Identification of hub biomarkers of myocardial infarction by single-cell sequencing, bioinformatics, and machine learning

Identification of hub biomarkers in coronary artery disease patients using machine learning and bioinformatic analyses

Understanding the molecular underpinnings of CAD is essential for developing effective therapeutic strategies. This study aims to identify and analyze differentially expressed hub biomarkers in the peripheral blood of CAD patients. Based on RNA-seq datasets from the Gene Expression Omnibus database, machine learning algorithms including LASSO, RF, and SVM-RFE were applied. Furthermore, the hub biomarkers were enriched to ascertain their roles in immune cell expression and signaling pathways through GO, KEGG, GSVE, and GSVA. An in vivo experiment was conducted to verify the hub biomarkers. Eleven hub biomarkers (ITM2B, GNA15, PLAU, GNG11, HIST1H2BH, SLC11A1, RPS7, DDIT4, CD83, GNLY, and S100A12) were identified and associated with CD8 + T cells and NK cells. They were mainly involved in immune responses, cardiac muscle contraction, oxidative phosphorylation, and apoptotic signaling pathways. Moreover, ITM2B had the most importance and significance to be the biomarker of CAD patients. In conclusion, these findings point to the possibility of ITM2B as a biomarker on the inflammatory pathogenesis of CAD and suggest new options for therapeutic intervention.

Artificial intelligence in traditional Chinese medicine: advances in multi-metabolite multi-target interaction modeling

Traditional Chinese Medicine (TCM) utilizes multi-metabolite and multi-target interventions to address complex diseases, providing advantages over single-target therapies. However, the active metabolites, therapeutic targets, and especially the combination mechanisms remain unclear. The integration of advanced data analysis and nonlinear modeling capabilities of artificial intelligence (AI) is driving the transformation of TCM into precision medicine. This review concentrates on the application of AI in TCM target prediction, including multi-omics techniques, TCM-specialized databases, machine learning (ML), deep learning (DL), and cross-modal fusion strategies. It also critically analyzes persistent challenges such as data heterogeneity, limited model interpretability, causal confounding, and insufficient robustness validation in practical applications. To enhance the reliability and scalability of AI in TCM target prediction, future research should prioritize continuous optimization of the AI algorithms using zero-shot learning, end-to-end architectures, and self-supervised contrastive learning.

MDFI promotes the proliferation and tolerance to chemotherapy of colorectal cancer cells by binding ITGB4/LAMB3 to activate the AKT signaling pathway

Colorectal cancer (CRC) is one of the most lethal cancers. Single-cell RNA sequencing (scRNA-seq) and protein-protein interactions (PPIs) have enabled the systematic study of CRC. In our research, the activation of the AKT pathway in CRC was analyzed by KEGG using single-cell sequencing data from the GSE144735 dataset. The correlation and PPIs of MDFI and ITGB4/LAMB3 were examined. The results were verified in the TCGA and CCLE and further tested by coimmunoprecipitation experiments. The effect of MDFI on the AKT pathway via ITGB4/LAMB3 was validated by knockdown and lentiviral overexpression experiments. The effect of MDFI on oxaliplatin/fluorouracil sensitivity was probed by colony formation assay and CCK8 assay. We discovered that MDFI was positively associated with ITGB4/LAMB3. In addition, MDFI was negatively associated with oxaliplatin/fluorouracil sensitivity. MDFI upregulated the AKT pathway by directly interacting with LAMB3 and ITGB4 in CRC cells, and enhanced the proliferation of CRC cells via the AKT pathway. Finally, MDFI reduced the sensitivity of CRC cells to oxaliplatin and fluorouracil. In conclusion, MDFI promotes the proliferation and tolerance to chemotherapy of colorectal cancer cells, partially through the activation of the AKT signaling pathway by the binding to ITGB4/LAMB3. Our findings provide a possible molecular target for CRC therapy.

Identification of mitochondria-related gene biomarkers associated with immune infiltration in acute myocardial infarction

Mitochondrial dysfunction has been known to contribute to the worsening of acute myocardial infarction (AMI). We screened differentially expressed genes (DEGs) between AMI and healthy individuals based on the GSE66360 dataset. We took the intersection of the obtained DEGs with 1,136 mitochondria-related genes. Finally, we screened out mitochondria-related DEGs (MitoDEGs). Eight MitoDEGs were identified as hub genes based on the random forest algorithm. Two mitochondria-related robust molecular clusters were identified by consensus clustering. Immune infiltration analysis showed that immune cell infiltration was significantly increased in the high-expression group of MitoDEGs. We obtained the potential drugs targeted at ALDH2, PMAIP1, and BCL2A1, such as disulfiram, obatoclax mesylate, and bortezomib. Quantitative reverse-transcription polymerase chain reaction further validated the expression of the MitoDEGs in the cell model of AMI. These findings reveal the potential role of MitoDEGs in AMI and provide new insights into risk stratification and individualized treatment of AMI patients.

Adiponectin protects against myocardial ischemia-reperfusion injury: a systematic review and meta-analysis of preclinical animal studies

BACKGROUND

Myocardial ischemia-reperfusion injury (MIRI) is widespread in the treatment of ischemic heart disease, and its treatment options are currently limited. Adiponectin (APN) is an adipocytokine with cardioprotective properties; however, the mechanisms of APN in MIRI are unclear. Therefore, based on preclinical (animal model) evidence, the cardioprotective effects of APN and the underlying mechanisms were explored.

METHODS

The literature was searched for the protective effect of APN on MIRI in six databases until 16 November 2023, and data were extracted according to selection criteria. The outcomes were the size of the myocardial necrosis area and hemodynamics. Markers of oxidation, apoptosis, and inflammation were secondary outcome indicators. The quality evaluation was performed using the animal study evaluation scale recommended by the Systematic Review Center for Laboratory animal Experimentation statement. Stata/MP 14.0 software was used for the summary analysis.

RESULTS

In total, 20 papers with 426 animals were included in this study. The pooled analysis revealed that APN significantly reduced myocardial infarct size [weighted mean difference (WMD) = 16.67 (95% confidence interval (CI) = 13.18 to 20.16, P < 0.001)] and improved hemodynamics compared to the MIRI group [Left ventricular end-diastolic pressure: WMD = 5.96 (95% CI = 4.23 to 7.70, P < 0.001); + dP/dtmax: WMD = 1393.59 (95% CI = 972.57 to 1814.60, P < 0.001); -dP/dtmax: WMD = 850.06 (95% CI = 541.22 to 1158.90, P < 0.001); Left ventricular ejection fraction: WMD = 9.96 (95% CI = 7.29 to 12.63, P < 0.001)]. Apoptosis indicators [caspase-3: standardized mean difference (SMD) = 3.86 (95% CI = 2.97 to 4.76, P < 0.001); TUNEL-positive cells: WMD = 13.10 (95% CI = 8.15 to 18.05, P < 0.001)], inflammatory factor levels [TNF-α: SMD = 4.23 (95% CI = 2.48 to 5.98, P < 0.001)], oxidative stress indicators [Superoxide production: SMD = 4.53 (95% CI = 2.39 to 6.67, P < 0.001)], and lactate dehydrogenase levels [SMD = 2.82 (95% CI = 1.60 to 4.04, P < 0.001)] were significantly reduced. However, the superoxide dismutase content was significantly increased [SMD = 1.91 (95% CI = 1.17 to 2.65, P < 0.001)].

CONCLUSION

APN protects against MIRI via anti-inflammatory, antiapoptotic, and antioxidant effects, and this effect is achieved by activating different signaling pathways.

Secrets and lies of host-microbial interactions: MHC restriction and trans-regulation of T cell trafficking conceal the role of microbial agents on the edge between health and multifactorial/complex diseases

Here we critically discuss data supporting the view that microbial agents (pathogens, pathobionts or commensals alike) play a relevant role in the pathogenesis of multifactorial diseases, but their role is concealed by the rules presiding over T cell antigen recognition and trafficking. These rules make it difficult to associate univocally infectious agents to diseases' pathogenesis using the paradigm developed for canonical infectious diseases. (Cross-)recognition of a variable repertoire of epitopes leads to the possibility that distinct infectious agents can determine the same disease(s). There can be the need for sequential infection/colonization by two or more microorganisms to develop a given disease. Altered spreading of infectious agents can determine an unwanted activation of T cells towards a pro-inflammatory and trafficking phenotype, due to differences in the local microenvironment. Finally, trans-regulation of T cell trafficking allows infectious agents unrelated to the specificity of T cell to modify their homing to target organs, thereby driving flares of disease. The relevant role of microbial agents in largely prevalent diseases provides a conceptual basis for the evaluation of more specific therapeutic approaches, targeted to prevent (vaccine) or cure (antibiotics and/or Biologic Response Modifiers) multifactorial diseases.

Immunological characterization of stroke-heart syndrome and identification of inflammatory therapeutic targets

Acute cardiac dysfunction caused by stroke-heart syndrome (SHS) is the second leading cause of stroke-related death. The inflammatory response plays a significant role in the pathophysiological process of cardiac damage. However, the mechanisms underlying the brain-heart interaction are poorly understood. Therefore, we aimed to analysis the immunological characterization and identify inflammation therapeutic targets of SHS. We analyzed gene expression data of heart tissue 24 hours after induction of ischemia stoke by MCAO or sham surgery in a publicly available dataset (GSE102558) from Gene Expression Omnibus (GEO). Bioinformatics analysis revealed 138 differentially expressed genes (DEGs) in myocardium of MCAO-treated compared with sham-treated mice, among which, immune and inflammatory pathways were enriched. Analysis of the immune cells infiltration showed that the natural killer cell populations were significantly different between the two groups. We identified five DIREGs, Aplnr, Ccrl2, Cdkn1a, Irak2, and Serpine1 and found that their expression correlated with specific populations of infiltrating immune cells in the cardiac tissue. RT-qPCR and Western blot methods confirmed significant changes in the expression levels of Aplnr, Cdkn1a, Irak2, and Serpine1 after MCAO, which may serve as therapeutic targets to prevent cardiovascular complications after stroke.

From multi-omics approaches to personalized medicine in myocardial infarction

Myocardial infarction (MI) is a prevalent cardiovascular disease characterized by myocardial necrosis resulting from coronary artery ischemia and hypoxia, which can lead to severe complications such as arrhythmia, cardiac rupture, heart failure, and sudden death. Despite being a research hotspot, the etiological mechanism of MI remains unclear. The emergence and widespread use of omics technologies, including genomics, transcriptomics, proteomics, metabolomics, and other omics, have provided new opportunities for exploring the molecular mechanism of MI and identifying a large number of disease biomarkers. However, a single-omics approach has limitations in understanding the complex biological pathways of diseases. The multi-omics approach can reveal the interaction network among molecules at various levels and overcome the limitations of the single-omics approaches. This review focuses on the omics studies of MI, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and other omics. The exploration extended into the domain of multi-omics integrative analysis, accompanied by a compilation of diverse online resources, databases, and tools conducive to these investigations. Additionally, we discussed the role and prospects of multi-omics approaches in personalized medicine, highlighting the potential for improving diagnosis, treatment, and prognosis of MI.

The prognostic nutritional index predicts all-cause mortality in critically ill patients with acute myocardial infarction

BACKGROUND

Malnutrition is common in patients with acute myocardial infarction (AMI) and is associated with a poor prognosis. The prognostic value of the prognostic nutritional index (PNI) in patients with AMI remains controversial. We aimed to explore the relationship between PNI and all-cause mortality in critically ill patients with AMI and evaluate the incremental prognostic value of PNI to commonly used prognostic assessment tools.

METHODS

The Medical Information Mart for Intensive Care-IV (MIMIC-IV) database was used to conduct a retrospective cohort analysis on 1180 critically ill patients with AMI. The primary endpoints were defined as 6-month and 1-year all-cause mortality. Cox regression analysis was used to investigate the relationship between admission PNI and all-cause mortality. The effect of adding PNI to sequential organ failure assessment (SOFA) score, or charlson comorbidity index (CCI) on its discriminative ability was assessed using C-statistic, net reclassification improvement (NRI), and integrated discrimination improvement (IDI).

RESULTS

Multivariate cox regression analysis demonstrated that the low PNI was regarded as an independent predictor of 1-year all-cause mortality in AMI patients admitted to ICU (adjusted Hazard Ratio: 95% CI = 1.75 (1.22-2.49)). The ROC test showed that admission PNI had a moderate predictive ability to predict all-cause mortality of critically ill patients with AMI. Furthermore, the net reclassification and integrated discrimination of the CCI alone model improved significantly with PNI. [C-statistic increased from 0.669 to 0.752, p < 0.001; NRI = 0.698, p < 0.001; IDI = 0.073, p < 0.001]. When PNI was added to the SOFA score, the C-statistic significantly improved from 0.770 to 0.805 (p < 0.001), and the NRI and IDI were estimated at 0.573 (p < 0.001) and 0.041 (p < 0.001), respectively.

CONCLUSION

PNI could be a novel predictor for identifying patients at high risk of 1-year all-cause mortality in critically ill patients with AMI. The addition of PNI to the SOFA score or CCI may be useful for very early risk stratification.

Machine learning in TCM with natural products and molecules: current status and future perspectives

Traditional Chinese medicine (TCM) has been practiced for thousands of years with clinical efficacy. Natural products and their effective agents such as artemisinin and paclitaxel have saved millions of lives worldwide. Artificial intelligence is being increasingly deployed in TCM. By summarizing the principles and processes of deep learning and traditional machine learning algorithms, analyzing the application of machine learning in TCM, reviewing the results of previous studies, this study proposed a promising future perspective based on the combination of machine learning, TCM theory, chemical compositions of natural products, and computational simulations based on molecules and chemical compositions. In the first place, machine learning will be utilized in the effective chemical components of natural products to target the pathological molecules of the disease which could achieve the purpose of screening the natural products on the basis of the pathological mechanisms they target. In this approach, computational simulations will be used for processing the data for effective chemical components, generating datasets for analyzing features. In the next step, machine learning will be used to analyze the datasets on the basis of TCM theories such as the superposition of syndrome elements. Finally, interdisciplinary natural product-syndrome research will be established by unifying the results of the two steps outlined above, potentially realizing an intelligent artificial intelligence diagnosis and treatment model based on the effective chemical components of natural products under the guidance of TCM theory. This perspective outlines an innovative application of machine learning in the clinical practice of TCM based on the investigation of chemical molecules under the guidance of TCM theory.

7-Hydroxyflavone Alleviates Myocardial Ischemia/Reperfusion Injury in Rats by Regulating Inflammation

Inflammation is the primary pathological process of myocardial ischemia/reperfusion injury (MI/RI). 7-Hydroxyflavone (HF), a natural flavonoid with a variety of bioactivities, plays a crucial role in various biological processes. However, its cardioprotective effects and the underlying mechanisms of MI/RI have not been investigated. This study aimed to explore whether pretreatment with HF could attenuate MI/RI-induced inflammation in rats and investigate its potential mechanisms. The results showed that pretreatment with HF could significantly improve the anatomic data and electrocardiograph parameters, reduce the myocardial infarct size, decrease markers of myocardial injury (aspartate transaminase, creatine kinase, lactate dehydrogenase, and cardiac troponin I), inhibit inflammatory cytokines (IL-1β, IL-6, and TNF-α), suppress oxidative stress, and recover the architecture of the cardiomyocytes. The cardioprotective effect of HF was connected with the regulation of the MAPK/NF-κB signaling pathway. What is more, molecular docking was carried out to prove that HF could be stably combined with p38, ERK1/2, JNK, and NF-κB. In summary, this is a novel study demonstrating the cardioprotective effects of HF against MI/RI in vivo. Consequently, these results demonstrate that HF can be considered a promising potential therapy for MI/RI.