ObjectiveTo construct a prognostic prediction model for hepatocellular carcinoma (HCC) based on disulfidptosis-associated genes (DAGs) and ferroptosis-associated genes (FAGs) using data from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases and explore the immune characteristics and antitumor drug sensitivity of HCC patients with high- and low-risk score. MethodsThe transcriptomic and clinical data of HCC were downloaded from the TCGA and ICGC databases. The expression levels of DAGs and FAGs were extracted. Subsequently, the differentially expressed and prognostically relevant DAGs and FAGs (DFAGs) were screened through differential expression and prognostic analysis. A prognostic prediction model for HCC was constructed by LASSO regression analysis. The prognostic value of risk factors was evaluated using univariate and multivariate Cox regression analyses, Kaplan-Meier survival analysis, receiver operating characteristic curves, principal component analysis, and t-distributed stochastic neighbor embedding. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to further elucidate the mechanisms of genes associated with HCC prognosis. The impact of risk factors on immune cells and immune cells functions was analyzed using single-sample gene set enrichment analysis. Based on the Genomics of Drug Sensitivity in Cancer database, the oncoPredict package was used to predict responses to antitumor drugs in for different risk groups. ResultsFour DFAGs (SLC7A11, SLC1A5, G6PD, and LRPPRC) with respective risk coefficients of 0.0350, 0.0442, 0.1597, and 0.0132 were selected to construct the prognostic prediction model. The risk score of prognostic prediction model was calculated as: Risk score =(0.0350×SLC7A11 expression level) + (0.0442×SLC1A5 expression level) + (0.159 7×G6PD expression level) + (0.013 2×LRPPRC expression level). The multivariate Cox regression analysis indicated that a high-risk score was an independent risk factor for HCC patient survival [HR (95%CI) = 5.414 (1.918, 15.279), P<0.001]. Both TCGA and ICGC datasets demonstrated that the high-risk patients had significantly worse survival than low-risk patients (P<0.001 and P=0.003, respectively). Enrichment analysis revealed that the risk-associated genes influenced HCC progression through multiple pathways, such as immune response, cell cycle, glycolysis, gluconeogenesis. Immune analysis showed that the high-risk patients exhibited increased infiltration of immunosuppressive cells, such as activated dendritic cells, macrophages, and regulatory T cells, while natural killer cell infiltration was significantly reduced. The drug sensitivity analysis suggested that the high-risk HCC patients might respond better to 5-fluorouracil, afatinib, cyclophosphamide, and lapatinib, whereas the low-risk patients might benefit more from oxaliplatin and sorafenib. ConclusionsHCC prognosis prediction model based on DFAGs in this study suggests a certain predictive value for the survival of HCC patients in the data from both TCGA and ICGC datasets. There are significant differences in pionts of immune cells infiltration and immune cells functions between high-risk and low-risk HCC patients. Additionally, significant differences exist in sensitivity to targeted drugs and chemotherapeutic drugs. This model can provide some references for immunotherapy, personalized treatment, and prognosis evaluation of HCC patients.
Objective A series of bioinformatics methods were used to identify ferroptosis related biomarkers in lupus nephritis (LN). Methods We retrieved sequencing data of GSE112943 from the GEO (Gene Expression Omnibus) database and screened LN differentially expressed genes. We searched for ferroptosis-related gene (FRG) through FerrDb database, and screened LN-FRG. We conducted enrichment analysis on the LN-FRGs using David online bioinformatics database and screened the core LN-FRG using cytoHubba. We used external data sets to verify the core LN-FRGs, constructed competing endogenous RNA networks, and conducted molecular docking analysis. Results A total of 37 LN-FRGs were selected through screening. These genes are mainly enriched in inflammation, immune regulation and ferroptosis related signaling pathways. Through the cytoHubba and external dataset validation, the key core LN-FRG of ATF3 (activating transcription factor 3) was ultimately identified, and its expression was significantly increased in LN (P<0.05). Molecular docking analysis showed that ATF3 was closely bound to SLC7A11 and NRF2, and may participate in the occurrence and development of LN through the microRNA-27-ATF3 regulation axis. Conclusion The pivotal gene ATF3 may participate in the inflammation and immune injury of LN through ferroptosis.
ObjectiveTo establish and validate the diagnostic model of ferroptosis genes for acute myocardial infarction (AMI) based on bioinformatics. MethodsFive AMI gene expression data were obtained from Gene Expression Omnibus (GEO), namely GSE66360, GSE48060, GSE60993, GSE83500, GSE34198. Among them, GSE66360 was used as the training set to perform differential analysis, and intersection of differential genes and ferroptosis genes was taken to obtain differentially expressed ferroptosis genes in AMI. GO and KEGG enrichment analysis was performed using Metascape website. Subsequently, random forest (RF) algorithm was used to screen out key genes with high classification performance according to the Keeny coefficient score, and artificial neural network (ANN) diagnostic model of AMI ferroptosis feature gene was constructed by model group GSE83500. The area under the receiver operating characteristic curve (AUC) of 10-fold cross-validation was used to evaluate the performance and generalization ability of the model, and 3 external independent datasets were used to verify the diagnostic performance of this model. The single sample gene setenrichment analysis was used to explore the difference in immune cell infiltration between infarcted myocardium and normal myocardium after AMI. In addition, correlation analysis between immune cells and key genes was also conducted. Finally, potential drugs that would prevent and treat AMI by regulating ferroptosis were screened out from the Coremin Medical platform. ResultsA total of 16 differentially expressed ferroptosis genes were obtained in the training set, GO enrichment analysis showed that they mainly participated in biological functions such as cellular response to biological stimuli and chemical stress, regulation of interleukin 17, etc. KEGG enrichment analysis showed that these genes were significantly enriched in NOD-like receptor signaling pathway, programmed cell necrosis, Leishmaniasis and other pathways. Four genes with good classification performance were screened out using RF algorithm, namely EPAS1, SLC7A5, FTH1, and ZFP36. The results of 10-fold cross-validation showed that the minimum AUC value was 0.746, the maximum value was 0.906, and the average value was 0.805. The AUC of the ANN model was 0.859, and the AUC values of the three independent validation sets were 0.763 (GSE48060), 0.673 (GSE60993), 0.698 (GSE34198). Immune cell infiltration found that macrophages, mast cells and monocytes were significantly active after AMI. Correlation analysis found that there were positive correlations between 4 key genes and activated dendritic cells, eosinophils and γδT cells. A total of 20 potential western medicines were predicted which could prevent and treat AMI by regulating ferroptosis, and the predicted potential Chinese medicine was mainly heat-clearing and detoxifying and blood-activating and removing blood stasis drugs. ConclusionThe identified AMI ferroptosis genes by bioinformatics method have certain diagnostic significance, which provides a reference for disease diagnosis and treatment.
Objective To observe the expression of hepcidin-ferroportin (FPN) pathway in adenine-induced chronic kidney disease (CKD) rat model and to explore the mechanism of its involvement in renal fibrosis in CKD. Methods A total of 20 6-week-old male SD rats without specific pathogen were selected. The rats were divided into control group and CKD group, with 10 rats in each group, using a simple random method. Rats were sacrificed at the end of the second and sixth weeks after modeling. The levels of serum creatinine (Scr), blood urea nitrogen (BUN) and 24 h urine protein quantification were measured. The pathological changes of rats were observed. The iron content of rat kidney tissue was detected by colorimetric method, and the level of serum hepcidin-25 was detected by enzyme linked immunosorbent assay method in both groups. Immunohistochemistry and reverse transcription-polymerase chain reaction were used to detect the renal protein and mRNA expression of α-smooth muscle actin (α-SMA), collagen type Ⅰ (Col-Ⅰ), FPN1, interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), nuclear factor kappa-B (NF-κB) P65. Results Compared with the control group, the levels of Scr, BUN, and 24 h urine protein quantification were higher in the CKD group at the end of the second and sixth weeks of modeling (P<0.05). The results of renal tissue staining showed that the CKD group had obvious glomerular structural disorders, tubular dilation, and interstitial collagen fiber deposition. Compared with the control group, the serum hepcidin-25 level and the iron content of kidney tissues in the CKD group were significantly higher, and correlation analysis suggested that both were positively correlated with the renal function of rats (P<0.05). Compared with the control group, the protein and mRNA expression levels of α-SMA, Col-Ⅰ, HAMP, IL-6, TNF-α, NF-κB P65 were higher (P<0.05), while FPN1 expression was lower in CKD group at the end of the second and sixth weeks of modeling (P<0.05). Correlation analysis results showed that HAMP mRNA expression was positively correlated with α-SMA, Col-Ⅰ, IL-6, TNF-α, and NF-κB p65 (P<0.001), which was negatively correlated with FPN1 mRNA expression (P<0.001). FPN1 mRNA expression was significantly negatively correlated with α-SMA, Col-Ⅰ (P<0.001). Conclusions Ferroptosis may be present in the adenine-induced rat model of CKD, and it may be involved in the process of renal fibrosis through the interaction of HAMP-FPN signaling pathway with the inflammatory response. Serum hepcidin-25 is expected to be a serological marker for the early diagnosis of CKD.
The morbidity and mortality of gallbladder cancer were rising. At present, there was no effective chemotherapy regimen, so it was of great practical significance to explore new therapy target. Ferroptosis is a non-apoptotic form of cell death characterized by iron-dependent lipid peroxidation and metabolic constraints. In recent years, it had become a research hotspot. Many studies had been carried out on the relevant biological mechanisms such as liver cancer, breast cancer, pancreatic cancer, and other cancer. At present, there are still few studies on ferroptosis in gallbladder cancer, and its relevant mechanisms need further in-depth analysis, which opens up a new research direction for exploring the treatment of gallbladder cancer.
Immunoglobulin A nephropathy (IgAN) is an immune-mediated chronic inflammatory disease with a complex pathogenesis and diverse clinical manifestations. Currently, there is no specific treatment plan. Programmed cell death is an active and orderly way of cell death controlled by genes in the body, which maintains the homeostasis of the body and the development of organs and tissues by participating in various molecular signaling pathways. In recent years, programmed cell death has played an important regulatory role in the occurrence and development of IgAN, involving complex signaling pathways. Under pathological conditions, it may relieve kidney damage through various pathways such as reducing oxidative stress, inhibiting inflammation, and improving energy metabolism. This article provides a review of the research progress of IgAN in apoptosis, autophagy, pyroptosis, ferroptosis,and cuproptosis in order to provide new therapeutic targets for IgAN.
Objective To summarize the papers about the research status and prospects of ferroptosis in hepatocellular carcinoma (HCC) and its drug resistance in recent years in order to provide directions and ideas for the treatment of HCC. Method The relevant literatures at home and abroad in recent years about ferroptosis in HCC and its drug resistance were reviewed. Results The mechanism of ferroptosis in the development and drug resistance of HCC was complicated, involving multiple protein and molecular pathways. Ferroptosis played an important role in improving chemotherapy and sorafenib resistance, and it had a broad application prospect in HCC. Conclusions The molecular mechanism of ferroptosis in HCC and its drug resistance has not been fully elucidated. Further research on the mechanism of ferroptosis in HCC may provide new molecular therapeutic targets for HCC. Ferroptosis has a broad application prospect in the treatment of HCC.
Objective To investigate the regulatory role of PLA2G4A targeting in ferroptosis and its sensitizing effect on the ferroptosis inducer Erastin. Methods PLA2G4A expression in lung adenocarcinoma (LUAD) was assessed by analyzing data from The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium databases, followed by immunohistochemical validation. PLA2G4A expression was knocked down in H1299 lung cancer cells using small interfering RNA. The correlation between PLA2G4A and ferroptosis marker genes was examined through gene correlation analysis and Western blotting. The regulatory relationship between PLA2G4A and ferrous ion (Fe2+) was analyzed using high-content fluorescence imaging. Cell proliferation after PLA2G4A inhibition and Erastin treatment was measured by CCK-8 assay. Flow cytometry and high-content fluorescence imaging were employed to evaluate the effects of PLA2G4A suppression combined with Erastin on intracellular Fe2+ and lipid peroxidation levels. Results Both mRNA (P<0.05) and protein (P<0.001) levels of PLA2G4A were significantly upregulated in LUAD tissues, and its high expression was associated with poor prognosis in LUAD patients (P<0.05). PLA2G4A expression was positively correlated with SLC7A11 expression (r=0.23, P<0.001). PLA2G4A knockdown suppressed SLC7A11 protein expression and increased cellular Fe2+ levels (P<0.01). Compared with the control group, PLA2G4A-silenced cells exhibited significantly reduced viability upon Erastin treatment (P<0.001). Furthermore, Erastin enhanced PLA2G4A targeting-induced Fe2+ accumulation and lipid peroxidation (P<0.001). Conclusion Targeting PLA2G4A induces ferroptosis in lung cancer cells by inhibiting SLC7A11 expression and enhances their sensitivity to Erastin.
Objective To identify genes of lipopolysaccharide (LPS) -induced acute lung injury (ALI) in mice base on bioinformatics and machine learning. Methods The acute lung injury dataset (GSE2411, GSE111241 and GSE18341) were download from the Gene Expression Database (GEO). Differential gene expression analysis was conducted. Gene ontology (GO) analysis, KEGG pathway analysis, GSEA enrichment analysis and protein-protein interaction analysis (PPI) network analysis were performed. LASSO-COX regression analysis and Support Vector Machine Expression Elimination (SVM-RFE) was utilized to identify key biomarkers. Receiver operator characteristic curve was used to evaluate the diagnostic ability. Validation was performed in GSE18341. Finally, CIBERSORT was used to analyze the composition of immune cells, and immunocorrelation analysis of biomarkers was performed. Results A total of 29 intersection DEGs were obtained after the intersection of GSE2411 and GSE111241 differentially expressed genes. Enrichment analysis showed that differential genes were mainly involved in interleukin-17, cytokine - cytokine receptor interaction, tumor necrosis factor and NOD-like receptor signaling pathways. Machine learning combined with PPI identified Gpx2 and Ifi44 were key biomarkers. Gpx2 is a marker of ferroptosis and Ifi44 is an type I interferon-induced protein, both of which are involved in immune regulation. Immunocorrelation analysis showed that Gpx2 and Ifi44 were highly correlated with Neutrophils, TH17 and M1 macrophage cells. Conclusion Gpx2 and Ifi44 have potential immunomodulatory abilities, and may be potential biomarkers for predicting and treating ALI in mince.
Neuropathic pain (NP) is a pathological state caused by damage or disease to the somatosensory nervous system. Programmed cell death (PCD) is an orderly process of cell death regulated by both intrinsic signals and external stimuli. In recent years, an increasing number of studies have shown that PCD plays a key regulatory role in the pathogenesis of NP. This article reviews the molecular mechanisms of various types of PCD and their specific roles in NP, in order to provide new research directions for the prevention, diagnosis, and treatment of NP.