Investigation of hub genes and their nonsynonymous single nucleotide polymorphism analysis in Plasmodium falciparum for designing therapeutic methodologies using next-generation sequencing approach

Construction of a five-gene-based prognostic model for relapsed/refractory acute lymphoblastic leukemia

BACKGROUND

Relapsed/refractory acute lymphoblastic leukemia (R/R ALL) continues to be a major cause of mortality in children worldwide, with around 15% of ALL patients experiencing relapse and approximately 10% eventually dying from the disease. Early identification of R/R ALL in children has posed a longstanding clinical challenge.

METHOD

Genetic analysis of survival outcomes in pediatric patients with ALL from the TARGET-ALL dataset revealed five risk score factors identified through the intersection of differential genes (relapse/non-relapse) from the GSE17703 and GSE6092 databases. A risk score equation was formulated using these factors and validated against prognostic data from 46 ALL cases at our institution. Patients from multiple datasets were stratified into high and low-score groups based on this equation. Protein-protein interaction networks (PPI) were then constructed using the intersecting differential genes from all three datasets to identify hub nodes and predict interacting transcription factors. Additionally, genes related to cell pyroptosis with varying expression across these datasets were screened, and a multifactorial ROC curve (incorporating risk score and differential expression of pyroptosis-related genes) was generated. Furthermore, relationships among variables in the predictive model were depicted using a nomogram, and model efficacy was assessed through decision curve analysis (DCA).

RESULTS

By analyzing the TARGET-ALL, GSE17703, and GSE6092 databases, we developed a prognostic risk assessment model for pediatric ALL incorporating BAG2, EPHA4, FBXO9, SNX10, and WNK1. Validation of this model was conducted using data from 46 pediatric ALL cases obtained from our institution. Following the identification of 27 differentially expressed genes, we constructed a PPI and identified the top 10 hub genes (PTPRC, BTK, LCK, PRKCQ, CD3D, CD27, CD3G, BLNK, RASGRP1, VPREB1). Using this network, we predicted the top 5 transcription factors (HOXB4, MYC, SOX2, E2F1, NANOG). ROC and DCA were conducted on pyroptosis-related genes exhibiting differential expression and risk scores. Subsequently, a nomogram was generated, demonstrating the effectiveness of the risk score in predicting prognosis for pediatric ALL patients.

CONCLUSIONS

We have developed a risk prediction model for pediatric R/R ALL utilizing the genes BAG2, EPHA4, FBXO9, SNX10, and WNK1. This model provides a scientific foundation for early identification of R/R ALL in children.

Bioinformatics and systems biology approach to identify the pathogenetic link of neurological pain and major depressive disorder

Neurological pain (NP) is always accompanied by symptoms of depression, which seriously affects physical and mental health. In this study, we identified the common hub genes (Co-hub genes) and related immune cells of NP and major depressive disorder (MDD) to determine whether they have common pathological and molecular mechanisms. NP and MDD expression data was downloaded from the Gene Expression Omnibus (GEO) database. Common differentially expressed genes (Co-DEGs) for NP and MDD were extracted and the hub genes and hub nodes were mined. Co-DEGs, hub genes, and hub nodes were analyzed for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. Finally, the hub nodes, and genes were analyzed to obtain Co-hub genes. We plotted Receiver operating characteristic (ROC) curves to evaluate the diagnostic impact of the Co-hub genes on MDD and NP. We also identified the immune-infiltrating cell component by ssGSEA and analyzed the relationship. For the GO and KEGG enrichment analyses, 93 Co-DEGs were associated with biological processes (BP), such as fibrinolysis, cell composition (CC), such as tertiary granules, and pathways, such as complement, and coagulation cascades. A differential gene expression analysis revealed significant differences between the Co-hub genes ANGPT2, MMP9, PLAU, and TIMP2. There was some accuracy in the diagnosis of NP based on the expression of ANGPT2 and MMP9. Analysis of differences in the immune cell components indicated an abundance of activated dendritic cells, effector memory CD8+ T cells, memory B cells, and regulatory T cells in both groups, which were statistically significant. In summary, we identified 6 Co-hub genes and 4 immune cell types related to NP and MDD. Further studies are needed to determine the role of these genes and immune cells as potential diagnostic markers or therapeutic targets in NP and MDD.

Computational prediction of the effects of non-synonymous single nucleotide polymorphisms on the GPI-anchor transamidase subunit GPI8p of Plasmodium falciparum

Drug resistance is increasingly evolving in malaria parasites; hence, it is important to discover and establish alternative drug targets. In this context, GPI-anchor transamidase (GPI-T) is a potential drug target primarily of its crucial role in the development and survival of the parasite in the GPI anchor biosynthesis pathway. The present investigation was undertaken to explore the plausible effects of nsSNP on the structure and functions of GPI-T subunit GPI8p of Plasmodium falciparum. The GPI8p (PF3D7_1128700) was analyzed using various sequence-based and structure-based computational tools such as SIFT, PROVEAN, PredictSNP, SNAP2, I-Mutant, MuPro, ConSurf, NetSurfP, MUSTER, COACH server and STRING server. Of the 34 nsSNPs submitted for functional analysis, 18 nsSNPs (R124 L, N143 K, Y145 F, V157I, T195S, K379E, I392 K, I437 T, Y438H, N439D, Y441H, N442D, N448D, N451D, D457A, D457Y, I458 L and N460 K) were predicted to have deleterious effects on the protein GPI8p. Additionally, I-Mutant 2.0 and MuPro both showed a decrease in stability after mutation as a result of these nsSNPs, suggesting the destabilization of protein. ConSurf findings suggest that most of the regions were highly conserved. In addition, COACH server was used to predict the ligand binding sites. It was found that no mutation was present at the predicted ligand binding site. The results of the STRING database showed that the protein GPI8p interacts with those proteins which either involve the biosynthetic process of attaching GPI anchor to protein or GPI anchor. The present study suggested that the GPI8p could be a novel target for anti-malarial drugs, which provides significant details for further experimentation.