LEF1-AS1 is implicated in the malignant development of glioblastoma via sponging miR-543 to upregulate EN2

Leveraging a disulfidptosis‑related lncRNAs signature for predicting the prognosis and immunotherapy of glioma

BACKGROUND

Gliomas, a prevalent form of primary brain tumors, are linked with a high mortality rate and unfavorable prognoses. Disulfidptosis, an innovative form of programmed cell death, has received scant attention concerning disulfidptosis-related lncRNAs (DRLs). The objective of this investigation was to ascertain a prognostic signature utilizing DRLs to forecast the prognosis and treatment targets of glioma patients.

METHODS

RNA-seq data were procured from The Cancer Genome Atlas database. Disulfidptosis-related genes were compiled from prior research. An analysis of multivariate Cox regression and the least absolute selection operator was used to construct a risk model using six DRLs. The risk signature's performance was evaluated via Kaplan-Meier survival curves and receiver operating characteristic curves. Additionally, functional analysis was carried out using GO, KEGG, and single-sample GSEA to investigate the biological functions and immune infiltration. The research also evaluated tumor mutational burden, therapeutic drug sensitivity, and consensus cluster analysis. Reverse transcription quantitative PCR was conducted to validate the expression level of DRLs.

RESULTS

A prognostic signature comprising six DRLs was developed to predict the prognosis of glioma patients. High-risk patients had significantly shorter overall survival than low-risk patients. The robustness of the risk model was validated by receiver operating characteristic curves and subgroup survival analysis. Risk model was used independently as a prognostic indicator for the glioma patients. Notably, the low-risk patients displayed a substantial decrease in the immune checkpoints, the proportion of immune cells, ESTIMATE and immune score. IC50 values from the different risk groups allowed us to discern three drugs for the treatment of glioma patients. Lastly, the potential clinical significance of six DRLs was determined.

CONCLUSIONS

A novel six DRLs signature was developed to predict prognosis and may provide valuable insights for patients with glioma seeking novel immunotherapy and targeted therapy.

Overexpression of TMEM150A in glioblastoma multiforme patients correlated with dismal prognoses and compromised immune statuses

Transmembrane proteins have exhibited a significant correlation with glioblastoma multiforme (GBM). The current study elucidates the roles of transmembrane protein 150A (TMEM150A) in GBM. Data on patients with GBM were collected from The Cancer Genome Atlas and Xena databases. The objective was to identify the expression levels of TMEM150A in patients with GBM, and evaluate its diagnostic and prognostic values, accomplished using the receiver operating characteristic and survival analyses. On a cellular level, Cell Counting Kit-8, Wound healing, and Transwell experiments were performed to gauge the impact of TMEM150A on cell growth and migration. The study further investigated the correlation between TMEM150A expression and immune status, along with ribonucleic acid (RNA) modifications in GBM. The findings demonstrated TMEM150A overexpression in the cancerous tissues of patients with GBM, with an area under the curve value of 0.95. TMEM150A overexpression was significantly correlated with poor prognostic indicators. TMEM150A overexpression and isocitrate dehydrogenase (IDH) mutation status were predictive of poor survival time among patients with GBM. In vitro experiments indicated that suppressing TMEM150A expression could inhibit GBM cell proliferation, migration, and invasion. Moreover, TMEM150A overexpression was associated with stromal, immune, and estimate scores, immune cells (such as the T helper (Th) 17 cells, Th2 cells, and regulatory T cells), cell markers, and RNA modifications. Therefore, TMEM150A overexpression might serve as a promising biomarker for predicting poor prognosis in patients with GBM. Inhibiting TMEM150A expression holds the potential for improving the survival time of patients with GBM.

Mechanism of LEF1-AS1 regulating HUVEC cells by targeting miR-489-3p/S100A11 axis

Background

The venous malformation is the most common congenital vascular malformation and exhibits the characteristics of local invasion and lifelong progressive development. Long noncoding RNA (lncRNA) regulates endothelial cells, vascular smooth muscle cells, macrophages, vascular inflammation, and metabolism and also affects the development of venous malformations. This study aimed to elucidate the role of the lncRNA LEF1-AS1 in the development of venous malformations and examine the interaction among LEF1-AS1, miR-489-3p, and S100A11 in HUVEC cells.

Methods

Venous malformation tissues, corresponding normal venous tissues, and HUVEC cells were used. Agilent human lncRNA microarray gene chip was used to screen differential genes, RNA expression was detected using quantitative reverse transcription PCR, and protein expression was detected using Western blotting. The proliferation, migration, and angiogenesis of HUVEC cells were assessed using CCK8, transwell, and in vitro angiogenesis tests.

Results

A total of 1,651 lncRNAs were screened using gene chip analysis, of which 1015 were upregulated and 636 were downregulated. The lncRNA LEF1-AS1 was upregulated with an obvious difference multiple, and the fold-change value was 11.03273. The results of the analysis performed using the StarBase bioinformatics prediction website showed that LEF1-AS1 and miR-489-3p possessed complementary binding sites and that miR-489-3p and S100A11 also had complementary binding sites. The findings of tissue experiments revealed that the expressions of LEF1-AS1 and S100A11 were higher in tissues with venous malformations than in normal tissues, whereas the expression of miR-489-3p was lower in venous malformations than in normal tissues. Cell culture experiments indicated that LEF1-AS1 promoted the proliferation, migration, and angiogenesis of HUVEC cells. In these cells, LEF1-AS1 targeted miR-489-3p, which in turn targeted S100A11. LEF1-AS1 acted as a competitive endogenous RNA and promoted the expression of S100A11 by competitively binding to miR-489-3p and enhancing the proliferation, migration, and angiogenesis of HUVEC cells. Thus, LEF1-AS1 participated in the occurrence and development of venous malformation.

Conclusions

The expression of LEF1-AS1 was upregulated in venous malformations, and the expression of S100A11 was increased by the adsorption of miR-489-3p to venous endothelial cells, thus enhancing the proliferation, migration, and angiogenesis of HUVEC cells. In conclusion, LEF1-AS1 is involved in the occurrence and development of venous malformations by regulating the miR-489-3p/S100A11 axis, which provides valuable insights into the pathogenesis of this disease and opens new avenues for its treatment.

Mechanistic insights into super-enhancer-driven genes as prognostic signatures in patients with glioblastoma

BACKGROUND

Glioblastoma (GBM) is one of the most common malignant brain tumors in adults and is characterized by high aggressiveness and rapid progression, poor treatment, high recurrence rate, and poor prognosis. Although super-enhancer (SE)-driven genes haven been recognized as prognostic markers for several cancers, whether it can be served as effective prognostic markers for patients with GBM has not been evaluated.

METHODS

We first combined histone modification data with transcriptome data to identify SE-driven genes associated with prognosis in patients with GBM. Second, we developed a SE-driven differentially expressed genes (SEDEGs) risk score prognostic model by univariate Cox analysis, KM survival analysis, multivariate Cox analysis and least absolute shrinkage and selection operator (LASSO) regression. Its reliability in predicting was verified by two external data sets. Third, through mutation analysis, immune infiltration, we explored the molecular mechanisms of prognostic genes. Next, Genomics of Drug Sensitivity in Cancer (GDSC) and the Connectivity Map (cMap) database were employed to assess different sensitivities to chemotherapeutic agents and small-molecule drug candidates between high- and low-risk patients. Finally, SEanalysis database was chosen to identify SE-driven transcription factors (TFs) regulating prognostic markers which will reveal a potential SE-driven transcriptional regulatory network.

RESULTS

First, we developed a 11-gene risk score prognostic model (NCF2, MTHFS, DUSP6, G6PC3, HOXB2, EN2, DLEU1, LBH, ZEB1-AS1, LINC01265, and AGAP2-AS1) selected from 1,154 SEDEGs, which is not only an independent prognostic factor for patients, but also can effectively predict the survival rate of patients. The model can effectively predict 1-, 2- and 3-year survival of patients and was validated in external Chinese Glioma Genome Atlas (CGGA) and Gene Expression Omnibus (GEO) datasets. Second, the risk score was positively correlated with the infiltration of regulatory T cell, CD4 memory activated T cell, activated NK cell, neutrophil, resting mast cell, M0 macrophage, and memory B cell. Third, we found that high-risk patients showed higher sensitivity than low-risk patients to both 27 chemotherapeutic agents and 4 small-molecule drug candidates which might benefit further precision therapy for GBM patients. Finally, 13 potential SE-driven TFs imply how SE regulates GBM patient's prognosis.

CONCLUSION

The SEDEG risk model not only helps to elucidate the impact of SEs on the course of GBM, but also provides a bright future for prognosis determination and choice of treatment for GBM patients.

Development of Prognostic Indicator Based on AU-Rich Elements-Related Genes in Glioblastoma

BACKGROUND

AREs (AU-rich elements) are important cis-acting short sequences in the 3'UTR (3'-untranslated region) that affect messenger RNA stability and translation. However, there were no systematic researches about AREs-related genes to predict the survival of patients with GBM (glioblastoma).

METHODS

Differentially expressed genes were acquired from The Cancer Genome Atlas and Chinese Glioma Genome Atlas databases. Differentially expressed AREs-related genes were filtered by overlapping differentially expressed genes and AREs-related genes. The prognostic genes were selected to construct a risk model. Patients with GBM were categorized into 2 risk groups depending on the medium value of risk score. Gene Set Enrichment Analysis was performed to explore the potential biological pathways. We explored the correlation between the risk model and immune cells. The chemotherapy sensitivity was predicted in different risk groups.

RESULTS

A risk model was constructed by 10 differentially expressed AREs-related genes (GNS, ANKH, PTPRN2, NELL1, PLAUR, SLC9A2, SCARA3, MAPK1, HOXB2, and EN2), and it could accurately predict the prognosis of patients with GBM. Higher risk scores for patients with GBM had a lower survival probability. The predictive power of risk model was decent. The risk score and treatment type were regarded as independent prognostic indicators. The mainly Gene Set Enrichment Analysis enrichment pathways were primary immunodeficiency and chemokine signaling pathway. Six immune cells were significant different in the 2 risk groups. There were higher abundance of macrophages M2 and neutrophils and higher sensitivity of 11 chemotherapy drugs in the high-risk group.

CONCLUSIONS

The 10 biomarkers might be important prognostic markers and potential therapeutic targets for patients with GBM.

A novel inflammation-related lncRNAs prognostic signature identifies LINC00346 in promoting proliferation, migration, and immune infiltration of glioma

In this study, a total of 13 inflammation-related lncRNAs with a high prognostic value were identified with univariate, multivariate Cox regression analysis, and LASSO analysis. LINC00346, which is one of the 13 lncRNAs identified, was positively associated with type 2 macrophage activation and the malignant degree of glioma. Fluorescence in situ hybridization (FISH) and immunohistochemical staining showed that LINC00346 was highly expressed in high-grade glioma, while type 2 macrophages key transcription factor STAT3 and surface marker CD204 were also highly expressed simultaneously. LINC00346 high-expression gliomas were more sensitive to the anti–PD-1 and anti-CTLA-4 therapy. LINC00346 was also associated with tumor proliferation and tumor migration validated by EdU, cell colony, formation CCK8, and transwell assays. These findings reveal novel biomarkers for predicting glioma prognosis and outline relationships between lncRNAs inflammation, and glioma, as well as possible immune checkpoint targets for glioma.

MiR-33a targets FOSL1 and EN2 as a clinical prognostic marker for sarcopenia by glioma

To determine the relationship between glioma and muscle aging and to predict prognosis by screening for co-expressed genes, this study examined the relationship between glioma and sarcopenia. The study identified eight co-downregulated miRNAs, three co-upregulated miRNAs, and seven genes associated with overall glioma survival, namely, KRAS, IFNB1, ALCAM, ERBB2, STAT3, FOSL1, and EN2. With a multi-factor Cox regression model incorporating FOSL1 and EN2, we obtained ROC curves of 0.702 and 0.709, respectively, suggesting that glioma prognosis can be predicted by FOSL1 and EN2, which are differentially expressed in both cancer and aged muscle. FOSL1 and EN2 were analyzed using Gene Set Enrichment Analysis to identify possible functional pathways. RT-qPCR and a dual-luciferase reporter gene system verified that hsa-miR-33a targets FOSL1 and EN2. We found that hsa-mir-33a co-targeting FOSL1 and EN2 has a good predictive value for glioblastoma and skeletal muscle reduction.

Long Non-Coding LEF1-AS1 Sponge miR-5100 Regulates Apoptosis and Autophagy in Gastric Cancer Cells via the miR-5100/DEK/AMPK-mTOR Axis

DEK and miR-5100 play critical roles in many steps of cancer initiation and progression and are directly or indirectly regulated by most promoters and repressors. LEF1-AS1 as a long non-coding RNA can regulate tumor development through sponge miRNA. The effect and regulatory mechanism of DEK on autophagy and apoptosis in gastric cancer (GC), and the role between miR-5100 and DEK or miR-5100 and LEF1-AS1 are still unclear. Our study found that DEK was highly expressed in gastric cancer tissues and cell lines, and knockdown of DEK inhibited the autophagy of cells, promoted apoptosis, and suppressed the malignant phenotype of gastric cancer. DEK regulates autophagy and apoptosis through the AMPK/mTOR signaling pathway. In addition, miR-5100 inhibits autophagy and promotes apoptosis in GC cells while LEF1-AS1 had the opposite effect. Studies have shown that miR-5100 acts by targeting the 3'UTR of DEK, and LEF1-AS1 regulates the expression of miR-5100 by sponging with mIR-5100. In conclusion, our results found that LEF1-AS1 and miR-5100 sponge function, and the miR-5100/DEK/AMPK/mTOR axis regulates autophagy and apoptosis in gastric cancer cells.

Long Noncoding RNA Mediated Regulation in Human Embryogenesis, Pluripotency, and Reproduction

Long noncoding RNAs (lncRNAs), a class of noncoding RNAs with more than 200 bp in length, are produced by pervasive transcription in mammalian genomes and regulate gene expression through various action mechanisms. Accumulating data indicate that lncRNAs mediate essential biological functions in human development, including early embryogenesis, induction of pluripotency, and germ cell development. Comprehensive analysis of sequencing data highlights that lncRNAs are expressed in a stage-specific and human/primate-specific pattern during early human development. They contribute to cell fate determination through interacting with almost all classes of cellular biomolecules, including proteins, DNA, mRNAs, and microRNAs. Furthermore, the expression of a few of lncRNAs is highly associated with the pathogenesis and progression of many reproductive diseases, suggesting that they could serve as candidate biomarkers for diagnosis or novel targets for treatment. Here, we review research on lncRNAs and their roles in embryogenesis, pluripotency, and reproduction. We aim to identify the underlying molecular mechanisms essential for human development and provide novel insight into the causes and treatments of human reproductive diseases.

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

Background

Breast cancer (BC) is an age‐related disease. Long noncoding RNAs (lncRNAs) have been proven to be crucial contributors in tumorigenesis. This study aims to develop a novel lncRNA‐based signature to predict elderly BC patients’ prognosis.

Methods

The RNA expression profiles and corresponding clinical information of 182 elderly BC patients were retrieved from The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs (DElncRNAs) between BC and adjacent normal samples were used to construct the signature in the training set through univariate Cox regression analysis, LASSO regression analysis, and multivariate Cox regression analysis. Kaplan–Meier analysis and time‐dependent receiver operating characteristic (ROC) analysis were used to evaluate the predictive performance. Besides, we developed the nomogram. Gene set enrichment analysis (GSEA) was performed to reveal the underlying molecular mechanisms.

Results

We constructed the five‐lncRNA signature (including LEF1‐AS1, MEF2C‐AS1, ST8SIA6‐AS1, LINC01224, and LINC02408) in the training set, which successfully divided the patients into low‐ and high‐risk groups with significantly different prognosis (p = 0.000049), and the AUC at 3 and 5 years of the signature was 0.779 and 0.788, respectively. The predictive performance of this signature was validated in the test and entire set. The 5‐lncRNA signature was an independent prognostic factor of OS (p = 0.007) and the nomogram constructed by independent prognostic factors was an accurate predictor of predicting overall survival probability. Besides, several pathways associated with tumorigenesis have been identified by GSEA.

Conclusions

The 5‐lncRNA signature and nomogram are reliable in predicting elderly BC patients’ prognosis and provide clues for clinical decision‐making.

Comprehensive In Silico Analysis of a Novel Serum Exosome-Derived Competitive Endogenous RNA Network for Constructing a Prognostic Model for Glioblastoma

Purpose

Glioblastoma (GBM) is one of the most aggressive brain tumors with high mortality, and tumor-derived exosomes provide new insight into the mechanisms of GBM tumorigenesis, metastasis and therapeutic resistance. We aimed to establish an exosome-derived competitive endogenous RNA (ceRNA) network for constructing a prognostic model for GBM.

Methods

We obtained the expression profiles of long noncoding RNAs (lncRNAs), miRNAs, and mRNAs from the GEO and TCGA databases and identified differentially expressed RNAs in GBM to construct a ceRNA network. By performing lasso and multivariate Cox regression analyses, we identified optimal prognosis-related differentially expressed lncRNAs (DElncRNAs) and generated a risk score model termed the exosomal lncRNA (exo-lncRNA) signature. The exo-lncRNA signature was subsequently validated in the CGGA GBM cohort. Finally, a novel prognostic nomogram was constructed based on the exo-lncRNA signature and clinicopathological parameters and validated in the CGGA external cohort. Based on the ceRNA hypothesis, oncocers were identified based on highly positive correlations between lncRNAs and mRNAs mediated by the same miRNAs. Furthermore, regression analyses were performed to assess correlations between the expression abundances of lncRNAs in tumors and exosomes.

Results

A total of 45 DElncRNAs, six DEmiRNAs, and 38 DEmRNAs were identified, and an exosome-derived ceRNA network was built. Three optimal prognostic-related DElncRNAs, HOTAIR (HR=0.341, P<0.001), SOX21-AS1 (HR=0.30, P<0.001), and STEAP3-AS1 (HR=2.47, P<0.001), were included to construct the exo-lncRNA signature, which was further proven to be an independent prognostic factor. The novel prognostic nomogram was constructed based on the exo-lncRNA signature, patient age, pharmacotherapy, radiotherapy, IDH mutation status, and MGMT promoter status, with a concordance index of 0.878. ROC and calibration plots both suggested that the nomogram had beneficial discrimination and predictive abilities. A total of 11 pairs of prognostic oncocers were identified. Regression analysis suggested excellent consistency of the expression abundance of the three exosomal lncRNAs between exosomes and tumor tissues.

Conclusions

Exosomal lncRNAs may serve as promising prognostic predictors and therapeutic targets. The prognostic nomogram based on the exo-lncRNA signature might provide an intuitive method for individualized survival prediction and facilitate better treatment strategies.

The role of miR-543 in human cancerous and noncancerous diseases

MicroRNA (miRNA) is a noncoding single-stranded RNA molecule that can regulate the posttranscriptional expression level of a gene by binding to the 3'-untranslated region (3'-UTR) of the target messenger RNA. miR-543 is a kind of miRNA, which plays an important role in the occurrence and development of various human cancerous and noncancerous diseases. miR-543 directly or indirectly regulates a large number of downstream target genes and plays an important role in cellular components, biological processes, and molecular functions. In addition, many studies have verified the regulatory mechanism, physiological role, biological function, and prognostic value of miR-543. Therefore, this article reviews the papers published in the past decade and elaborates on the research progress of miR-543 from the aspects of physiology and pathology, especially in cancerous and other noncancerous diseases. In particular, we pay attention to the expression patterns, direct targets, biological functions, related pathways, and prognostic value of miR-543 reported in experimental articles. And by comparing similar research articles, we point out existing controversies in this field to date, so as to facilitate further research in the future.