Use of Genome-Scale Integrated Analysis to Identify Key Genes and Potential Molecular Mechanisms in Recurrence of Lower-Grade Brain Glioma

Identification of new targets for glioblastoma therapy based on a DNA expression microarray

This study provides a comprehensive perspective on the deregulated pathways and impaired biological functions prevalent in human glioblastoma (GBM). In order to characterize differences in gene expression between individuals diagnosed with GBM and healthy brain tissue, we have designed and manufactured a specific, custom DNA microarray. The results obtained from differential gene expression analysis were validated by RT-qPCR. The datasets obtained from the analysis of common differential expressed genes in our cohort of patients were used to generate protein-protein interaction networks of functionally enriched genes and their biological functions. This network analysis, let us to identify 16 genes that exhibited either up-regulation (CDK4, MYC, FOXM1, FN1, E2F7, HDAC1, TNC, LAMC1, EIF4EBP1 and ITGB3) or down-regulation (PRKACB, MEF2C, CAMK2B, MAPK3, MAP2K1 and PENK) in all GBM patients. Further investigation of these genes and enriched pathways uncovered in this investigation promises to serve as a foundational step in advancing our comprehension of the molecular mechanisms underpinning GBM pathogenesis. Consequently, the present work emphasizes the critical role that the unveiled molecular pathways likely play in shaping innovative therapeutic approaches for GBM management. We finally proposed in this study a list of compounds that target hub of GBM-related genes, some of which are already in clinical use, underscoring the potential of those genes as targets for GBM treatment.

ABERRANT EXPRESSION OF COL14A1, CELRS3, and CTHRC1 IN BREAST CANCER СELLS

BACKGROUND

Collagens, which are the major components of the extracellular matrix involved in the regulation of tumor microenvironment, could be differentially expressed in breast cancer (BC) with different transcriptome profiling.

AIM

To analyze the transcript level expression of COL1A1, COL5A1, COL10A1, COL11A1, COL12A1, COL14A1, CTHRC1, and CELRS3 genes and the clinical relevance of their differential expression in BC.

MATERIALS AND METHODS

The transcript level expression of the genes was analyzed using the quantitative real-time PCR (qPCR) in tumor tissue of 60 BC patients.

RESULTS

Overexpression of COL1A1, COL5A1, COL10A1, COL11A1, COL12A1, CTHRC, and CELRS3 anddown-regulated expression of COL14A1 were observed. COL14A1 down-regulation was associated with aggressive, basal, and Her-2/neu BC subtypes (p = 0.031). Overexpression of CELSR3 was found to be associated with the older age of the patients (> 55 years, p = 0.049). Further analysis with the TCGA BC data set has shown a concordance in the differential expression of the above genes. Furthermore, overexpression of CTHRC1 was associated with poor overall survival (OS), particularly with poor prognosis (p = 0.00042) for the luminal BC subtype. On the other hand, CELSR3 overexpression was associated with mucinous tumors and poor prognosis in post-menopausal women. In silicotarget prediction identified several BC-associated miRNAs and members of miR-154, -515, and -10 families to perform a likely regulatory role in the above ECM genes.

CONCLUSION

The present study shows that the expression of COL14A1 and CTHRC1 may serve as potential biological markers for the detection of basal BC and the prognosis of survival for patients with the luminal subtype of BC.

The Oncogenic Role of Cyclin-Dependent Kinase Inhibitor 2C in Lower-Grade Glioma

Lower-grade gliomas (LGGs) are slow-growing, indolent tumors that usually affect younger patients and present a therapeutic challenge due to the heterogeneity of their clinical presentation. Dysregulation of cell cycle regulatory factors is implicated in the progression of many tumors, and drugs that target cell cycle machinery have shown efficacy as promising therapeutic approaches. To date, however, no comprehensive study has examined how cell cycle-related genes affect LGG outcomes. The cancer genome atlas (TCGA) data were used as the training set for differential analysis of gene expression and patient outcomes; the Chinese glioma genome atlas (CGGA) was used for validation. Levels of one candidate protein, cyclin-dependent kinase inhibitor 2C (CDKN2C), and its relationship to clinical prognosis were determined using a tissue microarray containing 34 LGG tumors. A nomogram was constructed to model the putative role of candidate factors in LGG. Cell type proportion analysis was performed to evaluate immune cell infiltration in LGG. Various genes encoding cell cycle regulatory factors showed increased expression in LGG and were significantly related to isocitrate dehydrogenase and chromosome arms 1p and 19q mutation status. CDKN2C expression independently predicted the outcome of LGG patients. High M2 macrophage values along with elevated CDKN2C expression were associated with poorer prognosis in LGG patients. CDKN2C plays an oncogenic role in LGG, which is associated with M2 macrophages.

Multi-omics data integration for subtype identification of Chinese lower-grade gliomas: a joint similarity network fusion approach

Lower-grade gliomas (LGG), characterized by heterogeneity and invasiveness, originate from the central nervous system. Although studies focusing on molecular subtyping and molecular characteristics have provided novel insights into improving the diagnosis and therapy of LGG, there is an urgent need to identify new molecular subtypes and biomarkers that are promising to improve patient survival outcomes. Here, we proposed a joint similarity network fusion (Joint-SNF) method to integrate different omics data types to construct a fused network using the Joint and Individual Variation Explained (JIVE) technique under the SNF framework. Focusing on the joint network structure, a spectral clustering method was employed to obtain subtypes of patients. Simulation studies show that the proposed Joint-SNF method outperforms the original SNF approach under various simulation scenarios. We further applied the method to a Chinese LGG data set including mRNA expression, DNA methylation and microRNA (miRNA). Three molecular subtypes were identified and showed statistically significant differences in patient survival outcomes. The five-year mortality rates of the three subtypes are 80.8%, 32.1%, and 34.4%, respectively. After adjusting for clinically relevant covariates, the death risk of patients in Cluster 1 was 5.06 times higher than patients in other clusters. The fused network attained by the proposed Joint-SNF method enhances strong similarities, thus greatly improves subtyping performance compared to the original SNF method. The findings in the real application may provide important clues for improving patient survival outcomes and for precision treatment for Chinese LGG patients. An R package to implement the method can be accessed in Github at https://github.com/Sameerer/Joint-SNF.

Establishment and Validation of a 5 m6A RNA Methylation Regulatory Gene Prognostic Model in Low-Grade Glioma

Background: The prognosis of low-grade glioma (LGG) is different from that of other intracranial tumors. Although many markers of LGG have been established, few are used in clinical practice. M6A methylation significantly affects the biological behavior of LGG tumors. Therefore, establishment of an LGG prognostic model based on m6A methylation regulatory genes is of great interest.

Methods: Data from 495 patients from The Cancer Genome Atlas (TCGA) and 172 patients from the Chinese Glioma Genome Atlas (CGGA) were analyzed. Univariate Cox analysis was used to identify methylation regulatory genes with prognostic significance. LASSO Cox regression was used to identify prognostic genes. Receiver operating characteristic (ROC) and Kaplan–Meier curves were used to verify the accuracy of the model. Gene Set Enrichment Analysis (GSEA) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to identify cellular pathways that were significantly associated with the prognosis of LGG.

Results: A glioma prognostic model based on five methylation regulatory genes was established. Compared with low-risk patients, patients identified as high risk had a poorer prognosis. There was a high degree of consistency between the internal training and internal validation CGGA cohorts and the external validation TCGA cohort. Furthermore, KEGG and GSEA analyses showed that the focal adhesion and cell cycle pathways were significantly upregulated in high-risk patients. This signature could be used to distinguish among patients with different immune checkpoint gene expression levels, which may inform immune checkpoint inhibitor (ICI) immunotherapy.

Conclusion: We comprehensively evaluated m6A methylation regulatory genes in LGG and constructed a prognostic model based on m6A methylation, which may improve prognostic prediction for patients with LGG.

Screening and Identification of Key Biomarkers in Lower Grade Glioma via Bioinformatical Analysis

Lower-grade glioma (LGG) is a common type of central nervous system tumor. Due to its complicated pathogenesis, the choice and timing of adjuvant therapy after tumor treatment are controversial. This study explored and identified potential therapeutic targets for lower-grade. The bioinformatics method was employed to identify potential biomarkers and LGG molecular mechanisms. Firstly, we selected and downloaded GSE15824, GSE50161, and GSE86574 from the GEO database, which included 40 LGG tissue and 28 normal brain tissue samples. GEO and VENN software identified of 206 codifference expressed genes (DEGs). Secondly, we applied the DAVID online software to investigate the DEG biological function and KEGG pathway enrichment, as well as to build the protein interaction visualization network through Cytoscape and STRING website. Then, the MCODE plug is used in the analysis of 22 core genes. Thirdly, the 22 core genes were analyzed with UNCLA software, of which 18 genes were associated with a worse prognosis. Fourthly, GEPIA was used to analyze the 18 selected genes, and 14 genes were found to be a significantly different expression between LGGs and normal brain tumor samples. Fifthly, hierarchical gene clustering was used to examine the 14 important gene expression differences in different histologies, as well as analysis of the KEGG pathway. Five of these genes were shown to be abundant in the natural killer cell-mediated cytokines (NKCC) and phagosome pathways. The five key genes that may be affected by the immune microenvironment play a crucial role in LGG development.

COL5A1 Serves as a Biomarker of Tumor Progression and Poor Prognosis and May Be a Potential Therapeutic Target in Gliomas

Glioma is the most common malignancy of the central nervous system. Although advances in surgical resection, adjuvant radiotherapy, and chemotherapy have been achieved in the last decades, the prognosis of gliomas is still dismal. COL5A1 is one of the collagen members with minor content but prominent functions. The present study examined the biological functions, prognostic value, and gene-associated tumor-infiltrating immune cells of COL5A1 through experiments and bioinformatics analysis. We found that the overexpression of COL5A1 was positively correlated with the increasing tumor malignancies and indicated poor prognosis in gliomas. Moreover, downregulation of COL5A1 could inhibit proliferation and migration of glioma cells and enhance their temozolomide sensitivities in vitro. Further bioinformatic analysis revealed that COL5A1 and its co-expressed genes participated in a number of pathways and biological processes involved in glioma progression. Finally, we evaluated the tumor-infiltrating immune cells of gliomas depending on COL5A1 and found that the percentages of the dendritic cells, which were known as the central mediator of tumor microenvironment in gliomas, were positively associated with the expression levels of COL5A1. Taken together, COL5A1 is an important biomarker and potential therapeutic target of gliomas.

Collagen Family Genes Associated with Risk of Recurrence after Radiation Therapy for Vestibular Schwannoma and Pan-Cancer Analysis

Background

The safety of radiotherapy techniques in the treatment of vestibular schwannoma (VS) shows a high rate of tumor control with few side effects. Neuropeptide Y (NPY) may have a potential relevance to the recurrence of VS. Further research is still needed on the key genes that determine the sensitivity of VS to radiation therapy.

Materials and Methods

Transcriptional microarray data and clinical information data from VS patients were downloaded from GSE141801, and vascular-related genes associated with recurrence after radiation therapy for VS were obtained by combining information from MSigDB. Logistics regression was applied to construct a column line graph prediction model for recurrence status after radiation therapy. Pan-cancer analysis was also performed to investigate the cooccurrence of these genes in tumorigenesis.

Results

We identified eight VS recurrence-related genes from the GSE141801 dataset. All of these genes were highly expressed in the VS recurrence samples. Four collagen family genes (COL5A1, COL3A1, COL4A1, and COL15A1) were further screened, and a model was constructed to predict the risk of recurrence of VS. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses revealed that these four collagen family genes play important roles in a variety of biological functions and cellular pathways. Pan-cancer analysis further revealed that the expression of these genes was significantly heterogeneous across immune phenotypes and significantly associated with immune infiltration. Finally, Neuropeptide Y (NPY) was found to be significantly and negatively correlated with the expression of COL5A1, COL3A1, and COL4A1.

Conclusions

Four collagen family genes have been identified as possible predictors of recurrence after radiation therapy for VS. Pan-cancer analysis reveals potential associations between the pathogenesis of VS and other tumorigenic factors. The relevance of NPY to VS was also revealed for the first time.

Comprehensive analysis of multi-omics data of recurrent gliomas identifies a recurrence-related signature as a novel prognostic marker

Tumor recurrence is a common clinical dilemma in diffuse gliomas. We aimed to identify a recurrence-related signature to predict the prognosis for glioma patients. In the public Chinese Glioma Genome Atlas dataset, we enrolled multi-omics data including genome, epigenome and transcriptome across primary and recurrent gliomas. We included RNA sequencing data from the batch 1 patients (325 patients) as the training set, while RNA sequencing data from the batch 2 patients (693 patients) were selected as the validation set. The R language was used for subsequent analysis. Compared with primary gliomas, more somatic mutations and copy number alterations were revealed in recurrent gliomas. In recurrent gliomas, we identified 113 genes whose methylation levels were significantly different from those of the primary glioma. Through differential expression analysis between primary and recurrent gliomas, we screened 121 recurrence-related genes. Based on these 121 gene expression profiles, consensus clustering of 325 patients yielded two robust groups with different molecular and prognostic features. We developed a recurrence-related risk signature with the lasso regression algorithm. High-risk group had shorter survival and earlier tumor recurrence than the low-risk group. Compared with traditional indicators, the signature showed better prognostic value. In addition, we constructed a nomogram model to predict glioma survival. Functional characteristics analysis found that the signature was associated with cell division and cell cycle. Immune analysis suggested that immunosuppressive status and macrophages might promote glioma recurrence. We demonstrated a novel 18-gene signature that could effectively predict recurrence and prognosis for glioma patients.

Collagen Type I α-1 Promotes Malignant Glioma Cell Proliferation and Is Associated with Glioma Prognosis

Collagen type I α-1 chain (COL1A1) is closely involved in the advancement of various tumors, yet the role of COL1A1 in the progression of glioma is not clear. Herein, we evaluated the effect of COL1A1 on glioma cell proliferation. The effect of COL1A1 on glioma cell proliferation was assessed through overexpression or knockdown of COL1A1. The CCK-8 and colony formation assays, as well as immunohistochemistry (IHC) were used to detect COL1A1 expression in different glioma grades. U-87MG as well as U-251MG cells were stably-inserted with lentivirus containing COL1A1 through transfection, we additionally used qRT-PCR as well as Western blot assay to validate their overexpression efficiencies. COL1A1 mRNA and protein levels were upregulated in the high-grade glioma (HGG) compared to the low-grade glioma (LGG). COL1A1 IHC score was remarkably higher in HGG than LGG. The staining index (SI) further showed that COL1A1 protein levels were higher in HGG than LGG. The Kaplan–Meier analysis showed that elevated COL1A1 mRNA levels were obviously correlated with lower overall survival (OS) and disease-free survival (DFS) for brain glioma patients. COL1A1 mRNA and protein levels were markedly upregulated in human glioma cell lines when compared with brain astrocyte cell lines. The high expression level of COL1A1 facilitated glioma cell proliferation. COL1A1 knockdown remarkably inhibited glioma cell proliferation. Thus, this research shows that COL1A1 promotes glioma cell proliferation and is closely related to glioma prognosis.

Collagen Type III Alpha 1 chain regulated by GATA-Binding Protein 6 affects Type II IFN response and propanoate metabolism in the recurrence of lower grade glioma

Some studies suggested the prognosis value of immune gene in lower grade glioma (LGG). Recurrence in LGG is a tough clinical problem for many LGG patients. Therefore, prognosis biomarker is required. Multivariate prognosis Cox model was constructed and then calculated the risk score. And differential expressed transcription factors (TFs) and differential expressed immune genes (DEIGs) were co‐analysed. Besides, significant immune cells/pathways were identified by single sample gene set enrichment analysis (ssGSEA). Moreover, gene set variation analysis (GSVA) and univariate Cox regression were applied to filter prognostic signalling pathways. Additionally, significant DEIG and immune cells/pathways, and significant DEIG and pathways were co‐analysed. Further, differential enriched pathways were identified by GSEA. In sum, a scientific hypothesis for recurrence LGG including TF, immune gene and immune cell/pathway was established. In our study, a total of 536 primary LGG samples, 2,498 immune genes and 318 TFs were acquired. Based on edgeR method, 2,164 DEGs, 2,498 DEIGs and 31 differentials expressed TFs were identified. A total of 106 DEIGs were integrated into multivariate prognostic model. Additionally, the AUC of the ROC curve was 0.860, and P value of Kaplan‐Meier curve < 0.001. GATA6 (TF) and COL3A1 (DEIG) were selected (R = 0.900, P < 0.001, positive) as significant TF‐immune gene links. Type II IFN response (P < 0.001) was the significant immune pathway. Propanoate metabolism (P < 0.001) was the significant KEGG pathway. We proposed that COL3A1 was positively regulated by GATA6, and by effecting type II IFN response and propanoate metabolism, COL3A1 involved in LGG recurrence.

Characterisation of the Expression of Neurotensin and Its Receptors in Human Colorectal Cancer and Its Clinical Implications

Introduction: Colorectal Cancer (CRC) accounts for 9% of cancer deaths globally. Hormonal pathways play important roles in some cancers. This study investigated the association of CRC expression of neurotensin (NTS), NTS receptors 1 and 3 (NTSR1 and NTSR3) and clinical outcomes. Methods: A prospective cohort study which quantifies the protein expression of NTS, NTSR1 and NTSR3 in human CRCs using immunohistochemistry. Expression levels were then compared with clinico-pathological outcome including histological grade, overall survival (OS) and disease-free survival (DFS). Results: Sixty-four patients were enrolled with median follow-up of 44.0 months. There was significantly higher expression of NTS in cancer tissue in CRC with higher T stages (p < 0.01), N stages (p = 0.03), and AJCC clinical stages (p = 0.04). There was significantly higher expression of NTS, NTSR1 and NTSR3 in cancer tissue compared to surrounding normal epithelium (median H-score 163.5 vs 97.3, p < 0.01). There was significantly shorter DFS in individuals with CRC with high levels of NTS compared to lower levels of NTS (35.8 months 95% CI 28.7–42.8 months vs 46.4 months 95% CI 42.2–50.5 months, respectively, p = 0.02). Above median NTS expression in cancer tissue was a significant risk factor for disease recurrence (HR 4.10, 95% CI 1.14–14.7, p = 0.03). Discussion: The expression of NTS and its receptors has the potential to be utilised as a predictive and prognostic marker in colorectal cancer for postoperative selection for adjuvant therapy and identify individuals for novel therapies targeting the neurotensinergic pathways. Conclusions: High NTS expression appears to be associated with more advanced CRC and worse DFS.

Comprehensive Transcriptomic Analysis and Experimental Validation Identify lncRNA HOXA-AS2/miR-184/COL6A2 as the Critical ceRNA Regulation Involved in Low-Grade Glioma Recurrence

Purpose

The recurrence and metastasis of glioma are closely related to complex regulatory networks among protein-coding genes, lncRNAs and microRNAs. The aim of this study was to investigate core genes, lncRNAs, miRNAs and critical ceRNA regulatory mechanisms, which are involved in lower-grade glioma (LGG) recurrence.

Materials and Methods

We employed multiple datasets from Chinese Glioma Genome Atlas (CGGA) database and The Cancer Genome Atlas (TCGA) to perform comprehensive transcriptomic analysis. Further in vitro experiments including cell proliferation assay, luciferase reporter assay, and Western blot were performed to validate our results.

Results

Recurrent LGG and glioblastoma (GBM) showed different transcriptome characteristics with less overlap of differentially expressed protein-coding genes (DEPs), lncRNAs (DELs) and miRNAs (DEMs) compared with primary samples. There were no overlapping gene in ontology (GO) terms related to GBM recurrence in the TCGA and CGGA databases, but there were overlaps associated with LGG recurrence. GO analysis and protein–protein interaction (PPI) network analysis identified three core genes: TIMP1, COL1A1 and COL6A2. By hierarchical cluster analysis of them, LGGs could be clustered as Low_risk and High_risk group. The High_risk group with high expression of TIMP1, COL1A1, and COL6A2 showed worse prognosis. By coexpression networks analysis, competing endogenous RNA (ceRNA) network analysis, cell proliferation assay and luciferase reporter assay, we confirmed that lncRNA HOXA-AS2 functioned as a ceRNA for miR-184 to regulate expression of COL6A2, which induced cell proliferation of low-grade glioma.

Conclusion

In this study, we revealed a 3-hub protein-coding gene signature to improve prognostic prediction in LGG, and identified a critical ceRNA regulation involved in LGG recurrence.

Bioinformatics Analysis of the Expression of ATP Binding Cassette Subfamily C Member 3 (ABCC3) in Human Glioma

Objective

To investigate the expression of the ABCC3 gene in human glioma and its correlation with the patient’s prognosis.

Methods

The cancer genome atlas (TCGA) database was used to analyze the differential expression of the ABCC3 gene in human glioma. The STRING database was used to construct the protein-protein interaction (PPI) network of the ABCC3 gene coding protein. The co-expression genes relevant to the ABCC3 gene were analyzed by the Pearson correlation test. A log-rank test was used to analyze the difference of overall survival (OS) and disease-free survival (DFS) between the high and low ABCC3 gene expression groups.

Results

The expression level of the ABCC3 gene in glioma tissues was lower than that of corresponding normal brain tissues. The PPI network contains 51 nodes with the average node degree of 13.3 and the local clustering coefficient of 0.72 which indicated that the PPI enrichment was significant (p<0.001). Ten hub genes (ABCC3,NR1I2,NR1H4,-CYP7A1,SLC10A1,CYP3A4,UGT1A1,UGT1A8,UGT1A6 and ALB) were identified by the cytoscape software. The KEGG analysis was enriched in drug metabolism - cytochrome P450 and PPAR signaling pathway. CFI gene expression level was positive correlated with the ABCC3 expression level (r=0.71, p<0.05). And the CNRIP1 gene expressed was negative correlated with ABCC3 expression (r=-0.43, p<0.05). The overall survival (HR=2.8, P<0.05) and disease-free survival rates (HR=2.0, P<0.05) of patients with ABCC3 low expression glioma were significantly higher than those of patients with high expression of ABCC3. Conclusion The expression level of the ABCC3 gene in glioma was decreased compared to normal brain tissue. The overall survival and disease-free survival of in the ABCC3 low-expression group was significant decreased.

High expression of EZR (ezrin) gene is correlated with the poor overall survival of breast cancer patients

Background

To evaluate the EZR (ezrin) gene expression in breast cancer and correlation with the prognosis through bioinformatics analysis and immunohistochemistry assay.

Methods

EZR gene expression in breast cancer and corresponding normal breast tissue was compared in the TCGA database. Protein‐protein interaction (PPI) network relevant EZR was established through the STRING database. The correlation between EZR expression and prognosis of breast cancer was analyzed by the log‐rank analysis from the TCGA. Ezrin protein (coded by EZR) expression was also examined by immunohistochemistry assay in 120 breast cancer patients.

Results

EZR expression level in tumor tissue was significantly upregulated compared to that of normal breast tissue of breast cancer patients (P < 0.05). In the PPI analysis, there were 51 nodes and 455 edges in the network. The top 10 hub genes of the network were identified. High expression of EZR mRNA was correlated with poor overall survival (OS) of the breast cancer patients (HR = 1.40, P = 0.038). However, the disease‐free survival (DFS) of breast cancer patients did not correlate with the EZR mRNA level (HR = 0.86, P = 0.44). The ezrin protein expression was positive with uniform brown‐yellow granules in the cell membrane, cavity surface and cytoplasm of the breast cancer cells. Of the included 120 cancer samples, 98 cases were positive for ezrin expression and 22 were negative. No correlation was found between ezrin expression site and patients’ clinicopathological features.

Conclusion

EZR is upregulated in breast cancer and can be used as potential biomarker for overall survival.