Prediction of a competing endogenous RNA co-expression network as a prognostic marker in glioblastoma

mRNA markers for survival prediction in glioblastoma multiforme patients: a systematic review with bioinformatic analyses

BACKGROUND

Glioblastoma multiforme (GBM) is a type of fast-growing brain glioma associated with a very poor prognosis. This study aims to identify key genes whose expression is associated with the overall survival (OS) in patients with GBM.

METHODS

A systematic review was performed using PubMed, Scopus, Cochrane, and Web of Science up to Journey 2024. Two researchers independently extracted the data and assessed the study quality according to the New Castle Ottawa scale (NOS). The genes whose expression was found to be associated with survival were identified and considered in a subsequent bioinformatic study. The products of these genes were also analyzed considering protein-protein interaction (PPI) relationship analysis using STRING. Additionally, the most important genes associated with GBM patients' survival were also identified using the Cytoscape 3.9.0 software. For final validation, GEPIA and CGGA (mRNAseq_325 and mRNAseq_693) databases were used to conduct OS analyses. Gene set enrichment analysis was performed with GO Biological Process 2023.

RESULTS

From an initial search of 4104 articles, 255 studies were included from 24 countries. Studies described 613 unique genes whose mRNAs were significantly associated with OS in GBM patients, of which 107 were described in 2 or more studies. Based on the NOS, 131 studies were of high quality, while 124 were considered as low-quality studies. According to the PPI network, 31 key target genes were identified. Pathway analysis revealed five hub genes (IL6, NOTCH1, TGFB1, EGFR, and KDR). However, in the validation study, only, the FN1 gene was significant in three cohorts.

CONCLUSION

We successfully identified the most important 31 genes whose products may be considered as potential prognosis biomarkers as well as candidate target genes for innovative therapy of GBM tumors.

Bioinformatics Analysis and Experimental Validation for Exploring Key Molecular Markers for Glioblastoma

Glioblastoma (GBM) is the most common primary intracranial malignancy with a very low survival rate. Exploring key molecular markers for GBM can help with early diagnosis, prognostic prediction, and recurrence monitoring. This study aims to explore novel biomarkers for GBM via bioinformatics analysis and experimental verification. Dataset GSE103229 was obtained from the GEO database to search differentially expressed lncRNA (DELs), mRNAs (DEMs), and miRNAs (DEMis). Hub genes were selected to establish competing endogenous RNA (ceRNA) networks. The GEPIA database was employed for the survival analysis and expression detection of hub genes. Hub gene expression in GBM tissue samples and cell lines was validated using RT-qPCR. Western blotting was employed for protein expression evaluation. SYT1 overexpression vector was transfected in GBM cells. CCK-8 assay and flow cytometry were performed to detect the malignant phenotypes of GBM cells. There were 901 upregulated and 1086 downregulated DEMs identified, which were prominently enriched in various malignancy-related functions and pathways. Twenty-two hub genes were selected from PPI networks. Survival analysis and experimental validation revealed that four hub genes were tightly associated with GBM prognosis and progression, including SYT1, GRIN2A, KCNA1, and SYNPR. The four genes were significantly downregulated in GBM tissues and cell lines. Overexpressing SYT1 alleviated the proliferation and promoted the apoptosis of GBM cells in vitro. We identify four genes that may be potential molecular markers of GBM, which may provide new ideas for improving early diagnosis and prediction of the disease.

Aberrant LYZ expression in tumor cells serves as the potential biomarker and target for HCC and promotes tumor progression via csGRP78

Hepatocellular carcinoma (HCC) takes the predominant malignancy of hepatocytes with bleak outcomes owing to high heterogeneity among patients. Personalized treatments based on molecular profiles will better improve patients' prognosis. Lysozyme (LYZ), a secretory protein with antibacterial function generally expressed in monocytes/macrophages, has been observed for the prognostic implications in different types of tumors. However, studies about the explicit applicative scenarios and mechanisms for tumor progression are still quite limited, especially for HCC. Here, based on the proteomic molecular classification data of early-stage HCC, we revealed that the LYZ level was elevated significantly in the most malignant HCC subtype and could serve as an independent prognostic predictor for HCC patients. Molecular profiles of LYZ-high HCCs were typical of those for the most malignant HCC subtype, with impaired metabolism, along with promoted proliferation and metastasis characteristics. Further studies demonstrated that LYZ tended to be aberrantly expressed in poorly differentiated HCC cells, which was regulated by STAT3 activation. LYZ promoted HCC proliferation and migration in both autocrine and paracrine manners independent of the muramidase activity through the activation of downstream protumoral signaling pathways via cell surface GRP78. Subcutaneous and orthotopic xenograft tumor models indicated that targeting LYZ inhibited HCC growth markedly in NOD/SCID mice. These results propose LYZ as a prognostic biomarker and therapeutic target for the subclass of HCC with an aggressive phenotype.

Identify and validate RUNX2 and LAMA2 as novel prognostic signatures and correlate with immune infiltrates in bladder cancer

Background

Muscle-invasive bladder cancer (MIBC) develops lymph node (LN) metastasis or distant metastasis, leading to recurrence and poor prognosis. The five-year survival rate of MIBC with LN or distant metastasis is only 8.1%; therefore, there is an urgent need to identify reliable biomarkers for prognosis and treatment regimen for patients with bladder cancer (BLCA).

Methods

SEER database was used to select important clinical characteristics for MIBC. Then, weighted gene co-expression network analysis (WGCNA) was employed to identify differentially expressed genes (DEGs) to recognize significant co-expression modules by calculating the correlation between the modules and clinical data. Furthermore, Cox regression and lasso analysis were applied to screen prognostic hub genes and establish the risk predictive model. Bladder cancer cell lines (UMUC3 and 5637) were used for experimental validation in vitro.

Results

Cox analysis of 122,600 MIBC patients showed that the N stage was the most important clinical factor. A total of 4,597 DEGs were calculated between N0 and N+ patients, and WGCNA with these DEGs in 368 samples revealed that expression of turquoise was positively and strongly correlated with the N stage. Eight genes were identified as important prognostic candidates using lasso regression based on Cox analysis and STRING database. Combining GEO datasets, literature, and clinical factors, we identified LAMA2 and RUNX2 as novel prognostic biomarkers. CCK8 assay showed that depletion of LAMA2 or RUNX2 significantly inhibited the proliferation of BLCA cells, and flow cytometry indicated that knockdown of LAMA2 or RUNX2 induced the apoptosis of BLCA cells. Transwell assay also showed that silencing of LAMA2 or RUNX2 weakened the migration and invasiveness of BLCA cells.

Conclusions

We constructed a new eight-gene risk model to provide novel prognostic biomarkers and therapeutic targets for BLCA.

Transcriptional Profiling of a Patient-Matched Cohort of Glioblastoma (IDH-Wildtype) for Therapeutic Target and Repurposing Drug Identification

Glioblastoma (GBM) is the most prevalent and aggressive adult brain tumor. Despite multi-modal therapies, GBM recurs, and patients have poor survival (~14 months). Resistance to therapy may originate from a subpopulation of tumor cells identified as glioma-stem cells (GSC), and new treatments are urgently needed to target these. The biology underpinning GBM recurrence was investigated using whole transcriptome profiling of patient-matched initial and recurrent GBM (recGBM). Differential expression analysis identified 147 significant probes. In total, 24 genes were validated using expression data from four public cohorts and the literature. Functional analyses revealed that transcriptional changes to recGBM were dominated by angiogenesis and immune-related processes. The role of MHC class II proteins in antigen presentation and the differentiation, proliferation, and infiltration of immune cells was enriched. These results suggest recGBM would benefit from immunotherapies. The altered gene signature was further analyzed in a connectivity mapping analysis with QUADrATiC software to identify FDA-approved repurposing drugs. Top-ranking target compounds that may be effective against GSC and GBM recurrence were rosiglitazone, nizatidine, pantoprazole, and tolmetin. Our translational bioinformatics pipeline provides an approach to identify target compounds for repurposing that may add clinical benefit in addition to standard therapies against resistant cancers such as GBM.

Crosstalk between SOX Genes and Long Non-Coding RNAs in Glioblastoma

Glioblastoma (GBM) continues to be the most devastating primary brain malignancy. Despite significant advancements in understanding basic GBM biology and enormous efforts in developing new therapeutic approaches, the prognosis for most GBM patients remains poor with a median survival time of 15 months. Recently, the interplay between the SOX (SRY-related HMG-box) genes and lncRNAs (long non-coding RNAs) has become the focus of GBM research. Both classes of molecules have an aberrant expression in GBM and play essential roles in tumor initiation, progression, therapy resistance, and recurrence. In GBM, SOX and lncRNAs crosstalk through numerous functional axes, some of which are part of the complex transcriptional and epigenetic regulatory mechanisms. This review provides a systematic summary of current literature data on the complex interplay between SOX genes and lncRNAs and represents an effort to underscore the effects of SOX/lncRNA crosstalk on the malignant properties of GBM cells. Furthermore, we highlight the significance of this crosstalk in searching for new biomarkers and therapeutic approaches in GBM treatment.

Network of miR-373/miR-520s-CD44 Axis Significantly Inhibits the Growth and Invasion of Human Glioblastoma Cells

BACKGROUND

The expression and regulation of microRNAs (miRNAs) play an important role in glioblastoma (GBM) tumorigenesis, progression and prognosis. Little is known about the role of the miRNA regulatory network of GBM risk-related genes in GBM growth and invasiveness.

METHODS

The UALCAN and Oncomine gene expression dataset were used to explore gene expression profiles in human GBM. The Kaplan-Meier method was performed to evaluate the prognostic values of the GBM-related genes. Multiple bioinformatics databases were analysed to predict the GBM-related genes targeted by miRNAs. A luciferase reporter assay and other molecular cell function experiments were conducted to reveal the mechanisms of interaction between the identified miRNAs and their targets.

RESULTS

The CD44 expression is significantly higher in GBM tissues than that in normal tissues, and negatively correlated with survival duration in GBM patients. In normal physiological conditions, CD44 expression is lower in various parts of the central nervous system than in other organ systems. The mRNA encoding CD44 is a direct target of miR-373 and miR-520s, and this finding was verified by molecular biology experiments. We further found that miR-373 and miR-520s expression was negatively associated with CD44 expression in GBM specimens, and that the miR-373 or miR-520s-CD44 interaction network significantly affected the growth and invasiveness of GBM cells.

CONCLUSION

The miR-373 and miR-520s exert their functions by suppressing CD44 expression in GBM cells, and their expression, together with that of CD44, could thus serve as a valuable biomarker of GBM prognosis.

Circular RNA circ_0002594 regulates PDGF-BB-induced proliferation and migration of human airway smooth muscle cells via sponging miR-139-5p/TRIM8 in asthma

BACKGROUND

Asthma is a chronic respiratory disease characterised by the contraction of smooth muscle and remodelling of the airway wall, which is correlated with increased airway smooth muscle mass. Circular RNA (circRNA) circ_0002594 has been reported as a pro-inflammatory factor in allergic asthma. Therefore, this study is designed to explore the role and mechanism of circ_0002594 in human airway smooth muscle cells (HASMC) proliferation and metastasis.

METHODS

Cell proliferative ability, invasion, and migration were detected by 3-(4, 5-dimethyl-2-thiazolyl)-2, 5-diphenyl-2-H-tetrazolium bromide (MTT), 5-ethynyl-2'-deoxyuridine (EdU), Transwell, and Wound healing assays. The protein levels of E-cadherin, N-cadherin, and tripartite motif 8 (TRIM8) were detected by western blot assay. The levels of interleukin-6 (IL-6) and IL-13 were detected using Enzyme-linked immunosorbent assays (ELISA). Levels of circ_0002594, microRNA-139-5p (miR-139-5p), TRIM8 were determined by real-time quantitative polymerase chain reaction (RT-qPCR). The binding between miR-139-5p and circ_0002594 or TRIM8 was predicted by Circinteractome or Starbase v2.0, and then verified by a dual-luciferase reporter and RNA Immunoprecipitation (RIP) assays.

RESULTS

Platelet-derived growth factor-BB (PDGF-BB) could trigger HASMC proliferation, metastasis, and inflammation. Circ_0002594 and TRIM8 were elevated in asthma patients and PDGF-BB-treated HASMC, and the miR-139-5p level was decreased. Furthermore, circ_0002594 knockdown could suppress PDGF-BB- stimulated HASMC damage. Mechanism analysis exhibited that circ_0002594 could regulate TRIM8 expression through sponging miR-139-5p.

CONCLUSION

Our findings revealed that circ_0002594 could act as a regulator in the airway remodelling during asthma development partly by the miR-139-5p/TRIM8 axis, hinting at an underlying therapeutic strategy for asthma.

METTL7B contributes to the malignant progression of glioblastoma by inhibiting EGR1 expression

Glioblastoma (GBM), a predominant central nervous system (CNS) malignancy, is correlated with high mortality and severe morbidity. Mammalian methyltransferase-like 7B (METTL7B) as a methyltransferase has been identified to participate in cancer progression. However, its function in GBM is elusive. Accordingly, we aimed to explore the effect of METTL7B on GBM. The expression of METTL7B and EGR2 in GBM patients and GBM cells were detected by qPCR, western blots and immunohistochemical staining. Cell viability was assessed by CCK-8 assays. Cell proliferation was determined by EdU, colony formation, and tumor sphere formation assays. METTL7B shRNA was injected into the Balb/c nude mice. The size and weight of isolated tumor was measured. And the expression levels of Ki67, METTL7B and EGR1 were examined by immunohistochemical staining. METTL7B was significantly elevated, while EGR1 was downregulated in clinical GBM tissues. METTL7B upregulation was associated with the low overall survival of GBM patients. Moreover, METTL7B depletion remarkably attenuated GBM cell proliferation. Mechanistically, METTL7B overexpression inhibited EGR1 expression in GBM cells. EGR1 knockdown rescued the inhibitory effect of METTL7B depletion on GBM cell proliferation. Meanwhile, METTL7B depletion arrested more GBM cells at the G0/G1, but fewer cells at the S phase, which EGR1 knockdown reversed these effects. Furthermore, tumorigenicity analysis revealed that METTL7B promotes tumor growth of GBM cells in vivo. METTL7B contributes to the malignant progression of GBM by inhibiting EGR1 expression. METTL7B and EGR1 may be utilized as the treatment targets for GBM therapy.

Non-coding RNA Activated by DNA Damage: Review of Its Roles in the Carcinogenesis

Long intergenic non-coding RNA 00657 (LINC00657) or "non-coding RNA activated by DNA damage" (NORAD) is an extremely conserved and copious long non-coding RNA (lncRNA). This transcript has pivotal role in the preservation of genome integrity. Several researches have appraised the role of NORAD in the evolution of human cancers with most of them indicating an oncogenic role for this lncRNA. Several miRNAs such as miR-199a-3p, miR-608, miR-155-5p, miR-590-3p, miR-495-3p, miR-608, miR-202-5p, miR-125a-3p, miR-144-3p, miR-202-5p, and miR-30a-5p have been recognized as targets of NORAD in different cancer cell lines. In addition, NORAD has interactions with cancer-related pathways, particularly STAT, TGF-β, Akt/mTOR, and PI3K/AKT pathway. Over-expression of NORAD has been related with poor clinical outcome of patients with diverse types of neoplasms. Collectively, NORAD is a prospective marker and target for combating cancer.

C-myc promotes miR-92a-2-5p transcription in rat ovarian granulosa cells after cadmium exposure

Cadmium (Cd) can induce ovarian injury by microRNAs (miRNAs), however, the molecular mechanism of miRNAs after Cd exposure have not known. In this study, 56-day-old adult female Sprague-Dawley (SD) rats were injection with PMSG, after 48 h, ovarian granulosa cells (GCs) were extracted and cultured for 24 h, then treated with 0, 2.5, 5, 10 and 20 μM Cd for 24 h. The results showed that expression levels of miR-92a-2-5p (upregulated) and Bcl2 (downregulated) changed significantly after Cd exposure. The messenger RNA (mRNA) and protein expression levels of DNMT1, DNMT3A, and DNMT3B had changed, but no obvious differences were found in miR-92a-2-5p single site methylation. The transcription factors C-MYC (upregulated), E2F1 (downregulated), and SP1 (downregulated), which target miRNAs significantly changed after exposure to Cd. The human ovarian GC tumor line (COV434) was used to knocked down C-myc, and the expression of miR-92a-2-5p was downregulated in the COV434-C-myc + 10 μM Cd group compared with COV434 cells. The N6-methyladenosine (m6A) methylation modification levels of long noncoding RNA (lncRNA) MT1JP and lncRNA CDKN2B-AS, which regulate miR-92a-2-5p were detected. In the 10 μM Cd group, m6A methylation levels at MT1JP-84, CDKN2B-AS-257, and CDKN2B-AS-329 were reduced. In summary, after Cd exposure, expression of miR-92a-2-5p, which targets the antiapoptotic gene Bcl2, was upregulated, which may be primarily related to upregulation of C-myc. MiR-92a-2-5p promoter DNA methylation may has no obvious effect on miR-92a-2-5p. Otherwise, the role of m6A methylation modified lncRNA MT1JP and lncRNA CDKN2B-AS in the regulation of miR-92a-2-5p needs further study.

Long, Noncoding RNA Dysregulation in Glioblastoma

Simple Summary

Developing effective therapies for glioblastoma (GBM), the most common primary brain cancer, remains challenging due to the heterogeneity within tumors and therapeutic resistance that drives recurrence. Noncoding RNAs are transcribed from a large proportion of the genome and remain largely unexplored in their contribution to the evolution of GBM tumors. Here, we will review the general mechanisms of long, noncoding RNAs and the current knowledge of how these impact heterogeneity and therapeutic resistance in GBM. A better understanding of the molecular drivers required for these aggressive tumors is necessary to improve the management and outcomes of this challenging disease.

Abstract

Transcription occurs across more than 70% of the human genome and more than half of currently annotated genes produce functional noncoding RNAs. Of these transcripts, the majority—long, noncoding RNAs (lncRNAs)—are greater than 200 nucleotides in length and are necessary for various roles in the cell. It is increasingly appreciated that these lncRNAs are relevant in both health and disease states, with the brain expressing the largest number of lncRNAs compared to other organs. Glioblastoma (GBM) is an aggressive, fatal brain tumor that demonstrates remarkable intratumoral heterogeneity, which has made the development of effective therapies challenging. The cooperation between genetic and epigenetic alterations drives rapid adaptation that allows therapeutic evasion and recurrence. Given the large repertoire of lncRNAs in normal brain tissue and the well-described roles of lncRNAs in molecular and cellular processes, these transcripts are important to consider in the context of GBM heterogeneity and treatment resistance. Herein, we review the general mechanisms and biological roles of lncRNAs, with a focus on GBM, as well as RNA-based therapeutics currently in development.

[Identification of Hub Genes for Ovarian Cancer Stem Cell Properties with Weighted Gene Co-expression Network Analysis]

OBJECTIVE

To investigate the significance of stemness-related genes in the diagnosis and treatment of ovarian cancer.

METHODS

Key modules and genes were identified with weighted gene co-expression network analysis (WGCNA). The signal pathways of high expression of key genes were analyzed by gene set enrichment analysis (GSEA) and single cell sequencing data. The chemosensitivity of ovarian cancer to chemotherapy drugs was estimated with pRRophetic. Flow cytometry was used to examine the expression of CD44 +CD117 +in SKOV3 cells and cancer stem cells. The expression of key genes in ovarian cancer stem cells was confirmed by qRT-PCR. The core genes were identified by GeneMANIA analysis.

RESULTS

According to the WGCNA results, 15 key genes were identified at the transcription level, all being highly expressed in many kinds of tumors. They were involved in the cell cycle, DNA repair, E2 target and G2M checkpoint pathway, and had significant correlation with chemosensitivity. The proportion of CD44 + CD117 + cells in SKOV3 cells and ovarian cancer stem cells were (1.20±0.34)% and (37.17±1.80)% respectively, with statistically significant difference ( P<0.05). qRT-PCR confirmed that seven key genes ( BUB1, CDC20, CCNB2, DLGAP5, KIF4 A, NEK2, NUSAP1) in the WGCNA results were highly expressed in ovarian cancer stem cells, and BUB1 might have played a core role.

CONCLUSION

Seven hub genes, especially BUB1, were identified by constructing gene co-expression network, which may become potential biomarkers of ovarian cancer gene.

Integrative Analysis of Multi-Omics Data Based on Blockwise Sparse Principal Components

The recent development of high-throughput technology has allowed us to accumulate vast amounts of multi-omics data. Because even single omics data have a large number of variables, integrated analysis of multi-omics data suffers from problems such as computational instability and variable redundancy. Most multi-omics data analyses apply single supervised analysis, repeatedly, for dimensional reduction and variable selection. However, these approaches cannot avoid the problems of redundancy and collinearity of variables. In this study, we propose a novel approach using blockwise component analysis. This would solve the limitations of current methods by applying variable clustering and sparse principal component (sPC) analysis. Our approach consists of two stages. The first stage identifies homogeneous variable blocks, and then extracts sPCs, for each omics dataset. The second stage merges sPCs from each omics dataset, and then constructs a prediction model. We also propose a graphical method showing the results of sparse PCA and model fitting, simultaneously. We applied the proposed methodology to glioblastoma multiforme data from The Cancer Genome Atlas. The comparison with other existing approaches showed that our proposed methodology is more easily interpretable than other approaches, and has comparable predictive power, with a much smaller number of variables.