role of lncRNAs in spermatogenesis, their potential as biomarkers for male infertility and the relationship between reproductive arrest and transgenerational effects. Finally, we suggest specific targets for the treatment of male infertility from the perspective of lncRNAs.

Identification of N6-methylandenosine related LncRNAs biomarkers associated with the overall survival of osteosarcoma.

PURPOSE: Osteosarcoma (OS) is a differentiation disease caused by the genetic and epigenetic differentiation of mesenchymal stem cells into osteoblasts. OS is a common, highly malignant tumor in children and adolescents. Fifteen to 20 % of the patients find distant metastases at their first visit. The purpose of our study was to identify biomarkers for tracking the prognosis and treatment of OS to improve the survival rate of patients. MATERIALS AND METHODS: In this study, which was based on Therapeutically Applicable Research to Generate Effective Treatments (TARGET), we searched for m6A related lncRNAs in OS. We constructed a network between lncRNA and m6A, and built an OS prognostic risk model. RESULTS: We identified 14,581 lncRNAs by using the dataset from TARGET. We obtained 111 m6A-related lncRNAs through a Pearson correlation analysis. A network was built between lncRNA and m6A genes. Eight m6A-related lncRNAs associated with survival were identified through a univariate Cox analysis. A selection operator (LASSO) Cox regression was used to construct a prognostic risk model with six genes (RP11-286E11.1, LINC01426, AC010127.3, DLGAP1-AS2, RP4-657D16.3, AC002398.11) obtained through least absolute shrinkage. We also discovered upregulated levels of DLGAP1-AS2 and m6A methylation in osteosarcoma tissues/cells compared with normal tissues/osteoblasts cells. CONCLUSION: We constructed a risk score prognosis model of m6A-related lncRNAs (RP11-286E11.1, LINC01426, AC010127.3, DLGAP1-AS2, RP4-657D16.3, AC002398.11) using the dataset downloaded from TRAGET. We verified the value of the model by dividing all samples into test groups and training groups. However, the role of m6A-related lncRNAs in osteosarcoma needs to be further researched by cell and in vivo studies.

An effective N6-methyladenosine-related long non-coding RNA prognostic signature for predicting the prognosis of patients with bladder cancer.

BACKGROUND: Bladder cancer (BLCA) typically has a poor prognosis due to high relapse and metastasis rates. A growing body of evidence indicates that N6-methyladenosine (m6A) and long non-coding RNAs (lncRNAs) play crucial roles in the progression of BLCA and the treatment response of patients with BLCA. Therefore, we conducted a comprehensive RNA-seq analysis of BLCA using data from The Cancer Genome Atlas (TCGA) to establish an m6A-related lncRNA prognostic signature (m6A-RLPS) for BLCA. METHODS: Consensus clustering analysis was used to investigate clusters of BLCA patients with varying prognoses. The least absolute shrinkage and selection operator Cox regression were used to develop the m6A-RLPS. The ESTIMATE and CIBERSORT algorithms were used to evaluate the immune composition. RESULTS: A total of 745 m6A-related lncRNAs were identified using Pearson correlation analysis (|R| > 0.4, p < 0.001). Fifty-one prognostic m6A-related lncRNAs were screened using univariate Cox regression analysis. Through consensus clustering analysis, patients were divided into two clusters (clusters 1 and 2) with different overall survival rates and tumor stages based on the differential expression of the lncRNAs. Enrichment analysis demonstrated that terms related to tumor biological processes and immune-related activities were increased in patient cluster 2, which was more likely to exhibit low survival rates. Nine m6A-related prognostic lncRNAs were finally determined and subsequently used to construct the m6A-RLPS, which was verified to be an independent predictor of prognosis using univariate and multivariate Cox regression analyses. Further, a nomogram based on age, tumor stage, and the m6A-RLPS was generated and showed high accuracy and reliability with respect to predicting the survival outcomes of BLCA patients. The prognostic signature was found to be strongly correlated to tumor-infiltrating immune cells and immune checkpoint expression. CONCLUSIONS: We established a novel m6A-RLPS with a favorable prognostic value for patients with BLCA. We believe that this prognostic signature can provide new insights into the tumorigenesis of BLCA and predict the treatment response in patients with BLCA.

Bioinformatic Tools for the Analysis and Prediction of ncRNA Interactions.

Noncoding RNAs (ncRNAs) play prominent roles in the regulation of gene expression via their interactions with other biological molecules such as proteins and nucleic acids. Although much of our knowledge about how these ncRNAs operate in different biological processes has been obtained from experimental findings, computational biology can also clearly substantially boost this knowledge by suggesting possible novel interactions of these ncRNAs with other molecules. Computational predictions are thus used as an alternative source of new insights through a process of mutual enrichment because the information obtained through experiments continuously feeds through into computational methods. The results of these predictions in turn shed light on possible interactions that are subsequently validated experimentally. This review describes the latest advances in databases, bioinformatic tools, and new in silico strategies that allow the establishment or prediction of biological interactions of ncRNAs, particularly miRNAs and lncRNAs. The ncRNA species described in this work have a special emphasis on those found in humans, but information on ncRNA of other species is also included.