Prognostic and Immunological Analysis of Disulfidoptosis-Related Ferroptosis Genes in Lung Adenocarcinoma

Prognostic and Immunological Analysis of Disulfidoptosis-Related Ferroptosis Genes in Lung Adenocarcinoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prognostic and Immunological Analysis of Disulfidoptosis-Related Ferroptosis Genes in Lung Adenocarcinoma Zichen Liu, Rongdi Yan, Yingxv Luo, Xin Zhang, Xin Zheng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5324542/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Disulfidoptosis, a newly identified form of regulated cell death (RCD), significantly influences the progression of lung adenocarcinoma (LUAD). This study identified 340 ferroptosis-related genes strongly correlated with disulfidoptosis through correlation analysis. By intersecting these genes with those from module genes selected via weighted gene co-expression network analysis (WGCNA), 31 genes were found. In the TCGA-LUAD cohort, Kaplan-Meier(KM) survival analysis initially screened these genes, leading to the selection of 6 Disulfidoptosis-Related Ferroptosis (DRF) genes (IL33, SLC2A1, CDCA3, KIF20A, FANCD2, RRM2) through further screening with Random Forest and SVM-RFE. Based on the expression levels of DRF genes, two distinct groups with differing prognostic and immune characteristics were identified. A machine learning-driven signature (MLS) of 24 DRF-related genes was then constructed using the RSF + SuperPC algorithm and validated in the TCGA-LUAD and GSE31210 datasets. Compared with 77 other signatures, MLS demonstrated superior performance in both datasets. A low MLS score was associated with immune activation, higher tumor mutation burden, and better survival probability. Conversely, a high MLS score correlated with poorer prognosis and reduced potential benefit from immune therapy, although treatments like Doramapimod might still offer benefits. The cell cycle pathway was a key factor distinguishing high from low MLS groups. Overall, MLS shows promise for predicting prognosis in LUAD patients and identifying those who might benefit from immune therapy. Additionally, DRF genes have potential clinical value for diagnosing and treating other cancers, as indicated by pan-cancer analysis. q-PCR experiments targeting select DRF genes confirmed their feasibility as diagnostic markers for LUAD. disulfidoptosis ferroptosis lung adenocarcinoma machine learning immune therapy prognosis drug sensitivity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Lung cancer is the second most common cancer worldwide and the leading cause of cancer-related deaths. LUAD is the most prevalent histological subtype, accounting for more than 40% of all lung cancer cases 1,2 . TNM staging has long been used as an important predictor of lung cancer prognosis 3 . However, studies have indicated that tumor heterogeneity may lead to prognostic differences among patients with the same stage of lung cancer 4 . Additionally, with the increasing use of immunotherapy for lung cancer, identifying patients who are more likely to benefit from such treatment has become a critical clinical challenge 5 . Anatomical TNM staging alone is not ideal for screening patients who are likely to respond to immunotherapy. Therefore, a comprehensive understanding of the molecular mechanisms underlying LUAD and the identification of new biomarkers for predicting long-term prognosis and response to immunotherapy are crucial for improving preventive strategies and effective interventions 6,7 . Recent studies have shown that cells overexpressing SLC7A11 can undergo rapid cell death due to SLC7A11-mediated depletion of NADPH and intracellular accumulation of toxic cystine, induced by disulfide stress. This process, known as disulfidoptosis, is a newly identified form of regulated cell death (RCD) distinct from known forms such as ferroptosis, necroptosis, and apoptosis. Disulfidoptosis typically occurs under glucose starvation conditions 8 . Regulation of disulfide proteases involves the formation and cleavage of disulfide bonds and the involvement of proteins such as NCKAP1, as well as redox-related signaling pathways. Disulfide proteases have potential as targets for cancer therapy. Inhibiting glucose transporter protein (GLUT) can have therapeutic effects on SLC7A11-overexpressing cancer cells by inducing disulfide proteases. Understanding the regulatory mechanisms and significance of disulfide proteases can provide insights into cellular homeostasis and offer potential strategies for targeted cancer therapy. Ferroptosis is an iron-dependent RCD characterized by excessive lipid peroxidation in the cell membrane, which inhibits antioxidant responses. Key features of ferroptosis include the oxidation of polyunsaturated fatty acids in phospholipids, the presence of redox-active iron, and the loss of lipid peroxidation repair mechanisms. GPX4, a crucial regulator of ferroptosis, inhibits its onset by reducing the formation of phospholipid hydroperoxides. Down-regulation of SLC7A11 triggers ferroptosis by impairing cysteine metabolism pathways 9–11 . Ferroptosis plays a significant role in various diseases, including organ damage, degenerative diseases, and tumors 12,13 . Moreover, therapeutic resistance in certain cancers is linked to overexpression of GPX4 and reduced expression of SLC7A11 14 . Although disulfidoptosis and ferroptosis are distinct forms of cell death, SLC7A11 can act as a common regulator of both, influencing intracellular iron levels and regulating ferroptosis resistance, and plays a vital role in the regulation of disulfidoptosis in lung cancer 10,15,16 . In this study, we identified and validated DRF genes using bioinformatics analysis. Based on different aggregated signatures, we developed a new machine learning-based score, the MLS, for LUAD patients. This score performed well compared to 77 published signatures. We also conducted immunological and drug sensitivity analyses to explore immune differences and identify potentially effective drugs among different patient subgroups. Our study contributes to a better understanding of disulfidoptosis-associated ferroptosis genes in LUAD development and may provide guidance for prognostic prediction and personalized therapy for various cancers, including LUAD. Materials and methods Data acquisition and processing RNA-Seq data on gene expression for 33 cancers, including LUAD, along with corresponding clinical data, were downloaded from TCGA (The Cancer Genome Atlas) database ( https://portal.gdc.cancer.gov/ ). Similar data from the validation dataset, GSE31210, were acquired from the GEO database (Gene Expression Omnibus ; https://www.ncbi.nlm.nih.gov/geo/ ). Somatic mutation data for TCGA-LUAD were acquired from the GDC (Genomic Data Commons; https://portal.gdc.cancer.gov/ ), and copy number variation (CNV) data were downloaded from UCSC Xena ( https://xenabrowser.net ). Tumor Immuno-Dysfunction and Rejection (TIDE) data for non-small cell lung cancer were obtained from the TIDE website ( http://tide.DRFci.harvard.edu/ ). Identifcation of DRF genes Twenty-three disulfidoptosis-related genes were identified from relevant studies 17,18 . The 564 ferroptosis-associated genes were obtained from the FerrDb V2 database ( http://www.zhounan.org/ ). The WGCNA R package was utilized to construct co-expression modules and clarify the relationships between these modules. Pearson correlation analysis was employed to screen for genes highly associated with disulfidoptosis and ferroptosis genes (|cor| > 0.3, p < 0.05), identifying those module genes significantly correlated with LUAD. The intersection of these gene sets was determined. Kaplan-Meier survival analyses were conducted to identify genes more strongly associated with prognosis (p < 0.05). Hyperparameter tuning of ten machine learning algorithms (Classification Tree, Glmnet, KNN, LDA, Logistic, Naive Bayes, NNET, Random Forest, SVM, Xgboost) was performed to select the best screening algorithm. The frequency of CNV of the finalized Disulfidoptosis-Related Ferroptosis (DRF) genes is presented in a bar chart, with their chromosomal positions shown using the "RCircos" package in R. Differential and survival analyses were conducted to validate these genes in the GSE31210 cohort, and the CIBERSORT algorithm was used to analyze the relationship between DRF genes and 20 immune cell types. Genome-wide genomic data for 33 cancers, including DRF gene expression in pan-cancers, survival analyses, and interlinkages, were obtained from TCGA. To better illustrate the intrinsic relationship between DRF and disulfidoptosis genes, the concept of "module" from the WGCNA algorithm was applied, with DRF and disulfidoptosis considered as a module. The association between the expression values of DRF/disulfidoptosis regulators and module signature genes was assessed based on intra-module connectivity. DRF/disulfidoptosis was defined as a regulator with a module membership (MM) greater than 0.75. For each cancer type, the pooled expression levels of the identified DRF/disulfidoptosis regulators were calculated as epigenetic module signature genes (EMEs). Clinical and immunological characterisation and enrichment analysis of DRF subgroups Consensus cluster analysis of LUAD samples in the TCGA dataset was performed based on DRF gene expression using the "ConsensusClusterPlus" package. Gene expression and clinical data, such as age, gender, and TNM stage, were visualized between subgroups using the "pheatmap" package. Overall survival (OS) times were then compared between these subgroups. Subsequently, the "LIMMA" R package was used to identify differentially expressed genes (DEGs) associated with Disulfidoptosis-Related Ferroptosis (DRF) groupings. DEGs were adjusted to a significance level of p 1.2. The "ClusterProfiler" package and "org.Hs.eg.db" were employed to perform GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment analyses based on the identified DEGs. The ssGSEA package and GSVA (Gene Set Variation Analysis) method were used to compute clustering values for immune-related pathways and immune cells to perform functional and pathway enrichment analyses on the subgroups, with results presented in heatmaps. The package of "estimate" was used to calculate 23 immune-related functional scores. Quantitative real-time polymerase chain reaction (qRT-PCR) Normal lung epithelial cells (BEAS-2B) and the human lung adenocarcinoma cell line (A549) were obtained from the Shandong University of Traditional Chinese Medicine. A fluorescence quantitative PCR instrument was acquired from Roche (Basel, Switzerland). Total RNA was extracted using TRNzol Universal reagent (Beijing, China). Complementary DNA (cDNA) was synthesized using the First Strand Synthesis Kit (KR116). The relative expression of genes was analyzed using the 2 − ΔΔCT method with actin as an internal reference gene and normal lung epithelial cells as the control. MLS prognostic model construction assessment and enrichment analysis In the TCGA-LUAD training cohort, the KM method was used to identify genes from DEGs significantly associated with OS (p < 0.001). Genes expressed in both TCGA-LUAD and GSE31210 were selected for inclusion in the integrated framework for constructing the MLS. Consistent models were developed based on 101 algorithm combinations in both the TCGA-LUAD training cohort and the GSE31210 validation cohort, and the average C-index of all models in both cohorts was calculated to assess predictive performance (Fig. 3 A). MLS scores for each sample in all cohorts were computed using the formula: risk_score = Σ(Expi * Coefi), where Coefi and Expi represent the risk factor and expression of each gene, respectively. Samples were categorized into low risk (score median) groups based on the median risk score. To illustrate the differences between high and low risk groups, we first depicted the process from DRF cluster grouping to risk grouping to final outcomes using Mulberry diagrams. Subsequently, MLS gene expression between high and low risk groups, as well as MLS scores of DRF subgroups, were analyzed. Finally, the "clusterSUR" package was used for survival analysis between high and low risk groups. To progressively validate the accuracy of the model, we conducted a systematic literature search that led to the inclusion of 77 published prognostic signatures for LUAD. These signatures were associated with features such as immunity, copper-induced cell death, and iron-dependent cell death. We then compared the MLS to these published signatures using the TCGA-LUAD and GSE31210 datasets. Subsequently, Gene Set Enrichment Analysis (GSEA) was used to analyze the pathways in high and low scoring groups. Immunoassay Six immune infiltration algorithms were employed to assess dissimilarities in immune cells between two scoring subgroups. Additionally, the TIDE (Tumor Immune Dysfunction and Exclusion) algorithm predicted the efficacy of anti-tumor immune drugs (anti-PD1, anti-CTLA4) based on TIDE scores, which were positively correlated with drug efficacy 19 . TIDE scores were also used to evaluate patient responses to immunotherapy. In the analysis of Tumor Mutational Burden (TMB), the maftools package was utilized to examine the connection between risk scores and TMB. The package of "survival survminer" was used to investigate the impact of TMB on LUAD prognosis, with a p-value < 0.05 considered statistically significant. Drug screening To evaluate the sensitivity of high and low scoring MLS groups to different drugs, the oncoPredict 20 software package was used to calculate IC50 values for 198 drugs from the GDSC2 database. This analysis aimed to identify potentially effective drugs for patients who are in the high and low scoring groups, with a significance threshold of p < e-10. Results Identification and pan-cancer analysis of the DRF gene Firstly, 340 ferroptosis-related genes associated with disulfidoptosis were identified through correlation studies. Using the "limma" package, TCGA-LUAD samples were categorized into tumor and normal tissues. Co-expression modules were constructed with the WGCNA package. Correlation analyses assessed the relationships between these modules and tumor versus normal tissues, leading to the identification of 13 modules with the optimal soft threshold power of 3 (Fig. 1 A, B). Among these modules, 8 were significantly correlated with tumors (P < 0.05), with 5 showing positive correlations and 3 showing negative correlations (Fig. 1 C). To identify relevant hub genes, we focused on the MEblue and MEbrown modules, which had the most significant positive and negative correlations (Pearson correlation coefficients of 0.52 and − 0.82, respectively; Fig. 1 D, E). From these modules, 1,437 related genes were selected, with 613 genes from the MEblue module. After intersecting these genes with the disulfidoptosis ferroptosis (DRF) genes, 31 genes were obtained. Kaplan-Meier survival analysis was used to screen for 10 candidate genes with strong prognostic relevance. SVM and Random Forest algorithms were then applied for hub gene screening (Fig. 2 A). The intersection of these two algorithms narrowed the 10 candidate genes down to 6 (IL33, SLC2A1, CDCA3, KIF20A, FANCD2, RRM2; Supplementary Table 1, 2). These six DRF genes demonstrated favorable receiver operating characteristic (ROC) values in both the TCGA and GSE31210 cohorts (Supplementary Fig. 1), leading to their identification as DRF genes for LUAD. Subsequent survival and differential analyses for the GSE31210 cohort validated the reliability of these genes (Supplementary Figs. 2, 3). The interrelationships among DRF genes in the genome-wide data for 33 cancers are illustrated in Fig. 2 B. Expression analysis revealed differential expression in 18 cancer types, indicating that IL33 could act as a protective factor for most tumors, while the other five genes appeared as risk factors (Fig. 2 C). Survival analysis results suggested that DRF genes can differentiate survival times across multiple cancers (Supplementary Table 3, P < 0.05). Notably, WGCNA was used to identify central regulators involved in the connectivity of DRF and disulfidoptosis regulators across 33 cancer types (Fig. 2 D). The analysis showed a strong correlation among central DRF regulators (R = 0.65, P = 4.7e-05; Fig. 2 E). In vitro experiments demonstrated significantly higher expression of CDCA3, KIF20A, FANCD2, RRM2, and SLC2A1 in the NSCLC (A549) cell line compared to normal lung epithelial cells (BEAS-2B), with statistically significant differences (Fig. 3 ), confirming the potential of DRF genes as tumor detection markers. Mutational landscape and immune profile of the DRF gene The chromosomal locations of DRF genes are shown in Fig. 4 A. The study revealed that CNV deletions were more prevalent in IL33, CDCA3, and KIF20A, while SLC2A1, RRM2, and FANCD2 were more frequently amplified (Fig. 4 B). These analyses demonstrated significant genetic and expression heterogeneity of DRF in lung adenocarcinoma, suggesting that imbalanced DRF expression plays a critical role in LUAD development. CIBERSORT analysis indicated that IL33 was positively correlated with various immune-activated cells and negatively correlated with immunosuppressive cells, such as regulatory T cells (Tregs) (Fig. 4 C). Clinical and immunological analysis of DRF subgroups To further investigate the clinical value and functional biological patterns of these DRFs, we performed clustering analyses on all tumor samples in the TCGA LUAD cohort based on the expression levels of the six DRFs. We compared the expression of DRF-related genes with LUAD subtypes to explore their relationship. Setting the clustering value (K) from 2 to 10, the best aggregation stability was found at K = 2 (Fig. 4 D). Consequently, the TCGA LUAD cohort was divided into cluster C1 (n = 347) and cluster C2 (n = 238) based on DRF expression. Heatmaps were used to display gene expression and TNM typing, staging, age (≤ 65 or > 65 years), and gender for the 585 LUAD samples. The IL33 gene expression level was significantly lower in the C1 group compared to the C2 group and was more closely associated with poor prognostic indicators (e.g., higher TNM staging and grading, older age), consistent with previous analyses (Fig. 4 E). Kaplan-Meier analysis revealed that the C2 group had a more favorable OS compared to the C1 group, which showed a survival disadvantage consistent with clinical parameters (P = 0.003, Fig. 4 F). Given the importance of tumor immunity in tumor development, we explored the level of cellular infiltration in the tumor microenvironment. ssGSEA scores indicated that the C2 group had richer infiltration of immune cells, such as immature B cells and natural killer(NK) cells. The poorer clinical outcome in the C1 group may be related to immunosuppression induced by a Type 2 T helper cell (Th2) subpopulation in CD4 + T cells 21 (Fig. 5 A). The TME (tumor microenvironment), which includes tumor cells, mesenchymal stromal cells, immune cells, cytokines, and chemokines, was assessed. Figure 5 B shows that the C2 group had higher StromalScore, ImmunityScore, and ESTIMATEScore, associated with lower tumor purity and better immune infiltration. This suggests that the C2 group may be linked to an immune microenvironment that promotes tumor death and is likely more sensitive to immunotherapy 22 . Analysis of the immune environment of the subgroups (Fig. 5 C) revealed that Type_II_IFN_Response was more prevalent in C2, indicating characteristics of the immune response. Parainflammation, which is crucial for maintaining homeostasis, was more prominent in C1, potentially driven by p53 mutations, which can promote cancer. Given the sensitivity of parainflammation to NSAIDs, aspirin may offer better therapeutic effects for patients in the C1 group 23 . To further investigate the pathways dominated by DRFs, we conducted GSVA. Figure 5 D illustrates that KEGG analysis revealed a significant enrichment of inflammatory metabolism-related pathways in the C2 group. In contrast, tumorigenic pathways, such as cell cycle and amino acid metabolism, were significantly enriched in the C1 group, which may be related to the previously mentioned parainflammation. Functional and pathway enrichment analysis of DRF groupings Using the limma package, 320 DEGs were identified from 37,992 genes, and these DEGs were used for subsequent enrichment analyses. GO analysis indicated that DRF-related genes were primarily associated with microtubule binding, extracellular matrix, and structural constituents in the cellular component (CC). These genes were related to condensed chromosomes, centromeric regions, and chromosomal regions. In terms of biological processes (BP), they were associated with nuclear chromosome segregation and mitotic nuclear division (Fig. 6 A-D). KEGG pathway analysis highlighted the cell cycle and p53 signaling pathway as the primary DRF-enriched pathways. The p53 signaling pathway regulates cellular processes such as metabolism, antioxidant defense, and ferroptosis 24 (Fig. 6 E-G). Validation of MLS risk model construction For sample selection, we chose 572 samples with available outcomes and non-zero survival times from TCGA-LUAD as the training group. Using the Kaplan-Meier method, 42 genes significantly associated with OS (p < 0.001) were identified from the 320 DEGs. Among these, 40 genes that were expressed in both TCGA-LUAD and GSE31210 were selected to be included in the integrated framework for constructing the MLS risk model. In the TCGA-LUAD training cohort, consistent models were constructed using 101 algorithm combinations. The average C-index for each model across all cohorts was calculated to evaluate their predictive ability, with the GSE31210 cohort used for model validation (Fig. 7 A). Ultimately, the RSF + SuperPC algorithm, which achieved the highest average C-index, was identified as the most valuable model. This model was developed using 24 hub genes (FAM83A, KRT6A, SLC2A1, RHOV, ANGPTL4, METTL7A, GAPDH, ECT2, LYPD3, GJB2, NAPSA, CYP4B1, LOXL2, CTSH, SFTPB, TMPRSS2, BTG2, ANLN, SLC34A2, CYP2B7P, PFKP, UBE2S, C16orf89, KPNA2). Samples with MLS scores above the mean (n = 286) were classified as the high-risk group, while the remaining samples were categorized as the low-MLS group. All patients in the high-MLS group exhibited a poorer prognosis (Fig. 7 B). The expression levels of the 24 genes showed statistical significance in both high and low scoring groups (Fig. 7 C). The Mulberry diagram indicated that the C2 group, which comprised a larger portion of the low-MLS group, was associated with better survival outcomes, with a statistically significant difference in scores between the C1 and C2 groups (Fig. 7 D, E). Furthermore, to comprehensively assess the predictive power of MLS, we included 77 different features in the study. MLS demonstrated a superior C-index in both TCGA-LUAD and GSE31210 datasets (12/78, 10/78; Fig. 7 F), thereby confirming the reliability of the MLS model. MLS immunological profile The CIBERSORT algorithm was used to analyze the correlation between the 24 MLS genes, MLS scores, and various immune cells. The results indicated that most MLS genes and MLS scores were negatively correlated with immune-activated cells and positively correlated with immune-suppressed cells. Additional analyses using five other immune infiltration algorithms also showed that immune cell infiltration (including dendritic cells, eosinophils, monocytes, and NK cells) was significantly higher in patients with low MLS scores compared to those with high MLS scores, suggesting an immune activation state (Fig. 8 A, B). This implies that LUAD with low MLS levels may be classified as "hot tumors." Conversely, T regulatory cells (Tregs) and tumor-associated fibroblasts (CAFs) were predominantly enriched in patients with high MLS scores, which likely contribute to tumor proliferation, invasion, drug resistance, and an immunosuppressive state, leading to poorer outcomes (Fig. 6 B, C). This suggests that LUAD with high MLS levels might be classified as "cold tumors," which are less sensitive to immunotherapy. TIDE scores were negatively correlated with risk scores (Fig. 8 C), indicating that low-risk patients may benefit more from immunotherapy. Increased TMB expression is associated with enhanced T cell activation. TMB has potential clinical utility in lung cancer and could serve as a biomarker for immunotherapy 25 . Our survival analyses showed that patients with lower MLS scores and higher TMB levels typically had significantly prolonged OS, suggesting that MLS could complement TMB in assessing patient prognosis (Fig. 8 D, E). GSEA analysis of MLS GSEA analysis for subgroups (Fig. 8 F) revealed that pathways enriched in low-scoring subgroups were associated with immune cell regulation and GnRH receptor signaling. These pathways, in addition to their role in regulating pituitary gonadotropin secretion, may predict survival and resistance to tumor proliferation, metastasis, and anti-angiogenesis in lung cancer patients 26 . The Hedgehog signaling pathway, utilized in developing tumor-targeting drugs, and VEGFR inhibitors, known for their effects on vascular smooth muscle and cancer cell proliferation, were also identified 27 . Interestingly, long-term depression was also enriched, suggesting a potential link between these pathways and tumor biology. Drug sensitivity analysis of MLS The drug sensitivity analysis identified nine drugs with varying effectiveness. The low-scoring subgroup samples showed greater sensitivity to drugs such as BMS-754807, Doramapimod, JQ1, OF-1, and SB505124. Conversely, the high-scoring subgroup samples were more sensitive to AZD7762, Dasatinib, Docetaxel, and WIKI4. These findings suggest that MLS may be useful in evaluating the efficacy of targeted therapies and chemotherapy (Fig. 9 A-I). Discussion LUAD, the most common type of lung cancer, is often diagnosed at an advanced stage and is highly aggressive. Despite significant advancements in the diagnosis and treatment of lung cancer, new biomarkers and genetic features have improved the ability to predict LUAD prognosis. The development of targeted therapies, immunotherapies, and other novel treatments has benefited patients considerably 28 . However, due to high resistance to conventional radiotherapy and the heterogeneous nature of LUAD, the prognosis remains poor, with a 5-year survival rate of only 15% 29 . Comprehensive analysis of patients' multi-omic data enhances our understanding of disease mechanisms, and identifying biomarkers and therapeutic strategies from these data will support clinical precision and personalized medicine. Recent studies have shown that SLC7A11 can transport cystine in response to glucose starvation, leading to disulfide stress, which disrupts the redox balance and causes disulfide bond death. This process, known as disulfidptosis, is characterized by abnormal formation of disulfide bonds in actin backbone proteins and the breakdown of F-actin 8,30 . Similar to ferroptosis, disulfidptosis can induce tumor cell death by altering the structure of cytoskeletal proteins. Elevated SLC7A11 expression has been observed in renal cancer tissues, where it is associated with tumor progression. Disulfidptosis induced by SLC7A11 contributes to cell death in glucose-deficient environments. Notably, SLC7A11 is a critical component of the upstream signaling pathway regulating ferroptosis, a form of cell death driven by oxidative stress and membrane lipid peroxidation, among other factors 13 . Specific genes in LUAD, such as STYK1 and LSH, play significant roles in regulating ferroptosis 31,32 . Recent research indicates that cell death occurs through complex and interdependent processes, involving extensive cellular interactions 33,34 . Thus, balancing ferroptosis and disulfidptosis could be a novel strategy to enhance outcomes and survival in LUAD patients. A recent study used four disulfidptosis/ferroptosis-related genes to predict prognosis and immune infiltration levels in LUAD patients 35 . In our study, we established a prognostic signature consisting of 24 DRF-related genes from the TCGA-LUAD cohort based on 6 newly identified DRF diagnostic genes and validated it in the GSE31210 dataset. In this study, we applied WGCNA along with Pearson correlation analysis, differential expression analysis, and KM survival analysis to selected DRF-related genes. While the LASSO algorithm is valuable for variable selection, it may sometimes favor less relevant predictors when genes are highly correlated with each other 36 . To address this issue, we compared 10 machine learning methods to identify the most important genes. Evaluation metrics included AUC, classification error rate, accuracy, precision, recall, sensitivity, and specificity. SVM-REF (support vector machine - recursive feature elimination) is a machine learning method that enhances classification accuracy and model robustness by removing less relevant features from the SVM-generated feature vector. Random Forest, another supervised machine learning algorithm, constructs and integrates multiple decision trees to improve prediction performance and stability. Among the six disulfidptosis-related ferroptosis genes identified, IL-33, a member of the IL-1 family, is closely linked to lung cancer progression. IL-33 enhances the activity of dendritic cells (DCs), CD8 + T cells, and NK cells, thereby inhibiting tumor growth and metastasis 37 . SLC2A1 expression is significantly elevated in lung cancer and correlates with poorer patient survival 38 . CDCA3 overexpression may be linked to the activation of various oncogenic signaling pathways and cancer progression, and correlates with sensitivity to platinum-based chemotherapy. Similarly, high KIF20A expression is associated with the activation of oncogenic pathways and poorer OS 39,40 . FANCD2 is recognized as a cancer susceptibility gene that coordinates DNA repair pathways and responds to DNA damage through the PI3K-Akt-mTOR signaling pathway, promoting tumor cell survival 41 . RRM2 is involved in tumorigenesis and resistance to chemotherapeutic agents 42,43 . DRF genes also show potential for aiding in the early diagnosis and treatment of other cancers. This research provides new insights into the mechanisms linking disulfidptosis and ferroptosis. Specifically, KIF20A and SLC2A1, as DRF genes, demonstrate potential for use in the diagnosis and treatment of hepatocellular carcinoma 44 . To explore the regulatory roles and immunological mechanisms of DRFs in tumor development, our study classified DRFs into two groups using cluster analysis. We examined the differences between DRF subgroups in terms of genomic, transcriptional, and immunological features, and their connections in LUAD. Investigating the potential links between disulfidptosis and ferroptosis can provide deeper insights into mechanisms such as anti-tumor immune activity and inhibition of tumor immune escape in the TME, and help guide the selection of effective immunotherapy regimens. Notably, the two subgroups showed significant differences in OS, TME, and immune infiltration levels. Group C2 was associated with high IL33 expression, immune activation, and better prognosis, indicating an immunoinflammatory phenotype. In contrast, Group C1 exhibited a lower estimate score and higher expression of immunosuppressive cells, which aligns with the immune desert subtype 45 . Additionally, the C1 group had higher TH2/TH1 ratios compared to the C2 group. TH2 cells, a subset of CD4 + T cells, secrete cytokines such as IL-4, IL-5 46 . TH1 cells, also a CD4 subset, primarily produce IFN-γ, IL-2, and TNF, and are involved in cellular immunity and the differentiation of cytotoxic T cells 47 . TH1 and TH2 cells maintain a balance through cytokine secretion, but this balance can be disrupted in the tumor microenvironment, leading to a shift from a Th1 to a Th2 response, known as the "Th1/Th2 shift" 48 . This shift is observed in many tumor patients, including those with lung cancer, and may be linked to tumor immune escape 49 . Machine learning algorithms are effective tools for analyzing multi-omics data. To understand the molecular differences between prognostic subtypes and enhance clinical utility, we used the TCGA-LUAD dataset as a training set and selected the best MLS through 101 algorithm combinations to address algorithm selection limitations. Overfitting is a common issue in model construction, where a model performs well in the training set but poorly in the validation set (64) . To mitigate this, we used the C-index of the validation cohort as a ranking criterion. Although the RSF + StepCox[forward] combination showed excellent performance in the training set, it was less effective in the validation set. In contrast, MLS demonstrated better prognostic value across all cohorts compared to other published signatures. Other signatures that performed better in the training set often failed to match this performance in the validation set, likely due to model overfitting. We analyzed the enrichment of numerous immune-related signatures between high and low score groups. We found that several oncogenic pathways were significantly activated in the high MLS group. This group also exhibited a high expression of Tregs cells, which may contribute to the immunosuppressive phenotype and a cold tumor environment. In contrast, the low MLS group had a higher TMB and a greater variety of immune cell types, suggesting stronger anti-tumor immunity and better prognosis. Survival analyses indicated a more favorable outcome for this group. Additionally, TIDE results showed that the low MLS group responded more effectively to immunotherapy, supporting the notion that MLS could aid in the early identification of immunotherapy-sensitive patients. Interestingly, while macrophages M2 are generally associated with anti-inflammatory responses and are more involved in angiogenesis, immunosuppression, and tumor metastasis 50 , our study observed the opposite. This discrepancy may be due to the heterogeneity among different M2 subpopulations, highlighting the complexity of macrophages in tumor immunity 51 . Despite the poor prognosis and limited benefit from immunotherapy in the high MLS group, Dasatinib and Docetaxel may still offer potential therapeutic benefits for this population. Our study has several limitations. First, the prognostic model was developed using retrospective data from public databases. Although the results indicate the potential of DRF scores in predicting LUAD prognosis, their clinical value needs to be validated with additional prospective real-world data. Second, the mechanisms underlying the relationship between disulfidptosis and ferroptosis require further investigation. Additionally, while bioinformatics analyses provided insights into the potential impact of DRF scores on immune cell infiltration, experimental validation is needed to confirm the correlation between DRF scores and immune activity. Conclusion In conclusion, we identified six genes related to disulfidoptosis and ferroptosis, and validated these DRF genes through in vitro experiments. Cluster analysis revealed two molecular subtypes of LUAD, highlighting significant differences in prognosis and immunological profiles between them. We developed a MLS that demonstrated strong performance across multiple cohorts, indicating its robust ability to predict patient prognosis. Comprehensive analyses, including functional, immunological, and pharmacological sensitivity assessments, confirmed the reliability and potential clinical utility of the DRF score for future precision medicine and clinical evaluation. Declarations Ethical Approval Not applicable Disclosure TCGA and GEO belong to public databases. The patients involved in the database have obtained ethical approval. Users can download relevant data for free for research and publish relevant articles. Our study is based on open-source data, so there are no ethical issues and other conflicts of interest. Competing interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest Author’s contributions LZC: Data analysis, Writing- Original draft preparation. YRD: Writing- Original draft preparation. ZX and LYX collected, processed and analyzed the data. ZX: Conception and design, Final approval of the version to be published. Funding This work was supported by the Chinese Medicine Inheritance and Innovation "Hundred Million" Talent Project (QI Huang Project) QI Huang Scholars Project, Shandong Province Taishan Scholars Specially Appointed Experts Program Project, and the 2021 Shandong Province Natural Science Foundation - General Project [No. ZR202103040386]. Data availability statement The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5324542","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":370526374,"identity":"148424be-4b29-4ad8-b2c7-fb7969a2cbf5","order_by":0,"name":"Zichen Liu","email":"","orcid":"","institution":"Shandong University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zichen","middleName":"","lastName":"Liu","suffix":""},{"id":370526375,"identity":"da5a78ec-e2bb-4178-a1bc-c7886498e195","order_by":1,"name":"Rongdi Yan","email":"","orcid":"","institution":"Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Rongdi","middleName":"","lastName":"Yan","suffix":""},{"id":370526376,"identity":"84929cb3-8b90-4fda-b1cc-85aa48d10248","order_by":2,"name":"Yingxv Luo","email":"","orcid":"","institution":"Shandong University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yingxv","middleName":"","lastName":"Luo","suffix":""},{"id":370526377,"identity":"1bc4fbc9-804d-4b0a-abb4-5b81762b9de5","order_by":3,"name":"Xin Zhang","email":"","orcid":"","institution":"Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Zhang","suffix":""},{"id":370526378,"identity":"ec0bd73a-a4eb-49c3-b984-b6d5f17aa72b","order_by":4,"name":"Xin Zheng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYDACCShpwMDYwJBQISEnT6KWMxbGhg3EaWEAagECxraKRIYDBHTIz24+9vBrm0WeOfvhxg8P50kkMDYwP3x0A48WxjnH0o1lzkgUW/YkNkskbpPIY2dgMzbOwaOFWSLHTFqiQiJxw4HENgaglmLGBh42aXxa2CTyv0lLGAC1nH8I1DJHIrHhAAEtPBI5bJIfQLbcANnSQIQWCYk0M2mGMyAtD5slEo5JGBs2E/CL/IzkZ5I/2+qADkt/+PFHTZ2cPHvzw8f4tIAAMw8ql4ByEGD8QYSiUTAKRsEoGMEAAD1RSeS5eZX9AAAAAElFTkSuQmCC","orcid":"","institution":"Qingdao Haici Hospital","correspondingAuthor":true,"prefix":"","firstName":"Xin","middleName":"","lastName":"Zheng","suffix":""}],"badges":[],"createdAt":"2024-10-24 09:23:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5324542/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5324542/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67782471,"identity":"af6589d6-421d-47a1-ac33-7a1536235c4a","added_by":"auto","created_at":"2024-10-29 16:07:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3031499,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of co-expression modules based on WGCNA. (A) Analysis of network topology for various soft-thresholding powers. (B) Gene dendrogram and module colors; each color represents a module. (C) Pearson correlation analysis between 13 modules and tumor tissue as well as normal tissue. Screening of hub genes in the (D) MEblue and (E) MEbrown modules, with module membership [MM] \u0026gt; 0.7 and gene significance [GS] \u0026gt; 0.3.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5324542/v1/58823e3a4b6971c6ccb2b587.png"},{"id":67783223,"identity":"1fc0ddb2-be18-42ca-bb85-ef0a332ad467","added_by":"auto","created_at":"2024-10-29 16:15:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2591311,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Comparison of 10 machine learning algorithms; (B) Interactions of DRF genes in pan-cancer analysis; (C) Differential expression of DRF in 18 types of cancer (BLCA, BRCA, CHOL, COAD, ESCA, GBM, HNSC, KICH, KIRC, KIRP, LIHC, LUAD, LUSC, PRAD, READ, STAD, THCA, UCEC); (D, E) WGCNA and correlation analysis confirm the relevance of DRF to disulfidoptosis in 33 cancer types.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5324542/v1/0bfa494c7e61a06274457e20.png"},{"id":67783793,"identity":"bdccb5d6-97ff-4564-9a06-c02c45a457f3","added_by":"auto","created_at":"2024-10-29 16:23:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1966724,"visible":true,"origin":"","legend":"\u003cp\u003eqPCR expression levels of 6 DRF genes in BEAS-2B and A549 cell lines. ns: not significant; *, P\u0026lt;0.05; **, P\u0026lt;0.01; ***, P\u0026lt;0.001. The primers used for the genes（5’-3’）IL33-F GCCTGTCAACAGCAGTCTAC; IL33-R CAACACCGTCACCTGATTCAT; SLC2A1-F GGCCAAGAGTGTGCTAAAGAA; SLC2A1-R ACAGCGTTGATGCCAGACAG; CDCA3-F CCGCTCTCCTACTCTTGGTATT; CDCA3-R GGCTGTCTTGCTTCCTCCTT; KIF20A-F CAGTCACAGCATCTTCTCAATCA; KIF20A-R TCAACCGTTCACCACTCTTCT; FANCD2-F CAGCAGACTCGCAGCAGAT; FANCD2-R GGTGATGAAGCAGCCTTGTG; RRM2-F TTGCCTGTGAAGCTCATTGG; RRM2-R CATCCTCTGATACTCGCCTACT; Actin-F ACACTGTGCCCATCTACG; Actin-R TGTCACGCACGATTTCC\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5324542/v1/2970ccec6262ffcc3abf345a.png"},{"id":67783225,"identity":"e564f34e-221b-4d61-a79d-8866855c5c60","added_by":"auto","created_at":"2024-10-29 16:15:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":741214,"visible":true,"origin":"","legend":"\u003cp\u003eDRF (A) Chromosome location; (B) CNV gain/loss; (C) CIBERSORT algorithm calculation of the relationship between DRF gene and immune cells; (D) DRF grouping C1, C2; (E, F) Heatmaps and survival analysis used to demonstrate expression levels and clinical characteristics of DRF grouping. ns: not significant; *, P\u0026lt;0.05; **, P\u0026lt;0.01; ***, P\u0026lt;0.001\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5324542/v1/2be1fbc7405b564a8e047f13.png"},{"id":67782477,"identity":"7c2195bd-21be-4af2-b726-94522383c3df","added_by":"auto","created_at":"2024-10-29 16:07:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2840873,"visible":true,"origin":"","legend":"\u003cp\u003e(A) TME-infiltrating cell composition between the two subtypes; (B) Correlation between DRF clusters and TME components; (C) Analysis of the immune environment of the subgroups.; (D) Functional differences between the two subtypes.\u003c/p\u003e\n\u003cp\u003ens: not significant; *, P\u0026lt;0.05; **, P\u0026lt;0.01; ***, P\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5324542/v1/bc8acf805e1dd3fe11582f79.png"},{"id":67782479,"identity":"74c0987b-b052-4c61-a54b-60c000ddf9e3","added_by":"auto","created_at":"2024-10-29 16:07:20","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2981185,"visible":true,"origin":"","legend":"\u003cp\u003eDRF grouping (A-D) GO analysis; (E-G) KEGG analysis. GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5324542/v1/c190b2d4b05c0a206574f1a0.png"},{"id":67782481,"identity":"72fef8f8-1f9e-4e5d-8665-8d761d1d18c0","added_by":"auto","created_at":"2024-10-29 16:07:20","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":4239583,"visible":true,"origin":"","legend":"\u003cp\u003eMLS model: (A) selection of the best model among 101 machine learning methods; (B, C) survival analysis and differential expression in high and low scoring groups; (D, E) Sankey diagram and volcano plot showing differences between groups C1 and C2 in MLS classification; (F) comparison of MLS (DRF) model with other published models. Risk Score=0.064895*FAM83A + 0.214337*KRT6A - 0.347418*SLC2A1 + 0.099551* RHOV + 0.222226*ANGPTL4 + 0.037792*METTL7A + 0.003043*GAPDH + 0.190001*ECT2 + 0.052498* LYPD3 - 0.045920*GJB2 - 0.006644*NAPSA + 0.078387*CYP4B1 + 0.124886*LOXL2 - 0.047164* CTSH - 0.201564* SFTPB + 0.087175*TMPRSS2 - 0.091927* BTG2 + 0.178454* ANLN - 0.110170* SLC34A2 - 0.072611* CYP2B7P + 0.001675* \u0026nbsp;PFKP + 0.072520* UBE2S + 0.270660* C16orf89 - 0.103629* KPNA2\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-5324542/v1/2be356db70693e147a9efd39.png"},{"id":67782474,"identity":"ff9611d8-81de-4e04-923c-0dd2cc1eb622","added_by":"auto","created_at":"2024-10-29 16:07:20","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":6694293,"visible":true,"origin":"","legend":"\u003cp\u003eMLS immune analysis and GSEA (A) Five immune infiltration algorithms were used to calculate immune features in different MLS groups. (B) The CIBERSORT algorithm was used to analyze the correlation between 24 MLS genes, MLS scores, and different immune cells. (C) TIDE scores for MLS groups. (D, E) The combined effect of TMB and MLS grouping on prognosis. (F) GSEA analysis for low-scoring MLS groups. ns: not significant; *, P\u0026lt;0.05; **, P\u0026lt;0.01; ***, P\u0026lt;0.001\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-5324542/v1/b3356a250819bc1ca8aad82f.png"},{"id":67783222,"identity":"f57cd382-3370-437f-9678-2a2e7e0e3a44","added_by":"auto","created_at":"2024-10-29 16:15:20","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":970641,"visible":true,"origin":"","legend":"\u003cp\u003eDrug sensitivity analysis of 9 types of drugs in high and low-scoring groups. ns: not significant; *, P\u0026lt;0.05; **, P\u0026lt;0.01; ***, P\u0026lt;0.001\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-5324542/v1/825c46a06ae60f80c7e3abd4.png"},{"id":70953909,"identity":"cd457262-1684-46bf-b3e9-82dd0fe0540c","added_by":"auto","created_at":"2024-12-09 14:02:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":26507715,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5324542/v1/8c08302b-d06b-42e9-84c8-8e92a8ec6b86.pdf"},{"id":67783224,"identity":"66fa8892-e9bf-4fc9-bb91-25dcc817337f","added_by":"auto","created_at":"2024-10-29 16:15:20","extension":"xls","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":11535,"visible":true,"origin":"","legend":"","description":"","filename":"77publicsignaturesforcomparing.xls","url":"https://assets-eu.researchsquare.com/files/rs-5324542/v1/1a1124f247300f272cb7d64f.xls"},{"id":67783227,"identity":"0a137477-d8e3-49e8-b5af-89d4095e788d","added_by":"auto","created_at":"2024-10-29 16:15:20","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":531271,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-5324542/v1/c6c318b37e9591294435eae3.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognostic and Immunological Analysis of Disulfidoptosis-Related Ferroptosis Genes in Lung Adenocarcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLung cancer is the second most common cancer worldwide and the leading cause of cancer-related deaths. LUAD is the most prevalent histological subtype, accounting for more than 40% of all lung cancer cases\u003csup\u003e1,2\u003c/sup\u003e. TNM staging has long been used as an important predictor of lung cancer prognosis\u003csup\u003e3\u003c/sup\u003e. However, studies have indicated that tumor heterogeneity may lead to prognostic differences among patients with the same stage of lung cancer\u003csup\u003e4\u003c/sup\u003e. Additionally, with the increasing use of immunotherapy for lung cancer, identifying patients who are more likely to benefit from such treatment has become a critical clinical challenge\u003csup\u003e5\u003c/sup\u003e. Anatomical TNM staging alone is not ideal for screening patients who are likely to respond to immunotherapy. Therefore, a comprehensive understanding of the molecular mechanisms underlying LUAD and the identification of new biomarkers for predicting long-term prognosis and response to immunotherapy are crucial for improving preventive strategies and effective interventions\u003csup\u003e6,7\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRecent studies have shown that cells overexpressing SLC7A11 can undergo rapid cell death due to SLC7A11-mediated depletion of NADPH and intracellular accumulation of toxic cystine, induced by disulfide stress. This process, known as disulfidoptosis, is a newly identified form of regulated cell death (RCD) distinct from known forms such as ferroptosis, necroptosis, and apoptosis. Disulfidoptosis typically occurs under glucose starvation conditions\u003csup\u003e8\u003c/sup\u003e. Regulation of disulfide proteases involves the formation and cleavage of disulfide bonds and the involvement of proteins such as NCKAP1, as well as redox-related signaling pathways. Disulfide proteases have potential as targets for cancer therapy. Inhibiting glucose transporter protein (GLUT) can have therapeutic effects on SLC7A11-overexpressing cancer cells by inducing disulfide proteases. Understanding the regulatory mechanisms and significance of disulfide proteases can provide insights into cellular homeostasis and offer potential strategies for targeted cancer therapy. Ferroptosis is an iron-dependent RCD characterized by excessive lipid peroxidation in the cell membrane, which inhibits antioxidant responses. Key features of ferroptosis include the oxidation of polyunsaturated fatty acids in phospholipids, the presence of redox-active iron, and the loss of lipid peroxidation repair mechanisms. GPX4, a crucial regulator of ferroptosis, inhibits its onset by reducing the formation of phospholipid hydroperoxides. Down-regulation of SLC7A11 triggers ferroptosis by impairing cysteine metabolism pathways\u003csup\u003e9\u0026ndash;11\u003c/sup\u003e. Ferroptosis plays a significant role in various diseases, including organ damage, degenerative diseases, and tumors\u003csup\u003e12,13\u003c/sup\u003e. Moreover, therapeutic resistance in certain cancers is linked to overexpression of GPX4 and reduced expression of SLC7A11\u003csup\u003e14\u003c/sup\u003e. Although disulfidoptosis and ferroptosis are distinct forms of cell death, SLC7A11 can act as a common regulator of both, influencing intracellular iron levels and regulating ferroptosis resistance, and plays a vital role in the regulation of disulfidoptosis in lung cancer\u003csup\u003e10,15,16\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this study, we identified and validated DRF genes using bioinformatics analysis. Based on different aggregated signatures, we developed a new machine learning-based score, the MLS, for LUAD patients. This score performed well compared to 77 published signatures. We also conducted immunological and drug sensitivity analyses to explore immune differences and identify potentially effective drugs among different patient subgroups. Our study contributes to a better understanding of disulfidoptosis-associated ferroptosis genes in LUAD development and may provide guidance for prognostic prediction and personalized therapy for various cancers, including LUAD.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eData acquisition and processing\u003c/p\u003e \u003cp\u003eRNA-Seq data on gene expression for 33 cancers, including LUAD, along with corresponding clinical data, were downloaded from TCGA (The Cancer Genome Atlas) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Similar data from the validation dataset, GSE31210, were acquired from the GEO database (Gene Expression Omnibus ;\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Somatic mutation data for TCGA-LUAD were acquired from the GDC (Genomic Data Commons; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and copy number variation (CNV) data were downloaded from UCSC Xena (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xenabrowser.net\u003c/span\u003e\u003cspan address=\"https://xenabrowser.net\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Tumor Immuno-Dysfunction and Rejection (TIDE) data for non-small cell lung cancer were obtained from the TIDE website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tide.DRFci.harvard.edu/\u003c/span\u003e\u003cspan address=\"http://tide.DRFci.harvard.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIdentifcation of DRF genes\u003c/p\u003e \u003cp\u003eTwenty-three disulfidoptosis-related genes were identified from relevant studies\u003csup\u003e17,18\u003c/sup\u003e. The 564 ferroptosis-associated genes were obtained from the FerrDb V2 database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.zhounan.org/\u003c/span\u003e\u003cspan address=\"http://www.zhounan.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The WGCNA R package was utilized to construct co-expression modules and clarify the relationships between these modules. Pearson correlation analysis was employed to screen for genes highly associated with disulfidoptosis and ferroptosis genes (|cor| \u0026gt; 0.3, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), identifying those module genes significantly correlated with LUAD. The intersection of these gene sets was determined. Kaplan-Meier survival analyses were conducted to identify genes more strongly associated with prognosis (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Hyperparameter tuning of ten machine learning algorithms (Classification Tree, Glmnet, KNN, LDA, Logistic, Naive Bayes, NNET, Random Forest, SVM, Xgboost) was performed to select the best screening algorithm. The frequency of CNV of the finalized Disulfidoptosis-Related Ferroptosis (DRF) genes is presented in a bar chart, with their chromosomal positions shown using the \"RCircos\" package in R. Differential and survival analyses were conducted to validate these genes in the GSE31210 cohort, and the CIBERSORT algorithm was used to analyze the relationship between DRF genes and 20 immune cell types. Genome-wide genomic data for 33 cancers, including DRF gene expression in pan-cancers, survival analyses, and interlinkages, were obtained from TCGA. To better illustrate the intrinsic relationship between DRF and disulfidoptosis genes, the concept of \"module\" from the WGCNA algorithm was applied, with DRF and disulfidoptosis considered as a module. The association between the expression values of DRF/disulfidoptosis regulators and module signature genes was assessed based on intra-module connectivity. DRF/disulfidoptosis was defined as a regulator with a module membership (MM) greater than 0.75. For each cancer type, the pooled expression levels of the identified DRF/disulfidoptosis regulators were calculated as epigenetic module signature genes (EMEs).\u003c/p\u003e \u003cp\u003eClinical and immunological characterisation and enrichment analysis of DRF subgroups\u003c/p\u003e \u003cp\u003eConsensus cluster analysis of LUAD samples in the TCGA dataset was performed based on DRF gene expression using the \"ConsensusClusterPlus\" package. Gene expression and clinical data, such as age, gender, and TNM stage, were visualized between subgroups using the \"pheatmap\" package. Overall survival (OS) times were then compared between these subgroups.\u003c/p\u003e \u003cp\u003eSubsequently, the \"LIMMA\" R package was used to identify differentially expressed genes (DEGs) associated with Disulfidoptosis-Related Ferroptosis (DRF) groupings. DEGs were adjusted to a significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 with |log2FoldChange (FC)| \u0026gt; 1.2. The \"ClusterProfiler\" package and \"org.Hs.eg.db\" were employed to perform GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment analyses based on the identified DEGs. The ssGSEA package and GSVA (Gene Set Variation Analysis) method were used to compute clustering values for immune-related pathways and immune cells to perform functional and pathway enrichment analyses on the subgroups, with results presented in heatmaps. The package of \"estimate\" was used to calculate 23 immune-related functional scores.\u003c/p\u003e \u003cp\u003eQuantitative real-time polymerase chain reaction (qRT-PCR)\u003c/p\u003e \u003cp\u003eNormal lung epithelial cells (BEAS-2B) and the human lung adenocarcinoma cell line (A549) were obtained from the Shandong University of Traditional Chinese Medicine. A fluorescence quantitative PCR instrument was acquired from Roche (Basel, Switzerland). Total RNA was extracted using TRNzol Universal reagent (Beijing, China). Complementary DNA (cDNA) was synthesized using the First Strand Synthesis Kit (KR116). The relative expression of genes was analyzed using the 2\u0026thinsp;\u0026minus;\u0026thinsp;ΔΔCT method with actin as an internal reference gene and normal lung epithelial cells as the control.\u003c/p\u003e \u003cp\u003eMLS prognostic model construction assessment and enrichment analysis\u003c/p\u003e \u003cp\u003eIn the TCGA-LUAD training cohort, the KM method was used to identify genes from DEGs significantly associated with OS (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Genes expressed in both TCGA-LUAD and GSE31210 were selected for inclusion in the integrated framework for constructing the MLS. Consistent models were developed based on 101 algorithm combinations in both the TCGA-LUAD training cohort and the GSE31210 validation cohort, and the average C-index of all models in both cohorts was calculated to assess predictive performance (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). MLS scores for each sample in all cohorts were computed using the formula: risk_score\u0026thinsp;=\u0026thinsp;Σ(Expi * Coefi), where Coefi and Expi represent the risk factor and expression of each gene, respectively. Samples were categorized into low risk (score\u0026thinsp;\u0026lt;\u0026thinsp;median) and high risk (score\u0026thinsp;\u0026gt;\u0026thinsp;median) groups based on the median risk score. To illustrate the differences between high and low risk groups, we first depicted the process from DRF cluster grouping to risk grouping to final outcomes using Mulberry diagrams. Subsequently, MLS gene expression between high and low risk groups, as well as MLS scores of DRF subgroups, were analyzed. Finally, the \"clusterSUR\" package was used for survival analysis between high and low risk groups.\u003c/p\u003e \u003cp\u003eTo progressively validate the accuracy of the model, we conducted a systematic literature search that led to the inclusion of 77 published prognostic signatures for LUAD. These signatures were associated with features such as immunity, copper-induced cell death, and iron-dependent cell death. We then compared the MLS to these published signatures using the TCGA-LUAD and GSE31210 datasets. Subsequently, Gene Set Enrichment Analysis (GSEA) was used to analyze the pathways in high and low scoring groups.\u003c/p\u003e \u003cp\u003eImmunoassay\u003c/p\u003e \u003cp\u003eSix immune infiltration algorithms were employed to assess dissimilarities in immune cells between two scoring subgroups. Additionally, the TIDE (Tumor Immune Dysfunction and Exclusion) algorithm predicted the efficacy of anti-tumor immune drugs (anti-PD1, anti-CTLA4) based on TIDE scores, which were positively correlated with drug efficacy\u003csup\u003e19\u003c/sup\u003e. TIDE scores were also used to evaluate patient responses to immunotherapy. In the analysis of Tumor Mutational Burden (TMB), the maftools package was utilized to examine the connection between risk scores and TMB. The package of \"survival survminer\" was used to investigate the impact of TMB on LUAD prognosis, with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e \u003cp\u003eDrug screening\u003c/p\u003e \u003cp\u003eTo evaluate the sensitivity of high and low scoring MLS groups to different drugs, the oncoPredict\u003csup\u003e20\u003c/sup\u003e software package was used to calculate IC50 values for 198 drugs from the GDSC2 database. This analysis aimed to identify potentially effective drugs for patients who are in the high and low scoring groups, with a significance threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;e-10.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eIdentification and pan-cancer analysis of the DRF gene\u003c/p\u003e\n\u003cp\u003eFirstly, 340 ferroptosis-related genes associated with disulfidoptosis were identified through correlation studies. Using the \u0026quot;limma\u0026quot; package, TCGA-LUAD samples were categorized into tumor and normal tissues. Co-expression modules were constructed with the WGCNA package. Correlation analyses assessed the relationships between these modules and tumor versus normal tissues, leading to the identification of 13 modules with the optimal soft threshold power of 3 (Fig. \u003cspan\u003e1\u003c/span\u003eA, B). Among these modules, 8 were significantly correlated with tumors (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with 5 showing positive correlations and 3 showing negative correlations (Fig. \u003cspan\u003e1\u003c/span\u003eC). To identify relevant hub genes, we focused on the MEblue and MEbrown modules, which had the most significant positive and negative correlations (Pearson correlation coefficients of 0.52 and \u0026minus;\u0026thinsp;0.82, respectively; Fig. \u003cspan\u003e1\u003c/span\u003eD, E). From these modules, 1,437 related genes were selected, with 613 genes from the MEblue module. After intersecting these genes with the disulfidoptosis ferroptosis (DRF) genes, 31 genes were obtained.\u003c/p\u003e\n\u003cp\u003eKaplan-Meier survival analysis was used to screen for 10 candidate genes with strong prognostic relevance. SVM and Random Forest algorithms were then applied for hub gene screening (Fig. \u003cspan\u003e2\u003c/span\u003eA). The intersection of these two algorithms narrowed the 10 candidate genes down to 6 (IL33, SLC2A1, CDCA3, KIF20A, FANCD2, RRM2; Supplementary Table\u0026nbsp;1, 2). These six DRF genes demonstrated favorable receiver operating characteristic (ROC) values in both the TCGA and GSE31210 cohorts (Supplementary Fig.\u0026nbsp;1), leading to their identification as DRF genes for LUAD. Subsequent survival and differential analyses for the GSE31210 cohort validated the reliability of these genes (Supplementary Figs.\u0026nbsp;2, 3).\u003c/p\u003e\n\u003cp\u003eThe interrelationships among DRF genes in the genome-wide data for 33 cancers are illustrated in Fig. \u003cspan\u003e2\u003c/span\u003eB. Expression analysis revealed differential expression in 18 cancer types, indicating that IL33 could act as a protective factor for most tumors, while the other five genes appeared as risk factors (Fig. \u003cspan\u003e2\u003c/span\u003eC). Survival analysis results suggested that DRF genes can differentiate survival times across multiple cancers (Supplementary Table 3, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Notably, WGCNA was used to identify central regulators involved in the connectivity of DRF and disulfidoptosis regulators across 33 cancer types (Fig. \u003cspan\u003e2\u003c/span\u003eD). The analysis showed a strong correlation among central DRF regulators (R\u0026thinsp;=\u0026thinsp;0.65, P\u0026thinsp;=\u0026thinsp;4.7e-05; Fig. \u003cspan\u003e2\u003c/span\u003eE). In vitro experiments demonstrated significantly higher expression of CDCA3, KIF20A, FANCD2, RRM2, and SLC2A1 in the NSCLC (A549) cell line compared to normal lung epithelial cells (BEAS-2B), with statistically significant differences (Fig. \u003cspan\u003e3\u003c/span\u003e), confirming the potential of DRF genes as tumor detection markers.\u003c/p\u003e\n\u003cp\u003eMutational landscape and immune profile of the DRF gene\u003c/p\u003e\n\u003cp\u003eThe chromosomal locations of DRF genes are shown in Fig. \u003cspan\u003e4\u003c/span\u003eA. The study revealed that CNV deletions were more prevalent in IL33, CDCA3, and KIF20A, while SLC2A1, RRM2, and FANCD2 were more frequently amplified (Fig. \u003cspan\u003e4\u003c/span\u003eB). These analyses demonstrated significant genetic and expression heterogeneity of DRF in lung adenocarcinoma, suggesting that imbalanced DRF expression plays a critical role in LUAD development. CIBERSORT analysis indicated that IL33 was positively correlated with various immune-activated cells and negatively correlated with immunosuppressive cells, such as regulatory T cells (Tregs) (Fig. \u003cspan\u003e4\u003c/span\u003eC).\u003c/p\u003e\n\u003cp\u003eClinical and immunological analysis of DRF subgroups\u003c/p\u003e\n\u003cp\u003eTo further investigate the clinical value and functional biological patterns of these DRFs, we performed clustering analyses on all tumor samples in the TCGA LUAD cohort based on the expression levels of the six DRFs. We compared the expression of DRF-related genes with LUAD subtypes to explore their relationship. Setting the clustering value (K) from 2 to 10, the best aggregation stability was found at K\u0026thinsp;=\u0026thinsp;2 (Fig. \u003cspan\u003e4\u003c/span\u003eD). Consequently, the TCGA LUAD cohort was divided into cluster C1 (n\u0026thinsp;=\u0026thinsp;347) and cluster C2 (n\u0026thinsp;=\u0026thinsp;238) based on DRF expression. Heatmaps were used to display gene expression and TNM typing, staging, age (\u0026le;\u0026thinsp;65 or \u0026gt;\u0026thinsp;65 years), and gender for the 585 LUAD samples. The IL33 gene expression level was significantly lower in the C1 group compared to the C2 group and was more closely associated with poor prognostic indicators (e.g., higher TNM staging and grading, older age), consistent with previous analyses (Fig. \u003cspan\u003e4\u003c/span\u003eE). Kaplan-Meier analysis revealed that the C2 group had a more favorable OS compared to the C1 group, which showed a survival disadvantage consistent with clinical parameters (P\u0026thinsp;=\u0026thinsp;0.003, Fig. \u003cspan\u003e4\u003c/span\u003eF).\u003c/p\u003e\n\u003cp\u003eGiven the importance of tumor immunity in tumor development, we explored the level of cellular infiltration in the tumor microenvironment. ssGSEA scores indicated that the C2 group had richer infiltration of immune cells, such as immature B cells and natural killer(NK) cells. The poorer clinical outcome in the C1 group may be related to immunosuppression induced by a Type 2 T helper cell (Th2) subpopulation in CD4\u0026thinsp;+\u0026thinsp;T cells\u003csup\u003e21\u003c/sup\u003e (Fig. \u003cspan\u003e5\u003c/span\u003eA). The TME (tumor microenvironment), which includes tumor cells, mesenchymal stromal cells, immune cells, cytokines, and chemokines, was assessed. Figure \u003cspan\u003e5\u003c/span\u003eB shows that the C2 group had higher StromalScore, ImmunityScore, and ESTIMATEScore, associated with lower tumor purity and better immune infiltration. This suggests that the C2 group may be linked to an immune microenvironment that promotes tumor death and is likely more sensitive to immunotherapy\u003csup\u003e22\u003c/sup\u003e. Analysis of the immune environment of the subgroups (Fig. \u003cspan\u003e5\u003c/span\u003eC) revealed that Type_II_IFN_Response was more prevalent in C2, indicating characteristics of the immune response. Parainflammation, which is crucial for maintaining homeostasis, was more prominent in C1, potentially driven by p53 mutations, which can promote cancer. Given the sensitivity of parainflammation to NSAIDs, aspirin may offer better therapeutic effects for patients in the C1 group \u003csup\u003e23\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eTo further investigate the pathways dominated by DRFs, we conducted GSVA. Figure \u003cspan\u003e5\u003c/span\u003eD illustrates that KEGG analysis revealed a significant enrichment of inflammatory metabolism-related pathways in the C2 group. In contrast, tumorigenic pathways, such as cell cycle and amino acid metabolism, were significantly enriched in the C1 group, which may be related to the previously mentioned parainflammation.\u003c/p\u003e\n\u003cp\u003eFunctional and pathway enrichment analysis of DRF groupings\u003c/p\u003e\n\u003cp\u003eUsing the limma package, 320 DEGs were identified from 37,992 genes, and these DEGs were used for subsequent enrichment analyses. GO analysis indicated that DRF-related genes were primarily associated with microtubule binding, extracellular matrix, and structural constituents in the cellular component (CC). These genes were related to condensed chromosomes, centromeric regions, and chromosomal regions. In terms of biological processes (BP), they were associated with nuclear chromosome segregation and mitotic nuclear division (Fig. \u003cspan\u003e6\u003c/span\u003eA-D). KEGG pathway analysis highlighted the cell cycle and p53 signaling pathway as the primary DRF-enriched pathways. The p53 signaling pathway regulates cellular processes such as metabolism, antioxidant defense, and ferroptosis\u003csup\u003e24\u003c/sup\u003e (Fig. \u003cspan\u003e6\u003c/span\u003eE-G).\u003c/p\u003e\n\u003cp\u003eValidation of MLS risk model construction\u003c/p\u003e\n\u003cp\u003eFor sample selection, we chose 572 samples with available outcomes and non-zero survival times from TCGA-LUAD as the training group. Using the Kaplan-Meier method, 42 genes significantly associated with OS (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were identified from the 320 DEGs. Among these, 40 genes that were expressed in both TCGA-LUAD and GSE31210 were selected to be included in the integrated framework for constructing the MLS risk model.\u003c/p\u003e\n\u003cp\u003eIn the TCGA-LUAD training cohort, consistent models were constructed using 101 algorithm combinations. The average C-index for each model across all cohorts was calculated to evaluate their predictive ability, with the GSE31210 cohort used for model validation (Fig. \u003cspan\u003e7\u003c/span\u003eA). Ultimately, the RSF\u0026thinsp;+\u0026thinsp;SuperPC algorithm, which achieved the highest average C-index, was identified as the most valuable model. This model was developed using 24 hub genes (FAM83A, KRT6A, SLC2A1, RHOV, ANGPTL4, METTL7A, GAPDH, ECT2, LYPD3, GJB2, NAPSA, CYP4B1, LOXL2, CTSH, SFTPB, TMPRSS2, BTG2, ANLN, SLC34A2, CYP2B7P, PFKP, UBE2S, C16orf89, KPNA2). Samples with MLS scores above the mean (n\u0026thinsp;=\u0026thinsp;286) were classified as the high-risk group, while the remaining samples were categorized as the low-MLS group. All patients in the high-MLS group exhibited a poorer prognosis (Fig. \u003cspan\u003e7\u003c/span\u003eB). The expression levels of the 24 genes showed statistical significance in both high and low scoring groups (Fig. \u003cspan\u003e7\u003c/span\u003eC). The Mulberry diagram indicated that the C2 group, which comprised a larger portion of the low-MLS group, was associated with better survival outcomes, with a statistically significant difference in scores between the C1 and C2 groups (Fig. \u003cspan\u003e7\u003c/span\u003eD, E). Furthermore, to comprehensively assess the predictive power of MLS, we included 77 different features in the study. MLS demonstrated a superior C-index in both TCGA-LUAD and GSE31210 datasets (12/78, 10/78; Fig. \u003cspan\u003e7\u003c/span\u003eF), thereby confirming the reliability of the MLS model.\u003c/p\u003e\n\u003cp\u003eMLS immunological profile\u003c/p\u003e\n\u003cp\u003eThe CIBERSORT algorithm was used to analyze the correlation between the 24 MLS genes, MLS scores, and various immune cells. The results indicated that most MLS genes and MLS scores were negatively correlated with immune-activated cells and positively correlated with immune-suppressed cells. Additional analyses using five other immune infiltration algorithms also showed that immune cell infiltration (including dendritic cells, eosinophils, monocytes, and NK cells) was significantly higher in patients with low MLS scores compared to those with high MLS scores, suggesting an immune activation state (Fig. \u003cspan\u003e8\u003c/span\u003eA, B). This implies that LUAD with low MLS levels may be classified as \u0026quot;hot tumors.\u0026quot; Conversely, T regulatory cells (Tregs) and tumor-associated fibroblasts (CAFs) were predominantly enriched in patients with high MLS scores, which likely contribute to tumor proliferation, invasion, drug resistance, and an immunosuppressive state, leading to poorer outcomes (Fig. \u003cspan\u003e6\u003c/span\u003eB, C). This suggests that LUAD with high MLS levels might be classified as \u0026quot;cold tumors,\u0026quot; which are less sensitive to immunotherapy. TIDE scores were negatively correlated with risk scores (Fig. \u003cspan\u003e8\u003c/span\u003eC), indicating that low-risk patients may benefit more from immunotherapy. Increased TMB expression is associated with enhanced T cell activation. TMB has potential clinical utility in lung cancer and could serve as a biomarker for immunotherapy\u003csup\u003e25\u003c/sup\u003e. Our survival analyses showed that patients with lower MLS scores and higher TMB levels typically had significantly prolonged OS, suggesting that MLS could complement TMB in assessing patient prognosis (Fig. \u003cspan\u003e8\u003c/span\u003eD, E).\u003c/p\u003e\n\u003cp\u003eGSEA analysis of MLS\u003c/p\u003e\n\u003cp\u003eGSEA analysis for subgroups (Fig. \u003cspan\u003e8\u003c/span\u003eF) revealed that pathways enriched in low-scoring subgroups were associated with immune cell regulation and GnRH receptor signaling. These pathways, in addition to their role in regulating pituitary gonadotropin secretion, may predict survival and resistance to tumor proliferation, metastasis, and anti-angiogenesis in lung cancer patients\u003csup\u003e26\u003c/sup\u003e. The Hedgehog signaling pathway, utilized in developing tumor-targeting drugs, and VEGFR inhibitors, known for their effects on vascular smooth muscle and cancer cell proliferation, were also identified\u003csup\u003e27\u003c/sup\u003e. Interestingly, long-term depression was also enriched, suggesting a potential link between these pathways and tumor biology.\u003c/p\u003e\n\u003cp\u003eDrug sensitivity analysis of MLS\u003c/p\u003e\n\u003cp\u003eThe drug sensitivity analysis identified nine drugs with varying effectiveness. The low-scoring subgroup samples showed greater sensitivity to drugs such as BMS-754807, Doramapimod, JQ1, OF-1, and SB505124. Conversely, the high-scoring subgroup samples were more sensitive to AZD7762, Dasatinib, Docetaxel, and WIKI4. These findings suggest that MLS may be useful in evaluating the efficacy of targeted therapies and chemotherapy (Fig. \u003cspan\u003e9\u003c/span\u003eA-I).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eLUAD, the most common type of lung cancer, is often diagnosed at an advanced stage and is highly aggressive. Despite significant advancements in the diagnosis and treatment of lung cancer, new biomarkers and genetic features have improved the ability to predict LUAD prognosis. The development of targeted therapies, immunotherapies, and other novel treatments has benefited patients considerably\u003csup\u003e28\u003c/sup\u003e. However, due to high resistance to conventional radiotherapy and the heterogeneous nature of LUAD, the prognosis remains poor, with a 5-year survival rate of only 15%\u003csup\u003e29\u003c/sup\u003e. Comprehensive analysis of patients' multi-omic data enhances our understanding of disease mechanisms, and identifying biomarkers and therapeutic strategies from these data will support clinical precision and personalized medicine.\u003c/p\u003e \u003cp\u003eRecent studies have shown that SLC7A11 can transport cystine in response to glucose starvation, leading to disulfide stress, which disrupts the redox balance and causes disulfide bond death. This process, known as disulfidptosis, is characterized by abnormal formation of disulfide bonds in actin backbone proteins and the breakdown of F-actin\u003csup\u003e8,30\u003c/sup\u003e. Similar to ferroptosis, disulfidptosis can induce tumor cell death by altering the structure of cytoskeletal proteins. Elevated SLC7A11 expression has been observed in renal cancer tissues, where it is associated with tumor progression. Disulfidptosis induced by SLC7A11 contributes to cell death in glucose-deficient environments. Notably, SLC7A11 is a critical component of the upstream signaling pathway regulating ferroptosis, a form of cell death driven by oxidative stress and membrane lipid peroxidation, among other factors\u003csup\u003e13\u003c/sup\u003e. Specific genes in LUAD, such as STYK1 and LSH, play significant roles in regulating ferroptosis\u003csup\u003e31,32\u003c/sup\u003e. Recent research indicates that cell death occurs through complex and interdependent processes, involving extensive cellular interactions\u003csup\u003e33,34\u003c/sup\u003e. Thus, balancing ferroptosis and disulfidptosis could be a novel strategy to enhance outcomes and survival in LUAD patients. A recent study used four disulfidptosis/ferroptosis-related genes to predict prognosis and immune infiltration levels in LUAD patients\u003csup\u003e35\u003c/sup\u003e. In our study, we established a prognostic signature consisting of 24 DRF-related genes from the TCGA-LUAD cohort based on 6 newly identified DRF diagnostic genes and validated it in the GSE31210 dataset.\u003c/p\u003e \u003cp\u003eIn this study, we applied WGCNA along with Pearson correlation analysis, differential expression analysis, and KM survival analysis to selected DRF-related genes. While the LASSO algorithm is valuable for variable selection, it may sometimes favor less relevant predictors when genes are highly correlated with each other\u003csup\u003e36\u003c/sup\u003e. To address this issue, we compared 10 machine learning methods to identify the most important genes. Evaluation metrics included AUC, classification error rate, accuracy, precision, recall, sensitivity, and specificity. SVM-REF (support vector machine - recursive feature elimination) is a machine learning method that enhances classification accuracy and model robustness by removing less relevant features from the SVM-generated feature vector. Random Forest, another supervised machine learning algorithm, constructs and integrates multiple decision trees to improve prediction performance and stability.\u003c/p\u003e \u003cp\u003eAmong the six disulfidptosis-related ferroptosis genes identified, IL-33, a member of the IL-1 family, is closely linked to lung cancer progression. IL-33 enhances the activity of dendritic cells (DCs), CD8\u0026thinsp;+\u0026thinsp;T cells, and NK cells, thereby inhibiting tumor growth and metastasis\u003csup\u003e37\u003c/sup\u003e. SLC2A1 expression is significantly elevated in lung cancer and correlates with poorer patient survival\u003csup\u003e38\u003c/sup\u003e. CDCA3 overexpression may be linked to the activation of various oncogenic signaling pathways and cancer progression, and correlates with sensitivity to platinum-based chemotherapy. Similarly, high KIF20A expression is associated with the activation of oncogenic pathways and poorer OS\u003csup\u003e39,40\u003c/sup\u003e. FANCD2 is recognized as a cancer susceptibility gene that coordinates DNA repair pathways and responds to DNA damage through the PI3K-Akt-mTOR signaling pathway, promoting tumor cell survival\u003csup\u003e41\u003c/sup\u003e. RRM2 is involved in tumorigenesis and resistance to chemotherapeutic agents\u003csup\u003e42,43\u003c/sup\u003e. DRF genes also show potential for aiding in the early diagnosis and treatment of other cancers. This research provides new insights into the mechanisms linking disulfidptosis and ferroptosis. Specifically, KIF20A and SLC2A1, as DRF genes, demonstrate potential for use in the diagnosis and treatment of hepatocellular carcinoma\u003csup\u003e44\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo explore the regulatory roles and immunological mechanisms of DRFs in tumor development, our study classified DRFs into two groups using cluster analysis. We examined the differences between DRF subgroups in terms of genomic, transcriptional, and immunological features, and their connections in LUAD. Investigating the potential links between disulfidptosis and ferroptosis can provide deeper insights into mechanisms such as anti-tumor immune activity and inhibition of tumor immune escape in the TME, and help guide the selection of effective immunotherapy regimens. Notably, the two subgroups showed significant differences in OS, TME, and immune infiltration levels. Group C2 was associated with high IL33 expression, immune activation, and better prognosis, indicating an immunoinflammatory phenotype. In contrast, Group C1 exhibited a lower estimate score and higher expression of immunosuppressive cells, which aligns with the immune desert subtype\u003csup\u003e45\u003c/sup\u003e. Additionally, the C1 group had higher TH2/TH1 ratios compared to the C2 group. TH2 cells, a subset of CD4\u0026thinsp;+\u0026thinsp;T cells, secrete cytokines such as IL-4, IL-5\u003csup\u003e46\u003c/sup\u003e. TH1 cells, also a CD4 subset, primarily produce IFN-γ, IL-2, and TNF, and are involved in cellular immunity and the differentiation of cytotoxic T cells\u003csup\u003e47\u003c/sup\u003e. TH1 and TH2 cells maintain a balance through cytokine secretion, but this balance can be disrupted in the tumor microenvironment, leading to a shift from a Th1 to a Th2 response, known as the \"Th1/Th2 shift\"\u003csup\u003e48\u003c/sup\u003e. This shift is observed in many tumor patients, including those with lung cancer, and may be linked to tumor immune escape\u003csup\u003e49\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMachine learning algorithms are effective tools for analyzing multi-omics data. To understand the molecular differences between prognostic subtypes and enhance clinical utility, we used the TCGA-LUAD dataset as a training set and selected the best MLS through 101 algorithm combinations to address algorithm selection limitations. Overfitting is a common issue in model construction, where a model performs well in the training set but poorly in the validation set \u003csup\u003e(64)\u003c/sup\u003e. To mitigate this, we used the C-index of the validation cohort as a ranking criterion. Although the RSF\u0026thinsp;+\u0026thinsp;StepCox[forward] combination showed excellent performance in the training set, it was less effective in the validation set. In contrast, MLS demonstrated better prognostic value across all cohorts compared to other published signatures. Other signatures that performed better in the training set often failed to match this performance in the validation set, likely due to model overfitting.\u003c/p\u003e \u003cp\u003eWe analyzed the enrichment of numerous immune-related signatures between high and low score groups. We found that several oncogenic pathways were significantly activated in the high MLS group. This group also exhibited a high expression of Tregs cells, which may contribute to the immunosuppressive phenotype and a cold tumor environment. In contrast, the low MLS group had a higher TMB and a greater variety of immune cell types, suggesting stronger anti-tumor immunity and better prognosis. Survival analyses indicated a more favorable outcome for this group. Additionally, TIDE results showed that the low MLS group responded more effectively to immunotherapy, supporting the notion that MLS could aid in the early identification of immunotherapy-sensitive patients. Interestingly, while macrophages M2 are generally associated with anti-inflammatory responses and are more involved in angiogenesis, immunosuppression, and tumor metastasis\u003csup\u003e50\u003c/sup\u003e, our study observed the opposite. This discrepancy may be due to the heterogeneity among different M2 subpopulations, highlighting the complexity of macrophages in tumor immunity\u003csup\u003e51\u003c/sup\u003e. Despite the poor prognosis and limited benefit from immunotherapy in the high MLS group, Dasatinib and Docetaxel may still offer potential therapeutic benefits for this population.\u003c/p\u003e \u003cp\u003eOur study has several limitations. First, the prognostic model was developed using retrospective data from public databases. Although the results indicate the potential of DRF scores in predicting LUAD prognosis, their clinical value needs to be validated with additional prospective real-world data. Second, the mechanisms underlying the relationship between disulfidptosis and ferroptosis require further investigation. Additionally, while bioinformatics analyses provided insights into the potential impact of DRF scores on immune cell infiltration, experimental validation is needed to confirm the correlation between DRF scores and immune activity.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, we identified six genes related to disulfidoptosis and ferroptosis, and validated these DRF genes through in vitro experiments. Cluster analysis revealed two molecular subtypes of LUAD, highlighting significant differences in prognosis and immunological profiles between them. We developed a MLS that demonstrated strong performance across multiple cohorts, indicating its robust ability to predict patient prognosis. Comprehensive analyses, including functional, immunological, and pharmacological sensitivity assessments, confirmed the reliability and potential clinical utility of the DRF score for future precision medicine and clinical evaluation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthical Approval\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Disclosure\u003c/p\u003e\n\u003cp\u003eTCGA and GEO belong to public databases. The patients involved in the database have obtained ethical approval. Users can download relevant data for free for research and publish relevant articles. Our study is based on open-source data, so there are no ethical issues and other conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Competing interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Author\u0026rsquo;s contributions\u003c/p\u003e\n\u003cp\u003eLZC: Data analysis, Writing- Original draft preparation. YRD: Writing- Original draft preparation. ZX and LYX collected, processed and analyzed the data. ZX: Conception and design, Final approval of the version to be published.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Funding\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Chinese Medicine Inheritance and Innovation \u0026quot;Hundred Million\u0026quot; Talent Project (QI Huang Project) QI Huang Scholars Project, Shandong Province Taishan Scholars Specially Appointed Experts Program Project, and the 2021 Shandong Province Natural Science Foundation - General Project [No. ZR202103040386].\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Data availability statement\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCappa C, Gregson K, Wardlaw T, et al. Birth registration: a child\u0026prime;s passport to protection[J]. Lancet Glob Health, 2014, 2(2): e67⁃68. 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Macrophage M1/M2 polarization. \u003cem\u003eEuropean journal of pharmacology\u003c/em\u003e \u003cstrong\u003e877\u003c/strong\u003e, 173090, doi:10.1016/j.ejphar.2020.173090 (2020).\u003c/li\u003e\n\u003cli\u003eLi, M.\u003cem\u003e et al.\u003c/em\u003e Metabolism, metabolites, and macrophages in cancer. \u003cem\u003eJournal of hematology \u0026amp; oncology\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 80, doi:10.1186/s13045-023-01478-6 (2023).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"disulfidoptosis, ferroptosis, lung adenocarcinoma, machine learning, immune therapy, prognosis, drug sensitivity","lastPublishedDoi":"10.21203/rs.3.rs-5324542/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5324542/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDisulfidoptosis, a newly identified form of regulated cell death (RCD), significantly influences the progression of lung adenocarcinoma (LUAD). This study identified 340 ferroptosis-related genes strongly correlated with disulfidoptosis through correlation analysis. By intersecting these genes with those from module genes selected via weighted gene co-expression network analysis (WGCNA), 31 genes were found. In the TCGA-LUAD cohort, Kaplan-Meier(KM) survival analysis initially screened these genes, leading to the selection of 6 Disulfidoptosis-Related Ferroptosis (DRF) genes (IL33, SLC2A1, CDCA3, KIF20A, FANCD2, RRM2) through further screening with Random Forest and SVM-RFE. Based on the expression levels of DRF genes, two distinct groups with differing prognostic and immune characteristics were identified. A machine learning-driven signature (MLS) of 24 DRF-related genes was then constructed using the RSF\u0026thinsp;+\u0026thinsp;SuperPC algorithm and validated in the TCGA-LUAD and GSE31210 datasets. Compared with 77 other signatures, MLS demonstrated superior performance in both datasets. A low MLS score was associated with immune activation, higher tumor mutation burden, and better survival probability. Conversely, a high MLS score correlated with poorer prognosis and reduced potential benefit from immune therapy, although treatments like Doramapimod might still offer benefits. The cell cycle pathway was a key factor distinguishing high from low MLS groups. Overall, MLS shows promise for predicting prognosis in LUAD patients and identifying those who might benefit from immune therapy. Additionally, DRF genes have potential clinical value for diagnosing and treating other cancers, as indicated by pan-cancer analysis. q-PCR experiments targeting select DRF genes confirmed their feasibility as diagnostic markers for LUAD.\u003c/p\u003e","manuscriptTitle":"Prognostic and Immunological Analysis of Disulfidoptosis-Related Ferroptosis Genes in Lung Adenocarcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-29 16:07:15","doi":"10.21203/rs.3.rs-5324542/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"53eebf8d-e895-4dcb-b2fb-91d2c7b74892","owner":[],"postedDate":"October 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-09T13:54:08+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-29 16:07:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5324542","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5324542","identity":"rs-5324542","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}