TGIF1 overexpression promotes glioma progression and worsens patient prognosis

Gene regulatory network topology governs resistance and treatment escape in glioma stem-like cells

Poor prognosis and drug resistance in glioblastoma (GBM) can result from cellular heterogeneity and treatment-induced shifts in phenotypic states of tumor cells, including dedifferentiation into glioma stem-like cells (GSCs). This rare tumorigenic cell subpopulation resists temozolomide, undergoes proneural-to-mesenchymal transition (PMT) to evade therapy, and drives recurrence. Through inference of transcriptional regulatory networks (TRNs) of patient-derived GSCs (PD-GSCs) at single-cell resolution, we demonstrate how the topology of transcription factor interaction networks drives distinct trajectories of cell-state transitions in PD-GSCs resistant or susceptible to cytotoxic drug treatment. By experimentally testing predictions based on TRN simulations, we show that drug treatment drives surviving PD-GSCs along a trajectory of intermediate states, exposing vulnerability to potentiated killing by siRNA or a second drug targeting treatment-induced transcriptional programs governing nongenetic cell plasticity. Our findings demonstrate an approach to uncover TRN topology and use it to rationally predict combinatorial treatments that disrupt acquired resistance in GBM.

Signature Construction and Disulfidptosis-Related Molecular Cluster Identification for Better Prediction of Prognosis in Glioma

Disulfidptosis is a newly discovered form of regulatory cell death. However, the identification of disulfidptosis-related molecular subtypes and potential biomarkers in gliomas and their prognostic predictive potential need to be further elucidated. RNA sequencing profiles and the relevant clinical data were obtained from the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). Disulfidptosis-related clusters were identified by unsupervised clustering analysis. Immune cell infiltration analysis and drug sensitivity analysis were used to explore the differences between clusters. Gene set enrichment analysis (GSEA) of differential genes between clusters was performed to explore the potential biological functions and signaling. A disulfidptosis-related scoring system (DRSS) was constructed based on a combined COX and LASSO analysis. Mendelian randomization (MR) analyses were used to further explore the causal relationship between levels of genes in DRSS and an increased risk of glioma. A prognosis nomogram was constructed based on the DRSS and 3 clinical features (age, WHO stage, and IDH status). The accuracy and stability of the prognosis nomogram were also validated in different cohorts. We identified two clusters that exhibited different prognoses, drug sensitivity profiles, and tumor microenvironment infiltration profiles. The overall survival (OS) of Cluster2 was significantly better than Cluster1. Cluster1 had an overall greater infiltration of immune cells compared to Cluster2. However, the Monocytes, activated B cells had higher infiltration abundance in Cluster2. GSEA results showed significant enrichment of immune-related biological processes in Cluster1, while Cluster2 was more enriched for functions related to neurotransmission and regulation. PER3, RAB34, NKX3-2, GPX7, FRA10AC1, and TGIF1 were finally included to construct DRSS. DRSS was independently related to prognosis. There was a significant difference in overall survival between the low-risk score group and the high-risk score group. Among six genes in DRSS, GPX7 levels were demonstrated to have a causal relationship with an increased risk of glioma. GPX7 may become a more promising biomarker for gliomas. The prognosis nomogram constructed based on the DRSS and three clinical features has considerable potential for predicting the prognosis of patients with glioma. Free online software for implementing this nomogram was established:  https://yekun-zhuang.shinyapps.io/DynNomapp/ . Our study established a novel glioma classification based on the disulfidptosis-related molecular subtypes. We constructed the DRSS and the prognosis nomogram to accurately stratify the prognosis of glioma patients. GPX7 was identified as a more promising biomarker for glioma. We provide important insights into the treatment and prognosis of gliomas.

[Effect of CircCCND1 on the Malignant Biological Behaviors of H446 Lung Cancer Cells by Regulating the MiR-340-5p/TGIF1 Axis]

BACKGROUND

Lung cancer is a common malignant tumor of the lung. To explore the molecular mechanism of the occurrence and development of lung cancer is a hot topic in current research. Cyclic RNA D1 (CircCCND1) is highly expressed in lung cancer and may be a potential target for the treatment of lung cancer. The aim of this study was to investigate the effect of CircCCND1 on the malignant biological behaviors of lung cancer cells by regulating the miR-340-5p/transforming growth factor β-induced factor homeobox 1 (TGIF1) axis.

METHODS

The expression of CircCCND1, miR-340-5p, and TGIF1 mRNA in human normal lung epithelial cells BEAS-2B and human lung cancer H446 cells were detected. H446 cells cultured in vitro were randomly divided into control group, CircCCND1 siRNA group, miR-340-5p mimics group, negative control group, and CircCCND1 siRNA+miR-340-5p inhibitor group. Cell proliferation, mitochondrial membrane potential, apoptosis, migration, and invasion were detected, and the expressions of CircCCND1, miR-340-5p, TGIF1 mRNA, BCL2-associated X protein (Bax), cleaved Caspase-3, N-cadherin, E-cadherin, and TGIF1 proteins in each group were detected. The targeting relationship of miR-340-5p with CircCCND1 and TGIF1 was verified.

RESULTS

Compared with BEAS-2B cells, CircCCND1 and TGIF1 mRNA were increased in H446 cells, and miR-340-5p expression was decreased (P<0.05). Knocking down CircCCND1 or up-regulating the expression of miR-340-5p inhibited the proliferation, migration and invasion of H446 cells, decreased the expression of TGIF1 mRNA and TGIF1 protein, and promoted cell apoptosis. Down-regulation of miR-340-5p could antagonize the inhibitory effect of CircCCND1 knockdown on the malignant biological behavior of H446 lung cancer cells. CircCCND1 may target the down-regulation of miR-340-5p, and miR-340-5p may target the down-regulation of TGIF1.

CONCLUSIONS

Knocking down CircCCND1 can inhibit the malignant behaviors of lung cancer H446 cells, which may be achieved through the regulation of miR-340-5p/TGIF1 axis.

Gene regulatory network topology governs resistance and treatment escape in glioma stem-like cells

Poor prognosis and drug resistance in glioblastoma (GBM) can result from cellular heterogeneity and treatment-induced shifts in phenotypic states of tumor cells, including dedifferentiation into glioma stem-like cells (GSCs). This rare tumorigenic cell subpopulation resists temozolomide, undergoes proneural-to-mesenchymal transition (PMT) to evade therapy, and drives recurrence. Through inference of transcriptional regulatory networks (TRNs) of patient-derived GSCs (PD-GSCs) at single-cell resolution, we demonstrate how the topology of transcription factor interaction networks drives distinct trajectories of cell state transitions in PD-GSCs resistant or susceptible to cytotoxic drug treatment. By experimentally testing predictions based on TRN simulations, we show that drug treatment drives surviving PD-GSCs along a trajectory of intermediate states, exposing vulnerability to potentiated killing by siRNA or a second drug targeting treatment-induced transcriptional programs governing non-genetic cell plasticity. Our findings demonstrate an approach to uncover TRN topology and use it to rationally predict combinatorial treatments that disrupts acquired resistance in GBM.

Integrating Machine Learning and Mendelian Randomization Determined a Functional Neurotrophin-Related Gene Signature in Patients with Lower-Grade Glioma

Recent researches reported that neurotrophins can promote glioma growth/invasion but the relevant model for predicting patients' survival in Lower-Grade Gliomas (LGGs) lacked. In this study, we adopted univariate Cox analysis, LASSO regression, and multivariate Cox analysis to determine a signature including five neurotrophin-related genes (NTGs), CLIC1, SULF2, TGIF1, TTF2, and WEE1. Two-sample Mendelian Randomization (MR) further explored whether these prognostic-related genes were genetic variants that increase the risk of glioma. A total of 1306 patients have been included in this study, and the results obtained from the training set can be verified by four independent validation sets. The low-risk subgroup had longer overall survival in five datasets, and its AUC values all reached above 0.7. The risk groups divided by the NTGs signature exhibited a distinct difference in targeted therapies from the copy-number variation, somatic mutation, LGG's surrounding microenvironment, and drug response. MR corroborated that TGIF1 was a potential causal target for increasing the risk of glioma. Our study identified a five-NTGs signature that presented an excellent survival prediction and potential biological function, providing new insight for the selection of LGGs therapy.

Quantitative study of bioinformatics analysis on glioma: a bibliometric analysis

Background

The bioinformatics analysis on glioma has been a hot point recently. The purpose of this study was to provide an overview of the research in this field using a bibliometric method.

Methods

The Web of Science Core Collection (WOSCC) database was used to search for literature related to the bioinformatics analysis of gliomas. Countries, institutions, authors, references, and keywords were analyzed using VOSviewer, CiteSpace, and Microsoft Excel software.

Result

China was the most productive country, while the USA was the most cited. Capital Medical University had the largest number of publications and citations. Institutions tend to collaborate more with other institutions in their countries rather than foreign ones. The most productive and most cited author was Jiang Tao. Two citation paths were identified, with literature in basic research journals often cited in clinical journals. Immune-related vocabularies appeared frequently in recent studies.

Conclusion

Glioma bioinformatics analyses spanned a wide range of fields. The international communication in this field urgently needs to be strengthened. Glioma bioinformatics approaches are developing from basic research to clinical applications. Recently, immune-related research has become a focus.

The complex interactions between the cellular and non-cellular components of the brain tumor microenvironmental landscape and their therapeutic implications

Glioblastoma (GBM), an aggressive high-grade glial tumor, is resistant to therapy and has a poor prognosis due to its universal recurrence rate. GBM cells interact with the non-cellular components in the tumor microenvironment (TME), facilitating their rapid growth, evolution, and invasion into the normal brain. Herein we discuss the complexity of the interactions between the cellular and non-cellular components of the TME and advances in the field as a whole. While the stroma of non-central nervous system (CNS) tissues is abundant in fibrillary collagens, laminins, and fibronectin, the normal brain extracellular matrix (ECM) predominantly includes proteoglycans, glycoproteins, and glycosaminoglycans, with fibrillary components typically found only in association with the vasculature. However, recent studies have found that in GBMs, the microenvironment evolves into a more complex array of components, with upregulated collagen gene expression and aligned fibrillary ECM networks. The interactions of glioma cells with the ECM and the degradation of matrix barriers are crucial for both single-cell and collective invasion into neighboring brain tissue. ECM-regulated mechanisms also contribute to immune exclusion, resulting in a major challenge to immunotherapy delivery and efficacy. Glioma cells chemically and physically control the function of their environment, co-opting complex signaling networks for their own benefit, resulting in radio- and chemo-resistance, tumor recurrence, and cancer progression. Targeting these interactions is an attractive strategy for overcoming therapy resistance, and we will discuss recent advances in preclinical studies, current clinical trials, and potential future clinical applications. In this review, we also provide a comprehensive discussion of the complexities of the interconnected cellular and non-cellular components of the microenvironmental landscape of brain tumors to guide the development of safe and effective therapeutic strategies against brain cancer.