Abstract Alzheimer’s disease (AD) is the most prevalent dementia disorder globally, and there are still no effective interventions for slowing or stopping the underlying pathogenic mechanisms. There is strong evidence implicating neural oxidative stress (OS) and ensuing neuroinflammation in the progressive neurodegeneration observed in the AD brain both during and prior to symptom emergence. Thus, OS-related biomarkers may be valuable for prognosis and provide clues to therapeutic targets during the early presymptomatic phase. In the current study, we gathered brain RNA-seq data of AD patients and matched controls from the Gene Expression Omnibus (GEO) to identify differentially expressed OS-related genes (OSRGs). These OSRGs were analyzed for cellular functions using the Gene Ontology (GO) database and used to construct a weighted gene co-expression network (WGCN) and protein-protein interaction (PPI) network. Receiver operating characteristic (ROC) curves were then constructed to identify network hub genes. A diagnostic model was established based on these hub genes using Least Absolute Shrinkage and Selection Operator (LASSO) and ROC analyses. Immune-related functions were examined by assessing correlations between hub gene expression and immune cell brain infiltration scores. Further, target drugs were predicted using the Drug-Gene Interaction database, while regulatory miRNAs and transcription factors were predicted using miRNet. In total, 156 candidate genes were identified among 11046 differentially expressed genes, 7098 genes in WGCN modules, and 446 OSRGs, and 5 hub genes (MAPK9, FOXO1, BCL2, ETS1, and SP1) were identified by ROC curve analyses. These hub genes were enriched in GO annotations “Alzheimer’s disease pathway,” “Parkinson’s Disease,” “Ribosome,” and “Chronic myeloid leukemia.” In addition, 78 drugs were predicted to target FOXO1, SP1, MAPK9, and BCL2, including fluorouracil, cyclophosphamide, and epirubicin. A hub gene-miRNA regulatory network with 43 miRNAs and hub gene-transcription factor (TF) network with 36 TFs were also generated. These hub genes may serve as biomarkers for AD diagnosis and provide clues to novel potential treatment targets. 

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Identification of oxidative stress-related genes differentially expressed in Alzheimer’s disease and construction of a hub gene-based diagnostic model

Human clear cell renal cell carcinoma (ccRCC) is the most common and frequently occurring histological subtype of RCC. Unlike other carcinomas, candidate predictive biomarkers for this type are in need to explore the molecular mechanism of ccRCC and identify candidate target genes for improving disease management. For this, we chose case-control-based studies from the Gene Expression Omnibus and subjected the gene expression microarray data to combined effect size meta-analysis for identifying shared genes signature. Further, we constructed a subnetwork of these gene signatures and evaluated topological parameters during the gene deletion analysis to get to the central hub genes, as they form the backbone of the network and its integrity. Parallelly, we carried out functional enrichment analysis using gene ontology and Elsevier disease pathway collection. We also performed microRNAs target gene analysis and constructed a regulatory network. We identified a total of 577 differentially expressed genes (DEGs), where 146 overexpressed and 431 underexpressed with a significant threshold of adjusted P values <0.05. Enrichment analysis of these DEGs' functions showed a relation to metabolic and cellular pathways like metabolic reprogramming in cancer, proteins with altered expression in cancer metabolic reprogramming, and glycolysis activation in cancer (Warburg effect). Our analysis revealed the potential role of PDHB and ATP5C1 in ccRCC by altering metabolic pathways and amyloid beta precursor protein (APP) role in altering cell-cycle growth for the tumour progression in ccRCC conditions. Identification of these candidate predictive genes paves the way for the development of biomarker-based methods for this carcinoma.

Network-based gene deletion analysis identifies candidate genes and molecular mechanism involved in clear cell renal cell carcinoma

\n\nThe Human Genome Project has unleashed the power of genomics\nin clinical practice as a choice of individualized therapy, particularly in cancer\ntreatment. Pharmacogenomics is an interdisciplinary field of genomics that deals with\ndrug response, based on individual genetic makeup.\n\n\n\nThe main genetic events associated with carcinogenesis activate oncogenes or\ninactivate tumor-suppressor genes. Therefore, drugs should be specific to inactivate or\nregulate these mutant genes and their protein products for effective cancer treatment. In\nthis review, we summarize how polymedication decisions in cancer treatments based on\nthe evaluation of cytochrome P450 (CYP450) polymorphisms are applied for pharmacogenetic\nassessment of anticancer therapy outcomes.\n\n\n\nHowever, multiple genetic events linked, inactivating a single mutant gene product,\nmay be insufficient to inhibit tumor progress. Thus, genomics and pharmacogenetics\ndirectly influence a patient’s response and aid in guiding clinicians to select the safest and\nmost effective combination of medications for a cancer patient from the initial prescription.\n\n\n\nThis review outlines the roles of oncogenes, the importance of cytochrome\nP450 (CYP450) in cancer susceptibility, and its impact on drug metabolism, proposing\ncombined approaches to achieve precision therapy.\n

G.K. Udayaraja

Papers

Network-based gene deletion analysis identifies candidate genes and molecular mechanism involved in clear cell renal cell carcinoma

Oncogenomics and CYP450 Implications in Personalized Cancer Therapy