CHD5 inhibits metastasis of neuroblastoma

Spatiotemporal deciphering of dynamic the FUS interactome during liquid-liquid phase separation in living cells

Liquid-liquid phase separations (LLPS) are membraneless organelles driven by biomolecule assembly and are implicated in cellular physiological activities. However, spatiotemporal deciphering of the dynamic proteome in living cells during LLPS formation remains challenging. Here, we introduce the Composition of LLPS proteome Assembly by Proximity labeling-assisted Mass spectrometry (CLAPM). We demonstrate that CLAPM can instantaneously label and monitor the FUS interactome shifts within intracellular droplets undergoing spatiotemporal LLPS. We report 129, 182 and 822 proteins specifically present in the LLPS droplets of HeLa, HEK 293 T and neuronal cells respectively. CLAPM further categorizes spatiotemporal dynamic proteome in droplets for living neuronal cells and identifies 596 LLPS-aboriginal proteins, 226 LLPS-dependent proteins and 58 LLPS-sensitive proteins. For validation, we uncover 11 previously unknown LLPS proteins in vivo. CLAPM provides a versatile tool to decipher proteins involved in LLPS and enables the accurate characterization of dynamic proteome in living cells.

RBPMS-AS1 sponges miR-19a-3p to restrain cervical cancer cells via enhancing PLCL1-mediated pyroptosis

Cervical cancer (CC) poses a threat to human health. Enhancing pyroptosis can prevent the proliferation and epithelial-mesenchymal transition (EMT) of tumor cells. This study aims to reveal the candidates that modulate pyroptosis in CC. Accordingly, the common microRNAs (miRNAs/miRs) that were sponged by RBPMS antisense RNA 1 (RBPMS-AS1) and could target Phospholipase C-Like 1 (PLCL1) were intersected. The expression of PBPMS-AS1/miR-19a-3p (candidate miRNA)/PLCL1 was predicted in cervical squamous cell carcinoma (CESC), by which the expression location of RBPMS-AS1 and the binding between RBPMS-AS1/PLCL1 and miR-19a-3p were analyzed. The targeting relationship between RBPMS-AS1/PLCL1 and miR-19a-3p was confirmed by dual-luciferase reporter assay. After the transfection, cell counting kit-8 assay, colony formation assay, quantitative reverse transcription PCR, and Western blot were implemented for cell viability and proliferation analysis as well as gene and protein expression quantification analysis. Based on the results, RBPMS-AS1 and PLCL1 were lowly expressed, yet miR-19a-3p was highly expressed in CESC. RBPMS-AS1 overexpression diminished the proliferation and expressions of N-cadherin, vimentin, and miR-19a-3p, yet enhanced those of E-cadherin, PLCL1, and pyroptosis-relevant proteins (inteleukin-1β, caspase-1, and gasdermin D N-terminal). However, the above RBPMS-AS1 overexpression-induced effects were counteracted in the presence of miR-19a-3p. There also existed a targeting relationship and negative interplay between PLCL1 and miR-19a-3p. In short, RBPMS-AS1 sponges miR-19a-3p and represses the growth and EMT of CC cells via enhancing PLCL1-mediated pyroptosis.

Integrated analysis of single-cell and bulk RNA-sequencing reveals a novel signature based on NK cell marker genes to predict prognosis and immunotherapy response in gastric cancer

Natural killer (NK) cells play essential roles in the tumor development, diagnosis, and prognosis of tumors. In this study, we aimed to establish a reliable signature based on marker genes in NK cells, thus providing a new perspective for assessing immunotherapy and the prognosis of patients with gastric cancer (GC). We analyzed a total of 1560 samples retrieved from the public database. We performed a comprehensive analysis of single-cell RNA-sequencing (scRNA-seq) data of gastric cancer and identified 377 marker genes for NK cells. By performing Cox regression analysis, we established a 12-gene NK cell-associated signature (NKCAS) for the Cancer Genome Atlas (TCGA) cohort, that assigned GC patients into a low-risk group (LRG) or a high-risk group (HRG). In the TCGA cohort, the areas under curve (AUC) value were 0.73, 0.81, and 0.80 at 1, 3, and 5 years. External validation of the predictive ability for the signature was then validated in the Gene Expression Omnibus (GEO) cohorts (GSE84437). The expression levels of signature genes were measured and validated in GC cell lines by real-time PCR. Moreover, NKCAS was identified as an independent prognostic factor by multivariate analysis. We combined this with a variety of clinicopathological characteristics (age, M stage, and tumor grade) to construct a nomogram to predict the survival outcomes of patients. Moreover, the LRG showed higher immune cell infiltration, especially CD8+ T cells and NK cells. The risk score was negatively associated with inflammatory activities. Importantly, analysis of the independent immunotherapy cohort showed that the LRG had a better prognosis and immunotherapy response when compared with the HRG. The identification of NK cell marker genes in this study suggests potential therapeutic targets. Additionally, the developed predictive signatures and nomograms may aid in the clinical management of GC.

Metabolic reprogramming of cancer cells by JMJD6-mediated pre-mRNA splicing associated with therapeutic response to splicing inhibitor

Dysregulated pre-mRNA splicing and metabolism are two hallmarks of MYC-driven cancers. Pharmacological inhibition of both processes has been extensively investigated as potential therapeutic avenues in preclinical and clinical studies. However, how pre-mRNA splicing and metabolism are orchestrated in response to oncogenic stress and therapies is poorly understood. Here, we demonstrate that jumonji domain containing 6, arginine demethylase, and lysine hydroxylase, JMJD6, acts as a hub connecting splicing and metabolism in MYC-driven human neuroblastoma. JMJD6 cooperates with MYC in cellular transformation of murine neural crest cells by physically interacting with RNA binding proteins involved in pre-mRNA splicing and protein homeostasis. Notably, JMJD6 controls the alternative splicing of two isoforms of glutaminase (GLS), namely kidney-type glutaminase (KGA) and glutaminase C (GAC), which are rate-limiting enzymes of glutaminolysis in the central carbon metabolism in neuroblastoma. Further, we show that JMJD6 is correlated with the anti-cancer activity of indisulam, a 'molecular glue' that degrades splicing factor RBM39, which complexes with JMJD6. The indisulam-mediated cancer cell killing is at least partly dependent on the glutamine-related metabolic pathway mediated by JMJD6. Our findings reveal a cancer-promoting metabolic program is associated with alternative pre-mRNA splicing through JMJD6, providing a rationale to target JMJD6 as a therapeutic avenue for treating MYC-driven cancers.

Circulating miRNAs and lncRNAs serve as biomarkers for early colorectal cancer diagnosis

BACKGROUND

Colorectal cancer (CRC), the third most prevalent and lethal disease, accounted for approximately 1.9 million new cases and claimed nearly 861,000 lives in 2018. It is imperative to develop a minimally invasive diagnostic technique for early identification of CRC. This would facilitate the selection of patient populations most suitable for clinical trials, monitoring disease progression, assessing treatment effectiveness, and enhancing overall patient care. Utilizing blood as a biomarker source is advantageous due to its minimal discomfort for patients, enabling better integration into clinical and follow-up trials. Recent findings indicate that long noncoding RNAs (lncRNAs) and microRNAs (miRNAs) are detectable in the blood of cancer patients, proving crucial in diagnosing various malignancies.

METHODS

In this case-control study, we collected plasma samples from 30 patients diagnosed with colorectal cancer (CRC) and 30 healthy volunteers. Following RNA extraction, we measured the expression levels of specific biomolecules, including miR-410, miR-211, miR-139, miR-197, lncRNA UICLM, lncRNA FEZF1-AS1, miR-129, lncRNA CCAT1, lncRNA BBOX1-AS1, and lncRNA LINC00698, using real-time quantitative polymerase chain reaction (RT-qPCR). The obtained data underwent analysis using the Mann-Whitney test for non-parametric data and the T-test for parametric data.

RESULTS

The level of miR-410, miR-211, miR-139, miR-197, lncRNA UICLM, lncRNA FEZF1-AS1 were significantly higher in patients with CRC than healthy controls (p < .05). Meanwhile, the level of miR-129, lncRNA CCAT1, lncRNA BBOX1-AS1, and lncRNA LINC00698 were higher in healthy controls than in CRC patients (p < .05).

CONCLUSION

MicroRNA (miRNA) and long noncoding RNAs (lncRNAs) have recently emerged as detectable entities in the blood of cancer patients, playing crucial roles in diagnosing various malignancies. However, their specific relevance in the diagnosis of colorectal cancer (CRC) remains underexplored. This study aimed to investigate miRNA and lncRNA profiles in the plasma fraction of human blood to discern significant differences in content and expression levels between CRC patients and healthy individuals. Our cohort comprised 30 CRC patients and 30 healthy controls, with no statistically significant differences (p < 0.05) in age or gender observed between the two groups. Noteworthy is the uniqueness of our study, as we identified a panel of three significant microRNAs and one significant lncRNA, providing a more reliable prediction compared to existing molecular markers in diagnosing CRC. The four genes examined, including miR-211, miR-129, miR-197, and lncRNA UICLM, demonstrated impeccable results in terms of sensitivity and specificity, suggesting their potential candidacy for inclusion in diagnostic panels. Further validation in a larger statistical population is recommended to confirm the robustness of these genes as promising markers for colorectal cancer diagnosis.

Development and validation of a novel stemness-related prognostic model for neuroblastoma using integrated machine learning and bioinformatics analyses

BACKGROUND

Neuroblastoma (NB) is a common solid tumor in children, with a dismal prognosis in high-risk cases. Despite advancements in NB treatment, the clinical need for precise prognostic models remains critical, particularly to address the heterogeneity of cancer stemness which plays a pivotal role in tumor aggressiveness and patient outcomes. By utilizing machine learning (ML) techniques, we aimed to explore the cancer stemness features in NB and identify stemness-related hub genes for future investigation and potential targeted therapy.

METHODS

The public dataset GSE49710 was employed as the training set for acquire gene expression data and NB sample information, including age, stage, and MYCN amplification status and survival. The messenger RNA (mRNA) expression-based stemness index (mRNAsi) was calculated and patients were grouped according to their mRNAsi value. Stemness-related hub genes were identified from the differentially expressed genes (DEGs) to construct a gene signature. This was followed by evaluating the relationship between cancer stemness and the NB immune microenvironment, and the development of a predictive nomogram. We assessed the prognostic outcomes including overall survival (OS) and event-free survival, employing machine learning methods to measure predictive accuracy through concordance indices and validation in an independent cohort E-MTAB-8248.

RESULTS

Based on mRNAsi, we categorized NB patients into two groups to explore the association between varying levels of stemness and their clinical outcomes. High mRNAsi was linked to the advanced International Neuroblastoma Staging System (INSS) stage, amplified MYCN, and elder age. High mRNAsi patients had a significantly poorer prognosis than low mRNAsi cases. According to the multivariate Cox analysis, the mRNAsi was an independent risk factor of prognosis in NB patients. After least absolute shrinkage and selection operator (LASSO) regression analysis, four key genes (ERCC6L, DUXAP10, NCAN, DIRAS3) most related to mRNAsi scores were discovered and a risk model was built. Our model demonstrated a significant prognostic capacity with hazard ratios (HR) ranging from 18.96 to 41.20, P values below 0.0001, and area under the receiver operating characteristic curve (AUC) values of 0.918 in the training set, suggesting high predictive accuracy which was further confirmed by external verification. Individuals with a low four-gene signature score had a favorable outcome and better immune responses. Finally, a nomogram for clinical practice was constructed by integrating the four-gene signature and INSS stage.

CONCLUSIONS

Our findings confirm the influence of CSC features in NB prognosis. The newly developed NB stemness-related four-gene signature prognostic signature could facilitate the prognostic prediction, and the identified hub genes may serve as promising targets for individualized treatments.

Epigenetic Dysregulation in MYCN-Amplified Neuroblastoma

Neuroblastoma (NB), a childhood cancer arising from the neural crest, poses significant clinical challenges, particularly in cases featuring amplification of the MYCN oncogene. Epigenetic factors play a pivotal role in normal neural crest and NB development, influencing gene expression patterns critical for tumorigenesis. This review delves into the multifaceted interplay between MYCN and known epigenetic modifications during NB genesis, shedding light on the intricate regulatory networks underlying the disease. We provide an extensive survey of known epigenetic mechanisms, encompassing DNA methylation, histone modifications, non-coding RNAs, super-enhancers (SEs), bromodomains (BET), and chromatin modifiers in MYCN-amplified (MNA) NB. These epigenetic changes collectively contribute to the dysregulated gene expression landscape observed in MNA NB. Furthermore, we review emerging therapeutic strategies targeting epigenetic regulators, including histone deacetylase inhibitors (HDACi), histone methyltransferase inhibitors (HMTi), and DNA methyltransferase inhibitors (DNMTi). We also discuss and summarize current drugs in preclinical and clinical trials, offering insights into their potential for improving outcomes for MNA NB patients.

Metabolic reprogramming of cancer cells by JMJD6-mediated pre-mRNA splicing is associated with therapeutic response to splicing inhibitor

Dysregulated pre-mRNA splicing and metabolism are two hallmarks of MYC-driven cancers. Pharmacological inhibition of both processes has been extensively investigated as potential therapeutic avenues in preclinical and clinical studies. However, how pre-mRNA splicing and metabolism are orchestrated in response to oncogenic stress and therapies is poorly understood. Here, we demonstrate that Jumonji Domain Containing 6, Arginine Demethylase and Lysine Hydroxylase, JMJD6, acts as a hub connecting splicing and metabolism in MYC-driven neuroblastoma. JMJD6 cooperates with MYC in cellular transformation by physically interacting with RNA binding proteins involved in pre-mRNA splicing and protein homeostasis. Notably, JMJD6 controls the alternative splicing of two isoforms of glutaminase (GLS), namely kidney-type glutaminase (KGA) and glutaminase C (GAC), which are rate-limiting enzymes of glutaminolysis in the central carbon metabolism in neuroblastoma. Further, we show that JMJD6 is correlated with the anti-cancer activity of indisulam, a "molecular glue" that degrades splicing factor RBM39, which complexes with JMJD6. The indisulam-mediated cancer cell killing is at least partly dependent on the glutamine-related metabolic pathway mediated by JMJD6. Our findings reveal a cancer-promoting metabolic program is associated with alternative pre-mRNA splicing through JMJD6, providing a rationale to target JMJD6 as a therapeutic avenue for treating MYC-driven cancers.

Chromosome 1p36 candidate gene ZNF436 predicts the prognosis of neuroblastoma: a bioinformatic analysis

BACKGROUND

Genetic 1p deletion is reported in 30% of all neuroblastoma and is associated with the unfavorable prognosis of neuroblastoma. The expressions and prognosis of 1p candidate genes in neuroblastoma are unclear.

METHODS

Public neuroblastoma cohorts were obtained for secondary analysis. The prognosis of 1p candidate genes in neuroblastoma was determined using Kaplan-Meier and cox regression analysis. The prediction of the nomogram model was determined using timeROC.

RESULTS

First, we confirmed the bad prognosis of 1p deletion in neuroblastoma. Moreover, zinc finger protein 436 (ZNF436) located at 1p36 region was down-regulated in 1p deleted neuroblastoma and higher ZNF436 expression was associated with the longer event free survival and overall survival of neuroblastoma. The expression levels of ZNF436 were lower in neuroblastoma patients with MYCN amplification or age at diagnosis ≥ 18months, or with stage 4 neuroblastoma. ZNF436 had robust predictive values of MYCN amplification and overall survival of neuroblastoma. Furthermore, the prognostic significance of ZNF436 in neuroblastoma was independent of MYCN amplification and age of diagnosis. Combinations of ZNF436 with MYCN amplification or age of diagnosis achieved better prognosis. At last, we constructed a nomogram risk model based on age, MYCN amplification and ZNF436. The nomogram model could predict the overall survival of neuroblastoma with high specificity and sensitivity.

CONCLUSIONS

Chromosome 1p36 candidate gene ZNF436 was a prognostic maker of neuroblastoma.

miR-124 and VAMP3 Act Antagonistically in Human Neuroblastoma

Neuroblastoma (NB) is the most common extracranial solid tumor that affects developing nerve cells in the fetus, infants, and children. miR-124 is a microRNA (miRNA) enriched in neuronal tissues, and VAMP3 (vesicle-associated membrane protein 3) has been reported to be an miR-124 target, although the relationship between NB and miR-124 or VAMP3 is unknown. Our current work identified that miR-124 levels are high in NB cases and that elevated miR-124 correlates with worse NB outcomes. Conversely, depressed VAMP3 correlates with worse NB outcomes. To investigate the mechanisms by which miR-124 and VAMP3 regulate NB, we altered miR-124 or VAMP3 expression in human NB cells and observed that increased miR-124 and reduced VAMP3 stimulated cell proliferation and suppressed apoptosis, while increased VAMP3 had the opposite effects. Genome-wide mRNA expression analyses identified gene and pathway changes which might explain the NB cell phenotypes. Together, our studies suggest that miR-124 and VAMP3 could be potential new markers of NB and targets of NB treatments.

Identification of the molecular subtypes and construction of risk models in neuroblastoma

The heterogeneity of neuroblastoma directly affects the prognosis of patients. Individualization of patient treatment to improve prognosis is a clinical challenge at this stage and the aim of this study is to characterize different patient populations. To achieve this, immune-related cell cycle genes, identified in the GSE45547 dataset using WGCNA, were used to classify cases from multiple datasets (GSE45547, GSE49710, GSE73517, GES120559, E-MTAB-8248, and TARGET) into subgroups by consensus clustering. ESTIMATES, CIBERSORT and ssGSEA were used to assess the immune status of the patients. And a 7-gene risk model was constructed based on differentially expressed genes between subtypes using randomForestSRC and LASSO. Enrichment analysis was used to demonstrate the biological characteristics between different groups. Key genes were screened using randomForest to construct neural network and validated. Finally, drug sensitivity was assessed in the GSCA and CellMiner databases. We classified the 1811 patients into two subtypes based on immune-related cell cycle genes. The two subtypes (Cluster1 and Cluster2) exhibited distinct clinical features, immune levels, chromosomal instability and prognosis. The same significant differences were demonstrated between the high-risk and low-risk groups. Through our analysis, we identified neuroblastoma subtypes with unique characteristics and established risk models which will improve our understanding of neuroblastoma heterogeneity.

Development of a novel autophagy-related gene model for gastric cancer prognostic prediction

Gastric cancer (GC) is a major global health issue and one of the leading causes of tumor-associated mortality worldwide. Autophagy is thought to play a critical role in the development and progression of GC, and this process is controlled by a set of conserved regulators termed autophagy-related genes (ATGs). However, the complex contribution of autophagy to cancers is not completely understood. Accordingly, we aimed to develop a prognostic model based on the specific role of ATGs in GC to improve the prediction of GC outcomes. First, we screened 148 differentially expressed ATGs between GC and normal tissues in The Cancer Genome Atlas (TCGA) cohort. Consensus clustering in these ATGs was performed, and based on that, 343 patients were grouped into two clusters. According to Kaplan–Meier survival analysis, cluster C2 had a worse prognosis than cluster C1. Then, a disease risk model incorporating nine differentially expressed ATGs was constructed based on the least absolute shrinkage and selection operator (LASSO) regression analysis, and the ability of this model to stratify patients into high- and low-risk groups was verified. The predictive value of the model was confirmed using both training and validation cohorts. In addition, the results of functional enrichment analysis suggested that GC risk is correlated with immune status. Moreover, autophagy inhibition increased sensitivity to cisplatin and exacerbated reactive oxygen species accumulation in GC cell lines. Collectively, the results indicated that this novel constructed risk model is an effective and reliable tool for predicting GC outcomes and could help with individual treatment through ATG targeting.

18F-FDG PET/CT-based radiomics nomogram could predict bone marrow involvement in pediatric neuroblastoma

Objective

To develop and validate an ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT)-based radiomics nomogram for non-invasively prediction of bone marrow involvement (BMI) in pediatric neuroblastoma.

Methods

A total of 133 patients with neuroblastoma were retrospectively included and randomized into the training set (n = 93) and test set (n = 40). Radiomics features were extracted from both CT and PET images. The radiomics signature was developed. Independent clinical risk factors were identified using the univariate and multivariate logistic regression analyses to construct the clinical model. The clinical-radiomics model, which integrated the radiomics signature and the independent clinical risk factors, was constructed using multivariate logistic regression analysis and finally presented as a radiomics nomogram. The predictive performance of the clinical-radiomics model was evaluated by receiver operating characteristic curves, calibration curves and decision curve analysis (DCA).

Results

Twenty-five radiomics features were selected to construct the radiomics signature. Age at diagnosis, neuron-specific enolase and vanillylmandelic acid were identified as independent predictors to establish the clinical model. In the training set, the clinical-radiomics model outperformed the radiomics model or clinical model (AUC: 0.924 vs. 0.900, 0.875) in predicting the BMI, which was then confirmed in the test set (AUC: 0.925 vs. 0.893, 0.910). The calibration curve and DCA demonstrated that the radiomics nomogram had a good consistency and clinical utility.

Conclusion

The ¹⁸F-FDG PET/CT-based radiomics nomogram which incorporates radiomics signature and independent clinical risk factors could non-invasively predict BMI in pediatric neuroblastoma.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13244-022-01283-8.

Pan-Cancer Analysis Identifies CHD5 as a Potential Biomarker for Glioma

The chromodomain helicase DNA binding domain 5 (CHD5) is required for neural development and plays an important role in the regulation of gene expression. Although CHD5 exerts a broad tumor suppressor effect in many tumor types, its specific functions regarding its expression levels, and impact on immune cell infiltration, proliferation and migration in glioma remain unclear. Here, we evaluated the role of CHD5 in tumor immunity in a pan-cancer multi-database using the R language. The Cancer Genome Atlas (TCGA), Genotype Tissue Expression (GTEx), and Cancer Cell Lines Encyclopedia (CCLE) datasets were utilized to determine the role of CHD5 in 33 types of cancers, including the expression level, prognosis, tumor progression, and immune microenvironment. Furthermore, we explored the effect of CHD5 on glioma proliferation and migration using the cell counting kit 8 (CCK-8) assay, transwell assays and western blot analysis. The findings from our pan-cancer analysis showed that CHD5 was differentially expressed in the tumor tissues as compared to the normal tissues. Survival analysis showed that CHD5 was generally associated with the prognosis of glioblastoma (GBM), low Grade Glioma (LGG) and neuroblastoma, where the low expression of CHD5 was associated with a worse prognosis in glioma patients. Then, we confirmed that the expression level of CHD5 was associated with tumor immune infiltration and tumor microenvironment, especially in glioma. Moreover, si-RNA mediated knockdown of CHD5 promoted the proliferation and migration of glioma cells in vitro. In conclusion, CHD5 was found to be differentially expressed in the pan-cancer analysis and might play an important role in antitumor immunity. CHD5 is expected to be a potential tumor prognostic marker, especially in glioma.

Feature Genes in Neuroblastoma Distinguishing High-Risk and Non-high-Risk Neuroblastoma Patients: Development and Validation Combining Random Forest With Artificial Neural Network

There is a significant difference in prognosis among different risk groups. Therefore, it is of great significance to correctly identify the risk grouping of children. Using the genomic data of neuroblastoma samples in public databases, we used GSE49710 as the training set data to calculate the feature genes of the high-risk group and non-high-risk group samples based on the random forest (RF) algorithm and artificial neural network (ANN) algorithm. The screening results of RF showed that EPS8L1, PLCD4, CHD5, NTRK1, and SLC22A4 were the feature differentially expressed genes (DEGs) of high-risk neuroblastoma. The prediction model based on gene expression data in this study showed high overall accuracy and precision in both the training set and the test set (AUC = 0.998 in GSE49710 and AUC = 0.858 in GSE73517). Kaplan–Meier plotter showed that the overall survival and progression-free survival of patients in the low-risk subgroup were significantly better than those in the high-risk subgroup [HR: 3.86 (95% CI: 2.44–6.10) and HR: 3.03 (95% CI: 2.03–4.52), respectively]. Our ANN-based model has better classification performance than the SVM-based model and XGboost-based model. Nevertheless, more convincing data sets and machine learning algorithms will be needed to build diagnostic models for individual organization types in the future.