NUP-98 Rearrangements Led to the Identification of Candidate Biomarkers for Primary Induction Failure in Pediatric Acute Myeloid Leukemia.

Conventional chemotherapy for acute myeloid leukemia regimens generally encompass an intensive induction phase, in order to achieve a morphological remission in terms of bone marrow blasts (<5%). The majority of cases are classified as Primary Induction Response (PIR); unfortunately, 15% of children do not achieve remission and are defined Primary Induction Failure (PIF). This study aims to characterize the gene expression profile of PIF in children with Acute Myeloid Leukemia (AML), in order to detect molecular pathways dysfunctions and identify potential biomarkers. Given that NUP98-rearrangements are enriched in PIF-AML patients, we investigated the association of NUP98-driven genes in primary chemoresistance. Therefore, 85 expression arrays, deposited on GEO database, and 358 RNAseq AML samples, from TARGET program, were analyzed for "Differentially Expressed Genes" (DEGs) between NUP98+ and NUP98-, identifying 110 highly confident NUP98/PIF-associated DEGs. We confirmed, by qRT-PCR, the overexpression of nine DEGs, selected on the bases of the diagnostic accuracy, in a local cohort of PIF patients: SPINK2, TMA7, SPCS2, CDCP1, CAPZA1, FGFR1OP2, MAN1A2, NT5C3A and SRP54. In conclusion, the integrated analysis of NUP98 mutational analysis and transcriptome profiles allowed the identification of novel putative biomarkers for the prediction of PIF in AML.

A random walk-based method to identify driver genes by integrating the subcellular localization and variation frequency into bipartite graph.

BACKGROUND: Cancer as a worldwide problem is driven by genomic alterations. With the advent of high-throughput sequencing technology, a huge amount of genomic data generates at every second which offer many valuable cancer information and meanwhile throw a big challenge to those investigators. As the major characteristic of cancer is heterogeneity and most of alterations are supposed to be useless passenger mutations that make no contribution to the cancer progress. Hence, how to dig out driver genes that have effect on a selective growth advantage in tumor cells from those tremendously and noisily data is still an urgent task. RESULTS: Considering previous network-based method ignoring some important biological properties of driver genes and the low reliability of gene interactive network, we proposed a random walk method named as Subdyquency that integrates the information of subcellular localization, variation frequency and its interaction with other dysregulated genes to improve the prediction accuracy of driver genes. We applied our model to three different cancers: lung, prostate and breast cancer. The results show our model can not only identify the well-known important driver genes but also prioritize the rare unknown driver genes. Besides, compared with other existing methods, our method can improve the precision, recall and fscore to a higher level for most of cancer types. CONCLUSIONS: The final results imply that driver genes are those prone to have higher variation frequency and impact more dysregulated genes in the common significant compartment. AVAILABILITY: The source code can be obtained at https://github.com/weiba/Subdyquency .

Decoding Common Features of Neurodegenerative Disorders: From Differentially Expressed Genes to Pathways.

BACKGROUND: Neurodegeneration is a progressive/irreversible loss of neurons, building blocks of our nervous system. Their degeneration gradually collapses the entire structural and functional system manifesting in myriads of clinical disorders categorized as Neurodegenerative Disorders (NDs) such as Alzheimer's Disease, (AD), Parkinson's Disease (PD), Frontotemporal Dementia (FTD) and Amyotrophic Lateral Sclerosis (ALS). NDs are characterized by a puzzling interplay of molecular and cellular defects affecting subset of neuronal populations in specific affected brain areas. OBJECTIVE: In present study, comparative in silico analysis was performed by utilizing gene expression datasets of AD, PD, FTD and ALS to identify potential common features to gain insights into complex molecular pathophysiology of the selected NDs. METHODS: Gene expression data of four disorders were subjected to the identification of Differential Gene Expression (DEG) and their mapping on biological processes, KEGG pathways and molecular functions. Detailed comparative analysis was performed to highlight the common grounds of these dis-orders at various stages. RESULTS: Astoundingly, 106 DEGs were found to be common across all disorders. Alongwith in total 100 GO terms and 7 KEGG pathways were found to be significantly enriched across all disorders. EGFR, CDC42 and CREBBP have been identified as the significantly interacting nodes in gene-gene in-teraction and in Protein-Protein Interaction (PPI) network as well. Furthermore, interaction of common DEGs targets with miRNA's has been scrutinized. CONCLUSION: The complex molecular underpinnings of these disorders are currently elusive. Despite heterogeneous clinical and pathological expressions, common features have been recognized in many NDs which provide evidence of their convergence.

Potential association between COVID-19 and neurological disorders: analysis of common genes and therapeutics.

As the COVID-19 pandemic persists, the increasing evidences suggest that the patients with COVID-19 may face the risks of the neurological complications and sequelae. To address this issue, we conducted a comprehensive study aimed at exploring the relationship between COVID-19 and various neurological disorders, with a particular focus on the shared dysregulated genes and the potential therapeutic targets. We selected six neurological disorders for investigation, including Alzheimer's disease, epilepsy, stroke, Parkinson's disease, and the sleep disorders. Through the bioinformatics analysis of the association between these disorders and COVID-19, we aimed to uncover the common molecular mechanisms and the potential treatment pathways. In this study, we utilized the publicly available RNA-Seq and microarray datasets, and employed tools such as Limma and DESeq2 for the differential gene analysis. Through the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, we explored the common biological features and pathways. Additionally, we focused on analyzing the regulatory roles of miRNA and transcription factors on the shared differentially expressed genes, and predicted the potential drugs interacting with these genes. These analyses contribute to a better understanding of the relationship between COVID-19 and the neurological disorders, and provide a theoretical basis for the future treatment strategies. Through this research, we aim to offer the deeper insights to the scientific community and present the new perspectives for the clinical practice in addressing the challenges of the neurological complications and sequelae faced by the COVID-19 patients.

Classification of subtypes and identification of dysregulated genes in sepsis.

Background: Sepsis is a clinical syndrome with high mortality. Subtype identification in sepsis is meaningful for improving the diagnosis and treatment of patients. The purpose of this research was to identify subtypes of sepsis using RNA-seq datasets and further explore key genes that were deregulated during the development of sepsis. Methods: The datasets GSE95233 and GSE13904 were obtained from the Gene Expression Omnibus database. Differential analysis of the gene expression matrix was performed between sepsis patients and healthy controls. Intersection analysis of differentially expressed genes was applied to identify common differentially expressed genes for enrichment analysis and gene set variation analysis. Obvious differential pathways between sepsis patients and healthy controls were identified, as were developmental stages during sepsis. Then, key dysregulated genes were revealed by short time-series analysis and the least absolute shrinkage and selection operator model. In addition, the MCPcounter package was used to assess infiltrating immunocytes. Finally, the dysregulated genes identified were verified using 69 clinical samples. Results: A total of 898 common differentially expressed genes were obtained, which were chiefly related to increased metabolic responses and decreased immune responses. The two differential pathways (angiogenesis and myc targets v2) were screened on the basis of gene set variation analysis scores. Four subgroups were identified according to median expression of angiogenesis and myc target v2 genes: normal, myc target v2, mixed-quiescent, and angiogenesis. The genes CHPT1, CPEB4, DNAJC3, MAFG, NARF, SNX3, S100A9, S100A12, and METTL9 were recognized as being progressively dysregulated in sepsis. Furthermore, most types of immune cells showed low infiltration in sepsis patients and had a significant correlation with the key genes. Importantly, all nine key genes were highly expressed in sepsis patients. Conclusion: This study revealed novel insight into sepsis subtypes and identified nine dysregulated genes associated with immune status in the development of sepsis. This study provides potential molecular targets for the diagnosis and treatment of sepsis.

Identification and analysis of dysregulated fatty acid metabolism genes in breast cancer subtypes.

Breast cancer is one of the most aggressive and lethal types of transformation among women. An anomaly of normal fatty acid metabolism is acknowledged as a critical trigger for malignant transformations including breast cancer, but the prospect of targeting fatty acid metabolism for the treatment of malignancy has remained unrecognized so far. It has been observed that specific fatty acid metabolism genes are involved in the commencement and development of breast cancer. These specific genes have also been observed to be related to different isotypes/molecular subtypes of breast cancer. The main purpose of this study was to scrutinize the prognostic significance, functional role, and expression pattern of fatty acid metabolism genes. In-Silico tools like TCGA BrCA, Gepia2, Ualcan Analysis, UCSC Xena, Kaplan-Meier plotter, Bc-gene EXminer, String, gene ontology, and KEGG databases, were used to assess the expression pattern of the fatty acid metabolism genes in breast cancer patients and also among the different molecular sub-types of breast cancer. Differential gene expression analysis revealed dysregulation of FABP4, FABP5, PLIN1, ï»¿PLIN2, PLIN4, PLIN5, LPIN1, MGLL, PNPLA2, PNPLA7, ACSL1, and ACOX2 showing a fold change > ± 1.5. Also, most of these genes show downregulation in Ualcan analysis of different isotypes/molecular subtypes of breast cancer. The study reveals that the screened genes i.e., FABP4, FABP5, PLIN1, PLIN2, PLIN4, PLIN5, LPIN1, MGLL, PNPLA2, PNPLA7, ACSL1, and ACOX2 can be used as biomarkers that reveal poor prognosis and may serve as therapeutic targets for the treatment of breast cancer.

Molecular imbalance mechanism of skin and blood leucocytes in severe burn patients at different burn times.

Background: Severe burns are a leading cause of injuries worldwide and are usually accompanied by considerable morbidity and mortality. The purpose of this study was to investigate the changes of gene expression in blood and skin at different times after severe burn. Methods: Firstly, the gene expression profiles of different burn time samples in GSE19743 and GSE8056 were analyzed. Secondly, the maladjusted gene network was identified by protein-protein interaction (PPI) network, and the genes in the network were enriched and analyzed. In addition, the key dysfunctional genes were identified by betweenness algorithm, and evaluated by survival analysis, Cox analysis, receiver operating characteristic (ROC) analysis. Finally, crosstalk analysis and enrichment analysis were carried out between the blood- and skin-specific differentially expressed genes (DEGs) at different burn times. Results: The results showed that there were common DEGs in the blood and skin at different burn times. Importantly, we screened out the key dysfunctional genes BIRC5, NCAM1, PCNA, TOP2A, and VEGFA, which were related to the course of burns. Enrichment analysis showed that these maladjusted genes were mainly involved in the immune inflammation-related signal pathway. Additionally, significant crosstalk was identified between blood- and skin-specific genes at different burn times, especially in the blood. The signal pathways involved in specific genes represent their own pathological characteristics. Conclusions: Both blood and skin tissues express common pathological changes and unique molecular mechanisms at different times after burn injury. The results of this study provide guidance for clinical personalized treatment.

Chemo-resistant Gastric Cancer Associated Gene Expression Signature: Bioinformatics Analysis Based on Gene Expression Omnibus.

BACKGROUND/AIM: This study aimed to identify biomarkers for predicting the prognosis of advanced gastric cancer patients who received docetaxel, cisplatin, and S-1 (DCS). MATERIALS AND METHODS: Gene expression profiles were obtained from the Gene Expression Omnibus database (GSE31811). Gene-Ontology-enrichment and KEGG-pathway analysis were used for evaluating the biological functions of differentially-expressed genes. Protein-protein interaction (PPI) network and Kaplan-Meier survival analyses were employed to assess the prognostic values of hub genes. RESULTS: A total of 1,486 differentially expressed genes (DEGs) were identified, including 13 up-regulated and 1,473 down-regulated genes. KEGG pathways such as metabolic pathways, cell adhesion molecules (CAMs), PI3K-Akt signaling pathway and pathways in cancer were significantly represented. In the PPI network, the top ten hub genes ranked by degree were GNG7, PLCB1, CALML5, FGFR4, GRB2, JAK3, ADCY7, ADCY9, GNAS and KDR. Five DEGs, including ANTXR1, EFNA5, GAMT, E2F2 and NRCAM, were associated with relapse-free survival and overall survival. CONCLUSION: ANTXR1, EFNA5, GAMT, E2F2 and NRCAM are potential biomarkers and therapeutic targets for DCS treatment in GC.

Dysregulated genes targeted by microRNAs and metabolic pathways in bladder cancer revealed by bioinformatics methods.

The present study aimed to identify bladder cancer-associated microRNAs (miRNAs) and target genes, and further analyze the potential molecular mechanisms involved in bladder cancer. The mRNA and miRNA expression profiling dataset GSE40355 was downloaded from the Gene Expression Omnibus database. The Limma package in R was used to identify differential expression levels. The Human microRNA Disease Database was used to identify bladder cancer-associated miRNAs and Target prediction programs were used to screen for miRNA target genes. Enrichment analysis was performed to identify biological functions. The Database for Annotation, Visualization and Integration Discovery was used to perform OMIM_DISEASE analysis, and then protein-protein interaction (PPI) analysis was performed to identify hubs with biological essentiality. ClusterONE plugins in cytoscape were used to screen modules and the InterPro database was used to perform protein domain enrichment analysis. A group of 573 disease dysregulated genes were identified in the present study. Enrichment analysis indicated that the muscle organ development and vascular smooth muscle contraction pathways were significantly enriched in terms of disease dysregulated genes. miRNAs targets (frizzled class receptor 8, EYA transcriptional coactivator and phosphatase 4, sacsin molecular chaperone, calcium voltage-gated channel auxiliary subunit ß2, peptidase inhibitor 15 and catenin α2) were mostly associated with bladder cancer. PPI analysis revealed that calmodulin 1 (CALM1), Jun proto-oncogene, AP-1 transcription factor subunit (JUN) and insulin like growth factor 1 (IGF1) were the important hub nodes. Additionally, protein domain enrichment analysis indicated that the serine/threonine protein kinase active site was enriched in module 1 extracted from the PPI network. Overall, the results suggested that the IGF signaling pathway and RAS/MEK/extracellular signal-regulated kinase transduction signaling may exert vital molecular mechanisms in bladder cancer, and that CALM1, JUN and IGF1 may be used as novel potential therapeutic targets.

The Networks of Genes Encoding Palmitoylated Proteins in Axonal and Synaptic Compartments Are Affected in PPT1 Overexpressing Neuronal-Like Cells.

CLN1 disease (OMIM #256730) is an early childhood ceroid-lipofuscinosis associated with mutated CLN1, whose product Palmitoyl-Protein Thioesterase 1 (PPT1) is a lysosomal enzyme involved in the removal of palmitate residues from S-acylated proteins. In neurons, PPT1 expression is also linked to synaptic compartments. The aim of this study was to unravel molecular signatures connected to CLN1. We utilized SH-SY5Y neuroblastoma cells overexpressing wild type CLN1 (SH-p.wtCLN1) and five selected CLN1 patients' mutations. The cellular distribution of wtPPT1 was consistent with regular processing of endogenous protein, partially detected inside Lysosomal Associated Membrane Protein 2 (LAMP2) positive vesicles, while the mutants displayed more diffuse cytoplasmic pattern. Transcriptomic profiling revealed 802 differentially expressed genes (DEGs) in SH-p.wtCLN1 (as compared to empty-vector transfected cells), whereas the number of DEGs detected in the two mutants (p.L222P and p.M57Nfs*45) was significantly lower. Bioinformatic scrutiny linked DEGs with neurite formation and neuronal transmission. Specifically, neuritogenesis and proliferation of neuronal processes were predicted to be hampered in the wtCLN1 overexpressing cell line, and these findings were corroborated by morphological investigations. Palmitoylation survey identified 113 palmitoylated protein-encoding genes in SH-p.wtCLN1, including 25 ones simultaneously assigned to axonal growth and synaptic compartments. A remarkable decrease in the expression of palmitoylated proteins, functionally related to axonal elongation (GAP43, CRMP1 and NEFM) and of the synaptic marker SNAP25, specifically in SH-p.wtCLN1 cells was confirmed by immunoblotting. Subsequent, bioinformatic network survey of DEGs assigned to the synaptic annotations linked 81 DEGs, including 23 ones encoding for palmitoylated proteins. Results obtained in this experimental setting outlined two affected functional modules (connected to the axonal and synaptic compartments), which can be associated with an altered gene dosage of wtCLN1. Moreover, these modules were interrelated with the pathological effects associated with loss of PPT1 function, similarly as observed in the Ppt1 knockout mice and patients with CLN1 disease.