LncRNA SNHG3 promotes gastric cancer cell proliferation and metastasis by regulating the miR-139-5p/MYB axis

Shedding light on function of long non-coding RNAs (lncRNAs) in glioblastoma

The brain tumors and especially glioblastoma, are affecting life of many people worldwide and due to their high mortality and morbidity, their treatment is of importance and has gained attention in recent years. The abnormal expression of genes is commonly observed in GBM and long non-coding RNAs (lncRNAs) have demonstrated dysregulation in this tumor. LncRNAs have length more than 200 nucleotides and they have been located in cytoplasm and nucleus. The current review focuses on the role of lncRNAs in GBM. There two types of lncRNAs in GBM including tumor-promoting and tumor-suppressor lncRNAs and overexpression of oncogenic lncRNAs increases progression of GBM. LncRNAs can regulate proliferation, cell cycle arrest and metastasis of GBM cells. Wnt, STAT3 and EZH2 are among the molecular pathways affected by lncRNAs in GBM and for regulating metastasis of GBM cells, these RNA molecules mainly affect EMT mechanism. LncRNAs are involved in drug resistance and can induce resistance of GBM cells to temozolomide chemotherapy. Furthermore, lncRNAs stimulate radio-resistance in GBM cells. LncRNAs increase PD-1 expression to mediate immune evasion. LncRNAs can be considered as diagnostic and prognostic tools in GBM and researchers have developed signature from lncRNAs in GBM.

Decoding long non‑coding RNAs: Friends and foes in cancer development (Review)

Cancer remains a formidable adversary, challenging medical advancements with its dismal prognosis, low cure rates and high mortality rates. Within this intricate landscape, long non‑coding RNAs (lncRNAs) emerge as pivotal players, orchestrating proliferation and migration of cancer cells. Harnessing the potential of lncRNAs as therapeutic targets and prognostic markers holds immense promise. The present comprehensive review delved into the molecular mechanisms underlying the involvement of lncRNAs in the onset and progression of the top five types of cancer. By meticulously examining lncRNAs across diverse types of cancer, it also uncovered their distinctive roles, highlighting their exclusive oncogenic effects or tumor suppressor properties. Notably, certain lncRNAs demonstrate diverse functions across different cancers, confounding the conventional understanding of their roles. Furthermore, the present study identified lncRNAs exhibiting aberrant expression patterns in numerous types of cancer, presenting them as potential indicators for cancer screening and diagnosis. Conversely, a subset of lncRNAs manifests tissue‑specific expression, hinting at their specialized nature and untapped significance in diagnosing and treating specific types of cancer. The present comprehensive review not only shed light on the intricate network of lncRNAs but also paved the way for further research and clinical applications. The unraveled molecular mechanisms offer a promising avenue for targeted therapeutics and personalized medicine, combating cancer proliferation, invasion and metastasis.

Long noncoding RNA SNHG3 interacts with microRNA-502-3p to mediate ITGA6 expression in liver hepatocellular carcinoma

SNHG3, a long noncoding RNA (lncRNA), has been linked to poor outcomes in patients with liver hepatocellular carcinoma (LIHC). In this study, we found that SNHG3 was overexpressed in LIHC and associated with poor outcomes in patients with LIHC. Functional assays, including colony formation, spheroid formation, and in vivo assays showed that SNHG3 promoted stemness of cancer stem cells (CSC) and tumor growth in vivo by interacting with microRNA-502-3p (miR-502-3p). miR-502-3p inhibitor repressed the tumor-suppressing effects of SNHG3 depletion. Finally, by RNA pull-down, dual-luciferase reporter assay, m6A methylation level detection, and m6A-IP-qPCR assays, we found that miR-502-3p targeted YTHDF3 to regulate the translation of integrin alpha-6 (ITGA6) and targeted HBXIP to inhibit the m6A modification of ITGA6 through methyltransferase-like 3 (METTL3). Our study revealed that SNHG3 controls the YTHDF3/ITGA6 and HBXIP/METTL3/ITGA6 pathways by repressing miR-502-3p expression to sustain the self-renewal properties of CSC in LIHC.

The putative role of ferroptosis in gastric cancer: a review

Ferroptosis is a unique cell death modality triggered by iron-dependent lipid peroxidation, with cysteine metabolism and glutathione-dependent antioxidant defence responses as the primary triggering mechanisms. Ferroptosis is an independent tumour suppression mechanism and has been implicated in various disorders. In tumourigenesis, ferroptosis plays a dual role in promoting and inhibiting tumours. P53, NFE2L2, BAP1, HIF, and other tumour suppressor genes regulate ferroptosis, releasing damage-associated molecular patterns or lipid metabolites to influence cellular immune responses. Ferroptosis is also involved in tumour suppression and metabolism. The combination of amino acid, lipid, and iron metabolism is involved in the initiation and execution of ferroptosis, and metabolic regulatory mechanisms also play roles in malignancies. Most investigations into ferroptosis in gastric cancer are concentrated on predictive models, not the underlying processes. This review investigates the underlying mechanisms of ferroptosis, tumour suppressor genes, and the tumour microenvironment.

Hub gene identification and molecular subtype construction for Helicobacter pylori in gastric cancer via machine learning methods and NMF algorithm

Helicobacter pylori (HP) is a gram-negative and spiral-shaped bacterium colonizing the human stomach and has been recognized as the risk factor of gastritis, peptic ulcer disease, and gastric cancer (GC). Moreover, it was recently identified as a class I carcinogen, which affects the occurrence and progression of GC via inducing various oncogenic pathways. Therefore, identifying the HP-related key genes is crucial for understanding the oncogenic mechanisms and improving the outcomes of GC patients. We retrieved the list of HP-related gene sets from the Molecular Signatures Database. Based on the HP-related genes, unsupervised non-negative matrix factorization (NMF) clustering method was conducted to stratify TCGA-STAD, GSE15459, GSE84433 samples into two clusters with distinct clinical outcomes and immune infiltration characterization. Subsequently, two machine learning (ML) strategies, including support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF), were employed to determine twelve hub HP-related genes. Beyond that, receiver operating characteristic and Kaplan-Meier curves further confirmed the diagnostic value and prognostic significance of hub genes. Finally, expression of HP-related hub genes was tested by qRT-PCR array and immunohistochemical images. Additionally, functional pathway enrichment analysis indicated that these hub genes were implicated in the genesis and progression of GC by activating or inhibiting the classical cancer-associated pathways, such as epithelial-mesenchymal transition, cell cycle, apoptosis, RAS/MAPK, etc. In the present study, we constructed a novel HP-related tumor classification in different datasets, and screened out twelve hub genes via performing the ML algorithms, which may contribute to the molecular diagnosis and personalized therapy of GC.

A comprehensive insight into the role of small nucleolar RNAs (snoRNAs) and SNHGs in human cancers

Long non-coding RNAs (lncRNAs), which comprise most non-coding RNAs (ncRNAs), have recently become a focus of cancer research. How many functional ncRNAs exist is still a matter of debate. Although insufficient evidence supports that most lncRNAs function as transcriptional by-products, it is widely known that an increasing number of lncRNAs play essential roles in cells. Small nucleolar RNAs (snoRNAs), 60-300 nucleotides in length, have been better studied than long non-coding RNAs (lncRNAs) and are predominantly present in the nucleolus. Most snoRNAs are encoded in introns of protein- and non-protein-coding genes called small nucleolar RNA host genes (SNHGs). In this article, we explore the biology and characteristics of SNHGs and their role in developing human malignancies. In addition, we provide an update on the ability of these snoRNAs to serve as prognostic and diagnostic variables in various forms of cancer.

Unveiling the connection: Long-chain non-coding RNAs and critical signaling pathways in breast cancer

Breast cancer is a complex and diverse condition that disrupts multiple signaling pathways essential for cell proliferation, survival, and differentiation. Recently, the significant involvement of long-chain non-coding RNAs (lncRNAs) in controlling key signaling pathways associated with breast cancer development has been discovered. This review aims to explore the interaction between lncRNAs and various pathways, including the AKT/PI3K/mTOR, Wnt/β-catenin, Notch, DNA damage response, TGF-β, Hedgehog, and NF-κB signaling pathways, to gain a comprehensive understanding of their roles in breast cancer. The AKT/PI3K/mTOR pathway regulates cell growth, survival, and metabolic function. Recent data suggests that specific lncRNAs can influence the functioning of this pathway, acting as either oncogenes or tumor suppressors. Dysregulation of this pathway is commonly observed in breast cancer cases. Moreover, breast cancer development has been associated with other pathways such as Wnt/β-catenin, Notch, TGF-β, Hedgehog, and NF-κB. Emerging studies have identified lncRNAs that modulate breast cancer's growth, progression, and metastasis by interacting with these pathways. To advance the development of innovative diagnostic tools and targeted treatment options, it is crucial to comprehend the intricate relationship between lncRNAs and vital signaling pathways in breast cancer. By fully harnessing the therapeutic potential of lncRNAs, there is a possibility of developing more effective and personalized therapy choices for breast cancer patients. Further investigation is necessary to comprehensively understand the role of lncRNAs within breast cancer signaling pathways and fully exploit their therapeutic potential.

A comprehensive review of the role of lncRNAs in gastric cancer (GC) pathogenesis, immune regulation, and their clinical applications

Gastric cancer (GC) is the fifth most common malignant tumor and the third leading cause of cancer-related deaths worldwide. Although numerous studies have been conducted on advanced GC, the molecular mechanisms behind it remain obscure. Long non-coding RNAs (lncRNAs) are a family of RNA transcripts capable of regulating target genes at transcriptional, post-transcriptional, and translational stages. They do this by modifying mRNAs, miRNAs, and proteins. These RNAs are critical regulators of many biological processes, including gene epigenetics, transcription, and post-transcriptional levels. This article highlights recent results on lncRNAs involved in drug resistance, proliferation, migration, angiogenesis, apoptosis, autophagy, and immune response in GC. The potential clinical implications of lncRNAs as biomarkers and therapeutic targets in GC are also discussed.

METTL3-mediated m6A modification of lncRNA SNHG3 accelerates gastric cancer progression by modulating miR-186-5p/cyclinD2 axis

OBJECTIVES

METTL3 as an m6A methyltransferase acts in diverse malignancies including gastric cancer (GC). We aimed to reveal the underlying mechanisms by which METTL3 contributes to gastric carcinogenesis.

METHODS

The association of METTL3 and SNHG3 with GC was analyzed by qRT-PCR, Western blot, and TCGA cohort. The functional experiments were implemented to uncover the role of METTL3 in GC. m6A dot blot and MeRIP were used to determine METTL3-mediated m6A modification of lncRNA SNHG3. The effect of METTL3 on SNHG3-mediated miR-186-5p/cyclinD2 axis was evaluated by luciferase gene report, RT-qPCR, and Western blot assays.

RESULTS

We found that METTL3 was remarkably elevated in GC tissues and correlated with poor survival in patients with GC. Silencing of METTL3 impaired GC cell growth and invasion, whereas restored METTL3 expression promoted these effects. Mechanistically, reduced expression of METTL3 decreased SNHG3 m6A level and caused a decrease in SNHG3 expression, which could further act as a sponge of miR-186-5p to upregulate cyclinD2. Overexpression of SNHG3 attenuated METTL3 knockdown-induced anti-proliferating and miR-186-5p upregulation and cyclinD2 downregulation.

CONCLUSION

We find that METTL3-mediated m6A modification of lncRNA SNHG3 accelerates GC progression by modulating miR-186-5p/cyclinD2 axis.

Identification of stemness index-related long noncoding RNA SNHG12 in human bladder cancer based on WGCNA

BACKGROUND

Cancer stem cells (CSCs) have an key role in the beginning, progression and treatment of bladder cancer. In the current study, our target was to identify CSCS-related genes in bladder cancer.

METHODS

Bladder cancer (BLCA) transcriptome data were acquired from The Cancer Genome Atlas (TCGA) database. WGCNA was used to screen genes connected with the mRNA expression-based stemness index (mRNAsi).Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were used to analyze the biological function of mRNAsi-related genes. Univariate Cox regression and LASSO Cox regression algorithms were applied to build a risk score model. Additionally, a ceRNA regulatory netwok based on key mRNAsi-related genes was established via TargetScan, miRDB, miRTarBas and miRcode database,and lncRNA SNHG12 was selected for further in vitro and invivo functional assays.

RESULTS

Between BLCA and normal samples were identified 1560 differentially expressed genes (DEGs).845 DEGs were most significantly associated with mRNAsi according to WGCNA analysis, which were mainly enriched in GO terms and KEGG pathways related to cell proliferation. Univariate Cox regression and LASSO Cox regression algorithms screened 25 mRNAsi-related genes to construct the risk score model with the significant ability to estimate prognosis of BLCA patients. A ceRNA network, including 8 lncRNA, 11 miRNA and 9 mRNAsi-related mRNA, was constructed.We found that lncRNAs ADAMTS9-AS1 and SNHG12 were observably related to the survival of BLCA patients. To verify this finding, we selected SNHG12 for further study. RT-PCR experiments revealed that SNHG12 was high expression in both bladder cancer tissues and cells.SNHG12 promoted proliferation, invasion, migration, apoptosis and stemness of bladder cancer cells in vitro and tumour proliferation in vivo.

CONCLUSION

Our study identified 25 biomarkers associated with stemness indices in BLCA and established a ceRNA network based on key mRNAsi-related genes.SNHG12 promoted BLCA proliferation, invasion, migration, apoptosis and stemness in vitro. It was also showed that SNHG12 promoted tumour growth.

A Genomic Instability-Related Long Noncoding RNA Signature for Predicting Hepatocellular Carcinoma Prognosis

Background. Long noncoding RNAs (lncRNAs) are found to be novel biomarkers for hepatocellular carcinoma (HCC) and play an important role in tumor progression. We established a genomic instability-related long noncoding RNA signature (GIlncSig) as an independent prognosis factor and also investigated its impact on prognosis significance. Method. Somatic mutation profiles, clinical characteristics, and RNA sequencing data were obtained from The Cancer Genome Atlas (TCGA) database. Lasso regression was used to construct GIlncSig. KEGG was used to identify the possible biological pathways. ESTIMATE and CIBERSORT algorithms were used to calculate the immune microenvironment scores and proportion of immune cells in HCC patients. The expression of LINC00501 was conducted by qRT-PCR. Cell proliferation was measured by EdU, CCK-8, and colony formation assay, and cell migration and invasion ability were measured by wound healing and transwell assay. Results. 135 genomic instability-related lncRNAs were identified, and GIlncSig was constructed using 13 independent lncRNAs with significant prognosis values. Based on the GIlncSig, high-risk group had worse clinical outcomes than low-risk group, while high-risk group also had higher UBQLN4, KRAS, ARID1A, and PIK3CA expression. Moreover, the efficiency of GIlncSig combining single-gene mutation was higher than single-gene mutation alone such as TP53. The results of CIBERSORT and ESTIMATE showed that GS group and GU group had significantly different immune infiltration. In addition, LINC00501 was identified as a potential biomarker in HCC with strong relationship with clinical characteristics. In vitro assays validated that LINC00501 promoted proliferation and migration of HCC cell lines. Conclusion. Our results showed that GIlncSig serves as a potential independent prognosis factor to predict HCC patients’ prognosis for exploring potential mechanism and therapy strategy. Besides, LINC00501 plays an important role in the progression of HCC, which may be a potential therapy target.

Prognostic microRNAs associated with phosphoserine aminotransferase 1 in gastric cancer as markers of bone metastasis

This study analyzed PSAT1-targeted miRNAs as a prognostic predictor for gastric cancer. The relationship between the clinical manifestations of gastric cancer in patients and phosphoserine aminotransferase 1 (PSAT1) was analyzed using correlation analysis. PSAT1 was highly expressed in gastric cancer, and its low expression was associated with a poor prognosis. By pan-cancer analysis, PSAT1 could affect the tumor immune microenvironment by immune infiltration analysis. Nine microRNAs targeting PSAT1 and associated with gastric cancer were screened by miRwalk and microRNA expression in TCGA tumor tissues. Six microRNAs were obtained by survival curve analysis, including hsa-miR-1-3p, hsa-miR-139-5p, hsa-miR-145-5p, hsa-miR-195-5p, hsa-miR-218-5p, and hsa-miR-497-5p. Based on the above six microRNAs, a model for bone metastasis prediction in gastric cancer prediction was constructed. An analysis of a decision curve was performed based on the microRNAs obtained to predict bone metastasis from gastric cancer. It had a positive area under the curve (AUC) value of 0.746, and the decision curve analysis (DCA) indicated that it was clinically significant. Dual-luciferase reporter genes indicated that hsa-miR-497-5p and PSAT1 were targeted, and qRT-PCR results confirmed that hsa-miR-497-5p could down-regulate PSAT1 expression. MicroRNAs targeting the regulation of PSAT1 expression can well predict the prognosis of gastric cancer.

Oncogenic role and potential regulatory mechanism of topoisomerase IIα in a pan-cancer analysis

Topoisomerase IIα (TOP2A) plays an oncogenic role in multiple tumor types. However, no pan-cancer analysis about the function and the upstream molecular mechanism of TOP2A is available. For the first time, we analyzed potential oncogenic roles of TOP2A in 33 cancer types via The Cancer Genome Atlas (TCGA) database. Overexpression of TOP2A was existed in almost all cancer types, and related to poor prognosis and advanced pathological stages in most cases. Besides, the high frequency of TOP2A genetic alterations was observed in several cancer types, and related to prognosis in some cases. Moreover, we conduct upstream miRNAs and lncRNAs of TOP2A to establish ceRNA networks in kidney renal clear cell carcinoma (SNHG3-miR-139-5p), kidney renal papillary cell carcinoma (TMEM147-AS1/N4BP2L2-IT2/THUMPD3-AS1/ERICD/TTN-AS1/SH3BP5-AS1/THRB-IT1/SNHG3/NEAT1-miR-139-5p), liver hepatocellular carcinoma (SNHG3/THUMPD3-AS1/NUTM2B-AS1/NUTM2A-AS1-miR-139-5p and SNHG6/GSEC/SNHG1/SNHG14/LINC00265/MIR3142HG-miR-101-3p) and lung adenocarcinoma (TYMSOS/HELLPAR/SNHG1/GSEC/SNHG6-miR-101-3p). TOP2A expression was generally positively correlated with cancer associated fibroblasts, M0 and M1 macrophages in most cancer types. Furthermore, TOP2A was positively associated with expression of immune checkpoints (CD274, CTLA4, HAVCR2, LAG3, PDCD1 and TIGIT) in most cancer types. Our first TOP2A pan-cancer study contributes to understanding the prognostic roles, immunological roles and potential upstream molecular mechanism of TOP2A in different cancers.

ncRNA-Mediated High Expression of LPCAT1 Correlates with Poor Prognosis and Tumor Immune Infiltration of Liver Hepatocellular Carcinoma

Purpose

To investigate the expression of LPCAT1 in liver hepatocellular carcinoma (LIHC) and its relationship with prognosis and immune infiltration and predict its upstream nonencoding RNAs (ncRNAs).

Method

In this study, expression analysis and survival analysis for LPCAT1 in pan cancers were first performed by using The Cancer Genome Atlas (TCGA) data, which suggested that LPCAT1 might be a potential LIHC oncogene. Then, ncRNAs contributing to the overexpression of LPCAT1 were explored in starBase by a combination of expression analysis, correlation analysis, and survival analysis. Immune cell infiltration of LPCAT1 in LIHC was finally investigated via Tumor Immune Estimation Resource (TIMER).

Result

SNHG3 was observed to be the most promising upstream lncRNA for the hsa-miR-139-5p/LPCAT1 axis in LIHC. In addition, the LPCAT1 level was significantly positively associated with tumor immune cell infiltration, biomarkers of immune cells, and immune checkpoint expression in LIHC.

Conclusion

To summarize, the upregulation of LPCAT1 mediated by ncRNAs is associated with poor prognosis, immune infiltration, and immune checkpoint expression in LIHC.

Identification of m6A-Related lncRNA to Predict the Prognosis of Patients with Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide. In the past decades, HCC treatment has achieved great progress; however, the overall prognosis remains poor. Therefore, it is the need of the hour to identify new prognostic biomarkers which can advance our understanding related to the underlying molecular mechanism of adverse prognosis and apply them to clinical work in prognosis prediction. In the present study, data of 576 HCC patients and 292 normal control cases from TCGA and ICGC databases were enrolled to our bioinformatic analysis. SNHG1 and SNHG3 were identified as overlapping genes in TCGA and ICGC databases using Pearson correlation analysis and univariate Cox regression analysis. Further, we used the median of the SNHG1 and SNHG3 expression values as the cutoff values to define the HCC patient groups with high or low expression level. The subsequent analysis revealed that abnormal high expression of SNHG1 or SNHG3 affected the immune infiltration patterns and the crosstalk among immune cells. Moreover, high expression of SNHG1 or SNHG3 resulted in drug resistant to AKT inhibitor VII, bexarotene, bicalutamide, dasatinib, erlotinib, and gefitinib. In addition, lower tumor neoantigen burden was observed in high SNHG1 or SNHG3 group. Further, we found significant relation between the aberrant upregulation of SNHG1 and SNHG3 in tumor grade and stage. We established a nomogram to systematically predict the 5- and 8-year overall survival of liver cancer patients with good accuracy. Finally, the in vitro assays suggest that SNHG1 and SNHG3 promote the proliferative, migratory, and invasive abilities of HCC cells.

Construction of a miRNA Signature Using Support Vector Machine to Identify Microsatellite Instability Status and Prognosis in Gastric Cancer

Background. The specific role and prognostic value of DNA repair and replication-associated miRNAs in gastric cancer (GC) have not been clearly elucidated. Therefore, comprehensive analysis of miRNAs in GC is crucial for proposing therapeutic strategies and survival prediction. Methods. Firstly, clinical information and transcriptome data of TCGA-GC were downloaded from the database. In the entire cohort, we performed differential analysis in all miRNAs and support vector machine (SVM) was used to eliminate redundant miRNAs. Subsequently, we combined survival data and cox regression analysis to construct a miRNA signature in the training cohort. In addition, we used PCA, Kaplan-Meier, and ROC analysis to explore the prognosis value of risk score in the training and testing cohort. It is worth noting that multiple algorithms were used to evaluate difference of immune microenvironment (TME), microsatellite instability (MSI), tumor mutational burden (TMB), and immunotherapy in different risk groups. Finally, we investigated the potential mechanism about miRNA signature. Results. We constructed miRNA signature based on the following 4 miRNAs: hsa-miR-139-5p, hsa-miR-139-3p, hsa-miR-146b-5p, and hsa-miR-181a-3p. Univariate and multivariate Cox regression analyses suggested that risk score is a risk factor and an independent prognostic factor in GC patients. The AUC value of ROC analysis showed a robust prediction accuracy in each cohort. Moreover, significant differences in immune functions, immune cell content, immune checkpoint, MSI status, and TMB score were excavated in different groups distinguished by risk score. Finally, based on the above four miRNA target genes, we revealed that the signature was enriched in DNA repair and replication. Conclusion. We have developed a robust risk-formula based on 4 miRNAs that provides accurate risk stratification and prognostic prediction for GC patients. In addition, different risk subgroups may potentially guide the choice of targeted therapy.