Identification of a disulfidptosis-related genes signature for prognostic implication in lung adenocarcinoma

Identification of disulfidptosis-related genes and subgroups in spinal cord injury

STUDY DESIGN

Bioinformatics analysis and experimental validation study.

OBJECTIVES

To investigate the role and expression patterns of disulfidptosis-related genes in spinal cord injury (SCI), identify potential pivotal genes, and explore possible therapeutic targets.

SETTING

Shanghai, China.

METHODS

Data acquisition and pre-processing: Screened 27 disulfidptosis-related genes based on literature and downloaded RNA-sequencing data of ASCI patients from GEO database (GSE151371); Identification of differentially expressed genes (DEGs): Used R package "limma" for differential gene expression analysis between ASCI samples and normal controls; Evaluating immune cell infiltration: Employed ssGSEA algorithm and CIBERSORT to determine immune cell abundance; Identification and functional verification of key genes: Intersected disulfidptosis-related genes with DEGs, and used machine learning techniques (Random Forest, Lasso, Support Vector Machine) to identify hub genes. Validated hub genes expression by real-time PCR; Construction of a diagnostic model: Developed a backpropagation neural network clinical prediction model based on hub genes and clinical features, and evaluated its performance using ROC curve. 6. Subcluster analysis: Performed consensus cluster analysis of ASCI samples and hub genes, and used GSVA to elucidate functional differences between subgroups.

RESULTS

Identified 7764 DEGs in ASCI, with GO and KEGG enrichment in inflammation and autophagy-related pathways; Found differences in immune cell infiltration between ASCI and control groups, and correlation between immune cells and DRGs; Determined seven hub genes (MYL6, NUBPL, CYFIP1, IQGAP1, FLNB, SLC7A11, CD2AP) through machine learning; Validated the expression of hub genes by qRT-PCR; Constructed a clinical diagnostic model with good predictive accuracy (overall dataset accuracy of 83.3%); Identified two subtypes of ASCI based on hub genes, with different immune infiltration and pathway activity.

CONCLUSION

Disulfidptosis is closely related to spinal cord injury. The identified hub genes and subtypes provide new insights for biomarker and therapeutic target research. The diagnostic model has potential for clinical application, but further studies are needed due to limitations such as small sample size.

SPONSORSHIP

This study was supported in part by the project of Youth Scientific and Technological Talents of PLA (2020QN06125), Changhong Talent Project in First affiliated hospital of Navy Medical University (Wei Xianzhao) and Basic Medical Research Project in First affiliated hospital of Navy Medical University (2023PY17). I want to reiterate that there is no prior publication of figures or tables and no conflict of interest in the submission of this manuscript. The graphical abstract is divided into two parts. The upper section sequentially illustrates the occurrence of disulfidptosis and changes in the immune microenvironment in the human body after SCI. The lower section displays the construction of a diagnostic model for SCI through the detection of changes in disulfidptosis-related genes, combined with patient clinical information.

Sulfide regulation and catabolism in health and disease

The metabolic pathway of sulfur-containing amino acids in organisms begins with methionine, which is metabolized to produce important sulfur-containing biomolecules such as adenosylmethionine, adenosylhomocysteine, homocysteine, cystine, and hydrogen sulfide (H2S). These sulfur-containing biomolecules play a wide range of physiological roles in the body, including anti-inflammation, antioxidant stress, DNA methylation, protein synthesis, etc., which are essential for maintaining cellular function and overall health. In contrast, dysregulation of the metabolic pathway of sulfur-containing amino acids leads to abnormal levels of sulfur-containing biomolecules, which produce a range of pathological consequences in multiple systems of the body, such as neurodegenerative diseases, cardiovascular diseases, and cancer. This review traces the milestones in the development of these sulfur-containing biomolecules from their initial discovery to their clinical applications and describes in detail the structure, physiochemical properties, metabolism, sulfide signaling pathway, physiopathological functions, and assays of sulfur-containing biomolecules. In addition, the paper also explores the regulatory role and mechanism of sulfur-containing biomolecules on cardiovascular diseases, liver diseases, neurological diseases, metabolic diseases and tumors. The focus is placed on donors of sulfur-containing biological macromolecule metabolites, small-molecule drug screening targeting H2S-producing enzymes, and the latest advancements in preclinical and clinical research related to hydrogen sulfide, including clinical trials and FDA-approved drugs. Additionally, an overview of future research directions in this field is provided. The aim is to enhance the understanding of the complex physiological and pathological roles of sulfur-containing biomolecules and to offer insights into developing effective therapeutic strategies for diseases associated with dysregulated sulfur-containing amino acid metabolism.

Disulfidptosis ‒ related lncRNAs are biomarkers of prognosis and immune response in Head and Neck Squamous Cell Carcinoma

OBJECTIVE

This study aims to explore the role of Disulfidptosis-Related long Non-Coding RNAs (DRlncRNAs) in the prognosis and immune infiltration of Head and Neck Squamous Cell Carcinoma (HNSCC).

METHODS

Using bioinformatics approaches, this study investigates the prognostic significance of DRlncRNAs in HNSCC patients and their potential association with the immune microenvironment. RNA sequencing data and clinical information for HNSCC were obtained from The Cancer Genome Atlas (TCGA) database. DRlncRNAs were identified through Pearson correlation analysis, and a prognostic model consisting of six DRlncRNAs was constructed using Least Absolute Shrinkage and Selection Operator (LASSO) regression, along with univariate and multivariate Cox analyses.

RESULTS

The predictive performance of the model was assessed using Receiver Operating Characteristic (ROC) curves and Principal Component Analysis (PCA), and further validated using calibration curves, a nomogram, and univariate/multivariate Cox analyses. In addition to functional enrichment analysis, the associations between the model and Tumor Mutation Burden (TMB), immune cell infiltration, and drug sensitivity were also examined.

CONCLUSION

We developed a novel predictive model composed of six DRlncRNAs to predict the prognosis of HNSCC patients and proposed potential clinical therapeutic targets from the perspective of disulfidptosis.

LEVEL OF EVIDENCE

Level 5.

Revealing VCAN as a Potential Common Diagnostic Biomarker of Renal Tubules and Glomerulus in Diabetic Kidney Disease Based on Machine Learning, Single-Cell Transcriptome Analysis and Mendelian Randomization

BACKGRUOUND

Diabetic kidney disease (DKD) is recognized as a significant complication of diabetes mellitus and categorized into glomerular DKDs and tubular DKDs, each governed by distinct pathological mechanisms and biomarkers.

METHODS

Through the identification of common features observed in glomerular and tubular lesions in DKD, numerous differentially expressed gene were identified by the machine learning, single-cell transcriptome and mendelian randomization.

RESULTS

The diagnostic markers versican (VCAN) was identified, offering supplementary options for clinical diagnosis. VCAN significantly highly expressed in glomerular parietal epithelial cell and proximal convoluted tubular cell. It was mainly involved in the up-regulation of immune genes and infiltration of immune cells like mast cell. Mendelian randomization analysis confirmed that serum VCAN protein levels were a risky factor for DKD, while there was no reverse association. It exhibited the good diagnostic potential for estimated glomerular filtration rate and proteinuria in DKD.

CONCLUSION

VCAN showed the prospects into DKD pathology and clinical indicator.

The miR-941/FOXN4/TGF-β feedback loop induces N2 polarization of neutrophils and enhances tumor progression of lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is a major subtype of lung cancer and one of the deadliest cancers in humans. Dysregulation of miRNA activity in tumor-associated neutrophils (TANs) in the tumor microenvironment plays an important role in the occurrence and development of LUAD.

Method

In this study, the miReact algorithm was used to analyze the single-cell RNA sequencing data of LUAD samples to reveal the miRNA profile characteristics of TANs in LUAD patients. The function of miR-941 was investigated in vivo and in vitro. The target gene and underlying signaling pathway of miR-941 were predicted and validated with qPCR, luciferase assay, WB and ELISA assay.

Results

The results indicated the crucial role of TANs, especially N2-TANs in LUAD and miR-941 activity was significantly upregulated in TANs of LUAD patients. MiR-941 overexpression promoted the proliferation, invasion, migration and anti-apoptosis of A549 and H1299. In vivo xenograft mouse model confirmed that miR-941 overexpression enhanced the growth of tumors formed by H1299 cells. Bioinformatics analysis showed that miR-941 targeted the tumor suppressor gene FOXN4, and we confirmed that FOXN4 overexpression could counteract the malignant effects of miR-941. In addition, miR-941 may drive LUAD progression through the FOXN4/TGF-β feedback signaling loop and participate in the N2-TAN polarization.

Conclusion

In summary, these findings reveal the key role of N2-TANs and the miR-941/FOXN4/TGF-β signaling loop in LUAD progression and provide potential therapeutic targets for future interventions.

Identification of disulfidptosis in esophageal squamous cell carcinoma based on single-cell and bulk RNA-seq data to predict prognosis and treatment response

Purpose

Our study aims to identify the molecular subtypes of genes associated with disulfidptosis in esophageal squamous cell carcinoma(ESCC), develop a prognostic model, and identify potential therapeutic targets.

Methods

Based on the GSE53625 expression profile data, we identified molecular subtypes with significant survival differences through consensus cluster analysis. Subsequently, univariate Cox, multivariate Cox, and LASSO-Cox regression analysis were used to establish risk stratification models. The transcriptome data of the TCGA-ESCC cohort and the GSE160269 single-cell sequencing dataset were integrated to verify the biological significance of the model, and further analyze the heterogeneity of the tumor immune microenvironment, explore the differences in the intercellular communication network, and screen potential targeted drugs, providing a theoretical basis for subsequent translational research.

Results

We identified two distinct patterns of disulfidptosis expression with significant differences in overall survival. Then, we constructed the prognostic signature of disulfidptosis, and results showed patients with high score had worse prognosis. Univariate and multivariate Cox analysis demonstrated that the constructed prognostic signature was an independent prognostic factor and was validated in an independent validation set. The two subgroups differed in the proportion of immune cell infiltration and related signaling pathways in ESCC. The exploration of immunotherapy data confirmed our prognostic signature also had certain predictive power for immunotherapy. Drug screening suggested AZD8186 and JQ1 as potential therapies for high-score patients.

Conclusion

This study provides a new prognostic signature for ESCC, explores new therapeutic targets, and provides new theoretical support for personalized treatment.

A Novel Disulfidptosis-Related Risk Signature for Prognostic Prediction in Patients With Ewing Sarcoma

Ewing sarcoma (ES) is a malignant bone tumor prevalent among children and adolescents. Disulfidptosis represents a novel form of cell death; however, the mechanism of disulfidptosis in ES remains unclear. Our aim is to explore the disulfidptosis-related prognostic signature in ES. Utilizing transcriptomic and clinical data of ES, disulfidptosis-related hub genes (DRHGs) were identified by differential gene expression analysis and Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analysis. A disulfidptosis-related risk score model (DRRS) was constructed based on these DRHGs. The performance of DRRS was assessed using survival analysis and receiver operating characteristic curve analysis. Immune cell infiltration in different risk subgroups and correlations between DRRS and antitumor reagents were also analyzed. In this study, we developed a disulfidptosis-related prognostic feature based on LRPPRC (leucine rich pentatricopeptide repeat containing), IQGAP1 (IQ motif containing GTPase activating protein 1), NDUFS1 (NADH:ubiquinone oxidoreductase core subunit S1), and TLN1 (talin 1), which may serve as a predictive and independent risk factor for ES. ES patients in the high-risk group exhibited a poorer prognosis, had a higher proportion of myeloid-derived suppressor cells (MDSCs) and M2 type of tumor-associated macrophages, and showed heightened sensitivity to some antitumor agents such as nilotinib and olaparib. This study is the first to construct a disulfidptosis-related prognostic signature that may predict the prognosis and immune response in ES patients, thereby providing a new reference for understanding the mechanisms of ES and guiding immunotherapy.

The complexities of cell death mechanisms: a new perspective in systemic sclerosis therapy

Systemic sclerosis, also termed scleroderma, is a severe and debilitating autoimmune disease characterized by fibrosis, an aberrant immune response, and vascular dysfunction. Cell death is essential to the body's continued normal development as it removes old or damaged cells. This process is governed by several mechanisms, including programmed cell death through apoptosis, necrosis, and pyroptosis, as well as metabolic processes, such as ferroptosis and cuproptosis. This review describes the signaling pathways associated with each form of cell death, examining the linkages between these pathways, and discussing how the dysregulation of cell death processes is involved in the development of autoimmune disorders such as systemic sclerosis. Existing and promising therapeutic strategies aimed at restoring the balance of cell death in systemic sclerosis and other autoimmune disorders are also emphasized.

Development and validation of M2 macrophage-related genes in a prognostic model of lung adenocarcinoma based on bulk RNA and ScRNA datasets

OBJECTIVE

This study aimed to investigate the correlation between M2 macrophages activity with the prognosis of lung adenocarcinoma (LUAD). We sought to identify key genes associated with M2 macrophage activity and examine their relationship with clinicopathological features to elucidate the underlying mechanism.

METHODS

Published datases were analyzed for differentially expressed genes. After quality control, batch effect removal, and annotation, the scRNA dataset identified M2 macrophage-associated differentially expressed genes in the LUAD group, which were cross-analyzed and referred to as M2 macrophage-linked genes. A risk model was generated using machine learing for these genes. Thereafter, two bulk RNA-seq datasets were used to evaluate the model. We computed risk scores for all samples and grouped them into low and high risk, aiding in the comparison of clinical characteristics, immune and stromal infiltration, and drug sensitivity. Finally, key genes were validated through immunohistochemistry in IPA samples.

RESULTS

We identified four key M2 macrophage-linked genes: TIMP1, CAV2, MIF, and SELENBP1. Survival durations in the high-riskscore cluster were lower across the TCGA-LUAD (P = 1.2 × 10-4), GSE14814 (P = 0.02), and GSE37745 (P = 0.01) data sets. The stromal score, fibroblast infiltration, and cytokinesis activation were increased in the high-risk subgroup. Neutrophil and endothelial cell infiltration and activation of the linolenic acid pathway occurred in the low-risk group. IHC confirmed that CAV2 and SELENBP1 expression was significantly reduced, while TIMP1 and MIF were significantly increased in LUAD, which was consistent with the bioinformatics findings.

CONCLUSION

The role of M2 macrophages in tumor progression could anticipate the prognosis of LUAD and develop novel immunotherapy strategies.

Integrated single-cell analysis reveals the regulatory network of disulfidptosis-related lncRNAs in bladder cancer: constructing a prognostic model and predicting treatment response

Background

Disulfidptosis is a newly discovered form of cell death, and long non-coding RNAs (lncRNAs) play a crucial role in tumor cell growth, migration, recurrence, and drug resistance, particularly in bladder cancer (BLCA). This study aims to investigate disulfidptosis-related lncRNAs (DRLs) as potential prognostic markers for BLCA patients.

Methods

Utilizing single-cell sequencing data, RNA sequencing data, and corresponding clinical information sourced from the GEO and TCGA databases, this study conducted cell annotation and intercellular communication analyses to identify differentially expressed disulfide death-related genes (DRGs). Subsequently, Pearson correlation and Cox regression analyses were employed to discern DRLs that correlate with overall survival. A prognostic model was constructed through LASSO regression analysis based on DRLs, complemented by multivariate Cox regression analysis. The performance of this model was rigorously evaluated using Kaplan-Meier analysis, receiver operating characteristic (ROC) curves, and area under the ROC curve (AUC). Furthermore, this investigation delved into the potential signaling pathways, immune status, tumor mutation burden (TMB), and responses to anticancer therapies associated with varying prognoses in patients with BLCA.

Results

We identified twelve differentially expressed DRGs and elucidated their corresponding intercellular communication relationships. Notably, epithelial cells function as ligands, signaling to other cell types, with the interactions between epithelial cells and both monocytes and endothelial cells exhibiting the strongest connectivity. This study identified six DRLs in BLCA-namely, C1RL-AS1, GK-AS1, AC134349.1, AC104785.1, AC011092.3, and AC009951.6, and constructed a nomogram to improve the predictive accuracy of the model. The DRL features demonstrated significant associations with various clinical variables, diverse immune landscapes, and drug sensitivity profiles in BLCA patients. Furthermore, RT-qPCR validation confirmed the aberrant expression levels of these DRLs in BLCA tissues, affirming the potential of DRL characteristics as prognostic biomarkers.

Conclusion

We established a DRLs model that serves as a predictive tool for the prognosis of BLCA patients, as well as for assessing tumor mutation burden, immune cell infiltration, and responses to immunotherapy and targeted therapies. Collectively, this study contributes valuable insights toward advancing precision medicine within the context of BLCA.

Construction of a novel disulfidptosis and cuproptosis-related lncRNA signature for predicting the clinical outcome and immune response in stomach adenocarcinoma

BACKGROUND

Disulfidptosis, a newly discovered form of cell death resulting from disulfide stress, remains unclear in its role in stomach adenocarcinoma (STAD). This study aimed to establish a novel disulfidptosis and cuproptosis-related lncRNAs (DCRLs) signature for STAD.

METHODS

We sourced RNA-seq data for STAD from the The Cancer Genome Atlas (TCGA) repository. STAD samples underwent nonnegative matrix factorization (NMF) clustering to identify distinct molecular subgroups, followed by Lasso-Cox regression to construct a prognostic model for DCRLs. Subsequently, the model's clinical predictive capacity was evaluated using a nomogram. The expression of risk lncRNAs was validated via quantitative reverse transcription polymerase chain reaction (qRT-PCR).

RESULTS

The samples were classified into three molecular subtypes based on DCRLs, with the C1 subtype demonstrating the worst prognosis. We identified four independent prognostic lncRNAs (AC016394.2, NUTM2A-AS1, OIP5-AS1, and LIMS1-AS1) and constructed a prognostic risk model. Survival analysis revealed that high-risk patients had a poorer prognosis. The model's risk score was strongly correlated with the tumor mutational burden (TMB), microsatellite instability (MSI), immune subtypes, and tumor-infiltrating immune cells (TIICs) in the tumor microenvironment (TME). Analysis utilizing the Tumor Immune Dysfunction and Exclusion (TIDE) revealed a higher risk of tumor immune evasion among high-risk patients. Moreover, the expression levels of four risk lncRNAs were higher in the majority of gastric cancer cell lines compared to normal cell lines.

CONCLUSION

Our study establishes a risk model that effectively predicts clinical outcomes and immune response in STAD.

Bioinformatics analysis of genes associated with disulfidptosis in spinal cord injury

Research findings indicate that programmed cell death (PCD) plays a pivotal role in the pathophysiology of spinal cord injury (SCI), and a recently discovered form of cell death, disulfidptosis, has emerged as a novel phenomenon. However, the characterization of disulfidptosis-related genes in SCI remains insufficiently explored. We retrieved SCI-related data from the Gene Expression Omnibus (GEO) database and identified three key genes associated with disulfidptosis in human SCI (CAPZB, SLC3A2, and TLN1), whose mediated signaling pathways are closely intertwined with SCI. Subsequent functional enrichment analysis suggested that these genes may regulate multiple pathways and exert corresponding roles in SCI pathology. Moreover, we predicted potential targeted drugs for the key genes along with their transcription factors and constructed an intricate regulatory network. CIBERSORT analysis revealed that CAPZB, SLC3A2, and TLN1 might be implicated in modulating changes within the immune microenvironment of individuals with SCI. Our study provides compelling evidence confirming the significant involvement of disulfidptosis following SCI while offering valuable insights into its underlying pathological mechanisms.

NDUFA11 may be the disulfidptosis-related biomarker of ischemic stroke based on integrated bioinformatics, clinical samples, and experimental analyses

Background

Programmed cell death plays an important role in neuronal injury and death after ischemic stroke (IS), leading to cellular glucose deficiency. Glucose deficiency can cause abnormal accumulation of cytotoxic disulfides, resulting in disulfidptosis. Ferroptosis, apoptosis, necroptosis, and autophagy inhibitors cannot inhibit this novel programmed cell death mechanism. Nevertheless, the potential mechanisms of disulfidptosis in IS remain unclear.

Methods

The GSE16561 dataset was used to screen for differentially expressed disulfidptosis-related biomarkers (DE-DRBs). A correlation between the DE-DRBs was detected. The optimal machine-learning (ML) model and predictor molecules were determined. The GSE58294 dataset was used to verify the accuracy of the optimal ML model. The DE-DRB expression was detected in the blood of patients with IS. Based on IS models, experimental analyses were performed to verify DE-DRB expression and the correlation between DE-DRBs.

Results

Leucine-rich pentatricopeptide repeat-containing (LRPPRC) and NADH dehydrogenase [ubiquinone] 1 alpha subcomplex subunit 11 (NDUFA11) were identified as DE-DRBs. The NADH: ubiquinone oxidoreductase core subunit S1 (NDUFS1) interacted with NDUFA11 and LRPPRC. The support vector machine (SVM) model was identified as the optimal ML model. The NDUFA11 expression level in the blood of patients with IS was 20.9% compared to that in normal controls. NDUFA11 expression was downregulated in the in vitro/in vivo models of IS. The number of formed complexes of NDUFS1 and NDUFA11 decreased in the in vitro/in vivo models of IS.

Conclusion

This research suggests that NDUFA11 is a specific DRB for IS and demonstrates alterations in the disulfidptosis-related protein complexes NDUFS1-NDUFA11.

Construction of a prognostic model based on disulfidptosis-related genes and identification of CCNA2 as a novel biomarker for hepatocellular carcinoma

BACKGROUND

Disulfidptosis, identified as an innovative form of cellular death subsequent to cuproptosis, is currently under investigation for its mechanisms in oncological contexts. In-depth analyses exploring the relationship between disulfidptosis-related genes (DRGs) and hepatocellular carcinoma (HCC) are currently limited.

METHODS

Transcriptomic data and clinical information were retrieved from the TCGA and GEO databases (GSE76427 and GSE54236), concentrating on the expression levels of 24 DRGs. Subsequently, multifactor and LASSO regression analyses were utilized to construct the 5-DRG prognostic signature. Immunohistochemistry (IHC) was employed to assess Cyclin A2 (CCNA2) protein expression levels. Quantitative real-time PCR (qRT-PCR) and western blot analyses were conducted to detect transcriptomic and protein expression of CCNA2-targeting short interfering RNA (siRNA). The Cell Counting Kit-8 (CCK-8) assay, EdU staining, and scratch experiments were employed to observe the proliferation and migration of hepatoma cell lines subsequent to CCNA2 inhibition.

RESULTS

Three HCC patterns were identified, among which pattern B exhibited the the most unfavorable survival outcomes. Five DRGs (STC2, PBK, CCNA2, SERPINE1, and SLC6A1) were involved to establish the 5-DRG prognostic signature. High-risk groups (HRGs) exhibited prolonged survival durations in comparison to low-risk groups (LRGs). Both bioinformatics analyses and experimental methodologies corroborated the association of CCNA2 with poor prognosis in HCC patients. Functional studies elucidated that interference with CCNA2 significantly inhibited proliferation and migration, while simultaneously promoting apoptosis in hepatoma cells and resulting in the downregulation of epithelial-mesenchymal transition (EMT)-related protein markers.

CONCLUSIONS

The 5-DRG prognostic signature is proficient in predicting clinical outcomes, informing therapeutic strategies, and elucidating the characteristics of the immune microenvironment in HCC patients. Furthermore, this study elucidates the potential of CCNA2 as an innovative biomarker for HCC.

Exploration on the construction of a bladder cancer prognostic model based on disulfidptosis-related lncRNAs and its clinical significance

Disulfidptosis is a novel programmed cell death mode that has been reported to play a role in oncogenesis. Increasing evidences suggest that the long non-coding RNAs (lncRNAs) play crucial roles in the initiation and progression of bladder cancer (BLCA). However, the role and prognostic value of disulfidptosis-related lncRNAs in BLCA remain unknown.The aim of this study was to construct and validate a disulfidptosis-related lncRNA risk model for predicting the prognosis of BLCA patients. A risk model consisting of 5 disulfidptosis-related lncRNAs was developed to predict the prognosis of BLCA patients. The overall survival (OS) of BLCA patients in the high-risk group was significantly shorter than that in the low-risk group (P < 0.05). The effectiveness of this model was validated using the receiver operating characteristic (ROC) curve analysis, and this model proved superior in prognostic accuracy compared with other clinical features. Furthermore, the tumor immune dysfunction and exclusion (TIDE) score in the high-risk group was significantly higher than that in the low-risk group, suggesting that the high-risk group had a less favorable response to immunotherapy. Simultaneously, patients in the low-risk group exhibited significantly higher sensitivity to CTLA-4 monoclonal antibody therapy compared to those in the high-risk group, suggesting potential benefits of immunotherapy for patients in the low-risk group. The combination of high risk and low tumor mutational burden (TMB) could further shortened the OS of BLCA patients. Lastly, the drug sensitivity analysis revealed that the BLCA cells in the high-risk group showed an increased sensitivity to cisplatin, sunitinib, cetuximab, axitinib, docetaxel, saracatinib, vinblastine and pazopanib compared with those in the low-risk group. According to the Quantitative real time PCR results, we found that five lncRNAs of the risk model were more highly expressed in BCa cell lines than human immortalized uroepithelial cell line. The disulfidptosis-related lncRNA risk model has a valuable effect in assessing the prognosis of BLCA patients.

Investigating the role of disulfidptosis related genes in radiotherapy resistance of lung adenocarcinoma

Background

Radiotherapy resistance is an important reason for high mortality in lung cancer patients, but the mechanism is still unclear. Dysregulation of cell proliferation and death plays a crucial role in the onset and progression of lung adenocarcinoma (LUAD). In recent times, a novel form of cellular demise called disulfidptosis, has attracted increasing attention. However, it is unclear whether the radiation-related disulfidptosis genes have prognostic role in LUAD.

Methods

A complete suite of bioinformatics tools was used to analyze the expression and prognostic significance of radiation-related disulfidptosis genes. Afterward, we investigated the predictive significance of the risk signature in tumor microenvironments (TME), somatic mutations, and immunotherapies. In addition, we conducted a series of experiments to verify the expression of differentially expressed radiotherapy related disulfidptosis genes (DERRDGs) in vitro.

Results

A total of 88 DERRDGs were found. We constructed and validated a novel prognostic model based on PRELP, FGFBP1, CIITA and COL5A1. The enrichment analysis showed the DERRDG affected tumor prognosis by influencing tumor microenvironments (TME) and immunotherapy. And we constructed nomogram to promote clinical application. In addition, q-PCR confirmed the significant differences in the expression of prognostic genes between A549 irradiation-resistance cell and A549. Finally, western-blot, IHC staining, and small interference experiment suggested that PRELP may be a potential biomarker for radiotherapy resistance, whose low expression was associated with poor outcomes in LUAD patients.

Conclusion

This study reveals the signature and possible underlying mechanisms of DERRDGs in LUAD and discovered the key gene PRELP, which helps to identify new prognostic biomarkers and provides a basis for future research.

Integrated analysis and experiments uncover the function of disulfidptosis in predicting immunotherapy effectiveness and delineating immune landscapes in uterine corpus endometrial carcinoma

Introduction

Recently, a novel type of metabolic-regulated cell demise titled disulfidptosis has been discovered. Studies have demonstrated its importance in immune responses against cancer and its impact on the proliferation of cancer cells. Nonetheless, the precise mechanism and roles of disulfidptosis are not fully understood, particularly regarding the prognosis for individuals with uterine corpus endometrial carcinoma (UCEC).

Methods

In this research, a distinctive disulfidptosis pattern was developed in UCEC, and by utilizing Non-negative Matrix Factorization (NMF) on 23 disulfidptosis related genes within the TCGA database, 3 distinct subgroups were distinguished. To collect data, we acquired gene expression profiles, somatic mutation information, copy number variation data, and corresponding clinical data from the TCGA and GEO database, specifically from UCEC patients. Cell line experiments and immunohistochemical (IHC) staining were conducted to validate the role of the LRPPRC in proliferation, migration and invasion.

Results

The genetic features and immune microenvironment of these subgroups were examined. It is worth mentioning that these subgroups offer important insights into comprehending the tumor microenvironment (TME) and the response of patients to immunotherapy and chemotherapy. Moreover, a disulfidptosis model was developed and validated, demonstrating a high level of accuracy in predicting the prognosis and outcomes of immunotherapy in UCEC patients. Additionally, a novel biomarker, LRPPRC, was identified, which can server as a promising predictor for forecasting prognosis in UCEC patients, with validation through tissue microarray staining and cell line experiments.

Discussion

This study has designed a classification system and a disulfidptosis model for UCEC, in addition to identifying a new biomarker, LRPPRC, for UCEC. These advancements serve as reliable and positive indicators for predicting outcomes and the efficacy of immunotherapy for each UCEC patient.

Development of a disulfidptosis-related prognostic model for endometrial cancer with potential therapeutic target

Prognosis biomarkers for endometrial cancer (EC) are in need. Recent evidence demonstrated the critical role of disulfidptosis, a novel cell death modality, in cancer. However, limited studies have developed a disulfidptosis-related gene model for EC. Disulfidptosis prognosis score of EC (disulfidptosis-PSEC) were constructed using disulfidptosis-related differently expression genes with the RNA data of 544 EC patients from The Cancer Genome Atlas (TCGA) dataset. Model was evaluated using time-dependent receiver operating characteristic curve analysis for overall survival (OS) and disease-free survival (DFS), along with the hazard ratio (HR) between risk groups. Then, the cellular and molecular profile for different risk groups were performed, along with drug target inference. Disulfidptosis-PSEC demonstrated outstanding prognostic value for OS and DFS, with 5-year area under curve of 0.71 (95% CI, 0.58-0.75) and 0.69 (95% CI, 0.62-0.76), respectively. Low risk group demonstrated better survival with an HR of 0.38 (95% CI, 0.24-0.59) and 0.46 (95% CI, 0.32-0.66) for OS and DFS, respectively. The model was independent of TCGA subtype. Low risk group were featured with more immune cell infiltration and less gene mutation. Serval drug targets, and the therapeutic value of serotonin receptor among copy number (CN)-low subpopulation, were identified. Disulfidptosis-PSEC was a potential prognosis biomarker for EC with targetable biological process.

Integrating multi-omics and machine learning survival frameworks to build a prognostic model based on immune function and cell death patterns in a lung adenocarcinoma cohort

INTRODUCTION

The programmed cell death (PCD) plays a key role in the development and progression of lung adenocarcinoma. In addition, immune-related genes also play a crucial role in cancer progression and patient prognosis. However, further studies are needed to investigate the prognostic significance of the interaction between immune-related genes and cell death in LUAD.

METHODS

In this study, 10 clustering algorithms were applied to perform molecular typing based on cell death-related genes, immune-related genes, methylation data and somatic mutation data. And a powerful computational framework was used to investigate the relationship between immune genes and cell death patterns in LUAD patients. A total of 10 commonly used machine learning algorithms were collected and subsequently combined into 101 unique combinations, and we constructed an immune-associated programmed cell death model (PIGRS) using the machine learning model that exhibited the best performance. Finally, based on a series of in vitro experiments used to explore the role of PSME3 in LUAD.

RESULTS

We used 10 clustering algorithms and multi-omics data to categorize TCGA-LUAD patients into three subtypes. patients with the CS3 subtype had the best prognosis, whereas patients with the CS1 and CS2 subtypes had a poorer prognosis. PIGRS, a combination of 15 high-impact genes, showed strong prognostic performance for LUAD patients. PIGRS has a very strong prognostic efficacy compared to our collection. In conclusion, we found that PSME3 has been little studied in lung adenocarcinoma and may be a novel prognostic factor in lung adenocarcinoma.

DISCUSSION

Three LUAD subtypes with different molecular features and clinical significance were successfully identified by bioinformatic analysis, and PIGRS was constructed using a powerful machine learning framework. and investigated PSME3, which may affect apoptosis in lung adenocarcinoma cells through the PI3K/AKT/Bcl-2 signaling pathway.

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.

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.

Effect of 23‑hydroxybetulinic acid on lung adenocarcinoma and its mechanism of action

The present study aimed to investigate the effect and mechanism of Pulsatilla compounds on lung adenocarcinoma. The representative drug chosen was the compound 23-HBA. GeneCards, Swiss target prediction, DisGeNET and TCMSP were used to screen out related genes, and MTT and flow cytometry assays were used to verify the inhibitory effect of Pulsatilla compounds on the proliferation of lung adenocarcinoma cells. Subsequently, the optimal target, peroxisome proliferator-activated receptor (PPAR)-γ, was selected using bioinformatics analysis, and its properties of low expression in lung adenocarcinoma cells and its role as a tumor suppressor gene were verified by western blot assay. The pathways related to immunity and inflammation, vascular function, cell proliferation, differentiation, development and apoptosis with the highest degree of enrichment and the mechanisms were explored through Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses. Finally, the clinical prognosis in terms of the survival rate of patients in whom the drug is acting on the target was analyzed using the GEPIA database. The results indicated that Pulsatilla compounds can inhibit the proliferation of lung adenocarcinoma cells by blocking the cell cycle at the G1 phase. Subsequently, the related PPAR-γ gene was verified as a tumor suppressor gene. Further analysis demonstrated that this finding was related to the PPAR signaling pathway and mitochondrial reactive oxygen species (ROS) production. Finally, the clinical prognosis was found to be improved, as the survival rate of patients was increased. In conclusion, Pulsatilla compounds were indicated to inhibit the viability and proliferation of lung adenocarcinoma H1299 cells, and the mechanism of action was related to PPAR-γ, the PPAR signaling pathway and mitochondrial ROS. The present study provides novel insight to further explore the treatment of lung adenocarcinoma.

Identifying and assessing a prognostic model based on disulfidptosis-related genes: implications for immune microenvironment and tumor biology in lung adenocarcinoma

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.

PPARγ Antagonists Exhibit Antitumor Effects by Regulating Ferroptosis and Disulfidptosis

Oral squamous cell carcinoma (OSCC) stands as a prevalent subtype of head and neck squamous cell carcinoma, leading to disease recurrence and low survival rates. PPARγ, a ligand-dependent nuclear transcription factor, holds significance in tumor development. However, the role of PPARγ in the development of OSCC has not been fully elucidated. Through transcriptome sequencing analysis, we discovered a notable enrichment of ferroptosis-related molecules upon treatment with PPARγ antagonist. We subsequently confirmed the occurrence of ferroptosis through transmission electron microscopy, iron detection, etc. Notably, ferroptosis inhibitors could not completely rescue the cell death caused by PPARγ inhibitors, and the rescue effect was the greatest when disulfidptosis and ferroptosis inhibitors coexisted. We confirmed that the disulfidptosis phenotype indeed existed. Mechanistically, through qPCR and Western blotting, we observed that the inhibition of PPARγ resulted in the upregulation of heme oxygenase 1 (HMOX1), thereby promoting ferroptosis, while solute carrier family 7 member 11 (SLC7A11) was also upregulated to promote disulfidptosis in OSCC. Finally, a flow cytometry analysis of flight and multiplex immunohistochemical staining was used to characterize the immune status of PPARγ antagonist-treated OSCC tissues in a mouse tongue orthotopic transplantation tumor model, and the results showed that the inhibition of PPARγ led to ferroptosis and disulfidptosis, promoted the aggregation of cDCs and CD8+ T cells, and inhibited the progression of OSCC. Overall, our findings reveal that PPARγ plays a key role in regulating cell death in OSCC and that targeting PPARγ may be a potential therapeutic approach for OSCC.

Constructing a disulfidptosis-related prognostic signature of hepatocellular carcinoma based on single-cell sequencing and weighted co-expression network analysis

Hepatocellular carcinoma (HCC) ranks as the second leading cause of cancer-related deaths globally. Disulfidptosis is a newly identified form of regulated cell death that is induced by glucose starvation. However, the clinical prognostic characteristics of disulfidptosis-associated genes in HCC remain poorly understood. We conducted an analysis of the single-cell datasets GSE149614 and performed weighted co-expression network analysis (WGCNA) on the Cancer Genome Atlas (TCGA) datasets to identify the genes related to disulfidptosis. A prognostic model was constructed using univariate COX and Lasso regression. Survival analysis, immune microenvironment analysis, and mutation analysis were performed. Additionally, a nomogram associated with disulfidptosis-related signature was constructed to identify the prognosis of HCC patients. Patients with HCC in the TCGA and GSE14520 datasets were categorized using a disulfidptosis-related model, revealing significant differences in survival times between the high- and low-disulfidptosis groups. High-disulfidptosis patients exhibited increased expression of immune checkpoint-related genes, implying that immunotherapy and certain chemotherapies may be beneficial for them. Meanwhile, the ROC and decision curves analysis (DCA) indicated that the nomogram has satisfying prognostic efficacy. Moreover, the experimental results of GATM in this prognostic model indicated that GATM is low expressed in HCC tissues, and GATM knockdown promotes the proliferation and migration of HCC cells. By analyzing single-cell and bulk multi-omics sequencing data, we developed a prognostic signature related to disulfidptosis and explored the relationship between high- and low-disulfidptosis groups in HCC. This study offers a novel reference for gaining a deeper understanding of the role of disulfidptosis in HCC.

Disulfidptosis, A Novel Cell Death Pathway: Molecular Landscape and Therapeutic Implications

Programmed cell death is pivotal for several physiological processes, including immune defense. Further, it has been implicated in the pathogenesis of developmental disorders and the onset of numerous diseases. Multiple modes of programmed cell death, including apoptosis, pyroptosis, necroptosis, and ferroptosis, have been identified, each with their own unique characteristics and biological implications. In February 2023, Liu Xiaoguang and his team discovered "disulfidptosis," a novel pathway of programmed cell death. Their findings demonstrated that disulfidptosis is triggered in glucose-starved cells exhibiting high expression of a protein called SLC7A11. Furthermore, disulfidptosis is marked by a drastic imbalance in the NADPH/NADP+ ratio and the abnormal accumulation of disulfides like cystine. These changes ultimately lead to the destabilization of the F-actin network, causing cell death. Given that high SLC7A11 expression is a key feature of certain cancers, these findings indicate that disulfidptosis could serve as the basis of innovative anti-cancer therapies. Hence, this review delves into the discovery of disulfidptosis, its underlying molecular mechanisms and metabolic regulation, and its prospective applications in disease treatment.

Disulfidptosis decoded: a journey through cell death mysteries, regulatory networks, disease paradigms and future directions

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.

Analysis of network expression and immune infiltration of disulfidptosis-related genes in chronic obstructive pulmonary disease

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is a globally prevalent respiratory disease, and programmed cell death plays a pivotal role in the development of COPD. Disulfidptosis is a newly discovered type of cell death that may be associated with the progression of COPD. However, the expression and role of disulfidptosis-related genes (DRGs) in COPD remain unclear.

METHODS

The expression of DRGs was identified by analyzing RNA sequencing (RNA-seq) data in COPD. Further, COPD patients were classified into two subtypes by unsupervised cluster analysis to reveal their differences in gene expression and immune infiltration. Meanwhile, hub genes associated with disulfidptosis were screened by weighted gene co-expression network analysis. Subsequently, the hub genes were validated experimentally in cells and animals. In addition, we screened potential therapeutic drugs through the hub genes.

RESULTS

We identified two distinct molecular clusters and observed significant differences in immune cell populations between them. In addition, we screened nine hub genes, and experimental validation showed that CDC71, DOHH, PDAP1, and SLC25A39 were significantly upregulated in cigarette smoke-induced COPD mouse lung tissues and bronchial epithelial cells (BEAS-2B) treated with cigarette smoke extract. Finally, we predicted 10 potential small molecule drugs such as Atovaquone, Taurocholic acid, Latamoxef, and Methotrexate.

CONCLUSION

We highlighted the strong association between COPD and disulfidptosis, with DRGs demonstrating a discriminative capacity for COPD. Additionally, the expression of certain novel genes, including CDC71, DOHH, PDAP1, and SLC25A39, is linked to COPD and may aid in the diagnosis and assessment of this condition.

Comprehensive analysis of scRNA-seq and bulk RNA-seq reveal the characteristics of disulfidptosis and a prognostic signature in BLCA

Disulfidptosis is a newly discovered mode of cell death. However, its biological mechanism in bladder cancer (BLCA) is still uncharacterized. In this investigation, we firstly examined the expression and mutation of disulfidptosis-related genes (DRGs) in BLCA. Two disulfidptosis phenotypes associated with DRGs expression patterns and immune cell infiltration were built. A disulfidptosis risk score signature was constructed based on ten differentially expressed genes (DEGs) between the disulfidptosis subtypes, which allowed patients to be stratified into high- and low-risk groups. We further confirmed that the disulfidptosis risk score signature has great power to predict prognosis, immune cell infiltration, and immunotherapy efficacy in BLCA. Additionally, we analyzed the differences in therapeutic sensitivities between high- and low-risk groups concerning targeted inhibitor therapy and immunotherapy. Analysis of single-cell RNA sequencing was conducted of the ten hub DRGs. Of the ten genes, we found that DUSP2 and SLCO1B3 were differentially expressed in BLCA tissues and adjacent normal tissues, and were markedly associated with patients' prognosis. Functional experiments revealed that overexpression of DUSP2 or knockdown of SLCO1B3 significantly inhibited cell proliferation, migration, and invasion in BLCA cells. In all, we present a fresh disulfidptosis-related prognostic signature, which has a remarkable capacity to characterize the immunological landscape and prognosis of BLCA patients.

Comprehensive analysis based on the disulfidptosis-related genes identifies hub genes and immune infiltration for pancreatic adenocarcinoma

Pancreatic adenocarcinoma (PAAD) is a prevalent and aggressive malignancy in the digestive tract, requiring accurate prediction and effective treatment strategies. Recently, the discovery of disulfidptosis, a novel form of programmed cell death characterized by abnormal disulfide accumulation, has sparked interest in its role in PAAD. In this study, we aimed to investigate the involvement of disulfidptosis-related genes (DRGs) in PAAD. Using publicly available databases, we conducted a comprehensive analysis exploring the complex relationships between DRGs and important aspects of PAAD, including gene expression, immune response, mutation, drug sensitivity, and functional enrichment. Notably, we observed significant heterogeneity among different disulfidptosis subclusters and identified specific differentially expressed genes in PAAD. Through machine learning techniques, we identified SLC7A11, S100A4, DIAPH3, PRDX1, and SLC7A7 as the most relevant hub genes. We further validated their significance in PAAD by considering their expression patterns, prognostic value, diagnostic potential, diagnostic model, and immune infiltration. This study presents exciting opportunities and challenges in unraveling the underlying mechanisms of PAAD prognosis. It also establishes a foundation for personalized cancer care and the development of innovative immunotherapeutic strategies. By shedding light on the role of DRGs, particularly hub genes, we enhance our understanding and management of PAAD.

Multi-omics analysis of disulfidptosis regulators and therapeutic potential reveals glycogen synthase 1 as a disulfidptosis triggering target for triple-negative breast cancer

Disruption of disulfide homeostasis during biological processes can have fatal consequences. Excess disulfides induce cell death in a novel manner, termed as "disulfidptosis." However, the specific mechanism of disulfidptosis has not yet been elucidated. To determine the cancer types sensitive to disulfidptosis and outline the corresponding treatment strategies, we firstly investigated the crucial functions of disulfidptosis regulators pan-cancer at multi-omics levels. We found that different tumor types expressed dysregulated levels of disulfidptosis regulators, most of which had an impact on tumor prognosis. Moreover, we calculated the disulfidptosis activity score in tumors and validated it using multiple independent datasets. Additionally, we found that disulfidptosis activity was correlated with classic biological processes and pathways in various cancers. Disulfidptosis activity was also associated with tumor immune characteristics and could predict immunotherapy outcomes. Notably, the disulfidptosis regulator, glycogen synthase 1 (GYS1), was identified as a promising target for triple-negative breast cancer and validated via in vitro and in vivo experiments. In conclusion, our study elucidated the complex molecular phenotypes and clinicopathological correlations of disulfidptosis regulators in tumors, laying a solid foundation for the development of disulfidptosis-targeting strategies for cancer treatment.

Integrated machine learning-driven disulfidptosis profiling: CYFIP1 and EMILIN1 as therapeutic nodes in neuroblastoma

BACKGROUND

Neuroblastoma (NB), a prevalent pediatric solid tumor, presents formidable challenges due to its high malignancy and intricate pathogenesis. The role of disulfidptosis, a novel form of programmed cell death, remains poorly understood in the context of NB.

METHODS

Gaussian mixture model (GMM)-identified disulfidptosis-related molecular subtypes in NB, differential gene analysis, survival analysis, and gene set variation analysis were conducted subsequently. Weighted gene co-expression network analysis (WGCNA) selected modular genes most relevant to the disulfidptosis core pathways. Integration of machine learning approaches revealed the combination of the Least absolute shrinkage and selection operator (LASSO) and Random Survival Forest (RSF) provided optimal dimensionality reduction of the modular genes. The resulting model was validated, and a nomogram assessed disulfidptosis characteristics in NB. Core genes were filtered and subjected to tumor phenotype and disulfidptosis-related experiments.

RESULTS

GMM clustering revealed three distinct subtypes with diverse prognoses, showing significant variations in glucose metabolism, cytoskeletal structure, and tumor-related pathways. WGCNA highlighted the red module of genes highly correlated with disulfide isomerase activity, cytoskeleton formation, and glucose metabolism. The LASSO and RSF combination yielded the most accurate and stable prognostic model, with a significantly worse prognosis for high-scoring patients. Cytological experiments targeting core genes (CYFIP1, EMILIN1) revealed decreased cell proliferation, migration, invasion abilities, and evident cytoskeletal deformation upon core gene knockdown.

CONCLUSIONS

This study showcases the utility of disulfidptosis-related gene scores for predicting prognosis and molecular subtypes of NB. The identified core genes, CYFIP1 and EMILIN1, hold promise as potential therapeutic targets and diagnostic markers for NB.

Regulation mechanisms of disulfidptosis-related genes in ankylosing spondylitis and inflammatory bowel disease

Introduction

Disulfidptosis is a recently identified form of cell death that contributes to maintaining the internal environment balance of an organism. However, the molecular basis of disulfidptosis in ulcerative colitis (UC), ankylosing spondylitis (AS), and Crohn's disease (CD) has not been thoroughly explored.

Methods

Firstly, the differentially expressed genes (DEGs) and disulfidptosis-associated genes (DAGs) were obtained through differential analysis between diseases (AS, CD, and UC) and control groups. After the disulfidptosis score was acquired using the single-sample gene set enrichment analysis (ssGSEA) algorithm, the DE-DAGs were screened by overlapping DAGs and DEGs of the three diseases. Next, the feature genes were selected through a combination of machine learning algorithms, receiver operating characteristic (ROC) curves, and expression analysis. Based on these feature genes, nomograms were created for AS, CD and UC. The co-feature genes were then identified by taking the intersections of the genes featured in all three diseases. Meanwhile, single-gene set enrichment analysis (GSEA) and the TF-mRNA-miRNA network were utilized to investigate the molecular mechanisms of the co-feature genes. To validate the expression differences of the co-feature genes between healthy controls and patients (AS and IBD), RT-PCR was performed. Lastly, mendelian randomization (MR) analysis was utilized to explore the causality between genetic variants of S100A12 with AS, UC and CD.

Results

In this study, 11 DE-DAGs were obtained. Functional enrichment analysis revealed their involvement in cytokine production and fatty acid biosynthesis. Latterly, AS/CD/UC -feature genes were derived, and they all had decent diagnostic performance. Through evaluation, the performance of the nomogram was decent for three diseases. Then, 2 co-feature genes (S100A12 and LILRA5) were obtained. The GSEA enrichment results indicated that the co-feature genes were mainly enriched in the cytokine-cytokine receptor interaction and drug metabolism cytochrome P450. As shown by functional experiments, there was a correlation between the mRNA expression of S100A12 with AS, UC and CD. Additionally, a causal connection between S100A12 and IBD was detected through MR analysis.

Discussion

In this study, 2 co-feature genes (S100A12 and LILRA5) were screened, and their functions were investigated in AS, CD and UC, providing a basis for further research into diagnosis and treatment.

Could the underlying biological basis of prognostic radiomics and deep learning signatures be explored in patients with lung cancer? A systematic review

OBJECTIVES

To summarize the underlying biological correlation of prognostic radiomics and deep learning signatures in patients with lung cancer and evaluate the quality of available studies.

METHODS

This study examined databases including the PubMed, Embase, Web of Science Core Collection, and Cochrane Library, for studies that elaborated on the underlying biological correlation with prognostic radiomics and deep learning signatures based on CT or PET/CT for predicting the prognosis in patients with lung cancer. Information about the patient and radiogenomic analyses was extracted for the included studies. The Radiomics Quality Score (RQS) and the Prediction Model Risk of Bias Assessment Tool were used to assess the quality of these studies.

RESULTS

Twelve studies were included with 7,338 patients from 2014 to 2022. All studies except for one were retrospective. Supervised machine learning was adopted in six studies, and the remaining used unsupervised machine learning methods. Gene sequencing and histopathological data were analyzed by 83.33% and 16.67% of the included studies, respectively. Gene set enrichment analysis and correlation analysis were most used to explore the biological meaning of prognostic signatures. The median RQS for supervised learning articles was 13.5 (range 12-19) and 7.0 (range 5-14) for unsupervised learning articles. The studies included in this report were assessed to have high risk of bias overall.

CONCLUSION

The biological basis for the interpretability of data-driven models mainly focused on genomics and histopathological factors, and it may improve the prognosis of lung cancer with more proper biological interpretation in the future.

Disulfidptosis-related lncRNA signatures assess immune microenvironment and drug sensitivity in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is associated with a high mortality rate, where resistance to immunotherapy and chemotherapy plays a crucial role. A newly identified form of cell death called disulfidptosis shows promise, but its biological mechanism in HCC remains uncertain. In this study, a prognostic model was developed for Disulfidptosis-related long non-coding RNAs (DRLs) from 370 HCC patients sourced from TCGA-LIHC, utilizing five key features: AC026356.1, AC073254.1, PXN-AS1 expression, AC026412.3, and AC099066.2. High-risk HCC patients had lower survival, CD4+ T cell infiltration, and elevated immune checkpoint gene expression. Furthermore, based on the features of DRLs, HCC was classified into three subtypes. Notably, patients belonging to different subtypes demonstrated varying overall survival rates, immune cell infiltration patterns, and sensitivity to immune therapy. Moreover, the novel DRL AC026412.3 (HR = 40.207) emerged as the most significant prognostic factor, exhibiting high expression across all HCC cells. Elevated expression of AC026412.3 promoted HCC cell proliferation and induced resistance to gefitinib. In conclusion, we have discovered five DRLs and constructed a prognostic risk model. Our findings validate the correlation between DRL-related prognostic models, tumor subtypes, and the HCC immune microenvironment along with its implications for immunotherapy. Moreover, further investigation into the molecular mechanisms of key biomarkers like AC026412.3 in the future will contribute significantly to advancing our comprehension of HCC's pathogenesis and drug resistance mechanisms.

Prognostic and immune infiltration features of disulfidptosis-related subtypes in breast cancer

Breast cancer (BC) is a prominent cause of cancer incidence and mortality around the world. Disulfidptosis, a type of cell death, can induce tumor cell death. The purpose of this study was to analyze the potential impact of disulfidptosis-related genes (DRGs) on the prognosis and immune infiltration features of BC. Based on DRGs, we conducted an unsupervised clustering analysis on gene expression data of BC in TCGA-BRCA dataset and identified two BC subtypes, cluster1 and cluster2, with cluster1 showing a higher likelihood of favorable survival. Through immune analysis, we found that cluster1 had lower proportions of infiltration in immune-related cells, including aDCs, DCs, NK_cells, Th2_cells, and Treg. Based on the immunophenoscore (IPS) results, we inferred that cluster1 might benefit more from immune checkpoint inhibitors targeting CTLA-4 and PD1. Targeted small molecule prediction results showed that patients with cluster2 BC might respond better to antagonistic small molecule compounds, including clofazimine, lenalidomide, and epigallocatechin. Differentially expressed genes between the two subtypes were found to be enriched in signaling pathways related to steroid hormone biosynthesis, ovarian steroidogenesis, and neutrophil extracellular trap formation, according to enrichment analyses. In conclusion, this study identified BC subtypes based on DRGs so as to help predict patient prognosis and provide valuable tools for guiding clinical management and precise treatment of BC patients.

Leveraging a disulfidptosis/ferroptosis-based signature to predict the prognosis of lung adenocarcinoma

BACKGROUND

Disulfidptosis and Ferroptosis are two novel forms of cell death. Although their mechanisms differ, research has shown that there is a relationship between the two. Investigating the connection between these two forms of cell death can further deepen our understanding of the development and progression of cancer, and provide better prediction models for accurate prognosis.

METHODS

In this study, RNA sequencing (RNA-seq) data, clinical data, single nucleotide polymorphism (SNP) data, and single-cell sequencing data were obtained from public databases. We used weighted gene co-expression network analysis (WGCNA) and unsupervised clustering to identify new Disulfidptosis/Ferroptosis-Related Genes (DFRG), and constructed a LASSO COX prognosis model that was externally validated. To further explore this novel signature, pathway and function analysis was performed, and differences in gene mutation frequency between high- and low-risk groups were studied. Importantly, we also conducted research on immune checkpoint, immune cell infiltration levels and immune resistance indicators, in addition to analyzing real clinical immunotherapy data.

RESULTS

We have identified four optimal disulfidptosis/ferroptosis-related genes (ODFRGs) that are differentially expressed and associated with the prognosis of Lung Adenocarcinoma (LUAD). These genes include GMPR, MCFD2, MRPL13, and SALL2. Based on these ODFRGs, we constructed a robust prognostic model in this study, and the high-risk group showed significantly lower overall survival (OS) compared to the low-risk group. Furthermore, this model can also predict the immunotherapy outcomes of LUAD patients to some extent.

A Disulfidptosis-Related Gene Signature Associated with Prognosis and Immune Cell Infiltration in Osteosarcoma

Osteosarcoma (OS) stands as a leading aggressive bone malignancy that primarily affects children and adolescents worldwide. A recently identified form of programmed cell death, termed Disulfidptosis, may have implications for cancer progression. Yet, its role in OS remains elusive. To elucidate this, we undertook a thorough examination of Disulfidptosis-related genes (DRGs) within OS. This involved parsing expression data, clinical attributes, and survival metrics from the TARGET and GEO databases. Our analysis unveiled a pronounced association between the expression of specific DRGs, particularly MYH9 and LRPPRC, and OS outcome. Subsequent to this, we crafted a risk model and a nomogram, both honed for precise prognostication of OS prognosis. Intriguingly, risks associated with DRGs strongly resonated with immune cell infiltration levels, myriad immune checkpoints, genes tethered to immunotherapy, and sensitivities to systematic treatments. To conclude, our study posits that DRGs, especially MYH9 and LRPPRC, hold potential as pivotal architects of the tumor immune milieu in OS. Moreover, they may offer predictive insights into treatment responses and serve as reliable prognostic markers for those diagnosed with OS.