Aberrant Methylation and Differential Expression of SLC2A1, TNS4, GAPDH, ATP8A2, and CASZ1 Are Associated with the Prognosis of Lung Adenocarcinoma

Role of the CASZ1 transcription factor in tissue development and disease

The zinc finger transcription factor gene, CASZ1/Castor (Castor zinc finger 1), initially identified in Drosophila, plays a critical role in neural, cardiac, and cardiovascular development, exerting a complex, multifaceted influence on cell fate and tissue morphogenesis. During neurogenesis, CASZ1 exhibits dynamic expression from early embryonic development to the perinatal period, constituting a key regulator in this process. Additionally, CASZ1 controls the transition between neurogenesis and gliomagenesis. During human cardiovascular system development, CASZ1 is essential for cardiomyocyte differentiation, cardiac morphogenesis, and vascular morphology homeostasis and formation. The deletion or inactivation of CASZ1 mutations can lead to human developmental diseases or tumors, including congenital heart disease, cardiovascular disease, and neuroblastoma. CASZ1 can be used as a biomarker for disease prevention and diagnosis as well as a prognostic indicator for cancer. This review explores the unique functions of CASZ1 in tissue morphogenesis and associated diseases, offering new insights for elucidating the molecular mechanisms underlying diseases and identifying potential therapeutic targets for disease prevention and treatment.

Study on Potential Differentially Expressed Genes in Idiopathic Pulmonary Fibrosis by Bioinformatics and Next-Generation Sequencing Data Analysis

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive lung disease with reduced quality of life and earlier mortality, but its pathogenesis and key genes are still unclear. In this investigation, bioinformatics was used to deeply analyze the pathogenesis of IPF and related key genes, so as to investigate the potential molecular pathogenesis of IPF and provide guidance for clinical treatment. Next-generation sequencing dataset GSE213001 was obtained from Gene Expression Omnibus (GEO), and the differentially expressed genes (DEGs) were identified between IPF and normal control group. The DEGs between IPF and normal control group were screened with the DESeq2 package of R language. The Gene Ontology (GO) and REACTOME pathway enrichment analyses of the DEGs were performed. Using the g:Profiler, the function and pathway enrichment analyses of DEGs were performed. Then, a protein-protein interaction (PPI) network was constructed via the Integrated Interactions Database (IID) database. Cytoscape with Network Analyzer was used to identify the hub genes. miRNet and NetworkAnalyst databaseswereused to construct the targeted microRNAs (miRNAs), transcription factors (TFs), and small drug molecules. Finally, receiver operating characteristic (ROC) curve analysis was used to validate the hub genes. A total of 958 DEGs were screened out in this study, including 479 up regulated genes and 479 down regulated genes. Most of the DEGs were significantly enriched in response to stimulus, GPCR ligand binding, microtubule-based process, and defective GALNT3 causes HFTC. In combination with the results of the PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network, hub genes including LRRK2, BMI1, EBP, MNDA, KBTBD7, KRT15, OTX1, TEKT4, SPAG8, and EFHC2 were selected. Cyclothiazide and rotigotinethe are predicted small drug molecules for IPF treatment. Our findings will contribute to identification of potential biomarkers and novel strategies for the treatment of IPF, and provide a novel strategy for clinical therapy.

Construction and validation of an efferocytosis-related prognostic signature in lung adenocarcinoma

BACKGROUND

Accumulating evidence highlights the potential significance of efferocytosis in tumor progression. This study is directed towards the construction of a prognostic risk model for lung adenocarcinoma (LUAD), grounded on efferocytosis-related genes (ERGs).

METHODS

Employing LASSO-COX regression analysis, a risk-prognostic model was formulated, centered on seven ERGs. Concurrently, a nomogram was established that incorporated patient clinical features and risk scores. The predictive accuracy of the risk model and the nomogram was substantiated via external validation sets. The landscapes of immune infiltration and genetic mutation were evaluated for high- and low-risk groups, with the expression of seven key genes validated through RT-PCR.

RESULTS

Our findings reveal that the high-risk group displayed considerably inferior survival outcomes in comparison to the low-risk group. A diminished abundance of immune cell infiltrates and a higher prevalence of gene mutations characterized the high-risk group. Genes with high expression were markedly enriched in pathways related to cell proliferation. The superior predictive performance of the risk model and nomogram was adequately substantiated by the external validation sets (GSE31210, GSE30219, and GSE50081). In addition, we discerned several potential therapeutic drugs demonstrating different sensitivities across patient risk groups. The differential expression of seven central genes was confirmed in A549, H1299, and BEAS-2B cell lines.

CONCLUSION

The constructed risk model and nomogram display high accuracy in predicting the survival and immune landscape of LUAD patients, thus providing invaluable prognostic tools in clinical scenarios.

Establishment of a Lymph Node Metastasis-Associated Prognostic Signature for Lung Adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is the most common histological subtype of non-small cell lung cancer (NSCLC) with a low 5-year survival rate, which may be associated with the presence of metastatic tumors at the time of diagnosis, especially lymph node metastasis (LNM). This study aimed to construct a LNM-related gene signature for predicting the prognosis of patients with LUAD.

Methods

RNA sequencing data and clinical information of LUAD patients were extracted from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Samples were divided into metastasis (M) and nonmetastasis (NM) groups based on LNM status. Differentially expressed genes (DEGs) between M and NM groups were screened, and then WGCNA was applied to identify key genes. Furthermore, univariate Cox and LASSO regression analyses were conducted to construct a risk score model, and the predictive performance of model was validated by GSE68465, GSE42127, and GSE50081. The protein and mRNA expression level of LNM-associated genes were detected by human protein atlas (HPA) and GSE68465.

Results

A prognostic model based on eight LNM-related genes (ANGPTL4, BARX2, GPR98, KRT6A, PTPRH, RGS20, TCN1, and TNS4) was developed. Patients in the high-risk group had poorer overall survival than those in the low-risk group, and validation analysis showed that this model had potential predictive value for patients with LUAD. HPA analysis supported the upregulation of ANGPTL4, KRT6A, BARX2, RGS20 and the downregulation of GPR98 in LUAD compared with normal tissues.

Conclusion

Our results indicated that the eight LNM-related genes signature had potential value in the prognosis of patients with LUAD, which may have important practical implications.

CASZ1 promotes migration, invasion, and metastasis of lung cancer cells by controlling expression of ITGAV

CASZ1, a zinc finger transcription factor with two isoforms, is known to play important roles in cardiac and neural development. The abnormal expression of CASZ1 is also frequently found in a variety of tumors but has different effects on different tumors; for example, it acts as a tumor suppressor in neuroblastoma but promotes cancer metastasis in ovarian cancer. However, the effect of CASZ1 in lung cancer, the most lethal cancer, remains unclear. Here, we found that the expression of CASZ1 in lung cancer is positively associated with cancer metastasis and poor prognosis. The overexpression of CASZ1b promotes lung cancer cell migration, invasion, and epithelial-mesenchymal transition and is associated with poor prognosis in lung cancer patients. The knockdown of CASZ1 resulted in the suppression of epithelial-mesenchymal transition, migration, and invasion of lung cancer cells and reduced metastasis in vivo. The results of an RNA-sequencing analysis of CASZ1-silenced cells showed that CASZ1 considerably affected the integrin-mediated pathways. CASZ1 bound to the ITGAV promoter and transcriptionally regulated ITGAV expression. Our findings demonstrate that CASZ1 plays an oncogenic role in lung cancer and that CASZ1 promotes lung cancer migration, invasion and metastasis is mediated by ITGAV.

Cell death-related biomarker SLC2A1 has a significant role in prognosis prediction and immunotherapy efficacy evaluation in pan-cancer

Introduction: SLC2A1, a member of the SLC transporter family, is involved in a variety of cell death modalities and has been found to be associated with the prognosis and immune microenvironment of a variety of tumors. However, there is a lack of systematic and comprehensive studies on the role of SLC2A1 in pan-cancer. Methods: The mRNA, promoter methylation, and protein expression levels of SLC2A1 in pan-cancer were comprehensively evaluated using GEPIA2.0, TIMER2.0, and UALCAN databases. UCSCXenaShiny based on the cancer genomic atlas pan-cancer data and GEPIA2.0 database were used to assess the prognostic significance of SLC2A1 in pan-cancer. Genetic alterations in SLC2A1 were also evaluated using cBioPortal. The relevance of SLC2A1 to immune infiltrating cells in pan-cancer was evaluated using the XCELL algorithm in combination with the TIMER2.0 database. The correlation of SLC2A1 with the efficacy of immune checkpoint blocker (ICB) therapy was evaluated using the tumor immune dysfunction and exclusion (TIDE) score. The correlation of SLC2A1 with numerous immune-related markers was also evaluated using the TISIDB database. The correlation of SLC2A1 with tumor biological function was evaluated at the single-cell level using the CancerSEA database. Finally, the biological function of SLC2A1 was comprehensively evaluated using gene set enrichment analysis (GSEA) and protein interaction networks. Results: SLC2A1 expression is aberrant in a variety of tumors and is strongly associated with the prognosis of several cancers. SLC2A1 is significantly associated with a variety of immune infiltrating cells including CD8⁺ T cells, myeloid-derived suppressor cells and macrophages in a variety of tumors. Meanwhile, the expression of SLC2A1 significantly correlated with multiple immune-related markers. In addition, SLC2A1 can also predict the effect of immune checkpoint blocker therapy in some tumors. In a functional analysis, SLC2A1 was significantly associated with hypoxia, epithelial-mesenchymal transition, mTORC1 signaling, and multiple metabolic pathways in pan-cancer. Conclusion: Our study systematically and comprehensively summarizes the prognostic significance and immune-related role of SLC2A1 in pan-cancer and reveals the potential mechanism of SLC2A1 in regulating the tumor microenvironment and tumor behavior, providing a new effective pan-applicable biomarker for prognostic prediction and the evaluation of immunotherapeutic strategies for tumors.

Identification of Cuproptosis-Related Subtypes in Lung Cancer, Characterization of Tumor Microenvironment Infiltration, and Establishment of a Prognostic Model

Cuproptosis, a recently found kind of programmed cell death, has been linked to tumor development, prognosis, and therapeutic response. The roles of cuproptosis-related genes (CRG) in the tumor microenvironment (TME) are, nevertheless, unknown. We evaluated alterations in CRG and assessed the related expression patterns in 1445 lung cancer (LC) samples from three separate datasets, analyzing genetic, and transcriptional domains. We discovered two separate molecular subtypes of CRG and discovered that various subtypes of CRG were connected with patient clinical features and prognosis. Furthermore, we discovered connections between distinct CRG subtypes and TME cell infiltration features. The CRG_score was then developed and validated for predicting overall survival (OS). Following that, we investigated the relationship between CRG_score and the cancer stem cell (CSC) index and chemotherapeutic treatment sensitivity. In addition, we created a very accurate nomogram to increase the clinical usefulness of CRG_score. The potential roles of CRG in the tumor-immune-microenvironment, clinical characteristics, and prognosis in LC are demonstrated by our multiplex study. These findings expand our understanding of CRG in LC and may open up new options for assessing LC patients' prognosis and generating more effective immunotherapeutic treatments.

Single-cell RNA-seq analysis to identify potential biomarkers for diagnosis, and prognosis of non-small cell lung cancer by using comprehensive bioinformatics approaches

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer and the leading cause of cancer-related deaths worldwide. Identification of gene biomarkers and their regulatory factors and signaling pathways is very essential to reveal the molecular mechanisms of NSCLC initiation and progression. Thus, the goal of this study is to identify gene biomarkers for NSCLC diagnosis and prognosis by using scRNA-seq data through bioinformatics techniques. scRNA-seq data were obtained from the GEO database to identify DEGs. A total of 158 DEGs (including 48 upregulated and 110 downregulated) were detected after gene integration. Gene Ontology enrichment and KEGG pathway analysis of DEGs were performed by FunRich software. A PPI network of DEGs was then constructed using the STRING database and visualized by Cytoscape software. We identified 12 key genes (KGs) including MS4A1, CCL5, and GZMB, by using two topological methods based on the PPI networking results. The diagnostic, expression, and prognostic potentials of the identified 12 key genes were assessed using the receiver operating characteristics (ROC) curve and a web-based tool, SurvExpress. From the regulatory network analysis, we extracted the 7 key transcription factors (TFs) (FOXC1, YY1, CEBPB, TFAP2A, SREBF2, RELA, and GATA2), and 8 key miRNAs (hsa-miR-124-3p, hsa-miR-34a-5p, hsa-miR-21-5p, hsa-miR-155-5p, hsa-miR-449a, hsa-miR-24-3p, hsa-let-7b-5p, and hsa-miR-7-5p) associated with the KGs were evaluated. Functional enrichment and pathway analysis, survival analysis, ROC analysis, and regulatory network analysis highlighted crucial roles of the key genes. Our findings might play a significant role as candidate biomarkers in NSCLC diagnosis and prognosis.

The Pharmacological Mechanism of Curcumin against Drug Resistance in Non-Small Cell Lung Cancer: Findings of Network Pharmacology and Bioinformatics Analysis

The pharmacological mechanism of curcumin against drug resistance in non-small cell lung cancer (NSCLC) remains unclear. This study aims to summarize the genes and pathways associated with curcumin action as an adjuvant therapy in NSCLC using network pharmacology, drug-likeness, pharmacokinetics, functional enrichment, protein-protein interaction (PPI) analysis, and molecular docking. Prognostic genes were identified from the curcumin-NSCLC intersection gene set for the following drug sensitivity analysis. Immunotherapy, chemotherapy, and targeted therapy sensitivity analyses were performed using external cohorts (GSE126044 and IMvigor210) and the CellMiner database. 94 curcumin-lung adenocarcinoma (LUAD) hub targets and 41 curcumin-lung squamous cell carcinoma (LUSC) hub targets were identified as prognostic genes. The anticancer effect of curcumin was observed in KEGG pathways involved with lung cancer, cancer therapy, and other cancers. Among the prognostic curcumin-NSCLC intersection genes, 20 LUAD and 8 LUSC genes were correlated with immunotherapy sensitivity in the GSE126044 NSCLC cohort; 30 LUAD and 13 LUSC genes were associated with immunotherapy sensitivity in the IMvigor210 cohort; and 12 LUAD and 13 LUSC genes were related to chemosensitivity in the CellMiner database. Moreover, 3 LUAD and 5 LUSC genes were involved in the response to targeted therapy in the CellMiner database. Curcumin regulates drug sensitivity in NSCLC by interacting with cell cycle, NF-kappa B, MAPK, Th17 cell differentiation signaling pathways, etc. Curcumin in combination with immunotherapy, chemotherapy, or targeted drugs has the potential to be effective for drug-resistant NSCLC. The findings of our study reveal the relevant key signaling pathways and targets of curcumin as an adjuvant therapy in the treatment of NSCLC, thus providing pharmacological evidence for further experimental research.

Prognostic Ability of Enhancer RNAs in Metastasis of Non-Small Cell Lung Cancer

(1) Background: Non-small cell lung cancer (NSCLC) is the most common lung cancer. Enhancer RNA (eRNA) has potential utility in the diagnosis, prognosis and treatment of cancer, but the role of eRNAs in NSCLC metastasis is not clear; (2) Methods: Differentially expressed transcription factors (DETFs), enhancer RNAs (DEEs), and target genes (DETGs) between primary NSCLC and metastatic NSCLC were identified. Prognostic DEEs (PDEEs) were screened by Cox regression analyses and a predicting model for metastatic NSCLC was constructed. We identified DEE interactions with DETFs, DETGs, reverse phase protein arrays (RPPA) protein chips, immunocytes, and pathways to construct a regulation network using Pearson correlation. Finally, the mechanisms and clinical significance were explained using multi-dimensional validation unambiguously; (3) Results: A total of 255 DEEs were identified, and 24 PDEEs were selected into the multivariate Cox regression model (AUC = 0.699). Additionally, the NSCLC metastasis-specific regulation network was constructed, and six key PDEEs were defined (ANXA8L1, CASTOR2, CYP4B1, GTF2H2C, PSMF1 and TNS4); (4) Conclusions: This study focused on the exploration of the prognostic value of eRNAs in the metastasis of NSCLC. Finally, six eRNAs were identified as potential markers for the prediction of metastasis of NSCLC.

Bioinformatics Characterization of Candidate Genes Associated with Gene Network and miRNA Regulation in Esophageal Squamous Cell Carcinoma Patients

The present study aimed to identify potential therapeutic targets for esophageal squamous cell carcinoma (ESCC). The gene expression profile GSE161533 contained 84 samples, in that 28 tumor tissues and 28 normal tissues encoded as ESCC patients were retrieved from the Gene Expression Omnibus database. The obtained data were validated and screened for differentially expressed genes (DEGs) between normal and tumor tissues with the GEO2R tool. Next, the protein–protein network (PPI) was constructed using the (STRING 2.0) and reconstructed with Cytoscape 3.8.2, and the top ten hub genes (HGsT10) were predicted using the Maximal Clique Centrality (MCC) algorithm of the CytoHubba plugin. The identified hub genes were mapped in GSE161533, and their expression was determined and compared with The Cancer Genome Atlas (TCGA.) ESCC patient’s samples. The overall survival rate for HGsT10 wild and mutated types was analyzed with the Gene Expression Profiling Interactive Analysis2 (GEPIA2) server and UCSC Xena database. The functional and pathway enrichment analysis was performed using the WebGestalt database with the reference gene from lumina human ref 8.v3.0 version. The promoter methylation for the HGsT10 was identified using the UALCAN server. Additionally, the miRNA-HGsT10 regulatory network was constructed to identify the top ten hub miRNAs (miRT10). Finally, we identified the top ten novel driving genes from the DEGs of GSE161533 ESCC patient’s sample using a multi-omics approach. It may provide new insights into the diagnosis and treatment for the ESCC affected patients early in the future.

The Protective Effect of Panax notoginseng Mixture on Hepatic Ischemia/Reperfusion Injury in Mice via Regulating NR3C2, SRC, and GAPDH

Panax notoginseng mixture (PNM) has the characteristics of multicomponent, multitarget, and multieffect, which can cope with the multidirectional and multidimensional complex pathological process caused by hepatic ischemia/reperfusion injury (HIRI). Our animal experiments showed that PNM composed of notoginseng, dogwood, and white peony root could significantly reduce the level of aspartate transaminase and alanine aminotransferase in the blood of mice with HIRI, indicating that this preparation had a protective effect on HIRI in mice. Therefore, on this basis, the molecular mechanism of PNM intervention in HIRI was further explored by network pharmacology. First, target genes corresponding to active components and HIRI were obtained through databases such as TCMSP, Pharm Mapper, Swiss Target Prediction, GeneCards, and so on. All target genes were standardized by Uniprot database, and a total of 291 target genes with their intersection were obtained. Then, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and biological processes (BPs) of 291 target genes were obtained through the online public platform of DAVID. A total of 177 KEGG pathways and 337 BPs were obtained by setting p < 0.01 and false discovery rate <0.05. The network mapping map of components and disease targets was drawn by Cytoscape, and the top 10 Hub target genes related to HIRI were obtained. At the same time, the String database was used to obtain the protein-protein interaction dataset, which was imported into Cytoscape, and the first 10 Hub target genes were obtained. The Hub target genes obtained by the above two methods were molecular docking with their corresponding small molecule compounds through DockThor online tool. The results showed that the docking of paeoniflorin with glyceraldehyde 3-phosphate dehydrogenase (GAPDH), paeoniflorin and loganin with SRC, ginsenoside Rb1 with NR3C2, ursolic acid and oleanolic acid with IL-6, paeoniflorin docking VEGFA, and MMP9. Finally, NR3C2, SRC, and GAPDH were identified as target genes in this study by referring to relevant literature reports. After verification by immunohistochemical experiments, compared with the sham group, the above three target genes were highly expressed in the HIRI group (p < 0.01). Compared with the HIRI group, the expression of three target genes in the PNM + HIRI group was significantly decreased (p < 0.01). The results showed that PNM could protect mouse HIRI by decreasing the expression of NR3C2, SRC, and GAPDH.

An 11-Gene Signature Risk-Prediction Model Based on Prognosis-Related miRNAs and Their Target Genes in Lung Adenocarcinoma

Aberrant expression of microRNAs may affect tumorigenesis and progression by regulating their target genes. This study aimed to construct a risk model for predicting the prognosis of patients with lung adenocarcinoma (LUAD) based on differentially expressed microRNA-regulated target genes. The miRNA sequencing data, RNA sequencing data, and patients’ LUAD clinical data were downloaded from the The Cancer Genome Atlas (TCGA) database. Differentially expressed miRNAs and genes were screened out by combining differential analysis with LASSO regression analysis to further screen out miRNAs associated with patients’ prognosis, and target gene prediction was performed for these miRNAs using a target gene database. Overlapping gene screening was performed for target genes and differentially expressed genes. LASSO regression analysis and survival analysis were then used to identify key genes. Risk score equations for prognostic models were established using multifactorial COX regression analysis to construct survival prognostic models, and the accuracy of the models was evaluated using subject working characteristic curves. The groups were divided into high- and low-risk groups according to the median risk score, and the correlation with the clinicopathological characteristics of the patients was observed. A total of 123 up-regulated miRNAs and 22 down-regulated miRNAs were obtained in this study. Five prognosis-related miRNAs were screened using LASSO regression analysis and Kaplan-Meier method validation, and their target genes were screened with the overlap of differentially expressed genes before multifactorial COX analysis finally resulted in an 11-gene risk model for predicting patient prognosis. The area under the ROC curve proved that the model has high accuracy. The 11-gene risk-prediction model constructed in this study may be an effective predictor of prognosis.

A Novel Mitochondrial-Related Nuclear Gene Signature Predicts Overall Survival of Lung Adenocarcinoma Patients

Background: Lung cancer is the leading cause of cancer-related death worldwide, of which lung adenocarcinoma (LUAD) is one of the main histological subtypes. Mitochondria are vital for maintaining the physiological function, and their dysfunction has been found to be correlated with tumorigenesis and disease progression. Although, some mitochondrial-related genes have been found to correlate with the clinical outcomes of multiple tumors solely. The integrated relationship between nuclear mitochondrial genes (NMGs) and the prognosis of LUAD remains unclear.

Methods: The list of NMGs, gene expression data, and related clinical information of LUAD were downloaded from public databases. Bioinformatics methods were used and obtained 18 prognostic related NMGs to construct a risk signature.

Results: There were 18 NMGs (NDUFS2, ATP8A2, SCO1, COX14, COA6, RRM2B, TFAM, DARS2, GARS, YARS2, EFG1, GFM1, MRPL3, MRPL44, ISCU, CABC1, HSPD1, and ETHE1) identified by LASSO regression analysis. The mRNA expression of these 18 genes was positively correlated with their relative linear copy number alteration (CNA). Meanwhile, the established risk signature could effectively distinguish high- and low-risk patients, and its predictive capacity was validated in three independent gene expression omnibus (GEO) cohorts. Notably, a significantly lower prevalence of actionable EGFR alterations was presented in patients with high-risk NMGs signature but accompanied with a more inflame immune tumor microenvironment. Additionally, multicomponent Cox regression analysis showed that the model was stable when risk score, tumor stage, and lymph node stage were considered, and the 1-, 3-, and 5-year AUC were 0.74, 0.75, and 0.70, respectively.

Conclusion: Together, this study established a signature based on NMGs that is a prognostic biomarker for LUAD patients and has the potential to be widely applied in future clinical settings.

CircHECTD1 Regulates Cell Proliferation and Migration by the miR-320-5p/SLC2A1 Axis in Glioblastoma Multiform

Glioblastoma multiform (GBM) is the most common and malignant primary brain cancer in adults, and thus, novel potential therapeutic targets for diagnosis and treatment are urgently needed. Circular RNAs (circRNAs) are a class of widespread and diverse endogenous RNAs that have been suggested as potential critical mediators during progression of various tumors. In this study, we investigated the involvement of circHECTD1 in GBM progression. CircHECTD1 Lentivirus, miR-320-5p mimic, and SLC2A1 Lentivirus were transduced into cancer cells independently or together. circHECTD1, miR-320-5p, and SLC2A1 level were detected by qRT-PCR. Western blot and qRT-PCR were applied to measure the expression of SLC2A1, CyclinD1, CDK2, and PCNA. Flow cytometry, EdU, colony formation, Transwell and wound-healing assays were conducted to assess cell proliferation and migration. Luciferase reporter assays were performed to determine the effect of miR-320-5p on circHECTD1 or SLC2A1. Xenograft experiments were implemented to evaluate tumor growth in vivo. CircHECTD1 expression led to the promotion of proliferation and migration of GBM cells. In addition, circHECTD1 acted as a ceRNA to interact with miR-320-5p, which targeted the solute carrier family 2 member 1 (SLC2A1). In vivo experiments also revealed that circHECTD1 promoted tumor growth. Collectively, our findings showed that the circHECTD1-miR-320-5p-SLC2A1 regulatory pathway promoted the progression of GBM, suggesting that circHECTD1 may be a therapeutic target for GBM.