Identification of disulfidptosis-related subgroups and prognostic signatures in lung adenocarcinoma using machine learning and experimental validation

Immune Regulation and Disulfidptosis in Atherosclerosis Influence Disease Progression and Therapy

Background: Atherosclerosis is a progressive and complex vascular pathology characterized by cellular heterogeneity, metabolic dysregulation, and chronic inflammation. Despite extensive research, the intricate molecular mechanisms underlying its development and progression remain incompletely understood. Methods: Single-cell RNA sequencing (scRNA-seq) was employed to conduct a comprehensive mapping of immune cell enrichment and interactions within atherosclerotic plaques, aiming to investigate the cellular and molecular complexities of these structures. This approach facilitated a deeper understanding of the heterogeneities present in smooth muscle cells, which were subsequently analyzed using pseudotime trajectory analysis to monitor the developmental trajectories of smooth muscle cell (SMC) subpopulations. An integrative bioinformatics approach, primarily utilizing Weighted Gene Co-expression Network Analysis (WGCNA) and machine learning techniques, identified Cathepsin C (CTSC), transforming growth factor beta-induced protein (TGFBI), and glia maturation factor-γ (GMFG) as critical biomarkers. A diagnostic risk score model was developed and rigorously tested through Receiver Operating Characteristic analysis. To illustrate the functional impact of CTSC on the regulation of plaque formation and SMC viability, both in vitro and in vivo experimental investigations were conducted. Results: An analysis revealed SMCs identified as the most prominent cellular type, exhibiting the highest density of disulfidptosis. Pseudotime trajectory analysis illuminated the dynamic activation pathways in SMCs, highlighting their significant role in plaque development and instability. Further characterization of macrophage subtypes demonstrated intercellular communication with SMCs, which exhibited specific signaling pathways, particularly between the proximal and core areas of plaques. The integrated diagnostic risk score model, which incorporates CTSC, TGFBI, and GMFG, proved to be highly accurate in distinguishing high-risk patients with elevated immune responses and systemic inflammation. Knockdown experiments of CTSC conducted in vitro revealed enhanced SMC survival rates, reduced oxidative stress, and inhibited apoptosis, while in vivo experiments confirmed a decrease in plaque burden and improvement in lipid profiles. Conclusions: This study emphasizes the significance of disulfidptosis in the development of atherosclerosis and identifies CTSC as a potential therapeutic target for stabilizing plaques by inhibiting SMC apoptosis and oxidative damage. Additionally, the risk score model serves as a valuable diagnostic tool for identifying high-risk patients and guiding precision treatment strategies.

Analysis and experimental validation of disulfidptosis related genes solute carrier family 3 member 2 (SLC3A2) in endometrial cancer

Disulfidptosis, a novel cell death paradigm triggered by disulfide stress, remains underexplored, particularly its implications for endometrial cancer (EC). This study focused on the prognostic significance of disulfidptosis-related genes (DRGs) in EC, highlighting the pivotal role of SLC3A2. To predict EC patient outcomes, we developed a model centered on DRGs, employing LASSO-Cox regression for its construction. The model revealed a strong correlation between DRG risk score, gene set enrichment analysis (GSEA), single-sample GSEA (ssGSEA), clinical characteristics, the tumor microenvironment (TME), and the response to immunotherapy. Key genes were pinpointed using random forest maps. To establish SLC3A2's oncogenic effects in EC, we conducted comprehensive studies including apoptosis, cell cycle, TRANSWELL, CCK-8, and tumor xenograft assays. SLC3A2 expression was further confirmed via qRT-PCR. The impact of SLC3A2 on EC's malignant behavior was corroborated through both in vitro and in vivo experiments.

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.

Signature Construction and Disulfidptosis-Related Molecular Cluster Identification for Better Prediction of Prognosis in Glioma

Disulfidptosis is a newly discovered form of regulatory cell death. However, the identification of disulfidptosis-related molecular subtypes and potential biomarkers in gliomas and their prognostic predictive potential need to be further elucidated. RNA sequencing profiles and the relevant clinical data were obtained from the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). Disulfidptosis-related clusters were identified by unsupervised clustering analysis. Immune cell infiltration analysis and drug sensitivity analysis were used to explore the differences between clusters. Gene set enrichment analysis (GSEA) of differential genes between clusters was performed to explore the potential biological functions and signaling. A disulfidptosis-related scoring system (DRSS) was constructed based on a combined COX and LASSO analysis. Mendelian randomization (MR) analyses were used to further explore the causal relationship between levels of genes in DRSS and an increased risk of glioma. A prognosis nomogram was constructed based on the DRSS and 3 clinical features (age, WHO stage, and IDH status). The accuracy and stability of the prognosis nomogram were also validated in different cohorts. We identified two clusters that exhibited different prognoses, drug sensitivity profiles, and tumor microenvironment infiltration profiles. The overall survival (OS) of Cluster2 was significantly better than Cluster1. Cluster1 had an overall greater infiltration of immune cells compared to Cluster2. However, the Monocytes, activated B cells had higher infiltration abundance in Cluster2. GSEA results showed significant enrichment of immune-related biological processes in Cluster1, while Cluster2 was more enriched for functions related to neurotransmission and regulation. PER3, RAB34, NKX3-2, GPX7, FRA10AC1, and TGIF1 were finally included to construct DRSS. DRSS was independently related to prognosis. There was a significant difference in overall survival between the low-risk score group and the high-risk score group. Among six genes in DRSS, GPX7 levels were demonstrated to have a causal relationship with an increased risk of glioma. GPX7 may become a more promising biomarker for gliomas. The prognosis nomogram constructed based on the DRSS and three clinical features has considerable potential for predicting the prognosis of patients with glioma. Free online software for implementing this nomogram was established:  https://yekun-zhuang.shinyapps.io/DynNomapp/ . Our study established a novel glioma classification based on the disulfidptosis-related molecular subtypes. We constructed the DRSS and the prognosis nomogram to accurately stratify the prognosis of glioma patients. GPX7 was identified as a more promising biomarker for glioma. We provide important insights into the treatment and prognosis of gliomas.

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

BACKGROUND

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

METHODS

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

RESULTS

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