Disulfide Stress Research Articles

The role of molecular subtypes and immune infiltration characteristics based on disulfidptosis-related genes in ovarian cancer

Ovarian cancer (OC) is the most fatal, gynecological malignancy. Compared with advanced ovarian cancer, the 5 year survival rate of early ovarian cancer is significantly improved, and predicting early detection and diagnosis is very important to improve the prognosis of OC. Recent research has found a new way of cell death: disulfidptosis. Under glucose starvation, abnormal accumulation of disulfide molecules such as Cystine in SLC7A11 overexpression cells induced disulfide stress to trigger cell death. Studies of disulfidptosis are still in their infancy and its role in ovarian cancer progression is unclear. In this study, we used a public database to detect the expression and mutations of disulfidptosis-related genes in OC. Cluster analysis was performed based on disulfidptosis-related genes, and disulfidptosis differential expression genes were analyzed. A prognostic risk model was constructed using three disulfidptosis-related genes, and the reasons for differences in prognosis were explored through immune infiltration analysis and drug sensitivity analysis. The prognostic characteristics of transcriptome based on disulfidptosis-related genes are closely related to the prognosis of OC patients. Finally, quantitative polymerase chain reaction (RT-qPCR) was used to detect the expression of three prognostic genes in clinical OC samples.Our study establishes a link between disulfidptosis and OC, providing new ideas for personalized and precise treatment of OC.

Thioredoxin reductase inhibition and glutathione depletion mediated by glaucocalyxin A promote intracellular disulfide stress in gastric cancer cells.

Thioredoxin reductase 1 (TXNRD1) has been identified as one of the promising chemotherapeutic targets in cancer cells. Therefore, a novel TXNRD1 inhibitor could accelerate chemotherapy in clinical anticancer research. In this study, glaucocalyxin A (GlauA), a natural diterpene extracted from Rabdosia japonica var. glaucocalyx, was identified as a novel inhibitor of TXNRD1. We found that GlauA effectively inhibited recombinant TXNRD1 and reduced its activity in gastric cancer cells without affecting the enzyme's expression level. Mechanistically, the selenocysteine residue (U498) of TXNRD1 was irreversibly modified by GlauA through a Michael addition. Additionally, GlauA formed a covalent adduct with glutathione (GSH) and disrupted cellular redox balance by depleting cellular GSH. The inhibition of TXNRD1 and depletion of GSH by GlauA conferred its cytotoxic effects in spheroid culture and Transwell assays in AGS cells. The disulfide stress induced cytotoxicity of GlauA could be mitigated by adding reducing agents, such as DTT and β-ME. Furthermore, the FDA-approval drug auranofin, a TXNRD1 inhibitor, triggered oligomerization of the cytoskeletal protein Talin-1 in AGS cells, indicating that inhibiting TXNRD1 triggered disulfide stress. In conclusion, this study uncovered GlauA as an efficient inhibitor of TXNRD1 and demonstrated the potential of TXNRD1 inhibition as an effective anticancer strategy by disrupting redox homeostasis and inducing disulfide stress.

Inducing disulfidptosis in tumors:potential pathways and significance.

Regulated cell death (RCD) is crucial for the elimination of abnormal cells. In recent years, strategies aimed at inducing RCD, particularly apoptosis, have become increasingly important in cancer therapy. However, the ability of tumor cells to evade apoptosis has led to treatment resistance and relapse, prompting extensive research into alternative death processes in cancer cells. A recent study identified a novel form of RCD known as disulfidptosis, which is linked to disulfide stress. Cancer cells import cystine from the extracellular environment via solute carrier family 7 member 11 (SLC7A11) and convert it to cysteine using nicotinamide adenine dinucleotide phosphate (NADPH). When NADPH is deficient or its utilization is impaired, cystine accumulates, leading to the formation of disulfide bonds in the actin cytoskeleton, triggering disulfidptosis. Disulfidptosis reveals a metabolic vulnerability in tumors, offering new insights into cancer therapy strategies. This review provides a detailed overview of the mechanisms underlying disulfidptosis, the current research progress, and limitations. It also highlights innovative strategies for inducing disulfidptosis and explores the potential of combining these approaches with traditional cancer therapies, particularly immunotherapy, to expedite clinical translation.

Identification of Disulfidptosis-Related LncRNA Subtypes, Establishment of a Prognostic Signature, and Characterization of Immune Infiltration in Ovarian Cancer.

Ovarian Cancer (OC) is a lethal malignant tumor with a poor prognosis. Disulfidptosis is a newly identified form of cell death caused by disulfide stress. Targeting disulfidptosis is a new metabolic therapeutic strategy in cancer treatment. We aimed to establish a disulfidptosis- related lncRNA signature for prognosis prediction and explore its treatment values in OC patients. Data from the TCGA and GTEx databases and a disulfidptosis gene set were used to establish a disulfidptosis-related lncRNA signature for prognosis prediction in OC patients. Then, we internally and externally (PCR) validated our model. We also built a nomogram to improve our model's predictive power. Afterward, GSEA was employed to explore our model's potential functions. The ESTIMATE, CIBERSORT, TIMER, and ssGSEA were applied to estimate the immune landscape. Finally, the drug sensitivity of certain drugs for OC patients was analyzed. We built a prognosis model based on seven drlncRNAs, including AL157871.2, HCP5, AC027348.1, AL109615.3, AL928654.1, LINC02585, and AC011445.1. Our model performed well by internal validation. PCR data also confirmed the same trend in the lncRNA levels. Furthermore, the nomogram-integrated age, grade, stage, and risk score could accurately predict the survival outcomes of OC patients. Subsequently, GSEA unveiled that our model genes enriched the Hedgehog signaling pathway, a key regulator in OC tumorigenesis. Our predictive signature was associated with immune checkpoints, such as PD-1(P < 0.01), PD-L1(P < 0.001), and CTLA4 (P < 0.01), which might help screen out OC patients who are sensitive to immunotherapy. Small molecule drugs, such as AZD-2281, GDC-0449, imatinib, and nilotinib, might benefit OC patients with different risk scores. Our disulfidptosis-related lncRNA signature comprised of AL157871.2, HCP5, AC027348.1, AL109615.3, AL928654.1, LINC02585, and AC011445.1 could serve as a prognostic biomarker and guidance to therapy response for OC patients.

Classification and functional analysis of disulfidptosis-associated genes in sepsis.

Sepsis represents a critical condition characterized by multiple-organ dysfunction resulting from inflammatory response to infection. Disulfidptosis is a newly identified type of programmed cell death that is intimately associated with the actin cytoskeleton collapse caused by glucose starvation and disulfide stress, but its role in sepsis is largely unknown. The study was to adopt a diagnostic and prognostic signature for sepsis with disulfidptosis based on the differentially expressed genes (DEGs) between sepsis and healthy people from GEO database. The disulfidptosis hub genes associated with sepsis were identified, and then developed consensus clustering and immune infiltration characteristics. Next, we evaluated disulfidptosis-related risk genes by using LASSO and Random Forest algorithms, and constructed the diagnostic sepsis model by nomogram. Finally, immune infiltration, GSVA analysis and mRNA-miRNA networks based on disulfidptosis-related DEGs were screened. There are five upregulated disulfidptosis-related genes and seven downregulated genes were filtered out. The six intersection disulfidptosis-related genes including LRPPRC, SLC7A11, GLUT, MYH9, NUBPL and GYS1 exhibited higher predictive ability for sepsis with an accuracy of 99.7%. In addition, the expression patterns of the critical genes were validated. The study provided a comprehensive view of disulfidptosis-based signatures to predict the prognosis, biological features and potential treatment directions for sepsis.

REDOX SIGNALLING IN THE PANCREAS IN HEALTH AND DISEASE.

This review addresses oxidative stress and redox signaling in the pancreas under physiological conditions as well as in acute pancreatitis, chronic pancreatitis, pancreatic cancer, and diabetes. Physiological redox homeodynamics is maintained mainly by NRF2/KEAP1, NF-κB, protein tyrosine phosphatases, peroxisome proliferator-activated receptor-γ co-activator 1α (PGC1α), and normal autophagy. Depletion of reduced glutathione in the pancreas is a hallmark of acute pancreatitis and is initially accompanied by disulfide stress, which is characterized by protein cysteinylation without increased glutathione oxidation. A cross-talk between oxidative stress, MAPKs, and NF-κB amplifies the inflammatory cascade, acting PP2A and PGC1α as key redox regulatory nodes. In acute pancreatitis, nitration of cystathionine-β synthase causes blockade of the trans-sulfuration pathway leading to increased homocysteine levels, whereas p53 triggers necroptosis in the pancreas through downregulation of sulfiredoxin, PGC1α, and peroxiredoxin 3. Chronic pancreatitis exhibits oxidative distress mediated by NADPH oxidase 1 and/or CYP2E1, which promotes cell death, fibrosis, and inflammation. Oxidative stress cooperates with mutant KRAS to initiate and promote pancreatic adenocarcinoma. Mutant KRAS increases mitochondrial ROS, which trigger acinar-to-ductal metaplasia and progression to PanIN. ROS are maintained at sufficient level to promote cell proliferation, whilst avoiding cell death or senescence through formation of NADPH and GSH, and activation of NRF-2, HIF-1/2α, and CREB. Redox signalling also plays a fundamental role in differentiation, proliferation, and insulin secretion of β-cells. However, ROS overproduction promotes β-cell dysfunction and apoptosis in type 1 and type 2 diabetes.

Disulfidptosis signature predicts immune microenvironment and prognosis of gastric cancer

BackgroundDisulfidptosis is a newly identified mechanism of cell death triggered by disulfide stress. Thus, gaining a comprehensive understanding of the disulfidptosis signature present in gastric cancer (GC) could greatly enhance the development of personalized treatment strategies for this disease.MethodsWe employed consensus clustering to identify various subtypes of disulfidptosis and examined the distinct tumor microenvironment (TME) associated with each subtype. The Disulfidptosis (Dis) score was used to quantify the subtype of disulfidptosis in each patient. Subsequently, we assessed the predictive value of Dis score in terms of GC prognosis and immune efficacy. Finally, we conducted in vitro experiments to explore the impact of Collagen X (COL10A1) on the progression of GC.ResultsTwo disulfidptosis-associated molecular subtypes (Discluster A and B) were identified, each with distinct prognosis, tumor microenvironment (TME), immune cell infiltration, and biological pathways. Discluster A, characterized by high expression of disulfidptosis genes, exhibited a high immune score but poor prognosis. Furthermore, the Dis score proved useful in predicting the prognosis and immune response in GC patients. Those in the low Dis score group showed better prognosis and increased sensitivity to immunotherapy. Finally, our experimental findings validated that downregulation of COL10A1 expression attenuates the proliferation and migration capabilities of GC cells while promoting apoptosis.ConclusionsThis study demonstrates that the disulfidptosis signature can assist in risk stratification and personalized treatment for patients with GC. The results offer valuable theoretical support for anti-tumor strategies.

Unraveling pathogenesis, biomarkers and potential therapeutic agents for endometriosis associated with disulfidptosis based on bioinformatics analysis, machine learning and experiment validation

BackgroundEndometriosis (EMs) is an enigmatic disease of yet-unknown pathogenesis. Disulfidptosis, a novel identified form of programmed cell death resulting from disulfide stress, stands a chance of treating diverse ailments. However, the potential roles of disulfidptosis-related genes (DRGs) in EMs remain elusive. This study aims to thoroughly explore the key disulfidptosis genes involved in EMs, and probe novel diagnostic markers and candidate therapeutic compounds from the aspect of disulfidptosis based on bioinformatics analysis, machine learning, and animal experiments.ResultsEnrichment analysis on key module genes and differentially expressed genes (DEGs) of eutopic and ectopic endometrial tissues in EMs suggested that EMs was closely related to disulfidptosis. And then, we obtained 20 and 16 disulfidptosis-related DEGs in eutopic and ectopic endometrial tissue, respectively. The protein-protein interaction (PPI) network revealed complex interactions between genes, and screened nine and ten hub genes in eutopic and ectopic endometrial tissue, respectively. Furthermore, immune infiltration analysis uncovered distinct differences in the immunocyte, human leukocyte antigen (HLA) gene set, and immune checkpoints in the eutopic and ectopic endometrial tissues when compared with health control. Besides, the hub genes mentioned above showed a close correlation with the immune microenvironment of EMs. Furthermore, four machine learning algorithms were applied to screen signature genes in eutopic and ectopic endometrial tissue, including the binary logistic regression (BLR), the least absolute shrinkage and selection operator (LASSO), the support vector machine-recursive feature elimination (SVM-RFE), and the extreme gradient boosting (XGBoost). Model training and hyperparameter tuning were implemented on 80% of the data using a ten-fold cross-validation method, and tested in the testing sets which determined the excellent diagnostic performance of these models by six indicators (Sensitivity, Specificity, Positive Predictive Value, Negative Predictive Value, Accuracy, and Area Under Curve). And seven eutopic signature genes (ACTB, GYS1, IQGAP1, MYH10, NUBPL, SLC7A11, TLN1) and five ectopic signature genes (CAPZB, CD2AP, MYH10, OXSM, PDLIM1) were finally identified based on machine learning. The independent validation dataset also showed high accuracy of the signature genes (IQGAP1, SLC7A11, CD2AP, MYH10, PDLIM1) in predicting EMs. Moreover, we screened 12 specific compounds for EMs based on ectopic signature genes and the pharmacological impact of tretinoin on signature genes was further verified in the ectopic lesion in the EMs murine model.ConclusionThis study verified a close association between disulfidptosis and EMs based on bioinformatics analysis, machine learning, and animal experiments. Further investigation on the biological mechanism of disulfidptosis in EMs is anticipated to yield novel advancements for searching for potential diagnostic biomarkers and revolutionary therapeutic approaches in EMs.

Comprehensive analysis of disulfidptosis-related genes reveals the effect of disulfidptosis in ulcerative colitis

Ulcerative colitis (UC) is a chronic inflammatory condition of the intestinal tract. Various programmed cell death pathways in the intestinal mucosa are crucial to the pathogenesis of UC. Disulfidptosis, a recently identified form of programmed cell death, has not been extensively reported in the context of UC. This study evaluated the expression of disulfidptosis-related genes (DRGs) in UC through public databases and assessed disulfide accumulation in the intestinal mucosal tissues of UC patients and dextran sulfate sodium (DSS)-induced colitis mice via targeted metabolomics. We utilized various bioinformatics techniques to identify UC-specific disulfidptosis signature genes, analyze their potential functions, and investigate their association with immune cell infiltration in UC. The mRNA and protein expression levels of these signature genes were confirmed in the intestinal mucosa of DSS-induced colitis mice and UC patients. A total of 24 DRGs showed differential expression in UC. Our findings underscore the role of disulfide stress in UC. Four UC-related disulfidptosis signature genes—SLC7A11, LRPPRC, NDUFS1, and CD2AP—were identified. Their relationships with immune infiltration in UC were analyzed using CIBERSORT, and their expression levels were validated by quantitative real-time PCR and western blotting. This study provides further insights into their potential functions and explores their links to immune infiltration in UC. In summary, disulfidptosis, as a type of programmed cell death, may significantly influence the pathogenesis of UC by modulating the homeostasis of the intestinal mucosal barrier.

Therapeutic and prognostic effect of disulfidptosis-related genes in lung adenocarcinoma

Disulfidptosis, a new form of cell death, may be induced by disulfide stress associated with cystine disulfide buildup, which can promote cell toxicity, leading to cell death. Nevertheless, the role of direct prognosis and the mechanism underlying the regulation of disulfidptosis-related genes (DRGs) in lung adenocarcinoma (LUAD) are still unknown. This study aimed to investigate the role of DRGs in LUAD prognosis and diagnosis through multiomics analysis. First, copy number variations (CNVs) and mutations in the 10 genes were assessed. Considering that five differentially expressed genes (DEGs) were associated with disulfidptosis, a novel DRG score that can be utilized to anticipate LUAD prognosis was developed. Next, the generated receiver operating characteristic (ROC) and survival curves demonstrated that the model had an excellent predictive quality in LUAD in both the training and validation cohorts. Meanwhile, substantial functional disparities between the high DRG group and the low DRG group were observed, and the second gap mitosis (G2M) checkpoint, E2 promoter-binding factor (E2F) targets, and myelocytomatosis (MYC) target activities were consistently higher in the high DRG group than in the low DRG group. Additionally, the T-cell dysfunction score and tumor inflammation signature (Merck18) were negatively correlated with DRGs, whereas myeloid-derived suppressor cells (MDSCs) were positively correlated with DRGs. Moreover, DRGs were negatively linked to most of the immunological checkpoints. Meanwhile, samples of low DRGs benefited more from immune checkpoint blockade (ICB). The correlation analysis between DRGs and clinical characteristics revealed increasing malignancy with increasing DRG scores. Drug sensitization experiment results indicated that sensitivity to cisplatin, vincristine, docetaxel, and gemcitabine was higher in the high DRG group than in the low DRG group. The function of model genes in LUAD was also verified using immunohistochemistry, quantitative reverse transcription-polymerase chain reaction (qRT-PCR), western blotting, 5-ethynyl-2′-deoxyuridine (EDU), and clonogenic formation.

Disulfidptosis: A Novel Prognostic Criterion and Potential Treatment Strategy for Diffuse Large B-Cell Lymphoma (DLBCL).

Diffuse Large B-cell Lymphoma (DLBCL), with its intrinsic genetic and epigenetic heterogeneity, exhibits significantly variable clinical outcomes among patients treated with the current standard regimen. Disulfidptosis, a novel form of regulatory cell death triggered by disulfide stress, is characterized by the collapse of cytoskeleton proteins and F-actin due to intracellular accumulation of disulfides. We investigated the expression variations of disulfidptosis-related genes (DRGs) in DLBCL using two publicly available gene expression datasets. The initial analysis of DRGs in DLBCL (GSE12453) revealed differences in gene expression patterns between various normal B cells and DLBCL. Subsequent analysis (GSE31312) identified DRGs strongly associated with prognostic outcomes, revealing eight characteristic DRGs (CAPZB, DSTN, GYS1, IQGAP1, MYH9, NDUFA11, NDUFS1, OXSM). Based on these DRGs, DLBCL patients were stratified into three groups, indicating that (1) DRGs can predict prognosis, and (2) DRGs can help identify novel therapeutic candidates. This study underscores the significant role of DRGs in various biological processes within DLBCL. Assessing the risk scores of individual DRGs allows for more precise stratification of prognosis and treatment strategies for DLBCL patients, thereby enhancing the effectiveness of clinical practice.

Disulfidptosis-related genes serve as potential prognostic biomarkers and indicate tumor microenvironment characteristics and immunotherapy response in prostate cancer

Disulfidptosis, a newly identified programmed cell death pathway in prostate cancer (PCa), is closely associated with intracellular disulfide stress and glycolysis. This study aims to elucidate the roles of disulfidptosis-related genes (DRGs) in the pathogenesis and progression of PCa, with the goal of improving diagnostic and therapeutic approaches. We analyzed PCa datasets and normal tissue transcriptome data from TCGA, GEO, and MSKCC. Using consensus clustering analysis and LASSO regression, we developed a risk scoring model, which was validated in an independent cohort. The model's predictive accuracy was confirmed through Kaplan–Meier curves, receiver operating characteristic (ROC) curves, and nomograms. Additionally, we explored the relationship between the risk score and immune cell infiltration, and examined the tumor microenvironment and somatic mutations across different risk groups. We also investigated responses to immunotherapy and drug sensitivity. Our analysis identified two disulfidosis subtypes with significant differences in survival, immune environments, and treatment responses. According to our risk score, the high-risk group exhibited poorer progression-free survival (PFS) and higher tumor mutational burden (TMB), associated with increased immune suppression. Functional enrichment analysis linked high-risk features to key cancer pathways, including the IL-17 signaling pathway. Moreover, drug sensitivity analysis revealed varied responses to chemotherapy, suggesting the potential for disulfidosis-based personalized treatment strategies. Notably, we identified PROK1 as a crucial prognostic marker in PCa, with its reduced expression correlating with disease progression. In summary, our study comprehensively assessed the clinical implications of DRGs in PCa progression and prognosis, offering vital insights for tailored precision medicine approaches.

Disulfidptosis-related lncRNAs signature predicting prognosis and immunotherapy effect in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is a prevalent malignant tumor worldwide, with high incidence and mortality rates. However, there is still a lack of specific and sensitive biomarkers for its early diagnosis and targeted treatment. Disulfidptosis is a newly identified mode of cell death that is characteristic of disulfide stress. Therefore, exploring the correlation between disulfidptosis-related long non-coding RNAs (DRGs-lncRNAs) and patient prognosis can provide new molecular targets for LUAD patients. The study analysed the transcriptome data and clinical data of LUAD patients in The Cancer Genome Atlas (TCGA) database, gene co-expression, and univariate Cox regression methods were used to screen for DRGs-lncRNAs related to prognosis. The risk score model of lncRNA was established by univariate and multivariate Cox regression models. TIMER, CIBERSORT, CIBERSORT-ABS, and other methods were used to analyze immune infiltration and further evaluate immune function analysis, immune checkpoints, and drug sensitivity. Real-time polymerase chain reaction (RT-PCR) was performed to detect the expression of DRGs-lncRNAs in LUAD cell lines. A total of 108 lncRNAs significantly associated with disulfidptosis were identified. A prognostic model was constructed by screening 10 lncRNAs with independent prognostic significance through single-factor Cox regression analysis, LASSO regression analysis, and multiple-factor Cox regression analysis. Survival analysis of patients through the prognostic model showed that there were obvious survival differences between the high- and low-risk groups. The risk score of the prognostic model can be used as an independent prognostic factor independent of other clinical traits, and the risk score increases with stage. Further analysis showed that the prognostic model was also different from tumor immune cell infiltration, immune function, and immune checkpoint genes in the high- and low-risk groups. Chemotherapy drug susceptibility analysis showed that high-risk patients were more sensitive to Paclitaxel, 5-Fluorouracil, Gefitinib, Docetaxel, Cytarabine, and Cisplatin. Additionally, RT-PCR analysis demonstrated differential expression of DRGs-lncRNAs between LUAD cell lines and the human bronchial epithelial cell line. The prognostic model of DRGs-lncRNAs constructed in this study has certain accuracy and reliability in predicting the survival prognosis of LUAD patients, and provides clues for the interaction between disulfidptosis and LUAD immunotherapy.

Developing a prognostic model using machine learning for disulfidptosis related lncRNA in lung adenocarcinoma

Disulfidptosis represents a novel cell death mechanism triggered by disulfide stress, with potential implications for advancements in cancer treatments. Although emerging evidence highlights the critical regulatory roles of long non-coding RNAs (lncRNAs) in the pathobiology of lung adenocarcinoma (LUAD), research into lncRNAs specifically associated with disulfidptosis in LUAD, termed disulfidptosis-related lncRNAs (DRLs), remains insufficiently explored. Using The Cancer Genome Atlas (TCGA)-LUAD dataset, we implemented ten machine learning techniques, resulting in 101 distinct model configurations. To assess the predictive accuracy of our model, we employed both the concordance index (C-index) and receiver operating characteristic (ROC) curve analyses. For a deeper understanding of the underlying biological pathways, we referred to the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) for functional enrichment analysis. Moreover, we explored differences in the tumor microenvironment between high-risk and low-risk patient cohorts. Additionally, we thoroughly assessed the prognostic value of the DRLs signatures in predicting treatment outcomes. The Kaplan–Meier (KM) survival analysis demonstrated a significant difference in overall survival (OS) between the high-risk and low-risk cohorts (p < 0.001). The prognostic model showed robust performance, with an area under the ROC curve exceeding 0.75 at one year and maintaining a value above 0.72 in the two and three-year follow-ups. Further research identified variations in tumor mutational burden (TMB) and differential responses to immunotherapies and chemotherapies. Our validation, using three GEO datasets (GSE31210, GSE30219, and GSE50081), revealed that the C-index exceeded 0.67 for GSE31210 and GSE30219. Significant differences in disease-free survival (DFS) and OS were observed across all validation cohorts among different risk groups. The prognostic model offers potential as a molecular biomarker for LUAD prognosis.

The CssRS two-component system of Bacillus subtilis contributes to teicoplanin and polymyxin B response.

CssRS is a two-component system that plays a pivotal role in mediating the secretion stress response in Bacillus subtilis. This system upregulates the synthesis of membrane-bound HtrA family proteases that cope with misfolded proteins that accumulate within the cell envelope as a result of overexpression or heat shock. Recent studies have shown the induction of CssRS-regulated genes in response to cell envelope stress. We investigated the induction of the CssRS-regulated htrA promoter in the presence of different cell wall- and membrane-active substances and observed induction of the CssRS-controlled genes by glycopeptides (vancomycin and teicoplanin), polymyxins B and E, certain β-lactams, and detergents. Teicoplanin was shown to elicit remarkably stronger induction than vancomycin and polymyxin B. Teicoplanin and polymyxin B induced the spxO gene expression in a CssRS-dependent fashion, resulting in increased activity of Spx, a master regulator of disulfide stress in Bacillus subtilis. The CssRS signaling pathway and Spx activity were demonstrated to be involved in Bacillus subtilis resistance to teicoplanin and polymyxin B.

Establishment of disulfidptosis-related LncRNA signature as biomarkers in colon adenocarcinoma.

Metabolic reprogramming is a hallmark of cancer and plays a key role in precision oncology treatment. Long non-coding RNAs (lncRNAs) regulate cancer cell behavior, including metabolism. Disulfidptosis, a newly identified form of regulated cell death triggered by glucose starvation, has yet to be fully understood in colon adenocarcinoma (COAD). This study aimed to confirm the existence and role of disulfidptosis in COAD and identify disulfidptosis-related lncRNAs that may be targeted to induce disulfidptosis in COAD. PI and F-actin staining were used to observe disulfidptosis in COAD cell lines. Disulfidptosis-related lncRNAs were identified based on the expression of disulfidptosis-associated genes in the TCGA-COAD database. A four-lncRNA signature for disulfidptosis was established. Subsequently, loss-of-function assays explored the roles of AC013652.1 and MCM3AP-AS1 in disulfidptosis. Disulfidptosis was observed in COAD cells under glucose starvation and could be reversed by agents that prevent disulfide stress, such as dithiothreitol (DTT) and tris-(2-carboxyethyl)-phosphine (TCEP). The prognostic value of disulfidptosis-associated genes in COAD patients was confirmed, with higher expression indicating longer survival. A disulfidptosis-related lncRNA signature comprising four lncRNAs was established based on the expression of these genes. Among these, AC013652.1 and MCM3AP-AS1 predicted worse prognoses. Furthermore, inhibiting AC013652.1 or MCM3AP-AS1 increased disulfidptosis-associated gene expression and cellular death, which could be reversed by DTT and TCEP. This study provides hitherto undocumented evidence of the existence of disulfidptosis and the prognostic value of disulfidptosis-associated genes in COAD. Importantly, we identified lncRNAs AC013652.1 and MCM3AP-AS1, which suppress disulfidptosis and may serve as potential therapeutic targets for COAD.

RNA sequencing-based approaches to identifying disulfidptosis-related diagnostic clusters and immune landscapes in osteoporosis.

Disulfidptosis, a newly recognized cell death triggered by disulfide stress, has garnered attention for its potential role in osteoporosis (OP) pathogenesis. Although sulfide-related proteins are reported to regulate the balance of bone metabolism in OP, the precise involvement of disulfidptosis regulators remains elusive. Herein, leveraging the GSE56815 dataset, we conducted an analysis to delineate disulfidptosis-associated diagnostic clusters and immune landscapes in OP. Subsequently, vertebral bone tissues obtained from OP patients and controls were subjected to RNA sequencing (RNA-seq) for the validation of key disulfidptosis gene expression. Our analysis unveiled seven significant disulfidptosis regulators, including FLNA, ACTB, PRDX1, SLC7A11, NUBPL, OXSM, and RAC1, distinguishing OP samples from controls. Furthermore, employing a random forest model, we identified four diagnostic disulfidptosis regulators including FLNA, SLC7A11, NUBPL, and RAC1 potentially predictive of OP risk. A nomogram model integrating these four regulators was constructed and validated using the GSE35956 dataset, demonstrating promising utility in clinical decision-making, as affirmed by decision curve analysis. Subsequent consensus clustering analysis stratified OP samples into two different disulfidptosis subgroups (clusters A and B) using significant disulfidptosis regulators, with cluster B exhibiting higher disulfidptosis scores and implicating monocyte immunity, closely linked to osteoclastogenesis. Notably, RNA-seq analysis corroborated the expression patterns of two disulfidptosis modulators, PRDX1 and OXSM, consistent with bioinformatics predictions. Collectively, our study sheds light on disulfidptosis patterns, offering potential markers and immunotherapeutic avenues for future OP management.

Prognostic Model Construction of Disulfidptosis-Related Genes and Targeted Anticancer Drug Research in Pancreatic Cancer.

Pancreatic cancer stands as one of the most lethal malignancies, characterized by delayed diagnosis, high mortality rates, limited treatment efficacy, and poor prognosis. Disulfidptosis, a recently unveiled modality of cell demise induced by disulfide stress, has emerged as a critical player intricately associated with the onset and progression of various cancer types. It has emerged as a promising candidate biomarker for cancer diagnosis, prognosis assessment, and treatment strategies. In this study, we have effectively established a prognostic risk model for pancreatic cancer by incorporating multiple differentially expressed long non-coding RNAs (DElncRNAs) closely linked to disulfide-driven cell death. Our investigation delved into the nuanced relationship between the DElncRNA-based predictive model for disulfide-driven cell death and the therapeutic responses to anticancer agents. Our findings illuminate that the high-risk subgroup exhibits heightened susceptibility to the small molecule compound AZD1208, positioning it as a prospective therapeutic agent for pancreatic cancer. Finally, we have elucidated the underlying mechanistic potential of AZD1208 in ameliorating pancreatic cancer through its targeted inhibition of the peroxisome proliferator-activated receptor-γ (PPARG) protein, employing an array of comprehensive analytical methods, including molecular docking and molecular dynamics (MD) simulations. This study explores disulfidptosis-related genes, paving the way for the development of targeted therapies for pancreatic cancer and emphasizing their significance in the field of oncology. Furthermore, through computational biology approaches, the drug AZD1208 was identified as a potential treatment targeting the PPARG protein for pancreatic cancer. This discovery opens new avenues for exploring targets and screening drugs for pancreatic cancer.

Identification of a disulfidptosis-related lncRNA signature for the prognostic and immune landscape prediction in head and neck squamous cell carcinoma

PurposeDisulfidptosis, a newly identified form of cell death, is triggered by disulfide stress. Herein, a unique signature was developed based on disulfidptosis-related lncRNAs (DRlncRNAs) for the prognostic and immune landscape prediction of head and neck squamous cell carcinoma (HNSCC).MethodsTranscriptome, somatic mutation, and clinical data were acquired at The Cancer Genome Atlas database. Individuals were partitioned into training and test cohorts at a 1:1 ratio to facilitate the development of a DRlncRNA signature using the least absolute shrinkage and selection operation method. Based on the median risk score, all HNSCC individuals were stratified into the high-risk group (HRG) and low-risk group (LRG). Kaplan–Meier survival and time-dependent receiver operating characteristic (ROC) analyses were used to estimate the prognostic value, and a nomogram was generated for survival prediction. To provide a more comprehensive assessment, the tumor microenvironment, functional enrichment, immune cell infiltration, and immunotherapeutic sensitivity were explored between LRG and HRG.ResultsA DRlncRNA signature was established with 10 DRlncRNAs. The corresponding values of areas under the ROC curves for 1–, 3–, and 5–year overall survival were 0.710, 0.692, and 0.640. A more favorable prognosis was noted in the patients with lower risk, along with higher immune scores, increased immune-related functions, and immune cell infiltration, as well as improved response to the immunotherapeutic intervention in comparison with individuals at higher risk.ConclusionThese findings demonstrate that the developed DRlncRNA signature holds promise as a reliable prognostic marker and predictor of immunotherapy response in HNSCC patients.

Integrated multiomic analysis reveals disulfidptosis subtypes in glioblastoma: implications for immunotherapy, targeted therapy, and chemotherapy.

Glioblastoma (GBM) presents significant challenges due to its malignancy and limited treatment options. Precision treatment requires subtyping patients based on prognosis. Disulfidptosis, a novel cell death mechanism, is linked to aberrant glucose metabolism and disulfide stress, particularly in tumors expressing high levels of SLC7A11. The exploration of disulfidptosis may provide a new perspective for precise diagnosis and treatment of glioblastoma. Transcriptome sequencing was conducted on samples from GBM patients treated at Tiantan Hospital (January 2022 - December 2023). Data from CGGA and TCGA databases were collected. Consensus clustering based on disulfidptosis features categorized GBM patients into two subtypes (DRGclusters). Tumor immune microenvironment, response to immunotherapy, and drug sensitivity were analyzed. An 8-gene disulfidptosis-based subtype predictor was developed using LASSO machine learning algorithm and validated on CGGA dataset. Patients in DRGcluster A exhibited improved overall survival (OS) compared to DRGcluster B. DRGcluster subtypes showed differences in tumor immune microenvironment and response to immunotherapy. The predictor effectively stratified patients into high and low-risk groups. Significant differences in IC50 values for chemotherapy and targeted therapy were observed between risk groups. Disulfidptosis-based classification offers promise as a prognostic predictor for GBM. It provides insights into tumor immune microenvironment and response to therapy. The predictor aids in patient stratification and personalized treatment selection, potentially improving outcomes for GBM patients.