Long Non-Coding RNA UBA6-AS1 Promotes the Malignant Properties of Glioblastoma by Competitively Binding to microRNA-760 and Enhancing Homeobox A2 Expression

Insights into the Role of LncRNAs and miRNAs in Glioma Progression and Their Potential as Novel Therapeutic Targets

Accumulating evidence supports that both long non-coding and micro RNAs (lncRNAs and miRNAs) are implicated in glioma tumorigenesis and progression. Poor outcome of gliomas has been linked to late-stage diagnosis and mostly ineffectiveness of conventional treatment due to low knowledge about the early stage of gliomas, which are not possible to observe with conventional diagnostic approaches. The past few years witnessed a revolutionary advance in biotechnology and neuroscience with the understanding of tumor-related molecules, including non-coding RNAs that are involved in the angiogenesis and progression of glioma cells and thus are used as prognostic biomarkers as well as novel therapeutic targets. The emerging research on lncRNAs and miRNAs highlights their crucial role in glioma progression, offering new insights into the disease. These non-coding RNAs hold significant potential as novel therapeutic targets, paving the way for innovative treatment approaches against glioma. This review encompasses a comprehensive discussion about the role of lncRNAs and miRNAs in gene regulation that is responsible for the promotion or the inhibition of glioma progression and collects the existing links between these key cancer-related molecules.

MetastaSite: Predicting metastasis to different sites using deep learning with gene expression data

Deep learning has massive potential in predicting phenotype from different omics profiles. However, deep neural networks are viewed as black boxes, providing predictions without explanation. Therefore, the requirements for these models to become interpretable are increasing, especially in the medical field. Here we propose a computational framework that takes the gene expression profile of any primary cancer sample and predicts whether patients’ samples are primary (localized) or metastasized to the brain, bone, lung, or liver based on deep learning architecture. Specifically, we first constructed an AutoEncoder framework to learn the non-linear relationship between genes, and then DeepLIFT was applied to calculate genes’ importance scores. Next, to mine the top essential genes that can distinguish the primary and metastasized tumors, we iteratively added ten top-ranked genes based upon their importance score to train a DNN model. Then we trained a final multi-class DNN that uses the output from the previous part as an input and predicts whether samples are primary or metastasized to the brain, bone, lung, or liver. The prediction performances ranged from AUC of 0.93–0.82. We further designed the model’s workflow to provide a second functionality beyond metastasis site prediction, i.e., to identify the biological functions that the DL model uses to perform the prediction. To our knowledge, this is the first multi-class DNN model developed for the generic prediction of metastasis to various sites.

Non-Coding RNAs and Brain Tumors: Insights Into Their Roles in Apoptosis

A major terrifying ailment afflicting the humans throughout the world is brain tumor, which causes a lot of mortality among pediatric and adult solid tumors. Several major barriers to the treatment and diagnosis of the brain tumors are the specific micro-environmental and cell-intrinsic features of neural tissues. Absence of the nutrients and hypoxia trigger the cells’ mortality in the core of the tumors of humans’ brains: however, type of the cells’ mortality, including apoptosis or necrosis, has been not found obviously. Current studies have emphasized the non-coding RNAs (ncRNAs) since their crucial impacts on carcinogenesis have been discovered. Several investigations suggest the essential contribution of such molecules in the development of brain tumors and the respective roles in apoptosis. Herein, we summarize the apoptosis-related non-coding RNAs in brain tumors.

Identification and validation of lncRNAs involved in m6A regulation for patients with ovarian cancer

Background

Both N6-methyladenosine (m6A) modification and lncRNAs play an important role in the carcinogenesis and cancer inhibition of ovarian cancer (OC). However, lncRNAs involved in m6A regulation (LI-m6As) have never been reported in OC. Herein, we aimed to identify and validate a signature based on LI-m6A for OC.

Methods

RNA sequencing profiles with corresponding clinical information associated with OC and 23 m6A regulators were extracted from TCGA. The Pearson correlation coefficient (PCC) between lncRNAs and 23 m6A regulators (|PCC|> 0.4 and p < 0.01) was calculated to identify LI-m6As. The LI-m6As with significant prognostic value were screened based on univariate Cox regression analysis to construct a risk model by LASSO Cox regression. Gene Set Enrichment Analysis (GSEA) was implemented to survey the biological functions of the risk groups. Several clinicopathological characteristics were utilized to evaluate their ability to predict prognosis, and a nomogram was constructed to evaluate the accuracy of survival prediction. Besides, immune microenvironment, checkpoint, and drug sensitivity in the two risk groups were compared using comprehensive algorithms. Finally, real-time qPCR analysis and cell counting kit-8 assays were performed on an alternative lncRNA, CACNA1G-AS1.

Results

The training cohort involving 258 OC patients and the validation cohort involving 111 OC patients were downloaded from TCGA. According to the PCC between the m6A regulators and lncRNAs, 129 LI-m6As were obtained to perform univariate Cox regression analysis and then 10 significant prognostic LI-m6As were identified. A prognostic signature containing four LI-m6As (AC010894.3, ACAP2-IT1, CACNA1G-AS1, and UBA6-AS1) was constructed according to the LASSO Cox regression analysis of the 10 LI-m6As. The prognostic signature was validated to show completely opposite prognostic value in the two risk groups and adverse overall survival (OS) in several clinicopathological characteristics. GSEA indicated that differentially expressed genes in disparate risk groups were enriched in several tumor-related pathways. At the same time, we found significant differences in some immune cells and chemotherapeutic agents between the two groups. An alternative lncRNA, CACNA1G-AS1, was proven to be upregulated in 30 OC specimens and 3 OC cell lines relative to control. Furthermore, knockdown of CACNA1G‐AS1 was proven to restrain the multiplication capacity of OC cells.

Conclusions

Based on the four LI-m6As (AC010894.3, ACAP2-IT1, CACNA1G-AS1, and UBA6-AS1), the risk model we identified can independently predict the OS and therapeutic value of OC. CACNA1G‐AS1 was preliminarily proved to be a malignant lncRNA.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02076-7.

LncRNA LINC00963 promotes osteogenic differentiation of hBMSCs and alleviates osteoporosis progression by targeting miRNA-760/ETS1 axis

Although long non-coding RNA LINC00963 has been reported to play a crucial regulatory role in osteoporosis (OP), its specific mechanism has not been well studied. Cell viability of human bone marrow mesenchymal stem cells (hBMSCs) transfected with short hairpin RNA targeting LINC00963 (sh-LINC00963) and negative control (sh-NC) was analysed by cell counting kit-8 (CCK-8) assay. Alkaline phosphatase (ALP) activity in hBMSCs transfected with sh-LINC00963 and sh-NC after induction by osteogenic medium (OM) on day 7 was detected. The protein expression levels of osteocalcin (OCN) and osteopontin (OPN) in hBMSCs transfected with sh-LINC00963 and sh-NC during OM induction on day 3 were detected by western blot. The relationship among LINC00963, miR-760, and E26 transformation specific-1 (ETS1) was determined by bioinformatics analysis, luciferase reporter assay, and RNA-binding protein immunoprecipitation (RIP) assay. A rat model with OP was established to confirm the role of LINC00963 in vivo. The expression level of LINC00963 was much lower in hBMSCs isolated from the discarded femoral head tissues of OP patients compared with that in health patients. Meanwhile, the expression level of LINC00963 was significantly increased and the expression level of miR-760 was decreased in hBMSCs during osteogenic induction. LINC00963 could bind to the 3'-untranslated region (3'-UTR) of miR-760 and negatively regulate the expression of miR-760, then promote the osteogenic differentiation in hBMSCs. ETS1 was identified as a target of miR-760. Moreover, overexpression of LINC00963 obviously reduced bone mineral density (BMD) of the left femur in OP rats and alleviated OP progression in vivo. Our results demonstrated that LINC00963 positively regulated the expression of ETS1 by directly targeting miR-760, and then promoted osteogenic differentiation of hBMSCs in vitro, and also attenuated OP progression in vivo, suggesting that LINC00963 might be a potential therapeutic target for OP.

Identification and Validation of Immune-Related lncRNA Signature as a Prognostic Model for Skin Cutaneous Melanoma

Purpose

Skin cutaneous melanoma (SKCM) is the most aggressive skin cancer that results in high morbidity and mortality rate worldwide. Immune-related long non-coding RNAs (IRlncRs) play an important role in regulating gene expression in tumors. Therefore, in this study, we aimed to identify IRlncRs signature that could predict prognosis and therapeutic targets for melanoma irrespective of the gene expression levels.

Methods

RNA-sequencing data were obtained from The Cancer Genome Atlas (TCGA). IRlncRs were identified using co-expression analysis and recognized using univariate analysis. The impact of IRlncRs on survival was analyzed using a modified least absolute shrinkage and selection operator (Lasso) regression model. A 1-year survival receiver operating characteristic curve was constructed, and the area under the curve was calculated to identify the optimal cut-off point to distinguish between high and low-risk groups in patients with SKCM. Furthermore, integrative analysis was performed to identify the impact of clinicopathological features, chemotherapeutic treatment, tumor-infiltrating immune cells, and mutant genes on survival.

Results

A total of 28 IRlncRs significantly associated with survival were identified. Seventeen IRlncRs pairs were used to build a survival risk model that could be used to distinguish between low and high-risk groups. The high-risk group was negatively associated with tumor-infiltrating immune cells and had a higher half inhibitory centration for chemotherapeutic agents such as cisplatin and vinblastine. Additionally, the high-risk group had a positive correlation with the expression of specific mutant genes such as BRAF and KIT.

Conclusion

Our findings demonstrate that some IRlncRs have a significant correlation with survival and therapeutic targets for SKCM patients and may provide new insight into the clinical diagnosis and treatment strategies for SKCM patients.