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Background: Limited supply of certain nutrients and deregulation of nucleus pulposus (NP) plays a key role in the pathogenesis of intervertebral disc degeneration (IVDD). However, whether nutrient deprivation-induced cell death, particularly disulfidptosis, contributes to the depletion of NP cells and the development of IVDD, is unknown. Methods: RNA-seq, single-cell RNA-seq, and Genome-wide DNA methylation datasets of nucleus pulposus tissue were collected for bioinformatic analysis. Predictive models of disulfidptosis related genes in IVDD were constructed by machine learning and their differential expression was analyzed. In addition, we performed cell subsets identification analysis, cell-cell communications analysis, and functional enrichment analysis of key genes in the core subset based on single-cell RNA-seq data of NP tissues isolated from one normal sample and one IVDD sample. Finally, glucose deprivation-induced disulfidptosis in human NP cells (HNPCs) was verified by various cell death inhibitors and disulfidptosis-related molecular markers. Results: We found the disulfidptosis signal was significantly activated in the IVDD group. Using single-cell RNA-seq analysis, we focused on the chondrocytes and found that disulfidptosis-related genes significantly highly expressed in the IVDD C4 chondrocyte subset, which was identified as a new disulfidptosis-associated cell subset. Correlation analysis revealed the negative correlation between SLC7A11 (driving gene of disulfidptosis) and the glucose transporter GLUTs (SLC2A1-4) family genes (suppressing genes of disulfidptosis) in the IVDD group. We also found obvious cell death in HNPC upon glucose starvation, while employment of various cell death inhibitors could not inhibit glucose starvation-induced death in HNPCs. Moreover, the accumulation of disulfide bonds in cytoskeletal proteins was indicated by slowed migration in non-reducible protein blotting experiments. 2-DG, a key disulfidptosis inhibitor, significantly rescued cell death caused by glucose starvation through lowering the NADP+/NADPH ratio. Conclusions: We validated the occurrence of disulfidptosis in HPNCs and identified a novel disulfidptosis-associated cell subset, followed by experimental verification of disulfidptosis in a glucose-limited context to mimic a fall in nutrient supply during the development disc degeneration. These findings provided new insights into the pathological mechanisms of IVDD and encourage us to explore potential therapeutic targets involved in the regulation of disulfidptosis for the prevention of intervertebral disc degeneration. [ABSTRACT FROM AUTHOR]

Background: The pattern of cell death known as disulfidptosis was recently discovered. Disulfidptosis, which may affect the growth of tumor cells, represents a potential new approach to treating tumors. Glycolysis affects tumor proliferation, invasion, chemotherapy resistance, the tumor microenvironment (TME), and immune evasion. However, the efficacy and therapeutic significance of disulfidptosis-related glycolysis genes (DRGGs) in stomach adenocarcinoma (STAD) remain uncertain. Methods: STAD clinical data and RNA sequencing data were downloaded from the TCGA database. DRGGs were screened using Cox regression and Lasso regression analysis to construct a prognostic risk model. The accuracy of the model was verified using survival studies, receiver operating characteristic (ROC) curves, column plots, and calibration curves. Additionally, our study investigated the relationships between the risk scores and immune cell infiltration, tumor mutational burden (TMB), and anticancer drug sensitivity. Results: We have successfully developed a prognosis risk model with 4 DRGGs (NT5E, ALG1, ANKZF1, and VCAN). The model showed excellent performance in predicting the overall survival of STAD patients. The DRGGs prognostic model significantly correlated with the TME, immune infiltrating cells, and treatment sensitivity. Conclusions: The risk model developed in this work has significant clinical value in predicting the impact of immunotherapy in STAD patients and assisting in the choice of chemotherapeutic medicines. It can correctly estimate the prognosis of STAD patients. [ABSTRACT FROM AUTHOR]

Breast cancer (BC) is a heterogeneous malignancy with a dismal prognosis. Disulfidptosis is a novel type of regulated cell death that happens in the presence of glucose deficiency and is linked to the metabolic process of glycolysis. However, the mechanism of action of disulfidptosis and glycolysis‐related genes (DGRG) in BC, as well as their prognostic value in BC patients, remain unknown. After identifying the differentially expressed DGRG in normal and BC tissues, a number of machine learning algorithms were utilized to select essential prognostic genes to develop a model, including SLC7A11, CACNA1H, SDC1, CHST1, and TFF3. The expression characteristics of these genes were then examined using single‐cell RNA sequencing, and BC was classified into three clusters using "ConsensusClusterPlus" based on these genes. The DGRG model's median risk score can categorize BC patients into high‐risk and low‐risk groups. Furthermore, we investigated variations in clinical landscape, immunoinvasion analysis, tumor immune dysfunction and rejection (TIDE), and medication sensitivity in patients in the DGRG model's high‐ and low‐risk groups. Patients in the low‐risk group performed better on immunological and chemotherapeutic therapies and had lower TIDE scores. In conclusion, the DGRG model we developed has significant clinical application potential because it can accurately predict the prognosis of BC, TME, and pharmacological treatment responses. [ABSTRACT FROM AUTHOR]

Cell death resistance represents a hallmark of cancer. Recent studies have identified metabolic cell death as unique forms of regulated cell death resulting from an imbalance in the cellular metabolism. This review discusses the mechanisms of metabolic cell death—ferroptosis, cuproptosis, disulfidptosis, lysozincrosis, and alkaliptosis—and explores their potential in cancer therapy. Our review underscores the complexity of the metabolic cell death pathways and offers insights into innovative therapeutic avenues for cancer treatment. [ABSTRACT FROM AUTHOR]

Cell death is a fundamental physiological process in all living organisms. Processes such as embryonic development, organ formation, tissue growth, organismal immunity, and drug response are accompanied by cell death. In recent years with the development of electron microscopy as well as biological techniques, especially the discovery of novel death modes such as ferroptosis, cuprotosis, alkaliptosis, oxeiptosis, and disulfidptosis, researchers have been promoted to have a deeper understanding of cell death modes. In this systematic review, we examined the current understanding of modes of cell death, including the recently discovered novel death modes. Our analysis highlights the common and unique pathways of these death modes, as well as their impact on surrounding cells and the organism as a whole. Our aim was to provide a comprehensive overview of the current state of research on cell death, with a focus on identifying gaps in our knowledge and opportunities for future investigation. We also presented a new insight for macroscopic intracellular survival patterns, namely that intracellular molecular homeostasis is central to the balance of different cell death modes, and this viewpoint can be well justified by the signaling crosstalk of different death modes. These concepts can facilitate the future research about cell death in clinical diagnosis, drug development, and therapeutic modalities. [ABSTRACT FROM AUTHOR]

Background: Disulfidptosis is an emerging form of cellular death resulting from the binding of intracellular disulfide bonds to actin cytoskeleton proteins. This study aimed to investigate the expression and prognostic significance of hub disulfidptosis-related lncRNAs (DRLRs) in R0 resected hepatocellular carcinoma (HCC) as well as their impact on the malignant behaviour of HCC cells. Methods: A robust signature for R0 resected HCC was constructed using least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression and was validated in an independent internal validation cohort to predict the prognosis of R0 HCC patients. Comprehensive bioinformatics analysis was performed on the hub DRLRs (KDM4A-AS1, MKLN1-AS, and TMCC1-AS1), followed by experimental validation using quantitative real-time polymerase chain reaction (qRT‒PCR) and cellular functional assays. Results: The signature served as an independent prognostic factor applicable to R0 HCC patients across different age groups, tumour stages, and pathological characteristics. Gene Ontology (GO) and gene set enrichment analysis (GSEA) revealed hub pathways associated with this signature. The high-risk group presented an increased abundance of M0 macrophages and activated memory CD4 T cells as well as elevated macrophage and major histocompatibility complex (MHC) class I expression. High-risk R0 HCC patients also presented increased tumour immune dysfunction and exclusion scores (TIDEs), mutation frequencies, and tumour mutational burdens (TMBs). Drug sensitivity analysis revealed that high-risk patients were more responsive to drugs, including GDC0810 and osimertinib. High expression levels of the three hub DRLRs were detected in R0 HCC tissues and HCC cell lines. Functional assays revealed that the three hub DRLRs enhanced HCC cell proliferation, migration, and invasion. Conclusions: A signature was constructed on the basis of three DRLRs, providing novel insights for personalized precision therapy in R0 HCC patients. [ABSTRACT FROM AUTHOR]

Background: Disulfidptosis 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. Methods: We 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. Results: Two 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. Conclusions: This 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. [ABSTRACT FROM AUTHOR]

In recent times, disulfidptosis, an intricate form of cellular demise, has garnered attention due to its impact on prognosis, tumor progression and treatment response. Nevertheless, the exact significance of disulfidptosis-related genes (DisRGs) in glioblastoma (GBM) remains enigmatic. The GEO and TCGA databases provided transcriptional and clinically relevant data on tumor samples, while the GTEx database provided data on healthy tissues. Disulfidptosis-related genes (DisRGs) were procured from previous scholarly investigations. The expression profile of DisRGs was initially scrutinized among patients diagnosed with GBM, subsequent to which their prognostic value was explored. Through consensus clustering, we constructed DisRGs-related clusters and gene subtypes. Our results established that the DisRG-related clusters had differentially expressed genes, resulting in a DisulfidptosisScore model, which had a positive prognostic value. The differential expression profile of 24 DisRGs between GBM samples and healthy samples was acquired. Through consensus cluster analysis, two distinct disulfidptosis subtypes, namely DisRGcluster A and DisRGcluster B, were identified. Then, the DisulfidptosisScore model including 4 characteristic genes was constructed.Notably, patients with GBM assigned with lower score demonstrated a considerably longer overall survival (OS) compared to those with higher score. We have effectively devised a prognostic model associated with disulfidptosis, presenting autonomous prognostic predictions for patients with GBM. These findings serve as a valuable addition to the current comprehension of disulfidptosis and offer fresh theoretical substantiation for the development of enhanced treatment strategies. [ABSTRACT FROM AUTHOR]

Background: Osteoarthritis (OA) is a disabling and highly prevalent condition affecting millions worldwide. Recently discovered, disulfidptosis represents a novel form of cell death induced by the excessive accumulation of cystine. Despite its significance, a systematic exploration of disulfidptosis-related genes (DRGs) in OA is lacking. Methods: This study utilized three OA-related datasets and DRGs. Differentially expressed (DE)-DRGs were derived by intersecting the differentially expressed genes (DEGs) from GSE114007 with DRGs. Feature genes underwent screening through three machine learning algorithms. High diagnostic value genes were identified using the receiver operating characteristic curve. Hub genes were confirmed through expression validation. These hub genes were then employed to construct a nomogram and conduct enrichment, immune, and correlation analyses. An additional validation of hub genes was performed through in vitro cell experiments. Results: SLC3A2 and PDLIM1 were designated as hub genes, displaying excellent diagnostic performance. PDLIM1 exhibited low expression in early chondrocyte differentiation, rising significantly in the late stage, while SLC3A2 showed high overall expression, declining in the late differentiation stage. Cellular experiments corroborated the correlation of SLC3A2 and PDLIM1 with chondrocyte inflammation. Conclusions: Two hub genes, SLC3A2 and PDLIM1, were identified in relation to disulfidptosis, providing potential directions for diagnosing and treating OA. [ABSTRACT FROM AUTHOR]

Background: Endometriosis (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. Results: Enrichment 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. Conclusion: This 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. [ABSTRACT FROM AUTHOR]

Background: Disulfidptosis and ferroptosis are forms of programmed cell death that may be associated with the pathogenesis of periodontitis. Our study developed periodontitis-associated biomarkers combining disulfidptosis and ferroptosis, which provides a new perspective on the pathogenesis of periodontitis. Methods: Firstly, we obtained the periodontitis dataset from public databases and found disulfidptosis- and ferroptosis-related differentially expressed transcripts based on the disulfidptosis and ferroptosis transcript sets. After that, transcripts that are tissue biomarkers for periodontitis were found using three machine learning methods. We also generated transcript subclusters from two periodontitis microarray datasets: GSE16134 and GSE23586. Furthermore, three transcripts with the best classification efficiency were further screened. Their expression and classification efficacy were validated using qRT-PCR. Finally, periodontal clinical indicators of 32 clinical patients were collected, and the correlation between three transcripts above and periodontal clinical indicators was analyzed. Results: We identified six transcripts that are tissue biomarkers for periodontitis, the top three transcripts with the best classification, and delineated two expression patterns in periodontitis. Conclusions: Our study found that disulfidptosis and ferroptosis were associated with immune responses and may involve periodontitis genesis. [ABSTRACT FROM AUTHOR]

Objective: Disulfidptosis is a newly recognized form of regulated cell death that has been linked to cancer progression and prognosis. Despite this association, the prognostic significance, immunological characteristics and treatment response of disulfidptosis-related lncRNAs (DRLs) in ovarian cancer have not yet been elucidated. Methods: The lncRNA data and clinical information for ovarian cancer and normal samples were obtained from the UCSC XENA. Differential expression analysis and Pearson analysis were utilized to identify core DRLs, followed by LASSO algorithm. Random Survival Forest was used to construct a prognostic model. The relationships between risk scores, RNA methylation, immune cell infiltration, mutation, responses to immunotherapy and drug sensitivity analysis were further examined. Additionally, qRT-PCR experiments were conducted to validate the expression of the core DRLs in human ovarian cancer cells and normal ovarian cells and the scRNA-seq data of the core DRLs were obtained from the GEO dataset, available in the TISCH database. Results: A total of 8 core DRLs were obtained to construct a prognostic model for ovarian cancer, categorizing all patients into low-risk and high-risk groups using an optimal cutoff value. The AUC values for 1-year, 3-year and 5-year OS in the TCGA cohort were 0.785, 0.810 and 0.863 respectively, proving a strong predictive capability of the model. The model revealed the high-risk group patients exhibited lower overall survival rates, higher TIDE scores and lower TMB levels compared to the low-risk group. Variations in immune cell infiltration and responses to therapeutic drugs were observed between the high-risk and low-risk groups. Besides, our study verified the correlations between the DRLs and RNA methylation. Additionally, qRT-PCR experiments and single-cell RNA sequencing data analysis were conducted to confirm the significance of the core DRLs at both cellular and scRNA-seq levels. Conclusion: We constructed a reliable and novel prognostic model with a DRLs cluster for ovarian cancer, providing a foundation for further researches in the management of this disease. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Objective To establish a scoring model for predicting the prognosis and drug sensitivity of colorectal cancer (CRC) based on the expression of disulfidptosis-related genes by bioinformatics analyses combined with the validation with CRC patient-derived organoids (CRC-PDOs). Methods NMF (nonnegative Matrix Factorization) algorithm, Cox and LASSO regression analyses were used to identify disulfidptosis-related genes with predictive value for CRC prognosis, and disulfidptosis-related risk scoring formula was constructed. The differential genes and enrichment pathways among different clusters were analyzed by GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes). The sensitivity of the high/low-risk clusters of CRC patients to chemotherapy drugs was predicted using the GDSC database and validated using CRC-PDOs. Results The results of NMF algorithm showed that CRC patients could be grouped into two clusters based on the disulfidptosis-related genes. COX regression analysis demonstrated that LRPPRC and SLC7A11 were the only two genes with significance to predict the prognosis of CRC patients (P=0.047, 0.033). Low expression of SLC7A11 or high expression of LRPPRC in tumors of CRC patients was significantly correlated with overall survival (OS) (P=0.004, 0.003). Based on LASSO regression analysis, the mortality risk scoring formula for disulfidptosis was as follows: Risk score=LRPPRC× (-0.670 5) +SLC7A11×0.311 2, and the GSE161158 dataset could be regrouped into high-risk and low-risk clusters accordingly. There were 125 differentially expressed genes (DEGs) between the two clusters. According to the GO and KEGG results, the up-regulated genes in high-risk cluster were mainly enriched in immune regulation, such as leukocyte chemotaxis, granulocyte migration and toll-like receptor binding. Low-risk cluster was characterized by pathways associated with sulfide metabolism, such as sulfur compound transmembrane transporter activity. Based on the GDSC database, the expression level of SLC7A11 and LRPPRC could predict chemotherapy drug sensitivity. As a representative, the efficacy of chemotherapy drug (irinotecan) on inhibiting the growth of CRC-PDOs was shown to be linearly correlated with the relative gene expression levels of SLC7A11 and LRPPRC in CRC tissues of patients (P=0.007, 0.040). Conclusion According to the results based on the bioinformatics analyses and drug sensitivity testing on CRC-PDOs, disulfidptosis risk score could predict the prognosis and drug sensitivity of CRC patients, with potential clinical application prospect. [ABSTRACT FROM AUTHOR]

Disulfidptosis, a regulated form of cell death, has been recently reported in cancers characterized by high SLC7A11 expression, including invasive breast carcinoma, lung adenocarcinoma, and hepatocellular carcinoma. However, its role in colon adenocarcinoma (COAD) has been infrequently discussed. In this study, we developed and validated a prognostic model based on 20 disulfidptosis-related genes (DRGs) using LASSO and Cox regression analyses. The robustness and practicality of this model were assessed via a nomogram. Subsequent correlation and enrichment analysis revealed a relationship between the risk score, several critical cancer-related biological processes, immune cell infiltration, and the expression of oncogenes and cell senescence-related genes. POU4F1, a significant component of our model, might function as an oncogene due to its upregulation in COAD tumors and its positive correlation with oncogene expression. In vitro assays demonstrated that POU4F1 knockdown noticeably decreased cell proliferation and migration but increased cell senescence in COAD cells. We further investigated the regulatory role of the DRG in disulfidptosis by culturing cells in a glucose-deprived medium. In summary, our research revealed and confirmed a DRG-based risk prediction model for COAD patients and verified the role of POU4F1 in promoting cell proliferation, migration, and disulfidptosis. [ABSTRACT FROM AUTHOR]

Abstract Background Limited supply of certain nutrients and deregulation of nucleus pulposus (NP) plays a key role in the pathogenesis of intervertebral disc degeneration (IVDD). However, whether nutrient deprivation-induced cell death, particularly disulfidptosis, contributes to the depletion of NP cells and the development of IVDD, is unknown. Methods RNA-seq, single-cell RNA-seq, and Genome-wide DNA methylation datasets of nucleus pulposus tissue were collected for bioinformatic analysis. Predictive models of disulfidptosis related genes in IVDD were constructed by machine learning and their differential expression was analyzed. In addition, we performed cell subsets identification analysis, cell-cell communications analysis, and functional enrichment analysis of key genes in the core subset based on single-cell RNA-seq data of NP tissues isolated from one normal sample and one IVDD sample. Finally, glucose deprivation-induced disulfidptosis in human NP cells (HNPCs) was verified by various cell death inhibitors and disulfidptosis-related molecular markers. Results We found the disulfidptosis signal was significantly activated in the IVDD group. Using single-cell RNA-seq analysis, we focused on the chondrocytes and found that disulfidptosis-related genes significantly highly expressed in the IVDD C4 chondrocyte subset, which was identified as a new disulfidptosis-associated cell subset. Correlation analysis revealed the negative correlation between SLC7A11 (driving gene of disulfidptosis) and the glucose transporter GLUTs (SLC2A1-4) family genes (suppressing genes of disulfidptosis) in the IVDD group. We also found obvious cell death in HNPC upon glucose starvation, while employment of various cell death inhibitors could not inhibit glucose starvation-induced death in HNPCs. Moreover, the accumulation of disulfide bonds in cytoskeletal proteins was indicated by slowed migration in non-reducible protein blotting experiments. 2-DG, a key disulfidptosis inhibitor, significantly rescued cell death caused by glucose starvation through lowering the NADP+/NADPH ratio. Conclusions We validated the occurrence of disulfidptosis in HPNCs and identified a novel disulfidptosis-associated cell subset, followed by experimental verification of disulfidptosis in a glucose-limited context to mimic a fall in nutrient supply during the development disc degeneration. These findings provided new insights into the pathological mechanisms of IVDD and encourage us to explore potential therapeutic targets involved in the regulation of disulfidptosis for the prevention of intervertebral disc degeneration. Graphical Abstract

Hongwei Xiang,* Bin Shen,* Chunmei Zhang, Rui Li Department of Ultrasound, The Third Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China*These authors contributed equally to this workCorrespondence: Rui Li, Email raylee7991@hospital.cqmu.edu.cnIntroduction: Cell death regulation holds a unique value in the field of cancer therapy. Recently, disulfidptosis has garnered substantial scientific attention. Previous studies have reported that sonodynamic therapy (SDT) based on reactive oxygen species (ROS) can regulate cancer cell death, achieving an limited anti-cancer effect. However, the integration of SDT with disulfidptosis as an anti-cancer strategy has not been extensively developed. In this study, we constructed an artificial membrane disulfidptosis sonosensitizer, specifically, a nanoliposome (SC@lip) coated with a combination of the chemotherapy medicine Sorafenib (Sora) and sonosensitizer Chlorin e6 (Ce6), to realize a one-stop enhanced SDT effect that induces disulfidptosis-like cancer cell death.Methods: Sorafenib and Ce6 were co-encapsulated into PEG-modified liposomes, and SC@Lip was constructed using a simple rotary evaporation phacoemulsification method. The cell phagocytosis, ROS generation ability, glutathione (GSH) depletion ability, lipid peroxidation (LPO), and disulfidptosis-like death mediated by SC@Lip under ultrasound (US) irradiation were evaluated. Based on a 4T1 subcutaneous tumor model, both the in vivo biological safety assessment and the efficacy of SDT were assessed.Results: SC@Lip exhibits high efficiency in cellular phagocytosis. After being endocytosed by 4T1 cells, abundant ROS were produced under SDT activation, and the cell survival rates were below 5%. When applied to a 4T1 subcutaneous tumor model, the enhanced SDT mediated by SC@Lip inhibited tumor growth and prolonged the survival time of mice. In vitro and in vivo experiments show that SC@Lip can enhance the SDT effect and trigger disulfidptosis-like cancer cell death, thus achieving anti-tumor efficacy both in vitro and in vivo.Conclusion: SC@Lip is a multifunctional nanoplatform with an artificial membrane, which can integrate the functions of sonosensitization and GSH depletion into a biocompatible nanoplatform, and can be used to enhance the SDT effect and promote disulfidptosis-like cancer cell death.Keywords: disulfidptosis, sonodynamic therapy, reactive oxygen species, liposomes, lipid peroxidation

Background: Emerging evidence suggests that cell deaths are involved in tumorigenesis and progression, which may be treated as a novel direction of cancers. Recently, a novel type of programmed cell death, disulfidptosis, was discovered. However, the detailed biological and clinical impact of disulfidptosis and related regulators remains largely unknown. Methods: In this work, we first enrolled pancancer datasets and performed multi-omics analysis, including gene expression, DNA methylation, copy number variation and single nucleic variation profiles. Then we deciphered the biological implication of disulfidptosis in clear cell renal cell carcinoma (ccRCC) by machine learning. Finally, a novel agent targeting at disulfidptosis in ccRCC was identified and verified. Results: We found that disulfidptosis regulators were dysregulated among cancers, which could be explained by aberrant DNA methylation and genomic mutation events. Disulfidptosis scores were depressed among cancers and negatively correlated with epithelial mesenchymal transition. Disulfidptosis regulators could satisfactorily stratify risk subgroups in ccRCC, and a novel subtype, DCS3, owning with disulfidptosis depression, insensitivity to immune therapy and aberrant genome instability were identified and verified. Moreover, treating DCS3 with NU1025 could significantly inhibit ccRCC malignancy. Conclusion: This work provided a better understanding of disulfidptosis in cancers and new insights into individual management based on disulfidptosis.

Disulfidptosis refers to a specific programmed cell death process characterized by the accumulation of disulfides. It has recently been reported in several cancers. However, the impact of disulfidptosis-related long non-coding RNAs (lncRNAs) on malignant tumors has remained largely unknown. In the present work, we screened prognostic disulfidptosis-related lncRNAs and studied their effects on lung adenocarcinoma. Relevant clinical data of lung adenocarcinoma cases were retrieved from The Cancer Genome Atlas (TCGA) database. RNA sequencing was used to identify differentially expressed disulfidptosis-related lncRNAs within lung adenocarcinoma. In addition, prognostic disulfidptosis-related lncRNAs were obtained through univariate Cox regression analysis. LASSO-COX was used to construct new disulfidptosis-related lncRNA signatures. Different statistical approaches were used to validate the practicability and accuracy of the disulfidptosis-related lncRNAs signatures. Furthermore, several bioinformatic approaches were used to study relevant heterogeneities in biological processes and pathways of diverse risk groups. Reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR) was conducted to analyze the expression of disulfidptosis-related lncRNAs. Finally, seven disulfidptosis-related lncRNA signatures were identified in lung adenocarcinoma cells. The prognosis prediction model constructed efficiently predicted patient survival. Subgroup analysis revealed significant differences in immune cell proportion, including T follicular helper cells and M0 macrophages. In addition, in vitro experimental results demonstrated significant differences in disulfidptosis-related lncRNAs. Altogether, the six disulfidptosis-related lncRNA signatures could serve as a potential prognostic biomarker for lung adenocarcinoma. Furthermore, these can be used as a prediction model in individualized immunotherapy for lung adenocarcinoma.

Abstract Alzheimer's disease is the predominant form of dementia, and disulfidptosis is the latest reported mode of cell death that impacts various disease processes. This study used bioinformatics to analyze genes associated with disulfidptosis in Alzheimer's disease comprehensively. Based on the public datasets, the differentially expressed genes associated with disulfidptosis were identified, and immune cell infiltration was investigated through correlation analysis. Subsequently, hub genes were determined by a randomforest model. A prediction model was constructed using logistic regression. In addition, the drug-target affinity was predicted by a graph neural network model, and the results were validated by molecular docking. Five hub genes (PPEF1, NEUROD6, VIP, NUPR1, and GEM) were identified. The gene set showed significant enrichment for AD-related pathways. The logistic regression model demonstrated an AUC of 0.952, with AUC values of 0.916 and 0.864 in validated datasets. The immune infiltration analysis revealed significant heterogeneity between the Alzheimer's disease and control groups. High-affinity drugs for hub genes were identified. Through our study, a disease prediction model was constructed using potential biomarkers, and drugs targeting the genes were predicted. These results contribute to further understanding of the molecular mechanisms underlying Alzheimer's disease.

Abstract Background Disulfidptosis is an emerging form of cellular death resulting from the binding of intracellular disulfide bonds to actin cytoskeleton proteins. This study aimed to investigate the expression and prognostic significance of hub disulfidptosis-related lncRNAs (DRLRs) in R0 resected hepatocellular carcinoma (HCC) as well as their impact on the malignant behaviour of HCC cells. Methods A robust signature for R0 resected HCC was constructed using least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression and was validated in an independent internal validation cohort to predict the prognosis of R0 HCC patients. Comprehensive bioinformatics analysis was performed on the hub DRLRs (KDM4A-AS1, MKLN1-AS, and TMCC1-AS1), followed by experimental validation using quantitative real-time polymerase chain reaction (qRT‒PCR) and cellular functional assays. Results The signature served as an independent prognostic factor applicable to R0 HCC patients across different age groups, tumour stages, and pathological characteristics. Gene Ontology (GO) and gene set enrichment analysis (GSEA) revealed hub pathways associated with this signature. The high-risk group presented an increased abundance of M0 macrophages and activated memory CD4 T cells as well as elevated macrophage and major histocompatibility complex (MHC) class I expression. High-risk R0 HCC patients also presented increased tumour immune dysfunction and exclusion scores (TIDEs), mutation frequencies, and tumour mutational burdens (TMBs). Drug sensitivity analysis revealed that high-risk patients were more responsive to drugs, including GDC0810 and osimertinib. High expression levels of the three hub DRLRs were detected in R0 HCC tissues and HCC cell lines. Functional assays revealed that the three hub DRLRs enhanced HCC cell proliferation, migration, and invasion. Conclusions A signature was constructed on the basis of three DRLRs, providing novel insights for personalized precision therapy in R0 HCC patients. Graphical Abstract

Abstract Background Disulfidptosis 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. Methods We 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. Results Two 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. Conclusions This 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.

Abstract Limited treatment options and poor prognosis present significant challenges in the treatment of lung squamous cell carcinoma (LUSC). Disulfidptosis impacts cancer progression and prognosis. We developed a prognostic signature using disulfidptosis-related long non-coding RNAs (lncRNAs) to predict the prognosis of LUSC patients. Gene expression matrices and clinical information for LUSC were downloaded from the TCGA database. Co-expression analysis identified 209 disulfidptosis-related lncRNAs. LASSO-Cox regression analysis identified nine key lncRNAs, forming the basis for establishing a prognostic model. The model’s validity was confirmed by Kaplan–Meier and ROC curves. Cox regression analysis identified the risk score (RS) as an independent prognostic factor inversely correlated with overall survival. A nomogram based on the RS demonstrated good predictive performance for LUSC patient prognosis. The relationship between RS and immune function was explored using ESTIMATE, CIBERSORT, and ssGSEA algorithms. According to the TIDE database, a negative correlation was found between RS and immune therapy responsiveness. The GDSC database revealed that 49 drugs were beneficial for the low-risk group and 25 drugs for the high-risk group. Silencing C10orf55 expression in SW900 cells reduced invasiveness and migration potential. In summary, this lncRNA model based on TCGA-LUSC data effectively predicts prognosis and assists clinical decision-making.

Background: In recent times, disulfidptosis, an intricate form of cellular demise, has garnered attention due to its impact on prognosis, tumor progression and treatment response. Nevertheless, the exact significance of disulfidptosis-related genes (DisRGs) in glioblastoma (GBM) remains enigmatic. Methods: The GEO and TCGA databases provided transcriptional and clinically relevant data on tumor samples, while the GTEx database provided data on healthy tissues. Disulfidptosis-related genes (DisRGs) were procured from previous scholarly investigations. The expression profile of DisRGs was initially scrutinized among patients diagnosed with GBM, subsequent to which their prognostic value was explored. Through consensus clustering, we constructed DisRGs-related clusters and gene subtypes. Our results established that the DisRG-related clusters had differentially expressed genes, resulting in a DisulfidptosisScore model, which had a positive prognostic value. Results: The differential expression profile of 24 DisRGs between GBM samples and healthy samples was acquired. Through consensus cluster analysis, two distinct disulfidptosis subtypes, namely DisRGcluster A and DisRGcluster B, were identified. Then, the DisulfidptosisScore model including 4 characteristic genes was constructed.Notably, patients with GBM assigned with lower score demonstrated a considerably longer overall survival (OS) compared to those with higher score. Conclusion: We have effectively devised a prognostic model associated with disulfidptosis, presenting autonomous prognostic predictions for patients with GBM. These findings serve as a valuable addition to the current comprehension of disulfidptosis and offer fresh theoretical substantiation for the development of enhanced treatment strategies.

Abstract Background Endometriosis (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. Results Enrichment 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. Conclusion This 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.

Abstract 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.

Abstract The role of SLC3A2, a gene implicated in disulfidptosis, has not been characterized in gliomas. This study aims to clarify the prognostic value of SLC3A2 and its influence on glioma. We evaluated the expression of SLC3A2 and its prognostic importance in gliomas using publicly accessible databases and our clinical glioma samples and with reliance on Meta and Cox regression analysis approaches. Functional enrichment analyses were performed to explore SLC3A2's function. Immune infiltration was evaluated using CIBERSORT, ssGSEA, and single-cell sequencing data. Additionally, Tumor immune dysfunction and exclusion (TIDE) and epithelial-mesenchymal transition scores were determined. CCK8, colony formation, migration, and invasion assays were utilized in vitro, and an orthotopic glioma xenograft model was employed in vivo, to investigate the role of SLC3A2 in gliomas. Bioinformatics analyses indicated high SLC3A2 expression correlates with adverse clinicopathological features and poor patient prognosis. Upregulated SLC3A2 influenced the tumor microenvironment by altering immune cell infiltration, particularly of macrophages, and tumor migration and invasion. SLC3A2 expression positively correlated with immune therapy indicators, including immune checkpoints and TIDE. Elevated SLC3A2 was revealed as an independent risk element for poor glioma prognosis through Cox regression analyses. In vitro experiments showed that reduced SLC3A2 expression decreased cell proliferation, migration, and invasion. In vivo, knockdown of SLC3A2 led to a reduction in tumor volume and prolonged survival in tumor-bearing mice. Therefore, SLC3A2 is a prognostic biomarker and associated with immune infiltration in gliomas.

Background: Colorectal cancer (CRC) is a leading cause of cancer mortality globally. This study aims to develop a prognostic model based on disulfidptosis-related genes to assess survival outcomes in CRC, highlighting the tumor microenvironment's role. Methods: The thought of traditional Chinese medicine syndrome differentiation and treatment runs through the whole study. We analyzed CRC tissue data from The Cancer Genome Atlas and the Gene Expression Omnibus using single-sample gene set enrichment and weighted gene correlation network analyses to identify prognostic markers and evaluate immune infiltration. We also investigated predictive drug sensitivities. Results: We identified seven disulfidptosis-related markers -- complement C1q A chain (C1QA), solute carrier family 11 member 1 (SLC11A1), cluster of differentiation 36 (CD36), cluster of differentiation 6 (CD6), interleukin 1 receptor associated kinase 3 (IRAK3), S100 calcium binding protein A8 (S100A8), and CD8 subunit alpha (CD8A) -- that significantly influence prognosis. Patients classified in the low-risk group demonstrated improved overall survival compared to those in the high-risk group across training (P = 0.0026) and validation cohorts (P = 0.032). Differential gene expression was significant in the high-risk group (P < 0.001), and prevalent mutations included APC regulator of WNT signaling pathway (APC), tumor protein P53 (TP53), Titin (TTN), and Kirsten rat sarcoma viral oncogene (KRAS). The risk score correlated linearly with tumor microenvironment attributes. The results of drug analysis showed that some traditional drugs may have anticancer effects through the vertical action of disulfidptosis. Conclusion: Our prognostic model, integrating seven disulfidptosis-related genes, categorizes CRC patients by survival probability and underscores these genes as potential biomarkers linked to the tumor microenvironment. These findings support their use in refining therapeutic strategies for CRC. [ABSTRACT FROM AUTHOR]

Abstract Disulfidptosis is a recently identified mode of regulated cell death. Regulating disulfidptosis in carcinoma is a promising therapeutic approach. Long non-coding RNAs (lncRNAs) have been reported to be related to the occurrence and development of many cancers. Disulfidptosis-related lncRNAs (DRLs) in HPV-negative oral squamous cell carcinoma (OSCC) have not been studied. Based on The Cancer Genome Atlas (TCGA) database, least absolute shrinkage selection operator (LASSO) analysis and Cox regression analysis were used to identify overall survival related DRLs and construct the signature. Kaplan–Meier, time-dependent receiver operating characteristics (ROC) and principal component analyses (PCA) were explored to demonstrate the prediction potential of the signature. Subgroup analysis stratified by different clinicopathological characteristics were conducted. Nomogram was established by DRLs signature and independent clinicopathological characteristics. The calibration plots were performed to reveal the accuracy of nomogram. Immune cell subset infiltration, immunotherapy response, drug sensitivity analysis, and tumor mutation burden (TMB) were conducted. Underlying functions and pathways were explored by Gene Set Enrichment Analysis (GSEA) analysis. Previous lncRNA signatures of OSCC were retrieved from PubMed for further validation. Gene expression omnibus (GEO) datasets (GSE41613 and GSE85446) were merged as an external validation for DRLs signature. Consensus clustering analysis of DRLs signature and experimental validation of DRLs were also explored. This research sheds light on the robust performance of DRLs signature in survival prediction, immune cell infiltration, immune escape, and immunotherapy of HPV-negative OSCC.

Abstract 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.

Abstract Background Clear cell renal cell carcinoma (ccRCC) is the most prevalent subtype of renal tumors and is associated with a unfavorable prognosis. Disulfidptosis is a recently identified form of cell death mediated by disulfide bonds. Numerous studies have highlighted the significance of immune checkpoint genes (ICGs) in ccRCC. Nevertheless, the involvement of disulfidptosis-related immune checkpoint genes (DRICGs) in ccRCC remains poorly understood. Methods The mRNA expression profiles and clinicopathological data of ccRCC patients were obtained from The Cancer Genome Atlas and Gene Expression Omnibus (GEO) databases. The associations between disulfidptosis-related genes (DRGs) and immune checkpoint genes (ICGs) were assessed to identify DRICGs. Cox regression analysis and least absolute shrinkage and selection operator (LASSO) analysis were conducted to construct a risk signature. Results A total of 39 differentially expressed immune-related candidate genes were identified. A prognostic signature was constructed utilizing nine DRICGs (CD276, CD80, CD86, HLA-E, LAG3, PDCD1LG2, PVR, TIGIT, and TNFRSF4) and validated using GEO data. The risk model functioned as an independent prognostic indicator for ccRCC, while the associated nomogram provided a reliable scoring system for ccRCC. Gene set enrichment analysis indicated enrichment of phospholipase D, antigen processing and presentation, and ascorbate and aldarate metabolism-related signaling pathways in the high-risk group. Furthermore, the DRICGs exhibited correlations with the infiltration of various immune cells. It is noteworthy that patients with ccRCC categorized into distinct risk groups based on this model displayed varying sensitivities to potential therapeutic agents. Conclusions The novel DRICG-based risk signature is a reliable indicator for the prognosis of ccRCC patients. Moreover, it also aids in drug selection and correlates with the tumour immune microenvironment in ccRCC.

Kaibo Hu,1,2,&ast; Yanghuan Ou,1,&ast; Leyang Xiao,1,2,&ast; Ruonan Gu,1,2 Fei He,1,2 Jie Peng,1,2 Yuan Shu,1,2 Ting Li,1,2 Liang Hao1 1Department of Orthopedics, Second Affiliated Hospital of Nanchang University, Nanchang, 330006, People’s Republic of China; 2The Second Clinical Medical College, Nanchang University, Nanchang, 330006, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Liang Hao, Tel +86 136 0700 8562, Email ndefy07018@ncu.edu.cnBackground: Osteoarthritis (OA) is a major cause of human disability. Despite receiving treatment, patients with the middle and late stage of OA have poor survival outcomes. Therefore, within the framework of predictive, preventive, and personalized medicine (PPPM/3PM), early personalized diagnosis of OA is particularly prominent. PPPM aims to accurately identify disease by integrating multiple omic techniques; however, the efficiency of currently available methods and biomarkers in predicting and diagnosing OA should be improved. Disulfidptosis, a novel programmed cell death mechanism and appeared in particular metabolic status, plays a mysterious characteristic in the occurrence and development of OA, which warrants further investigation.Methods: In this study, we integrated three public datasets from the Gene Expression Omnibus (GEO) database, including 26 OA samples and 20 normal samples. Via a series of bioinformatic analysis and machine learning, we identified the diagnostic biomarkers and several subtypes of OA. Moreover, the expression of these biomarkers were verified in our in-house cohort and the single cell dataset.Results: Three significant regulators of disulfidptosis (NCKAP1, OXSM, and SLC3A2) were identified through differential expression analysis and machine learning. And a nomogram constructed based on these three regulators exhibited ideal efficiency in predicting early- and late-stage OA. Furthermore, based on the expression of three regulators, we identified two disulfidptosis-related subtypes of OA with different infiltration of immune cells and personalized expression level of immune checkpoints. Notably, the expression of the three regulators was demonstrated in a single-cell RNA profile and verified in the synovial tissue in our in-house cohort including 6 OA patients and 6 normal people. Finally, an efficient disulfidptosis-mediated diagnostic model was constructed for OA, with the AUC value of 97.6923% in the training set and 93.3333% and 100% in two validation sets.Conclusion: Overall, with regard to PPPM, this study provided novel insights into the role of disulfidptosis regulators in the personalized diagnosis and treatment of OA.Keywords: osteoarthritis, disulfidptosis, perspective of predictive, preventive and personalized medicine, immune microenvironment, diagnostic model

Abstract 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

Abstract Background Hepatocellular carcinoma (HCC) is a highly prevalent and deadly cancer, with limited treatment options for advanced-stage patients. Disulfidptosis is a recently identified mechanism of programmed cell death that occurs in SLC7A11 high-expressing cells due to glucose starvation-induced disintegration of the cellular disulfide skeleton. We aimed to explore the potential of disulfidptosis, as a prognostic and therapeutic marker in HCC. Methods We classified HCC patients into two disulfidptosis subtypes (C1 and C2) based on the transcriptional profiles of 31 disulfrgs using a non-negative matrix factorization (NMF) algorithm. Further, five genes (NEIL3, MMP1, STC2, ADH4 and CFHR3) were screened by Cox regression analysis and machine learning algorithm to construct a disulfidptosis scoring system (disulfS). Cell proliferation assay, F-actin staining and PBMC co-culture model were used to validate that disulfidptosis occurs in HCC and correlates with immunotherapy response. Results Our results suggests that the low disulfidptosis subtype (C2) demonstrated better overall survival (OS) and progression-free survival (PFS) prognosis, along with lower levels of immunosuppressive cell infiltration and activation of the glycine/serine/threonine metabolic pathway. Additionally, the low disulfidptosis group showed better responses to immunotherapy and potential antagonism with sorafenib treatment. As a total survival risk factor, disulfS demonstrated high predictive efficacy in multiple validation cohorts. We demonstrated the presence of disulfidptosis in HCC cells and its possible relevance to immunotherapeutic sensitization. Conclusion The present study indicates that novel biomarkers related to disulfidptosis may serve as useful clinical diagnostic indicators for liver cancer, enabling the prediction of prognosis and identification of potential treatment targets. Graphical Abstract

Mingyue Fu,1,2,&ast; Diliaremu Aihemaiti,1,2,&ast; Haowen Fu,1,2,&ast; Na Li,1,2 Yifan Yuan,3 Mei Ye1,2 1Department of Gastroenterology, Zhongnan Hospital, Wuhan University, Wuhan, Hubei, People’s Republic of China; 2Hubei Clinical Centre and Key Laboratory of Intestinal and Colorectal Diseases, Zhongnan Hospital, Wuhan University, Wuhan, Hubei, People’s Republic of China; 3Center of Gerontology and Geriatrics, West China Hospital, Sichuan University, Chengdu, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Yifan Yuan; Mei Ye, Email yifanyuan95@163.com; wumeiye08@163.comBackground: Crohn’s disease (CD) is a persistent inflammatory condition that impacts the gastrointestinal system and is characterized by a multifaceted pathogenesis involving genetic, immune, and environmental components. This study primarily investigates the relationship between gene expression and immune cell infiltration in CD, focusing on disulfidptosis—a novel form of cell death caused by abnormal disulfide accumulation—and its impact on various immune cell populations. By identifying key disulfidptosis-related genes (DRGs) and exploring their association with distinct gene expression subtypes, this research aims to enhance our understanding of CD and potentially other autoimmune diseases.Methods: Gene expression data from intestinal biopsy samples were collected from both individuals with CD and healthy controls, and these data were retrieved from the GEO database. Through gene expression level comparisons, various differentially expressed genes (DEGs) were identified. Subsequently, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed to reveal the biological processes and pathways linked to these DEGs. Later, immune cell infiltration was evaluated. Hub candidate DRGs were identified using machine learning algorithms. Validation of the expression of hub DRGs was carried out using quantitative real-time polymerase chain reaction. The hub DRGs were subjected to unsupervised hierarchical clustering to classify CD patients into subtypes. The characteristics of each subtype were then analyzed.Results: Two hub DRGs (NDUFA11 and LRPPRC) were identified. NDUFA11 showed a significantly positive association with the abundance of Th17 cells. Conversely, higher expression levels of LRPPRC were associated with a reduced abundance of various immune cells, particularly monocytes. CD patients were classified into two disulfidptosis-related subtypes. Cluster B patients exhibited lower immune infiltration and milder clinical presentation.Conclusion: LRPPRC and NDUFA11 are identified as hub DRGs in CD, with potential roles in disulfidptosis and immune regulation. The disulfidptosis subtypes provide new insights into disease progression.Keywords: Crohn’s disease, disulfidptosis, machine learning, expression pattern, immune cell infiltration

Abstract Purpose 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. Methods 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. Results 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. Conclusions 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.

Abstract Background This research strived to construct a new signature utilizing disulfidptosis-related ferroptosis (SRF) genes to anticipate response to immunotherapy, prognosis, and drug sensitivity in individuals with colorectal cancer (CRC). Methods The data for RNA sequencing as well as corresponding clinical information of individuals with CRC, were extracted from The Cancer Genome Atlas (TCGA) dataset. SRF were constructed with the help of the random forest (RF), least absolute shrinkage and selection operator (LASSO), and stepwise regression algorithms. To validate the SRF model, we applied it to an external cohort, GSE38832. Prognosis, immunotherapy response, drug sensitivity, molecular functions of genes, and somatic mutations of genes were compared across the high- and low-risk groups (categories). Following this, all statistical analyses were conducted with the aid of the R (version 4.23) software and various packages of the Cytoscape (version 3.8.0) tool. Results SRF was developed based on five genes (ATG7, USP7, MMD, PLIN4, and THDC2). Both univariate and multivariate Cox regression analyses established SRF as an independent, prognosis-related risk factor. Individuals from the high-risk category had a more unfavorable prognosis, elevated tumor mutational burden (TMB), and significant immunosuppressive status. Hence, they might have better outcomes post-immunotherapy and might benefit from the administration of pazopanib, lapatinib, and sunitinib. Conclusion In conclusion, SRF can act as a new biomarker for prognosis assessment. Moreover, it is also a good predictor of drug sensitivity and immunotherapy response in CRC but should undergo optimization before implementation in clinical settings.

Zhe Xiong,1,2 Ying Fang,1,2 Shuangshuang Lu,1 Qiuyue Sun,1,3 Yuhui Sun,1,3 Pengcheng Yang,4,&ast; Jin Huang1,&ast; 1Department of Gastroenterology, The Affiliated Changzhou No.2 People’s Hospital of Nanjing Medical University, Changzhou, Jiangsu Province, People’s Republic of China; 2Graduate School of Dalian Medical University, Dalian, Liaoning Province, China; 3Graduate School of Nanjing Medical University, Nanjing, Jiangsu Province, People’s Republic of China; 4Department of Gastroenterology, Hengshanqiao People’s Hospital, Changzhou, Jiangsu Province, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Pengcheng Yang; Jin Huang, Email 496808936@qq.com; hj042153@hotmail.comBackground: Ulcerative colitis (UC) is a nonspecific inflammatory disease confined to the intestinal mucosa and submucosa, and its prevalence significantly increases each year. Disulfidptosis is a recently discovered new form of cell death that has been suggested to be involved in multiple diseases. The aim of this study was to explore the relevance of disulfidptosis in UC.Methods: First, the UC datasets were downloaded from the Gene Expression Omnibus (GEO) database, and UC samples were typed based on upregulated disulfidptosis-related genes (DRGs). Then, weighted gene co-expression network analysis (WGCNA) was performed on the datasets and molecular subtypes of UC, respectively, to obtain candidate signature genes. After validation of the validation set and qRT-PCR, we constructed a nomogram model by signature genes to predict the risk of UC. Finally, single-cell sequencing analysis was used to study the heterogeneity of UC and to demonstrate the expression of DRGs and signature genes at the single-cell level.Results: A total of 7 DRGs were significantly upregulated in the expression profiles of UC, and 180 UC samples were divided into two subtypes based on these DRGs. Five candidate signature genes were obtained by intersecting two key gene modules selected by WGCNA. After evaluation, four signature genes with diagnostic relevance (COL4A1, PRRX1, NNMT, and PECAM1) were eventually identified. The nomogram model showed excellent prediction ability. Finally, in the single-cell analysis, there were eight cell types (including B cells, T cells, monocyte, smooth muscle cells, epithelial cells, neutrophil, endothelial cells and NK cells) were identified. The signature genes were significantly expressed mainly in endothelial cells and smooth muscle cells.Conclusion: In this study, subtypes related to disulfidptosis were identified, and single-cell analysis was performed to understand the pathogenesis of UC from a new perspective. Four signature genes were screened and a prediction model with high accuracy was established. This provides novel insights for early diagnosis and therapeutic targets in UC.Keywords: ulcerative colitis, disulfidptosis, molecular clusters, WGCNA, nomogram

Abstract Cell death is an important part of the life cycle, serving as a foundation for both the orderly development and the maintenance of physiological equilibrium within organisms. This process is fundamental, as it eliminates senescent, impaired, or aberrant cells while also promoting tissue regeneration and immunological responses. A novel paradigm of programmed cell death, known as disulfidptosis, has recently emerged in the scientific circle. Disulfidptosis is defined as the accumulation of cystine by cancer cells with high expression of the solute carrier family 7 member 11 (SLC7A11) during glucose starvation. This accumulation causes extensive disulfide linkages between F-actins, resulting in their contraction and subsequent detachment from the cellular membrane, triggering cellular death. The RAC1-WRC axis is involved in this phenomenon. Disulfidptosis sparked growing interest due to its potential applications in a variety of pathologies, particularly oncology, neurodegenerative disorders, and metabolic anomalies. Nonetheless, the complexities of its regulatory pathways remain elusive, and its precise molecular targets have yet to be definitively identified. This manuscript aims to meticulously dissect the historical evolution, molecular underpinnings, regulatory frameworks, and potential implications of disulfidptosis in various disease contexts, illuminating its promise as a groundbreaking therapeutic pathway and target.

Abstract Background Disulfidptosis is a novel form of programmed cell death induced by high SLC7A11 expression under glucose starvation conditions, unlike other known forms of cell death. However, the roles of disulfidptosis in cancers have yet to be comprehensively well-studied, particularly in ccRCC. Methods The expression profiles and somatic mutation of DGs from the TCGA database were investigated. Two DGs clusters were identified by unsupervised consensus clustering analysis, and a disulfidptosis-related prognostic signature (DR score) was constructed. Furthermore, the predictive capacity of the DR score in prognosis was validated by several clinical cohorts. We also developed a nomogram based on the DR score and clinical features. Then, we investigated the differences in the clinicopathological information, TMB, tumor immune landscapes, and biological characteristics between the high- and low-risk groups. We evaluated whether the DR score is a robust tool for predicting immunotherapy response by the TIDE algorithm, immune checkpoint genes, submap analysis, and CheckMate immunotherapy cohort. Results We identified two DGs clusters with significant differences in prognosis, tumor immune landscapes, and clinical features. The DR score has been demonstrated as an independent risk factor by several clinical cohorts. The high-risk group patients had a more complicated tumor immune microenvironment and suffered from more tumor immune evasion in immunotherapy. Moreover, patients in the low-risk group had better prognosis and response to immunotherapy, particularly in anti-PD1 and anti-CTLA-4 inhibitors, which were verified in the CheckMate immunotherapy cohort. Conclusion The DR score can accurately predict the prognosis and immunotherapy response and assist clinicians in providing a personalized treatment regime for ccRCC patients.

Abstract Osteosarcoma (OS) is the most common malignant bone tumor with high pathological heterogeneity. Our study aimed to investigate disulfidptosis-related modification patterns in OS and their relationship with survival outcomes in patients with OS. We analyzed the single-cell-level expression profiles of disulfidptosis-related genes (DSRGs) in both OS microenvironment and OS subclusters, and HMGB1 was found to be crucial for intercellular regulation of OS disulfidptosis. Next, we explored the molecular clusters of OS based on DSRGs and related immune cell infiltration using transcriptome data. Subsequently, the hub genes of disulfidptosis in OS were screened by applying multiple machine models. In vitro and patient experiments validated our results. Three main disulfidptosis-related molecular clusters were defined in OS, and immune infiltration analysis suggested high immune heterogeneity between distinct clusters. The in vitro experiment confirmed decreased cell viability of OS after ACTB silencing and higher expression of ACTB in patients with lower immune scores. Our study systematically revealed the underlying relationship between disulfidptosis and OS at the single-cell level, identified disulfidptosis-related subtypes, and revealed the potential role of ACTB expression in OS disulfidptosis.

Abstract Tumor is a local tissue hyperplasia resulted from cancerous transformation of normal cells under the action of various physical, chemical and biological factors. The exploration of tumorigenesis mechanism is crucial for early prevention and treatment of tumors. Epigenetic modification is a common and important modification in cells, including DNA methylation, histone modification, non-coding RNA modification and m6A modification. The normal mode of cell death is programmed by cell death-related genes; however, recent researches have revealed some new modes of cell death, including pyroptosis, ferroptosis, cuproptosis and disulfidptosis. Epigenetic regulation of various cell deaths is mainly involved in the regulation of key cell death proteins and affects cell death by up-regulating or down-regulating the expression levels of key proteins. This study aims to investigate the mechanism of epigenetic modifications regulating pyroptosis, ferroptosis, cuproptosis and disulfidptosis of tumor cells, explore possible triggering factors in tumor development from a microscopic point of view, and provide potential targets for tumor therapy and new perspective for the development of antitumor drugs or combination therapies.

The disulfide stress-induced cell death known as disulfidptosis is characterized by the disintegration of cytoskeletal proteins and F-actin as a result of an excessive buildup of disulfides within the cell. The relationship between disulfidptosis-associated long non-coding RNA (lncRNA) in hepatocellular carcinoma (HCC) progression is still not clearly understood. In this article, we aim to explore the crucial role of lncRNA in HCC.

Disulfidptosis is a recently identified mode of regulated cell death. Regulating disulfidptosis in carcinoma is a promising therapeutic approach. Long non-coding RNAs (lncRNAs) have been reported to be related to the occurrence and development of many cancers. Disulfidptosis-related lncRNAs (DRLs) in HPV-negative oral squamous cell carcinoma (OSCC) have not been studied. Based on The Cancer Genome Atlas (TCGA) database, least absolute shrinkage selection operator (LASSO) analysis and Cox regression analysis were used to identify overall survival related DRLs and construct the signature. Kaplan–Meier, time-dependent receiver operating characteristics (ROC) and principal component analyses (PCA) were explored to demonstrate the prediction potential of the signature. Subgroup analysis stratified by different clinicopathological characteristics were conducted. Nomogram was established by DRLs signature and independent clinicopathological characteristics. The calibration plots were performed to reveal the accuracy of nomogram. Immune cell subset infiltration, immunotherapy response, drug sensitivity analysis, and tumor mutation burden (TMB) were conducted. Underlying functions and pathways were explored by Gene Set Enrichment Analysis (GSEA) analysis. Previous lncRNA signatures of OSCC were retrieved from PubMed for further validation. Gene expression omnibus (GEO) datasets (GSE41613 and GSE85446) were merged as an external validation for DRLs signature. Consensus clustering analysis of DRLs signature and experimental validation of DRLs were also explored. This research sheds light on the robust performance of DRLs signature in survival prediction, immune cell infiltration, immune escape, and immunotherapy of HPV-negative OSCC. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Clear cell renal cell carcinoma (ccRCC) is the most prevalent subtype of renal tumors and is associated with a unfavorable prognosis. Disulfidptosis is a recently identified form of cell death mediated by disulfide bonds. Numerous studies have highlighted the significance of immune checkpoint genes (ICGs) in ccRCC. Nevertheless, the involvement of disulfidptosis-related immune checkpoint genes (DRICGs) in ccRCC remains poorly understood. Methods: The mRNA expression profiles and clinicopathological data of ccRCC patients were obtained from The Cancer Genome Atlas and Gene Expression Omnibus (GEO) databases. The associations between disulfidptosis-related genes (DRGs) and immune checkpoint genes (ICGs) were assessed to identify DRICGs. Cox regression analysis and least absolute shrinkage and selection operator (LASSO) analysis were conducted to construct a risk signature. Results: A total of 39 differentially expressed immune-related candidate genes were identified. A prognostic signature was constructed utilizing nine DRICGs (CD276, CD80, CD86, HLA-E, LAG3, PDCD1LG2, PVR, TIGIT, and TNFRSF4) and validated using GEO data. The risk model functioned as an independent prognostic indicator for ccRCC, while the associated nomogram provided a reliable scoring system for ccRCC. Gene set enrichment analysis indicated enrichment of phospholipase D, antigen processing and presentation, and ascorbate and aldarate metabolism-related signaling pathways in the high-risk group. Furthermore, the DRICGs exhibited correlations with the infiltration of various immune cells. It is noteworthy that patients with ccRCC categorized into distinct risk groups based on this model displayed varying sensitivities to potential therapeutic agents. Conclusions: The novel DRICG-based risk signature is a reliable indicator for the prognosis of ccRCC patients. Moreover, it also aids in drug selection and correlates with the tumour immune microenvironment in ccRCC. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Introduction: Lung cancer, with the highest global mortality rate among cancers, presents a grim prognosis, often diagnosed at an advanced stage in nearly 70% of cases. Recent research has unveiled a novel mechanism of cell death termed disulfidptosis, which is facilitated by glucose scarcity and the protein SLC7A11. Methods: Utilizing the least absolute shrinkage and selection operator (LASSO) regression analysis combined with Cox regression analysis, we constructed a prognostic model focusing on disulfidptosis-related genes. Nomograms, correlation analyses, and enrichment analyses were employed to assess the significance of this model. Among the genes incorporated into the model, CHRNA5 was selected for further investigation regarding its role in LUAD cells. Biological functions of CHRNA5 were assessed using EdU, transwell, and CCK-8 assays. Results: The efficacy of the model was validated through internal testing and an external validation set, with further evaluation of its robustness and clinical applicability using a nomogram. Subsequent correlation analyses revealed associations between the risk score and infiltration of various cancer types, as well as oncogene expression. Enrichment analysis also identified associations between the risk score and pivotal biological processes and KEGG pathways. Our findings underscore the significant impact of CHRNA5 on LUAD cell proliferation, migration, and disulfidptosis. Conclusion: This study successfully developed and validated a robust prognostic model centered on disulfidptosis-related genes, providing a foundation for predicting prognosis in LUAD patients. [ABSTRACT FROM AUTHOR]

Background: Hepatocellular carcinoma (HCC) is a highly prevalent and deadly cancer, with limited treatment options for advanced-stage patients. Disulfidptosis is a recently identified mechanism of programmed cell death that occurs in SLC7A11 high-expressing cells due to glucose starvation-induced disintegration of the cellular disulfide skeleton. We aimed to explore the potential of disulfidptosis, as a prognostic and therapeutic marker in HCC. Methods: We classified HCC patients into two disulfidptosis subtypes (C1 and C2) based on the transcriptional profiles of 31 disulfrgs using a non-negative matrix factorization (NMF) algorithm. Further, five genes (NEIL3, MMP1, STC2, ADH4 and CFHR3) were screened by Cox regression analysis and machine learning algorithm to construct a disulfidptosis scoring system (disulfS). Cell proliferation assay, F-actin staining and PBMC co-culture model were used to validate that disulfidptosis occurs in HCC and correlates with immunotherapy response. Results: Our results suggests that the low disulfidptosis subtype (C2) demonstrated better overall survival (OS) and progression-free survival (PFS) prognosis, along with lower levels of immunosuppressive cell infiltration and activation of the glycine/serine/threonine metabolic pathway. Additionally, the low disulfidptosis group showed better responses to immunotherapy and potential antagonism with sorafenib treatment. As a total survival risk factor, disulfS demonstrated high predictive efficacy in multiple validation cohorts. We demonstrated the presence of disulfidptosis in HCC cells and its possible relevance to immunotherapeutic sensitization. Conclusion: The present study indicates that novel biomarkers related to disulfidptosis may serve as useful clinical diagnostic indicators for liver cancer, enabling the prediction of prognosis and identification of potential treatment targets. [ABSTRACT FROM AUTHOR]