N6-methyladenosine Modification-Related Long Non-Coding RNAs are Potential Biomarkers for Predicting the Prognosis of Patients With Osteosarcoma

LncRNA SNHG12 regulated by WTAP aggravated the oxygen-glucose deprivation/reperfusion-induced injury in bEnd.3 cell

OBJECTIVES

Previous studies have identified abnormal expression of lncRNA SNHG12 in ischemic stroke, but the underlying molecular mechanism remains unclear.

MATERIALS AND METHODS

Through database predictions, m6A methylation sites were found on SNHG12, suggesting post-transcriptional modification. To further elucidate the role of SNHG12 and m6A methyltransferase WTAP in oxygen-glucose deprivation/reperfusion (OGD/R)-induced damage in cerebral microvascular endothelial cells, we conducted investigations. Additionally, we examined the impact of m6A methyltransferase WTAP on SNHG12 expression.

RESULTS

Overexpressing SNHG12 in bEnd.3 cells was found to inhibit cell proliferation and promote apoptosis, as well as activate the production of reactive oxygen species and inflammatory cytokines (E-selectin, IL-6 and MCP-1), along with angiogenic proteins (VEGFA and FGFb). Conversely, SNHG12 knockdown alleviated OGD/R-induced damage to BEnd.3 cells, resulting in improved cell proliferation, reduced apoptosis, decreased ROS and LDH production, as well as diminished expression of inflammatory cytokines (E-selectin, IL-6 and MCP-1) and angiogenic proteins (VEGFA and FGFb). Furthermore, WTAP was found to positively regulate SNHG12 expression, and WTAP knockdown in bEnd.3 cells under the OGD/R conditions inhibited cell proliferation, promoted apoptosis, and increased ROS and LDH production.

CONCLUSION

These findings suggest that WTAP may play a crucial role in SNHG12-mediated OGD/R-induced damage in bEnd.3 cells. More molecular experiments are needed to further analyze its mechanism. Overall, our study helps to enrich our understanding of the dysregulation of SNHG12 in ischemic stroke.

A glycometabolic gene signature associating with immune infiltration and chemosensitivity and predicting the prognosis of patients with osteosarcoma

Background

Accumulating evidence has suggested that glycometabolism plays an important role in the pathogenesis of tumorigenesis. However, few studies have investigated the prognostic values of glycometabolic genes in patients with osteosarcoma (OS). This study aimed to recognize and establish a glycometabolic gene signature to forecast the prognosis, and provide therapeutic options for patients with OS.

Methods

Univariate and multivariate Cox regression, LASSO Cox regression, overall survival analysis, receiver operating characteristic curve, and nomogram were adopted to develop the glycometabolic gene signature, and further evaluate the prognostic values of this signature. Functional analyses including Gene Ontology (GO), kyoto encyclopedia of genes and genomes analyses (KEGG), gene set enrichment analysis, single-sample gene set enrichment analysis (ssGSEA), and competing endogenous RNA (ceRNA) network, were used to explore the molecular mechanisms of OS and the correlation between immune infiltration and gene signature. Moreover, these prognostic genes were further validated by immunohistochemical staining.

Results

A total of four genes including PRKACB, SEPHS2, GPX7, and PFKFB3 were identified for constructing a glycometabolic gene signature which had a favorable performance in predicting the prognosis of patients with OS. Univariate and multivariate Cox regression analyses revealed that the risk score was an independent prognostic factor. Functional analyses indicated that multiple immune associated biological processes and pathways were enriched in the low-risk group, while 26 immunocytes were down-regulated in the high-risk group. The patients in high-risk group showed elevated sensitivity to doxorubicin. Furthermore, these prognostic genes could directly or indirectly interact with other 50 genes. A ceRNA regulatory network based on these prognostic genes was also constructed. The results of immunohistochemical staining showed that SEPHS2, GPX7, and PFKFB3 were differentially expressed between OS tissues and adjacent normal tissues.

Conclusion

The preset study constructed and validated a novel glycometabolic gene signature which could predict the prognosis of patients with OS, identify the degree of immune infiltration in tumor microenvironment, and provide guidance for the selection of chemotherapeutic drugs. These findings may shed new light on the investigation of molecular mechanisms and comprehensive treatments for OS.

Construction of a cuproptosis-related lncRNA signature for predicting prognosis and immune landscape in osteosarcoma patients

BACKGROUND

Long noncoding RNAs (lncRNAs) influence the onset of osteosarcoma. Cuproptosis is a novel cell death mechanism. We attempted to identify a cuproptosis-related lncRNA signature to predict the prognosis and immune landscape in osteosarcoma patients.

METHODS

Transcriptional and clinical data of 85 osteosarcoma patients were derived from the TARGET database and randomly categorized into the training and validation cohorts. We implemented the univariate and multivariate Cox regression, along with LASSO regression analyses for developing a cuproptosis-related lncRNA risk model. Kaplan-Meier curves, C-index, ROC curves, univariate and multivariate Cox regression, and nomogram were used to assess the capacity of this risk model to predict the osteosarcoma prognosis. Gene ontology, KEGG, and Gene Set Enrichment (GSEA) analyses were conducted for determining the potential functional differences existing between the high-risk and low-risk patients. We further conducted the ESTIMATE, single-smaple GSEA, and CIBERSORT analyses for identifying the different immune microenvironments and immune cells infiltrating both the risk groups.

RESULTS

We screened out four cuproptosis-related lncRNAs (AL033384.2, AL031775.1, AC110995.1, and LINC00565) to construct the risk model in the training cohort. This risk model displayed a good performance to predict the overall survival of osteosarcoma patients, which was confirmed by using the validation and the entire cohort. Further analyses showed that the low-risk patients have more immune activation and immune cells infiltrating as well as a good response to immunotherapy.

CONCLUSIONS

We developed a novel cuproptosis-related lncRNA signature with high reliability and accuracy for predicting outcome and immunotherapy response in osteosarcoma patients, which provides new insights into the personalized treatment of osteosarcoma.

The Cuproptosis-Related Long Noncoding RNA Signature Predicts Prognosis and Tumour Immune Analysis in Osteosarcoma

Background

Cuprotopsis is a type of programmed cell death discovered in recent years. Long noncoding RNAs (lncRNAs) play an important regulatory role in programmed cell death. The effect of cuproptosis-related lncRNAs on osteosarcoma is unknown. Our work, based on cuproptosis-related lncRNAs, proposes a gene signature to assess the prognosis of patients with osteosarcoma.

Methods

Osteosarcoma gene expression data from The Cancer Genome Atlas (TCGA), clinical features of osteosarcoma and RNA sequencing data of normal adipose tissue were obtained from the UCSC Xena database. A cuproptosis-related lncRNA risk model was established to calculate the risk score. At the same time, cluster analysis, clinicopathological analysis, functional enrichment analysis, and prediction of compounds with potential therapeutic value were evaluated. We analyzed whether there was a correlation between the risk score and tumour immunity. RT-qPCR was used to verify the expression level of lncRNA.

Results

Nine lncRNAs (AC124798.1, AC006033.2, AL450344.2, AL512625.2, LINC01060, LINC00837, AC004943.2, AC064836.3, and AC100821.2) were identified to create a risk model and indicate the prognosis of patients with osteosarcoma. The high-risk group had a worse prognosis than the low-risk group. Analysis of clinicopathological features, principal component analysis, receiver operating characteristic curve, c-index curve, and comparative analysis of models proved that the model is reliable. Functional enrichment analysis suggests that the risk score may correlate with cell energy metabolism and tumour-related biological function. Three potentially therapeutic compounds have been predicted. These analyses may be beneficial to the treatment of osteosarcoma in the future. RT-qPCR verified the expression level of three lncRNA (LINC01060, NKILA, and SNHG8).

Conclusions

Cuproptosis-related lncRNAs have a strong relationship with osteosarcoma patients. Nine lncRNA models can effectively forecast the prognosis of osteosarcoma and may play a significant role in the individualized treatment of osteosarcoma patients in the future.

The development of a novel signature based on the m6A RNA methylation regulator-related ceRNA network to predict prognosis and therapy response in sarcomas

Background: N⁶ methyladenosine (m⁶A)-related noncoding RNAs (including lncRNAs and miRNAs) are closely related to the development of cancer. However, the gene signature and prognostic value of m⁶A regulators and m⁶A-associated RNAs in regulating sarcoma (SARC) development and progression remain largely unexplored. Therefore, further research is required.

Methods: We obtained expression data for RNA sequencing (RNA-seq) and miRNAs of SARC from The Cancer Genome Atlas (TCGA) datasets. Correlation analysis and two target gene prediction databases (miRTarBase and LncBase v.2) were used to deduce m⁶A-related miRNAs and lncRNAs, and Cytoscape software was used to construct ceRNA-regulating networks. Based on univariate Cox regression and least absolute shrinkage and selection operator (LASSO) Cox regression analyses, an m⁶A-associated RNA risk signature (m6Ascore) model was established. Prognostic differences between subgroups were explored using Kaplan–Meier (KM) analysis. Risk score-related biological phenotypes were analyzed in terms of functional enrichment, tumor immune signature, and tumor mutation signature. Finally, potential immunotherapy features and drug sensitivity predictions for this model were also discussed.

Results: A total of 16 miRNAs, 104 lncRNAs, and 11 mRNAs were incorporated into the ceRNA network. The risk score was obtained based on RP11-283I3.6, hsa-miR-455-3p, and CBLL1. Patients were divided into two risk groups using the risk score, with patients in the low-risk group having longer overall survival (OS) than those in the high-risk group. The receiver operating characteristic (ROC) curves indicated that risk characteristic performed well in predicting the prognosis of patients with SARC. In addition, lower m6Ascore was also positively correlated with the abundance of immune cells such as monocytes and mast cells activated, and several immune checkpoint genes were highly expressed in the low-m6Ascore group. According to our analysis, lower m6Ascore may lead to better immunotherapy response and OS outcomes. The risk signature was significantly associated with the chemosensitivity of SARC. Finally, a nomogram was constructed to predict the OS in patients with SARC. The concordance index (C-index) for the nomogram was 0.744 (95% CI: 0.707–0.784). The decision curve analysis (DCA), calibration plot, and ROC curve all showed that this nomogram had good predictive performance.

Conclusion: This m6Ascore risk model based on m⁶A RNA methylation regulator-related RNAs may be promising for clinical prediction of prognosis and might contain potential biomarkers for treatment response prediction for SARC patients.