A Review: Multi-Omics Approach to Studying the Association between Ionizing Radiation Effects on Biological Aging

Integration of Multi-Omics Data and Machine Learning to Identify Antioxidant Biomarkers in Type 1 Diabetes

The identification of biomarkers for early diagnosis and monitoring the progression of Type 1 Diabetes (T1DM) is essential for improving disease management. This study integrates multi-omics data with machine learning to identify antioxidant stress proteins in serum as potential biomarkers. Serum samples from mice treated with varying doses of streptozotocin (STZ) and human transcriptomic data from the gene expression omnibus (GEO) database were analyzed using weighted gene co-expression network analysis (WGCNA). Proteomic analysis of 25 T1DM and 25 healthy controls using LC-MS/MS revealed 33 differentially expressed proteins enriched in oxidative stress pathways. Machine learning algorithms, including Random Forest and SVM-RFE, identified five key proteins: GPX3, GSTP1, PRDX6, SOD1, and MSRB2. GPX3 demonstrated the highest diagnostic value, with a significant correlation to clinical parameters such as HbA1c and fasting plasma glucose. Functional validation showed GPX3 overexpression protected pancreatic β-cells from H2O2-induced oxidative damage and alleviated symptoms and pathological changes in T1DM mice. These results suggest that GPX3 is a promising biomarker for diagnosing and tracking T1DM progression, offering new insights into oxidative stress management in T1DM.

Metabolic crossroads: unravelling immune cell dynamics in gastrointestinal cancer drug resistance

Metabolic reprogramming within the tumor microenvironment (TME) plays a critical role in driving drug resistance in gastrointestinal cancers (GI), particularly through the pathways of fatty acid oxidation and glycolysis. Cancer cells often rewire their metabolism to sustain growth and reshape the TME, creating conditions such as nutrient depletion, hypoxia, and acidity that impair antitumor immune responses. Immune cells within the TME also undergo metabolic alterations, frequently adopting immunosuppressive phenotypes that promote tumor progression and reduce the efficacy of therapies. The competition for essential nutrients, particularly glucose, between cancer and immune cells compromises the antitumor functions of effector immune cells, such as T cells. Additionally, metabolic by-products like lactate and kynurenine further suppress immune activity and promote immunosuppressive populations, including regulatory T cells and M2 macrophages. Targeting metabolic pathways such as fatty acid oxidation and glycolysis presents new opportunities to overcome drug resistance and improve therapeutic outcomes in GI cancers. Modulating these key pathways has the potential to reinvigorate exhausted immune cells, shift immunosuppressive cells toward antitumor phenotypes, and enhance the effectiveness of immunotherapies and other treatments. Future strategies will require continued research into TME metabolism, the development of novel metabolic inhibitors, and clinical trials evaluating combination therapies. Identifying and validating metabolic biomarkers will also be crucial for patient stratification and treatment monitoring. Insights into metabolic reprogramming in GI cancers may have broader implications across multiple cancer types, offering new avenues for improving cancer treatment.

Curcumin nanoparticles alleviate brain mitochondrial dysfunction and cellular senescence in γ-irradiated rats

Despite the diverse applications of γ radiation in radiotherapy, industrial processes, and sterilization, it causes hazardous effects on living organisms, such as cellular senescence, persistent cell cycle arrest, and mitochondrial dysfunction. This study evaluated the efficacy of curcumin nanoparticles (CNPs) in mitigating mitochondrial dysfunction and cellular senescence induced by γ radiation in rat brain tissues. Four groups of male Wistar albino rats (n = 8 per group) were included: (Gr1) the control group; (Gr2) the CNPs group (healthy rats receiving oral administration of curcumin nanoparticles at a dose of 10 mg/kg/day, three times per week for eight weeks); (Gr3) the irradiated group (rats exposed to a single dose of 10 Gy head γ irradiation); and (Gr4) the irradiated + CNPs group (irradiated rats treated with CNPs). The data obtained demonstrated that oral administration of CNPs for eight weeks attenuated oxidative stress in γ-irradiated rats by lowering the brain's lipid peroxidation level [malondialdehyde (MDA)] and enhancing antioxidant markers [superoxide dismutase (SOD), reduced glutathione (GSH), and total antioxidant capacity (TAC)] (P < 0.05). In addition, CNPs significantly increased mitochondrial function by improving complex I, complex II, and ATP production levels compared to the irradiated group. In irradiated rats, CNPs also showed anti-neuroinflammatory effects by reducing brain interleukin 6 (IL-6), tumor necrosis factor-alpha (TNF-α), and nuclear factor-kappa B (NF-ĸB) levels (P < 0.05). Moreover, CNPs administered to irradiated rats significantly reduced brain β-galactosidase activity and the expression levels of p53, p21, and p16 genes (P < 0.05) while concurrently inducing a significant increase in AMPK mRNA expression compared to the irradiated group. In conclusion, CNPs ameliorated the neurotoxicity of γ radiation and hold promise as a novel agent to delay cellular senescence via their combined antioxidant, anti-inflammatory, and mitochondrial-enhancing properties.

Analysis of total RNA as a potential biomarker of Parkinson's disease in silico

Knowledge about total RNA molecules in Parkinson's disease is limited. This gene expression profiling study was conducted with a preclinical experimental design using a mouse model to examine the molecular-biological characteristics and the pathological implication of total RNA gene interaction in Parkinson's disease in silico. In silico analysis of total RNA molecules, the Gene Expression Omnibus database, published results, and preliminary findings of available patient samples apply. The potential signaling network and the effect of the interaction of molecules with total RNA was predicted and confirmed. The research consists of four parts. At first, we analyzed the control and MPTP groups. In the second part, we analyzed FVB-N control and MPTP. In the third part, we analyzed controls. In the fourth part, we analyzed MTPT separately. The constructed network contains total RNA, where the Kyoto Encyclopedia of Genes and Genomes database analysis showed that genes from the signaling pathway are involved in the development and complications of Parkinson's disease in male and female rats. Identified total RNA and genes are involved in altered signaling. There is direct interconnection and interdependence of interactions in the signaling network. Results identified the significant total-RNA molecules of the signaling pathway that connect other molecules. In silico analysis shows upregulated and downregulated genes in Parkinson's disease rats. Preliminary data shows that total RNA molecules interact with other genes, and they are applicable in Parkinson's disease course monitoring, shedding light on how factors impact the expression of genes and offering strategies for management.

From Genomics to Metabolomics: Molecular Insights into Osteoporosis for Enhanced Diagnostic and Therapeutic Approaches

Osteoporosis (OP) is a prevalent skeletal disorder characterized by decreased bone mineral density (BMD) and increased fracture risk. The advancements in omics technologies-genomics, transcriptomics, proteomics, and metabolomics-have provided significant insights into the molecular mechanisms driving OP. These technologies offer critical perspectives on genetic predispositions, gene expression regulation, protein signatures, and metabolic alterations, enabling the identification of novel biomarkers for diagnosis and therapeutic targets. This review underscores the potential of these multi-omics approaches to bridge the gap between basic research and clinical applications, paving the way for precision medicine in OP management. By integrating these technologies, researchers can contribute to improved diagnostics, preventative strategies, and treatments for patients suffering from OP and related conditions.

Navigating the Metaverse: A New Virtual Tool with Promising Real Benefits for Breast Cancer Patients

BC, affecting both women and men, is a complex disease where early diagnosis plays a crucial role in successful treatment and enhances patient survival rates. The Metaverse, a virtual world, may offer new, personalized approaches to diagnosing and treating BC. Although Artificial Intelligence (AI) is still in its early stages, its rapid advancement indicates potential applications within the healthcare sector, including consolidating patient information in one accessible location. This could provide physicians with more comprehensive insights into disease details. Leveraging the Metaverse could facilitate clinical data analysis and improve the precision of diagnosis, potentially allowing for more tailored treatments for BC patients. However, while this article highlights the possible transformative impacts of virtual technologies on BC treatment, it is important to approach these developments with cautious optimism, recognizing the need for further research and validation to ensure enhanced patient care with greater accuracy and efficiency.

Translating genetic findings to epigenetics: identifying the mechanisms associated with aging after high-radiation exposure on earth and in space

Purpose

Exposure to radiation is a health concern within and beyond the Earth's atmosphere for aircrew and astronauts in their respective austere environments. The biological effects of radiation exposure from a multiomics standpoint are relatively unexplored and stand to shed light on tailored monitoring and treatment for those in these career fields. To establish a reference variable for genetic damage, biological age seems to be closely associated with the effect of radiation. Following a genetic-based study, this study explores the epigenetic landscape of radiation exposure along with its associative effects on aging processes.

Methods

We imported the results of the genetics-based study that was a secondary analysis of five publicly available datasets (noted as Data1). The overlap of these genes with new data involving methylation data from two datasets (noted as Data2) following similar secondary analysis procedures is the basis of this study. We performed the standard statistical analysis on these datasets along with supervised and unsupervised learning to create preranked gene lists used for functional analysis in Ingenuity Pathway Analysis (IPA).

Results

There were 664 genes of interest from Data1 and 577 genes from Data2. There were 40 statistically significant methylation probes within 500 base pairs of the gene's transcription start site and 10 probes within 100 base pairs, which are discussed in depth. IPA yielded 21 significant pathways involving metabolism, cellular development, cell death, and diseases. Compared to gold standards for gestational age, we observed relatively low error and standard deviation using newly identified biomarkers.

Conclusion

We have identified 17 methylated genes that exhibited particular interest and potential in future studies. This study suggests that there are common trends in oxidative stress, cell development, and metabolism that indicate an association between aging processes and the effects of ionizing radiation exposure.