Identification of Potential Prognostic Genes for Neuroblastoma

Investigation of differentially expressed genes related to cellular senescence between high-risk and non-high-risk groups in neuroblastoma

Object

This study aims to identify differentially expressed genes (DEGs) between high-risk and non-high-risk groups in neuroblastoma (NB), construct a prognostic model, and establish a risk score formula.

Materials and methods

The NB dataset GSE49710 (n = 498) from the GEO database served as the training cohort to select DEGs between high-risk and non-high-risk NB groups. Cellular senescence-related genes were obtained from the Aging Atlas database. Intersection genes from both datasets were identified as key genes of cellular senescence-related genes (SRGs). A prognostic model was constructed using Univariate Cox regression analysis and the Lasso algorithm with SRGs. Validation was performed using the E-MTAB-8248 cohort (n = 223). The expression levels of AURKA and CENPA were evaluated via RT-qPCR in two clinical NB sample groups.

Results

Eight SRGs were identified, and a prognostic model comprising five genes related to cellular senescence was constructed. AURKA and CENPA showed significant expression in clinical samples and were closely associated with cellular senescence.

Conclusion

The prognostic model consisted with five cellular senescence related genes effectively predicts the prognosis of NB patients. AURKA and CENPA represent promising targets in NB for predicting cellular senescence, offering potential insights for NB therapy.

Hyaluronic Acid Interacting Molecules Mediated Crosstalk between Cancer Cells and Microenvironment from Primary Tumour to Distant Metastasis

Hyaluronic acid (HA) is a prominent component of the extracellular matrix, and its interactions with HA-interacting molecules (HAIMs) play a critical role in cancer development and disease progression. This review explores the multifaceted role of HAIMs in the context of cancer, focusing on their influence on disease progression by dissecting relevant cellular and molecular mechanisms in tumour cells and the tumour microenvironment. Cancer progression can be profoundly affected by the interactions between HA and HAIMs. They modulate critical processes such as cell adhesion, migration, invasion, and proliferation. The TME serves as a dynamic platform in which HAIMs contribute to the formation of a unique niche. The resulting changes in HA composition profoundly influence the biophysical properties of the TME. These modifications in the TME, in conjunction with HAIMs, impact angiogenesis, immune cell recruitment, and immune evasion. Therefore, understanding the intricate interplay between HAIMs and HA within the cancer context is essential for developing novel therapeutic strategies. Targeting these interactions offers promising avenues for cancer treatment, as they hold the potential to disrupt critical aspects of disease progression and the TME. Further research in this field is imperative for advancing our knowledge and the treatment of cancer.

BAP31 Promotes Angiogenesis via Galectin-3 Upregulation in Neuroblastoma

Neuroblastoma (NB) is one of the highly vascularized childhood solid tumors, and understanding the molecular mechanisms underlying angiogenesis in NB is crucial for developing effective therapeutic strategies. B-cell receptor-associated protein 31 (BAP31) has been implicated in tumor progression, but its role in angiogenesis remains unexplored. This study investigated BAP31 modulation of pro-angiogenic factors in SH-SY5Y NB cells. Through protein overexpression, knockdown, antibody blocking, and quantification experiments, we demonstrated that overexpression of BAP31 led to increased levels of vascular endothelial growth factor A (VEGFA) and Galectin-3 (GAL-3), which are known to promote angiogenesis. Conditioned medium derived from BAP31-overexpressing neuroblastoma cells stimulated migration and tube formation in endothelial cells, indicating its pro-angiogenic properties. Also, we demonstrated that BAP31 enhances capillary tube formation by regulating hypoxia-inducible factor 1 alpha (HIF-1α) and its downstream target, GAL-3. Furthermore, GAL-3 downstream proteins, Jagged 1 and VEGF receptor 2 (VEGFR2), were up-regulated, and blocking GAL-3 partially inhibited the BAP31-induced tube formation. These findings suggest that BAP31 promotes angiogenesis in NB by modulating GAL-3 and VEGF signaling, thereby shaping the tumor microenvironment. This study provides novel insights into the pro-angiogenic role of BAP31 in NB.

Plasticity of gene expression in the nervous system by exposure to environmental odorants that inhibit HDACs

Eukaryotes respond to secreted metabolites from the microbiome. However, little is known about the effects of exposure to volatiles emitted by microbes or in the environment that we are exposed to over longer durations. Using Drosophila melanogaster, we evaluated a yeast-emitted volatile, diacetyl, found at high levels around fermenting fruits where they spend long periods of time. Exposure to the diacetyl molecules in headspace alters gene expression in the antenna. In vitro experiments demonstrated that diacetyl and structurally related volatiles inhibited conserved histone deacetylases (HDACs), increased histone-H3K9 acetylation in human cells, and caused changes in gene expression in both Drosophila and mice. Diacetyl crosses the blood-brain barrier and exposure caused modulation of gene expression in the mouse brain, therefore showing potential as a neuro-therapeutic. Using two separate disease models previously known to be responsive to HDAC inhibitors, we evaluated the physiological effects of volatile exposure. Diacetyl exposure halted proliferation of a neuroblastoma cell line in culture. Exposure to diacetyl vapors slowed progression of neurodegeneration in a Drosophila model for Huntington's disease. These changes strongly suggest that certain volatiles in the surroundings can have profound effects on histone acetylation, gene expression, and physiology in animals.

Plasticity of gene expression in the nervous system by exposure to environmental odorants that inhibit HDACs

Eukaryotes are often exposed to microbes and respond to their secreted metabolites, such as the microbiome in animals or commensal bacteria in roots. Little is known about the effects of long-term exposure to volatile chemicals emitted by microbes, or other volatiles that we are exposed to over a long duration. Using the model system Drosophila melanogaster, we evaluate a yeast emitted volatile, diacetyl, found in high levels around fermenting fruits where they spend long periods of time. We find that exposure to just the headspace containing the volatile molecules can alter gene expression in the antenna. Experiments showed that diacetyl and structurally related volatile compounds inhibited human histone-deacetylases (HDACs), increased histone-H3K9 acetylation in human cells, and caused wide changes in gene expression in both Drosophila and mice. Diacetyl crosses the blood-brain barrier and exposure causes modulation of gene expression in the brain, therefore has potential as a therapeutic. Using two separate disease models known to be responsive to HDAC-inhibitors, we evaluated physiological effects of volatile exposure. First, we find that the HDAC inhibitor also halts proliferation of a neuroblastoma cell line in culture as predicted. Next, exposure to vapors slows progression of neurodegeneration in a Drosophila model for Huntington's disease. These changes strongly suggest that unbeknown to us, certain volatiles in the surroundings can have profound effects on histone acetylation, gene expression and physiology in animals.

Construction of predictive model of interstitial fibrosis and tubular atrophy after kidney transplantation with machine learning algorithms

Background: Interstitial fibrosis and tubular atrophy (IFTA) are the histopathological manifestations of chronic kidney disease (CKD) and one of the causes of long-term renal loss in transplanted kidneys. Necroptosis as a type of programmed death plays an important role in the development of IFTA, and in the late functional decline and even loss of grafts. In this study, 13 machine learning algorithms were used to construct IFTA diagnostic models based on necroptosis-related genes. Methods: We screened all 162 "kidney transplant"-related cohorts in the GEO database and obtained five data sets (training sets: GSE98320 and GSE76882, validation sets: GSE22459 and GSE53605, and survival set: GSE21374). The training set was constructed after removing batch effects of GSE98320 and GSE76882 by using the SVA package. The differentially expressed gene (DEG) analysis was used to identify necroptosis-related DEGs. A total of 13 machine learning algorithms-LASSO, Ridge, Enet, Stepglm, SVM, glmboost, LDA, plsRglm, random forest, GBM, XGBoost, Naive Bayes, and ANNs-were used to construct 114 IFTA diagnostic models, and the optimal models were screened by the AUC values. Post-transplantation patients were then grouped using consensus clustering, and the different subgroups were further explored using PCA, Kaplan-Meier (KM) survival analysis, functional enrichment analysis, CIBERSOFT, and single-sample Gene Set Enrichment Analysis. Results: A total of 55 necroptosis-related DEGs were identified by taking the intersection of the DEGs and necroptosis-related gene sets. Stepglm[both]+RF is the optimal model with an average AUC of 0.822. A total of four molecular subgroups of renal transplantation patients were obtained by clustering, and significant upregulation of fibrosis-related pathways and upregulation of immune response-related pathways were found in the C4 group, which had poor prognosis. Conclusion: Based on the combination of the 13 machine learning algorithms, we developed 114 IFTA classification models. Furthermore, we tested the top model using two independent data sets from GEO.

A Relationship Prediction Method for Magnaporthe oryzae-Rice Multi-Omics Data Based on WGCNA and Graph Autoencoder

Magnaporthe oryzae Oryzae (MoO) pathotype is a devastating fungal pathogen of rice; however, its pathogenic mechanism remains poorly understood. The current research is primarily focused on single-omics data, which is insufficient to capture the complex cross-kingdom regulatory interactions between MoO and rice. To address this limitation, we proposed a novel method called Weighted Gene Autoencoder Multi-Omics Relationship Prediction (WGAEMRP), which combines weighted gene co-expression network analysis (WGCNA) and graph autoencoder to predict the relationship between MoO-rice multi-omics data. We applied WGAEMRP to construct a MoO-rice multi-omics heterogeneous interaction network, which identified 18 MoO small RNAs (sRNAs), 17 rice genes, 26 rice mRNAs, and 28 rice proteins among the key biomolecules. Most of the mined functional modules and enriched pathways were related to gene expression, protein composition, transportation, and metabolic processes, reflecting the infection mechanism of MoO. Compared to previous studies, WGAEMRP significantly improves the efficiency and accuracy of multi-omics data integration and analysis. This approach lays out a solid data foundation for studying the biological process of MoO infecting rice, refining the regulatory network of pathogenic markers, and providing new insights for developing disease-resistant rice varieties.

Multifaceted role of NF-κB in hepatocellular carcinoma therapy: Molecular landscape, therapeutic compounds and nanomaterial approaches

The predominant kind of liver cancer is hepatocellular carcinoma (HCC) that its treatment have been troublesome difficulties for physicians due to aggressive behavior of tumor cells in proliferation and metastasis. Moreover, stemness of HCC cells can result in tumor recurrence and angiogenesis occurs. Another problem is development of resistance to chemotherapy and radiotherapy in HCC cells. Genomic mutations participate in malignant behavior of HCC and nuclear factor-kappaB (NF-κB) has been one of the oncogenic factors in different human cancers that after nuclear translocation, it binds to promoter of genes in regulating their expression. Overexpression of NF-κB has been well-documented in increasing proliferation and invasion of tumor cells and notably, when its expression enhances, it induces chemoresistance and radio-resistance. Highlighting function of NF-κB in HCC can shed some light on the pathways regulating progression of tumor cells. The first aspect is proliferation acceleration and apoptosis inhibition in HCC cells mediated by enhancement in expression level of NF-κB. Moreover, NF-κB is able to enhance invasion of HCC cells via upregulation of MMPs and EMT, and it triggers angiogenesis as another step for increasing spread of tumor cells in tissues and organs. When NF-κB expression enhances, it stimulates chemoresistance and radio-resistance in HCC cells and by increasing stemness and population of cancer-stem cells, it can provide the way for recurrence of tumor. Overexpression of NF-κB mediates therapy resistance in HCC cells and it can be regulated by non-coding RNAs in HCC. Moreover, inhibition of NF-κB by anti-cancer and epigenetic drugs suppresses HCC tumorigenesis. More importantly, nanoparticles are considered for suppressing NF-κB axis in cancer and their prospectives and results can also be utilized for treatment of HCC. Nanomaterials are promising factors in treatment of HCC and by delivery of genes and drugs, they suppress HCC progression. Furthermore, nanomaterials provide phototherapy in HCC ablation.

Development of gene model combined with machine learning technology to predict for advanced atherosclerotic plaques

BACKGROUND

Atherosclerosis, as a major cause of stroke, is responsible for a quarter of deaths worldwide. In particular, rupture of late-stage plaques in large vessels such as the carotid artery can lead to serious cardiovascular disease. The aim of our study was to establish a genetic model combined with machining leaning techniques to screen out gene signatures and predict for advanced atherosclerosis plaques.

METHODS

The microarray dataset GSE28829 and GSE43292 which were publicly obtained from the Gene Expression Omnibus database were utilized to screen for potential predictive genes. Differentially expressed genes (DEGs) were identified by using the "limma" R package. Gene Ontology (GO) and Kyoto Encyclopedia of Genes Genomes (KEGG) analyses of these DEGs were performed by Metascape. Later, Random Forest (RF) algorithm was applied to further screen out top-30 genes which contribute the most. The expression data of top 30-DEGs were converted into a "Gene Score". Finally, we developed a model based on artificial neural network (ANN) to predict advanced atherosclerotic plaques. The model later was validated in an independent test dataset GSE104140.

RESULTS

A total of 176 DEGs were identified in the training datasets. GO and KEGG enrichment analysis revealed that these genes were enriched in leukocyte-mediated immune response, cytokine- cytokine interactions, and immunoinflammatory signaling. Further, top-30 genes (including 25 upregulated and 5 downregulated DEGs) were screened as predictors by RF algorithm. The predictive model was developed with a significantly predictive value (AUC = 0.913) in the training datasets, and was validated with an independent dataset GSE104140 (AUC = 0.827).

CONCLUSION

In present study, our prediction model was established and showed satisfactory predictive power in both training and test datasets. In addition, this is the first study adopted bioinformatics methods combined with machine learning techniques (RF and ANN) to explore and predict for the advanced atherosclerotic plaques. However, further investigations were needed to verify the screened DEGs and predictive effectiveness of this model.

Variant rs8400 enhances ALKBH5 expression through disrupting miR-186 binding and promotes neuroblastoma progression

Objective

AlkB homolog 5 (ALKBH5) has been proven to be closely related to tumors. However, the role and molecular mechanism of ALKBH5 in neuroblastomas have rarely been reported.

Methods

The potential functional single-nucleotide polymorphisms (SNPs) in ALKBH5 were identified by National Center for Biotechnology Information (NCBI) dbSNP screening and SNPinfo software. TaqMan probes were used for genotyping. A multiple logistic regression model was used to evaluate the effects of different SNP loci on the risk of neuroblastoma. The expression of ALKBH5 in neuroblastoma was evaluated by Western blotting and immunohistochemistry (IHC). Cell counting kit-8 (CCK-8), plate colony formation and 5-ethynyl-2'-deoxyuridine (EdU) incorporation assays were used to evaluate cell proliferation. Wound healing and Transwell assays were used to compare cell migration and invasion. Thermodynamic modelling was performed to predict the ability of miRNAs to bind to ALKBH5 with the rs8400 G/A polymorphism. RNA sequencing, N6-methyladenosine (m6A) sequencing, m6A methylated RNA immunoprecipitation (MeRIP) and a luciferase assay were used to identify the targeting effect of ALKBH5 on SPP1.

Results

ALKBH5 was highly expressed in neuroblastoma. Knocking down ALKBH5 inhibited the proliferation, migration and invasion of cancer cells. miR-186-3p negatively regulates the expression of ALKBH5, and this ability is affected by the rs8400 polymorphism. When the G nucleotide was mutated to A, the ability of miR-186-3p to bind to the 3'-UTR of ALKBH5 decreased, resulting in upregulation of ALKBH5. SPP1 is the downstream target gene of the ALKBH5 oncogene. Knocking down SPP1 partially restored the inhibitory effect of ALKBH5 downregulation on neuroblastoma. Downregulation of ALKBH5 can improve the therapeutic efficacy of carboplatin and etoposide in neuroblastoma.

Conclusions

We first found that the rs8400 G>A polymorphism in the m6A demethylase-encoding gene ALKBH5 increases neuroblastoma susceptibility and determines the related mechanisms. The aberrant regulation of ALKBH5 by miR-186-3p caused by this genetic variation in ALKBH5 promotes the occurrence and development of neuroblastoma through the ALKBH5-SPP1 axis.

Signature literature review reveals AHCY, DPYSL3, and NME1 as the most recurrent prognostic genes for neuroblastoma

Neuroblastoma is a childhood neurological tumor which affects hundreds of thousands of children worldwide, and information about its prognosis can be pivotal for patients, their families, and clinicians. One of the main goals in the related bioinformatics analyses is to provide stable genetic signatures able to include genes whose expression levels can be effective to predict the prognosis of the patients. In this study, we collected the prognostic signatures for neuroblastoma published in the biomedical literature, and noticed that the most frequent genes present among them were three: AHCY, DPYLS3, and NME1. We therefore investigated the prognostic power of these three genes by performing a survival analysis and a binary classification on multiple gene expression datasets of different groups of patients diagnosed with neuroblastoma. Finally, we discussed the main studies in the literature associating these three genes with neuroblastoma. Our results, in each of these three steps of validation, confirm the prognostic capability of AHCY, DPYLS3, and NME1, and highlight their key role in neuroblastoma prognosis. Our results can have an impact on neuroblastoma genetics research: biologists and medical researchers can pay more attention to the regulation and expression of these three genes in patients having neuroblastoma, and therefore can develop better cures and treatments which can save patients' lives.

Combining multiple cell death pathway-related risk scores to develop neuroblastoma cell death signature

PURPOSE

Cell death plays an important role in tumourigenesis and progression; nevertheless, the clinical significance of cell death-related genes in neuroblastoma remains incompletely understood.

METHODS

We separately constructed the corresponding risk scores for each of the eight cell death pathways separately and assessed their predictive performance. Through Cox regression analysis, these eight risk scores were integrated to obtain final cell death risk scores (CDRS). We evaluated the predictive performance of CDRS in multiple datasets and compared its accuracy with the clinical characteristics of patients and some existing prognostic models for neuroblastoma. We then explored the differences in immune infiltration between the high and low CDRS groups, and the significance of CDRS on EFS and disease progression.

RESULTS

All eight risk scores have high predictive accuracy, with the Immunogenic-RS being the most accurate and the cuproptosis-RS the least accurate. Model genes are mainly enriched in a variety of cancer-related pathways and are closely related to the clinical characteristics. CDRS showed superior and robust predictive performance in multiple datasets and was more accurate than the clinical characteristics of patients and some existing prognostic models for neuroblastoma. High CDRS group featured distinct immune cold tumor profiles and may have poorer immune checkpoint inhibitor efficacy. CDRS had significance in predicting EFS and disease progression.

CONCLUSION

We integrated risk scores associated with multiple cell death pathways to develop a high-performing and robust neuroblastoma signature. CDRS was a promising tool that may help with risk assessment and prediction of overall prognosis, and thus improve clinical outcomes.

Identification of Lipocalin 2 as a Ferroptosis-Related Key Gene Associated with Hypoxic-Ischemic Brain Damage via STAT3/NF-κB Signaling Pathway

Hypoxic-ischemic brain damage (HIBD) is a common cause of death or mental retardation in newborns. Ferroptosis is a novel form of iron-dependent cell death driven by lipid peroxidation, and recent studies have confirmed that ferroptosis plays an important role in the development of HIBD. However, HIBD ferroptosis-related biomarkers remain to be discovered. An artificial neural network (ANN) was established base on differentially expressed genes (DEGs) related to HIBD and ferroptosis and validated by external dataset. The protein-protein interaction (PPI) network, support vector machine-recursive feature elimination (SVM-RFE) algorithms, and random forest (RF) algorithm were utilized to identify core genes of HIBD. An in vitro model of glutamate-stimulated HT22 cell HIBD was constructed, and glutamate-induced ferroptosis and mitochondrial structure and function in HT22 cells were examined by propidium iodide (PI) staining, flow cytometry, Fe²⁺ assay, Western blot, JC-1 kit, and transmission electron microscopy (TEM). In addition, Western blot and immunofluorescence assays were used to detect the NF-κB/STAT3 pathway. An HIBD classification model was constructed and presented excellent performance. The PPI network and two machine learning algorithms indicated two hub genes in HIBD. Lipocalin 2 (LCN2) was the core gene correlated with the risk of HIBD according to the results of differential expression analysis and logistic regression diagnostics. Subsequently, we verified in an in vitro model that LCN2 is highly expressed in glutamate-induced ferroptosis in HT22 cells. More importantly, LCN2 silencing significantly inhibited glutamate-stimulated ferroptosis in HT22 cells. We also found that glutamate-stimulated HT22 cells produced mitochondrial dysfunction. Furthermore, in vitro experiments confirmed that NF-κB and STAT3 were activated and that silencing LCN2 could have the effect of inhibiting their activation. In short, our findings reveal a molecular mechanism by which LCN2 may promote ferroptosis in HIBD through activation of the NF-κB/STAT3 pathway, providing new and unique insights into LCN2 as a biomarker for HIBD and suggesting new preventive and therapeutic strategies for HIBD.

Integration of the Connectivity Map and Pathway Analysis to Predict Plant Extract’s Medicinal Properties—The Study Case of Sarcopoterium spinosum L

Medicinal properties of plants are usually identified based on knowledge of traditional medicine or using low-throughput screens for specific pharmacological activities. The former is very biased since it requires prior knowledge of plants’ properties, while the latter depends on a specific screening system and will miss medicinal activities not covered by the screen. We sought to enrich our understanding of the biological activities of Sarcopoterium spinosum L. root extract based on transcriptome changes to uncover a plurality of possible pharmacological effects without the need for prior knowledge or functional screening. We integrated Gene Set Enrichment Analysis of the RNAseq data to identify pathways affected by the treatment of cells with the extract and perturbational signatures in the CMAP database to enhance the validity of the results. Activities of signaling pathways were measured using immunoblotting with phospho-specific antibodies. Mitochondrial membrane potential was assessed using JC-1 staining. SARS-CoV-2-induced cell killing was assessed in Vero E6 and A549 cells using an MTT assay. Here, we identified transcriptome changes following exposure of cultured cells to the medicinal plant Sarcopoterium spinosum L. root extract. By integrating algorithms of GSEA and CMAP, we confirmed known anti-cancer activities of the extract and predicted novel biological effects on oxidative phosphorylation and interferon pathways. Experimental validation of these pathways uncovered strong activation of autophagy, including mitophagy, and excellent protection from SARS-CoV-2 infection. Our study shows that gene expression analysis alone is insufficient for predicting biological effects since some of the changes reflect compensatory effects, and additional biochemical tests provide necessary corrections. This study defines the advantages and limitations of transcriptome analysis in predicting the biological and medicinal effects of the Sarcopoterium spinosum L. extract. Such analysis could be used as a general approach for predicting the medicinal properties of plants.

The Machine-Learning-Mediated Interface of Microbiome and Genetic Risk Stratification in Neuroblastoma Reveals Molecular Pathways Related to Patient Survival

Simple Summary

Neuroblastoma is a highly heterogeneous malignancy with a wide range of outcomes from spontaneous regression to fatal chemoresistant disease, as currently treated according to the risk stratification of the Children’s Oncology Group (COG), resulting in some high COG risk patients receiving excessive treatment, due to lacking predictors for treatment response. Here, we sought to complement COG risk classification by using the tumor intracellular microbiome, which is part of the tumor’s molecular signature. We determine that an intra-tumor microbial gene abundance score, namely M-score, separates the high COG-risk patients into two subpopulations (M_high and M_low) with higher accuracy in risk stratification than the current COG risk assessment, thus sparing a subset of high COG-risk patients from being subjected to traditional high-risk therapies.

Abstract

Currently, most neuroblastoma patients are treated according to the Children’s Oncology Group (COG) risk group assignment; however, neuroblastoma’s heterogeneity renders only a few predictors for treatment response, resulting in excessive treatment. Here, we sought to couple COG risk classification with tumor intracellular microbiome, which is part of the molecular signature of a tumor. We determine that an intra-tumor microbial gene abundance score, namely M-score, separates the high COG-risk patients into two subpopulations (M_high and M_low) with higher accuracy in risk stratification than the current COG risk assessment, thus sparing a subset of high COG-risk patients from being subjected to traditional high-risk therapies. Mechanistically, the classification power of M-scores implies the effect of CREB over-activation, which may influence the critical genes involved in cellular proliferation, anti-apoptosis, and angiogenesis, affecting tumor cell proliferation survival and metastasis. Thus, intracellular microbiota abundance in neuroblastoma regulates intracellular signals to affect patients’ survival.

Construction of Novel Gene Signature-Based Predictive Model for the Diagnosis of Acute Myocardial Infarction by Combining Random Forest With Artificial Neural Network

Background

Acute myocardial infarction (AMI) is one of the most common causes of mortality around the world. Early diagnosis of AMI contributes to improving prognosis. In our study, we aimed to construct a novel predictive model for the diagnosis of AMI using an artificial neural network (ANN), and we verified its diagnostic value via constructing the receiver operating characteristic (ROC).

Methods

We downloaded three publicly available datasets (training sets GSE48060, GSE60993, and GSE66360) from Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were identified between 87 AMI and 78 control samples. We applied the random forest (RF) and ANN algorithms to further identify novel gene signatures and construct a model to predict the possibility of AMI. Besides, the diagnostic value of our model was further validated in the validation sets GSE61144 (7 AMI patients and 10 controls), GSE34198 (49 AMI patients and 48 controls), and GSE97320 (3 AMI patients and 3 controls).

Results

A total of 71 DEGs were identified, of which 68 were upregulated and 3 were downregulated. Firstly, 11 key genes in 71 DEGs were screened with RF classifier for the classification of AMI and control samples. Then, we calculated the weight of each key gene using ANN. Furthermore, the diagnostic model was constructed and named neuralAMI, with significant predictive power (area under the curve [AUC] = 0.980). Finally, our model was validated with the independent datasets GSE61144 (AUC = 0.900), GSE34198 (AUC = 0.882), and GSE97320 (AUC = 1.00).

Conclusion

Machine learning was used to develop a reliable predictive model for the diagnosis of AMI. The results of our study provide potential gene biomarkers for early disease screening.

Pyroptosis-Related Gene Signature Predicts the Prognosis and Immune Infiltration in Neuroblastoma

Neuroblastoma is the most common pediatric extracranial solid tumor. The 5-year survival rate for high-risk neuroblastoma is less than 50%, despite multimodal treatment. Pyroptosis, an inflammatory type of programmed cell death, manifested pro-tumor and anti-tumor roles in the adult tumor. Thus, we aimed to elucidate the function of pyroptosis in neuroblastoma. We classified neuroblastoma patients into two clusters based on the pyroptosis gene expression. We found high pyroptosis neuroblastoma manifested favorable overall survival and more anti-tumor immune cell infiltration. Based on the results of a stepwise Cox regression analysis, we built a four-gene predictive model including NLRP3, CASP3, IL18, and GSDMB. The model showed excellent predictive performance in internal and external validation. Our findings highlight that high pyroptosis positively correlated with neuroblastoma outcomes and immune landscape, which may pave the way for further studies on inducing pyroptosis therapy in high-risk neuroblastoma treatment.

Identification and Validation of Inflammatory Response-Related Gene Signatures to Predict the Prognosis of Neuroblastoma

Background. Neuroblastoma (NB) is the third most common malignant tumor in children. The inflammation is believed to be closely related to NB patients’ prognosis. However, there is no comprehensive research to study the role of inflammatory response-related gene (IRRG) in NB patients. Methods. We downloaded the gene expression profiles of NB patients from GEO and TARGET database, and the expression of 200 IRRGs was extracted. Then, we performed differentially analysis between INSS stage 4 and INSS stage 4S NB patients. The univariate and multivariate Cox regression analyses were performed to screen out the overall survival- (OS-) and event-free survival- (EFS-) related IRRGs in GSE49710, and two signatures were constructed; both signatures were evaluated by Kaplan-Meier (K-M) survival curve and receiver operating characteristic (ROC) curve. Finally, the TARGET cohort was used to validate IRRG signatures, and the independence of the prognostic IRRG signatures was evaluated by integrating clinical information. Results. We screened out 10 OS-related IRRGs and 11 EFS-related IRRGs. Then, we identified that OS- and EFS-related IRRG signatures and found that the OS and EFS of NB patients in the low-risk group were significantly superior than those in the high-risk group (both $P$ value < 0.0001). The AUC values of 3-, 5-, and 7-year OS are 0.910, 0.933, and 0.921, respectively, and 3-, 5-, and 7-year EFS are 0.840, 0.835, and 0.837, respectively. In addition, we found that both IRRG signatures can be used as independent prognostic indicators for patients with NB. Both IRRG signatures still have good predictive ability in validation cohort. Conclusions. We constructed and validated two prognostic gene signatures based on IRRGs. Our study helped us to better understand the role of inflammation in NB and provided new insights for the prognosis assessment and treatment strategy for NB patients.

Novel Gene Signatures Predicting Primary Non-response to Infliximab in Ulcerative Colitis: Development and Validation Combining Random Forest With Artificial Neural Network

Background: While infliximab has revolutionized the treatment of ulcerative colitis, primary non-response is difficult to predict, which limits effective disease management. The study aimed to establish a novel genetic model to predict primary non-response to infliximab in patients with ulcerative colitis.

Methods: Publicly available mucosal expression profiles of infliximab-treated ulcerative colitis patients (GSE16879, GSE12251) were utilized to identify potential predictive gene panels. The random forest algorithm and artificial neural network were applied to further screen for predictive signatures and establish a model to predict primary non-response to infliximab.

Results: A total of 28 downregulated and 2 upregulated differentially expressed genes were identified as predictors. The novel model was successfully established on the basis of the molecular prognostic score system, with a significantly predictive value (AUC = 0.93), and was validated with an independent dataset GSE23597 (AUC = 0.81).

Conclusion: Machine learning was used to construct a predictive model based on the molecular prognostic score system. The novel model can predict primary non-response to infliximab in patients with ulcerative colitis, which aids in clinical-decision making.

Identification of the Novel Methylated Genes' Signature to Predict Prognosis in INRG High-Risk Neuroblastomas

BACKGROUND

Neuroblastomas are the most frequent extracranial pediatric solid tumors. The prognosis of children with high-risk neuroblastomas has remained poor in the past decade. A powerful signature is required to identify factors associated with prognosis and improved treatment selection. Here, we identified a strong methylation signature that favored the earlier diagnosis of neuroblastoma in patients.

METHODS

Gene methylation (GM) data of neuroblastoma patients from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) were analyzed using a multivariate Cox regression analysis (MCRA) and univariate Cox proportional hazards regression analysis (UCPHRA).

RESULTS

The methylated genes' signature consisting of eight genes (NBEA, DDX28, TMED8, LOC151174, EFNB2, GHRHR, MIMT1, and SLC29A3) was selected. The signature divided patients into low- and high-risk categories, with statistically significant survival rates (median survival time: 25.08 vs. >128.80 months, log-rank test, P < 0.001) in the training group, and the validation of the signature's risk stratification ability was carried out in the test group (log-rank test, P < 0.01, median survival time: 30.48 vs. >120.36 months). The methylated genes' signature was found to be an independent predictive factor for neuroblastoma by MCRA. Functional enrichment analysis suggested that these methylated genes were related to butanoate metabolism, beta-alanine metabolism, and glutamate metabolism, all playing different significant roles in the process of energy metabolism in neuroblastomas.

CONCLUSIONS

The set of eight methylated genes could be used as a new predictive and prognostic signature for patients with INRG high-risk neuroblastomas, thus assisting in treatment, drug development, and predicting survival.

A Deep-Learning Model With the Attention Mechanism Could Rigorously Predict Survivals in Neuroblastoma

BACKGROUND

Neuroblastoma is one of the most devastating forms of childhood cancer. Despite large amounts of attempts in precise survival prediction in neuroblastoma, the prediction efficacy remains to be improved.

METHODS

Here, we applied a deep-learning (DL) model with the attention mechanism to predict survivals in neuroblastoma. We utilized 2 groups of features separated from 172 genes, to train 2 deep neural networks and combined them by the attention mechanism.

RESULTS

This classifier could accurately predict survivals, with areas under the curve of receiver operating characteristic (ROC) curves and time-dependent ROC reaching 0.968 and 0.974 in the training set respectively. The accuracy of the model was further confirmed in a validation cohort. Importantly, the two feature groups were mapped to two groups of patients, which were prognostic in Kaplan-Meier curves. Biological analyses showed that they exhibited diverse molecular backgrounds which could be linked to the prognosis of the patients.

CONCLUSIONS

In this study, we applied artificial intelligence methods to improve the accuracy of neuroblastoma survival prediction based on gene expression and provide explanations for better understanding of the molecular mechanisms underlying neuroblastoma.

Identify potential miRNA-mRNA regulatory networks contributing to high-risk neuroblastoma

Neuroblastoma (NB) is a common tumor in children, usually in the retroperitoneum. After various treatments, low- and intermediate-risk patients have achieved good results, but the prognosis of high-risk patients is still very poor. Therefore, it is necessary to find new effective targets for the treatment of high-risk patients. In this study, comprehensive bioinformatics analysis was used to identify the differentially expressed genes (DEG and DEM) between high-risk patients and non-high-risk patients, and it was identified that ADRB2 may affect the survival status of high-risk patients due to miR -30a-5p regulation. The GSE49710, GSE73517, and GSE121513 datasets were downloaded from the Gene Expression Synthesis (GEO) database, and DEG and DEM were selected. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were applied to the selected DEGs. The STRING database and Cytoscape software were used to construct protein-protein interaction (PPI) networks and perform modular analysis of the DEGs. The TARGET data set containing information on overall survival days were used for the prognostic analysis of central genes. We identified a total of 255 DEGs from GSE49710 and GSE73517, and 193 DEMs from GSE121513. We identified the 5 most important central genes from the PPI network, performed a prognostic analysis in the target data set, and verified their expression using RT-qPCR to select the most important ADRB2 gene to predict miRNA. Integrating the differential miRNA predicted by miRDB and miRSystem and GSE121513 between the targeted miRNA and the prognosis, miR-30a-5p was finally identified as the targeted miRNA of ADRB2.

Overexpression of TICRR and PPIF confer poor prognosis in endometrial cancer identified by gene co-expression network analysis

The incidence of endometrial cancer (EC) is intensively increasing. However, due to the complexity and heterogeneity of EC, the molecular targeted therapy is still limited. The reliable and accurate biomarkers for tumor progression are urgently demanded. After normalizing the data from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA), we utilized limma and WGCNA packages to identify differentially expressed genes (DEGs). The copy number variations of candidate genes were investigated by cBioPortal. Enrichment pathways analysis was performed by ClueGO and CluePedia. The methylation status was explored by UALCAN. ROC curve and survival analysis were conducted by SPSS and Kaplan–Meier. Infiltration immune cells in microenvironment were analyzed by TISIDB. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were applied to explore potential biological pathways. Immunohistochemistry staining (IHC), cell proliferation, cell apoptosis, colony formation, migration, invasion and scratch-wound assays were performed to investigate the function of key genes in vitro. In this study, four expression profile datasets were integrated to identify candidate genes. Combined with WGCNA analysis, the top ten candidates were screened out, whose abnormal methylation patterns were extremely correlated with their expression level and they were associated with tumor grades and predicted poor survival. GSEA and GSVA demonstrated they were involved in DNA replication and cell cycle transition in EC. Gene silencing of TICRR and PPIF dramatically inhibited cell growth, migration and epithelial-mesenchymal transition (EMT) and enhanced progesterone sensitivity. Additionally, from DrugBank database, cyclosporine may be effective for PPIF targeted therapy. By integrative bioinformatics analysis and in vitro experiments, our study shed novel light on the molecular mechanisms of EC. TICRR and PPIF may promise to be potential therapeutic targets for endometrial cancer.

Identification of MYCN-Related Gene as a Potential Biomarker for Neuroblastoma Prognostic Model by Integrated Analysis and Quantitative Real-Time PCR

Neuroblastoma (NB) has the highest incidence of all extracranial solid tumors in children and is highly lethal. This study aims to establish a prognostic model of NB with MYCN-related genes. We determined the gene expression profiles of 900 NB samples from the UCSC database and four Gene Expression Omnibus (GEO) data sets, and performed a comprehensive bioinformatics analysis and clinical sample verification. After univariate Cox regression, least absolute shrinkage and selection operator (Lasso), and multivariate Cox regression analyses, four (AKR1C1, CHD5, PDE4DIP, and PRKACB) genes were finally selected and used to construct a risk score prognostic model. In the UCSC data set, the high-risk group exhibited a significantly worse prognosis than the low-risk group. In addition, the nomogram, which includes prognostic markers and clinical factors, demonstrates high prognostic value. Finally, the differential expression of the four genes in the model was verified by quantitative real-time PCR in clinical tissues. These findings of MYCN-related genes provide a new and reliable prognostic model for NB related to MYCN.

Proteoglycans as Mediators of Cancer Tissue Mechanics

Proteoglycans are a diverse group of molecules which are characterized by a central protein backbone that is decorated with a variety of linear sulfated glycosaminoglycan side chains. Proteoglycans contribute significantly to the biochemical and mechanical properties of the interstitial extracellular matrix where they modulate cellular behavior by engaging transmembrane receptors. Proteoglycans also comprise a major component of the cellular glycocalyx to influence transmembrane receptor structure/function and mechanosignaling. Through their ability to initiate biochemical and mechanosignaling in cells, proteoglycans elicit profound effects on proliferation, adhesion and migration. Pathologies including cancer and cardiovascular disease are characterized by perturbed expression of proteoglycans where they compromise cell and tissue behavior by stiffening the extracellular matrix and increasing the bulkiness of the glycocalyx. Increasing evidence indicates that a bulky glycocalyx and proteoglycan-enriched extracellular matrix promote malignant transformation, increase cancer aggression and alter anti-tumor therapy response. In this review, we focus on the contribution of proteoglycans to mechanobiology in the context of normal and transformed tissues. We discuss the significance of proteoglycans for therapy response, and the current experimental strategies that target proteoglycans to sensitize cancer cells to treatment.

DNA methylation of AHCY may increase the risk of ischemic stroke

Genetic factors play an important role in the pathogenesis of ischemic stroke. Of these, epigenetic modifications provide a new direction for the study of ischemic stroke pathogenesis. This study aimed to determine the correlation between DNA methylation of the gene encoding S-adenosylhomocysteine hydrolase (AHCY) and the risk of ischemic stroke in 64 ischemic stroke patients and 138 patients with traumatic brain injury (control group). The methylation level of AHCY was analyzed using quantitative methylation-specific polymerase chain reaction. Statistically significant differences in AHCY methylation levels were observed between the case group [medians (interquartile range): 0.13% (0.09%, 0.27%)] and the control group [0.06% (0.00%, 0.17%), p < 0.0001], and these associations remained significant in both male (p = 0.003) and female (p = 0.0005) subjects. A subgroup analysis by age revealed a considerably higher percentage of methylated AHCY in the case group than the control group in all age groups (age < 60 years, p = 0.007; age ≥ 60 years, p < 0.0001). A receiver operating characteristic (ROC) curve analysis revealed a trend toward a role for AHCY methylation as an indicator of risk in all ischemic patients [area under the curve (AUC) = 0.70, p = 0.0001], male patients (AUC = 0.67, p = 0.004), and female patients (AUC = 0.75, p = 0.0002). Our study confirmed a significant association between the AHCY DNA methylation level and the risk of ischemic stroke, suggesting that this gene methylation pattern may be a potential diagnostic marker of ischemic stroke.

ERCC6L promotes the progression of hepatocellular carcinoma through activating PI3K/AKT and NF-κB signaling pathway

Background

Excision Repair Cross-Complementation group 6-like (ERCC6L) has been shown to exhibit carcinogenic effect in several malignant tumors. However, the function and molecular mechanism of the ERCC6L in hepatocellular carcinoma (HCC) have not been investigated extensively.

Methods

Immunohistochemistry analyses were used to detect ERCC6L expression in a HCC tissue microarray, and the Chi-square test was used to assess the correlation between ERCC6L expression and patients’ clinicopathological features. shRNA was used to down-regulation ERCC6L expression in HCC cell lines. MTT assay, plate clone formation assay, flow cytometry, caspase 3/7 activity and migration assays were performed to evaluate the impact of ERCC6L on HCC cells in vitro. Nude mice xenograft models were used to assess the role of ERCC6L in vivo. The regulatory of mechanism of PI3K/AKT pathway was evaluated by western blotting.

Results

ERCC6L was highly expressed in HCC tissue compared with tumor adjacent tissues in 90 paired samples. ERCC6L expression positively correlated with gender, tumor encapsulation, and pathological stage. Patients with low ERCC6L expression had significantly longer OS than those with high ERCC6L expression. Knockdown of ERCC6L expression significantly inhibited proliferation, invasion and metastasis in vitro and tumor growth in vivo, and it promoted cell cycle arrest and apoptosis. Mechanistic analyses revealed that PI3K/AKT and NF-κB signaling pathway were inhibited by silencing ERCC6L.

Conclusion

These results demonstrate that ERCC6L plays a critical role in HCC progression, and thereby might be a potential therapeutic target for HCC patients.

METTL14 Gene Polymorphisms Confer Neuroblastoma Susceptibility: An Eight-Center Case-Control Study

Neuroblastoma is the primary cause of cancer death in childhood. METTL14 is tightly linked to cancer. However, whether single-nucleotide polymorphisms (SNPs) in the METTL14 gene could predispose to neuroblastoma susceptibility lacks evidence. With an epidemiology case-control study, associations between METTL14 gene SNPs and overall risk for neuroblastoma were estimated in 898 cases and 1,734 controls. Following that, stratified analysis was performed. Among the five analyzed SNPs, rs298982 G>A and rs62328061 A>G exhibited a significant association with decreased susceptibility to neuroblastoma, whereas the associations with increased neuroblastoma susceptibility were observed for rs9884978 G>A and rs4834698 T>C. Moreover, subjects carrying two to five risk genotypes were more inclined to develop neuroblastoma than those with zero to one risk genotypes. The stratified analysis further demonstrated the protective effect of rs298982 G>A and rs62328061 A>G, as well as the predisposing effect of rs4834698 T>C and two to five risk genotypes, in certain subgroups. Haplotype analysis was performed. Moreover, false-positive report probability analysis validated the reliability of the significant results. The expression quantitative trait locus analysis revealed that rs298982 is correlated with the expression levels of its surrounding genes. Our results suggest that some SNPs in the METTL14 gene are associated with predisposition to neuroblastoma.

Association between METTL3 gene polymorphisms and neuroblastoma susceptibility: A nine-centre case-control study

Neuroblastoma ranks as the most commonly seen and deadly solid tumour in infancy. The aberrant activity of m6 A-RNA methyltransferase METTL3 is involved in human cancers. Therefore, functional genetic variants in the METTL3 gene may contribute to neuroblastoma risk. In the current nine-centre case-control study, we aimed to analyse the association between the METTL3 gene single nucleotide polymorphisms (SNPs) and neuroblastoma susceptibility. We genotyped four METTL3 gene SNPs (rs1061026 T>G, rs1061027 C>A, rs1139130 A>G, and rs1263801 G>C) in 968 neuroblastoma patients and 1814 controls in China. We found significant associations between these SNPs and neuroblastoma risk in neither single-locus nor combined analyses. Interestingly, in the stratified analysis, we observed a significant risk association with rs1061027 AA in subgroups of children ≤ 18 months of age (adjusted OR = 1.87, 95% CI = 1.03-3.41, P = .040) and females (adjusted OR = 1.86, 95% CI = 1.07-3.24, P = .028). Overall, we identified a significant association between METTL3 gene rs1061027 C>A polymorphism and neuroblastoma risk in children ≤18 months of age and females. Our findings provide novel insights into the genetic determinants of neuroblastoma.

Characterization of lncRNA-Perturbed TLR-Signaling Network Identifies Novel lncRNA Prognostic Biomarkers in Colorectal Cancer

Increasing evidence has suggested that long non-coding RNAs (lncRNAs) are critical regulators in the Toll-like receptors (TLR)-signaling network to modulate colorectal cancer (CRC) development and progression. However, the mechanism and clinical significance for lncRNAs regulating TLR signaling pathways in CRC remained largely unknown. In this study, we performed an integrative network analysis of transcriptomics by focusing on a lncRNA-perturbed TLR-signaling network, identifying 280 lncRNAs and 122 mRNAs. We found a profound phenomenon that abnormal expression of some lncRNAs can perturb the TLR-signaling network to contribute to CRC development and progression. Furthermore, we identified a novel TLR-related prognostic gene signature (TLRLncSig) composed of three lncRNAs (MCHR2, AC011472.4, and AC063944.1), and one mRNA (CDKN2B). Utilizing TLRLncSig could classify CRC patients of training set into two groups with significantly different overall survival. The prognostic value of the TLRLncSig was further validated in the other two independent CRC datasets with different platforms. Results of multivariate and stratification analysis indicated that the TLRLncSig is an independent prognostic factor, and our study underscores the clinical significance of TLR-related lncRNAs in CRC development and progression.

Downregulation of PIF1, a potential new target of MYCN, induces apoptosis and inhibits cell migration in neuroblastoma cells

Neuroblastoma (NB) is one of the most common malignant tumors in children. Chemotherapy resistance is one of the significant challenges in the treatment of high-risk NB patients, and it is necessary to search for new valid targets for NB treatment. This study aims to explore the possible role of PIF1 in NB by using bioinformatic analysis and downregulation of PIF1 with specific siRNA. Kyoto genome encyclopedia and R language based gene ontology was used to analyze the differentially expressed genes (DEGs) (including PIF1) when MYCN expression was silenced in NB cells. Analysis based on the R2 database showed a lower expression of PIF1 correlated with good prognosis in NB patients. Downregulation of MYCN expression by transfecting MYCN siRNA (#1, #2) into NB cells decreased the PIF1 expression at both mRNA and protein levels, while upregulation of MYCN expression by transfecting MYCN overexpressed plasmid increased the PIF1 expression. We further found that downregulation of PIF1 expression by transfecting PIF1 siRNA (#1, #2) into NB cells, increased the number of apoptotic cells, inhibited the cell survival, decreased the ability of cell migration and induced a cell cycle arrest at G1 phase. These data indicated that PIF1, as a potential new target of MYCN, maybe a novel target for NB treatment.

LIN28A gene polymorphisms modify neuroblastoma susceptibility: A four-centre case-control study

Neuroblastoma ranks the most common seen solid tumour in childhood. Overexpression of LIN28A gene has been linked to the development of multiple human malignancies, but the relationship between LIN28A single nucleotide polymorphisms (SNPs) and neuroblastoma susceptibility is still under debate. Herein, we evaluated the correlation of four potentially functional LIN28A SNPs (rs3811464 G>A, rs3811463 T>C, rs34787247 G>A, and rs11247957 G>A) and neuroblastoma susceptibility in 505 neuroblastoma patients and 1070 controls from four independent hospitals in China. The correlation strengths were determined by using odds ratios (ORs) and corresponding 95% confidence intervals (CIs). Among these SNPs, rs34787247 G>A exhibited a significant association with increased susceptibility in neuroblastoma (GA vs GG: adjusted OR = 1.30, 95% CI = 1.03-1.64; AA vs GG: adjusted OR = 2.51, 95% CI = 1.36-4.64, AA/GA vs GG: adjusted OR = 1.42, 95% CI = 1.12-1.80, AA vs GG/GA: adjusted OR = 2.39, 95% CI = 1.29-4.42). Furthermore, the combined analysis of risk genotypes revealed that subjects carrying three risk genotypes (adjusted OR = 1.64, 95% CI = 1.02-2.63) are more inclined to develop neuroblastoma than those without risk genotype, and so do carriers of 1-4 risk genotypes (adjusted OR = 1.26, 95% CI = 1.01-1.56). Stratification analysis further revealed risk effect of rs3811464 G>A, rs34787247 G>A and 1-4 risk genotypes in some subgroups. Haplotype analysis of these four SNPs yields two haplotypes significantly correlated with increased neuroblastoma susceptibility. Overall, our finding indicated that LIN28A SNPs, especially rs34787247 G>A, may increase neuroblastoma risk.

Cellular components in tumor microenvironment of neuroblastoma and the prognostic value

Background

Tumor microenvironment (TME) contributes to tumor development, progression, and treatment response. In this study, we detailed the cell composition of the TME in neuroblastoma (NB) and constructed a cell risk score model to predict the prognosis of NB.

Methods

xCell score was calculated through transcriptomic data from the datasets GSE49711 and GSE45480 based on the xCell algorithm. The random forest method was employed to select important features and the coefficient was obtained via multivariate cox regression analysis to construct a prognostic model, and the performance was validated in another two independent datasets, GSE16476 and TARGET-NBL.

Results

We found that both immune and non-immune cells varies significantly in different prognostic groups, and were correlated with survival time. The proposed prognostic cell risk score (pCRS) model we constructed can be an independent prognostic indicator for overall survival (OS) and event-free survival (EFS) (training: OS, HR 1.579, EFS, HR 1.563; validation: OS, HR 1.665, 3.848, EFS, HR 2.203, all p-values < 0.01) and only independent prognostic factor in International Neuroblastoma Risk Group high risk patients (HR 1.339, 3.631; p-value 1.76e–2, 3.71e–5), rather than MYCN amplification. Besides, pCRS model showed good performance in grouping, in discriminating MYCN status, the area under the curve (AUC) was 0.889, 0.933, and 0.861 in GSE49711, GSE45480, and GSE16476, respectively. In separating high risk groups, the AUC was 0.904 in GSE49711.

Conclusion

This study details the cellular components in the TME of NB through gene expression data, the proposed pCRS model might provide a basis for treatment selection of high risk patients or targeting cellular components of TME in NB.

Development and Validation of an RNA-Seq-Based Prognostic Signature in Neuroblastoma

Objective: The stratification of neuroblastoma (NBL) prognosis remains difficult. RNA-based signatures might be able to predict prognosis, but independent cross-platform validation is still rare.

Methods: RNA-Seq-based profiles from NBL patients were acquired and then analyzed. The RNA-Seq prognostic index (RPI) and the clinically adjusted RPI (RCPI) were successively established in the training cohort (TARGET-NBL) and then verified in the validation cohort (GSE62564). Survival prediction was assessed using a time-dependent receiver operating characteristic (ROC) curve and area under the ROC curve (AUC). Functional enrichment analysis of the genes was conducted using bioinformatics methods.

Results: In the training cohort, 10 gene pairs were eventually integrated into the RPI. In both cohorts, the high-risk group had poor overall survival (OS) (P < 0.001 and P < 0.001, respectively) and favorable event-free survival (EFS) (P = 0.00032 and P = 0.06, respectively). ROC curve analysis also showed that the RPI predicted OS (60 month AUC values of 0.718 and 0.593, respectively) and EFS (60 month AUC values of 0.627 and 0.852, respectively) well in both the training and validation cohorts. Clinicopathological indicators associated with prognosis in the univariate and multivariate regression analyses were identified and added to the RPI to form the RCPI. The RCPI was also used to divide populations into different risk groups, and the high-risk group had poor OS (P < 0.001 and P < 0.001, respectively) and EFS (P < 0.05 and P < 0.05, respectively). Finally, the RCPI had higher accuracy than the RPI for the prediction of OS (60 month AUC values of 0.730 and 0.852, respectively) and EFS (60 month AUC values of 0.663 and 0.763, respectively) in both the training and validation cohorts. Moreover, these differentially expressed genes may be involved in certain NBL-related events.

Conclusions: The RCPI could reliably categorize NBL patients based on different risks of death.