A disulfidptosis-related glucose metabolism and immune response prognostic model revealing the immune microenvironment in lung adenocarcinoma.

Background: Lung adenocarcinoma accounts for the majority of lung cancer cases and impact survival rate of patients severely. Immunotherapy is an effective treatment for lung adenocarcinoma but is restricted by many factors including immune checkpoint expression and the inhibitory immune microenvironment. This study aimed to explore the immune microenvironment in lung adenocarcinoma via disulfidptosis. Methods: Public datasets of lung adenocarcinoma from the TCGA and GEO was adopted as the training and validation cohort. Based on the differences in the expression of disulfidptosis -related genes, a glucose metabolism and immune response prognostic model was constructed. The prognostic value and clinical relationship of the model were further explored. Immune-related analyses were performed according to CIBERSORT, ssGSEA, TIDE, IPS. Results: We verified that the model could accurately predict the survival expectancy of lung adenocarcinoma patients. Patients with lung adenocarcinoma and a low-risk score had better survival outcomes according to the model. Moreover, the high-risk group tended to have an immunosuppressive effect, as reflected by the immune cell components, phenotypes and functions. We also found that the clinically relevant immune checkpoint CTLA-4 was significantly higher in low-risk group (P<0.05), indicating that the high-risk group may suffer worse tumor immunotherapy efficacy. Finally, we found that this model has accurate predictive value for the efficacy of immune checkpoint blockade in non-small cell lung cancer (P<0.05). Conclusion: The prognostic model demonstrated the feasibility of predicting survival and immunotherapy efficacy via disulfidptosis-related genes and will facilitate the development of personalized anticancer therapy.

Disulfidptosis-related long non-coding RNA signature predicts the prognosis, tumor microenvironment, immunotherapy, and antitumor drug options in colon adenocarcinoma.

This study aims to investigate the role and prognostic significance of long non-coding RNAs (lncRNAs) associated with disulfidptosis in colon adenocarcinoma (COAD). The TCGA database's clinical data and transcriptome profiles were employed. Analysis of previous studies identified 10 disulfidptosis-related genes (DRGs). We used these genes to construct a signature that could independently and accurately predict the prognosis of patients with COAD. The Kaplan-Meier (K-M) curve analysis showed that the lower-risk group had a better prognosis. With the help of multivariate Cox regression analysis, the risk score produced from the patient's signature might independently predict the outcomes. Utilizing a nomogram, the receiver operating characteristic (ROC) curve, and principal component analysis (PCA), the signature's predictive ability was also confirmed. It's interesting to note that immunotherapy, especially PD-1 immune checkpoint suppression, was more likely to benefit low-risk patients. The IC50 levels for certain anticancer agents were lower in the high-risk group. Finally, qRT-PCR analyses in colon cancer cell lines revealed elevated levels of lncRNAs CASC9, ZEB1-AS1, ATP2A1-AS1, SNHG7, AL683813.1, and AP003555.1, and reduced levels of FAM160A1-DT and AC112220.2, compared to normal cell lines. This signature offers insights into prognosis, tumor microenvironment, and options for immunotherapy and antitumor drugs in patients with COAD.

Integrated analysis of disulfidoptosis-related genes identifies NRP1 as a novel biomarker promoting proliferation of gastric cancer via glutamine mediated energy metabolism.

The incidence and mortality of gastric cancer rank fifth and fourth worldwide among all malignancies, respectively. Additionally, disulfidoptosis, a recently identified form of cellular demise, is closely linked to the initiation and advancement of malignancies. This study aims to create a novel signature of disulfidptosis-related genes (DRGs) and to further explore its association with the tumor immune microenvironment. Based on our comprehensive study, a prognostic signature consisting of 31 DRGs in stomach adenocarcinoma (STAD) was identified and characterized. Through the integrative analyses involving gene expression profiling, machine learning algorithms, and Cox regression models, the prognostic significance of these DRGs was demonstrated. Our findings highlight their strong predictive power in assessing overall survival across diverse patient datasets, and their better performance than traditional clinicopathological factors. Moreover, the DRGs signature showed association with the characteristics of the tumor microenvironment, which has implications for the immune modulation and therapeutic strategies in STAD. Specifically, NRP1 emerged as a key DRG with elevated expression in STAD, showing correlation with the advanced stages of diseases and poorer outcomes. Functional studies further revealed the role of NRP1 in promoting STAD cell proliferation through the modulation of glutamine metabolism. Overall, our study underscores the clinical relevance of DRGs as biomarker and potential therapeutic targets in STAD management, providing insights into disease biology and personalized treatments.

Construction of lncRNA prognostic model related to disulfidptosis in lung adenocarcinoma.

Background: Lung cancer is one of the malignant tumors with the highest rates of morbidity and mortality worldwide. One of the most common histological types of lung cancer is lung adenocarcinoma (LUAD). Despite the fact that development in medicine has significantly improved some patients' prognoses, the overall survival (OS) rate is still very low. In glucose-deficient SLC7A11-overexpressed cancer cells, the accumulation of disulfide molecules leads to abnormal disulfide bonding between actin cytoskeletal proteins, interferes with their tissues, and eventually leads to actin network collapse and cell death. This mode of cell death is called disulfidptosis. Studies have shown that disulfidptosis may be a new target for cancer treatment. However, the role of disulfidptosis in LUAD is still unknown. Methods: LUAD transcriptome and clinical information from The Cancer Genome Atlas (TCGA) was downloaded. The co-expression analysis, Least Absolute Shrinkage and Selection Operator (LASSO) regression, and Cox regression analysis was performed to screen the disulfidptosis-related lncRNAs (DRLs) and build the prognostic model. Kaplan-Meier curve, Cox regression analysis, and receiver operating characteristic (ROC) curve was used to validate the model. Then a nomogram is made to predict the prognosis of LUAD patients. Finally, fresh-collected clinical samples were used to verify the expression of DRLs in LUAD. Results: The prognostic model with six DRLs was developed to predict the prognosis of LUAD, with superior prognosis value compared to other clinical variables. The Cox regression analysis revealed that T stage, N stage and the risk score were identified as independent variables that affected LUAD prognosis. ROC curve revealed that the model has a moderate diagnostic value, with an AUC of 1-year 0.684, 3-year 0.664, and 5-year 0.588. Moreover, nine medications connected to LUAD treatment were acquired through drug sensitivity analysis. LUAD tissue validation showed that AC012073.1, AC012615.1, EMSLR, and SNHG12 were highly expressed, while AL606834.1 and AL365181.2 with low expression. Conclusion: Six DRLs were screened and verified to construct the prognostic model, which can accurately predict the LUAD prognosis. It establishes a basis for further exploration into the molecular mechanisms underlying LUAD and identification of potential biomarkers for diagnosis, prognosis, and therapeutic targets.

Disulfidptosis-associated LncRNA signature predicts prognosis and immune response in kidney renal clear cell carcinoma.

BACKGROUND: Kidney renal clear cell carcinoma (KIRC) represents a significant proportion of renal cell carcinomas and is characterized by high aggressiveness and poor prognosis despite advancements in immunotherapy. Disulfidptosis, a novel cell death pathway, has emerged as a critical mechanism in various cellular processes, including cancer. This study leverages machine learning to identify disulfidptosis-related long noncoding RNAs (DRlncRNAs) as potential prognostic biomarkers in KIRC, offering new insights into tumor pathogenesis and treatment avenues. RESULTS: Our analysis of data from The Cancer Genome Atlas (TCGA) led to the identification of 431 DRlncRNAs correlated with disulfidptosis-related genes. Five key DRlncRNAs (SPINT1-AS1, AL161782.1, OVCH1-AS1, AC131009.3, and AC108673.3) were used to develop a prognostic model that effectively distinguished between low- and high-risk patients with significant differences in overall survival and progression-free survival. The low-risk group had a favorable prognosis associated with a protective immune microenvironment and a better response to targeted drugs. Conversely, the high-risk group displayed aggressive tumor features and poor immunotherapy outcomes. Validation through qRT‒PCR confirmed the differential expression of these DRlncRNAs in KIRC cells compared to normal kidney cells, underscoring their potential functional significance in tumor biology. CONCLUSIONS: This study established a robust link between disulfidptosis-related lncRNAs and patient prognosis in KIRC, underscoring their potential as prognostic biomarkers and therapeutic targets. The differential expression of these lncRNAs in tumor versus normal tissue further highlights their relevance in KIRC pathogenesis. The predictive model not only enhances our understanding of KIRC biology but also provides a novel stratification tool for precision medicine approaches, improving treatment personalization and outcomes in KIRC patients.

Disulfidptosis-related gene expression reflects the prognosis of drug-resistant cancer patients and inhibition of MYH9 reverses sorafenib resistance.

The onset of drug resistance in advanced cancer patients markedly diminishes their prognosis. Recently, disulfidptosis, a novel form of cell death, has been identified, triggered by excessive disulfide formation leading to cell shrinkage and F-actin contraction. Previous studies have identified 15 essential genes (FLNA, FLNB, MYH9, TLN1, ACTB, MYL6, MYH10, CAPZB, DSTN, IQGAP1, ACTN4, PDLIM1, CD2AP, INF2, SLC7A11) associated with disulfidptosis. This study sourced pan-cancer mRNA expression data from Xena to thoroughly evaluate the molecular and clinical characteristics of disulfidptosis-related genes. Through unsupervised clustering, mRNA expression data identified the expression levels of disulfidptosis-related genes and potential clusters related to this form of cell death. Kaplan-Meier survival curves illustrated the correlation between different clusters and overall survival. The findings reveal that high expression of disulfidptosis-related genes is linked to poor survival in liver cancer. The GDSC database was utilized to analyze the relationship between disulfidptosis-related genes and the AUC of 198 drugs. The results demonstrate that 12 disulfidptosis-related genes influence sorafenib resistance, as revealed by the intersection of differential genes related to sorafenib resistance from the GSE109211 dataset. Among them, the MYH9 gene was found to play a crucial role in both. Finally, experimental evidence confirmed that MYH9 mitigates sorafenib resistance in hepatocellular carcinoma through disulfidptosis-like changes. This study identifies disulfidptosis as a promising avenue for enhancing the sensitivity of tumor cells to drugs, offering new therapeutic perspectives for future research on disulfidptosis and drug resistance in cancer patients.

Disulfidoptosis as a Novel Mechanism of Neuronal Death: Insights from Creutzfeldt-Jakob Disease.

BACKGROUND: Sporadic Creutzfeldt-Jakob Disease (SCJD) is a severe neurodegenerative disorder characterized by rapid progression and extensive neuronal loss. Disulfidptosis is an innovative type of programmed cell demise characterized by an accumulation of disulfide bonds within the cellular cytoplasm, subsequently triggering functional disruption and cell demise. METHODS: Through literature review and analysis, we identified 18 candidate disulfidptosis-related genes (DRGs) involved in cellular processes. The dataset used for analysis, GSE124571, was obtained from the GEO database. Gene-gene and protein-protein interactions were analyzed using the GeneMANIA and STRING databases, respectively. We also performed enrichment analysis, DEGs analysis, WGCNA analysis, immune infiltration, consensus clustering and matrix correlation. RESULTS: The analysis showed that 12 out of 18 DRGs were significantly changed between SCJD and control groups. The DRGs had strong interactions such as physical interactions, co-expression and genetic interactions and were enriched in biological processes and pathways related to actin cytoskeletal regulation. The study most notably identified three hub genes (WASF2, TLN1 and G6PD) important for SCJD and emphasized the functional significance of the identified hub genes. The role of the immune system in the pathogenesis of SCJD The study found that the composition of immune cells in SCJD brain tissue is altered. Consensus clustering provided insights into immune infiltration and hub gene expression in SCJD subgroup. CONCLUSION: Our study reveals the possible involvement of disulfidptosis in SCJD and highlights the significance of identified hub genes as potential biomarkers and therapeutic targets for SCJD.

Disulfidptosis signature predicts immune microenvironment and prognosis of gastric cancer.

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.

Comprehensive evaluation of disulfidptosis in intestinal immunity and biologic therapy response in Ulcerative Colitis.

Objective: Ulcerative Colitis (UC) manifests as a chronic inflammatory condition of the intestines, marked by ongoing immune system dysregulation. Disulfidptosis, a newly identified cell death mechanism, is intimately linked to the onset and advancement of inflammation. However, the role of disulfidptosis in UC remains unclear. Methods: We screened differentially expressed genes (DEGs) associated with disulfidptosis in multiple UC datasets, narrowed down the target gene number using lasso regression, and conducted immune infiltration analysis and constructed a clinical diagnostic model. Additionally, we explored the association between disulfidptosis-related key genes and disease remission in UC patients receiving biologic therapy. Finally, we confirmed the expression of key genes in FHC cells and UC tissue samples. Results: In the differential analysis, we identified 20 DEGs associated with disulfidptosis. Immune infiltration results revealed that five genes (PDLIM1, SLC7A11, MYH10, NUBPL, OXSM) exhibited strong correlations with immune cells and pathways. Using GO, KEGG and WGCNA analyses, we discovered that gene modules highly correlated with disulfidptosis-related gene expression were significantly enriched in inflammation-related pathways. Additionally, we developed a nomogram based on these five immune-related disulfidptosis genes for UC diagnosis, showing robust diagnostic capability and clinical efficacy. Kaplan-Meier survival analysis revealed a significant link between changes in the expression levels of these cell genes and disease remission in UC patients receiving biologic therapy. In line with previous studies, similar expression changes of the target gene were seen in both UC cell models and tissue samples. Conclusions: This study utilized bioinformatic analysis and machine learning to identify and analyze features associated with disulfidptosis in multiple UC datasets. This enhances our comprehension of the role disulfidptosis plays in intestinal immunity and inflammation in UC, providing new perspectives for developing innovative treatments for UC.

Bioinformatics Identification and Experimental Verification of Disulfidptosis-Related Genes in the Progression of Osteoarthritis.

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.

Development of a disulfidptosis-related lncRNA prognostic signature for enhanced prognostic assessment and therapeutic strategies in lung squamous cell carcinoma.

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.

Elevated SLC3A2 associated with poor prognosis and enhanced malignancy in gliomas.

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.

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.

Identification of disulfidptosis- and ferroptosis-related transcripts in periodontitis by bioinformatics analysis and experimental validation.

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.

A disulfidptosis-related lncRNAs cluster to forecast the prognosis and immune landscapes of ovarian cancer.

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.

Pan-cancer analysis of disulfidptosis with potential implications in prognosis, immune microenvironment, and drug resistance in human cancer.

To get a systematic assessment of disulfidptosis-related genes across human cancers and explore the predictive role of disulfidptosis in cancer drug sensitivity. We developed a score-level model to quantify the level of disulfidptosis in 33 human cancers using TCGA data. The mRNA expression and protein levels of disulfidptosis-related genes in human cancer cells and tissues were detected and retrieved from the Human Protein Atlas. Multiomics bioinformatic analyses were performed to evaluate disulfidptosis-related gene characteristics as well as the effect of disulfidptosis on the cancer immune microenvironment and drug resistance. Thirty cancers showed significantly different expression levels of disulfidptosis-related genes between normal and tumor samples. The mRNA expression and protein level of disulfidptosis-related genes were consistent with TCGA databases in lung cancer and hepatocellular carcinoma. We also found that altered levels of the disulfidptosis score expression were usually related to patient prognosis, and high expression of disulfidptosis-related genes was associated with drug resistance in different cancer types. Our study illustrates the characterization of disulfidptosis in multiple cancer types and highlights its potential value as a predictive biomarker of drug response, which can pave the way for further investigation of the prognostic and therapeutic potential of disulfidptosis.

Exploring the immune landscape of disulfidptosis in ulcerative colitis and the role of modified gegen qinlian decoction in mediating disulfidptosis to alleviate colitis in mice.

ETHNOPHARMACOLOGICAL RELEVANCE: Ulcerative colitis (UC), a recurrent inflammatory bowel disease, continues to challenge effective pharmacologic management. Disulfidptosis, a recently identified form of cell death, appears implicated in the progression of various diseases. Scientific studies have demonstrated that Modified Gegen Qinlian decoction (MGQD) alleviates UC symptoms. However, the underlying mechanisms remain inadequately elucidated. AIM OF THE STUDY: This study investigated the role of disulfidptosis in UC and explored the potential of MGQD to ameliorate UC by mediating disulfidptosis. METHODS: Microarray data were utilized to identify disulfidptosis-related genes stably expressed in UC, and integrated genomic analyses were conducted to elucidate the landscape of disulfidptosis in UC. Subsequently, C57BL/6J mice were administered 3% dextran sodium sulfate (DSS) to induce experimental colitis and treated with MGQD. Quantitative real-time polymerase chain reaction and immunohistochemical analysis of colonic tissues from colitis mice were performed to validate the microarray data findings. Finally, molecular docking was employed to explore the binding interactions between MGQD components and disulfidptosis biomarkers. RESULTS: Myosin heavy chain 10 (MYH10) and filamin A (FLNA) were identified as stably expressed in UC, demonstrating high diagnostic value for the disease. Correlation analysis indicated that disulfidptosis-related genes are associated with elevated levels of immune cells in UC. Single gene set enrichment analysis further clarified that these genes might be involved in the pathological processes of UC via immune-related pathways. Subsequent animal experiments revealed that MYH10 and FLNA were significantly upregulated in mice with colitis, a condition reversed by MGQD treatment. Molecular docking results showed that MYH10 and FLNA serve as stable binding targets for the primary components of MGQD. CONCLUSIONS: The study identified a connection between the disulfidptosis-related landscape and immune infiltration in UC, suggesting that MGQD may modulate disulfidptosis by inhibiting MYH10 and FLNA, thereby alleviating UC.

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

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.

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.

Correlation of disulfidptosis and periodontitis: New insights and clinical significance.

OBJECTIVES: This study aims to investigate and predict the therapeutic agents associated with disulfidptosis in periodontitis. DESIGN: The dataset GSE10334 was downloaded from the Gene Expression Omnibus (GEO) database and used to train a least absolute shrinkage and selection operator (LASSO) regression and support vector machine recursive feature elimination (SVM-RFE) algorithm to identify genes associated with disulfidptosis in periodontitis. GSE16134 validation sets, polymerase chain reaction (PCR), and gingival immunofluorescence were used to verify the results.Single-gene Gene Set Enrichment Analysis (GSEA) was performed to explore the potential mechanisms and functions of the characterized genes. Immune infiltration and correlation analyses were performed, and competing endogenous RNA (ceRNA) networks were constructed. Effective therapeutic drugs were then predicted using the DGIdb database, and molecular docking was used to validate binding affinity. RESULTS: Six genes (SLC7A11, SLC3A2, RPN1, NCKAP1, LRPPRC, and NDUFS1) associated with disulfidptosis in periodontitis were obtained. Validation results from external datasets and experiments were consistent with the screening results. Single-gene GSEA analysis was mainly enriched for antigen presentation and immune-related pathways and functions.Immune infiltration and correlation analyses revealed significant regulatory relationships between these genes and plasma cells, resting dendritic cell, and activated NK cells. The ceRNA network was visualized. And ME-344, NV-128, and RILUZOLE, which have good affinity to target genes, were identified as promising agents for the treatment of periodontitis. CONCLUSIONS: SLC7A11, SLC3A2, RPN1, NCKAP1, LRPPRC, and NDUFS1 are targets associated with disulfidptosis in periodontitis, and ME-344, NV-128, and RILUZOLE are promising agents for the treatment of periodontitis.