Gene knockdown of CENPA reduces sphere forming ability and stemness of glioblastoma initiating cells

E2F1-driven CENPM expression promotes glycolytic reprogramming and tumorigenicity in glioblastoma

Centromere protein M (CENPM), traditionally associated with chromosome segregation, is now recognized for its significant role in cancer biology. Particularly in glioblastoma (GBM), where less is known about CENPM compared to other centromere proteins (CENPs), it appears crucially involved in regulating tumor cell proliferation, invasion, and metabolic reprogramming-key factors in GBM's aggressiveness. Initial analyses using the GEPIA database (TCGA/GTEx datasets) reveal distinct patterns of CENPM expression in GBM, suggesting its potential as a therapeutic target. Our study manipulated CENPM expression through shRNA-mediated knockdown and vector-based overexpression in GBM cell lines LN229 and U251. Knockdown resulted in a 50% reduction in cell proliferation and a 70% decrease in invasion, accompanied by diminished glycolytic markers such as glucose consumption, lactate production, and ATP levels. Conversely, overexpression of CENPM enhanced both metabolic activity and invasive capacities. The introduction of the glycolytic inhibitor 2-DG effectively reversed the effects of CENPM modulation, highlighting a dependency on glycolytic pathways. Moreover, we identified E2F1 as a key regulator of CENPM, linking it to GBM's metabolic alterations. In vivo studies using a BALB/c nude mouse xenograft model demonstrated that CENPM knockdown significantly inhibits tumor growth, with treated groups showing a 60% reduction in tumor volume over four weeks. These findings underscore the E2F1-CENPM axis as a promising target for therapeutic strategies, aiming to disrupt the metabolic and invasive pathways facilitated by CENPM in GBM. These insights establish a foundation for targeting the metabolic dependencies of tumor cells, potentially leading to innovative treatments for GBM.

A Multi-Drug Concentration Gradient Mixing Chip: A Novel Platform for High-Throughput Drug Combination Screening

Combinatorial drug therapy has emerged as a critically important strategy in medical research and patient treatment and involves the use of multiple drugs in concert to achieve a synergistic effect. This approach can enhance therapeutic efficacy while simultaneously mitigating adverse side effects. However, the process of identifying optimal drug combinations, including their compositions and dosages, is often a complex, costly, and time-intensive endeavor. To surmount these hurdles, we propose a novel microfluidic device capable of simultaneously generating multiple drug concentration gradients across an interlinked array of culture chambers. This innovative setup allows for the real-time monitoring of live cell responses. With minimal effort, researchers can now explore the concentration-dependent effects of single-agent and combination drug therapies. Taking neural stem cells (NSCs) as a case study, we examined the impacts of various growth factors-epithelial growth factor (EGF), platelet-derived growth factor (PDGF), and fibroblast growth factor (FGF)-on the differentiation of NSCs. Our findings indicate that an overdose of any single growth factor leads to an upsurge in the proportion of differentiated NSCs. Interestingly, the regulatory effects of these growth factors can be modulated by the introduction of additional growth factors, whether singly or in combination. Notably, a reduced concentration of these additional factors resulted in a decreased number of differentiated NSCs. Our results affirm that the successful application of this microfluidic device for the generation of multi-drug concentration gradients has substantial potential to revolutionize drug combination screening. This advancement promises to streamline the process and accelerate the discovery of effective therapeutic drug combinations.

Prognostic model of hepatocellular carcinoma based on cancer grade

BACKGROUND

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer. With highly invasive biological characteristics and a lack of obvious clinical manifestations, HCC usually has a poor prognosis and ranks fourth in cancer mortality. The aetiology and exact molecular mechanism of primary HCC are still unclear.

AIM

To select the characteristic genes that are significantly associated with the prognosis of HCC patients and construct a prognosis model of this malignancy.

METHODS

By comparing the gene expression levels of patients with different cancer grades of HCC, we screened out differentially expressed genes associated with tumour grade. By protein-protein interaction (PPI) network analysis, we obtained the top 2 PPI networks and hub genes from these differentially expressed genes. By using least absolute shrinkage and selection operator Cox regression, 13 prognostic genes were selected for feature extraction, and a prognostic risk model of HCC was established.

RESULTS

The model had significant prognostic ability in HCC. We also analysed the biological functions of these prognostic genes.

CONCLUSION

By comparing the gene profiles of patients with different stages of HCC, We have constructed a prognosis model consisting of 13 genes that have important prognostic value. This model has good application value and can be explained clinically.

CENP-A: A Histone H3 Variant with Key Roles in Centromere Architecture in Healthy and Diseased States

Centromeres are key architectural components of chromosomes. Here, we examine their construction, maintenance, and functionality. Focusing on the mammalian centromere- specific histone H3 variant, CENP-A, we highlight its coevolution with both centromeric DNA and its chaperone, HJURP. We then consider CENP-A de novo deposition and the importance of centromeric DNA recently uncovered with the added value from new ultra-long-read sequencing. We next review how to ensure the maintenance of CENP-A at the centromere throughout the cell cycle. Finally, we discuss the impact of disrupting CENP-A regulation on cancer and cell fate.

High copy number variations, particular transcription factors, and low immunity contribute to the stemness of prostate cancer cells

Background

Tumor metastasis is the main cause of death of cancer patients, and cancer stem cells (CSCs) is the basis of tumor metastasis. However, systematic analysis of the stemness of prostate cancer cells is still not abundant. In this study, we explore the effective factors related to the stemness of prostate cancer cells by comprehensively mining the multi-omics data from TCGA database.

Methods

Based on the prostate cancer transcriptome data in TCGA, gene expression modules that strongly relate to the stemness of prostate cancer cells are obtained with WGCNA and stemness scores. Copy number variation of stemness genes of prostate cancer is calculated and the difference of transcription factors between prostate cancer and normal tissues is evaluated by using CNV (copy number variation) data and ATAC-seq data. The protein interaction network of stemness genes in prostate cancer is constructed using the STRING database. Meanwhile, the correlation between stemness genes of prostate cancer and immune cells is analyzed.

Results

Prostate cancer with higher Gleason grade possesses higher cell stemness. The gene set highly related to prostate cancer stemness has higher CNV in prostate cancer samples than that in normal samples. Although the transcription factors of stemness genes have similar expressions, they have different contributions between normal and prostate cancer tissues; and particular transcription factors enhance the stemness of prostate cancer, such as PUM1, CLOCK, SP1, TCF12, and so on. In addition, the lower tumor immune microenvironment is conducive to the stemness of prostate cancer. CD8 + T cells and M1 macrophages may play more important role in the stemness of prostate cancer than other immune cells in the tumor microenvironment. Finally, EZH2 is found to play a central role in stemness genes and is negatively correlated with resting mast cells and positively correlated with activated memory CD4 + T cells.

Conclusions

Based on the systematic and combined analysis of multi-omics data, we find that high copy number variation, specific transcription factors, and low immune microenvironment jointly contribute to the stemness of prostate cancer cells. These findings may provide us new clues and directions for the future research on stemness of prostate cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-02870-x.

Bioinformatics Analysis Reveals Biomarkers With Cancer Stem Cell Characteristics in Lung Squamous Cell Carcinoma

Background

Tumor stem cells play important roles in the survival, proliferation, metastasis and recurrence of tumors. We aimed to identify new prognostic biomarkers for lung squamous cell carcinoma (LUSC) based on the cancer stem cell theory.

Methods

RNA-seq data and relevant clinical information were downloaded from The Cancer Genome Atlas (TCGA) database. Weighted gene coexpression network analysis (WGCNA) was applied to identify significant modules and hub genes, and prognostic signatures were constructed with the prognostic hub genes.

Results

LUSC patients in the TCGA database have higher mRNA expression-based stemness index (mRNAsi) in tumor tissue than in adjacent normal tissue. In addition, some clinical features and outcomes were highly correlated with the mRNAsi. WGCNA revealed that the pink and yellow modules were the most significant modules related to the mRNAsi; the top 10 hub genes in the pink module were enriched mostly in epidermal development, the secretory granule membrane, receptor regulator activity and the cytokine-cytokine receptor interaction. The protein–protein interaction (PPI) network revealed that the top 10 hub genes were significantly correlated with each other at the transcriptional level. In addition, the top 10 hub genes were all highly expressed in LUSC, and some were differentially expressed in different TNM stages. Regarding the survival analysis, the nomogram of a prognostic signature with three hub genes showed high predictive value.

Conclusion

mRNAsi-related hub genes could be a potential biomarker of LUSC.

The Oncogenic Role of CENPA in Hepatocellular Carcinoma Development: Evidence from Bioinformatic Analysis

OBJECTIVE

This study is aimed at investigating the predictive value of CENPA in hepatocellular carcinoma (HCC) development.

METHODS

Using integrated bioinformatic analysis, we evaluated the CENPA mRNA expression in tumor and adjacent tissues and correlated it with HCC survival and clinicopathological features. A Cox regression hazard model was also performed.

RESULTS

CENPA mRNA was significantly upregulated in tumor tissues compared with that in adjacent tissues, which were validated in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) series (all P < 0.01). In the Kaplan-Meier plotter platform, the high level of CENPA mRNA was significantly correlated with overall survival (OS), disease-free survival (DFS), recurrence-free survival (RFS), and progression-free survival (PFS) in HCC patients (all log rank P < 0.01). For validation in GSE14520 and pan-TCGA dataset, HCC patients with CNEPA mRNA overexpression had poor OS compared with those with low CENPA mRNA (log rank P = 0.025 and P < 0.0001, respectively), and those with high CENPA had poor DFS in TCGA (log rank P = 0.0001). Additionally, CENPA mRNA were upregulated in HCC patients with alpha-fetoprotein (AFP) elevation, advanced TNM stage, larger tumor size, advanced AJCC stage, advanced pathology grade, and vascular invasion (all P < 0.05). A Cox regression model including CENPA, OIP5, and AURKB could predict OS in HCC patients effectively (AUC = 0.683).

CONCLUSION

Overexpressed in tumors, CENPA might be an oncogenic factor in the development of HCC patients.

Surface-Directed Structural Transition of Amyloidogenic Aggregates and the Resulting Neurotoxicity

The transition of amyloidogenic species into ordered structures (i.e., prefibrillar oligomers, protofibrils, mature fibrils, and amyloidogenic aggregates) is closely associated with many neurodegenerative disease pathologies. It is increasingly appreciated that the liquid-solid interface contributes to peptide aggregation under physiological conditions. However, much remains to be explored on the molecular mechanism of surface-directed amyloid formation. We herein demonstrate that physical environmental conditions (i.e., negatively charged surface) affect amyloid formation. Nontoxic amyloid aggregates quickly develop into intertwisting fibrils on a negatively charged mica surface. These fibrillar structures show significant cytotoxicity on both neuroblastoma cell-lines (SH-SY5Y) and primary neural stem cells. Our results suggest an alternative amyloid development pathway, following which Aβ peptides form large amyloidogenic aggregates upon stimulation, and later transit into neurotoxic fibrillar structures while being trapped and aligned by a negatively charged surface. Conceivably, the interplay between chemical and physical environmental conditions plays important roles in the development of neurodegenerative diseases.

Ultra-multiplexed analysis of single-cell dynamics reveals logic rules in differentiation

Dynamical control of cellular microenvironments is highly desirable to study complex processes such as stem cell differentiation and immune signaling. We present an ultra-multiplexed microfluidic system for high-throughput single-cell analysis in precisely defined dynamic signaling environments. Our system delivers combinatorial and time-varying signals to 1500 independently programmable culture chambers in week-long live-cell experiments by performing nearly 106 pipetting steps, where single cells, two-dimensional (2D) populations, or 3D neurospheres are chemically stimulated and tracked. Using our system and statistical analysis, we investigated the signaling landscape of neural stem cell differentiation and discovered "cellular logic rules" that revealed the critical role of signal timing and sequence in cell fate decisions. We find synergistic and antagonistic signal interactions and show that differentiation pathways are highly redundant. Our system allows dissection of hidden aspects of cellular dynamics and enables accelerated biological discovery.

Identification of four hub genes associated with adrenocortical carcinoma progression by WGCNA

BACKGROUND

Adrenocortical carcinoma (ACC) is a rare and aggressive malignant cancer in the adrenal cortex with poor prognosis. Though previous research has attempted to elucidate the progression of ACC, its molecular mechanism remains poorly understood.

METHODS

Gene transcripts per million (TPM) data were downloaded from the UCSC Xena database, which included ACC (The Cancer Genome Atlas, n = 77) and normal samples (Genotype Tissue Expression, n = 128). We used weighted gene co-expression network analysis to identify gene connections. Overall survival (OS) was determined using the univariate Cox model. A protein-protein interaction (PPI) network was constructed by the search tool for the retrieval of interacting genes.

RESULTS

To determine the critical genes involved in ACC progression, we obtained 2,953 significantly differentially expressed genes and nine modules. Among them, the blue module demonstrated significant correlation with the "Stage" of ACC. Enrichment analysis revealed that genes in the blue module were mainly enriched in cell division, cell cycle, and DNA replication. Combined with the PPI and co-expression networks, we identified four hub genes (i.e., TOP2A, TTK, CHEK1, and CENPA) that were highly expressed in ACC and negatively correlated with OS. Thus, these identified genes may play important roles in the progression of ACC and serve as potential biomarkers for future diagnosis.

Morozova-Roche L, Zhang C. HEWL interacts with dissipated oleic acid micelles, and decreases oleic acid cytotoxicity

Senile plaques are well-known hallmarks of Alzheimer's Diseases (AD). However, drugs targeting tangles of the protein tau and plaques of β-amyloid have no significant effect on disease progression, and the studies on the underlying mechanism of AD remain in high demand. Growing evidence supports the protective role of senile plaques in local inflammation driven by S100A9. We herein demonstrate that oleic acid (OA) micelles interact with hen egg white lysozyme (HEWL) and promote its amyloid formation. Consequently, SH-SY5Y cell line and mouse neural stem cells are rescued from OA toxicity by co-aggregation of OA and HEWL. Using atomic force microscopy in combination with fluorescence microscopy, we revealed that HEWL forms round-shaped aggregates in the presence of OA micelles instead of protofibrils of HEWL alone. These HEWL amyloids act as a sink for toxic OA micelles and their co-aggregate form large clumps, suggesting a protective function in amyloid and OA cytotoxicity.

Current Challenges and Opportunities in Treating Glioblastoma

Glioblastoma multiforme (GBM), the most common and aggressive primary brain tumor, has a high mortality rate despite extensive efforts to develop new treatments. GBM exhibits both intra- and intertumor heterogeneity, lending to resistance and eventual tumor recurrence. Large-scale genomic and proteomic analysis of GBM tumors has uncovered potential drug targets. Effective and "druggable" targets must be validated to embark on a robust medicinal chemistry campaign culminating in the discovery of clinical candidates. Here, we review recent developments in GBM drug discovery and delivery. To identify GBM drug targets, we performed extensive bioinformatics analysis using data from The Cancer Genome Atlas project. We discovered 20 genes, BOC, CLEC4GP1, ELOVL6, EREG, ESR2, FDCSP, FURIN, FUT8-AS1, GZMB, IRX3, LITAF, NDEL1, NKX3-1, PODNL1, PTPRN, QSOX1, SEMA4F, TH, VEGFC, and C20orf166AS1 that are overexpressed in a subpopulation of GBM patients and correlate with poor survival outcomes. Importantly, nine of these genes exhibit higher expression in GBM versus low-grade glioma and may be involved in disease progression. In this review, we discuss these proteins in the context of GBM disease progression. We also conducted computational multi-parameter optimization to assess the blood-brain barrier (BBB) permeability of small molecules in clinical trials for GBM treatment. Drug delivery in the context of GBM is particularly challenging because the BBB hinders small molecule transport. Therefore, we discuss novel drug delivery methods, including nanoparticles and prodrugs. Given the aggressive nature of GBM and the complexity of targeting the central nervous system, effective treatment options are a major unmet medical need. Identification and validation of biomarkers and drug targets associated with GBM disease progression present an exciting opportunity to improve treatment of this devastating disease.

Biomimetic models to examine microenvironmental regulation of glioblastoma stem cells

Glioblastoma multiforme (GBM), a malignant brain tumor, is the deadliest form of human cancer with low survival rates because of its highly invasive nature. In recent years, there has been a growing appreciation for the role that glioblastoma stem cells (GSCs) play during tumorigenesis and tumor recurrence of GBM. GSCs are a specialized subset of GBM cells with stem cell-like features that contribute to tumor initiation and therapeutic resistance. Thus, to enhance therapeutic efficiency and improve survival, targeting GSCs and their microenvironmental niche appears to be a promising approach. To develop this approach, understanding GSC-microenvironment interactions is crucial. This review discusses various biomimetic model systems to understand the impact of biophysical, biochemical, and cellular microenvironmental cues on GSC behaviors. These models include two-dimensional or matrix-free environment models, engineered biomaterial-based three-dimensional models, co-culture models, and mouse and rat in vivo models. These systems have been used to study the effects of biophysical factors, modulation of signaling pathways, extracellular matrix components, and culture conditions on the GSC phenotype. The advantages and disadvantages of these model systems and their impact in the field of GSC research are discussed.