Role of Necroptosis and Immune Infiltration in Human Stanford Type A Aortic Dissection: Novel Insights from Bioinformatics Analyses

Identification of Key Immune Infiltration Related Genes Involved in Aortic Dissection Using Bioinformatic Analyses and Experimental Verification

Purpose

Immune microenvironment plays an important role in aortic dissection (AD). Therefore, novel immune biomarkers may facilitate AD prevention, diagnosis, and treatment. This study aimed at mining key immune-related genes and relevant mechanisms involved in AD pathogenesis.

Patients and Methods

Key immune cells in AD were identified by ssGESA algorithm. Next, genes associated with key immune cells were screened by weighted gene coexpression network analysis (WGCNA). Then hub immune genes were picked from protein-protein interaction network of overlapped genes from differential expression and WGCNA analyses by cytohubba plug-in. Their diagnostic potential was evaluated in two independent cohorts from GEO database. In addition, the expressions of hub immune genes were determined by quantitative RT-PCR, immunohistochemistry, and Western blotting in dissected and normal aortic tissues.

Results

Activated B cells, CD56dim natural killer cells, eosinophils, gamma delta T cells, immature B cells, natural killer cells and type 17 T helper cells were identified as key immune cells in AD. Thereafter, a gene module significantly correlated with key immune cells were found by WGCNA method. Subsequently, KDR, IGF1, NOS3, PECAM1, GAPDH, FLT1, DLL4, CDH5, VWF, and TEK were identified as hub immune cell related genes by PPI network analysis, which may be potential diagnostic markers for AD, as evidenced by ROC curves. Moreover, the decreased expression of VWF in AD was validated at both mRNA and protein levels, and its expression was significantly positive correlated with the marker of smooth muscle cells, ACTA2, in AD. Further immunofluorescent results showed that VWF was colocalized with ACTA2 in aortic tissues.

Conclusion

We identified key immune cells and hub immune cell-related genes involved in AD. Moreover, we found that VWF was co-expressed with the smooth muscle cell marker ACTA2, indicating the important role of VWF in smooth muscle cell loss in AD pathogenesis.

Construction and evaluation of endometriosis diagnostic prediction model and immune infiltration based on efferocytosis-related genes

Background: Endometriosis (EM) is a long-lasting inflammatory disease that is difficult to treat and prevent. Existing research indicates the significance of immune infiltration in the progression of EM. Efferocytosis has an important immunomodulatory function. However, research on the identification and clinical significance of efferocytosis-related genes (EFRGs) in EM is sparse. Methods: The EFRDEGs (differentially expressed efferocytosis-related genes) linked to datasets associated with endometriosis were thoroughly examined utilizing the Gene Expression Omnibus (GEO) and GeneCards databases. The construction of the protein-protein interaction (PPI) and transcription factor (TF) regulatory network of EFRDEGs ensued. Subsequently, machine learning techniques including Univariate logistic regression, LASSO, and SVM classification were applied to filter and pinpoint diagnostic biomarkers. To establish and assess the diagnostic model, ROC analysis, multivariate regression analysis, nomogram, and calibration curve were employed. The CIBERSORT algorithm and single-cell RNA sequencing (scRNA-seq) were employed to explore immune cell infiltration, while the Comparative Toxicogenomics Database (CTD) was utilized for the identification of potential therapeutic drugs for endometriosis. Finally, immunohistochemistry (IHC) and reverse transcription quantitative polymerase chain reaction (RT-qPCR) were utilized to quantify the expression levels of biomarkers in clinical samples of endometriosis. Results: Our findings revealed 13 EFRDEGs associated with EM, and the LASSO and SVM regression model identified six hub genes (ARG2, GAS6, C3, PROS1, CLU, and FGL2). Among these, ARG2, GAS6, and C3 were confirmed as diagnostic biomarkers through multivariate logistic regression analysis. The ROC curve analysis of GSE37837 (AUC = 0.627) and GSE6374 (AUC = 0.635), along with calibration and DCA curve assessments, demonstrated that the nomogram built on these three biomarkers exhibited a commendable predictive capacity for the disease. Notably, the ratio of nine immune cell types exhibited significant differences between eutopic and ectopic endometrial samples, with scRNA-seq highlighting M0 Macrophages, Fibroblasts, and CD8 Tex cells as the cell populations undergoing the most substantial changes in the three biomarkers. Additionally, our study predicted seven potential medications for EM. Finally, the expression levels of the three biomarkers in clinical samples were validated through RT-qPCR and IHC, consistently aligning with the results obtained from the public database. Conclusion: we identified three biomarkers and constructed a diagnostic model for EM in this study, these findings provide valuable insights for subsequent mechanistic research and clinical applications in the field of endometriosis.

MALT1 Positively Relates to T Helper 1 and T Helper 17 cells, and Serves as a Potential Biomarker for Predicting 30-Day Mortality in Stanford Type A Aortic Dissection Patients

Mucosa-associated lymphoid tissue 1 (MALT1) regulates inflammation and T helper (Th) cell differentiation, which may participate in the progression of Stanford type A aortic dissection (TAAD). This study intended to assess the association of MALT1 expression with prognosis in TAAD patients. In this prospective study, MALT1 expression was measured by reverse transcription-quantitative polymerase chain reaction assay from peripheral blood samples in 100 TAAD patients and 100 non-AD controls (non-AD patients with chest pain) before treatment. Besides, Th1, Th2, and Th17 cells of TAAD patients before treatment were measured by flow cytometry assay, and their 30-day mortality was recorded. MALT1 expression was ascended in TAAD patients vs. non-AD controls (P < 0.001). In TAAD patients, elevated MALT1 expression was linked with hypertension complication (P = 0.009), increased systolic blood pressure (r = 0.291, P = 0.003), C-reactive protein (CRP) (r = 0.286, P = 0.004), and D-dimer (r = 0.359, P < 0.001). Additionally, MALT1 expression was positively correlated with Th1 cells (r = 0.312, P = 0.002) and Th17 cells (r = 0.397, P < 0.001), but not linked with Th2 cells (r = -0.166, P = 0.098). Notably, the 30-day mortality of TAAD patients was 28.0%. MALT1 expression [odds ratio (OR) = 1.936, P = 0.004], CRP (OR = 1.108, P = 0.002), D-dimer (OR = 1.094, P = 0.003), and surgery timing (emergency vs. selective) (OR = 8.721, P = 0.024) independently predicted increased risk of death within 30 days in TAAD patients. Furthermore, the combination of the above-mentioned independent factors had an excellent ability in predicting 30-day mortality with the area under curve of 0.949 (95% confidence interval: 0.909-0.989). MALT1 expression relates to increased Th1 cells, Th17 cells, and 30-day mortality risk in TAAD patients.

Bioinformatics analysis of rheumatoid arthritis tissues identifies genes and potential drugs that are expressed specifically

Studies have implicated necroptosis mechanisms in orthopaedic-related diseases, since necroptosis is a unique regulatory cell death pattern. However, the role of Necroptosis-related genes in rheumatoid arthritis (RA) has not been well described. We downloaded RA-related data information and Necroptosis-related genes from the Gene Expression Omnibus (GEO), Kyoto Gene and Genome Encyclopedia (KEGG) database, and Genome Enrichment Analysis (GSEA), respectively. We identified 113 genes associated with RA-related necroptosis, which was closely associated with the cytokine-mediated signaling pathway, necroptosis and programmed necrosis. Subsequently, FAS, MAPK8 and TNFSF10 were identified as key genes among 48 Necroptosis-associated differential genes by three machine learning algorithms (LASSO, RF and SVM-RFE), and the key genes had good diagnostic power in distinguishing RA patients from healthy controls. According to functional enrichment analysis, these genes may regulate multiple pathways, such as B-cell receptor signaling, T-cell receptor signaling pathways, chemokine signaling pathways and cytokine-cytokine receptor interactions, and play corresponding roles in RA. Furthermore, we predicted 48 targeted drugs against key genes and 31 chemical structural formulae based on targeted drug prediction. Moreover, key genes were associated with complex regulatory relationships in the ceRNA network. According to CIBERSORT analysis, FAS, MAPK8 and TNFSF10 may be associated with changes in the immune microenvironment of RA patients. Our study developed a diagnostic validity and provided insight to the mechanisms of RA. Further studies will be required to test its diagnostic value for RA before it can be implemented in clinical practice.

Prognosis analysis of necroptosis-related genes in colorectal cancer based on bioinformatic analysis

Background: Colorectal cancer (CRC) is one gastrointestinal malignancy, accounting for 10% of cancer diagnoses and cancer-related deaths worldwide each year. Therefore, it is urgent to identify genes involved in CRC predicting the prognosis.

Methods: CRC’s data were acquired from the Gene Expression Omnibus (GEO) database (GSE39582 and GSE41258 datasets) and The Cancer Genome Atlas (TCGA) database. The differentially expressed necroptosis-related genes (DENRGs) were sorted out between tumor and normal tissues. Univariate Cox regression analysis and least absolute shrinkage and selectionator operator (LASSO) analysis were applied to selected DENRGs concerning patients’ overall survival and to construct a prognostic biomarker. The effectiveness of this biomarker was assessed by the Kaplan–Meier curve and the receiver operating characteristic (ROC) analysis. The GSE39582 dataset was utilized as external validation for the prognostic signature. Moreover, using univariate and multivariate Cox regression analyses, independent prognostic factors were identified to construct a prognostic nomogram. Next, signaling pathways regulated by the signature were explored through the gene set enrichment analysis (GSEA). The single sample gene set enrichment analysis (ssGSEA) algorithm and tumor immune dysfunction and exclusion (TIDE) were used to explore immune correlation in the two groups, high-risk and low-risk ones. Finally, prognostic genes’ expression was examined in the GSE41258 dataset.

Results: In total, 27 DENRGs were filtered, and a necroptosis-related prognostic signature based on 6 DENRGs was constructed, which may better understand the overall survival (OS) of CRC. The Kaplan–Meier curve manifested the effectiveness of the prognostic signature, and the ROC curve showed the same result. In addition, univariate and multivariate Cox regression analyses revealed that age, pathology T, and risk score were independent prognostic factors, and a nomogram was established. Furthermore, the prognostic signature was most significantly associated with the apoptosis pathway. Meanwhile, 24 immune cells represented significant differences between two groups, like the activated B cell. Furthermore, 32 immune checkpoints, TIDE scores, PD-L1 scores, and T-cell exclusion scores were significantly different between the two groups. Finally, a 6-gene prognostic signature represented different expression levels between tumor and normal samples significantly in the GSE41258 dataset.

Conclusion: Our study established a signature including 6 genes and a prognostic nomogram that could significantly assess the prognosis of patients with CRC.