Crosstalk of disulfidptosis-related subtypes, establishment of a prognostic signature and immune infiltration characteristics in bladder cancer based on a machine learning survival framework

Global research trends on biomarkers for cancer immunotherapy: Visualization and bibliometric analysis

The global burden of cancer continues to grow, posing a significant public health challenge. Although cancer immunotherapy has shown significant efficacy, the response rate is not high. Therefore, the objective of our research was to identify the latest research trends and hotspots on biomarkers from 1993 to 2023. Data were collected from the database Web of Science core collection. Bibliometric analysis and visualization were conducted with CiteSpace(6.3.1), VOSviewer (v1.6.20), R-bibliometrix(v4.3.3), and Microsoft Excel(2019). A total of 2686 literatures were retrieved. The sheer annual volume of publications has shown a rapid upward trend since 2015. The United States has generated the most publications and Harvard University ranked as a leading institution. The global biomarker research on immune checkpoint inhibitors (ICIs) revealed regional differences and in-depth explorations should be promoted in developing countries. Although China has become the second largest country in terms of publication, the average citation per paper and the total link strength were both lower than the other countries. The research on biomarkers mainly concentrated upon the following aspects: PD-1/PD-L1, CTLA-4, gene expression, adverse events, total mutational burden (TMB), body mass index (BMI), gut microbiota, cd8(+)/cd4(+) t-cells, and blood-related biomarkers such as lactate dehydrogenase (LDH), neutrophil-lymphocyte ratio (NLR), cytokines. Furthermore, "artificial intelligence" and "machine learning" have become the most important research hotspot over the last 2 y, which will help us to identify useful biomarkers from complex big data and provide a basis for precise medicine for malignant tumors.

Integrated Analysis of Disulfidptosis-Related Genes Identifies CD2AP as a Potential Therapeutic Target for Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a deadly cancer with limited treatment options for patients at advanced stages. It is urgent to develop reliable prognostic risk models and identify more biomarkers to improve the clinical outcomes of patients with HCC. Disulfidptosis is a newly discovered form of regulated cell death (RCD), and research on the comprehensive roles of disulfidptosis-related genes (DRGs) in HCC prognosis and development remains limited. In this paper, we systematically analyzed the expression levels and prognostic profiles of 26 DRGs in HCC samples from The Cancer Genome Atlas (TCGA) cohort and developed a prognostic risk model using seven hub DRGs. The independent prognostic value of the risk model was further validated in the external cohort. The overall survival of patients with HCC in the low-risk group was significantly longer than that of those in the high-risk group. Subsequently, the protein level of CD2-associated protein (CD2AP) was found to be highly expressed in HCC clinical tissues and associated with the severity of HCC. In vitro experiments demonstrated that the down-regulation of CD2AP attenuated the proliferation, migration, invasion, and epithelial-mesenchymal transition (EMT) abilities of HCC cells. Taken together, our study revealed that the DRG CD2AP may serve as a potential biomarker for HCC and offer support for prognosis prediction of patients with HCC.

Artificial intelligence networks for assessing the prognosis of gastrointestinal cancer to immunotherapy based on genetic mutation features: a systematic review and meta-analysis

BACKGROUND AND AIM

Artificial intelligence (AI) networks offer significant potential for predicting immunotherapy outcomes in gastrointestinal cancers by analyzing genetic mutation profiles. Their application in prognosis remains underexplored. This systematic review and meta-analysis aim to evaluate the effectiveness of AI-based models, which refers to systems utilizing artificial intelligence to analyze data and make predictions, in predicting immunotherapy responses in gastrointestinal cancers using genetic mutation features.

METHODS

This study, adhering to PRISMA guidelines, aimed to evaluate AI networks for predicting gastrointestinal cancer prognosis in response to immunotherapy using genetic mutation features. A search in PubMed, WOS, and Scopus identified relevant studies. Data extraction and quality assessment were conducted, and statistical analysis included pooled estimates for sensitivity, specificity, accuracy, and AUC. Regression models and imputation methods addressed missing values, ensuring accurate and robust results. STATA version 18 was used to analyze the data.

RESULT

A total of 45 studies, all published in 2024, involving 14,047 participants in training sets and 10,885 participants in test sets, were included. The pooled results of AI model performance for gastrointestinal cancers based on genetic mutation features were: AUC = 0.86 (95% CI: 0.86-0.87), Sensitivity = 83% (95% CI: 83%-84%), Specificity = 72% (95% CI: 72%-73%), and Accuracy = 82% (95% CI: 82%-83%). Heterogeneity was low to moderate, and no publication bias was detected. Subgroup analysis showed higher AUC for gastric cancer models (AUC: 0.87) and lower for pancreatic cancer models (AUC: 0.52).

CONCLUSION

AI networks demonstrate promising potential in predicting immunotherapy outcomes for gastrointestinal cancers based on genetic mutation features. This systematic review highlights their effectiveness in stratifying patients and optimizing treatment decisions. However, further large-scale studies are needed to validate AI models and integrate them into clinical practice for improved precision in cancer immunotherapy.

Disulfidptosis in tumor progression

Disulfidptosis, a regulated cell death modality driven by the cystine transporter solute carrier family 7 member 11 (SLC7A11), is characterized by actin cytoskeleton collapse under glucose starvation. This review systematically elucidates the pivotal role of disulfidptosis in tumor metabolic reprogramming, with a focus on its molecular mechanisms and distinctions from other cell death pathways. The core mechanisms include SLC7A11-mediated cystine overload and NRF2/c-Myc-regulated pentose phosphate pathway activation. By integrating multiomics data and single-cell transcriptomics, we comprehensively decipher the heterogeneous expression patterns of disulfidptosis-related genes (DRGs) and their dynamic interplay with immune microenvironment remodeling. Furthermore, the coexpression networks of DRGs and disulfidptosis-related long noncoding RNAs (DRLs) offer novel insights into tumor diagnosis, prognosis, and targeted therapy. Therapeutically, SLC7A11 inhibitors (e.g., HG106) and glucose transporter inhibitors (e.g., BAY-876) demonstrate efficacy by exploiting metabolic vulnerabilities, whereas natural compounds synergizing with immune checkpoint blockade provide strategies to counteract immunosuppressive microenvironments. Through interdisciplinary collaboration and clinical translation, disulfidptosis research holds transformative potential in redefining precision oncology.

Based on disulfidptosis, unveiling the prognostic and immunological signatures of Asian hepatocellular carcinoma and identifying the potential therapeutic target ZNF337-AS1

BACKGROUND

Disulfidptosis is a newly discovered programmed cell death pathway that may be connected to tumorigenesis and development, showing promise as a novel treatment strategy for cancer. This study aims to construct a prognostic model of disulfidptosis-related Long non-coding RNAs (DRLRs) within the Asian HCC population and to investigate the impact of DRLRs on HCC.

METHODS

Utilising a combination of univariate Cox, Lasso-Cox, and multivariate Cox analyses, five pivotal DRLRs (AC099850.3, ZNF337-AS1, LINC01138, AL031985.3, AC131009.1) were identified, forming a robust prognostic signature. Subsequent validations included Receiver Operating Characteristic (ROC) and Concordance Index analyses, alongside Principal Component Analysis. Comprehensive bioinformatics analysis was performed on the hub DRLRs, followed by experimental validation using quantitative real-time polymerase chain reaction and cellular functional assays.

RESULTS

The risk score independently predicted prognosis, outperforming traditional clinical-pathological factors across varying ages, tumour stages, and pathological classifications in the cohort. A nomogram integrating these variables demonstrated capability in forecasting survival. Multivariate analysis confirmed that the risk score and AJCC TNM staging are independent prognostic factors for predicting overall survival (OS) in Asian HCC patients (both P < 0.001). The prognostic model's ROC area under the ROC values for 1-, 3-, and 5-year predictions were 0.837, 0.794, and 0.783, respectively, indicating its strong diagnostic and prognostic value. Pathway and immune landscape analyses elucidated the biological underpinnings and immune modulations associated with the high-risk group. Immune landscape analysis indicated that both immunescore (P < 0.001) and estimatescore (P < 0.05) were significantly decreased in the high-risk group, with both specific and non-specific immune responses being significantly suppressed, while the tumour immune dysfunction and exclusion score was notably increased (P < 0.001). Tumour mutational burden (TMB) analysis revealed a significantly higher TMB in the high-risk group (P = 0.033) and shorter OS for HCC patients in the high TMB subgroup (P = 0.002). Notably, Potential chemotherapeutic agents (PFI3, 5-Fluorouracil, BPD-00008900, GDC0810, and AZ6102) were identified for high-risk group. Experimental validations through quantitative PCR and in vitro assays confirmed the deregulation of these DRLRs in HCC, with functional studies highlighting the potential of ZNF337-AS1 silencing in curtailing tumour invasiveness.

CONCLUSION

Our investigations validate a DRLR-based risk scoring model as an effective prognostic tool for Asian HCC. This model not only enhances understanding of disulfidptosis's role in HCC but also facilitates personalised treatment strategies, potentially improving patient outcomes.

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.

Disulfidptosis related immune genes drive prognostic model development and tumor microenvironment characterization in bladder urothelial carcinoma

The intricate nature and varied forms of bladder urothelial carcinoma (BLCA) highlight the need for new indicators to define tumor prognosis. Disulfidptosis, a novel form of cell death, is closely linked to BLCA progression, prognosis, and treatment outcomes. Our current goal is to develop a novel disulfidptosis-related immune prognostic model to enhance BLCA treatment strategies. Utilizing RNA-seq data from The Cancer Genome Atlas (TCGA) , which included 419 patients (19 normal, 400 tumor), we performed weighted gene co-expression network analysis (WGCNA) to identify disulfidptosis-associated immune genes. Through multivariate Cox regression, and the least absolute shrinkage and selection operator (LASSO) regularization, we established a disulfidptosis-related immune risk scoring system. A nomogram combining risk score and clinical features predicted prognosis. Model performance was validated through survival curve analysis and independent validation cohort. Immune checkpoints, cell infiltration, and tumor mutation load were assessed. Differential gene enrichment analysis was conducted. Prognostic genes were validated via in vitro experiments. Eight immune genes related to disulfidptosis were identified and verified in BLCA prognosis. A prognostic model outperformed previous ones in predicting overall survival (OS) for high- and low-risk groups. Patients with low-risk scores had higher OS rates and tumor mutation burden (TMB) compared to high-risk score patients. CD4 memory T cells, CD8 T cells, M1 macrophages, and resting NK cells were found to be higher in the low-risk group. Immune checkpoint inhibitor (ICI) treatment may be more effective for the low-risk score group. High-risk score group exhibited stronger correlation with cancer malignant pathways. Knocking out tumor necrosis factor receptor superfamily member 12 A (TNFRSF12A) inhibits BLCA cell proliferation and invasion while overexpressing it has the opposite effect. We constructed a novel risk score model that combines disulfidptosis and immune genes, demonstrating good prognostic prediction performance. We discovered and verified that the TNFRSF12A gene is an oncogene in BLCA, which may help provide personalized guidance for individualized treatment and immunotherapy selection for BLCA patients to a certain extent.

Disulfidptosis as a key regulator of glioblastoma progression and immune cell impairment

Background

Glioblastoma, associated with poor prognosis and impaired immune function, shows potential interactions between newly identified disulfidptosis mechanisms and T cell exhaustion, yet these remain understudied.

Methods

Key genes were identified using Lasso regression, followed by multivariate analysis to develop a prognostic model. Single-cell pseudotemporal analysis explored disulfidptosis T-cell exhaustion (Tex) signaling in cell differentiation. Immune infiltration was assessed via ssGSEA, while transwell assays and immunofluorescence examined the effects of disulfidptosis-Tex genes on glioma cell behavior and immune response.

Results

Eleven disulfidptosis-Tex genes were found critical for glioblastoma survival outcomes. This gene set underpinned a model predicting patient prognosis. Single-cell analysis showed high disulfidptosis-Tex activity in endothelial cells. Memory T cell populations were linked to these genes. SMC4 inhibition reduced LN299 cell migration and increased chemotherapy sensitivity, decreasing CD4 and CD8 T cell activation.

Conclusions

Disulfidptosis-Tex genes are pivotal in glioblastoma progression and immune interactions, offering new avenues for improving anti-glioblastoma therapies through modulation of T cell exhaustion.

Comprehensive analysis of disulfidptosis-related genes and the immune microenvironment in heart failure

Background

Heart failure (HF) is a prevalent cardiovascular disease that currently lacks effective treatment options due to its intricate pathogenesis. A recent study has linked disulfidoptosis, a novel form of cell demise, with the development of a range of diseases. Nonetheless, the effect of disulfidoptosis on the immune microenvironment of HF is not well comprehended. In this paper, bioinformatics analysis was performed to investigate how disulfidptosis-related genes (DRGs) affect the immune microenvironment of HF.

Methods

The expression of four DRGs was initially examined using bulk RNA-Seq and single-cell RNA sequencing data. A predictive model was subsequently developed. Consensus clustering was used to distinguish between the two clusters of DRGs. The effect of these DRGs on the characteristics of the immune microenvironment was further explored, such as infiltrating immune cells, immune response gene sets, and HLAs genes.

Results

All four DRGs were dysregulated in HF samples. The predictive model based on these four DRGs effectively differentiated between HF patients and healthy individuals, which was validated in the experiment. These four DRGs were strongly associated with the abundance of infiltrating monocytes. Moreover, our analysis identified two distinct clusters of DRGs and these clusters exhibited differences in terms of immune cell abundance, immune response, and HLA gene expression. The biological functions associated with these differences were also revealed.

Conclusion

Our discovery underscores the pivotal role of DRGs in shaping the diversity and intricacy of the immune microenvironment in HF.

Integrated Bioinformatics and Experimental Validation to Identify a Disulfidptosis-Related lncRNA Model for Prognostic Prediction in Papillary Renal Cell Carcinoma

AIMS

This study aimed to construct a prognostic model for papillary renal cell carcinoma (pRCC) utilizing disulfidptosis-associated long non-coding RNAs (lncRNAs). Additionally, it investigated the potential of these lncRNAs in predicting immune responses and drug sensitivity in pRCC.

BACKGROUND

LncRNAs have been implicated in the progression and prognosis of pRCC. Recently, disulfidptosis, an emerging form of regulated cell death, has shown potential as a therapeutic approach for cancer. However, the potential association between disulfidptosis-related lncRNAs and pRCC remains unclear.

METHODS

We analyzed transcriptome profiling and clinical data of pRCC patients from The Cancer Genome Atlas database. Using Pearson correlation analysis, we identified lncRNAs associated with disulfidptosis. Based on the disulfidptosis-related lncRNAs that were correlated with overall survival (OS), we constructed a novel prediction model using least absolute shrinkage and selection operator, univariable Cox regression, and multivariable Cox regression analyses. The model's utility was assessed through Kaplan-Meier survival, receiver operating characteristics, and principal component analyses. Moreover, functional analysis helped identify potential prognostic mechanisms, and the prediction of chemical drugs for pRCC was also performed. Finally, qRT-PCR validated the expression of prognostic lncRNAs in pRCC cells and patient samples.

RESULTS

Our prediction model was based on nine disulfidptosis-related lncRNAs. Evaluation and validation analyses demonstrated that the model had excellent, consistent, and independent prognostic value for pRCC patients, with area under the curve values of 0.954, 0.910, and 0.830 for 1-, 3-, and 5-year OS, respectively. Through functional analysis, we discovered a significant correlation between the identified prognostic signature and immunity. Additionally, in terms of chemotherapy sensitivity, our analysis indicated that the low-risk group exhibited higher sensitivity to sunitinib and pazopanib. Furthermore, the expression patterns of the identified lncRNAs were validated in samples obtained from pRCC cells and patients.

CONCLUSION

This study successfully established and validated a novel disulfidptosis-related prediction model. The findings suggest the potential involvement of immune-related pathways in lncRNA signature-associated survival. This model holds promise for differentiating prognosis and improving personalized therapeutic strategies for pRCC in clinical practice.

Dissecting the functions and regulatory mechanisms of disulfidoptosis-related RPN1 in pan-cancer: modulation of immune microenvironment and cellular senescence

Introduction

Cancer's inherent heterogeneity, marked by diverse genetic and molecular alterations, presents significant challenges for developing effective treatments. One such alteration is the regulation of disulfidoptosis, a recently discovered programmed cell death pathway. RPN1, a key regulator associated with disulfidoptosis, may influence various aspects of tumor biology, including immune evasion and cellular senescence. This study aims to dissect the role of RPN1 in pan-cancer and its potential as a therapeutic target.

Methods

We employed a pan-cancer analysis to explore RPN1 expression and its association with clinical outcomes across multiple tumor types. Immune cell infiltration and expression of immune checkpoint genes were analyzed in relation to RPN1. Additionally, cellular senescence markers were assessed in RPN1 knockdown tumor cells. Gene regulatory mechanisms were studied through gene copy number variations, DNA methylation analysis, and transcriptional regulation by SP1.

Results

RPN1 is overexpressed in a wide range of tumor types and correlates with poor clinical outcomes, including overall survival, disease-specific survival, and progression-free intervals. Our analysis shows that RPN1 is involved in immune evasion, correlating with the presence of myeloid dendritic cells, macrophages, and tumor-associated fibroblasts, and influencing T-cell activity. RPN1 knockdown led to reduced tumor cell proliferation and induced cellular senescence, marked by increased senescence-associated biomarkers and β-galactosidase activity. RPN1 expression was found to be regulated by gene copy number variations, reduced DNA methylation, and transcriptional control via SP1.

Discussion

These findings highlight RPN1 as a key pan-cancer regulator, influencing immune microenvironment interactions and cellular senescence. The regulation of disulfidoptosis by RPN1 presents a promising avenue for therapeutic intervention. Targeting RPN1 could enhance immunotherapy efficacy and help mitigate tumor progression, offering a potential strategy for cancer treatment.

Prognostic model based on disulfidptosis-related lncRNAs for predicting survival and therapeutic response in bladder cancer

Background

With poor treatment outcomes and prognosis, bladder cancer remains a focus for clinical research in the precision oncology era. However, the potential of disulfidptosis, a novel cell death mechanism, and its related long non-coding RNAs to support selective cancer cell killing in this disease is still unclear.

Methods

We identified key disulfidptosis-related lncRNAs in bladder cancer, constructed a prognostic risk model with potential therapeutic targets, and confirmed the findings through quantitative PCR analysis.

Results

We identified five crucial lncRNAs (AC005840.4, AC010331.1, AL021707.6, MIR4435-2HG and ARHGAP5-AS1) and integrated them into a predictive model centered on disulfidptosis-associated lncRNAs. Reliability and validity tests demonstrated that the lncRNA prediction index associated with disulfidptosis effectively discerns patients' prognosis outcomes. Additionally, high-risk patients exhibited elevated expression levels of genes involved in the PI3K-Akt signaling pathway, extracellular matrix organization, and immune escape mechanisms, which are associated with poor prognosis. Notably, high-risk patients demonstrated higher sensitivity to Sorafenib, Oxaliplatin and MK-2206, underscoring the promise of these lncRNAs as precise therapeutic targets in bladder cancer.

Conclusion

By revealing the predictive importance of disulfidptosis-associated lncRNAs in bladder cancer, our research offers new perspectives and pinpoints potential therapeutic targets in clinical environments.

Diagnostic and prognostic value of disulfidptosis-related genes in sepsis

Background

Sepsis is a disease associated with high morbidity and mortality rates, especially among the elderly and patients in intensive care units. Disulfidptosis, a newly identified form of cell death triggered by disulfide stress, is emerging as a significant factor in disease progression. This study aimed to explore the diagnostic and prognostic value of disulfidptosis-related genes in sepsis.

Methods

We obtained two datasets from the Gene Expression Omnibus (GEO) database to conduct our analysis. Functional enrichment analysis was performed to identify relevant biological pathways. A protein-protein interaction network was constructed to identify hub genes critical to sepsis. Additionally, we analyzed the immune infiltration status in sepsis patients. The diagnostic value of these hub genes for sepsis was evaluated using nomograms, receiver operating characteristic (ROC) curves, and calibration curves in both training and validation datasets. Finally, a miRNA-immune-related hub genes (miRNA-IHGs) regulatory network was developed to elucidate the synergistic interactions between miRNAs and their target genes.

Results

A total of 3,469 differentially expressed genes (DEGs) were identified, of which seven were related to disulfidptosis (DR-DEGs). Functional enrichment analysis showed that DR-DEGs were significantly enriched in pathways related to actin dynamics. Five hub genes (MYH10, ACTN4, MYH9, FLNA, and IQGAP1) were identified as central to these processes. The analysis of immune infiltration revealed significantly lower levels of 11 immune cell types, while macrophages and regulatory T cells were significantly elevated in sepsis patients. The area under the ROC curves (AUCs) of the IHGs risk prediction model were 0.917 and 0.894 for the training and validation sets, respectively. A miRNA-IHGs regulatory network, comprising 17 nodes and 27 edges, was constructed, with MYH9 being the most frequently regulated by miRNAs.

Conclusion

The pathophysiological process of sepsis appears to involve disulfidptosis, highlighting it as a potential new therapeutic targets for sepsis management.

Integrated analysis of disulfidptosis-related genes SLC7A11, SLC3A2, RPN1 and NCKAP1 across cancers

Disulfidptosis, a newly identified form of regulated cell death associated with disruption of disulfide bond formation in the endoplasmic reticulum, involves the dysregulation of disulfidptosis-related genes (DRGs) that may contribute to cancer development and progression. However, the molecular mechanisms and clinical implications of DRGs in different cancer types remain poorly characterized. Therefore, in this comprehensive study, we investigated the expression, prognostic value, and functional roles of four recently identified DRGs (SLC7A11, SLC3A2, RPN1, and NCKAP1) across various cancers. Especially, in clinical samples of glioblastoma, we found that RPN1 was significantly correlated with patient survival. Through mutation landscape analysis, we identified diverse missense mutations in these DRGs, with NCKAP1 exhibiting the highest mutation frequency (5.9% in skin cutaneous melanoma). Additionally, we observed positive correlations between these DRGs and tumor stemness (DNAss DNA stemness score and RNAss RNA stemness score) as well as RNA modifications, particularly m6A modification, in several cancer types. Furthermore, high expression of SLC7A11, RPN1, and NCKAP1 was positively associated with infiltration of T-helper type 2 (Th2) cells in various cancers, while high expression of SLC7A11, SLC3A2, and RPN1 correlated with tumor mutational burden (TMB) in 10, 4, and 8 tumor types, respectively. Utilizing a protein-protein interaction network, we identified the RHO GTPases Activate WASPs and WAVEs pathway as significantly enriched, suggesting the involvement of these DRGs in cancer-related signaling pathways. Collectively, our findings provide novel insights into the molecular mechanisms and clinical implications of DRGs in pan-cancer, highlighting their potential as biomarkers and therapeutic targets for cancer treatment.

Therapeutic Potential of lncRNAs in Regulating Disulfidptosis for Cancer Treatment

Non-coding RNAs (lncRNAs) play critical roles in various cellular processes, including a novel form of regulated cell death known as disulfidptosis, characterized by accumulating protein disulfide bonds and severe endoplasmic reticulum stress. This review highlights the therapeutic potential of lncRNAs in regulating disulfidptosis for cancer treatment, emphasizing their influence on key pathway components such as GPX4, SLC7A11, and PDIA family members. Recent studies have demonstrated that targeting specific lncRNAs can sensitize cancer cells to disulfidptosis, offering a promising approach to cancer therapy. The regulation of disulfidptosis by lncRNAs involves various signaling pathways, including oxidative stress, ER stress, and calcium signaling. This review also discusses the molecular mechanisms underlying lncRNA regulation of disulfidptosis, the challenges of developing lncRNA-based therapies, and the future potential of this rapidly advancing field in cancer research.

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.

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.

Identification of Disulfidptosis-Related Genes in Ischemic Stroke by Combining Single-Cell Sequencing, Machine Learning Algorithms, and In Vitro Experiments

BACKGROUND

Ischemic stroke (IS) is a severe neurological disorder with a pathogenesis that remains incompletely understood. Recently, a novel form of cell death known as disulfidptosis has garnered significant attention in the field of ischemic stroke research. This study aims to investigate the mechanistic roles of disulfidptosis-related genes (DRGs) in the context of IS and to examine their correlation with immunopathological features.

METHODS

To enhance our understanding of the mechanistic underpinnings of disulfidptosis in IS, we initially retrieved the expression profile of peripheral blood from human IS patients from the GEO database. We then utilized a suite of machine learning algorithms, including LASSO, random forest, and SVM-RFE, to identify and validate pivotal genes. Furthermore, we developed a predictive nomogram model, integrating multifactorial logistic regression analysis and calibration curves, to evaluate the risk of IS. For the analysis of single-cell sequencing data, we employed a range of analytical tools, such as "Monocle" and "CellChat," to assess the status of immune cell infiltration and to characterize intercellular communication networks. Additionally, we utilized an oxygen-glucose deprivation (OGD) model to investigate the effects of SLC7A11 overexpression on microglial polarization.

RESULTS

This study successfully identified key genes associated with disulfidptosis and developed a reliable nomogram model using machine learning algorithms to predict the risk of ischemic stroke. Examination of single-cell sequencing data showed a robust correlation between disulfidptosis levels and the infiltration of immune cells. Furthermore, "CellChat" analysis elucidated the intricate characteristics of intercellular communication networks. Notably, the TNF signaling pathway was found to be intimately linked with the disulfidptosis signature in ischemic stroke. In an intriguing finding, the OGD model demonstrated that SLC7A11 expression suppresses M1 polarization while promoting M2 polarization in microglia.

CONCLUSION

The significance of our findings lies in their potential to shed light on the pathogenesis of ischemic stroke, particularly by underscoring the pivotal role of disulfidptosis-related genes (DRGs). These insights could pave the way for novel therapeutic strategies targeting DRGs to mitigate the impact of ischemic stroke.

Construction of a Novel Disulfidptosis-Related lncRNA Prognostic Signature in Pancreatic Cancer

Pancreatic cancer is a lethal, extremely aggressive gastrointestinal tumor with a poor prognosis and limited treatment alternatives. Disulfidptosis is a newly defined type of cell death with potential influence on cancer. Research on the association between disulfidptosis and pancreatic cancer is scarce. The expression data of disulfidptosis-related genes were downloaded from The Cancer Genome Atlas-Pancreatic Adenocarcinoma (TCGA). Disulfidptosis-related lncRNA signature (DRLS) was developed through the Cox and the least absolute shrinkage and selection operator (LASSO) analysis. Differences in enrichment functions, mutational landscape, immune microenvironment, and predicted therapeutic efficacy between high- and low-risk groups were assessed. Consensus clustering analysis was applied to identify the DRLS-related subtypes. Among 98 disulfidptosis-related lncRNAs, 5 lncRNAs were screened thus constructing a prognostic DRLS. DRLS showed high predictive accuracy and was an independent prognostic factor for pancreatic cancer. According to the risk scores calculated from the signature, samples were categorized into high- and low- risk groups. Overall, low-risk patients had a better prognosis, lower mutational occurrences, higher immune cell infiltration and more sensitivity to anti-tumor agents. The DRLS performed well in predicting prognosis and revealed intimate correlation with biological function, mutation status and immune infiltration landscape of pancreatic cancer, providing some insights for future research on the relationship between disulfidptosis and pancreatic cancer.

Unveiling disulfidptosis-related biomarkers and predicting drugs in Alzheimer's disease

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

Construction of a prognostic model for disulfidptosis-related long noncoding RNAs in R0 resected hepatocellular carcinoma and analysis of their impact on malignant behavior

BACKGROUND

Disulfidptosis is an emerging form of cellular death resulting from the binding of intracellular disulfide bonds to actin cytoskeleton proteins. This study aimed to investigate the expression and prognostic significance of hub disulfidptosis-related lncRNAs (DRLRs) in R0 resected hepatocellular carcinoma (HCC) as well as their impact on the malignant behaviour of HCC cells.

METHODS

A robust signature for R0 resected HCC was constructed using least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression and was validated in an independent internal validation cohort to predict the prognosis of R0 HCC patients. Comprehensive bioinformatics analysis was performed on the hub DRLRs (KDM4A-AS1, MKLN1-AS, and TMCC1-AS1), followed by experimental validation using quantitative real-time polymerase chain reaction (qRT‒PCR) and cellular functional assays.

RESULTS

The signature served as an independent prognostic factor applicable to R0 HCC patients across different age groups, tumour stages, and pathological characteristics. Gene Ontology (GO) and gene set enrichment analysis (GSEA) revealed hub pathways associated with this signature. The high-risk group presented an increased abundance of M0 macrophages and activated memory CD4 T cells as well as elevated macrophage and major histocompatibility complex (MHC) class I expression. High-risk R0 HCC patients also presented increased tumour immune dysfunction and exclusion scores (TIDEs), mutation frequencies, and tumour mutational burdens (TMBs). Drug sensitivity analysis revealed that high-risk patients were more responsive to drugs, including GDC0810 and osimertinib. High expression levels of the three hub DRLRs were detected in R0 HCC tissues and HCC cell lines. Functional assays revealed that the three hub DRLRs enhanced HCC cell proliferation, migration, and invasion.

CONCLUSIONS

A signature was constructed on the basis of three DRLRs, providing novel insights for personalized precision therapy in R0 HCC patients.

Unveiling disulfidptosis-related genes in HBV-associated hepatocellular carcinoma: an integrated study incorporating transcriptome and Mendelian randomization analyses

Disulfidptosis, a recently unveiled mechanism of demise, has been linked to an unfavorable prognosis in the context of hepatocellular carcinoma (HCC). However, few studies have focused on the causal link between disulfidptosis and HBV-related HCC (HBV-HCC). In this study, the Mendelian randomization (MR) analysis demonstrated that the risk of HCC increased with increasing genetic susceptibility to HBV, and the genetic changes of disulfidptosis were significantly associated with the increased risk of HBV-HCC. Within both the TCGA and GEO cohorts, it is possible to accurately forecast the prognosis of HBV-HCC by utilizing a risk score that is derived from a combination of GYS1, RPN1, SLC7A11, LRPPRC and CAPZB genes. GYS1, a potential therapeutic target for HBV-HCC, exhibits a remarkable positive correlation with immune infiltration and MSI when compared to other molecules. Furthermore, we demonstrated that silencing GYS1 effectively inhibits the tumor proliferation and metastasis of HBV-HCC in vitro and in vivo. Overall, this study expands the understanding of the potential roles of disulfidptosis in HBV-HCC and highlights GYS1 as a promising target for HBV-HCC.

A disulfidptosis-associated long noncoding RNA signature to predict low-grade glioma classification, prognosis, tumor microenvironment, and therapy regimens: Observational study

This study aimed to investigate the function of disulfidptosis-associated long noncoding RNAs (DAlncRNAs) in low-grade gliomas (LGG) through bioinformatics analysis and construct a signature to predict the classification, prognosis, tumor microenvironment, and selection of immunotherapy and chemotherapy in LGG. Genomic, clinical, and mutational information of 526 patients with LGG was retrieved from The Cancer Genome Atlas repository. A nonnegative matrix factorization algorithm was applied to classify patients with LGG. Univariate, LASSO regression, and multivariate Cox regression analyses were performed to determine prognostic DAlncRNAs. Following the median risk score, we defined the sample as a high-risk (HR) or low-risk group. Finally, survival, receiver operating characteristic curve, risk curve, principal component, independent prognosis, risk difference, functional enrichment, tumor microenvironment, immune cell infiltration, mutation, and drug sensitivity analyses were performed. Patients were classified into C1 and C2 subtypes associated with disulfidptosis. Eight prognostic DAlncRNAs (AC003035.2, AC010157.2, AC010273.3, AC011444.3, AC092667.1, AL450270.1, AL645608.2, and LINC01571) were identified, and a prognostic signature of LGG was developed. The DAlncRNA-based signature was found to be an independent prognostic factor in patients with LGG, thereby constructing a nomogram. In addition, in the HR group, immune function was more active and the tumor mutation burden was higher. The patients were mainly composed of subtype C2, and their prognosis was worse. Immunotherapy and chemotherapy were predicted in the HR and low-risk groups, respectively. Our study, based on DAlncRNAs, highlights 2 disulfidptosis-associated LGG subtypes with different prognostic and immune characteristics and creates a novel disulfidptosis-associated prognostic signature, which may inform the classification, prognosis, molecular pathogenesis, and therapeutic strategies for patients with LGG.

LIG1 is a novel marker for bladder cancer prognosis: evidence based on experimental studies, machine learning and single-cell sequencing

Background

Bladder cancer, a highly fatal disease, poses a significant threat to patients. Positioned at 19q13.2-13.3, LIG1, one of the four DNA ligases in mammalian cells, is frequently deleted in tumour cells of diverse origins. Despite this, the precise involvement of LIG1 in BLCA remains elusive. This pioneering investigation delves into the uncharted territory of LIG1's impact on BLCA. Our primary objective is to elucidate the intricate interplay between LIG1 and BLCA, alongside exploring its correlation with various clinicopathological factors.

Methods

We retrieved gene expression data of para-carcinoma tissues and bladder cancer (BLCA) from the GEO repository. Single-cell sequencing data were processed using the "Seurat" package. Differential expression analysis was then performed with the "Limma" package. The construction of scale-free gene co-expression networks was achieved using the "WGCNA" package. Subsequently, a Venn diagram was utilized to extract genes from the positively correlated modules identified by WGCNA and intersect them with differentially expressed genes (DEGs), isolating the overlapping genes. The "STRINGdb" package was employed to establish the protein-protein interaction (PPI) network.Hub genes were identified through the PPI network using the Betweenness Centrality (BC) algorithm. We conducted KEGG and GO enrichment analyses to uncover the regulatory mechanisms and biological functions associated with the hub genes. A machine-learning diagnostic model was established using the R package "mlr3verse." Mutation profiles between the LIG1^high and LIG1^low groups were visualized using the BEST website. Survival analyses within the LIG1^high and LIG1^low groups were performed using the BEST website and the GENT2 website. Finally, a series of functional experiments were executed to validate the functional role of LIG1 in BLCA.

Results

Our investigation revealed an upregulation of LIG1 in BLCA specimens, with heightened LIG1 levels correlating with unfavorable overall survival outcomes. Functional enrichment analysis of hub genes, as evidenced by GO and KEGG enrichment analyses, highlighted LIG1's involvement in critical function such as the DNA replication, cellular senescence, cell cycle and the p53 signalling pathway. Notably, the mutational landscape of BLCA varied significantly between LIG1high and LIG1low groups.Immune infiltrating analyses suggested a pivotal role for LIG1 in immune cell recruitment and immune regulation within the BLCA microenvironment, thereby impacting prognosis. Subsequent experimental validations further underscored the significance of LIG1 in BLCA pathogenesis, consolidating its functional relevance in BLCA samples.

Conclusions

Our research demonstrates that LIG1 plays a crucial role in promoting bladder cancer malignant progression by heightening proliferation, invasion, EMT, and other key functions, thereby serving as a potential risk biomarker.

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.

Exploring prognostic DNA methylation genes in bladder cancer: a comprehensive analysis

The current study aimed to investigate the status of genes with prognostic DNA methylation sites in bladder cancer (BLCA). We obtained bulk transcriptome sequencing data, methylation data, and single-cell sequencing data of BLCA from public databases. Initially, Cox survival analysis was conducted for each methylation site, and genes with more than 10 methylation sites demonstrating prognostic significance were identified to form the BLCA prognostic methylation gene set. Subsequently, the intersection of marker genes associated with epithelial cells in single-cell sequencing analysis was obtained to acquire epithelial cell prognostic methylation genes. Utilizing ten machine learning algorithms for multiple combinations, we selected key genes (METRNL, SYT8, COL18A1, TAP1, MEST, AHNAK, RPP21, AKAP13, RNH1) based on the C-index from multiple validation sets. Single-factor and multi-factor Cox analyses were conducted incorporating clinical characteristics and model genes to identify independent prognostic factors (AHNAK, RNH1, TAP1, Age, and Stage) for constructing a Nomogram model, which was validated for its good diagnostic efficacy, prognostic prediction ability, and clinical decision-making benefits. Expression patterns of model genes varied among different clinical features. Seven immune cell infiltration prediction algorithms were used to assess the correlation between immune cell scores and Nomogram scores. Finally, drug sensitivity analysis of Nomogram model genes was conducted based on the CMap database, followed by molecular docking experiments. Our research offers a reference and theoretical basis for prognostic evaluation, drug selection, and understanding the impact of DNA methylation changes on the prognosis of BLCA.

Disulfidptosis‑related lncRNA prognosis model to predict survival therapeutic response prediction in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the most common pathological type of lung cancer, and disulfidptosis is a newly discovered mechanism of programmed cell death. However, the effects of disulfidptosis-related lncRNAs (DR-lncRNAs) in LUAD have yet to be fully elucidated. The aim of the present study was to identify and validate a novel lncRNA-based prognostic marker that was associated with disulfidptosis. RNA-sequencing and associated clinical data were obtained from The Cancer Genome Atlas database. Univariate Cox regression and lasso algorithm analyses were used to identify DR-lncRNAs and to establish a prognostic model. Kaplan-Meier curves, receiver operating characteristic curves, principal component analysis, Cox regression, nomograms and calibration curves were used to assess the reliability of the prognostic model. Functional enrichment analysis, immune infiltration analysis, somatic mutation analysis, tumor microenvironment and drug predictions were applied to the risk model. Reverse transcription-quantitative PCR was subsequently performed to validate the mRNA expression levels of the lncRNAs in normal cells and tumor cells. These analyses enabled a DR-lncRNA prognosis signature to be constructed, consisting of nine lncRNAs; U91328.1, LINC00426, MIR1915HG, TMPO-AS1, TDRKH-AS1, AL157895.1, AL512363.1, AC010615.2 and GCC2-AS1. This risk model could serve as an independent prognostic tool for patients with LUAD. Numerous immune evaluation algorithms indicated that the low-risk group may exhibit a more robust and active immune response against the tumor. Moreover, the tumor immune dysfunction exclusion algorithm suggested that immunotherapy would be more effective in patients in the low-risk group. The drug-sensitivity results showed that patients in the high-risk group were more sensitive to treatment with crizotinib, erlotinib or savolitinib. Finally, the expression levels of AL157895.1 were found to be lower in A549. In summary, a novel DR-lncRNA signature was constructed, which provided a new index to predict the efficacy of therapeutic interventions and the prognosis of patients with LUAD.

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.

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.

Integrative nomogram model based on anoikis-related genes enhances prognostic evaluation in colorectal cancer

Background

Revealing the role of anoikis resistance plays in CRC is significant for CRC diagnosis and treatment. This study integrated the CRC anoikis-related key genes (CRC-AKGs) and established a novel model for improving the efficiency and accuracy of the prognostic evaluation of CRC.

Methods

CRC-ARGs were screened out by performing differential expression and univariate Cox analysis. CRC-AKGs were obtained through the LASSO machine learning algorithm and the LASSO Risk-Score was constructed to build a nomogram clinical prediction model combined with the clinical predictors. In parallel, this work developed a web-based dynamic nomogram to facilitate the generalization and practical application of our model.

Results

We identified 10 CRC-AKGs and a risk-related prognostic Risk-Score was calculated. Multivariate COX regression analysis indicated that the Risk-Score, TNM stage, and age were independent risk factors that significantly associated with the CRC prognosis(p < 0.05). A prognostic model was built to predict the outcome with satisfied accuracy (3-year AUC = 0.815) for CRC individuals. The web interactive nomogram (https://yuexiaozhang.shinyapps.io/anoikisCRC/) showed strong generalizability of our model. In parallel, a substantial correlation between tumor microenvironment and Risk-Score was discovered in the present work.

Conclusion

This study reveals the potential role of anoikis in CRC and sets new insights into clinical decision-making in colorectal cancer based on both clinical and sequencing data. Also, the interactive tool provides researchers with a user-friendly interface to input relevant clinical variables and obtain personalized risk predictions or prognostic assessments based on our established model.

Disulfidptosis: disulfide stress-induced novel cell death pathway

The aberrant accumulation of intracellular disulfides in solute carrier family 7 member 11 (SLC7A11)high cells under glucose starvation induces disulfidptosis. Disulfidptosis has shown potential in tumor diagnosis and treatment.

Establishment of a diagnostic model of endometriosis based on disulfidptosis-related genes

OBJECTIVES

We aimed to establish a diagnostic model of endometriosis (EM) based on disulfidptosis-related genes (DRGs).

MATERIALS AND METHODS

The mRNA expression data of EM were downloaded from the gene expression omnibus database and subjected to differential analysis, and co-expression analysis was performed based on 10 disulfidptosis genes to acquire DRGs. The differentially expressed DRGs were subjected to biofunctional analysis. Lasso analysis and support vector machine-recursive feature elimination (SVM-RFE) analysis were employed to extract the intersection of feature genes as biomarkers, and the diagnostic values of biomarkers for EM were evaluated based on receiver operating characteristic curves. The correlations between biomarkers and the immune microenvironment were assessed by Pearson analysis of biomarkers and immune cell infiltration levels.

RESULTS

Transforming growth factor β stimulated protein clone 22 domain family member 4 (TSC22D4), and F-box/SPRY domain-containing protein 1 (FBXO45) worked as the diagnostic classifiers in EM, with an obvious decrease in FBXO45 expression and an evident increase in TSC22D4 expression. The areas under the curves of FBXO45 and TSC22D4 were 0.752 and 0.706, respectively, and the area of FBXO45 combined with TSC22D4 reached 0.865, suggesting that TSC22D4 and FBXO45 had high predictive values. The diagnostic markers were closely correlated with immune cell infiltration.

CONCLUSION

The diagnostic markers constructed based on disulfidptosis are good predictors for EM, which have close correlations with EM.

A novel artificial intelligence network to assess the prognosis of gastrointestinal cancer to immunotherapy based on genetic mutation features

Background

Immune checkpoint inhibitors (ICIs) have revolutionized gastrointestinal cancer treatment, yet the absence of reliable biomarkers hampers precise patient response prediction.

Methods

We developed and validated a genomic mutation signature (GMS) employing a novel artificial intelligence network to forecast the prognosis of gastrointestinal cancer patients undergoing ICIs therapy. Subsequently, we explored the underlying immune landscapes across different subtypes using multiomics data. Finally, UMI-77 was pinpointed through the analysis of drug sensitization data from the Genomics of Drug Sensitivity in Cancer (GDSC) database. The sensitivity of UMI-77 to the AGS and MKN45 cell lines was evaluated using the cell counting kit-8 (CCK8) assay and the plate clone formation assay.

Results

Using the artificial intelligence network, we developed the GMS that independently predicts the prognosis of gastrointestinal cancer patients. The GMS demonstrated consistent performance across three public cohorts and exhibited high sensitivity and specificity for 6, 12, and 24-month overall survival (OS) in receiver operating characteristic (ROC) curve analysis. It outperformed conventional clinical and molecular features. Low-risk samples showed a higher presence of cytolytic immune cells and enhanced immunogenic potential compared to high-risk samples. Additionally, we identified the small molecule compound UMI-77. The half-maximal inhibitory concentration (IC50) of UMI-77 was inversely related to the GMS. Notably, the AGS cell line, classified as high-risk, displayed greater sensitivity to UMI-77, whereas the MKN45 cell line, classified as low-risk, showed less sensitivity.

Conclusion

The GMS developed here can reliably predict survival benefit for gastrointestinal cancer patients on ICIs therapy.

Pan-cancer analysis of the disulfidptosis-related gene RPN1 and its potential biological function and prognostic significance in gliomas

Background

Numerous studies have shown a strong correlation between disulfidptosis and various cancers. However, the expression and function of RPN1, a crucial gene in disulfidptosis, remain unclear in the context of cancer.

Methods

Gene expression and clinical information on lung adenocarcinoma were obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. RPN1 expression was analyzed using the Timer2.0 and the Human Protein Atlas (HPA) databases. Prognostic significance was assessed using Cox regression analysis and Kaplan-Meier curves. Genetic mutations and methylation levels were examined using the cBioPortal and UALCAN platforms, respectively. The relationship between RPN1 and tumor mutation burden (TMB) and microsatellite instability (MSI) across different cancer types was analyzed using the Spearman correlation coefficient. The relationship between RPN1 and immune cell infiltration was analyzed using the Timer2.0 database, whereas variations in drug sensitivity were explored using the CellMiner database. Receiver operating characteristic curves validated RPN1's diagnostic potential in glioma, and its correlation with immune checkpoint inhibitors (ICIs) was assessed using Spearman's correlation coefficient. Single-sample gene set enrichment analysis elucidated a link between RPN1 and immune cells and pathways. In addition, a nomogram based on RPN1 was developed to predict patient prognosis. The functional impact of RPN1 on glioma cells was confirmed using scratch and Transwell assays.

Result

RPN1 was aberrantly expressed in various cancers and affected patient prognosis. The main mutation type of RPN1 in the cancer was amplified. RPN1 exhibited a positive correlation with myeloid-derived suppressor cells, neutrophils, and macrophages, and a negative correlation with CD8+ T cells and hematopoietic stem cells. RPN1 expression was associated with TMB and MSI in various cancers. The expression of RPN1 affected drug sensitivity in cancer cells. RPN1 was positively correlated with multiple ICIs in gliomas. RPN1 also affected immune cell infiltration into the tumor microenvironment. RPN1 was an independent prognostic factor for gliomas, and the nomogram demonstrated excellent predictive performance. Interference with RPN1 expression reduces the migratory and invasive ability of glioma cells.

Conclusion

RPN1 exerts multifaceted effects on different stages of cancer, including immune infiltration, prognosis, and treatment outcomes. RPN1 expression affects the prognosis and immune microenvironment infiltration in patients with glioma, making RPN1 a potential target for the treatment of glioma.

Prognostic and therapeutic model based on disulfidptosis-related genes for patients with clear cell renal cell carcinoma

Disulfidptosis, a newly discovered mode of cell death caused by excessive accumulation of intracellular disulfide compounds, is closely associated with tumor development. This study focused on the relationship between disulfidptosis and clear cell renal cell carcinoma (ccRCC). Firstly, the characterizations of disulfidptosis-related genes (DRGs) in ccRCC were showed, which included number variation (CNV), single nucleotide variation (SNV), DNA methylation, mRNA expression and gene mutation. Then, the ccRCC samples were classified into three clusters through unsupervised clustering based on DRGs. Survival and pathway enrichment differences were evaluated among the three clusters. Subsequently, the differentially expressed genes (DEGs) among the three clusters were screened by univariate Cox, LASSO, and multivariate Cox analysis, and five key DEGs were obtained. Based on the five key DEGs, the ccRCC samples were reclassified into two geneclusters and the survival differences and immune cell infiltration between two geneclusters was investigated. In next step, ccRCC samples were divided into two groups according to PCA scores of five key DEGs, namely high PCA score group (HPSG) and low PCA score group (LPSG). On this basis, differences in survival prognosis, immune cell infiltration and correlation with immune checkpoint, as well as differences in sensitivity to targeted drugs were compared between HPSG and LPSG. The expression levels of four immune checkpoints were higher in HPSG than in LPSG, whereas the LPSG was more sensitive to targeted drug therapy than the HPSG. Finally, validation experiments on HDAC4 indicated that HDAC4 could increase the proliferation and colony formation ability of ccRCC cells.

Multi-Omics Insights into Disulfidptosis-Related Genes Reveal RPN1 as a Therapeutic Target for Liver Cancer

Disulfidptosis, a newly identified mode of programmed cell death, is yet to be comprehensively elucidated with respect to its multi-omics characteristics in tumors, specific pathogenic mechanisms, and antitumor functions in liver cancer. This study included 10,327 tumor and normal tissue samples from 33 cancer types. In-depth analyses using various bioinformatics tools revealed widespread dysregulation of disulfidptosis-related genes (DRGs) in pan-cancer and significant associations with prognosis, genetic variations, tumor stemness, methylation levels, and drug sensitivity. Univariate and multivariate Cox regression and LASSO regression were used to screen and construct prognosis-related hub DRGs and predictive models in the context of liver cancer. Subsequently, single cell analysis was conducted to investigate the subcellular localization of RPN1, a hub DRG, in various solid tumors. Western blotting was performed to validate the expression of RPN1 at both cellular and tissue levels. Additionally, functional experiments, including CCK8, EdU, clone, and transwell assays, indicated that RPN1 knockdown promoted the proliferative and invasive capacities of liver cancer cells. Therefore, this study elucidated the multi-omics characteristics of DRGs in pan-cancer and established a prognostic model for liver cancer. Additionally, this study revealed the molecular functions of RPN1 in liver cancer, suggesting its potential as a therapeutic target for this disease.

Identification of Key Disulfidptosis-Related Genes and Their Association with Gene Expression Subtypes in Crohn's Disease

Background

Crohn's disease (CD) is a persistent inflammatory condition that impacts the gastrointestinal system and is characterized by a multifaceted pathogenesis involving genetic, immune, and environmental components. This study primarily investigates the relationship between gene expression and immune cell infiltration in CD, focusing on disulfidptosis-a novel form of cell death caused by abnormal disulfide accumulation-and its impact on various immune cell populations. By identifying key disulfidptosis-related genes (DRGs) and exploring their association with distinct gene expression subtypes, this research aims to enhance our understanding of CD and potentially other autoimmune diseases.

Methods

Gene expression data from intestinal biopsy samples were collected from both individuals with CD and healthy controls, and these data were retrieved from the GEO database. Through gene expression level comparisons, various differentially expressed genes (DEGs) were identified. Subsequently, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed to reveal the biological processes and pathways linked to these DEGs. Later, immune cell infiltration was evaluated. Hub candidate DRGs were identified using machine learning algorithms. Validation of the expression of hub DRGs was carried out using quantitative real-time polymerase chain reaction. The hub DRGs were subjected to unsupervised hierarchical clustering to classify CD patients into subtypes. The characteristics of each subtype were then analyzed.

Results

Two hub DRGs (NDUFA11 and LRPPRC) were identified. NDUFA11 showed a significantly positive association with the abundance of Th17 cells. Conversely, higher expression levels of LRPPRC were associated with a reduced abundance of various immune cells, particularly monocytes. CD patients were classified into two disulfidptosis-related subtypes. Cluster B patients exhibited lower immune infiltration and milder clinical presentation.

Conclusion

LRPPRC and NDUFA11 are identified as hub DRGs in CD, with potential roles in disulfidptosis and immune regulation. The disulfidptosis subtypes provide new insights into disease progression.

Identification of disulfidptosis-associated genes and characterization of immune cell infiltration in thyroid carcinoma

OBJECTIVE

The primary objective of this study is to conduct a comprehensive screening and analysis of differentially expressed genes related to disulfidoptosis (DEDRGs) in thyroid carcinoma (THCA). This entails delving into the intricate characterization of immune cell infiltration within the THCA context and subsequently formulating and validating a novel prognostic model.

METHOD

To achieve our objectives, we first delineated two distinct subtypes of disulfidoptosis-related genes (DRGs) via consensus clustering methodology. Subsequently, employing the limma R package, we identified the DEDRGs critical for our investigation. These DEDRGs underwent meticulous validation across various databases, alongside an in-depth analysis of gene regulation. Employing functional enrichment techniques, we explored the potential molecular mechanisms underlying disulfidoptosis in THCA. Furthermore, we scrutinized the immune landscape within the two identified subtypes utilizing CIBERSORT and ESTIMATE algorithms. The construction of the prognostic model for THCA entailed intricate methodologies including univariate, multivariate Cox regression, and LASSO regression algorithms. The validity and efficacy of our prognostic model were corroborated through Kaplan-Meier survival curves and ROC curves. Additionally, a nomogram was meticulously formulated to facilitate the prediction of patient prognosis. To fortify our findings, we conducted a comprehensive Bayesian co-localization analysis coupled with rigorous in vitro experimentation, aimed at unequivocally establishing the validity of the identified DEDRGs.

RESULT

Our analyses unveiled Cluster C1, characterized by elevated expression levels of DEDRGs, as harboring a favorable prognosis accompanied by abundant immune cell infiltration. Correlation analyses underscored predominantly positive associations among the DEDRGs, further affirming their significance in THCA. Differential expression patterns of DEDRGs between tumor samples and normal tissues were evident across the GEPIA and HPA databases. Insights from the TIMER database underscored a robust correlation between DEDRGs and immune cell infiltration. KEGG analysis elucidated the enrichment of DEDRGs primarily in pivotal pathways including MAPK, PPAR signaling pathway, and Proteoglycans in cancer. Furthermore, analyses using CIBERSORT and ESTIMATE algorithms shed light on the crucial role played by DEDRGs in shaping the immune microenvironment. The prognostic model, anchored by five genes intricately associated with THCA prognosis, exhibited commendable predictive accuracy and was intricately linked to the tumor immune microenvironment. Notably, patients categorized with low-risk scores stood to potentially benefit more from immunotherapy. The validation of DEDRGs unequivocally underscores the protective role of INF2 in THCA.

CONCLUSION

In summary, our study delineates two discernible subtypes intricately associated with DRGs, revealing profound disparities in immune infiltration and survival prognosis within the THCA milieu. The implications of our findings extend to potential treatment strategies for THCA patients, which could entail targeted interventions directed towards DEDRGs and prognostic genes, thereby influencing disulfidptosis and the immune microenvironment. Moreover, the robust predictive capability demonstrated by our prognostic model, based on the five genes (ANGPTL7, FIRRE, ODAPH, PROKR1, SFRP5), underscores its potential clinical utility in guiding personalized therapeutic approaches for THCA patients.

Unveiling the hidden role of disulfidptosis in kidney renal clear cell carcinoma: a prognostic signature for personalized treatment

The role of disulfidptosis in kidney renal clear cell carcinoma (KIRC) remains unknown. This study investigated disulfidptosis-related biomarkers for KIRC prognosis prediction and individualized treatment. KIRC patients were clustered by disulfidptosis profiles. Differential expression analysis, survival models, and machine learning were used to construct the disulfidptosis-related prognostic signature (DRPS). Characterizations of the tumor immune microenvironment, genetic drivers, drug sensitivity, and immunotherapy response were explored according to the DRPS risk stratification. Markers included in the signature were validated using single-cell, spatial transcriptomics, quantitative RT-qPCR, and immunohistochemistry. In the discovery cohort, we unveiled two clusters of KIRC patients that differed significantly in disulfidptosis regulator expressions and overall survival (OS). After multiple feature selection steps, a DRPS prognostic model with four features (CHAC1, COL7A1, FOXM1, SHOX2) was constructed and validated. Combined with clinical factors, the model demonstrated robust performance in the discovery and external validation cohorts (5-year AUC = 0.793 and 0.846, respectively). KIRC patients with high-risk scores are characterized by inferior OS, less tumor purity, and increased infiltrations of fibroblasts, M1 macrophages, and B cells. High-risk patients also have higher frequencies of BAP1 and AHNAK2 mutation. Besides, the correlation between the DRPS score and the chemotherapy-response signature indicated the potential effect of Gefitinib for high-risk patients. Among the signature genes, FOXM1 is highly expressed in cycling tumor cells and exhibits spatial aggregation, while others are expressed sparsely within tumor samples. The DRPS model enables improved clinical management and personalized KIRC therapy. The identified biomarkers and immune characteristics offer new mechanistic insight into disulfidptosis in KIRC.

Molecular characterization, clinical value, and cancer-immune interactions of genes related to disulfidptosis and ferroptosis in colorectal cancer

BACKGROUND

This research strived to construct a new signature utilizing disulfidptosis-related ferroptosis (SRF) genes to anticipate response to immunotherapy, prognosis, and drug sensitivity in individuals with colorectal cancer (CRC).

METHODS

The data for RNA sequencing as well as corresponding clinical information of individuals with CRC, were extracted from The Cancer Genome Atlas (TCGA) dataset. SRF were constructed with the help of the random forest (RF), least absolute shrinkage and selection operator (LASSO), and stepwise regression algorithms. To validate the SRF model, we applied it to an external cohort, GSE38832. Prognosis, immunotherapy response, drug sensitivity, molecular functions of genes, and somatic mutations of genes were compared across the high- and low-risk groups (categories). Following this, all statistical analyses were conducted with the aid of the R (version 4.23) software and various packages of the Cytoscape (version 3.8.0) tool.

RESULTS

SRF was developed based on five genes (ATG7, USP7, MMD, PLIN4, and THDC2). Both univariate and multivariate Cox regression analyses established SRF as an independent, prognosis-related risk factor. Individuals from the high-risk category had a more unfavorable prognosis, elevated tumor mutational burden (TMB), and significant immunosuppressive status. Hence, they might have better outcomes post-immunotherapy and might benefit from the administration of pazopanib, lapatinib, and sunitinib.

CONCLUSION

In conclusion, SRF can act as a new biomarker for prognosis assessment. Moreover, it is also a good predictor of drug sensitivity and immunotherapy response in CRC but should undergo optimization before implementation in clinical settings.

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 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.

Integrated analysis of disulfidptosis-related immune genes signature to boost the efficacy of prognostic prediction in gastric cancer

BACKGROUND

Gastric cancer (GC) remains a malignant tumor with high morbidity and mortality, accounting for approximately 1,080,000 diagnosed cases and 770,000 deaths worldwide annually. Disulfidptosis, characterized by the stress-induced abnormal accumulation of disulfide, is a recently identified form of programmed cell death. Substantial studies have demonstrated the significant influence of immune clearance on tumor progression. Therefore, we aimed to explore the intrinsic correlations between disulfidptosis and immune-related genes (IRGs) in GC, as well as the potential value of disulfidptosis-related immune genes (DRIGs) as biomarkers.

METHODS

This study incorporated the single-cell RNA sequencing (scRNA-seq) dataset GSE183904 and transcriptome RNA sequencing of GC from the TCGA database. Disulfidptosis-related genes (DRGs) and IRGs were derived from the representative literature on both cell disulfidptosis and immunity. The expression and distribution of DRGs were investigated at the single-cell level in different GC cell types. Pearson correlation analysis was used to identify the IRGs closely related to disulfidptosis. The prognostic signature of DRIGs was established using Cox and LASSO analyses. We then analyzed and evaluated the differences in long-term prognosis, Gene Set Enrichment Analysis (GSEA), immune infiltration, mutation profile, CD274 expression, and response to chemotherapeutic drugs between the two groups. A tissue array containing 63 paired GC specimens was used to verify the expression of 4 DRIGs and disulfidptosis regulator SLC7A11 through immunohistochemistry staining.

RESULTS

The scRNA-seq analysis found that SLC7A11, SLC3A2, RPN1 and NCKAP1 were enriched in specific cell types and closely related to immune infiltration. Four DIRGs (GLA, HIF-1α, VPS35 and CDC37) were successfully identified to establish a signature to potently predict the survival time of GC patients. Patients with high risk scores generally experienced worse prognoses and exhibited greater resistant to classical chemotherapy drugs. Furthermore, the expression of GLA, HIF-1α, VPS35, CDC37 and SLC7A11 were elevated in GC tissues. A high expression of GLA, HIF-1α, VPS35 or CDC37 was associated with more advanced clinical stage of GC and increased SLC7A11 expression.

CONCLUSION

Current study first highlights the potential value of DRIGs as biomarkers in GC. We successfully constructed a robust model incorporating four DRIGs to accurately predict the survival time and clinicopathological characteristics of GC patients.

Machine learning-driven prognostic analysis of cuproptosis and disulfidptosis-related lncRNAs in clear cell renal cell carcinoma: a step towards precision oncology

Cuproptosis and disulfidptosis, recently discovered mechanisms of cell death, have demonstrated that differential expression of key genes and long non-coding RNAs (lncRNAs) profoundly influences tumor development and affects their drug sensitivity. Clear cell renal cell carcinoma (ccRCC), the most common subtype of kidney cancer, presently lacks research utilizing cuproptosis and disulfidptosis-related lncRNAs (CDRLRs) as prognostic markers. In this study, we analyzed RNA-seq data, clinical information, and mutation data from The Cancer Genome Atlas (TCGA) on ccRCC and cross-referenced it with known cuproptosis and disulfidptosis-related genes (CDRGs). Using the LASSO machine learning algorithm, we identified four CDRLRs-ACVR2B-AS1, AC095055.1, AL161782.1, and MANEA-DT-that are strongly associated with prognosis and used them to construct a prognostic risk model. To verify the model's reliability and validate these four CDRLRs as significant prognostic factors, we performed dataset grouping validation, followed by RT-qPCR and external database validation for differential expression and prognosis of CDRLRs in ccRCC. Gene function and pathway analysis were conducted using Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA) for high- and low-risk groups. Additionally, we have analyzed the tumor mutation burden (TMB) and the immune microenvironment (TME), employing the oncoPredict and Immunophenoscore (IPS) algorithms to assess the sensitivity of diverse risk categories to targeted therapeutics and immunosuppressants. Our predominant objective is to refine prognostic predictions for patients with ccRCC and inform treatment decisions by conducting an exhaustive study on cuproptosis and disulfidptosis.

Effect of disulfidptosis-related genes SLC3A2, SLC7A11 and FLNB polymorphisms on risk of autoimmune thyroiditis in a Chinese population

PURPOSE

This study aimed to evaluate the associations between disulfidptosis related genes-SLC3A2, SLC7A11 and FLNB polymorphisms and risk of autoimmune thyroiditis (AIT).

METHODS

Six SNPs in the SLC3A2, SLC7A11 and FLNB were genotyped in 650 AIT cases and 650 controls using a MassARRAY platform.

RESULTS

Minor alleles of SLC3A2-rs12794763, rs1059292 and FLNB-rs839240 might lead to a higher risk of AIT (p < 0.001), while SLC7A11-rs969319-C allele tends to decrease the risk of the disease (p = 0.006). Genetic model analysis showed that SLC3A2-rs12794763, SLC3A2-rs1059292 and FLNB-rs839240 polymorphisms were risk factors for AIT (p < 0.001); while SLC7A11-rs969319 showed a protective role for the disease in all genetic models (p < 0.005). Stratification analysis showed that SLC3A2-rs1059292 and rs12794763 were correlated with higher risk of AIT regardless of sex (p < 0.05). Moreover, FLNB-rs839240 exhibited higher risk of disease only in females (p < 0.05). By contrast, SLC7A11-rs969319 showed a protective role only in females (p < 0.05).

CONCLUSION

Our results shed new light on the association between disulfidptosis-related genes and AIT risk.

Machine learning and single-cell transcriptome profiling reveal regulation of fibroblast activation through THBS2/TGFβ1/P-Smad2/3 signalling pathway in hypertrophic scar

Hypertrophic scar (HS) is a chronic inflammatory skin disorder characterized by excessive deposition of extracellular matrix, and the mechanisms underlying their formation remain poorly understood. We analysed scRNA-seq data from samples of normal skin and HS. Using the hdWGCNA method, key gene modules of fibroblasts in HS were identified. Non-negative matrix factorization was employed to perform subtype analysis of HS patients using these gene modules. Multiple machine learning algorithms were applied to screen and validate accurate gene signatures for identifying and predicting HS, and a convolutional neural network (CNN) based on deep learning was established and validated. Quantitative reverse transcription-polymerase chain reaction and western blotting were performed to measure mRNA and protein expression. Immunofluorescence was used for gene localization analysis, and biological features were assessed through CCK8 and wound healing assay. Single-cell sequencing revealed distinct subpopulations of fibroblasts in HS. HdWGCNA identified key gene characteristics of this population, and pseudotime analysis was conducted to investigate gene variation during fibroblast differentiation. By employing various machine learning algorithms, the gene range was narrowed down to three key genes. A CNN was trained using the expression of these key genes and immune cell infiltration, enabling diagnosis and prediction of HS. Functional experiments demonstrated that THBS2 is associated with fibroblast proliferation and migration in HS and affects the formation and development of HS through the TGFβ1/P-Smad2/3 pathway. Our study identifies unique fibroblast subpopulations closely associated with HS and provides biomarkers for the diagnosis and treatment of HS.

A disulfidptosis-related lncRNA signature for predicting prognosis and evaluating the tumor immune microenvironment of lung adenocarcinoma

As a novel form of regulated cell death (RCD), disulfidptosis offering a significant opportunity in better understanding of tumor pathogenesis and therapeutic strategies. Long non-coding RNAs (lncRNAs) regulate the biology functions of tumor cells by engaging with a range of targets. However, the prognostic value of disulfidptosis-related lncRNAs (DRlncRNAs) in lung adenocarcinoma (LUAD) remains unclear. Therefore, our study aimed at establishing a prognostic model for LUAD patients based on DRlncRNAs. RNA-seq data and clinical information were obtained from The Cancer Genome Atlas (TCGA) database. Subsequently, a prognostic model based on DRlncRNAs was constructed using LASSO and COX regression analysis. Patients were stratified into high- and low-risk groups based on their risk scores. Differences between the high-risk and low-risk groups were investigated in terms of overall survival (OS), functional enrichment, tumor immune microenvironment (TIME), somatic mutations, and drug sensitivity. Finally, the role of lncRNA GSEC in LUAD was validated through in vitro experiments. Using the prognostic model consists of 5 DRlncRNAs (AL365181.2, GSEC, AC093673.1, AC012615.1, AL606834.1), the low-risk group exhibited a markedly superior survival in comparison to the high-risk group. The significant differences were observed among patients from different risk groups in OS, immune cell infiltration, immune checkpoint expression, immunotherapy response, and mutation landscape. Experimental results from cellular studies demonstrate the knockdown of lncRNA GSEC leading to a significant reduction in the proliferation and migration abilities of LUAD cells. Our prognostic model, constructed using 5 DRlncRNAs, exhibited the capacity to independently predict the survival of LUAD patients, providing the potentially significant assistance in prognosis prediction, and treatment effects optimization. Moreover, our study established a foundation for further research on disulfidptosis in LUAD and proposed new perspectives for the treatment of LUAD.

A novel disulfidptosis-related prognostic gene signature and experimental validation identify ACTN4 as a novel therapeutic target in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is a prevalent form of malignancy globally. Disulfidptosis is novel programmed cell death pathway based on disulfide proteins, may have a positive impact on the development of LUAD treatment strategies.

OBJECTIVE

To investigate the impact of disulfidptosis-related genes (DRGs) on the prognosis of LUAD, developed a risk model to facilitate the diagnosis and prognostication of patients. We also explored ACTN4 (DRGs) as a new therapeutic biomarker for LUAD.

METHODS

We investigated the expression patterns of DRGs in both LUAD and noncancerous tissues. To assess the prognostic value of the DRGs, we developed risk models through univariate Cox analysis and lasso regression. The expression and function of ACTN4 was evaluated by qRT-PCR, immunohistochemistry and in vitro experiments. The TIMER examined the association between ACTN4 expression and immune infiltration in LUAD.

RESULTS

Ten differentially expressed DRGs were identified. And ACTN4 was identified as potential risk factors through univariate Cox regression analysis (P < 0.05). ACTN4 expression and riskscore were used to construct a risk model to predict overall survival in LUAD, and high-risk demonstrated a significantly higher mortality rate compared to the low-risk cohort. qRT-PCR and immunohistochemistry assays indicated ACTN4 was upregulated in LUAD, and the upregulation was associated with clinicopathologic features. In vitro experiments showed the knockdown of ACTN4 expression inhibited the proliferation in LUAD cells. The TIMER analysis demonstrated a correlation between the expression of ACTN4 and the infiltration of diverse immune cells. Elevated ACTN4 expression was associated with a reduction in memory B cell count. Additionally, the ACTN4 expression was associated with m6A modification genes.

CONCLUSIONS

Our study introduced a prognostic model based on DRGs, which could forecast the prognosis of patients with LUAD. The biomarker ACTN4 exhibits promise for the diagnosis and management of LUAD, given its correlation with tumor immune infiltration and m6A modification.

Construction of disulfidptosis-based immune response prediction model with artificial intelligence and validation of the pivotal grouping oncogene c-MET in regulating T cell exhaustion

Background

Given the lack of research on disulfidptosis, our study aimed to dissect its role in pan-cancer and explore the crosstalk between disulfidptosis and cancer immunity.

Methods

Based on TCGA, ICGC, CGGA, GSE30219, GSE31210, GSE37745, GSE50081, GSE22138, GSE41613, univariate Cox regression, LASSO regression, and multivariate Cox regression were used to construct the rough gene signature based on disulfidptosis for each type of cancer. SsGSEA and Cibersort, followed by correlation analysis, were harnessed to explore the linkage between disulfidptosis and cancer immunity. Weighted correlation network analysis (WGCNA) and Machine learning were utilized to make a refined prognosis model for pan-cancer. In particular, a customized, enhanced prognosis model was made for glioma. The siRNA transfection, FACS, ELISA, etc., were employed to validate the function of c-MET.

Results

The expression comparison of the disulfidptosis-related genes (DRGs) between tumor and nontumor tissues implied a significant difference in most cancers. The correlation between disulfidptosis and immune cell infiltration, including T cell exhaustion (Tex), was evident, especially in glioma. The 7-gene signature was constructed as the rough model for the glioma prognosis. A pan-cancer suitable DSP clustering was made and validated to predict the prognosis. Furthermore, two DSP groups were defined by machine learning to predict the survival and immune therapy response in glioma, which was validated in CGGA. PD-L1 and other immune pathways were highly enriched in the core blue gene module from WGCNA. Among them, c-MET was validated as a tumor driver gene and JAK3-STAT3-PD-L1/PD1 regulator in glioma and T cells. Specifically, the down-regulation of c-MET decreased the proportion of PD1+ CD8+ T cells.

Conclusion

To summarize, we dissected the roles of DRGs in the prognosis and their relationship with immunity in pan-cancer. A general prognosis model based on machine learning was constructed for pan-cancer and validated by external datasets with a consistent result. In particular, a survival-predicting model was made specifically for patients with glioma to predict its survival and immune response to ICIs. C-MET was screened and validated for its tumor driver gene and immune regulation function (inducing t-cell exhaustion) in glioma.