Application of angiogenesis-related genes associated with immune infiltration in the molecular typing and diagnosis of acute myocardial infarction.

BACKGROUND: Angiogenesis has been discovered to be a critical factor in developing tumors and ischemic diseases. However, the role of angiogenesis-related genes (ARGs) in acute myocardial infarction (AMI) remains unclear. METHODS: The GSE66360 dataset was used as the training cohort, and the GSE48060 dataset was used as the external validation cohort. The random forest (RF) algorithm was used to identify the signature genes. Consensus clustering analysis was used to identify robust molecular clusters associated with angiogenesis. The ssGSEA was used to analyze the correlation between ARGs and immune cell infiltration. In addition, we constructed miRNA-gene, transcription factor network, and targeted drug network of signature genes. RT-qPCR was used to verify the expression levels of signature genes. RESULTS: Seven signature ARGs were identified based on the RF algorithm. Receiver operating characteristic curves confirmed the classification accuracy of the risk predictive model based on signature ARGs (area under the curve [AUC] = 0.9596 in the training cohort and AUC = 0.7773 in the external validation cohort). Subsequently, the ARG clusters were identified by consensus clustering. Cluster B had a more generalized high expression of ARGs and was significantly associated with immune infiltration. The miRNA and transcription factor network provided new ideas for finding potential upstream targets and biomarkers. Finally, the results of RT-qPCR were consistent with the bioinformatics analysis, further validating our results. CONCLUSIONS: Angiogenesis is closely related to AMI, and characterizing the angiogenic features of patients with AMI can help to risk-stratify patients and provide personalized treatment.

Multi-omics and single-cell sequencing analyses reveal the potential significance of circadian pathways in cancer therapy.

BACKGROUND: Circadian rhythm disturbance is an independent risk factor for cancer. However, few studies have been reported on circadian rhythm related genes (CRGs) in cancer, so it is important to further explore the impact of CRGs in pan-cancer. RESEARCH DESIGN AND METHODS: The Cancer Genome Atlas database was used to collect cancer-related data such as copy number variation, single nucleotide variants, methylation, and survival differences. Immunohistochemistry (IHC) was used to verify the expression of circadian rhythm hub genes. The circadian pathway scores (CRS) were calculated using single-sample gene enrichment analysis. TIMER and GEPIA databases were used for immune-cell integration and assessment. Single-cell sequencing data was used to evaluate the abundance of CRS in tumor microenvironment cells. RESULTS: In this study, we found that the expression of circadian pathway varies between tumors. CSNK1E was significantly up-regulated in most tumors and CRY2 was significantly down-regulated in most tumors. The protein interaction network suggested CRY2 as the core gene and IHC verified its significant low expression in KIRC. In addition, CRGs were found to be protective factors in most tumors and have the potential to act as specific immune markers in different tumors. CRS was significantly lower in abundance in most tumors. CRS was significantly associated with overall survival in tumor patients and associated with the expression of many immune cells in the tumor immune microenvironment. CRS is significantly associated with tumor mutational burden and microsatellite instability scores in most tumors and may serve as a potential immunotherapeutic marker. CONCLUSIONS: The circadian rhythm pathway may be a breakthrough point in regulating the tumor microenvironment meanwhile a suitable immunotherapy method in the future.

Machine learning identifies ferroptosis-related genes as potential diagnostic biomarkers for osteoarthritis.

Background: Osteoarthritis (OA) is one of the most common forms of degenerative arthritis and a major cause of pain and disability. Ferroptosis, a novel mode of cell death, has been verified to participate in the development of OA, but its mechanism is still unclear. This paper analyzed the ferroptosis-related genes (FRGs) in OA and explored their potential clinical value. Methods: We downloaded data through the GEO database and screened for DEGs. Subsequently, FRGs were obtained using two machine learning methods, LASSO regression and SVM-RFE. The accuracy of the FRGs as disease diagnosis was identified using ROC curves and externally validated. The CIBERSORT analyzed the immune microenvironment rug regulatory network constructed through the DGIdb. The competitive endogenous RNA (ceRNA) visualization network was constructed to search for possible therapeutic targets. The expression levels of FRGs were verified by qRT-PCR and immunohistochemistry. Results: In this study, we found 4 FRGs. The ROC curve showed that the combined 4 FRGs had the highest diagnostic value. Functional enrichment analysis showed that the 4 FRGs in OA could influence the development of OA through biological oxidative stress, immune response, and other processes. qRT-PCR and immunohistochemistry verified the expression of these key genes, further confirming our findings. Monocytes and macrophages are heavily infiltrated in OA tissues, and the persistent state of immune activation may promote the progression of OA. ETHINYL ESTRADIOL was a possible targeted therapeutic agent for OA. Meanwhile, ceRNA network analysis identified some lncRNAs that could regulate the FRGs. Conclusion: We identify 4 FRGs (AQP8, BRD7, IFNA4, and ARHGEF26-AS1) closely associated with bio-oxidative stress and immune response, which may become early diagnostic and therapeutic targets for OA.

Cuproptosis correlates with immunosuppressive tumor microenvironment based on pan-cancer multiomics and single-cell sequencing analysis.

Recent studies suggest that cuproptosis, a novel mode of cell death, may be associated with the development of cancer. However, no studies are showing its role in tumorigenesis, progression, and prognosis. In the present study, we comprehensively analyzed the expression difference, gene variation and methylation modification of cuproptosis-related genes (CRGs) in pan-cancer. Then, Single sample gene set enrichment analysis (ssGSEA) was used to calculate individual cuproptosis scores (CS). The association of CS with copy number variation, clinical features, immune-related genes, TMB, MSI, and tumor immune dysfunction and exclusion (TIDE) was comprehensively assessed. Single-cell transcriptome sequencing (scRNA-seq) to analyze the activation of cuproptosis in the tumor microenvironment. Immunohistochemistry (IHC) were used to validate the expression of cuproptosis hub-gene. Our study shows that CRGs were significantly expressed in a variety of tumors, and CDKN2A had the highest mutation frequency (49%) in all tumors. A significant increase in the CS was observed in most cancers and were associated with poor prognosis in the majority of tumors. CS was significantly negatively correlated with tumor microenvironment scores in more than 10 tumors and positively correlated with PD-L1 in 11 tumors, suggesting involvement in tumor immune escape. scRNA-seq suggests that CRG scores significantly increased in the cancer cells. This study opens avenues for further research on the role of cuproptosis in the occurrence and development of cancer and the development of targeted therapies based on cuproptosis.

Comprehensive multi-omics analysis of the m7G in pan-cancer from the perspective of predictive, preventive, and personalized medicine.

Background: The N7-methylguanosine modification (m7G) of the 5' cap structure in the mRNA plays a crucial role in gene expression. However, the relation between m7G and tumor immune remains unclear. Hence, we intended to perform a pan-cancer analysis of m7G which can help explore the underlying mechanism and contribute to predictive, preventive, and personalized medicine (PPPM / 3PM). Methods: The gene expression, genetic variation, clinical information, methylation, and digital pathological section from 33 cancer types were downloaded from the TCGA database. Immunohistochemistry (IHC) was used to validate the expression of the m7G regulator genes (m7RGs) hub-gene. The m7G score was calculated by single-sample gene-set enrichment analysis. The association of m7RGs with copy number variation, clinical features, immune-related genes, TMB, MSI, and tumor immune dysfunction and exclusion (TIDE) was comprehensively assessed. CellProfiler was used to extract pathological section characteristics. XGBoost and random forest were used to construct the m7G score prediction model. Single-cell transcriptome sequencing (scRNA-seq) was used to assess the activation state of the m7G in the tumor microenvironment. Results: The m7RGs were highly expressed in tumors and most of the m7RGs are risk factors for prognosis. Moreover, the cellular pathway enrichment analysis suggested that m7G score was closely associated with invasion, cell cycle, DNA damage, and repair. In several cancers, m7G score was significantly negatively correlated with MSI and TMB and positively correlated with TIDE, suggesting an ICB marker potential. XGBoost-based pathomics model accurately predicts m7G scores with an area under the ROC curve (AUC) of 0.97. Analysis of scRNA-seq suggests that m7G differs significantly among cells of the tumor microenvironment. IHC confirmed high expression of EIF4E in breast cancer. The m7G prognostic model can accurately assess the prognosis of tumor patients with an AUC of 0.81, which was publicly hosted at https://pan-cancer-m7g.shinyapps.io/Panca-m7g/. Conclusion: The current study explored for the first time the m7G in pan-cancer and identified m7G as an innovative marker in predicting clinical outcomes and immunotherapeutic efficacy, with the potential for deeper integration with PPPM. Combining m7G within the framework of PPPM will provide a unique opportunity for clinical intelligence and new approaches. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-022-00305-1.