Comparison of RNA-seq and microarray-based models for clinical endpoint prediction

In vivo cisplatin-resistant neuroblastoma metastatic model reveals tumour necrosis factor receptor superfamily member 4 (TNFRSF4) as an independent prognostic factor of survival in neuroblastoma

Neuroblastoma is the most common solid extracranial tumour in children. Despite major advances in available therapies, children with drug-resistant and/or recurrent neuroblastoma have a dismal outlook with 5-year survival rates of less than 20%. Therefore, tackling relapsed tumour biology by developing and characterising clinically relevant models is a priority in finding targetable vulnerability in neuroblastoma. Using matched cisplatin-sensitive KellyLuc and resistant KellyCis83Luc cell lines, we developed a cisplatin-resistant metastatic MYCN-amplified neuroblastoma model. The average number of metastases per mouse was significantly higher in the KellyCis83Luc group than in the KellyLuc group. The vast majority of sites were confirmed as having lymph node metastasis. Their stiffness characteristics of lymph node metastasis values were within the range reported for the patient samples. Targeted transcriptomic profiling of immuno-oncology genes identified tumour necrosis factor receptor superfamily member 4 (TNFRSF4) as a significantly dysregulated MYCN-independent gene. Importantly, differential TNFRSF4 expression was identified in tumour cells rather than lymphocytes. Low TNFRSF4 expression correlated with poor prognostic indicators in neuroblastoma, such as age at diagnosis, stage, and risk stratification and significantly associated with reduced probability of both event-free and overall survival in neuroblastoma. Therefore, TNFRSF4 Low expression is an independent prognostic factor of survival in neuroblastoma.

NetActivity enhances transcriptional signals by combining gene expression into robust gene set activity scores through interpretable autoencoders

Grouping gene expression into gene set activity scores (GSAS) provides better biological insights than studying individual genes. However, existing gene set projection methods cannot return representative, robust, and interpretable GSAS. We developed NetActivity, a machine learning framework that generates GSAS based on a sparsely-connected autoencoder, where each neuron in the inner layer represents a gene set. We proposed a three-tier training that yielded representative, robust, and interpretable GSAS. NetActivity model was trained with 1518 GO biological processes terms and KEGG pathways and all GTEx samples. NetActivity generates GSAS robust to the initialization parameters and representative of the original transcriptome, and assigned higher importance to more biologically relevant genes. Moreover, NetActivity returns GSAS with a more consistent definition and higher interpretability than GSVA and hipathia, state-of-the-art gene set projection methods. Finally, NetActivity enables combining bulk RNA-seq and microarray datasets in a meta-analysis of prostate cancer progression, highlighting gene sets related to cell division, key for disease progression. When applied to metastatic prostate cancer, gene sets associated with cancer progression were also altered due to drug resistance, while a classical enrichment analysis identified gene sets irrelevant to the phenotype. NetActivity is publicly available in Bioconductor and GitHub.

Analgesic candidate adenosine A3 receptors are expressed by perineuronal peripheral macrophages in human dorsal root ganglion and spinal cord microglia

ABSTRACT

Adenosine receptors are a family of purinergic G protein-coupled receptors that are widely distributed in bodily organs and in the peripheral and central nervous systems. Recently, antihyperalgesic actions have been suggested for the adenosine A3 receptor, and its agonists have been proposed as new neuropathic pain treatments. We hypothesized that these receptors may be expressed in nociceptive primary afferent neurons. However, RNA sequencing across species, eg, rat, mouse, dog, and human, suggests that dorsal root ganglion (DRG) expression of ADORA3 is inconsistent. In rat and mouse, Adora3 shows very weak to no expression in DRG, whereas it is well expressed in human DRG. However, the cell types in human DRG that express ADORA3 have not been delineated. An examination of DRG cell types using in situ hybridization clearly detected ADORA3 transcripts in peripheral macrophages that are in close apposition to the neuronal perikarya but not in peripheral sensory neurons. By contrast, ADORA1 was found primarily in neurons, where it is broadly expressed at low levels. These results suggest that a more complex or indirect mechanism involving modulation of macrophage and/or microglial cells may underlie the potential analgesic action of adenosine A3 receptor agonism.

Integration of Computational Pipeline to Streamline Efficacious Drug Nomination and Biomarker Discovery in Glioblastoma

Glioblastoma multiforme (GBM) is the deadliest, most heterogeneous, and most common brain cancer in adults. Not only is there an urgent need to identify efficacious therapeutics, but there is also a great need to pair these therapeutics with biomarkers that can help tailor treatment to the right patient populations. We built patient drug response models by integrating patient tumor transcriptome data with high-throughput cell line drug screening data as well as Bayesian networks to infer relationships between patient gene expression and drug response. Through these discovery pipelines, we identified agents of interest for GBM to be effective across five independent patient cohorts and in a mouse avatar model: among them are a number of MEK inhibitors (MEKis). We also predicted phosphoglycerate dehydrogenase enzyme (PHGDH) gene expression levels to be causally associated with MEKi efficacy, where knockdown of this gene increased tumor sensitivity to MEKi and overexpression led to MEKi resistance. Overall, our work demonstrated the power of integrating computational approaches. In doing so, we quickly nominated several drugs with varying known mechanisms of action that can efficaciously target GBM. By simultaneously identifying biomarkers with these drugs, we also provide tools to select the right patient populations for subsequent evaluation.

RNA-sequencing of Human Kidney Allografts and Delineation of T-Cell Genes, Gene Sets, and Pathways Associated With Acute T Cell-mediated Rejection

BACKGROUND

Delineation of T-cell genes, gene sets, pathways, and T-cell subtypes associated with acute T cell-mediated rejection (TCMR) may improve its management.

METHODS

We performed bulk RNA-sequencing of 34 kidney allograft biopsies (16 Banff TCMR and 18 no rejection [NR] biopsies) from 34 adult recipients of human kidneys. Computational analysis was performed to determine the differential intragraft expression of T-cell genes at the level of single-gene, gene set, and pathways.

RESULTS

T-cell signaling pathway gene sets for plenary T-cell activation were overrepresented in TCMR biopsies compared with NR biopsies. Heightened expression of T-cell signaling genes was validated using external TCMR biopsies. Pro- and anti-inflammatory immune gene sets were enriched, and metabolism gene sets were depleted in TCMR biopsies compared with NR biopsies. Gene signatures of regulatory T cells, Th1 cells, Th2 cells, Th17 cells, T follicular helper cells, CD4 tissue-resident memory T cells, and CD8 tissue-resident memory T cells were enriched in TCMR biopsies compared with NR biopsies. T-cell exhaustion and anergy were also molecular attributes of TCMR. Gene sets associated with antigen processing and presentation, and leukocyte transendothelial migration were overexpressed in TCMR biopsies compared with NR biopsies. Cellular deconvolution of graft infiltrating cells by gene expression patterns identified CD8 T cell to be the most abundant T-cell subtype infiltrating the allograft during TCMR.

CONCLUSIONS

Our delineation of intragraft T-cell gene expression patterns, in addition to yielding new biological insights, may help prioritize T-cell genes and T-cell subtypes for therapeutic targeting.

CKLF instigates a "cold" microenvironment to promote MYCN-mediated tumor aggressiveness

Solid tumors, especially those with aberrant MYCN activation, often harbor an immunosuppressive microenvironment to fuel malignant growth and trigger treatment resistance. Despite this knowledge, there are no effective strategies to tackle this problem. We found that chemokine-like factor (CKLF) is highly expressed by various solid tumor cells and transcriptionally up-regulated by MYCN. Using the MYCN-driven high-risk neuroblastoma as a model system, we demonstrated that as early as the premalignant stage, tumor cells secrete CKLF to attract CCR4-expressing CD4+ cells, inducing immunosuppression and tumor aggression. Genetic depletion of CD4+ T regulatory cells abolishes the immunorestrictive and protumorigenic effects of CKLF. Our work supports that disrupting CKLF-mediated cross-talk between tumor and CD4+ suppressor cells represents a promising immunotherapeutic approach to battling MYCN-driven tumors.

Comparison of RNA-Seq and microarray in the prediction of protein expression and survival prediction

Gene expression profiling using RNA-sequencing (RNA-seq) and microarray technologies is widely used in cancer research to identify biomarkers for clinical endpoint prediction. We compared the performance of these two methods in predicting protein expression and clinical endpoints using The Cancer Genome Atlas (TCGA) datasets of lung cancer, colorectal cancer, renal cancer, breast cancer, endometrial cancer, and ovarian cancer. We calculated the correlation coefficients between gene expression measured by RNA-seq or microarray and protein expression measured by reverse phase protein array (RPPA). In addition, after selecting the top 103 survival-related genes, we compared the random forest survival prediction model performance across test platforms and cancer types. Both RNA-seq and microarray data were retrieved from TCGA dataset. Most genes showed similar correlation coefficients between RNA-seq and microarray, but 16 genes exhibited significant differences between the two methods. The BAX gene was recurrently found in colorectal cancer, renal cancer, and ovarian cancer, and the PIK3CA gene belonged to renal cancer and breast cancer. Furthermore, the survival prediction model using microarray was better than the RNA-seq model in colorectal cancer, renal cancer, and lung cancer, but the RNA-seq model was better in ovarian and endometrial cancer. Our results showed good correlation between mRNA levels and protein measured by RPPA. While RNA-seq and microarray performance were similar, some genes showed differences, and further clinical significance should be evaluated. Additionally, our survival prediction model results were controversial.

A prognostic metabolism-related gene signature associated with the tumor immune microenvironment in neuroblastoma

Neuroblastoma (NB) is the most prevalent malignant solid tumor in children. Tumor metabolism, including lipid, amino acid, and glucose metabolism, is intricately linked to the genesis and progression of tumors. This study aimed to establish a prognostic gene signature for NB patients, based on metabolism-related genes, and to investigate a treatment approach that could enhance the survival rate of high-risk NB patients. From the NB dataset GSE49710, we identified metabolism-related gene markers utilizing the "limma" R package and univariate Cox analysis combined with least absolute shrinkage and selection operator (LASSO) regression analysis. We explored the correlation between these gene markers and the overall survival of NB patients. Gene set enrichment analysis (GSEA) and single-sample GSEA algorithms were used to assess the differences in metabolism and immune status. Furthermore, we examined the association between metabolic subgroups and drug responsiveness. Concurrently, data downloaded from TARGET and MTAB were used for external verification. Using multicolor immunofluorescence and immunohistochemistry, we investigated the relationship between the lipid metabolism-related gene ELOVL6 with both the International Neuroblastoma Staging System classification of NB and survival rate. Finally, we explored the effect of high ELOVL6 expression on the immune microenvironment in NB using flow cytometry. We identified an eight-gene signature comprising metabolism-related genes in NB: ELOVL6, OSBPL9, RPL27A, HSD17B3, ACHE, AKR1C1, PIK3R1, and EPHX2. This panel effectively predicted disease-free survival, and was validated using an internal dataset from GSE49710 and two external datasets from the TARGET and MTAB databases. Moreover, our findings confirmed that ELOVL6 fosters an immunosuppressive microenvironment and contributes to the malignant progression in NB. The eight-gene signature is significant in predicting the prognosis of NB, effectively classifying patients into high- and low-risk groups. This classification may guide the development of innovative treatment strategies for these patients. Notably, the signature gene ELOVL6 markedly encourages an immunosuppressive microenvironment and malignant progression in NB.

Reactivation of the G1 enhancer landscape underlies core circuitry addiction to SWI/SNF

Several cancer core regulatory circuitries (CRCs) depend on the sustained generation of DNA accessibility by SWI/SNF chromatin remodelers. However, the window when SWI/SNF is acutely essential in these settings has not been identified. Here we used neuroblastoma (NB) cells to model and dissect the relationship between cell-cycle progression and SWI/SNF ATPase activity. We find that SWI/SNF inactivation impairs coordinated occupancy of non-pioneer CRC members at enhancers within 1 hour, rapidly breaking their autoregulation. By precisely timing inhibitor treatment following synchronization, we show that SWI/SNF is dispensable for survival in S and G2/M, but becomes acutely essential only during G1 phase. We furthermore developed a new approach to analyze the oscillating patterns of genome-wide DNA accessibility across the cell cycle, which revealed that SWI/SNF-dependent CRC binding sites are enriched at enhancers with peak accessibility during G1 phase, where they activate genes involved in cell-cycle progression. SWI/SNF inhibition strongly impairs G1-S transition and potentiates the ability of retinoids used clinically to induce cell-cycle exit. Similar cell-cycle effects in diverse SWI/SNF-addicted settings highlight G1-S transition as a common cause of SWI/SNF dependency. Our results illustrate that deeper knowledge of the temporal patterns of enhancer-related dependencies may aid the rational targeting of addicted cancers.

An omics review and perspective of researches on intrahepatic cholestasis of pregnancy

Intrahepatic cholestasis of pregnancy (ICP) is one of the common pregnancy complications that may threaten the health of both pregnant women and their fetuses. Hence, it is of vital importance to identify key moleculars and the associated functional pathways of ICP, which will help us to better understand the pathological mechanisms as well as to develop precise clinical biomarkers. The emerging and developing of multiple omics approaches enable comprehensive studies of the genome, transcriptome, proteome and metabolome of clinical samples. The present review collected and summarized the omics based studies of ICP, aiming to provide an overview of the current progress, limitations and future directions. Briefly, these studies covered a broad range of research contents by the comparing of different experimental groups including ICP patients, ICP subtypes, ICP fetuses, ICP models and other complications. Correspondingly, the studied samples contain various types of clinical samples, in vitro cultured tissues, cell lines and the samples from animal models. According to the main research objectives, we further categorized these studies into two groups: pathogenesis and diagnosis analyses. The pathogenesis studies identified tens of functional pathways that may represent the key regulatory events for the occurrence, progression, treatment and fetal effects of ICP. On the other hand, the diagnosis studies tested more than 40 potential models for the early-prediction, diagnosis, grading, prognosis or differential diagnosis of ICP. Apart from these achievements, we also evaluated the limitations of current studies, and emphasized that many aspects of clinical characteristics, sample processing, and analytical method can greatly affect the reliability and repeatability of omics results. Finally, we also pointed out several new directions for the omics based analyses of ICP and other perinatal associated conditions in the future.

5-Hydroxymethylcytosine Profiling of Cell-Free DNA Identifies Bivalent Genes That Are Prognostic of Survival in High-Risk Neuroblastoma

PURPOSE

Neuroblastoma is the most common extracranial solid tumor in childhood. We previously showed that circulating cell-free DNA (cfDNA) and tumor biopsy derived 5-hydroxymethylcytosime (5-hmC) profiles identified patients with neuroblastoma who experienced subsequent relapse. Here, we hypothesized that 5-hmC modifications selectively enriched in cfDNA compared with tumor biopsy samples would identify epigenetic changes associated with aggressive tumor behavior and identify novel biomarkers of outcome in patients with high-risk neuroblastoma.

METHODS

5-hmC profiles from cfDNA (n = 64) and tumor biopsies (n = 48) were compared. Two neuroblastoma cell lines underwent chromatin immunoprecipitation followed by sequencing (ChIP-Seq) for H3K27me3, H3K4me3, and H3K27ac; kethoxal-associated single-stranded DNA sequencing; hmC-Seal for 5-hmC; and RNA-sequencing (RNA-Seq). Genes enriched for both H3K27me3 and H3K4me3 in the included cell lines were defined as bivalent. Using bivalent genes defined in vitro, a bivalent signature was established in three publicly available cohorts of patients with neuroblastoma through gene set variation analysis. Differences between tumors with high or low bivalent signatures were assessed by the Kaplan-Meier method and Cox proportional hazards models.

RESULTS

In cfDNA compared with tumor biopsy derived 5-hmC profiles, we found increased 5-hmC deposition on Polycomb Repressive Complex 2 target genes, a finding previously described in the context of bivalent genes. We identified 313 genes that bore bivalent chromatin marks, were enriched for mediators of neuronal differentiation, and were transcriptionally repressed across a panel of heterogeneous neuroblastoma cell lines. In three distinct clinical cohorts, low bivalent signature was significantly and independently associated with worse clinical outcome in patients with high-risk neuroblastoma.

CONCLUSION

Low expression of bivalent genes is a biomarker of worse outcome in patients with high-risk neuroblastoma.

Revealing the biology behind MRI signatures in high grade glioma

Magnetic resonance imaging (MRI) measurements are routinely collected during the treatment of high-grade gliomas (HGGs) to characterize tumor boundaries and guide surgical tumor resection. Using spatially matched MRI and transcriptomics we discovered HGG tumor biology captured by MRI measurements. We strategically overlaid the spatially matched omics characterizations onto a pre-existing transcriptional map of glioblastoma multiforme (GBM) to enhance the robustness of our analyses. We discovered that T1+C measurements, designed to capture vasculature and blood brain barrier (BBB) breakdown and subsequent contrast extravasation, also indirectly reveal immune cell infiltration. The disruption of the vasculature and BBB within the tumor creates a permissive infiltrative environment that enables the transmigration of anti-inflammatory macrophages into tumors. These relationships were validated through histology and enrichment of genes associated with immune cell transmigration and proliferation. Additionally, T2-weighted (T2W) and mean diffusivity (MD) measurements were associated with angiogenesis and validated using histology and enrichment of genes involved in neovascularization. Furthermore, we establish an unbiased approach for identifying additional linkages between MRI measurements and tumor biology in future studies, particularly with the integration of novel MRI techniques. Lastly, we illustrated how noninvasive MRI can be used to map HGG biology spatially across a tumor, and this provides a platform to develop diagnostics, prognostics, or treatment efficacy biomarkers to improve patient outcomes.

Identification of NCAPG as an Essential Gene for Neuroblastoma Employing CRISPR-Cas9 Screening Database and Experimental Verification

Neuroblastoma is the most common extracranial solid tumor in children. Patients with neuroblastoma have a poor prognosis. The development of therapy targets and the ability to predict prognosis will be enhanced through further exploration of the genetically related genes of neuroblastoma. The present investigation utilized CRISPR-Cas9 genome-wide screening based on the DepMap database to determine essential genes for neuroblastoma cells' continued survival. WGCNA analysis was used to determine the progression-related genes, and a prognostic signature was constructed. The signature gene, NCAPG, was downregulated in neuroblastoma cells to explore its impact on various cellular processes. This research used DepMap and WGCNA to pinpoint 45 progression-related essential genes for neuroblastoma. A risk signature comprising NCAPG and MAD2L1 was established. The suppression of NCAPG prevented neuroblastoma cells from proliferating, migrating, and invading. The results of flow cytometric analysis demonstrated that NCAPG inhibition caused cell cycle arrest during the G2 and S phases and the activation of apoptosis. Additionally, NCAPG downregulation activated the p53-mediated apoptotic pathway, inducing cell apoptosis. The present work showed that NCAPG knockdown reduced neuroblastoma cell progression and may serve as a basis for further investigation into diagnostic indicators and therapy targets for neuroblastoma.

Target Genes of c-MYC and MYCN with Prognostic Power in Neuroblastoma Exhibit Different Expressions during Sympathoadrenal Development

Deregulation of the MYC family of transcription factors c-MYC (encoded by MYC), MYCN, and MYCL is prevalent in most human cancers, with an impact on tumor initiation and progression, as well as response to therapy. In neuroblastoma (NB), amplification of the MYCN oncogene and over-expression of MYC characterize approximately 40% and 10% of all high-risk NB cases, respectively. However, the mechanism and stage of neural crest development in which MYCN and c-MYC contribute to the onset and/or progression of NB are not yet fully understood. Here, we hypothesized that subtle differences in the expression of MYCN and/or c-MYC targets could more accurately stratify NB patients in different risk groups rather than using the expression of either MYC gene alone. We employed an integrative approach using the transcriptome of 498 NB patients from the SEQC cohort and previously defined c-MYC and MYCN target genes to model a multigene transcriptional risk score. Our findings demonstrate that defined sets of c-MYC and MYCN targets with significant prognostic value, effectively stratify NB patients into different groups with varying overall survival probabilities. In particular, patients exhibiting a high-risk signature score present unfavorable clinical parameters, including increased clinical risk, higher INSS stage, MYCN amplification, and disease progression. Notably, target genes with prognostic value differ between c-MYC and MYCN, exhibiting distinct expression patterns in the developing sympathoadrenal system. Genes associated with poor outcomes are mainly found in sympathoblasts rather than in chromaffin cells during the sympathoadrenal development.

Analysis of High-Risk Neuroblastoma Transcriptome Reveals Gene Co-Expression Signatures and Functional Features

Neuroblastoma represents a neoplastic expansion of neural crest cells in the developing sympathetic nervous system and is childhood's most common extracranial solid tumor. The heterogeneity of gene expression in different types of cancer is well-documented, and genetic features of neuroblastoma have been described by classification, development stage, malignancy, and progression of tumors. Here, we aim to analyze RNA sequencing datasets, publicly available in the GDC data portal, of neuroblastoma tumor samples from various patients and compare them with normal adrenal gland tissue from the GTEx data portal to elucidate the gene expression profile and regulation networks they share. Our results from the differential expression, weighted correlation network, and functional enrichment analyses that we performed with the count data from neuroblastoma and standard normal gland samples indicate that the analysis of transcriptome data from 58 patients diagnosed with high-risk neuroblastoma shares the expression pattern of 104 genes. More importantly, our analyses identify the co-expression relationship and the role of these genes in multiple biological processes and signaling pathways strongly associated with this disease phenotype. Our approach proposes a group of genes and their biological functions to be further investigated as essential molecules and possible therapeutic targets of neuroblastoma regardless of the etiology of individual tumors.

CUDC-907, a dual PI3K/histone deacetylase inhibitor, increases meta-iodobenzylguanidine uptake (123/131I-mIBG) in vitro and in vivo: a promising candidate for advancing theranostics in neuroendocrine tumors

BACKGROUND

Neuroblastoma (NB) and pheochromocytoma/paraganglioma (PHEO/PGL) are neuroendocrine tumors. Imaging of these neoplasms is performed by scintigraphy after injection of radiolabeled meta-iodobenzylguanidine (mIBG), a norepinephrine analog taken up by tumoral cells through monoamine transporters. The pharmacological induction of these transporters is a promising approach to improve the imaging and therapy (theranostics) of these tumors.

METHODS

Transporters involved in mIBG internalization were identified by using transfected Human Embryonic Kidney (HEK) cells. Histone deacetylase inhibitors (HDACi) and inhibitors of the PI3K/AKT/mTOR pathway were tested in cell lines to study their effect on mIBG internalization. Studies in xenografted mice were performed to assess the effect of the most promising HDACi on 123I-mIBG uptake.

RESULTS

Transfected HEK cells demonstrated that the norepinephrine and dopamine transporter (NET and DAT) avidly internalizes mIBG. Sodium-4-phenylbutyrate (an HDACi), CUDC-907 (a dual HDACi and PI3K inhibitor), BGT226 (a PI3K inhibitor) and VS-5584 and rapamycin (two inhibitors of mTOR) increased mIBG internalization in a neuroblastoma cell line (IGR-NB8) by 2.9-, 2.1-, 2.5-, 1.5- and 1.3-fold, respectively, compared with untreated cells. CUDC-907 also increased mIBG internalization in two other NB cell lines and in one PHEO cell line. We demonstrated that mIBG internalization occurs primarily through the NET. In xenografted mice with IGR-NB8 cells, oral treatment with 5 mg/kg of CUDC-907 increased the tumor uptake of 123I-mIBG by 2.3- and 1.9-fold at 4 and 24 h post-injection, respectively, compared to the untreated group.

CONCLUSIONS

Upregulation of the NET by CUDC-907 lead to a better internalization of mIBG in vitro and in vivo.

Correcting batch effects in large-scale multiomics studies using a reference-material-based ratio method

BACKGROUND

Batch effects are notoriously common technical variations in multiomics data and may result in misleading outcomes if uncorrected or over-corrected. A plethora of batch-effect correction algorithms are proposed to facilitate data integration. However, their respective advantages and limitations are not adequately assessed in terms of omics types, the performance metrics, and the application scenarios.

RESULTS

As part of the Quartet Project for quality control and data integration of multiomics profiling, we comprehensively assess the performance of seven batch effect correction algorithms based on different performance metrics of clinical relevance, i.e., the accuracy of identifying differentially expressed features, the robustness of predictive models, and the ability of accurately clustering cross-batch samples into their own donors. The ratio-based method, i.e., by scaling absolute feature values of study samples relative to those of concurrently profiled reference material(s), is found to be much more effective and broadly applicable than others, especially when batch effects are completely confounded with biological factors of study interests. We further provide practical guidelines for implementing the ratio based approach in increasingly large-scale multiomics studies.

CONCLUSIONS

Multiomics measurements are prone to batch effects, which can be effectively corrected using ratio-based scaling of the multiomics data. Our study lays the foundation for eliminating batch effects at a ratio scale.

Identification of hub genes and microRNAs with prognostic values in esophageal cancer by integrated analysis

Esophageal cancer (EC) is the eighth most common cancer in the world, and the sixth most common cause of cancer-related mortality. The aim of the present study was to identify cell and molecular mechanisms involved in EC, and to provide the potential targets for diagnosis and treatment. Here, a microarray dataset (GSE20347) was screened to find differentially expressed genes (DEGs). Different bioinformatic methods were used to analyze the identified DEGs. The up-regulated DEGs were significantly involved in different biological processes and pathways including extracellular matrix organization and ECM-receptor interaction. FN1, CDK1, AURKA, TOP2A, FOXM1, BIRC5, CDC6, UBE2C, TTK, and TPX2 were identified as the most important genes among the up-regulated DEGs. Our analysis showed that has-miR-29a-3p, has-miR-29b-3p, has-miR-29c-3p, and has-miR-767-5p had the largest number of common targets among the up-regulated DEGs. These findings strengthen the understanding of EC development and progression, as well as representing potential markers for EC diagnosis and treatment.

LRP8-mediated selenocysteine uptake is a targetable vulnerability in MYCN-amplified neuroblastoma

Ferroptosis has emerged as an attractive strategy in cancer therapy. Understanding the operational networks regulating ferroptosis may unravel vulnerabilities that could be harnessed for therapeutic benefit. Using CRISPR-activation screens in ferroptosis hypersensitive cells, we identify the selenoprotein P (SELENOP) receptor, LRP8, as a key determinant protecting MYCN-amplified neuroblastoma cells from ferroptosis. Genetic deletion of LRP8 leads to ferroptosis as a result of an insufficient supply of selenocysteine, which is required for the translation of the antiferroptotic selenoprotein GPX4. This dependency is caused by low expression of alternative selenium uptake pathways such as system Xc- . The identification of LRP8 as a specific vulnerability of MYCN-amplified neuroblastoma cells was confirmed in constitutive and inducible LRP8 knockout orthotopic xenografts. These findings disclose a yet-unaccounted mechanism of selective ferroptosis induction that might be explored as a therapeutic strategy for high-risk neuroblastoma and potentially other MYCN-amplified entities.

Screening of hub inflammatory bowel disease biomarkers and identification of immune-related functions based on basement membrane genes

BACKGROUND

Inflammatory bowel disease (IBD), including Crohn's disease (CD) and ulcerative colitis (UC), is a chronic, inflammatory, and autoimmune disease, but its specific etiology and pathogenesis are still unclear. This study aimed to better discover the causative basement membrane (BM) genes of their subtypes and their associations.

METHODS

The differential expression of BM genes between CD and UC was analyzed and validated by downloading relevant datasets from the GEO database. We divided the samples into 3 groups for comparative analysis. Construction of PPI networks, enrichment of differential gene functions, screening of Lasso regression models, validation of ROC curves, nomogram for disease prediction and other analytical methods were used. The immune cell infiltration was further explored by ssGSEA analysis, the immune correlates of hub BM genes were found, and finally, the hub central genes were screened by machine learning.

RESULTS

We obtained 6 candidate hub BM genes related to cellular immune infiltration in the CD and UC groups, respectively, and further screened the central hub genes ADAMTS17 and ADAMTS9 through machine learning. And in the ROC curve models, AUC > 0.7, indicating that this characteristic gene has a more accurate predictive effect on IBD. We also found that the pathogenicity-related BM genes of the CD and UC groups were mainly concentrated in the ADAMTS family (ADAMTS17 and ADAMTS9). Addition there are some differences between the two subtypes, and the central different hub BM genes are SPARC, POSTN, and ADAMTS2.

CONCLUSIONS

In the current study, we provided a nomogram model of CD and UC composed of BM genes, identified central hub genes, and clarified the similarities and differences between CD and UC. This will have potential value for preclinical, clinical, and translational guidance and differential research in IBD.

Caffeine Supplementation and FOXM1 Inhibition Enhance the Antitumor Effect of Statins in Neuroblastoma

High-risk neuroblastoma exhibits transcriptional activation of the mevalonate pathway that produces cholesterol and nonsterol isoprenoids. A better understanding of how this metabolic reprogramming contributes to neuroblastoma development could help identify potential prevention and treatment strategies. Here, we report that both the cholesterol and nonsterol geranylgeranyl-pyrophosphate branches of the mevalonate pathway are critical to sustain neuroblastoma cell growth. Blocking the mevalonate pathway by simvastatin, a cholesterol-lowering drug, impeded neuroblastoma growth in neuroblastoma cell line xenograft, patient-derived xenograft (PDX), and TH-MYCN transgenic mouse models. Transcriptional profiling revealed that the mevalonate pathway was required to maintain the FOXM1-mediated transcriptional program that drives mitosis. High FOXM1 expression contributed to statin resistance and led to a therapeutic vulnerability to the combination of simvastatin and FOXM1 inhibition. Furthermore, caffeine synergized with simvastatin to inhibit the growth of neuroblastoma cells and PDX tumors by blocking statin-induced feedback activation of the mevalonate pathway. This function of caffeine depended on its activity as an adenosine receptor antagonist, and the A2A adenosine receptor antagonist istradefylline, an add-on drug for Parkinson's disease, could recapitulate the synergistic effect of caffeine with simvastatin. This study reveals that the FOXM1-mediated mitotic program is a molecular statin target in cancer and identifies classes of agents for maximizing the therapeutic efficacy of statins, with implications for treatment of high-risk neuroblastoma.

SIGNIFICANCE

Caffeine treatment and FOXM1 inhibition can both enhance the antitumor effect of statins by blocking the molecular and metabolic processes that confer statin resistance, indicating potential combination therapeutic strategies for neuroblastoma. See related commentary by Stouth et al., p. 2091.

Defining the landscape of circular RNAs in neuroblastoma unveils a global suppressive function of MYCN

Circular RNAs (circRNAs) are a regulatory RNA class. While cancer-driving functions have been identified for single circRNAs, how they modulate gene expression in cancer is not well understood. We investigate circRNA expression in the pediatric malignancy, neuroblastoma, through deep whole-transcriptome sequencing in 104 primary neuroblastomas covering all risk groups. We demonstrate that MYCN amplification, which defines a subset of high-risk cases, causes globally suppressed circRNA biogenesis directly dependent on the DHX9 RNA helicase. We detect similar mechanisms in shaping circRNA expression in the pediatric cancer medulloblastoma implying a general MYCN effect. Comparisons to other cancers identify 25 circRNAs that are specifically upregulated in neuroblastoma, including circARID1A. Transcribed from the ARID1A tumor suppressor gene, circARID1A promotes cell growth and survival, mediated by direct interaction with the KHSRP RNA-binding protein. Our study highlights the importance of MYCN regulating circRNAs in cancer and identifies molecular mechanisms, which explain their contribution to neuroblastoma pathogenesis.

RNA Sequencing of Pooled Samples Effectively Identifies Differentially Expressed Genes

Analysis of gene expression changes across the genome provides a powerful, unbiased tool for gaining insight into molecular mechanisms. We have effectively used RNA sequencing to identify differentially expressed genes in long-lived genetic mutants in C. elegans to advance our understanding of the genetic pathways that control longevity. Although RNA sequencing costs have come down, cost remains a barrier to examining multiple strains and time points with a sufficient number of biological replicates. To circumvent this, we have examined the efficacy of identifying differentially expressed genes by sequencing a pooled RNA sample from long-lived isp-1 mitochondrial mutant worms. We found that sequencing a pooled RNA sample could effectively identify genes that were found to be significantly upregulated in the two individually sequenced RNA-seq experiments. Finally, we compared the genes significantly upregulated in the two individually sequenced RNA-seq experiments to two previous microarray experiments to come up with a high-confidence list of modulated genes in long-lived isp-1 mutant worms. Overall, this work demonstrates that RNA sequencing of pooled RNA samples can be used to identify differentially expressed genes.

Identification of a novel gene signature for neuroblastoma differentiation using a Boolean implication network

Although induction of differentiation represents an effective strategy for neuroblastoma treatment, the mechanisms underlying neuroblastoma differentiation are poorly understood. We generated a computational model of neuroblastoma differentiation consisting of interconnected gene clusters identified based on symmetric and asymmetric gene expression relationships. We identified a differentiation signature consisting of series of gene clusters comprised of 1251 independent genes that predicted neuroblastoma differentiation in independent datasets and in neuroblastoma cell lines treated with agents known to induce differentiation. This differentiation signature was associated with patient outcomes in multiple independent patient cohorts and validated the role of MYCN expression as a marker of neuroblastoma differentiation. Our results further identified novel genes associated with MYCN via asymmetric Boolean implication relationships that would not have been identified using symmetric computational approaches and that were associated with both neuroblastoma differentiation and patient outcomes. Our differentiation signature included a cluster of genes involved in intracellular signaling and growth factor receptor trafficking pathways that is strongly associated with neuroblastoma differentiation, and we validated the associations of UBE4B, a gene within this cluster, with neuroblastoma cell and tumor differentiation. Our findings demonstrate that Boolean network analyses of symmetric and asymmetric gene expression relationships can identify novel genes and pathways relevant for neuroblastoma tumor differentiation that could represent potential therapeutic targets.

Microisolation of Spatially Characterized Single Populations of Neurons for RNA Sequencing from Mouse and Postmortem Human Brain Tissues

Single-cell and single-population RNA sequencing (RNA-seq) is a rapidly evolving new field of intense investigation. Recent studies indicate unique transcriptomic profiles are derived based on the spatial localization of neurons within circuits and regions. Individual neuronal subtypes can have vastly different transcriptomic fingerprints, well beyond the basic excitatory neuron and inhibitory neuron designations. To study single-population gene expression profiles of spatially characterized neurons, we have developed a methodology combining laser capture microdissection (LCM), RNA purification of single populations of neurons, and subsequent library preparation for downstream applications, including RNA-seq. LCM provides the benefit of isolating single neurons characterized by morphology or via transmitter-identified and/or receptor immunoreactivity and enables spatial localization within the sample. We utilize unfixed human postmortem and mouse brain tissue that is frozen to preserve RNA quality in order to isolate the desired neurons of interest. Microisolated neurons are then pooled for RNA purification utilizing as few as 250 individual neurons from a tissue section, precluding extraneous nonspecific tissue contaminants. Library preparation is performed from picogram RNA quantities extracted from LCM-captured neurons. Single-population RNA-seq analysis demonstrates that microisolated neurons from both postmortem human and mouse brain tissues are viable for transcriptomic profiling, including differential gene expression assessment and bioinformatic pathway inquiry.

Hedgehog signaling is a potential therapeutic target for vascular calcification

BACKGROUND

Patients with chronic kidney disease (CKD) suffered from vascular calcification (VC), one major contributor for their increased mortality rate. Hedgehog (Hh) signaling plays a crucial role in physiological bone mineralization and is associated with several cardiovascular diseases. However, the molecular changes underlying VC is ill defined and it remains unclear whether Hh signaling intervention affects VC.

METHODS

We constructed human primary vascular smooth muscle cell (VSMC) calcification model and performed RNA sequencing. Alizarin red staining and calcium content assay were conducted to identify the occurrence of VC. Three different R packages were applied to determine differentially expressed genes (DEGs). Enrichment analysis and protein-protein interaction (PPI) network analysis were carried out to explore the biological roles of DEGs. qRT-PCR assay was then applied to validate the expression of key genes. By using Connectivity Map (CMAP) analysis, several small molecular drugs targeting these key genes were obtained, including SAG (Hedgehog signaling activator) and cyclopamine (CPN) (Hedgehog signaling inhibitor), which were subsequently used to treat VSMC.

RESULTS

Obvious Alizarin red staining and increased calcium content identified the occurrence of VC. By integrating results from three R packages, we totally obtained 166 DEGs (86 up-regulated and 80 down-regulated), which were significantly enriched in ossification, osteoblast differentiation, and Hh signaling. PPI network analysis identified 10 key genes and CMAP analysis predicted several small molecular drugs targeting these key genes including chlorphenamine, isoeugenol, CPN and phenazopyridine. Notably, our in vitro experiment showed that SAG markedly alleviated VSMC calcification, whereas CPN significantly exacerbated VC.

CONCLUSIONS

Our research provided deeper insight to the pathogenesis of VC and indicated that targeting Hh signaling pathway may represent a potential and effective therapy for VC.

Genealyzer: web application for the analysis and comparison of gene expression data

BACKGROUND

Gene expression profiling is a widely adopted method in areas like drug development or functional gene analysis. Microarray data of gene expression experiments is still commonly used and widely available for retrospective analyses. However, due to to changes of the underlying technologies data sets from different technologies are often difficult to compare and thus a multitude of already available data becomes difficult to use. We present a web application that abstracts away mathematical and programmatical details in order to enable a convenient and customizable analysis of microarray data for large-scale reproducibility studies. In addition, the web application provides a feature that allows easy access to large microarray repositories.

RESULTS

Our web application consists of three basic steps which are necessary for a differential gene expression analysis as well as Gene Ontology (GO) enrichment analysis and the comparison of multiple analysis results. Genealyzer can handle Affymetrix data as well as one-channel and two-channel Agilent data. All steps are visualized with meaningful plots. The application offers flexible analysis while being intuitively operable.

CONCLUSIONS

Our web application provides a unified platform for analysing microarray data, while allowing users to compare the results of different technologies and organisms. Beyond reproducibility, this also offers many possibilities for gaining further insights from existing study data, especially since data from different technologies or organisms can also be compared. The web application can be accessed via this URL: https://genealyzer.item.fraunhofer.de/ . Login credentials can be found at the end.

Urine as liquid gold: the transcriptional landscape of acute rejection defined by urinary cell mRNA profiling of kidney allograft recipients

PURPOSE OF REVIEW

Because all functioning nephrons contribute to urine formation, we reasoned that urine would be a suitable substitute to kidney allograft biopsy to discern human kidney allograft status. In view of compelling data that ribonucleic acid (RNA) sequencing outperforms microarray-based profiling, we performed RNA sequencing of urinary cells and kidney allograft biopsies to define the transcriptional landscape of allograft rejection.

RECENT FINDINGS

Whole genome transcriptome profiling identified unique and shared gene signatures of acute T cell mediated rejection (TCMR) and antibody mediated rejection (AMR). We found that biopsy rejection signatures are enriched in urinary cells and that the immune cellular landscape is more diverse and enriched in urine compared to biopsies. Towards a patient friendly protocol for urinary cell messenger RNA (mRNA) profiling, we developed a filtration-based protocol for the initial processing of urine at home and demonstrated excellent performance characteristics of the filter- based protocol.

SUMMARY

Acute rejection signatures are enriched in urinary cells. Urinary cell mRNA profiles are diagnostic and prognostic of acute rejection and could serve as yardsticks of in-vivo immune status. RNA sequencing yields insights into mechanisms of rejection and helps prioritize therapeutic targets. The filtration protocol for home processing of urine may optimize immune surveillance.

Multiple Genes with Potential Tumor Suppressive Activity Are Present on Chromosome 10q Loss in Neuroblastoma and Are Associated with Poor Prognosis

Neuroblastoma (NB) is a tumor affecting the peripheral sympathetic nervous system that substantially contributes to childhood cancer mortality. Despite recent advances in understanding the complexity of NB, the mechanisms determining its progression are still largely unknown. Some recurrent segmental chromosome aberrations (SCA) have been associated with poor survival. However, the prognostic role of most SCA has not yet been investigated. We examined a cohort of 260 NB primary tumors at disease onset for the loss of chromosome 10q, by array-comparative genomic hybridization (a-CGH) and Single Nucleotide Polymorphism (SNP) array and we found that 26 showed 10q loss, while the others 234 displayed different SCA. We observed a lower event-free survival for NB patients displaying 10q loss compared to patients with tumors carrying other SCA. Furthermore, analyzing the region of 10q loss, we identified a cluster of 75 deleted genes associated with poorer outcome. Low expression of six of these genes, above all CCSER2, was significantly correlated to worse survival using in silico data from 786 NB patients. These potential tumor suppressor genes can be partly responsible for the poor prognosis of NB patients with 10q loss.

Inferring Gene Regulatory Networks from RNA-seq Data Using Kernel Classification

Gene expression profiling is one of the most recognized techniques for inferring gene regulators and their potential targets in gene regulatory networks (GRN). The purpose of this study is to build a regulatory network for the budding yeast Saccharomyces cerevisiae genome by incorporating the use of RNA-seq and microarray data represented by a wide range of experimental conditions. We introduce a pipeline for data analysis, data preparation, and training models. Several kernel classification models; including one-class, two-class, and rare event classification methods, are used to categorize genes. We test the impact of the normalization techniques on the overall performance of RNA-seq. Our findings provide new insights into the interactions between genes in the yeast regulatory network. The conclusions of our study have significant importance since they highlight the effectiveness of classification and its contribution towards enhancing the present comprehension of the yeast regulatory network. When assessed, our pipeline demonstrates strong performance across different statistical metrics, such as a 99% recall rate and a 98% AUC score.

Differentially expressed genes in systemic sclerosis: Towards predictive medicine with new molecular tools for clinicians

Systemic sclerosis (SSc) is a rare and chronic autoimmune disease characterized by a pathogenic triad of immune dysregulation, vasculopathy, and progressive fibrosis. Clinical tools commonly used to assess patients, such as the modified Rodnan skin score, difference between limited or diffuse forms of skin involvement, presence of lung, heart or kidney involvement, or of various autoantibodies, are important prognostic factors, but still fail to reflect the large heterogeneity of the disease. SSc treatment options are diverse, ranging from conventional drugs to autologous hematopoietic stem cell transplantation, and predicting response is challenging. Genome-wide technologies, such as high throughput microarray analyses and RNA sequencing, allow accurate, unbiased, and broad assessment of alterations in expression levels of multiple genes. In recent years, many studies have shown robust changes in the gene expression profiles of SSc patients compared to healthy controls, mainly in skin tissues and peripheral blood cells. The objective analysis of molecular patterns in SSc is a powerful tool that can further classify SSc patients with similar clinical phenotypes and help predict response to therapy. In this review, we describe the journey from the first discovery of differentially expressed genes to the identification of enriched pathways and intrinsic subsets identified in SSc, using machine learning algorithms. Finally, we discuss the use of these new tools to predict the efficacy of various treatments, including stem cell transplantation. We suggest that the use of RNA gene expression-based classifications according to molecular subsets may bring us one step closer to precision medicine in Systemic Sclerosis.

Leveraging transcriptomics for precision diagnosis: Lessons learned from cancer and sepsis

Diagnostics require precision and predictive ability to be clinically useful. Integration of multi-omic with clinical data is crucial to our understanding of disease pathogenesis and diagnosis. However, interpretation of overwhelming amounts of information at the individual level requires sophisticated computational tools for extraction of clinically meaningful outputs. Moreover, evolution of technical and analytical methods often outpaces standardisation strategies. RNA is the most dynamic component of all -omics technologies carrying an abundance of regulatory information that is least harnessed for use in clinical diagnostics. Gene expression-based tests capture genetic and non-genetic heterogeneity and have been implemented in certain diseases. For example patients with early breast cancer are spared toxic unnecessary treatments with scores based on the expression of a set of genes (e.g., Oncotype DX). The ability of transcriptomics to portray the transcriptional status at a moment in time has also been used in diagnosis of dynamic diseases such as sepsis. Gene expression profiles identify endotypes in sepsis patients with prognostic value and a potential to discriminate between viral and bacterial infection. The application of transcriptomics for patient stratification in clinical environments and clinical trials thus holds promise. In this review, we discuss the current clinical application in the fields of cancer and infection. We use these paradigms to highlight the impediments in identifying useful diagnostic and prognostic biomarkers and propose approaches to overcome them and aid efforts towards clinical implementation.

An omics approach to delineating the molecular mechanisms that underlie the biological effects of physical plasma

The use of physical plasma to treat cancer is an emerging field, and interest in its applications in oncology is increasing rapidly. Physical plasma can be used directly by aiming the plasma jet onto cells or tissue, or indirectly, where a plasma-treated solution is applied. A key scientific question is the mechanism by which physical plasma achieves selective killing of cancer over normal cells. Many studies have focused on specific pathways and mechanisms, such as apoptosis and oxidative stress, and the role of redox biology. However, over the past two decades, there has been a rise in omics, the systematic analysis of entire collections of molecules in a biological entity, enabling the discovery of the so-called "unknown unknowns." For example, transcriptomics, epigenomics, proteomics, and metabolomics have helped to uncover molecular mechanisms behind the action of physical plasma, revealing critical pathways beyond those traditionally associated with cancer treatments. This review showcases a selection of omics and then summarizes the insights gained from these studies toward understanding the biological pathways and molecular mechanisms implicated in physical plasma treatment. Omics studies have revealed how reactive species generated by plasma treatment preferentially affect several critical cellular pathways in cancer cells, resulting in epigenetic, transcriptional, and post-translational changes that promote cell death. Finally, this review considers the outlook for omics in uncovering both synergies and antagonisms with other common cancer therapies, as well as in overcoming challenges in the clinical translation of physical plasma.

Transcriptomic changes in single yeast cells under various stress conditions

BACKGROUND

The stress response of Saccharomyces cerevisiae has been extensively studied in the past decade. However, with the advent of recent technology in single-cell transcriptome profiling, there is a new opportunity to expand and further understanding of the yeast stress response with greater resolution on a system level. To understand transcriptomic changes in baker's yeast S. cerevisiae cells under stress conditions, we sequenced 117 yeast cells under three stress treatments (hypotonic condition, glucose starvation and amino acid starvation) using a full-length single-cell RNA-Seq method.

RESULTS

We found that though single cells from the same treatment showed varying degrees of uniformity, technical noise and batch effects can confound results significantly. However, upon careful selection of samples to reduce technical artifacts and account for batch-effects, we were able to capture distinct transcriptomic signatures for different stress conditions as well as putative regulatory relationships between transcription factors and target genes.

CONCLUSION

Our results show that a full-length single-cell based transcriptomic analysis of the yeast may help paint a clearer picture of how the model organism responds to stress than do bulk cell population-based methods.

A Simple, Test-Based Method to Control the Overestimation Bias in the Analysis of Potential Prognostic Tumour Markers

The early evaluation of prognostic tumour markers is commonly performed by comparing the survival of two groups of patients identified on the basis of a cut-off value. The corresponding hazard ratio (HR) is usually estimated, representing a measure of the relative risk between patients with marker values above and below the cut-off. A posteriori methods identifying an optimal cut-off are appropriate when the functional form of the relation between the marker distribution and patient survival is unknown, but they are prone to an overestimation bias. In the presence of a small sample size, which is typical of rare diseases, the external validation sets are hardly available and internal cross-validation could be unfeasible. We describe a new method to obtain an unbiased estimate of the HR at an optimal cut-off, exploiting the simple relation between the HR and the associated p-value estimated by a random permutation analysis. We validate the method on both simulated data and set of gene expression profiles from two large, publicly available data sets. Furthermore, a reanalysis of a previously published study, which included 134 Stage 4S neuroblastoma patients, allowed for the identification of E2F1 as a new gene with potential oncogenic activity. This finding was confirmed by an immunofluorescence analysis on an independent cohort.

Assessing Outlier Probabilities in Transcriptomics Data When Evaluating a Classifier

Outliers in the training or test set used to fit and evaluate a classifier on transcriptomics data can considerably change the estimated performance of the model. Hence, an either too weak or a too optimistic accuracy is then reported and the estimated model performance cannot be reproduced on independent data. It is then also doubtful whether a classifier qualifies for clinical usage. We estimate classifier performances in simulated gene expression data with artificial outliers and in two real-world datasets. As a new approach, we use two outlier detection methods within a bootstrap procedure to estimate the outlier probability for each sample and evaluate classifiers before and after outlier removal by means of cross-validation. We found that the removal of outliers changed the classification performance notably. For the most part, removing outliers improved the classification results. Taking into account the fact that there are various, sometimes unclear reasons for a sample to be an outlier, we strongly advocate to always report the performance of a transcriptomics classifier with and without outliers in training and test data. This provides a more diverse picture of a classifier's performance and prevents reporting models that later turn out to be not applicable for clinical diagnoses.

A novel ganglioside-related risk signature can reveal the distinct immune landscape of neuroblastoma and predict the immunotherapeutic response

Introduction

Gangliosides play an essential role in cancer development and progression. However, the involvement of gangliosides in the prognosis and tumor microenvironment (TME) of neuroblastoma is not entirely understood.

Methods

Consensus clustering analysis was performed to identify ganglioside-mediated molecular subtypes. LASSO-Cox analysis was conducted to identify independent prognostic genes, and a novel risk signature was constructed. The risk signature was validated internally and externally. We further explored the independent prognosis value, immune landscape, drug susceptibility, and tumor dedifferentiation of the risk signature. The role of the signature gene B3GALT4 in neuroblastoma was explored in vitro.

Results

Seventeen ganglioside-related genes were differentially expressed between INSS stage 4 and other stages, and two ganglioside-related clusters with distinct prognoses were identified. A novel risk signature integrating ten ganglioside-related prognostic genes was established. Across the train set and external validation sets, the risk signature presented high predictive accuracy and discrimination. The risk signature was an independent prognostic factor and constructed a nomogram combining multiple clinical characteristics. In the high-score group, the deficiency in antigen processing and presenting machinery, lack of immune cell infiltration, and escaping NK cells contributed substantially to immune escape. The low-score group was more responsive to immune checkpoint blockade therapy, while the high-score group showed substantial sensitivity to multiple chemotherapeutic drugs. Besides, the risk score was significantly positively correlated with the stemness index and reduced considerably in all-trans retinoic acid-treated neuroblastoma cell lines, indicating high dedifferentiation in the high-score group. Additionally, neuroblastoma cells with downregulation of B3GALT4 present with increased proliferation, invasion, and metastasis abilities in vitro.

Conclusion

The novel ganglioside-related risk signature highlights the role of ganglioside in neuroblastoma prognosis and immune landscape and helps optimize chemotherapy and immunotherapy for neuroblastoma.

Measuring biological age using omics data

Age is the key risk factor for diseases and disabilities of the elderly. Efforts to tackle age-related diseases and increase healthspan have suggested targeting the ageing process itself to 'rejuvenate' physiological functioning. However, achieving this aim requires measures of biological age and rates of ageing at the molecular level. Spurred by recent advances in high-throughput omics technologies, a new generation of tools to measure biological ageing now enables the quantitative characterization of ageing at molecular resolution. Epigenomic, transcriptomic, proteomic and metabolomic data can be harnessed with machine learning to build 'ageing clocks' with demonstrated capacity to identify new biomarkers of biological ageing.

ViLoN-a multi-layer network approach to data integration demonstrated for patient stratification

With more and more data being collected, modern network representations exploit the complementary nature of different data sources as well as similarities across patients. We here introduce the Variation of information fused Layers of Networks algorithm (ViLoN), a novel network-based approach for the integration of multiple molecular profiles. As a key innovation, it directly incorporates prior functional knowledge (KEGG, GO). In the constructed network of patients, patients are represented by networks of pathways, comprising genes that are linked by common functions and joint regulation in the disease. Patient stratification remains a key challenge both in the clinic and for research on disease mechanisms and treatments. We thus validated ViLoN for patient stratification on multiple data type combinations (gene expression, methylation, copy number), showing substantial improvements and consistently competitive performance for all. Notably, the incorporation of prior functional knowledge was critical for good results in the smaller cohorts (rectum adenocarcinoma: 90, esophageal carcinoma: 180), where alternative methods failed.

Multi-omics integration reveals a six-malignant cell maker gene signature for predicting prognosis in high-risk neuroblastoma

Background

Neuroblastoma is the most common extracranial solid tumor of childhood, arising from the sympathetic nervous system. High-risk neuroblastoma (HRNB) remains a major therapeutic challenge with low survival rates despite the intensification of therapy. This study aimed to develop a malignant-cell marker gene signature (MMGS) that might serve as a prognostic indicator in HRNB patients.

Methods

Multi-omics datasets, including mRNA expression (single-cell and bulk), DNA methylation, and clinical information of HRNB patients, were used to identify prognostic malignant cell marker genes. MMGS was established by univariate Cox analysis, LASSO, and stepwise multivariable Cox regression analysis. Kaplan–Meier (KM) curve and time-dependent receiver operating characteristic curve (tROC) were used to evaluate the prognostic value and performance of MMGS, respectively. MMGS further verified its reliability and accuracy in the independent validation set. Finally, the characteristics of functional enrichment, tumor immune features, and inflammatory activity between different MMGS risk groups were also investigated.

Results

We constructed a prognostic model consisting of six malignant cell maker genes (MAPT, C1QTNF4, MEG3, NPW, RAMP1, and CDT1), which stratified patients into ultra-high-risk (UHR) and common-high-risk (CHR) group. Patients in the UHR group had significantly worse overall survival (OS) than those in the CHR group. MMGS was verified as an independent predictor for the OS of HRNB patients. The area under the curve (AUC) values of MMGS at 1-, 3-, and 5-year were 0.78, 0.693, and 0.618, respectively. Notably, functional enrichment, tumor immune features, and inflammatory activity analyses preliminarily indicated that the poor prognosis in the UHR group might result from the dysregulation of the metabolic process and immunosuppressive microenvironment.

Conclusion

This study established a novel six-malignant cell maker gene prognostic model that can be used to predict the prognosis of HRNB patients, which may provide new insight for the treatment and personalized monitoring of HRNB patients.

GDPD5 Related to Lipid Metabolism Is a Potential Prognostic Biomarker in Neuroblastoma

Neuroblastoma (NB) is an extracranial solid tumor in children with poor prognosis in high-risk patients and its pathogenesis and prognostic markers urgently need to be explored. This study aimed to explore potential biomarkers related to NB from the aspect of lipid metabolism. Fifty-eight lipid metabolism-related differentially expressed genes between high-risk NB and non-high-risk NB in the GSE49710 dataset were analyzed using bioinformatics, including 45 down-regulated genes and 13 up-regulated genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis identified steroid hormone biosynthesis as an abnormal metabolic pathway in high-risk NB. Survival analysis established a three-gene prognostic model, including ACHE, GDPD5 and PIK3R1. In the test data, the AUCs of the established prognostic models used to predict patient survival at 1, 3 and 5 years were 0.84, 0.90 and 0.91, respectively. Finally, in the SH-SY5Y cell line, it was verified that overexpression of GDPD5 can inhibit cell proliferation and migration, as well as affect the lipid metabolism of SH-SY5Y, but not the sugar metabolism. hsa-miR-592 was predicted to be a potential target miRNA of GDPD5 by bioinformatics. In conclusion, this study develops a lipid-metabolism-related gene-based prognostic model for NB and demonstrates that GDPD5 inhibits SH-SY5Y proliferation and migration and may be targeted by hsa-miR-592 and inhibit SH-SY5Y fat synthesis.

Identification of key candidate genes and biological pathways in neuropathic pain

BACKGROUND

Neuropathic pain is a common chronic pain, characterized by spontaneous pain and mechanical allodynia. The incidence of neuropathic pain is on the rise due to infections, higher rates of diabetes and stroke, and increased use of chemotherapy drugs in cancer patients. At present, due to its pathophysiological process and molecular mechanism remaining unclear, there is a lack of effective treatment and prevention methods in clinical practice. Now, we use bioinformatics technology to integrate and filter hub genes that may be related to the pathogenesis of neuropathic pain, and explore their possible molecular mechanism by functional annotation and pathway enrichment analysis.

METHODS

The expression profiles of GSE24982, GSE2884, GSE2636 and GSE30691 were downloaded from the Gene Expression Omnibus（GEO）database, and these datasets include 93 neuropathic pain Rattus norvegicus and 59 shame controls. After the four datasets were all standardized by quantiles, the differentially expressed genes (DEGs) between NPP Rattus norvegicus and the shame controls were finally identified by the robust rank aggregation (RRA) analysis method. In order to reveal the possible underlying biological function of DEGs, the Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway enrichment analysis of DEGs were performed. In addition, a Protein-protein Interaction (PPI) network was also established. At the end of our study, a high throughput sequencing dataset GSE117526 was used to corroborate our calculation results.

RESULTS

Through RRA analysis of the above four datasets GSE24982, GSE2884, GSE2636, and GSE30691, we finally obtained 231 DEGs, including 183 up-regulated genes and 47 down-regulated genes. Arranging 231 DEGs in descending order according to |log2 fold change (FC)|, we found that the top 20 key genes include 14 up-regulated genes and 6 down-regulated genes. The most down-regulated hub gene abnormal expressed in NPP was Egf17 (P-value = 0.008), Camk2n2 (P-value = 0.002), and Lep (P-value = 0.02), and the most up-regulated hub gene abnormal expressed in NPP was Nefm (P-value = 1.08E-06), Prx (P-value = 2.68E-07), and Stip1 (P-value = 4.40E-07). In GO functional annotation analysis results, regulation of ion transmembrane transport (GO:0034765; P-value = 1.45E-09) was the most remarkable enriched for biological process, synaptic membrane (GO:0097060; P-value = 2.95E-08) was the most significantly enriched for cellular component, channel activity (GO:0015267; P-value = 2.44E-06) was the most prominent enriched for molecular function. In KEGG pathway enrichment analysis results, the top three notable enrichment pathways were Neuroactive ligand-receptor interaction (rno04080; P-value = 3.46E-08), Calcium signaling pathway (rno04020; P-value = 5.37E-05), and Osteoclast differentiation (rno04380; P-value = 0.000459927). Cav1 and Lep appeared in the top 20 genes in both RRA analysis and PPI analysis, while Nefm appeared in RRA analysis and datasets GSE117526 validation analysis, so we finally identified these three genes as hub genes.

CONCLUSIONS

Our research identified the hub genes and signal pathways of neuropathic pain, enriched the pathophysiological mechanism of neuropathic pain to some extent, and provided a possible basis for the targeted therapy of neuropathic pain.

E2F3 gene expression is a potential negative prognostic marker for localised and MYCN not-amplified neuroblastoma: Results of in silico analysis of 786 samples

BACKGROUND

Neuroblastoma (NB) is an enigmatic childhood malignancy characterised by a wide range of clinical behaviour. Many potential oncogenes for NB have recently been identified. Among them, E2 transcription factor 3 (E2F3) expression was associated with a poor survival in 134 stage 4S patients, but evidence for other stage groups remains poorly investigated.

METHODS

We have analysed the expression of E2F3 gene from a database of 786 NB samples. Overall and event-free survivals (EFS) were assessed by the Kaplan-Meier method, splitting the data on the median and tertile expression values. The Cox model was applied to control for the confounding by stage, age and MYCN amplification. Validation was performed by an in silico analysis of an independent cohort of 283 NB patients. Furthermore, an immunofluorescence analysis on 48 formalin-fixed, paraffin-embedded NB specimens was also performed.

RESULTS

E2F3 overexpression was associated with a poor survival (EFS = 84%, 95% CI: 79%-95%, for low expression levels; EFS = 62%, 95% CI: 56%-68% for middle levels; EFS = 30%, 95% CI: 24%-36%, for high levels, p < .001). This association was confirmed in multivariable analysis and was more evident in patients with MYCN not-amplified and localised stages. Immunofluorescence results and the validation on an independent cohort of NB primary samples confirmed these findings.

CONCLUSIONS

E2F3 is a new potential prognostic marker in NB with favourable characteristics at diagnosis. Further studies are needed to elucidate the potential role of E2F3 in NB oncogenesis and progression, in order to identify new targets for therapeutic interventions.

Reliable assessment of telomere maintenance mechanisms in neuroblastoma

BACKGROUND

Telomere maintenance mechanisms (TMM) are a hallmark of high-risk neuroblastoma, and are conferred by activation of telomerase or alternative lengthening of telomeres (ALT). However, detection of TMM is not yet part of the clinical routine, and consensus on TMM detection, especially on ALT assessment, remains to be achieved.

METHODS

Whole genome sequencing (WGS) data of 68 primary neuroblastoma samples were analyzed. Telomere length was calculated from WGS data or by telomere restriction fragment analysis (n = 39). ALT was assessed by C-circle assay (CCA, n = 67) and detection of ALT-associated PML nuclear bodies (APB) by combined fluorescence in situ hybridization and immunofluorescence staining (n = 68). RNA sequencing was performed (n = 64) to determine expression of TERT and telomeric long non-coding RNA (TERRA). Telomerase activity was examined by telomerase repeat amplification protocol (TRAP, n = 15).

RESULTS

Tumors were considered as telomerase-positive if they harbored a TERT rearrangement, MYCN amplification or high TERT expression (45.6%, 31/68), and ALT-positive if they were positive for APB and CCA (19.1%, 13/68). If all these markers were absent, tumors were considered TMM-negative (25.0%, 17/68). According to these criteria, the majority of samples were classified unambiguously (89.7%, 61/68). Assessment of additional ALT-associated parameters clarified the TMM status of the remaining seven cases with high likelihood: ALT-positive tumors had higher TERRA expression, longer telomeres, more telomere insertions, a characteristic pattern of telomere variant repeats, and were associated with ATRX mutations.

CONCLUSIONS

We here propose a workflow to reliably detect TMM in neuroblastoma. We show that unambiguous classification is feasible following a stepwise approach that determines both, activation of telomerase and ALT. The workflow proposed in this study can be used in clinical routine and provides a framework to systematically and reliably determine telomere maintenance mechanisms for risk stratification and treatment allocation of neuroblastoma patients.

The Evolving Role of Next Generation Sequencing in Pediatric Neurosurgery: a Call for Action for Research, Clinical Practice, and Optimization of Care

NGS (Next-Generation Sequencing) is one of the most promising technologies that have truly revolutionized many aspects of clinical practice in recent years. It has been and is increasingly applied in many disciplines of medicine; however, it appears that pediatric neurosurgery despite its great potential has not truly embraced this new technology and is hesitant to employ it in its routine practice and guidelines. In this review, we briefly summarized the developments that lead to the establishment of NGS technology, reviewed the current applications and potentials of NGS in the disorders treated by pediatric neurosurgeons, and lastly discuss the steps we need to take to better harness NGS in pediatric neurosurgery.

Particle Swarm Optimization Artificial Intelligence technique for gene signature discovery in transcriptomic cohorts

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EBPSO identifies unique, accurate, and succinct gene signatures.

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Key genes within the signatures provide biological insights its associated functions.

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A web-based micro-framework developed for ease of use and real-time visualizations.

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A promising alternative to traditional single gene signature generation.

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Downstream analysis will better translate these signatures towards clinical translation.

The development of gene signatures is key for delivering personalized medicine, despite only a few signatures being available for use in the clinic for cancer patients. Gene signature discovery tends to revolve around identifying a single signature. However, it has been shown that various highly predictive signatures can be produced from the same dataset. This study assumes that the presentation of top ranked signatures will allow greater efforts in the selection of gene signatures for validation on external datasets and for their clinical translation. Particle swarm optimization (PSO) is an evolutionary algorithm often used as a search strategy and largely represented as binary PSO (BPSO) in this domain. BPSO, however, fails to produce succinct feature sets for complex optimization problems, thus affecting its overall runtime and optimization performance. Enhanced BPSO (EBPSO) was developed to overcome these shortcomings. Thus, this study will validate unique candidate gene signatures for different underlying biology from EBPSO on transcriptomics cohorts. EBPSO was consistently seen to be as accurate as BPSO with substantially smaller feature signatures and significantly faster runtimes. 100% accuracy was achieved in all but two of the selected data sets. Using clinical transcriptomics cohorts, EBPSO has demonstrated the ability to identify accurate, succinct, and significantly prognostic signatures that are unique from one another. This has been proposed as a promising alternative to overcome the issues regarding traditional single gene signature generation. Interpretation of key genes within the signatures provided biological insights into the associated functions that were well correlated to their cancer type.

SMARCE1 promotes neuroblastoma tumorigenesis through assisting MYCN-mediated transcriptional activation

SMARCE1 gene, encoding a core subunit of SWI/SNF chromatin remodeling complex, is situated on chromosome 17q21-ter region that is frequently gained in neuroblastoma. However, its role in the tumorigenesis remains unknown. Here, we showed that high expression of SMARCE1 was associated with poor prognosis of patients with neuroblastoma, especially those with MYCN amplification. Knockdown of SMARCE1 reduced proliferation, colony formation, and tumorigenicity of neuroblastoma cells. Mechanistically, SMARCE1 directly interacted with MYCN, which was necessary for MYCN-mediated transcriptional activation of downstream target genes including PLK1, ODC1, and E2F2. Overexpression of PLK1, ODC1 or E2F2 significantly reversed the inhibiting effect of SMARCE1 knockdown on the proliferation, colony formation, and tumorigenicity of MYCN-amplified neuroblastoma cells. Moreover, we revealed that MYCN directly regulated SMARCE1 transcription through binding to a non-canonical E-box of SMARCE1 promoter, thus enhancing SMARCE1-MYCN cooperativity. These findings establish SMARCE1 is a critical oncogenic factor in neuroblastoma and provide a new potential target for treatment of neuroblastoma with 17q21-ter gain and MYCN amplification.

The Time Sequence of Gene Expression Changes after Spinal Cord Injury

Gene expression changes following spinal cord injury (SCI) are time-dependent, and an accurate understanding of these changes can be crucial in determining time-based treatment options in a clinical setting. We performed RNA sequencing of the contused spinal cord of rats at five different time points from the very acute to chronic stages (1 hour, 1 day, 1 week, 1 month, and 3 months) following SCI. We identified differentially expressed genes (DEGs) and Gene Ontology (GO) terms at each time point, and 14,257 genes were commonly expressed at all time points. The biological process of the inflammatory response was increased at 1 hour and 1 day, and the cellular component of the integral component of the synaptic membrane was increased at 1 day. DEGs associated with cell activation and the innate immune response were highly enriched at 1 week and 1 month, respectively. A total of 2841 DEGs were differentially expressed at any of the five time points, and 18 genes (17 upregulated and 1 downregulated) showed common expression differences at all time points. We found that interleukin signaling, neutrophil degranulation, eukaryotic translation, collagen degradation, LGI–ADAM interactions, GABA receptor, and L1CAM-ankyrin interactions were prominent after SCI depending on the time post injury. We also performed gene–drug network analysis and found several potential antagonists and agonists which can be used to treat SCI. We expect to discover effective treatments in the clinical field through further studies revealing the efficacy and safety of potential drugs.

PRMT5 activates AKT via methylation to promote tumor metastasis

Protein arginine methyltransferase 5 (PRMT5) is the primary methyltransferase generating symmetric-dimethyl-arginine marks on histone and non-histone proteins. PRMT5 dysregulation is implicated in multiple oncogenic processes. Here, we report that PRMT5-mediated methylation of protein kinase B (AKT) is required for its subsequent phosphorylation at Thr308 and Ser473. Moreover, pharmacologic or genetic inhibition of PRMT5 abolishes AKT1 arginine 15 methylation, thereby preventing AKT1 translocation to the plasma membrane and subsequent recruitment of its upstream activating kinases PDK1 and mTOR2. We show that PRMT5/AKT signaling controls the expression of the epithelial-mesenchymal-transition transcription factors ZEB1, SNAIL, and TWIST1. PRMT5 inhibition significantly attenuates primary tumor growth and broadly blocks metastasis in multiple organs in xenograft tumor models of high-risk neuroblastoma. Collectively, our results suggest that PRMT5 inhibition augments anti-AKT or other downstream targeted therapeutics in high-risk metastatic cancers.