The determination of a causal association between gut microbiota and a range of dyslipidemia remains uncertain. To clarify these associations, we employed a two-sample Mendelian randomization (MR) analysis utilizing the inverse-variance weighted (IVW) method. This comprehensive analysis investigated the genetic variants that exhibited a significant association (p < 5 × 10⁻⁸) with 129 distinct gut microbiota genera and their potential link to different types of dyslipidemia. The results indicated a potential causal association between 22 gut microbiota genera and dyslipidemia in humans. Furthermore, these findings suggested that the impact of gut microbiota on dyslipidemia regulation is dependent on the specific phylum, family, and genus. Bacillota phylum demonstrated the greatest diversity, with 15 distinct genera distributed among eight families. Notably, gut microbiota-derived from the Lachnospiraceae and Lactobacillaceae families exhibit statistically significant associations with lipid levels that contribute to overall health (p < 0.05). The sensitivity analysis indicated that our findings possess robustness (p > 0.05). The findings of our investigation provide compelling evidence that substantiates a causal association between the gut microbiota and dyslipidemia in the human body. It is noteworthy to highlight the significant influence of the Bacillota phylum as a crucial regulator of lipid levels, and the families Lachnospiraceae and Lactobacillaceae should be recognized as probiotics that significantly contribute to this metabolic process.

Mitochondrial DNA (mtDNA) copy number and telomere length (TL) are dynamic factors that have been linked to the aging process in organisms. However, the causal relationship between these variables remains uncertain. In this research, instrumental variables (IVs) related to mtDNA copy number and TL were obtained from publicly available genome-wide association studies (GWAS). Through bidirectional Mendelian randomization (MR) analysis, we examined the potential causal relationship between these factors. The forward analysis, with mtDNA copy number as the exposure and TL as the outcome, did not reveal a significant effect (B=-0.004, P>0.05). On the contrary, upon conducting a reverse analysis, it was found that there exists a positive causal relationship (B=0.054, P<0.05). Sensitivity analyses further confirmed the reliability of these results. The outcomes of this study indicate a one-way positive causal relationship, indicating that telomere shortening in the aging process may lead to a decrease in mtDNA copy number, providing new perspectives on their biological mechanisms.

The aim of this study was to assess the causal relationships between mineral metabolism disorders, representative of trace elements, and key aging biomarkers: telomere length (TL) and mitochondrial DNA copy number (mtDNA-CN). Utilizing bidirectional Mendelian randomization (MR) analysis in combination with the two-stage least squares (2SLS) method, we explored the causal relationships between mineral metabolism disorders and these aging indicators. Sensitivity analysis can be used to determine the reliability and robustness of the research results. The results confirmed that a positive causal relationship was observed between mineral metabolism disorders and TL (p < 0.05), while the causal relationship with mtDNA-CN was not significant (p > 0.05). Focusing on subgroup analyses of specific minerals, our findings indicated a distinct positive causal relationship between iron metabolism disorders and both TL and mtDNA-CN (p < 0.05). In contrast, disorders in magnesium and phosphorus metabolism did not exhibit significant causal effects on either aging biomarker (p > 0.05). Moreover, reverse MR analysis did not reveal any significant causal effects of TL and mtDNA-CN on mineral metabolism disorders (p > 0.05). The combination of 2SLS with MR analysis further reinforced the positive causal relationship between iron levels and both TL and mtDNA-CN (p < 0.05). Notably, the sensitivity analysis did not indicate significant pleiotropy or heterogeneity within these causal relationships (p > 0.05). These findings highlight the pivotal role of iron metabolism in cellular aging, particularly in regulating TL and sustaining mtDNA-CN, offering new insights into how mineral metabolism disorders influence aging biomarkers. Our research underscores the importance of trace element balance, especially regarding iron intake, in combating the aging process. This provides a potential strategy for slowing aging through the adjustment of trace element intake, laying the groundwork for future research into the relationship between trace elements and healthy aging.

Background: In the purpose of identifying reliable biomarkers for evaluating prognosis, monitoring recurrence and exploring new therapeutic targets, it is quite necessary to screen for the genetic changes and potential molecular mechanisms of the occurrence and development of gastric cancer (GC) from the aspects of race and region. Methods: Target datasets were retrieved from Gene Expression Omnibus (GEO) database with “gastric cancer” as the key word, and corresponding data was downloaded. The differentially expressed genes (DEGs) was obtained by using limma R package, and the Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway for DEGs were analyzed in Enirchr database. Protein-protein interaction (PPI) network and molecular module were also constructed through STRING database and Cytoscape software. Survival analyses were completed for DEGs in GEO and Kaplan-Meier plotter database via cross validation. Finally, the correlation between gene expression and the infiltration cell levels in tumor microenvironment (TME) was explored based on the tumor immune estimation resource (TIMER) database. Results: Five GC-related microarray datasets were selected and used for differential analysis, and 222 DEGs were identified. GO analyses of DEGs were mainly involved in cell metabolism and the formation of extracellular matrix (ECM). The top enriched pathways of DEGs were protein digestion and absorption, ECM-receptor interaction, focal adhesion (FA), PI3K-Akt signaling pathway. Survival analyses of DEGs revealed that the expression levels of CTSK and COL4A2 were significantly associated with poor prognosis of GC patients in Asian. Specifically, the high expression of CTSK had a closely related to the infiltration level of inflammatory cell in TME. Conclusions: CTSK and COL4A2 could play a critical role in the pathogenesis of GC and act as the promising prognostic biomarkers. CTSK could induce the formation of immunosuppressive TME and promote the immune escape of GC cells.

Hepatocellular carcinoma (HCC) is an aggressive cancer type with poor prognosis; thus, there is especially necessary and urgent to screen potential prognostic biomarkers for early diagnosis and novel therapeutic targets. In this study, we downloaded target data sets from the GEO database, and obtained codifferentially expressed genes using the limma R package and identified key genes through the protein–protein interaction network and molecular modules, and performed GO and KEGG pathway analyses for key genes via the clusterProfiler package and further determined their correlations with clinicopathological features using the Oncomine database. Survival analysis was completed in the GEPIA and the Kaplan–Meier plotter database. Finally, correlations between key genes, cell types infiltrated in the tumor microenvironment (TME), and hypoxic signatures were explored based on the TIMER database. From the results, 11 key genes related to the cell cycle were determined, and high levels of these key genes’ expression were focused on advanced and higher grade status HCC patients, as well as in samples of TP53 mutation and vascular invasion. Besides, the 11 key genes were significantly associated with poor prognosis of HCC and also were positively related to the infiltration level of MDSCs in the TME and the HIF1A and VEGFA of hypoxic signatures, but a negative correlation was found with endothelial cells (ECs) and hematopoietic stem cells. The result determined that 11 key genes (RRM2, NDC80, ECT2, CCNB1, ASPM, CDK1, PRC1, KIF20A, DTL, TOP2A, and PBK) could play a vital role in the pathogenesis of HCC, drive the communication between tumor cells and the TME, and act as probably promising diagnostic, therapeutic, and prognostic biomarkers in HCC patients.

Ischemic stroke (IS) greatly threatens human health resulting in high mortality and substantial loss of function. Recent studies have shown that the outcome of IS has sex specific, but its mechanism is still unclear. This study is aimed at identifying the sexually dimorphic to peripheral immune response in IS progression, predicting potential prognostic biomarkers that can lead to sex‐specific outcome, and revealing potential treatment targets. Gene expression dataset GSE37587, including 68 peripheral whole blood samples which were collected within 24 hours from known onset of symptom and again at 24‐48 hours after onset (20 women and 14 men), was downloaded from the Gene Expression Omnibus (GEO) datasets. First, using Bioconductor R package, two kinds of differentially expressed genes (DEGs) (nonsex‐specific‐ and sex‐specific‐DEGs) were screened by follow‐up (24‐48 hours) vs. baseline (24 hours). 30 nonsex‐specific DEGs (1 upregulated and 29 downregulated), 79 female‐specific DEGs (25 upregulated and 54 downregulated), and none of male‐specific DEGs were obtained finally. Second, bioinformatics analysis of female‐specific DEGs was performed. Gene Ontology (GO) functional annotation analysis shows that DEGs were mainly enriched in translational initiation, cytosolic ribosome, and structural constituent of ribosome. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis shows that the top 6 enrichment pathways are ribosome, nuclear factor‐­kappa B (NF‐kappa B) signaling pathway, apoptosis, mineral absorption, nonalcoholic fatty liver disease, and pertussis. Three functional modules were clustered in the protein–protein interaction (PPI) network of DEGs. The top 10 key genes of the PPI network constructed were selected, including RPS14, RPS15A, RPS24, FAU, RPL27, RPL31, RPL34, RPL35A, RSL24D1, and EEF1B2. Sex difference of ribosome in stroke‐induced peripheral immunosuppression may be the potential mechanism of sex disparities in outcome after IS, and women are more likely to have stroke‐induced immunosuppression. RPS14, RPS15A, RPS24, FAU, RPL27, RPL31, RPL34, RPL35A, RSL24D1, and EEF1B2 may be novel prognostic biomarkers and potential therapeutic targets for IS.