Cell Death-related Genes Research Articles

Identification of hub programmed cell death-related genes and immune infiltration in Crohn’s disease using bioinformatics

BackgroundCrohn’s disease (CD) is an immune-mediated disorder characterized by immune cell infiltration that induces persistent chronic inflammation of the gastrointestinal tract. Programmed cell death (PCD) plays a critical role in the pathogenesis of CD. This study identified vital PCD-related genes in CD based on immune infiltration using bioinformatic analysis.MethodsWe obtained two CD datasets from the Gene Expression Omnibus (GEO) database and examined immune cell infiltration to investigate immune cell dysregulation in CD. PCD-related genes were retrieved from the GeneCards database. Based on the differentially expressed genes (DEGs) and PCD gene sets, PCD-related DEGs were identified. Candidate hub genes were identified using a protein-protein interaction (PPI) network, and their diagnostic effectiveness was predicted using receiver operating characteristic (ROC) curve analysis. Functional enrichment and immune infiltration analyses were used to assess the distinct roles of the hub genes. Finally, the miRWalk and ENCORI databases were used to predict which microRNAs (miRNAs) regulated the hub genes and to verify gene expression in CD colonic tissues via transcriptome sequencing.ResultsA total of 335 PCD-related DEGs and 3 hub genes (MMP1, SAA1, and PLAU) were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional analyses indicated the enrichment of these genes in the immune response. Infiltration analysis of immune cells showed abundant endothelial cells, plasma cells, dendritic cells, and monocytes in the CD samples. Based on the correlation analysis, the three hub genes were positively correlated with monocytes and negatively correlated with CD8 naïve T-cells. MMP1, SAA1, and PLAU correlated with the pathogenicity of CD and had good diagnostic value for CD. The three hub genes were highly expressed in the CD tissues, as confirmed using transcriptome sequencing.ConclusionThis study identified MMP1, SAA1, and PLAU as hub genes involved in PCD in patients with CD. These genes regulate immune cell function and their expression levels are closely related to immune cell infiltration. These findings provide novel insights into the mechanisms underlying CD pathogenesis. The identified PCD genes and regulatory miRNAs are potential biomarkers and therapeutic targets for CD.

Development and validation of a prognostic risk score model for hepatocellular carcinoma in the Asian population based on immunogenic cell death-related genes

BackgroundHepatocellular carcinoma (HCC), the predominant form of liver cancer, is marked by limited therapeutic success and unfavorable prognoses. Its etiology varies regionally, with hepatitis B virus (HBV) being the predominant cause in most of Asia. Immunogenic cell death (ICD), a specific type of cell death, has been extensively linked to HCC treatment in numerous studies. This research aims to explore the significance of ICD-related genes in the Asian HCC cohort, potentially offering novel approaches for HCC management.MethodsWe initially obtained transcriptomic and clinical data pertinent to Asian HCC from the TCGA database. Subsequently, we classified the samples into distinct subgroups according to ICD gene expression levels and conducted analyses of the tumor microenvironment and enrichment. Furthermore, we randomly allocated the samples into training and testing cohorts, thereafter developing and validating an ICD gene-based prognostic model tailored for the Asian HCC population.ResultsThe Asian HCC samples were categorized into two subgroups: high and low ICD expression. In the low ICD expression group, we observed diminished infiltration of immune and stromal cells, increased tumor purity, and improved prognosis. Moreover, we devised a 5-gene risk-score prognostic model comprising BAX, CASP8, HMGB1, HSP90AA1, and IL6, demonstrating efficacy in prognostic predictions for the Asian HCC cohort.ConclusionOur investigation unveils new perspectives on the influence of ICDs within Asian HCC populations. The derived 5-gene risk-score prognostic model, based on ICDs, not only serves as a tool for assessing prognosis in Asian HCC cases but also suggests potential therapeutic targets for HCC treatment.

The landscape of immunogenic cell death-related genes predicts the overall survival and immune infiltration status of Non-small-cell Lung Carcinoma

The landscape of immunogenic cell death-related genes predicts the overall survival and immune infiltration status of Non-small-cell Lung Carcinoma

The overlooked shadow killer: Elizabethkingia anophelis as a potent pathogen in American bullfrogs (Aquarana catesbeiana)

Elizabethkingia anophelis has garnered significant attention due to its rising prevalence in clinical settings, yet its impact on animal infections remains underexplored. In this study, we isolated the strain AWYL-1 from infected American bullfrogs (Aquarana catesbeiana) and conducted comprehensive characterization of its biological properties. Morphologically and biochemically, AWYL-1 resembled the known bullfrog pathogen Elizabethkingia miricola, and exhibited multidrug resistance, being sensitive only to doxycycline. AWYL-1 demonstrated high virulence, with a 100 % mortality rate in bullfrogs at a concentration of 1 × 109 CFU/mL within 14 days, regardless of the infection route, including immersion and intramuscular injection. Gene expression analysis revealed significant upregulation of immune-related genes (e.g., P65, CXCL10, IFNγ) and cell death-related genes (e.g., BNIP3, Caspase-8) in the kidney and spleen, particularly at 72 h post-infection. Furthermore, experimental infection in mice resulted in a 50 % mortality rate, confirming the cross-species infection potential of AWYL-1. Histopathological examination revealed multi-organ hyperemia, edema, and inflammation, with significant pathological changes in the liver, spleen, kidneys, and lungs. These findings suggest that E. anophelis poses a zoonotic risk, capable of infecting both amphibians and mammals. This study provides critical data for the prevention and control of E. anophelis infections in aquaculture, highlighting the need for ongoing vigilance and further research into its potential impact on public health.

BcIRF5 activates bcTBK1 phosphorylation to enhance PANoptosis during GCRV infection

TBK1 is an important IFN antiviral signalling factor, and in previous work black carp TBK1 (bcTBK1) and black carp IRF5 (bcIRF5) together promoted cell death in GCRV-infected cells. In this research, bcTBK1 and bcIRF5 were investigated both in vivo and in vitro to delineate their individual and combined functions. This study demonstrated that both bcTBK1 and bcIRF5 expressions were modulated in response to GCRV infection across the intestine, gill, kidney and spleen. In bcgill cells, overexpression of bcTBK1 and bcIRF5 initially suppressed the expression of cell death-related genes, including RIPK1, caspase1, caspase3 and bax, but this suppression was negated upon GCRV infection. In vivo, mRNA expression levels of RIPK1 and related genes varied by tissue following bcTBK1 or bcIRF5 overexpression and GCRV infection. Notably, intracellular co-overexpression of bcTBK1 and bcIRF5 led to significant upregulation of caspase3, caspase1, bax, and IL1β, along with enhanced caspase3 activity post-GCRV infection. This co-expression correlated with higher survival rates in black carp during GCRV infection and increased caspase3 mRNA in the spleen and gills. Hematoxylin-eosin (HE) staining indicated disorganized spleen tissue and edematous, hyperplastic gill changes in co-transfected groups after infection. TUNEL staining of tissue sections showed that DNA breakage was significantly stronger in the co-transfected group than in the other groups during GCRV infection. Further phosphorylation experiments showed that bcIRF5 promoted phosphorylation modification of bcTBK1. Thus, these data suggest that bcIRF5 activates bcTBK1 by enhancing its phosphorylation and promotes PANoptosis in GCRV-infected cells.

Myelination potential and injury susceptibility of grey versus white matter human oligodendrocytes.

Increasing evidence indicates heterogeneity in functional and molecular properties of oligodendrocyte lineage cells both during development and under pathologic conditions. In multiple sclerosis, remyelination of grey matter lesions exceeds that in white matter. Here we used cells derived from grey matter versus white matter regions of surgically resected human brain tissue samples, to compare the capacities of human A2B5-positive progenitor cells and mature oligodendrocytes to ensheath synthetic nanofibers, and relate differences to the molecular profiles of these cells. For both cell types, the percentage of ensheathing cells was greater for grey matter versus white matter cells. For both grey matter and white matter samples, the percentage of cells ensheathing nanofibers was greater for A2B5-positive cells versus mature oligodendrocytes. Grey matter A2B5-positive cells were more susceptible than white matter A2B5-positive cells to injury induced by metabolic insults. Bulk RNA sequencing indicated that separation by cell type (A2B5-positive vs mature oligodendrocytes) is more significant than by region but segregation for each cell type by region is apparent. Molecular features of grey matter versus white matter derived A2B5-positive and mature oligodendrocytes were lower expression of mature oligodendrocyte genes and increased expression of early oligodendrocyte lineage genes. Genes and pathways with increased expression in grey matter derived cells with relevance for myelination included those related to responses to external environment, cell-cell communication, cell migration, and cell adhesion. Immune and cell death related genes were up-regulated in grey matter derived cells. We observed a significant number of up-regulated genes shared between the stress/injury and myelination processes, providing a basis for these features. In contrast to oligodendrocyte lineage cells, no functional or molecular heterogeneity was detected in microglia maintained in vitro, likely reflecting the plasticity of these cells ex vivo. The combined functional and molecular data indicate that grey matter human oligodendrocytes have increased intrinsic capacity to myelinate but also increased injury susceptibility, in part reflecting their being at a stage earlier in the oligodendrocyte lineage.

Integrative analysis of multiple cell death model for precise prognosis and drug response prediction in gastric cancer.

Gastric cancer (GC) is a common upper gastrointestinal tumor. However, the evaluation of prognosis and treatment response in patients with gastric cancer remains a challenge. Programmed cell death (PCD) is one of the important terminal paths for the cells of metazoans, and is involved in a variety of biological events that include morphogenesis, maintenance of tissue homeostasis, and elimination of harmful cells. The objective of this project is to investigate the predictive significance of cell death pathways and create prognostic signatures associated to cell death, with the purpose of forecasting prognosis and providing guidance for the treatment of gastric cancer. Gene transcription profiles and corresponding clinical data of gastric cancer patients were collected from The Cancer Genome Atlas (TCGA-STAD, n = 448) and the Gene Expression Comprehensive Database (GSE84437, n = 483). Thirteen types of cell death-related genes, including apoptosis, necroptosis, pyroptosis, ferroptosis, autophagy, cuprotosis, parthanatos, entotic cell death, netotic cell death, lysosome-dependent cell death, alkaliptosis, oxeiptosis, and disulfidptosis, were analysed. Cell death-related genes associated with prognosis were identified in the TCGA-STAD training cohort using Lasso-Cox regression to generate a risk score. Patients were categorized into high and low-risk groups based on the median risk score for survival difference analysis. Cell death-related genes associated with prognosis were identified in the TCGA-STAD training cohort using Lasso-Cox regression to generate a risk score. Additionally, the response to immunotherapy in the high-risk and low-risk groups was calculated using the oncoPredict algorithm. Futhermore, the model genes were validated in the GEO validation set. A total of 324 differential programmed cell death (PCD)-related genes were identified, and 65 were selected through single-factor Cox analysis. Six PCD-related genes were ultimately identified by Lasso regression to construct a prognostic risk score model. The log-rank test revealed that patients in the high-risk group had inferior survival time compared with those in the low-risk group. The area under the ROC curve (AUC) for the training group at years 1, 3, and 5 were 0.684, 0.713, 0.743, respectively, while the AUC for the validation cohort at years 1, 3, and 5 were 0.695, 0.704, and 0.707, respectively. Unsupervised clustering identified potential subtypes included in the model, and a survival difference was also observed between the two subgroups. Multifactor Cox results, combined with clinical information, demonstrated that the prognostic risk score can serve as an independent prognostic factor, irrespective of other clinical features. By comprehensively analyzing multiple cell death patterns, we have established a novel model that accurately forecasts the clinical prognosis and drug sensitivity of gastric cancer. It was found that all 12 representative drugs may not be suitable for patients in high-risk groups.

Unveiling the impact of cell death-related genes and immune dynamics on drug resistance in lung adenocarcinoma: a risk score model and functional insights

Lung adenocarcinoma (LUAD), characterized by its heterogeneity and complex pathogenesis, is the focus of this study which investigates the association between cell death-related genes and LUAD. Through machine learning, a risk score model was developed using the Coxboost rsf algorithm, demonstrating strong prognostic accuracy in both validation (GSE30219, GSE31210, GSE72094) and training (TCGA-LUAD) datasets with C-indices of 0.93, 0.67, 0.68, and 0.64, respectively. The study reveals that the expression of Keratin 18 (KRT18), a key cytoskeletal protein, varies across LUAD cell lines (DV-90, PC-9, A549) compared to normal bronchial epithelial cells (BEAS-2B), suggesting its potential role in LUAD's pathogenesis. Kaplan–Meier survival curves further validate the model, indicating longer survival in the low-risk group. A comprehensive analysis of gene expression, functional differences, immune infiltration, and mutations underscores significant variations between risk groups, highlighting the high-risk group's immunological dysfunction. This points to a more intricate tumor immune environment and the possibility of alternative therapeutic strategies. The study also delves into drug sensitivity, showing distinct responses between risk groups, underscoring the importance of risk stratification in treatment decisions for LUAD patients. Additionally, it explores KRT18's epigenetic regulation and its correlation with immune cell infiltration and immune regulatory molecules, suggesting KRT18's significant role in the tumor immune landscape. This research not only offers a valuable prognostic tool for LUAD but also illuminates the complex interplay between cell death-related genes, drug sensitivity, and immune infiltration, positioning KRT18 as a potential therapeutic or prognostic target to improve patient outcomes by personalizing LUAD treatment strategies.

Identification of Renal Ischemia-Reperfusion Injury Subtypes and Predictive Model for Graft Loss after Kidney Transplantation Based on Programmed Cell Death-Related Genes

Introduction: Ischemia-reperfusion injury (IRI) is detrimental to kidney transplants and may contribute to poor long-term outcomes of transplantation. Programmed cell death (PCD), a regulated cell death form triggered by IRI, is often indicative of an unfavorable prognosis following transplantation. However, given the intricate pathophysiology of IRI and the considerable variability in clinical conditions during kidney transplantation, the specific patterns of cell death within renal tissues remain ambiguous. Consequently, accurately predicting the outcomes for transplanted kidneys continues to be a formidable challenge. Methods: Eight Gene Expression Omnibus datasets of biopsied transplanted kidney samples post-IRI and 1,548 PCD-related genes derived from 18 PCD patterns were collected in our study. Consensus clustering was performed to identify distinct IRI subtypes based on PCD features (IRI PCD subtypes). Differential enrichment analysis of cell death, metabolic signatures, and immune infiltration across these subtypes was evaluated. Three machine learning algorithms were used to identify PCD patterns related to prognosis. Genes associated with graft loss were screened for each PCD type. A predictive model for graft loss was constructed using 101 combinations of 10 machine learning algorithms. Results: Four IRI subtypes were identified: PCD-A, PCD-B, PCD-C, and PCD-D. PCD-A, characterized by high enrichment of multiple cell death patterns, significant metabolic paralysis, and immune infiltration, showed the poorest prognosis among the four subtypes. While PCD-D involved the least kind of cell death patterns with the features of extensive activation of metabolic pathways and the lowest immune infiltration, correlating with the best prognosis in the four subtypes. Using various machine learning algorithms, 10 cell death patterns and 42 PCD-related genes were identified as positively correlated with graft loss. The predictive model demonstrated high sensitivity and specificity, with area under the curve values for 0.5-, 1-, 2-, 3-, and 4-year graft survival at 0.888, 0.91, 0.926, 0.923, and 0.923, respectively. Conclusion: Our study explored the comprehensive features of PCD patterns in transplanted kidney samples post-IRI. The prediction model shows great promise in forecasting graft loss and could aid in risk stratification in patients following kidney transplantation.

Machine learning-based cell death marker for predicting prognosis and identifying tumor immune microenvironment in prostate cancer

BackgroundProstate cancer (PCa) incidence and mortality rates are rising, necessitating precise prognostic tools to guide personalized treatment. Dysregulation of programmed cell death pathways in tumor suppression and cancer development has garnered increasing attention, providing a new research direction for identifying biomarkers and potential therapeutic targets. MethodsIntegrating multiple database resources, we constructed and optimized a prognostic signature based on the expression of programmed cell death-related genes (PCDRG) using ten machine learning algorithms. Model performance and prognostic effects were further evaluated. We analyzed the relationships between signature and clinicopathological features, somatic mutations, drug sensitivity, and the tumor immune microenvironment, and constructed a nomogram. The expression level of PCDRGs were evaluated and compared. ResultsOf 1560 PCDRGs, 149 were differentially expressed in PCa, with 34 associated with biochemical recurrence. The PCDRG-derived index (PCDI), constructed using the random forest algorithm, exhibited optimal prognostic performance, successfully stratifying PCa patients into two groups with significant prognostic differences. Patients with high PCDI scores exhibited poorer survival and lower immunotherapy benefit. PCDI was closely associated with the infiltration of specific immune cells, particularly positive correlations with macrophages and T helper cells, and negative correlations with neutrophils, suggesting that PCDI may influence the tumor immune microenvironment, thereby affecting patient prognosis and treatment response. PCDI was associated with age, pathological stage, somatic mutations, and drug sensitivity. The PCDI-based nomogram demonstrated excellent performance in predicting biochemical recurrence in PCa patients. Finally, the differential expression of these PCDRGs was verified based on cell lines and PCa patient expression profile data. ConclusionThis study developed an effective prognostic indicator for prostate cancer, PCDI, using machine learning approaches. PCDI reflects the link between aberrant programmed cell death pathways and disease progression and treatment response.

The Fungicide Ipconazole Can Activate Mediators of Cellular Damage in Rat Brain Regions.

This study aimed to investigate the toxicity of the fungicide ipconazole on oxidative status, cell death and inflammasome complex activation in the hypothalamus, cerebral cortex, striatum and hippocampus of rats. Female albino rats were randomly divided into a control group and four groups treated with ipconazole at doses of 1, 5, 10 and 20 mg/kg b.w., administered for six days. Ipconazole significantly increased MDA and ROS levels in all brain regions studied, while reducing catalase enzyme activity. The molecular expression of cell death-related genes (AKT1, APAF1, BNIP3, CASP3 and BAX) and the inflammasome complex (CASP1, IL1β, IL6, NLRP3, NFĸB and TNFα) was also assessed, showing increased expression in at least one brain region. The findings demonstrate that ipconazole induces central nervous system toxicity in mammals, highlighting its potential role as a risk factor in the development of neurodegenerative disorders in individuals exposed to this contaminant.

Immunogenic cell death-related genes predict prognosis and response to immunotherapy in lung squamous cell carcinoma.

Lung squamous cell carcinoma (LUSC) is a malignancy with limited therapeutic options. Immunogenic cell death (ICD) has the potential to enhance the efficacy of cancer therapy by triggering immune responses. We aimed to explore the potential of ICD-based classification in predicting prognosis and response to immunotherapy for LUSC. RNA-seq information and clinical data of LUSC patients were obtained from The Cancer Genome Atlas (TCGA) dataset. ICD-related gene expressions in LUSC samples were analyzed by consensus clustering. Subsequently, differentially expressed genes (DEGs) between different ICD-related subsets were analyzed. Tumor mutation burden, immune cell infiltration, and survival analyses were conducted between different ICD subsets. Finally, an ICD-related risk signature was constructed and evaluated in LUSC patients, and the immunotherapy responses based on the gene expressions were also forecasted. ICD-high and ICD-low groups were defined, and 1466 DEGs were identified between the two subtypes. These DEGs were mainly enriched in collagen-containing extracellular matrix, cytokine-cytokine receptor interaction, the PI3K-Akt signaling pathway, and neuroactive ligand-receptor interaction. Furthermore, the ICD-low group exhibited a favorable prognosis, enhanced TTN and MUC16 mutation frequencies, increased infiltrating immune cells, and downregulated immune checkpoint expressions. Furthermore, we demonstrated that an ICD-related model (based on CD4, NLRP3, NT5E, and TLR4 genes) could forecast the prognosis of LUSC, and ICD risk scores were lower in the responder group. In summary, the predicted values of ICD-related genes (CD4, NLRP3, NT5E, and TLR4) for the prognosis and response to immunotherapy in LUSC were verified in the study, which benefits immunotherapy-based interventions for LUSC patients.

Rapid function analysis of OsiWAK1 using a Dual-Luciferase assay in rice

In the past decade, the exploration of genetic resources in rice has significantly enhanced the efficacy of rice breeding. However, the exploration of genetic resources is hindered by the identification of candidate genes. To expedite the identification of candidate genes, this study examined tapetum programmed cell death-related genes OsiWAK1, OsPDT1, EAT1, TDR, and TIP2 to assess the efficacy of the Dual-Luciferase (Dual-LUC) assay in rapidly determining gene relationships. The study found that, in the Dual-LUC assay, OsiWAK1 and its various recombinant proteins exhibit comparable activation abilities on the EAT1 promoter, potentially indicating a false positive. However, the Dual-LUC assay can reveal that OsiWAK1 impacts both the function of its upstream regulatory factor OsPDT1 and the TDR/TIP2 transcription complex. By rapidly studying the relationship between diverse candidate genes and regulatory genes in a well-known trait via the Dual-LUC assay, this study provides a novel approach to expedite the determination of candidate genes such as genome-wide association study.

Machine learning-based biomarker screening for acute myeloid leukemia prognosis and therapy from diverse cell-death patterns

Acute myeloid leukemia (AML) exhibits pronounced heterogeneity and chemotherapy resistance. Aberrant programmed cell death (PCD) implicated in AML pathogenesis suggests PCD-related signatures could serve as biomarkers to predict clinical outcomes and drug response. We utilized 13 PCD pathways, including apoptosis, pyroptosis, ferroptosis, autophagy, necroptosis, cuproptosis, parthanatos, entotic cell death, netotic cell death, lysosome-dependent cell death, alkaliptosis, oxeiptosis, and disulfidptosis to develop predictive models based on 73 machine learning combinations from 10 algorithms. Bulk RNA-sequencing, single-cell RNA-sequencing transcriptomic data, and matched clinicopathological information were obtained from the TCGA-AML, Tyner, and GSE37642-GPL96 cohorts. These datasets were leveraged to construct and validate the models. Additionally, in vitro experiments were conducted to substantiate the bioinformatics findings. The machine learning approach established a 6-gene pan-programmed cell death-related genes index (PPCDI) signature. Validation in two external cohorts showed high PPCDI associated with worse prognosis in AML patients. Incorporating PPCDI with clinical variables, we constructed several robust prognostic nomograms that accurately predicted prognosis of AML patients. Multi-omics analysis integrating bulk and single-cell transcriptomics revealed correlations between PPCDI and immunological features, delineating the immune microenvironment landscape in AML. Patients with high PPCDI exhibited resistance to conventional chemotherapy like doxorubicin but retained sensitivity to dasatinib and methotrexate (FDA-approved drugs for other leukemias), suggesting the potential of PPCDI to guide personalized therapy selection in AML. In summary, we developed a novel PPCDI model through comprehensive analysis of diverse programmed cell death pathways. This PPCDI signature demonstrates great potential in predicting clinical prognosis and drug sensitivity phenotypes in AML patients.

Subchronic intranasal lipopolysaccharide exposure induces pulmonary autoimmunity and glomerulonephritis in NZBWF1 mice

Lupus, a systemic autoimmune disease shaped by gene-environment interplay, often progresses to endstage renal failure. While subchronic systemic exposure to bacterial lipopolysaccharide (LPS) triggers autoimmunity and glomerulonephritis in lupus-prone mice, it is unknown if inhaling LPS, which is common in certain occupations, can similarly trigger lupus. Here we determined how subchronic intranasal (IN) LPS instillation influences autoimmunity and glomerulonephritis development in lupusprone NZBWF1 female mice. Briefly, mice were IN-instilled with vehicle or E. coli LPS (0.8 μg/g) twice weekly for 5 wk, followed by necropsy. For systemic comparison, additional cohorts of mice were injected with LPS intraperitoneally (IP) using identical doses/timing. Lungs were assessed for inflammatory and autoimmune responses and then related to systemic autoimmunity and glomerulonephritis. IN/LPS exposure induced in the lung: i) leukocyte infiltration, ii)mRNA signatures for cytokines, chemokines, IFN-regulated, and cell death-related genes, iii) ectopic lymphoid tissue formation, and iv)diverse IgM and IgG autoantibodies (AAbs). Pulmonary effects coincided with enlarged spleens, elevated plasma IgG AAbs, and inflamed IgG-containing kidney glomeruli. In contrast, IP/LPS treatment induced systemic autoimmunity and glomerulonephritis without pulmonary manifestations. Taken together, these preclinical findings suggest the lung could serve as a critical nexus for triggering autoimmunity by respirable LPS in genetically predisposed individuals.

A novel mitochondrial function-associated programmed cell death-related prognostic signature for predicting the prognosis of early breast cancer.

Purpose: To screen mitochondrial function-associated PCD-related biomarkers and construct a risk model for predicting the prognosis of early breast cancer. Methods: Data on gene expression levels and clinical information were obtained from the TCGA database, and GSE42568 and GSE58812 datasets were obtained from GEO database. The mitochondrial function-associated programmed cell death (PCD) related genes in early breast cancer were identified, then LASSO logistic regression, SVM-RFE, random forest (RF), and multiple Cox logistic regression analysis were employed to construct a prognostic risk model. Differences in immune infiltration, drug sensitivity, and immunotherapy response were evaluated between groups. Lastly, the qRT-PCR was employed to confirm the key genes. Results: Total 1,478 DEGs were screened between normal and early breast cancer groups, and these DEGs were involved in PI3K-Akt signaling pathway, focal adhesion, and ECM-receptor interaction pathways. Then total 178 mitochondrial function-associated PCD related genes were obtained, followed by a four mitochondrial function-associated PCD related genes prognostic model and nomogram were built. In addition, total 2 immune checkpoint genes were lowly expressed in the high-risk group, including CD47 and LAG3, and the fraction of some immune cells in high- and low-risk groups had significant difference, such as macrophage, eosinophil, mast cell, etc., and the Top3 chemotherapeutics with significant differences were included FH535, MK.2206, and bicalutamide. Finally, the qRT-qPCR results shown that the CREB3L1, CAPG, SPINT1 and GRK3 mRNA expression were in line with the bioinformatics analysis results. Conclusion: Four mitochondrial function-associated PCD-related genes were identified, including CREB3L1, CAPG, SPINT1, and GRK3, and the prognostic risk model and nomogram were established for predicting the survival of early breast cancer patient. The chemotherapeutics, containing FH535, MK.2206, and bicalutamide, might be used for early breast cancer.

Deciphering the tumour microenvironment of clear cell renal cell carcinoma: Prognostic insights from programmed death genes using machine learning.

Clear cell renal cell carcinoma (ccRCC), a prevalent kidney cancer form characterised by its invasiveness and heterogeneity, presents challenges in late-stage prognosis and treatment outcomes. Programmed cell death mechanisms, crucial in eliminating cancer cells, offer substantial insights into malignant tumour diagnosis, treatment and prognosis. This study aims to provide a model based on 15 types of Programmed Cell Death-Related Genes (PCDRGs) for evaluating immune microenvironment and prognosis in ccRCC patients. ccRCC patients from the TCGA and arrayexpress cohorts were grouped based on PCDRGs. A combination model using Lasso and SuperPC was constructed to identify prognostic gene features. The arrayexpress cohort validated the model, confirming its robustness. Immune microenvironment analysis, facilitated by PCDRGs, employed various methods, including CIBERSORT. Drug sensitivity analysis guided clinical treatment decisions. Single-cell data enabled Programmed Cell Death-Related scoring, subsequent pseudo-temporal and cell-cell communication analyses. A PCDRGs signature was established using TCGA-KIRC data. External validation in the arrayexpress cohort underscored the model's superiority over traditional clinical features. Furthermore, our single-cell analysis unveiled the roles of PCDRG-based single-cell subgroups in ccRCC, both in pseudo-temporal progression and intercellular communication. Finally, we performed CCK-8 assay and other experiments to investigate csf2. In conclusion, these findings reveal that csf2 inhibit the growth, infiltration and movement of cells associated with renal clear cell carcinoma. This study introduces a PCDRGs prognostic model benefiting ccRCC patients while shedding light on the pivotal role of programmed cell death genes in shaping the immune microenvironment of ccRCC patients.

Chaperone- and PTM-mediated activation of IRF1 tames radiation-induced cell death and the inflammatory response.

The key role of structural cells in immune modulation has been revealed with the advent of single-cell multiomics, but the underlying mechanism remains poorly understood. Here, we revealed that the transcriptional activation of interferon regulatory factor 1 (IRF1) in response to ionizing radiation, cytotoxic chemicals and SARS-CoV-2 viral infection determines the fate of structural cells and regulates communication between structural and immune cells. Radiation-induced leakage of mtDNA initiates the nuclear translocation of IRF1, enabling it to regulate the transcription of inflammation- and cell death-related genes. Novel posttranslational modification (PTM) sites in the nuclear localization sequence (NLS) of IRF1 were identified. Functional analysis revealed that mutation of the acetylation site and the phosphorylation sites in the NLS blocked the transcriptional activation of IRF1 and reduced cell death in response to ionizing radiation. Mechanistically, reciprocal regulation between the single-stranded DNA sensors SSBP1 and IRF1, which restrains radiation-induced and STING/p300-mediated PTMs of IRF1, was revealed. In addition, genetic deletion or pharmacological inhibition of IRF1 tempered radiation-induced inflammatory cell death, and radiation mitigators also suppressed SARS-CoV-2 NSP-10-mediated activation of IRF1. Thus, we revealed a novel cytoplasm-oriented mechanism of IRF1 activation in structural cells that promotes inflammation and highlighted the potential effectiveness of IRF1 inhibitors against immune disorders.

Bioinformatics analysis of signature genes related to cell death in keratoconus

Keratoconus is corneal disease in which the progression of conical dilation of cornea leads to reduced visual acuity and even corneal perforation. However, the etiology mechanism of keratoconus is still unclear. This study aims to identify the signature genes related to cell death in keratoconus and examine the function of these genes. A dataset of keratoconus from the GEO database was analysed to identify the differentially expressed genes (DEGs). A total of 3558 DEGs were screened from GSE151631. The results of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that they mainly involved in response to hypoxia, cell–cell adhesion, and IL-17 signaling pathway. Then, the cell death-related genes datasets were intersected with the above 3558 DEGs to obtain 70 ferroptosis-related DEGs (FDEGs), 32 autophagy-related DEGs (ADEGs), six pyroptosis-related DEGs (PDEGs), four disulfidptosis-related DEGs (DDEGs), and one cuproptosis-related DEGs (CDEGs). After using Least absolute shrinkage and selection operator (LASSO), Random Forest analysis, and receiver operating characteristic (ROC) curve analysis, one ferroptosis-related gene (TNFAIP3) and five autophagy-related genes (CDKN1A, HSPA5, MAPK8IP1, PPP1R15A, and VEGFA) were screened out. The expressions of the above six genes were significantly decreased in keratoconus and the area under the curve (AUC) values of these genes was 0.944, 0.893, 0.797, 0.726, 0.882 and 0.779 respectively. GSEA analysis showed that the above six genes mainly play an important role in allograft rejection, asthma, and circadian rhythm etc. In conclusion, the results of this study suggested that focusing on these genes and autoimmune diseases will be a beneficial perspective for the keratoconus etiology research.

Analysis of cell death-related genes to evaluate the prognostic and immunotherapeutic value in bladder cancer

To enhance therapeutic approaches, we created a distinctive pattern utilizing the cell demise indicator (CDI) to predict the effectiveness of immunotherapy in individuals with bladder carcinoma (BLCA). Hub prognostic CDIs were identified from the TCGA database using differential gene expression and survival analysis, encompassing 763 genes across 13 death modes. The subtype assessment was employed to evaluate the impact of these genes on the prognosis and immunotherapeutic outcomes in patients with BLCA. The LASSO regression method was used to identify significant CDIs, while Cox regression and nomogram analyses were conducted to explore the impact of CDIs on prognosis. CHMP4C and GSDMB were selected as the hub genes for the following research. Subsequently, These two central genes underwent further investigation to explore their association with immunotherapy, followed by an analysis of their potential regulatory network. Subtype analysis showed that these CDIs were significantly associated with the prognosis and immunotherapy of BLCA patients. The regulatory network in BLCA was evaluated through the establishment of the lncRNA XIST/NEAT1-CDIs-miR-146a-5p/miR-429 axis. Immunohistochemical analysis revealed a significant up-regulation of CHMP4C in bladder cancer tissues, which was strongly associated with an unfavorable prognosis for BLCA patients. Moreover, our findings provide compelling evidence that CHMP4C plays a pivotal role in promoting BLCA progression through the activation of the epithelial-mesenchymal transition (EMT) pathway. These findings highlight the negative impact of CHMP4C on BLCA patient prognosis, while also providing insights into the oncogenic mechanisms and immunotherapy in which CHMP4C may be involved.