Disulfidptosis and pentose phosphate pathway-associated prognosis signature guides immunotherapy for lung adenocarcinoma patients

Disulfidptosis and pentose phosphate pathway-associated prognosis signature guides immunotherapy for lung adenocarcinoma patients | 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 Disulfidptosis and pentose phosphate pathway-associated prognosis signature guides immunotherapy for lung adenocarcinoma patients hanyu Zhou, xiao yun, jun wu, xinzhu Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5051024/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 Background Disulfidptosis is a form of cell death, where generation of nicotinamide adenine dinucleotide phosphate (NADPH) through the pentose phosphate pathway (PPP) play an important role. The discovery of disulfidptosis provides new in-sights into lung adenocarcinoma (LUAD) therapeutics. Research Design and Methods: Disulfidptosis regulators (DSRs) was used to identify subgroups. Meanwhile, WGCNA and single-cell analysis were performed to identify genes related to disulfidptosis and PPP (DPRGs). To determine the risk signature, clinical features were analyzed, as well as prognostic pre-dictive ability, tumor immune microenvironment (TIME), immunotherapeutic response and drug sensitivity. Finally, the results were experimentally verified in vitro and vivo. Results We identified two DSR and DPRG clusters associated with distinct immune profiles involved in regulating different biological processes. The risk signature was effective in assessing LUAD prognosis in patients. It showed a strong correlation with TIME and could predict the immunotherapy response. After LRRC61 knockdown, the proliferation, migration and anti-apoptotic ability of LUAD cells were significantly reduced. Moreover, the xenograft tumors showed tumour growth was promoted when overexpressing LRRC61. Conclusion We analyzed DSRs and DPRGs in LUAD and developed an evaluation system that assesses the risk and guides the clinical application of drugs, including chemotherapeutic and immunotherapeutic agents. Disulfidptosis Lung adenocarcinoma Pentose phosphate pathway Tumor immune microenvironment Prognosis Immunotherapy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Immunotherapy has demonstrated sustained clinical relevance in lung adenocarcinoma (LUAD) 1 , and the clinical use of various immune checkpoint inhibitors (ICIs) in advanced LUAD has been approved 2 . Despite reported efficacy, many patients receiving immunotherapy continue to have poor prognoses. Therefore, it is essential to thoroughly investigate the intrinsic regulatory mechanisms of the tumor immune microenvironment (TIME) in patients with LUAD 3 . Cell death is a physiological process that maintains biological development and internal environmental homeostasis. Targeting cell death-related pathways to kill cancer cells is a major direction in cancer therapy 4 . Recently, Gan et al. discovered and identified a new type of cell death, disulfidptosis, which offers new possibilities for cancer therapy 5 . Tumor cells typically require a high expression of solute carrier family 7 member 11 (SLC7A11) to uptake more cystine for glutathione synthesis to balance the oxidative effects arising from their highly active metabolism. However, ingested cystine must be reduced to cysteine promptly to eliminate toxicity 6 . This process requires the consumption of large amounts of nicotinamide adenine dinucleotide phosphate (NADPH) generated via the pentose phosphate pathway (PPP). Otherwise, excessive accumulation of cystine may cause tumor cell death. When cells with high SLC7A11 levels lack glucose, disulfide accumulates abnormally, causing disulfide stress, which leads to the contraction of actin filaments, destruction of the cytoskeletal structure, and consequently cell death 7 . SLC7A11 is overexpressed in patients with LUAD and is positively correlated with tumor progression 8 . Therefore, we sought to explore the role of disulfidptosis in LUAD, particularly in relation to TIME. The PPP, which is closely related to disulfidptosis, is a major glucose catabolism pathway that differs from glycolysis 9 . The PPP is essential for cancer cells to meet their anabolic needs and respond to oxidative damage 10 . Additionally, activated immune cells rely on the PPP to regulate the immune response 11 . However, despite the well-known role of the PPP in promoting tumor progression and immune regulation, there is a lack of systematic studies on its effects in LUAD. Therefore, exploring the effects of the PPP on the TIME in LUAD is worth considering, as it may help develop effective immunotherapeutic strategies. In this study, we analyzed the characteristics of disulfide regulators (DSRs) in LUAD patients using data from public databases and performed a consensus clustering analysis based on DSR expression. By performing WGCNA and single-cell analysis, we identified genes associated with disulfidptosis and PPP, respectively, and identified DSRs and PPP-related genes (DPRGs). Using DPRGs to construct a risk signature, we systematically analyzed clinical prognosis, TIME, and response to immunotherapy in LUAD patients. We hypothesized that the risk signature would effectively differentiate LUAD patients, allowing for more individualized and effective therapeutic strategies. 2. Materials and Methods 2.1 Transcriptome dataset collection We obtained transcriptome data and clinical information for patients with LUAD from the TCGA-LUAD cohort in The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/ ), as well as from datasets GSE68645 and GSE26939 in the Gene Expression Omnibus database (GEO, https://www.ncbi.nlm.nih.gov/geo/ ). To eliminate batch effects on data from different databases, we used the sva R package. We retrieved a gene set containing nine DSRs from the report by Gan et al. (5). Additionally, we obtained thirty pentose phosphate pathway-related genes (PRGs) from the Reactome database ( https://reactome.org/ , R-HSA-71336) and the Kyoto Encyclopedia of Genes and Genomes (KEGG, https://www.genome.jp/kegg/ ) database (has00030). 2.2 Weighted Gene Coexpression Network Analysis (WGCNA) In this study, we utilized WGCNA to identify genes associated with disulfidptosis and PPP. Outliers were removed, and soft thresholds were selected based on the scale-free topology criterion to construct an expression network. Gene modules related to disulfidptosis and PPP were then identified. For the analysis, a scale-free topological fit index of 1–20 was used, with a screening criterion of 0.9, and a minimum of 30 genes per module was set. 2.3 Single-cell Analysis and AUCell Raw data for single-cell analysis were downloaded from GSE149655. The raw count matrices were merged and converted into an expression matrix. The following pre-processing steps were applied: samples were excluded if fewer than three cells expressed the genes, if fewer than 200 genes occurred in a single cell, or if the cells contained too many mitochondrial genes and an abnormal number of expressed genes. Next, the data were normalized to enable practical comparison between different cell samples. Principal component analysis (PCA) was then used to reduce the dimensionality of the data and reveal the principal factors of the different subgroups. Clustering operations were performed using the Leiden algorithm, and the resolution was set to 0.25. Cell types were annotated, and marker genes were compared across taxa based on known cell markersTo identify the most significant genes associated with disulfidptosis and PPP, we employed the AUCell R software package, which assigned a disulfidptosis activity fraction and PPP activity fraction to each cell. The area under the curve (AUC) scores of the DRGs and PRGs were calculated to determine their enrichment levels in the expressed genes of each cell. Subsequently, cells were classified based on their median AUC score. 2.4 Consensus clustering analysis Consensus clustering was performed using the expression levels of the nine DSRs. The overlapping genes between differentially expressed genes (DEGs) among the DSR clusters, gene modules identified by WGCNA, and DEGs obtained from the scRNA-Seq analysis, were selected as DPRGs for further analysis. Using univariate Cox regression analysis, we screened the DPRGs that were associated with overall survival (OS). Subsequently, clustering analysis based on these OS-related DPRGs yielded two DPRG clusters. 2.5 Analysis of infiltrating immune cell population The abundance of infiltrating immune cells was quantified using multiple software packages, including the Estimation of STromal and Immune cells in MAlignant Tumor tissues Expression algorithm 12 , single-sample gene set enrichment analysis (ssGSEA), xCELL 13 , TIMER 14 , quanTIseq 15 , MCP-counter 16 , EPIC and CIBERSORT 17 . 2.6 Enrichment analysis The dataset c2.cp.kegg.v2022.1.Hs.symbols was obtained from MsigDB ( https://www.gsea-msigdb.org/gsea/msigdb ) and used for differential functional identification through the GSVA R package. GSEA 4.3.2 software was used to perform gene set enrichment analysis (GSEA) of the curated gene sets. 2.7 Construction of the risk signatures By performing the least absolute shrinkage and selection operator (LASSO) Cox regression on the OS-related-DPRGs, we constructed a risk signature containing seven genes. The risk score was determined using the formula: Σ(coefficient_k*expression_k), where coefficient_k and expression_k are the risk coefficient and expression of each gene, respectively. After calculating the risk scores for each sample, based on the median score, the patients were divided into low-risk and high-risk groups. 2.8 Evaluation of the risk signatures The prognostic significance of the risk signature was assessed using receiver-operating characteristic (ROC) curve analysis, Cox regression, and Kaplan–Meier survival analysis, and the concordance index was calculated for both the training and test sets. Nomograms were generated using the rms R package, and their accuracy was evaluated using cumulative hazard and calibration curves. To assess the benefits of the intervention with nomograms and risk scores for LUAD patients, Decision Curve Analysis (DCA) was performed. 2.9 Analyses of immune checkpoints expression and immunotherapy response in different risk signature subgroups The limma R package was used to examine variations in immune checkpoint expression between the subgroups. Additionally, we assessed the responses of patients in different clusters and signature subgroups to immunotherapy according to the Tumor Immune Dysfunction and Exclusion (TIDE) score 18 as well as The Cancer Immunome Atlas ( https://tcia.at/home ). 2.10 Examination of the drug sensitivity The OncoPredict R package 19 was used to screen for sensitive drugs between different risk signature subgroups. 2.11 Pancancer analysis of LRRC61 We assessed the differential expression of LRRC61 and its prognostic impact on multiple cancers. In addition, we performed a correlation analysis of LRRC61 expression with the degree of immune cell infiltration and immune checkpoint expression. 2.12 Cell culture and tissue collection All cell lines were purchased from the Institute of Biochemistry and Cell Biology (Shanghai, China) and were maintained in Dulbecco's modified Eagle's medium (Gibco, Life Technologies, CA, USA) supplemented with 10% fetal bovine serum (FBS) (Hyclone, Logan, UT, USA) and 100 µg/mL penicillin-streptomycin (Hyclone). Eight LUAD and matching paracancerous non-tumor tissue samples were obtained during routine surgery at the First Affiliated Hospital of Nanjing Medical University, and the specimens were stored at − 80°C until further testing. 2.13 Reverse transcription-quantitative PCR Total RNA was isolated from tissues using TRIzol reagent (Takara Bio, Shiga, Japan) and reverse-transcribed to cDNA using a PrimeScriptTM RT reagent Kit (Takara). SYBR® Premix Ex Taq (Takara) was used for qRT-PCR analysis. Detailed primer sequences are listed in Supplementary Table S1 . 2.14 Immunohistochemistry Staining Slices of tissue were blocked and subjected to suitable concentrations of primary antibody (Sigma-Aldrich, #HPA019355) at 4°C. Biotinylated sections were treated for 30 min with the secondary antibody (Dako, Denmark). Finally, the slices were stained with a Dako diaminobenzidine (DAB) kit and counterstained with hematoxylin (Baso, Zhuhai, China). 2.15 RNA interference For the knockdown of LRRC61, specific small interference RNAs (siRNAs) and scrambled siRNA (si-NC) were transfected into A549 and H1299 cells using LipofectamineTM 3000 (Invitrogen, Carlsbad, CA, USA). Target RNAs were measured 48 h after transfection using qRT-PCR. The siRNA sequences are listed in Supplementary Table S1 . 2.16 Cell proliferation assay LUAD cells were planted in 96-well plates, followed by the addition of 20 µL CCK8 solution to each well. At 1, 2, 3, 4, and 5 days, the absorbance at 450 nm (OD450) was determined using a microplate reader (Thermo Fisher, Multiskan 51119000, MA, USA). 2.17 Colony formation The cells were maintained in 6-well plates for 2 weeks. Cell colonies were stained with crystal violet solution for 1.5 h after being fixed with 4% formaldehyde for 10 min. The mean colony counts were calculated. 2.18 Transwell assay For the migration assay, 1 × 105 cells were plated in the top chamber of the Transwell chamber, and 10% FBS culture medium was added to the lower chamber. The cells that migrated across the membrane were photographed and counted under a microscope (Nikon, Ts2R, Japan). 2.19 Apoptosis assay Cells were seeded into 6-well plates. Then we stained cells for 15 min with the annexin V-FITC/PI Apoptosis Detection Kit (Beyotime Institute of Biotechnology, Jiangsu, China). Samples were analyzed using FACSCanto TM II flow cytometry (Becton, Dickinson and Company). 2.20 Animal studies Nanjing Medical University’s Committee on Ethics for Animal Experiments ap-proved the experimental procedures (IACUC-1706007). We used 6–8-week-old male BALB/c nude mice (SPF grade) from the Nanjing Medical University Animal Center. H1299 (1 × 106) cells were injected into the right side of each mouse. Every 3 days, the tumour volume was measured. Six mice were randomly divided into the control and LRRC61 groups. The mice were euthanized at a right time. Tumor width and length were recorded, and tumor volume cal-culated using the formula: [width 2 × length]/2. In order to euthanize the mice, an appropriate amount of mice were put in a euthanasia box, then carbon dioxide was introduced. When the signs of life disappear completely, make sure that there is no breathing and no heartbeat, we remove the body. This approach meets the requirements of animal ethics. 2.21 Statistical analysis The Wilcoxon rank test and Kruskal–Wallis test were used to comparing two independent groups and three or more independent groups, respectively. The differences in patient survival were evaluated using Kaplan-Meier and log-rank tests. Spearman's rank correlation analysis was used for the correlation analysis. The R software (version 4.2.3) and GraphPad Prism 9 were used for all statistical analyses. 3. Results 3.1 Characteristics and prognostic value of DSRs in LUAD After removing the batch effect of GSE68545 and TCGA-LUAD (Figs. 1A and 1B), we analyzed the nine DSRs using the combined data. Their chromosomal distribution and CNVs are displayed in Fig. 1C and 1D, respectively. Among them, RAC1, SLC2A1, NCKAP1, SLC3A2, ABI2, and BRK1 exhibited significant copy number amplification, while WASF2, CYFIP1, and SLC7A11 showed copy number deletions. Moreover, the expression levels of DSRs were markedly different between LUAD tumors and healthy tissues (Fig. 1E). The network diagram in Fig. 1F illustrates the interactions and prognostic values of the DSRs, where only BRK1 played a favorable prognostic role, and all other DSRs were prognostic risk factors. We also conducted Kaplan-Meier survival analysis of individual DSRs separately, and the outcomes were consistent with the network diagram (Fig. 1G). Additionally, SLC7A11, SLC3A2, NCKAP1, RAC1, and SLC2A1 were identified as independent prognostic factors in patients with LUAD (Fig. 1H). 3.2 Identification of distinct clusters and their functions based on DSR expression To investigate whether DSR expression was associated with clinical characteristics in LUAD, we performed a consensus clustering analysis based on the nine DSRs. We identified two DSR clusters (Fig. 2A, Table S2), and Kaplan–Meier survival analysis showed that patients in cluster B had a higher likelihood of survival (Fig. 2B). The DSR clusters effectively differentiated patients with LUAD, as shown by PCA, t-SNE, and UMAP analysis (Figs. 2C–2E). The expression of DSRs and associated LUAD clinical characteristics in the two DSR clusters is depicted in Fig. 2F. Differential expression analysis revealed that all DSRs except RAC1 were more highly expressed in cluster A (Fig. 2G). We also observed variations in immune infiltration levels between the two DSR clusters, with cluster A having higher ratios of CD56dim neutrophils, type 2 T helper cells, and natural killer cells, while the majority of the remaining immune cells were significantly more abundant in cluster B (Fig. 2H). Finally, we identified 3519 DEGs between the two DSR clusters for further analysis. To investigate the biological roles of the DSRs in the different DSR clusters, we performed GSEA (Figs. 2I and 2J) and GSVA (Figs. 2K and 2L). Consistent with the results of the immune infiltration analysis, cluster B showed a stronger correlation with the immune response, which could explain the better survival outcome of LUAD patients in cluster B. On the other hand, cluster A was associated with the metabolism of various compounds involved in biochemical processes, including the disulfidptosis process (highlighted in blue) and the pentose phosphate pathway (highlighted in green), which is closely related to disulfidptosis. 3.3 Identification of gene modules highly associated with disulfidptosis and pentose phosphate pathways by WGCNA To identify genes associated with DSR and PPP, we performed WGCNA. We chose 5 as the soft threshold based on the scale-free topology fit index to generate a proximity matrix (Fig. 3A). After module cutting with a dynamic tree and merging similar gene modules, 12 gene modules were identified and assigned various color designations (Fig. 3B) which were then correlated with DSR and PPP (Fig. 3C). Among them, the black (Fig. 3D), pink (Fig. 3E), salmon (Fig. 3F), and turquoise modules (Fig. 3G) had a strong correlation with PPP, while the midnight blue (Fig. 3H), salmon (Fig. 3I), and turquoise (Fig. 3J) modules showed a strong correlation with DSR. A total of 6305 genes from these modules were selected for further analysis. 3.4 Identification of genes highly associated with disulfidptosis and pentose phosphate pathways by single-cell analysis We conducted a single-cell analysis to investigate genes associated with DSR and PPP. To ensure sample validity, we removed doublets and filtered cells based on mitochondrial gene ratio (Figure S1 A). The number of tagged transcripts was positively correlated with the number of detected genes (Figure S1 B), and we corrected for cell cycle heterogeneity (Figure S1 C). After quality control, we removed batch effects (Fig. 4A), and we used t-SNE dimensionality reduction to classify cells into 28 clusters (Fig. 4B) that were validated using cell markers (Fig. 4C). Gene-specific expression for different cell types is shown in Figure S1 D. We used the AUCell R package to separately score the enrichment of DSRs and PRGs (Table S3) in each cell (Figs. 4D and 4F) and classified cells into low and high groups based on the median DSR-AUC score/PPP-AUC score (Figs. 4E and 4G). B cells, mononuclear phagocytes, fibroblasts, and epithelial cells showed higher DSR-AUC scores, whereas B cells and mononuclear phagocytes had higher PPP-AUC scores. We identified 1983 DEGs between the low and high DSR-AUC groups and 1846 DEGs between the low and high PPP-AUC groups for further analysis. GSVA revealed that in addition to being associated with disulfidptosis and PPP (marked in yellow), DSRs and PRGs may participate in pathways such as oxidative phosphorylation, ferroptosis, and protein export (marked in green) that differentiate the low and high AUC groups (Figs. 4H and 4I). 3.5 Construction and assessment of risk signature based on OS-related DPRGs Based on the previous analysis, we identified 2050 DSRs and PRGs (DPRGs) (Figure S2A), out of which 302 OS-related DPRGs were selected (p < 0.0001) using univariate COX regression analysis (Table S4). Functional enrichment analysis of these OS-related DPRGs revealed their association with PPP and platinum drug resistance (Figure S2B). We performed a consensus clustering analysis of LUAD patients in the study cohort using these OS-related DPRGs, resulting in two DPRG clusters (Figure S2C). The DPRG clusters effectively stratified LUAD patients (Figure S2D-S2F), with patients in cluster B demonstrating higher survival rates, as evidenced by Kaplan–Meier survival analysis (Figure S2G). Additionally, most DSRs were expressed at lower levels in DPRG cluster B (Figure S2H). Finally, we performed LASSO on the OS-related DPRGs to establish corresponding risk signatures (Figs. 5A and 5B). The risk signature consisted of seven genes (RTN4, ACHE, ABAT, EGLN1, FAM117A, LRRC61, and ADM), and their correlations with DSRs and PRGs are shown in Figure S3A and Figure S3B, respectively. All seven risk model genes can be independently presented as prognostic indicators in patients with LUAD, as demonstrated in Fig. 5C. To generate risk scores for patients in the TCGA-BLCA and GSE68465 cohorts, we utilized the expression and coefficient of each candidate gene in the risk signatures (Table S5). We evenly divided the patient population into training and test subsets to analyze the accuracy of the signatures. To validate the risk signature, we conducted Kaplan–Meier OS analysis (Figs. 5D–5F). For all categories, survival was significantly greater in the low-risk group. The risk signature exhibited good predictive ability at 1-, 3-, and 5-year time points, according to time-dependent ROC curves (Figs. 5G–5I). The relationships among the DSR cluster, risk score, and LUAD patient survival status are summarized in a Sankey plot in Fig. 5J. We then examined the distribution and variations in risk scores between the DSR and DPRG clusters (Figures S3C, S3D). Furthermore, there were notable differences in survival status, gender, T-stage, N-stage, and TNM stage between the low-risk and high-risk groups of LUAD patients (Fig. 5K), and the distribution statistics are shown in Figs. 5L–5P. 3.6 Value for prognosis of the risk signature In Fig. 6A, it is evident that the risk score acts as an independent prognostic factor, and the concordance index values indicate that the risk signature exhibits the best predictive ability compared to other clinical characteristics (Fig. 6B). We also created a nomogram that plots the prognostic value of the risk signature along with clinical features to quantify their predictive abilities in patients with LUAD (Fig. 6C). To assess the precision of their predictive abilities, we conducted cumulative hazard and calibration assessments (Figs. 6D, 6E). Additionally, decision curve analysis at 1-, 3-, and 5-year time points demonstrated that interventions based on the nomogram and risk score resulted in greater benefits for LUAD patients than those based on other clinical traits (Figs. 6F–6H). Finally, we conducted KEGG (Fig. 6I) and GO (Fig. 6J) functional enrichment analysis using GSEA, which suggested that low risk was due to the immune response, while the high risk was associated with cell cycle, PPP, actin cytoskeleton regulation, cancer pathways, and disulfide oxidoreductase. This indicates a significant link between the risk model we developed and disulfidptosis. 3.7 Correlations of the risk signature scores with TIME and immunotherapy response Given the functional enrichment results that suggest a correlation between the risk score and the immune response, we were particularly interested in exploring the interaction between the risk score and the tumor immune microenvironment (TIME). Figure 7A and Figure S4A illustrate the relationship between the risk score and the infiltration of immune cells, and the degree of immune cell infiltration for each gene in the risk signature is plotted in Fig. 7B. There were variations in immune checkpoint expression levels between the low-risk and high-risk groups (Fig. 7C), and the candidate genes for the risk signature showed different correlations with immune checkpoints (Fig. 7D). The high-risk group exhibited higher expression levels of immunosuppressive factors regarding cytokines (Figure S4B). There were observable differences in immune cell quantity and tumor purity (ESTIMATE score) between the low-risk and high-risk groups (Fig. 7E). Furthermore, there was a difference in TIDE scores for the efficacy of immunotherapy between the two groups (Fig. 7F). While the immunophenoscore score for the CTLA4–PD1 + immunophenotype did not differ significantly between the two groups, all other groups showed differences in immunotherapy response (Figs. 7G–7J). Additionally, the combined analysis of risk signatures and immune checkpoints could more effectively differentiate the prognosis of LUAD patients (Figure S5). Finally, we performed a drug sensitivity analysis of the DPRG risk signature. As the high-risk group was insensitive to clinically used chemotherapeutic agents (Figure S6A), we screened for potential therapeutic agents that might benefit them (Figure S6B). 3.8 Function enrichment, expression and prognosis analysis of LRRC61 We discovered that LRRC61, one of the candidate genes for the risk signature, is involved in disulfide formation and is associated with PPP and cancer pathways (Figs. 8A–8D). Due to the limited research on LRRC61 to date, we aimed to investigate its characteristics in cancer. The expression of LRRC61 was found to be high in numerous tumor tissues (Fig. 8E) and can be utilized as an independent prognostic factor (Fig. 8F). Specifically, high expression of LRRC61 was correlated with LUAD progression (Fig. 8G) and poor overall survival (Figs. 8H–8J). In a pan-cancer analysis, LRRC61 was also linked with immune infiltration (Figure S7) and immune checkpoints (Figure S8). 3.9 Role of LRRC61 as an oncogene of LUAD cells in vitro We validated LRRC61 expression in eight pairs of LUAD samples and their corresponding noncancerous lung samples using qRT-PCR (Fig. 9A) and IHC staining (Fig. 9B). We observed that LRRC61 was upregulated in LUAD tissues and cells (Fig. 9C), which is consistent with the RNA-seq results. Subsequently, we successfully silenced LRRC61 in A549 and H1299 cells using specific siRNAs. LRRC61 knockdown inhibited the proliferation (Figs. 9D – 9F), migration (Fig. 9G), and apoptosis resistance (Fig. 9H) of A549 and H1299 cells. These results indicate that LRRC61 functions as an oncogene during the development of LUAD tumors. We established subcutaneous tumor formation models by 6–8-week-old male BALB/c nude mice (SPF grade) to evaluate the effects of LRRC61 in vivo. Images shows the xenograft tumors of the LRRC61 group were bigger than that of the control group. Moreover, tumor volume and tumor weight were measured to evaluate nude mice. 4. Discussion Recent research has identified a new form of cell death induced by disulfide stress called disulfidptosis 20 . In the presence of glucose starvation or NADPH depletion, excessive accumulation of disulfide molecules in cells with high SLC7A11 expression causes disulfide stress. Aberrant disulfide bonds across the actin cytoskeleton led to actin cytoskeletal collapse and cell death. This study also confirmed that NADPH is essential for providing reducing power to break down abnormally accumulated disulfide bonds, thereby inhibiting disulfidptosis 20 . Therefore, NADPH, which is mainly derived from PPP, plays an essential role in both preventing disulfidptosis and reversing disulfide stress. In this study, we included PPP-related genes (PRGs) and analyzed disulfidptosis in LUAD using an integrated approach. In addition to SLC7A11, SLC3A2 (which encodes the SLC7A11 chaperone) 21 , 22 , and SLC2A1 (glucose transporter), Gan et al. demonstrated that the Rac-WAVE regulatory complex (WRC)-mediated lamellipodia formation promotes disulfidptosis, while deletion of either component of the WRC could inhibit disulfidptosis. Therefore, we selected RAC1 and components of the WRC (NCKAP1, WASF2, CYFIP1, ABI2, and BRK1) as DSRs. All nine DSRs were associated with the prognosis of LUAD except WASF2. Among them, five (SLC7A11, SLC3A2, NCKAP1, RAC1, and SLC2A1) could be considered independent prognostic risk factors. In this study, the patient cohort was divided into two distinct DSR clusters based on DSR expression, with patients in cluster A showing higher expression and worse overall prognosis, while patients in cluster B exhibited higher immune infiltration, which is known to have a positive effect on prognosis 23 – 25 . Enrichment function comparison of the two DSR clusters revealed that cluster B was associated with favorable immune responses, while cluster A was involved in metabolic pathways related to disulfidptosis and PPP. The analysis based on single-cell data also showed that PRGs were significantly involved in the disulfidptosis process. After conducting the aforementioned analysis, we obtained the DPRGs and utilized them for secondary clustering analysis. The resulting DPRG clusters could effectively distinguish between LUAD patients and provide a dependable foundation for further analysis. The functional annotation of DPRGs demonstrated their involvement in regulating the cell cycle, immune response, and platinum drug resistance, in addition to PPP. Given that platinum drugs are frequently employed in LUAD treatment regimens, it is clinically relevant to employ DPRGs for assessing varying susceptibilities to platinum drugs in patients with LUAD. 26 – 28 . The risk signatures constructed using DPRGs are highly effective in predicting the prognosis of LUAD, and LUAD patients with low-risk scores still benefit from immune response modulation. Despite the benefits of immunotherapy, most patients experience disease progression 29 . Therefore, there is an urgent need to optimize immunotherapy regimens 30 . Multi-targeted combination immunotherapy can significantly improve immunotherapy efficacy and has been reported in a variety of tumors including lung cancer, pancreatic cancer and colorectal cancer 31 – 33 . Relationship between cancer immune response and mechanisms of resistance to immunotherapy has been under investigation for a long time. combination therapies (e.g., immunotherapy with chemotherapy, radiation therapy and targeted therapy), and discuss combination therapies approved by the US Food and Drug Administration demonstrated benefits to patients. Many targeted therapies such as targeting cytokines and other soluble immunoregulatory factors, ACT, virotherapy, innate immune modifiers and cancer vaccines 34 , as well as combination therapies that exploit alternative immune targets and other therapeutic modalities were studied 32 . We found that the risk signature related to DPRGs could be implemented to guide immunotherapy planning, given that patients with high-risk scores expressed stimulatory immunological checkpoints much less than those with low-risk scores did, and as a result, responded to immunotherapy less effectively.We believe that our study will also bring benefits to oncology patients, especially LUAD patients. TIME is involved in tumorigeneses, progressions, and metastases 35 . The intra-tumor immune landscape is a critical factor influencing patient survival and response to immunotherapy 36 . TIME plays a significant role in the progression of lung cancers 37 , 38 and is linked to prognosis 39 . Multiple algorithms to assess the abundance of immune infiltration showed that risk score was significantly positively correlated with neutrophil and negatively correlated with T cells and B cells, consistent with reported 40 , 41 . Tumor-associated neutrophils enhance the proliferation of tumor cells through pathways such as neutrophil extracellular traps 42 . This result suggests that risk score may weaken immune response and promote tumor development by regulating TIME. In addition to being associated with immune cells and immune checkpoints, the expression of other critical molecules in the TIME regulatory network, such as cytokines, chemokines, and growth factors 43 , also differed significantly between the low-risk and high-risk groups. Therefore, the DPRG-related risk signature may have significant clinical value in regulating the TIME network and preventing immune escape. LRRC61, a functionally enriched component gene in the risk signature, is associated with disulfidptosis and PPP. It is highly expressed in several cancer types and demonstrates prognostic value, including in LUAD. In vitro experiments showed that LRRC61 knockdown successfully reduced the ability of LUAD cells to proliferate, invade, migrate, and prevent apoptosis. Therefore, the mechanism by which LRRC61 exerts oncogenic effects in LUAD through the regulation of disulfidptosis and PPP deserves further investigation. This study has several limitations. The study subjects were enrolled based on the limited resources available in public databases, and the retrospective analysis may be subject to selection bias, which affects accuracy. Moreover, clinical information on patients with LUAD in public databases is not comprehensive, which may have led to the omission of additional characteristics associated with disulfidptosis and PPP. 5. Conclusions Through the analysis of bulk RNA-seq and scRNA-seq, we identified DPRGs. By constructing a DPRG-related risk signature, we revealed the relevance of disulfidptosis and PPP in LUAD to the clinical characteristics, prognosis, TIME, and immunotherapeutic response of patients, and provided insight into the underlying regulatory mechanisms. These promising predictive tools may help the development of more effective and personalized treatment strategies for patients with LUAD. Declarations Patents: None Ethics approval and consent to participate: IACUC-2210031 Consent for publication: Not applicable Competing interests: The authors declare that they have no competing interests. Funding: This study was supported by grants from the National Natural Science Foundation of China (Grant Nos. 82172889), and the Clinical Frontier Technology of Jiangsu Provincial Department of Science and Technology (BE2020783). Author Contribution Zhou HY and Xiao Y wrote the main manuscript and completed most of the experiments. Wu J and Wang XZ provided ideas and reviewed data. All authors reviewed the manuscript. Data Availability Data is provided within the manuscript or supplementary information files. References Guo X, et al. Global characterization of T cells in non-small-cell lung cancer by single-cell sequencing. Nat Med. 2018;24:978–85. 10.1038/s41591-018-0045-3 . Yang L, et al. Single-cell transcriptome analysis revealed a suppressive tumor immune microenvironment in EGFR mutant lung adenocarcinoma. J Immunother Cancer. 2022;10. 10.1136/jitc-2021-003534 . Ramos-Casals M, et al. Immune-related adverse events of checkpoint inhibitors. Nat reviews Disease primers. 2020;6. 10.1038/s41572-020-0160-6 . Mou Y, et al. Ferroptosis, a new form of cell death: opportunities and challenges in cancer. J Hematol Oncol. 2019;12. 10.1186/s13045-019-0720-y . Liu X, et al. Actin cytoskeleton vulnerability to disulfide stress mediates disulfidptosis. Nat Cell Biol. 2023;25:404–14. 10.1038/s41556-023-01091-2 . Liu X, et al. Cystine transporter regulation of pentose phosphate pathway dependency and disulfide stress exposes a targetable metabolic vulnerability in cancer. Nat Cell Biol. 2020;22:476–86. 10.1038/s41556-020-0496-x . Hu K, et al. Suppression of the SLC7A11/glutathione axis causes synthetic lethality in KRAS-mutant lung adenocarcinoma. J Clin Investig. 2020;130:1752–66. 10.1172/JCI124049 . Jiang P, Du W, Wu M. Regulation of the pentose phosphate pathway in cancer. Protein Cell. 2014;5:592–602. 10.1007/s13238-014-0082-8 . Patra KC, Hay N. The pentose phosphate pathway and cancer. Trends Biochem Sci. 2014;39:347–54. 10.1016/j.tibs.2014.06.005 . Ma H, et al. c-Src facilitates tumorigenesis by phosphorylating and activating G6PD. Oncogene. 2021;40:2567–80. 10.1038/s41388-021-01673-0 . Ghergurovich JM, et al. A small molecule G6PD inhibitor reveals immune dependence on pentose phosphate pathway. Nat Chem Biol. 2020;16:731–9. 10.1038/s41589-020-0533-x . Yoshihara K, et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun. 2013;4:2612. 10.1038/ncomms3612 . Aran D, Hu Z, Butte AJ. xCell: digitally portraying the tissue cellular heterogeneity landscape. Genome Biol. 2017;18:220. 10.1186/s13059-017-1349-1 . Li T, et al. A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells. Cancer Res. 2017;77:e108–10. 10.1158/0008-5472.CAN-17-0307 . Finotello F, et al. Molecular and pharmacological modulators of the tumor immune contexture revealed by deconvolution of RNA-seq data. Genome Med. 2019;11:34. 10.1186/s13073-019-0638-6 . Becht E, et al. Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression. Genome Biol. 2016;17:218. 10.1186/s13059-016-1070-5 . Newman AM, et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015;12:453–7. 10.1038/nmeth.3337 . Jiang P, et al. Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat Med. 2018;24:1550–8. 10.1038/s41591-018-0136-1 . Maeser D, Gruener RF, Huang RS. oncoPredict: an R package for predicting in vivo or cancer patient drug response and biomarkers from cell line screening data. Brief Bioinform. 2021;22. 10.1093/bib/bbab260 . (!!!. INVALID CITATION !!!). Koppula P, Zhuang L, Gan B. Cystine transporter SLC7A11/xCT in cancer: ferroptosis, nutrient dependency, and cancer therapy. Protein Cell. 2021;12:599–620. 10.1007/s13238-020-00789-5 . Kunda P, Craig G, Dominguez V, Baum B, Abi. Sra1, and Kette control the stability and localization of SCAR/WAVE to regulate the formation of actin-based protrusions. Curr biology: CB. 2003;13:1867–75. 10.1016/j.cub.2003.10.005 . Leonetti A, et al. Molecular basis and rationale for combining immune checkpoint inhibitors with chemotherapy in non-small cell lung cancer. Drug Resist updates: reviews commentaries Antimicrob anticancer Chemother. 2019;46:100644. 10.1016/j.drup.2019.100644 . Abbasi J. Potential New Immune-Checkpoint Inhibitor Partner for Lung Cancer. JAMA. 2021;326:901. 10.1001/jama.2021.14765 . Sun J, et al. Characterization of immune landscape in papillary thyroid cancer reveals distinct tumor immunogenicity and implications for immunotherapy. Oncoimmunology. 2021;10:e1964189. 10.1080/2162402X.2021.1964189 . Rudin CM, et al. Pembrolizumab or Placebo Plus Etoposide and Platinum as First-Line Therapy for Extensive-Stage Small-Cell Lung Cancer: Randomized, Double-Blind, Phase III KEYNOTE-604 Study. J Clin oncology: official J Am Soc Clin Oncol. 2020;38:2369–79. 10.1200/JCO.20.00793 . Gadgeel S, et al. Updated Analysis From KEYNOTE-189: Pembrolizumab or Placebo Plus Pemetrexed and Platinum for Previously Untreated Metastatic Nonsquamous Non-Small-Cell Lung Cancer. J Clin oncology: official J Am Soc Clin Oncol. 2020;38:1505–17. 10.1200/JCO.19.03136 . Jassem J, et al. Updated Overall Survival Analysis From IMpower110: Atezolizumab Versus Platinum-Based Chemotherapy in Treatment-Naive Programmed Death-Ligand 1-Selected NSCLC. J Thorac oncology: official publication Int Association Study Lung Cancer. 2021;16:1872–82. 10.1016/j.jtho.2021.06.019 . Passaro A, Brahmer J, Antonia S, Mok T, Peters S. Managing Resistance to Immune Checkpoint Inhibitors in Lung Cancer: Treatment and Novel Strategies. J Clin oncology: official J Am Soc Clin Oncol. 2022;40:598–610. 10.1200/JCO.21.01845 . Shi R, et al. APOBEC-mediated mutagenesis is a favorable predictor of prognosis and immunotherapy for bladder cancer patients: evidence from pan-cancer analysis and multiple databases. Theranostics. 2022;12:4181–99. 10.7150/thno.73235 . Lahiri A, et al. Lung cancer immunotherapy: progress, pitfalls, and promises. Mol Cancer. 2023;22:40. 10.1186/s12943-023-01740-y . Zhu S, et al. Combination strategies to maximize the benefits of cancer immunotherapy. J Hematol Oncol. 2021;14. 10.1186/s13045-021-01164-5 . Agarwal P, Le DT, Boland PM. Immunotherapy in colorectal cancer. Adv Cancer Res. 2021;151:137–96. 10.1016/bs.acr.2021.03.002 . Lorentzen CL, Haanen JB, Met O, Svane IM. Clinical advances and ongoing trials on mRNA vaccines for cancer treatment. Lancet Oncol. 2022;23:e450–8. 10.1016/S1470-2045(22)00372-2 . Sun S, et al. Development and validation of an immune-related prognostic signature in lung adenocarcinoma. Cancer Med. 2020;9:5960–75. 10.1002/cam4.3240 . Zhu M, et al. Clinical Significance and Immunometabolism Landscapes of a Novel Recurrence-Associated Lipid Metabolism Signature In Early-Stage Lung Adenocarcinoma: A Comprehensive Analysis. Front Immunol. 2022;13:783495. 10.3389/fimmu.2022.783495 . Sorin M, et al. Single-cell spatial landscapes of the lung tumour immune microenvironment. Nature. 2023;614:548–54. 10.1038/s41586-022-05672-3 . Zuo S, Wei M, Wang S, Dong J, Wei J. Pan-Cancer Analysis of Immune Cell Infiltration Identifies a Prognostic Immune-Cell Characteristic Score (ICCS) in Lung Adenocarcinoma. Front Immunol. 2020;11:1218. 10.3389/fimmu.2020.01218 . Shinohara S, et al. New evaluation of the tumor immune microenvironment of non-small cell lung cancer and its association with prognosis. J Immunother Cancer. 2022;10. 10.1136/jitc-2021-003765 . Linde IL et al. Neutrophil-activating therapy for the treatment of cancer. Cancer cell 41, 356–372 e310, 10.1016/j.ccell.2023.01.002 (2023). Paijens ST, Vledder A, de Bruyn M, Nijman HW. Tumor-infiltrating lymphocytes in the immunotherapy era. Cell Mol Immunol. 2021;18:842–59. 10.1038/s41423-020-00565-9 . Xiao Y et al. Cathepsin C promotes breast cancer lung metastasis by modulating neutrophil infiltration and neutrophil extracellular trap formation. Cancer cell 39, 423–437 e427, 10.1016/j.ccell.2020.12.012 (2021). Gajewski TF, Schreiber H, Fu YX. Innate and adaptive immune cells in the tumor microenvironment. Nat Immunol. 2013;14:1014–22. 10.1038/ni.2703 . Tables Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.xlsx SupplementaryMaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5051024","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":351831875,"identity":"64a1ddb0-403c-4498-829c-97b536962f12","order_by":0,"name":"hanyu Zhou","email":"","orcid":"","institution":"The Third Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"hanyu","middleName":"","lastName":"Zhou","suffix":""},{"id":351831876,"identity":"9baca50b-83fb-42e9-aa83-bbb0429fc35e","order_by":1,"name":"xiao yun","email":"","orcid":"","institution":"The Third Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"xiao","middleName":"","lastName":"yun","suffix":""},{"id":351831878,"identity":"18a767e3-81a3-469c-9f64-750a4d77fec0","order_by":2,"name":"jun wu","email":"","orcid":"","institution":"The Third Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"jun","middleName":"","lastName":"wu","suffix":""},{"id":351831880,"identity":"6b7720df-5520-493a-8f62-5a75484d5615","order_by":3,"name":"xinzhu Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArElEQVRIiWNgGAWjYBACAwYGNjCDn5n58APStEi2s6UZkKbF4DyPggRRWszZT6c9+FBxWM74MA9Qf41NNEEtlj252w1nnEkzNjvMe+ABw7G03AaCDjuQu02at80mcdthvgQDxobDRGg5/xakRSJxczOPgQRxWm5AbdnATKwWyxlvIX6ROAwM5ARi/GLOn7sNHGL8/YcPP/hQY0NYCypIIE35KBgFo2AUjAJcAABR6T2DH/yYlAAAAABJRU5ErkJggg==","orcid":"","institution":"Harbin Medical University","correspondingAuthor":true,"prefix":"","firstName":"xinzhu","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-09-08 04:23:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5051024/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5051024/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67193470,"identity":"42fdef63-01ef-4701-b417-7e9b2e036daa","added_by":"auto","created_at":"2024-10-22 08:52:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2478569,"visible":true,"origin":"","legend":"\u003cp\u003eTranscriptional and genetic characteristics of DSRs in LUAD. (A) Batch effect between datasets, TCGA-LUAD and GSE68465. (B) Removing the batch effect. (C) Chromosomal distribution of 9 DSRs. (D) CNV of DSRs in LUAD patients. (E) Differences in DSR expression levels between LUAD and normal specimens. (F) Interactions among DSRs as risk/favorable factors in LUAD. (G) Kaplan–Meier curves of the indicated DSRs for LUAD patients. (H) Univariate analyses of the independent prognostic value of DSRs. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, and ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001.\u003cstrong\u003eTable 1\u003c/strong\u003e. This is a table. Tables should be placed in the main text near to the first time they are cited.\u003c/p\u003e","description":"","filename":"Figure142.png","url":"https://assets-eu.researchsquare.com/files/rs-5051024/v1/f9cd25348de5368661cca041.png"},{"id":67193477,"identity":"0b29c065-c5b2-49f7-b24f-cd743a181907","added_by":"auto","created_at":"2024-10-22 08:52:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3361046,"visible":true,"origin":"","legend":"\u003cp\u003eClinical characteristics in two DSR clusters, and functional enrichment analysis of the genes in the clusters. (A) Consensus clustering based on DSRs (k = 2). (B) Kaplan–Meier curves for LUAD patients in two DSR clusters. (C–E) PCA, t-SNE, and UMAP analysis for two DSR clusters, respectively. (F) Clinical characteristics and expression of nine DSRs in the two clusters. (G) DSRs with differential expression between the two clusters. (H) Differences in immune cell infiltration between the two DSR clusters were analyzed by ssGSEA. (I, J) Gene set enrichment analysis of KEGG (I) and GO (J) terms for two DSR clusters. (K, L) Gene Set Variation Analysis of KEGG (K) and GO (L) terms for two DSR clusters. k, the number of clusters identified. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, and ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure236.png","url":"https://assets-eu.researchsquare.com/files/rs-5051024/v1/040f36b0b753ac28592d3f59.png"},{"id":67194701,"identity":"0e82c949-59e0-400a-9269-3d5accc647b3","added_by":"auto","created_at":"2024-10-22 09:00:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2315763,"visible":true,"origin":"","legend":"\u003cp\u003eWGCNA of LUAD to identify similar gene networks with PPP and DSR. (A) The scale independence (left) and mean connectivity (right) of WGCNA. (B) Gene dendrograms and color matching the gene modules. (C) Correlation of gene modules with PPP and DSR. (D–G) Correlation of module members with specified colors and gene significance for PPP. (H–J) Correlation of module members with specified colors and gene significance for DSR.\u003c/p\u003e","description":"","filename":"Figure329.png","url":"https://assets-eu.researchsquare.com/files/rs-5051024/v1/91d13e35a11d0fe7d8b57cc5.png"},{"id":67194703,"identity":"4d2c1fe3-7029-4646-a0fa-f058fd72d505","added_by":"auto","created_at":"2024-10-22 09:00:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3412828,"visible":true,"origin":"","legend":"\u003cp\u003eScRNA-Seq analysis of LUAD to identify genes highly associated with PPP and DSR. (A) The cell distribution of the samples. (B) UMAP plots of GSE149655 and 28 cell clusters were classified. (C) Cells were annotated into seven different types of cells. (D) All cells were assigned corresponding DSR-AUC scores. (E) Cells were divided into two groups (low and high) according to DSR-AUC score. (F) All cells were assigned corresponding PPP-AUC scores. (G) Cells were divided into two groups (low and high) according to PPP-AUC score. (H, I) Gene Set Variation Analysis of KEGG terms for low- and high-DSR-AUC score groups (H), and low- and high-PPP-AUC score groups (I). ***\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure425.png","url":"https://assets-eu.researchsquare.com/files/rs-5051024/v1/5f467e5bb3a03bcb5bde6662.png"},{"id":67194702,"identity":"d78655ee-cddf-4743-9c98-9205b01989fa","added_by":"auto","created_at":"2024-10-22 09:00:47","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2047289,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction and evaluation of risk signature based on OS-related DPRGs. (A, B) LASSO coefficient distribution (A) and cross-validation (B) of the OS-related DPRGs. (C) Univariate analyses of the independent prognostic value of the seven genes in risk signature. (D–F) Kaplan–Meier OS curves for LUAD patients in low-risk and high-risk groups of risk signature in the indicated subgroups. (G–I) ROC curves validate the prognostic capability of risk signature in the indicated subgroups at 1, 3, and 5 years, respectively. (J) Sankey diagram of DSR clusters, risk scores, and survival status in LUAD. (K) Differences in the distribution of clinical traits among low-risk and high-risk groups. (L–P) Statistical analysis of indicated clinical traits with differences in low-risk and high-risk groups. **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, and ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure512.png","url":"https://assets-eu.researchsquare.com/files/rs-5051024/v1/040efcfecad83070dde84029.png"},{"id":67193472,"identity":"a24932ed-ae06-4074-bd38-394af9575027","added_by":"auto","created_at":"2024-10-22 08:52:47","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1932684,"visible":true,"origin":"","legend":"\u003cp\u003ePrognostic value of the risk signature. (A) Forest plot of Cox survival analysis. (B) Concordance index values of the risk scores from risk signature. (C) OS probability of LUAD patients at 1, 3, and 5 years was predicted based on nomograms created utilizing the risk signature. (D) Cumulative hazard of nomogram. (E) Calibration curves showing the deviation from the predicted results of the nomogram. (F–H) Decision curve analysis at 1 (F), 3 (G), and 5 (H) years. (I–J) Gene set enrichment analysis of KEGG (I) and GO (J) terms for low-risk and high-risk groups. *\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05, and ***\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure610.png","url":"https://assets-eu.researchsquare.com/files/rs-5051024/v1/df8a6d8adece8451e6e19c30.png"},{"id":67193475,"identity":"d007e9e2-d519-4e36-b04b-15fac520c893","added_by":"auto","created_at":"2024-10-22 08:52:47","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":3050654,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations of the risk signature with TIME, and immunotherapy response in LUAD. (A) Correlation of risk score with infiltrating immune cell populations. (B) Correlation of the seven genes in risk signature with infiltrating immune cell populations. (C) Comparison of immune checkpoint expression between the low-risk and high-risk groups. (D) Correlation of the seven genes in risk signature with immune checkpoint expression. (E) Comparison of TIME scores between low-risk and high-risk groups. (F) Variation in TIDE scores between low-risk and high-risk groups. (G–J) Differences in response to the indicated immunotherapies between low-risk and high-risk groups. *\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.01, and ***\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure77.png","url":"https://assets-eu.researchsquare.com/files/rs-5051024/v1/48cf9007a740d2b161257387.png"},{"id":67194704,"identity":"45d5467b-cd0e-442a-89ac-92a8ddd2599a","added_by":"auto","created_at":"2024-10-22 09:00:47","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":3779695,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment and expression analysis of LRRC61. (A–D) Gene set enrichment analysis of GO-BP (A), GO-MF (B), KEGG terms (C), and hallmark gene sets (D) for LRRC61. (E) Pan-cancer expression profile of LRRC61. (F) Prognostic analysis of LRRC61 in pan-cancer. (G) Differential expression of LRRC61 in diverse TNM stages and normal samples. (H–J) Kaplan–Meier curves of LRCC61 for LUAD patients in the indicated datasets. *\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05, and ****\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"Figure85.png","url":"https://assets-eu.researchsquare.com/files/rs-5051024/v1/d0760d042de5458159177256.png"},{"id":67193474,"identity":"9160b3aa-7940-448f-93d4-089dfc526a46","added_by":"auto","created_at":"2024-10-22 08:52:47","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":189086,"visible":true,"origin":"","legend":"\u003cp\u003eLRRC61 promotes LUAD progression in vitro. (A) LRRC61 expression validated by qRT-PCR in 8 paired samples of LUAD tissues and adjacent normal tissues. (B) Representative immunohistochemistry (IHC) images of LRRC61 in LUAD tissues and corresponding normal tissues. Scale bar, 100 μm. (C) qRT-PCR analysis of LRRC61 expression in normal bronchial epithelial cells and various LUAD cell lines. (D–E) Effect of LRRC61 knockdown on proliferation of A549 (D) and H1299 cells (E) assessed using CCK-8 assay. (F) Effect of LRRC61 knockdown on proliferation of A549 and H1299 cells assessed using plate cloning assay. (G) Transwell migration assays of A549 and H1299 cells after LRRC61 knockdown. Scale bar, 100 μm. (H) Apoptosis assays of A549 and H1299 cells after LRRC61 knockdown. (I) Images of the xenograft tumors of the control and LRRC61 overexpress group. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.01, and ***\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.001.The data are presented as mean ± SD (n = 6).\u003c/p\u003e","description":"","filename":"Figure9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5051024/v1/e3bd70b116f3df7fbfa9501c.jpg"},{"id":87461144,"identity":"7aa76162-6edc-434b-87d3-3470b24f096c","added_by":"auto","created_at":"2025-07-24 06:02:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":24789790,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5051024/v1/ef2565a8-1abf-436a-990d-249baadf9b43.pdf"},{"id":67193480,"identity":"e3aedee8-c1c9-47db-a49c-1a5120d8362a","added_by":"auto","created_at":"2024-10-22 08:52:47","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":45291,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5051024/v1/53d71ed73a98734559e4104e.xlsx"},{"id":67193481,"identity":"cc778952-01d1-4c21-8628-a77f0c40b846","added_by":"auto","created_at":"2024-10-22 08:52:47","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":6017941,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-5051024/v1/3bf0eec0ab822383a40d37ab.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Disulfidptosis and pentose phosphate pathway-associated prognosis signature guides immunotherapy for lung adenocarcinoma patients","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eImmunotherapy has demonstrated sustained clinical relevance in lung adenocarcinoma (LUAD)\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, and the clinical use of various immune checkpoint inhibitors (ICIs) in advanced LUAD has been approved\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Despite reported efficacy, many patients receiving immunotherapy continue to have poor prognoses. Therefore, it is essential to thoroughly investigate the intrinsic regulatory mechanisms of the tumor immune microenvironment (TIME) in patients with LUAD\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCell death is a physiological process that maintains biological development and internal environmental homeostasis. Targeting cell death-related pathways to kill cancer cells is a major direction in cancer therapy\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Recently, Gan et al. discovered and identified a new type of cell death, disulfidptosis, which offers new possibilities for cancer therapy\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Tumor cells typically require a high expression of solute carrier family 7 member 11 (SLC7A11) to uptake more cystine for glutathione synthesis to balance the oxidative effects arising from their highly active metabolism. However, ingested cystine must be reduced to cysteine promptly to eliminate toxicity\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. This process requires the consumption of large amounts of nicotinamide adenine dinucleotide phosphate (NADPH) generated via the pentose phosphate pathway (PPP). Otherwise, excessive accumulation of cystine may cause tumor cell death. When cells with high SLC7A11 levels lack glucose, disulfide accumulates abnormally, causing disulfide stress, which leads to the contraction of actin filaments, destruction of the cytoskeletal structure, and consequently cell death\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. SLC7A11 is overexpressed in patients with LUAD and is positively correlated with tumor progression\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Therefore, we sought to explore the role of disulfidptosis in LUAD, particularly in relation to TIME.\u003c/p\u003e \u003cp\u003eThe PPP, which is closely related to disulfidptosis, is a major glucose catabolism pathway that differs from glycolysis\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. The PPP is essential for cancer cells to meet their anabolic needs and respond to oxidative damage\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Additionally, activated immune cells rely on the PPP to regulate the immune response\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. However, despite the well-known role of the PPP in promoting tumor progression and immune regulation, there is a lack of systematic studies on its effects in LUAD. Therefore, exploring the effects of the PPP on the TIME in LUAD is worth considering, as it may help develop effective immunotherapeutic strategies.\u003c/p\u003e \u003cp\u003eIn this study, we analyzed the characteristics of disulfide regulators (DSRs) in LUAD patients using data from public databases and performed a consensus clustering analysis based on DSR expression. By performing WGCNA and single-cell analysis, we identified genes associated with disulfidptosis and PPP, respectively, and identified DSRs and PPP-related genes (DPRGs). Using DPRGs to construct a risk signature, we systematically analyzed clinical prognosis, TIME, and response to immunotherapy in LUAD patients. We hypothesized that the risk signature would effectively differentiate LUAD patients, allowing for more individualized and effective therapeutic strategies.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Transcriptome dataset collection\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWe obtained transcriptome data and clinical information for patients with LUAD from the TCGA-LUAD cohort in The Cancer Genome Atlas (TCGA, \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), as well as from datasets GSE68645 and GSE26939 in the Gene Expression Omnibus database (GEO, \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). To eliminate batch effects on data from different databases, we used the sva R package. We retrieved a gene set containing nine DSRs from the report by Gan et al. (5). Additionally, we obtained thirty pentose phosphate pathway-related genes (PRGs) from the Reactome database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://reactome.org/\u003c/span\u003e\u003cspan address=\"https://reactome.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, R-HSA-71336) and the Kyoto Encyclopedia of Genes and Genomes (KEGG, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genome.jp/kegg/\u003c/span\u003e\u003cspan address=\"https://www.genome.jp/kegg/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database (has00030).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Weighted Gene Coexpression Network Analysis (WGCNA)\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn this study, we utilized WGCNA to identify genes associated with disulfidptosis and PPP. Outliers were removed, and soft thresholds were selected based on the scale-free topology criterion to construct an expression network. Gene modules related to disulfidptosis and PPP were then identified. For the analysis, a scale-free topological fit index of 1\u0026ndash;20 was used, with a screening criterion of 0.9, and a minimum of 30 genes per module was set.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Single-cell Analysis and AUCell\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eRaw data for single-cell analysis were downloaded from GSE149655. The raw count matrices were merged and converted into an expression matrix. The following pre-processing steps were applied: samples were excluded if fewer than three cells expressed the genes, if fewer than 200 genes occurred in a single cell, or if the cells contained too many mitochondrial genes and an abnormal number of expressed genes. Next, the data were normalized to enable practical comparison between different cell samples. Principal component analysis (PCA) was then used to reduce the dimensionality of the data and reveal the principal factors of the different subgroups. Clustering operations were performed using the Leiden algorithm, and the resolution was set to 0.25. Cell types were annotated, and marker genes were compared across taxa based on known cell markersTo identify the most significant genes associated with disulfidptosis and PPP, we employed the AUCell R software package, which assigned a disulfidptosis activity fraction and PPP activity fraction to each cell. The area under the curve (AUC) scores of the DRGs and PRGs were calculated to determine their enrichment levels in the expressed genes of each cell. Subsequently, cells were classified based on their median AUC score.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Consensus clustering analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eConsensus clustering was performed using the expression levels of the nine DSRs. The overlapping genes between differentially expressed genes (DEGs) among the DSR clusters, gene modules identified by WGCNA, and DEGs obtained from the scRNA-Seq analysis, were selected as DPRGs for further analysis. Using univariate Cox regression analysis, we screened the DPRGs that were associated with overall survival (OS). Subsequently, clustering analysis based on these OS-related DPRGs yielded two DPRG clusters.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Analysis of infiltrating immune cell population\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe abundance of infiltrating immune cells was quantified using multiple software packages, including the Estimation of STromal and Immune cells in MAlignant Tumor tissues Expression algorithm\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, single-sample gene set enrichment analysis (ssGSEA), xCELL\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, TIMER\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, quanTIseq\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, MCP-counter\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, EPIC and CIBERSORT\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Enrichment analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe dataset c2.cp.kegg.v2022.1.Hs.symbols was obtained from MsigDB (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org/gsea/msigdb\u003c/span\u003e\u003cspan address=\"https://www.gsea-msigdb.org/gsea/msigdb\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and used for differential functional identification through the GSVA R package. GSEA 4.3.2 software was used to perform gene set enrichment analysis (GSEA) of the curated gene sets.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Construction of the risk signatures\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eBy performing the least absolute shrinkage and selection operator (LASSO) Cox regression on the OS-related-DPRGs, we constructed a risk signature containing seven genes. The risk score was determined using the formula: Σ(coefficient_k*expression_k), where coefficient_k and expression_k are the risk coefficient and expression of each gene, respectively. After calculating the risk scores for each sample, based on the median score, the patients were divided into low-risk and high-risk groups.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Evaluation of the risk signatures\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe prognostic significance of the risk signature was assessed using receiver-operating characteristic (ROC) curve analysis, Cox regression, and Kaplan\u0026ndash;Meier survival analysis, and the concordance index was calculated for both the training and test sets. Nomograms were generated using the rms R package, and their accuracy was evaluated using cumulative hazard and calibration curves. To assess the benefits of the intervention with nomograms and risk scores for LUAD patients, Decision Curve Analysis (DCA) was performed.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Analyses of immune checkpoints expression and immunotherapy response in different risk signature subgroups\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe limma R package was used to examine variations in immune checkpoint expression between the subgroups. Additionally, we assessed the responses of patients in different clusters and signature subgroups to immunotherapy according to the Tumor Immune Dysfunction and Exclusion (TIDE) score\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e as well as The Cancer Immunome Atlas (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tcia.at/home\u003c/span\u003e\u003cspan address=\"https://tcia.at/home\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Examination of the drug sensitivity\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe OncoPredict R package\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e was used to screen for sensitive drugs between different risk signature subgroups.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.11 Pancancer analysis of LRRC61\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWe assessed the differential expression of LRRC61 and its prognostic impact on multiple cancers. In addition, we performed a correlation analysis of LRRC61 expression with the degree of immune cell infiltration and immune checkpoint expression.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.12 Cell culture and tissue collection\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAll cell lines were purchased from the Institute of Biochemistry and Cell Biology (Shanghai, China) and were maintained in Dulbecco's modified Eagle's medium (Gibco, Life Technologies, CA, USA) supplemented with 10% fetal bovine serum (FBS) (Hyclone, Logan, UT, USA) and 100 \u0026micro;g/mL penicillin-streptomycin (Hyclone).\u003c/p\u003e \u003cp\u003eEight LUAD and matching paracancerous non-tumor tissue samples were obtained during routine surgery at the First Affiliated Hospital of Nanjing Medical University, and the specimens were stored at \u0026minus;\u0026thinsp;80\u0026deg;C until further testing.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.13 Reverse transcription-quantitative PCR\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTotal RNA was isolated from tissues using TRIzol reagent (Takara Bio, Shiga, Japan) and reverse-transcribed to cDNA using a PrimeScriptTM RT reagent Kit (Takara). SYBR\u0026reg; Premix Ex Taq (Takara) was used for qRT-PCR analysis. Detailed primer sequences are listed in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e2.14 Immunohistochemistry Staining\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eSlices of tissue were blocked and subjected to suitable concentrations of primary antibody (Sigma-Aldrich, #HPA019355) at 4\u0026deg;C. Biotinylated sections were treated for 30 min with the secondary antibody (Dako, Denmark). Finally, the slices were stained with a Dako diaminobenzidine (DAB) kit and counterstained with hematoxylin (Baso, Zhuhai, China).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e2.15 RNA interference\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFor the knockdown of LRRC61, specific small interference RNAs (siRNAs) and scrambled siRNA (si-NC) were transfected into A549 and H1299 cells using LipofectamineTM 3000 (Invitrogen, Carlsbad, CA, USA). Target RNAs were measured 48 h after transfection using qRT-PCR. The siRNA sequences are listed in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e2.16 Cell proliferation assay\u003c/h2\u003e \u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e LUAD cells were planted in 96-well plates, followed by the addition of 20 \u0026micro;L CCK8 solution to each well. At 1, 2, 3, 4, and 5 days, the absorbance at 450 nm (OD450) was determined using a microplate reader (Thermo Fisher, Multiskan 51119000, MA, USA).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e2.17 Colony formation\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe cells were maintained in 6-well plates for 2 weeks. Cell colonies were stained with crystal violet solution for 1.5 h after being fixed with 4% formaldehyde for 10 min. The mean colony counts were calculated.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e2.18 Transwell assay\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFor the migration assay, 1 \u0026times; 105 cells were plated in the top chamber of the Transwell chamber, and 10% FBS culture medium was added to the lower chamber. The cells that migrated across the membrane were photographed and counted under a microscope (Nikon, Ts2R, Japan).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e2.19 Apoptosis assay\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eCells were seeded into 6-well plates. Then we stained cells for 15 min with the annexin V-FITC/PI Apoptosis Detection Kit (Beyotime Institute of Biotechnology, Jiangsu, China). Samples were analyzed using FACSCanto TM II flow cytometry (Becton, Dickinson and Company).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e2.20 Animal studies\u003c/h2\u003e \u003cp\u003e Nanjing Medical University\u0026rsquo;s Committee on Ethics for Animal Experiments ap-proved the experimental procedures (IACUC-1706007). We used 6\u0026ndash;8-week-old male BALB/c nude mice (SPF grade) from the Nanjing Medical University Animal Center. H1299 (1 \u0026times; 106) cells were injected into the right side of each mouse. Every 3 days, the tumour volume was measured. Six mice were randomly divided into the control and LRRC61 groups. The mice were euthanized at a right time. Tumor width and length were recorded, and tumor volume cal-culated using the formula: [width 2 \u0026times; length]/2. In order to euthanize the mice, an appropriate amount of mice were put in a euthanasia box, then carbon dioxide was introduced. When the signs of life disappear completely, make sure that there is no breathing and no heartbeat, we remove the body. This approach meets the requirements of animal ethics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e2.21 Statistical analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe Wilcoxon rank test and Kruskal\u0026ndash;Wallis test were used to comparing two independent groups and three or more independent groups, respectively. The differences in patient survival were evaluated using Kaplan-Meier and log-rank tests. Spearman's rank correlation analysis was used for the correlation analysis. The R software (version 4.2.3) and GraphPad Prism 9 were used for all statistical analyses.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Characteristics and prognostic value of DSRs in LUAD\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAfter removing the batch effect of GSE68545 and TCGA-LUAD (Figs.\u0026nbsp;1A and 1B), we analyzed the nine DSRs using the combined data. Their chromosomal distribution and CNVs are displayed in Fig.\u0026nbsp;1C and 1D, respectively. Among them, RAC1, SLC2A1, NCKAP1, SLC3A2, ABI2, and BRK1 exhibited significant copy number amplification, while WASF2, CYFIP1, and SLC7A11 showed copy number deletions. Moreover, the expression levels of DSRs were markedly different between LUAD tumors and healthy tissues (Fig.\u0026nbsp;1E). The network diagram in Fig.\u0026nbsp;1F illustrates the interactions and prognostic values of the DSRs, where only BRK1 played a favorable prognostic role, and all other DSRs were prognostic risk factors. We also conducted Kaplan-Meier survival analysis of individual DSRs separately, and the outcomes were consistent with the network diagram (Fig.\u0026nbsp;1G). Additionally, SLC7A11, SLC3A2, NCKAP1, RAC1, and SLC2A1 were identified as independent prognostic factors in patients with LUAD (Fig.\u0026nbsp;1H).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Identification of distinct clusters and their functions based on DSR expression\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo investigate whether DSR expression was associated with clinical characteristics in LUAD, we performed a consensus clustering analysis based on the nine DSRs. We identified two DSR clusters (Fig.\u0026nbsp;2A, Table S2), and Kaplan\u0026ndash;Meier survival analysis showed that patients in cluster B had a higher likelihood of survival (Fig.\u0026nbsp;2B). The DSR clusters effectively differentiated patients with LUAD, as shown by PCA, t-SNE, and UMAP analysis (Figs.\u0026nbsp;2C\u0026ndash;2E). The expression of DSRs and associated LUAD clinical characteristics in the two DSR clusters is depicted in Fig.\u0026nbsp;2F. Differential expression analysis revealed that all DSRs except RAC1 were more highly expressed in cluster A (Fig.\u0026nbsp;2G). We also observed variations in immune infiltration levels between the two DSR clusters, with cluster A having higher ratios of CD56dim neutrophils, type 2 T helper cells, and natural killer cells, while the majority of the remaining immune cells were significantly more abundant in cluster B (Fig.\u0026nbsp;2H). Finally, we identified 3519 DEGs between the two DSR clusters for further analysis.\u003c/p\u003e \u003cp\u003eTo investigate the biological roles of the DSRs in the different DSR clusters, we performed GSEA (Figs.\u0026nbsp;2I and 2J) and GSVA (Figs.\u0026nbsp;2K and 2L). Consistent with the results of the immune infiltration analysis, cluster B showed a stronger correlation with the immune response, which could explain the better survival outcome of LUAD patients in cluster B. On the other hand, cluster A was associated with the metabolism of various compounds involved in biochemical processes, including the disulfidptosis process (highlighted in blue) and the pentose phosphate pathway (highlighted in green), which is closely related to disulfidptosis.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Identification of gene modules highly associated with disulfidptosis and pentose phosphate pathways by WGCNA\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo identify genes associated with DSR and PPP, we performed WGCNA. We chose 5 as the soft threshold based on the scale-free topology fit index to generate a proximity matrix (Fig.\u0026nbsp;3A). After module cutting with a dynamic tree and merging similar gene modules, 12 gene modules were identified and assigned various color designations (Fig.\u0026nbsp;3B) which were then correlated with DSR and PPP (Fig.\u0026nbsp;3C). Among them, the black (Fig.\u0026nbsp;3D), pink (Fig.\u0026nbsp;3E), salmon (Fig.\u0026nbsp;3F), and turquoise modules (Fig.\u0026nbsp;3G) had a strong correlation with PPP, while the midnight blue (Fig.\u0026nbsp;3H), salmon (Fig.\u0026nbsp;3I), and turquoise (Fig.\u0026nbsp;3J) modules showed a strong correlation with DSR. A total of 6305 genes from these modules were selected for further analysis.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Identification of genes highly associated with disulfidptosis and pentose phosphate pathways by single-cell analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWe conducted a single-cell analysis to investigate genes associated with DSR and PPP. To ensure sample validity, we removed doublets and filtered cells based on mitochondrial gene ratio (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA). The number of tagged transcripts was positively correlated with the number of detected genes (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB), and we corrected for cell cycle heterogeneity (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eC). After quality control, we removed batch effects (Fig.\u0026nbsp;4A), and we used t-SNE dimensionality reduction to classify cells into 28 clusters (Fig.\u0026nbsp;4B) that were validated using cell markers (Fig.\u0026nbsp;4C). Gene-specific expression for different cell types is shown in Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eD. We used the AUCell R package to separately score the enrichment of DSRs and PRGs (Table S3) in each cell (Figs.\u0026nbsp;4D and 4F) and classified cells into low and high groups based on the median DSR-AUC score/PPP-AUC score (Figs.\u0026nbsp;4E and 4G). B cells, mononuclear phagocytes, fibroblasts, and epithelial cells showed higher DSR-AUC scores, whereas B cells and mononuclear phagocytes had higher PPP-AUC scores. We identified 1983 DEGs between the low and high DSR-AUC groups and 1846 DEGs between the low and high PPP-AUC groups for further analysis. GSVA revealed that in addition to being associated with disulfidptosis and PPP (marked in yellow), DSRs and PRGs may participate in pathways such as oxidative phosphorylation, ferroptosis, and protein export (marked in green) that differentiate the low and high AUC groups (Figs.\u0026nbsp;4H and 4I).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Construction and assessment of risk signature based on OS-related DPRGs\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eBased on the previous analysis, we identified 2050 DSRs and PRGs (DPRGs) (Figure S2A), out of which 302 OS-related DPRGs were selected (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) using univariate COX regression analysis (Table S4). Functional enrichment analysis of these OS-related DPRGs revealed their association with PPP and platinum drug resistance (Figure S2B). We performed a consensus clustering analysis of LUAD patients in the study cohort using these OS-related DPRGs, resulting in two DPRG clusters (Figure S2C). The DPRG clusters effectively stratified LUAD patients (Figure S2D-S2F), with patients in cluster B demonstrating higher survival rates, as evidenced by Kaplan\u0026ndash;Meier survival analysis (Figure S2G). Additionally, most DSRs were expressed at lower levels in DPRG cluster B (Figure S2H).\u003c/p\u003e \u003cp\u003eFinally, we performed LASSO on the OS-related DPRGs to establish corresponding risk signatures (Figs.\u0026nbsp;5A and 5B). The risk signature consisted of seven genes (RTN4, ACHE, ABAT, EGLN1, FAM117A, LRRC61, and ADM), and their correlations with DSRs and PRGs are shown in Figure S3A and Figure S3B, respectively. All seven risk model genes can be independently presented as prognostic indicators in patients with LUAD, as demonstrated in Fig.\u0026nbsp;5C. To generate risk scores for patients in the TCGA-BLCA and GSE68465 cohorts, we utilized the expression and coefficient of each candidate gene in the risk signatures (Table S5). We evenly divided the patient population into training and test subsets to analyze the accuracy of the signatures.\u003c/p\u003e \u003cp\u003eTo validate the risk signature, we conducted Kaplan\u0026ndash;Meier OS analysis (Figs.\u0026nbsp;5D\u0026ndash;5F). For all categories, survival was significantly greater in the low-risk group. The risk signature exhibited good predictive ability at 1-, 3-, and 5-year time points, according to time-dependent ROC curves (Figs.\u0026nbsp;5G\u0026ndash;5I). The relationships among the DSR cluster, risk score, and LUAD patient survival status are summarized in a Sankey plot in Fig.\u0026nbsp;5J. We then examined the distribution and variations in risk scores between the DSR and DPRG clusters (Figures S3C, S3D). Furthermore, there were notable differences in survival status, gender, T-stage, N-stage, and TNM stage between the low-risk and high-risk groups of LUAD patients (Fig.\u0026nbsp;5K), and the distribution statistics are shown in Figs.\u0026nbsp;5L\u0026ndash;5P.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Value for prognosis of the risk signature\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn Fig.\u0026nbsp;6A, it is evident that the risk score acts as an independent prognostic factor, and the concordance index values indicate that the risk signature exhibits the best predictive ability compared to other clinical characteristics (Fig.\u0026nbsp;6B). We also created a nomogram that plots the prognostic value of the risk signature along with clinical features to quantify their predictive abilities in patients with LUAD (Fig.\u0026nbsp;6C). To assess the precision of their predictive abilities, we conducted cumulative hazard and calibration assessments (Figs.\u0026nbsp;6D, 6E). Additionally, decision curve analysis at 1-, 3-, and 5-year time points demonstrated that interventions based on the nomogram and risk score resulted in greater benefits for LUAD patients than those based on other clinical traits (Figs.\u0026nbsp;6F\u0026ndash;6H). Finally, we conducted KEGG (Fig.\u0026nbsp;6I) and GO (Fig.\u0026nbsp;6J) functional enrichment analysis using GSEA, which suggested that low risk was due to the immune response, while the high risk was associated with cell cycle, PPP, actin cytoskeleton regulation, cancer pathways, and disulfide oxidoreductase. This indicates a significant link between the risk model we developed and disulfidptosis.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Correlations of the risk signature scores with TIME and immunotherapy response\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eGiven the functional enrichment results that suggest a correlation between the risk score and the immune response, we were particularly interested in exploring the interaction between the risk score and the tumor immune microenvironment (TIME). Figure\u0026nbsp;7A and Figure S4A illustrate the relationship between the risk score and the infiltration of immune cells, and the degree of immune cell infiltration for each gene in the risk signature is plotted in Fig.\u0026nbsp;7B. There were variations in immune checkpoint expression levels between the low-risk and high-risk groups (Fig.\u0026nbsp;7C), and the candidate genes for the risk signature showed different correlations with immune checkpoints (Fig.\u0026nbsp;7D). The high-risk group exhibited higher expression levels of immunosuppressive factors regarding cytokines (Figure S4B). There were observable differences in immune cell quantity and tumor purity (ESTIMATE score) between the low-risk and high-risk groups (Fig.\u0026nbsp;7E). Furthermore, there was a difference in TIDE scores for the efficacy of immunotherapy between the two groups (Fig.\u0026nbsp;7F). While the immunophenoscore score for the CTLA4\u0026ndash;PD1\u0026thinsp;+\u0026thinsp;immunophenotype did not differ significantly between the two groups, all other groups showed differences in immunotherapy response (Figs.\u0026nbsp;7G\u0026ndash;7J). Additionally, the combined analysis of risk signatures and immune checkpoints could more effectively differentiate the prognosis of LUAD patients (Figure S5). Finally, we performed a drug sensitivity analysis of the DPRG risk signature. As the high-risk group was insensitive to clinically used chemotherapeutic agents (Figure S6A), we screened for potential therapeutic agents that might benefit them (Figure S6B).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Function enrichment, expression and prognosis analysis of LRRC61\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWe discovered that LRRC61, one of the candidate genes for the risk signature, is involved in disulfide formation and is associated with PPP and cancer pathways (Figs.\u0026nbsp;8A\u0026ndash;8D). Due to the limited research on LRRC61 to date, we aimed to investigate its characteristics in cancer. The expression of LRRC61 was found to be high in numerous tumor tissues (Fig.\u0026nbsp;8E) and can be utilized as an independent prognostic factor (Fig.\u0026nbsp;8F). Specifically, high expression of LRRC61 was correlated with LUAD progression (Fig.\u0026nbsp;8G) and poor overall survival (Figs.\u0026nbsp;8H\u0026ndash;8J). In a pan-cancer analysis, LRRC61 was also linked with immune infiltration (Figure S7) and immune checkpoints (Figure S8).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec33\" class=\"Section2\"\u003e \u003ch2\u003e3.9 Role of LRRC61 as an oncogene of LUAD cells \u003cem\u003ein vitro\u003c/em\u003e\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWe validated LRRC61 expression in eight pairs of LUAD samples and their corresponding noncancerous lung samples using qRT-PCR (Fig.\u0026nbsp;9A) and IHC staining (Fig.\u0026nbsp;9B). We observed that LRRC61 was upregulated in LUAD tissues and cells (Fig.\u0026nbsp;9C), which is consistent with the RNA-seq results. Subsequently, we successfully silenced LRRC61 in A549 and H1299 cells using specific siRNAs. LRRC61 knockdown inhibited the proliferation (Figs.\u0026nbsp;9D \u0026ndash; 9F), migration (Fig.\u0026nbsp;9G), and apoptosis resistance (Fig.\u0026nbsp;9H) of A549 and H1299 cells. These results indicate that LRRC61 functions as an oncogene during the development of LUAD tumors. We established subcutaneous tumor formation models by 6\u0026ndash;8-week-old male BALB/c nude mice (SPF grade) to evaluate the effects of LRRC61 in vivo. Images shows the xenograft tumors of the LRRC61 group were bigger than that of the control group. Moreover, tumor volume and tumor weight were measured to evaluate nude mice.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eRecent research has identified a new form of cell death induced by disulfide stress called disulfidptosis \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. In the presence of glucose starvation or NADPH depletion, excessive accumulation of disulfide molecules in cells with high SLC7A11 expression causes disulfide stress. Aberrant disulfide bonds across the actin cytoskeleton led to actin cytoskeletal collapse and cell death. This study also confirmed that NADPH is essential for providing reducing power to break down abnormally accumulated disulfide bonds, thereby inhibiting disulfidptosis \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Therefore, NADPH, which is mainly derived from PPP, plays an essential role in both preventing disulfidptosis and reversing disulfide stress. In this study, we included PPP-related genes (PRGs) and analyzed disulfidptosis in LUAD using an integrated approach.\u003c/p\u003e\u003cp\u003eIn addition to SLC7A11, SLC3A2 (which encodes the SLC7A11 chaperone)\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, and SLC2A1 (glucose transporter), Gan et al. demonstrated that the Rac-WAVE regulatory complex (WRC)-mediated lamellipodia formation promotes disulfidptosis, while deletion of either component of the WRC could inhibit disulfidptosis. Therefore, we selected RAC1 and components of the WRC (NCKAP1, WASF2, CYFIP1, ABI2, and BRK1) as DSRs. All nine DSRs were associated with the prognosis of LUAD except WASF2. Among them, five (SLC7A11, SLC3A2, NCKAP1, RAC1, and SLC2A1) could be considered independent prognostic risk factors.\u003c/p\u003e\u003cp\u003eIn this study, the patient cohort was divided into two distinct DSR clusters based on DSR expression, with patients in cluster A showing higher expression and worse overall prognosis, while patients in cluster B exhibited higher immune infiltration, which is known to have a positive effect on prognosis\u003csup\u003e\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Enrichment function comparison of the two DSR clusters revealed that cluster B was associated with favorable immune responses, while cluster A was involved in metabolic pathways related to disulfidptosis and PPP. The analysis based on single-cell data also showed that PRGs were significantly involved in the disulfidptosis process.\u003c/p\u003e\u003cp\u003eAfter conducting the aforementioned analysis, we obtained the DPRGs and utilized them for secondary clustering analysis. The resulting DPRG clusters could effectively distinguish between LUAD patients and provide a dependable foundation for further analysis. The functional annotation of DPRGs demonstrated their involvement in regulating the cell cycle, immune response, and platinum drug resistance, in addition to PPP. Given that platinum drugs are frequently employed in LUAD treatment regimens, it is clinically relevant to employ DPRGs for assessing varying susceptibilities to platinum drugs in patients with LUAD.\u003csup\u003e\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe risk signatures constructed using DPRGs are highly effective in predicting the prognosis of LUAD, and LUAD patients with low-risk scores still benefit from immune response modulation. Despite the benefits of immunotherapy, most patients experience disease progression\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Therefore, there is an urgent need to optimize immunotherapy regimens\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Multi-targeted combination immunotherapy can significantly improve immunotherapy efficacy and has been reported in a variety of tumors including lung cancer, pancreatic cancer and colorectal cancer\u003csup\u003e\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Relationship between cancer immune response and mechanisms of resistance to immunotherapy has been under investigation for a long time. combination therapies (e.g., immunotherapy with chemotherapy, radiation therapy and targeted therapy), and discuss combination therapies approved by the US Food and Drug Administration demonstrated benefits to patients. Many targeted therapies such as targeting cytokines and other soluble immunoregulatory factors, ACT, virotherapy, innate immune modifiers and cancer vaccines\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, as well as combination therapies that exploit alternative immune targets and other therapeutic modalities were studied\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. We found that the risk signature related to DPRGs could be implemented to guide immunotherapy planning, given that patients with high-risk scores expressed stimulatory immunological checkpoints much less than those with low-risk scores did, and as a result, responded to immunotherapy less effectively.We believe that our study will also bring benefits to oncology patients, especially LUAD patients.\u003c/p\u003e\u003cp\u003eTIME is involved in tumorigeneses, progressions, and metastases\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. The intra-tumor immune landscape is a critical factor influencing patient survival and response to immunotherapy\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. TIME plays a significant role in the progression of lung cancers\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e and is linked to prognosis\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Multiple algorithms to assess the abundance of immune infiltration showed that risk score was significantly positively correlated with neutrophil and negatively correlated with T cells and B cells, consistent with reported\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Tumor-associated neutrophils enhance the proliferation of tumor cells through pathways such as neutrophil extracellular traps\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. This result suggests that risk score may weaken immune response and promote tumor development by regulating TIME. In addition to being associated with immune cells and immune checkpoints, the expression of other critical molecules in the TIME regulatory network, such as cytokines, chemokines, and growth factors\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e, also differed significantly between the low-risk and high-risk groups. Therefore, the DPRG-related risk signature may have significant clinical value in regulating the TIME network and preventing immune escape.\u003c/p\u003e\u003cp\u003eLRRC61, a functionally enriched component gene in the risk signature, is associated with disulfidptosis and PPP. It is highly expressed in several cancer types and demonstrates prognostic value, including in LUAD. In vitro experiments showed that LRRC61 knockdown successfully reduced the ability of LUAD cells to proliferate, invade, migrate, and prevent apoptosis. Therefore, the mechanism by which LRRC61 exerts oncogenic effects in LUAD through the regulation of disulfidptosis and PPP deserves further investigation.\u003c/p\u003e\u003cp\u003eThis study has several limitations. The study subjects were enrolled based on the limited resources available in public databases, and the retrospective analysis may be subject to selection bias, which affects accuracy. Moreover, clinical information on patients with LUAD in public databases is not comprehensive, which may have led to the omission of additional characteristics associated with disulfidptosis and PPP.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"5. Conclusions","content":" \u003cp\u003eThrough the analysis of bulk RNA-seq and scRNA-seq, we identified DPRGs. By constructing a DPRG-related risk signature, we revealed the relevance of disulfidptosis and PPP in LUAD to the clinical characteristics, prognosis, TIME, and immunotherapeutic response of patients, and provided insight into the underlying regulatory mechanisms. These promising predictive tools may help the development of more effective and personalized treatment strategies for patients with LUAD.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003ePatents: \u003c/h2\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003ch2\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eIACUC-2210031\u003c/p\u003e\n\u003ch2\u003eConsent for publication:\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eCompeting interests:\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding:\u003c/h2\u003e\n\u003cp\u003eThis study was supported by grants from the National Natural Science Foundation of China (Grant Nos. 82172889), and the Clinical Frontier Technology of Jiangsu Provincial Department of Science and Technology (BE2020783).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eZhou HY and Xiao Y wrote the main manuscript and completed most of the experiments. Wu J and Wang XZ provided ideas and reviewed data. All authors reviewed the manuscript.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eData is provided within the manuscript or supplementary information files.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGuo X, et al. Global characterization of T cells in non-small-cell lung cancer by single-cell sequencing. Nat Med. 2018;24:978\u0026ndash;85. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41591-018-0045-3\u003c/span\u003e\u003cspan address=\"10.1038/s41591-018-0045-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang L, et al. Single-cell transcriptome analysis revealed a suppressive tumor immune microenvironment in EGFR mutant lung adenocarcinoma. J Immunother Cancer. 2022;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/jitc-2021-003534\u003c/span\u003e\u003cspan address=\"10.1136/jitc-2021-003534\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRamos-Casals M, et al. Immune-related adverse events of checkpoint inhibitors. Nat reviews Disease primers. 2020;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41572-020-0160-6\u003c/span\u003e\u003cspan address=\"10.1038/s41572-020-0160-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMou Y, et al. Ferroptosis, a new form of cell death: opportunities and challenges in cancer. J Hematol Oncol. 2019;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13045-019-0720-y\u003c/span\u003e\u003cspan address=\"10.1186/s13045-019-0720-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu X, et al. Actin cytoskeleton vulnerability to disulfide stress mediates disulfidptosis. Nat Cell Biol. 2023;25:404\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41556-023-01091-2\u003c/span\u003e\u003cspan address=\"10.1038/s41556-023-01091-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu X, et al. Cystine transporter regulation of pentose phosphate pathway dependency and disulfide stress exposes a targetable metabolic vulnerability in cancer. Nat Cell Biol. 2020;22:476\u0026ndash;86. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41556-020-0496-x\u003c/span\u003e\u003cspan address=\"10.1038/s41556-020-0496-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu K, et al. Suppression of the SLC7A11/glutathione axis causes synthetic lethality in KRAS-mutant lung adenocarcinoma. J Clin Investig. 2020;130:1752\u0026ndash;66. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1172/JCI124049\u003c/span\u003e\u003cspan address=\"10.1172/JCI124049\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang P, Du W, Wu M. Regulation of the pentose phosphate pathway in cancer. Protein Cell. 2014;5:592\u0026ndash;602. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s13238-014-0082-8\u003c/span\u003e\u003cspan address=\"10.1007/s13238-014-0082-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatra KC, Hay N. The pentose phosphate pathway and cancer. Trends Biochem Sci. 2014;39:347\u0026ndash;54. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.tibs.2014.06.005\u003c/span\u003e\u003cspan address=\"10.1016/j.tibs.2014.06.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa H, et al. c-Src facilitates tumorigenesis by phosphorylating and activating G6PD. Oncogene. 2021;40:2567\u0026ndash;80. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41388-021-01673-0\u003c/span\u003e\u003cspan address=\"10.1038/s41388-021-01673-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGhergurovich JM, et al. A small molecule G6PD inhibitor reveals immune dependence on pentose phosphate pathway. Nat Chem Biol. 2020;16:731\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41589-020-0533-x\u003c/span\u003e\u003cspan address=\"10.1038/s41589-020-0533-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoshihara K, et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun. 2013;4:2612. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/ncomms3612\u003c/span\u003e\u003cspan address=\"10.1038/ncomms3612\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAran D, Hu Z, Butte AJ. xCell: digitally portraying the tissue cellular heterogeneity landscape. Genome Biol. 2017;18:220. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13059-017-1349-1\u003c/span\u003e\u003cspan address=\"10.1186/s13059-017-1349-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi T, et al. A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells. Cancer Res. 2017;77:e108\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1158/0008-5472.CAN-17-0307\u003c/span\u003e\u003cspan address=\"10.1158/0008-5472.CAN-17-0307\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFinotello F, et al. Molecular and pharmacological modulators of the tumor immune contexture revealed by deconvolution of RNA-seq data. Genome Med. 2019;11:34. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13073-019-0638-6\u003c/span\u003e\u003cspan address=\"10.1186/s13073-019-0638-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBecht E, et al. Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression. Genome Biol. 2016;17:218. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13059-016-1070-5\u003c/span\u003e\u003cspan address=\"10.1186/s13059-016-1070-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNewman AM, et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015;12:453\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nmeth.3337\u003c/span\u003e\u003cspan address=\"10.1038/nmeth.3337\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang P, et al. Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat Med. 2018;24:1550\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41591-018-0136-1\u003c/span\u003e\u003cspan address=\"10.1038/s41591-018-0136-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaeser D, Gruener RF, Huang RS. oncoPredict: an R package for predicting in vivo or cancer patient drug response and biomarkers from cell line screening data. Brief Bioinform. 2021;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/bib/bbab260\u003c/span\u003e\u003cspan address=\"10.1093/bib/bbab260\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e(!!!. INVALID CITATION !!!).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoppula P, Zhuang L, Gan B. Cystine transporter SLC7A11/xCT in cancer: ferroptosis, nutrient dependency, and cancer therapy. Protein Cell. 2021;12:599\u0026ndash;620. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s13238-020-00789-5\u003c/span\u003e\u003cspan address=\"10.1007/s13238-020-00789-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKunda P, Craig G, Dominguez V, Baum B, Abi. Sra1, and Kette control the stability and localization of SCAR/WAVE to regulate the formation of actin-based protrusions. Curr biology: CB. 2003;13:1867\u0026ndash;75. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cub.2003.10.005\u003c/span\u003e\u003cspan address=\"10.1016/j.cub.2003.10.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeonetti A, et al. Molecular basis and rationale for combining immune checkpoint inhibitors with chemotherapy in non-small cell lung cancer. Drug Resist updates: reviews commentaries Antimicrob anticancer Chemother. 2019;46:100644. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.drup.2019.100644\u003c/span\u003e\u003cspan address=\"10.1016/j.drup.2019.100644\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbbasi J. Potential New Immune-Checkpoint Inhibitor Partner for Lung Cancer. JAMA. 2021;326:901. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jama.2021.14765\u003c/span\u003e\u003cspan address=\"10.1001/jama.2021.14765\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun J, et al. Characterization of immune landscape in papillary thyroid cancer reveals distinct tumor immunogenicity and implications for immunotherapy. Oncoimmunology. 2021;10:e1964189. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/2162402X.2021.1964189\u003c/span\u003e\u003cspan address=\"10.1080/2162402X.2021.1964189\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRudin CM, et al. Pembrolizumab or Placebo Plus Etoposide and Platinum as First-Line Therapy for Extensive-Stage Small-Cell Lung Cancer: Randomized, Double-Blind, Phase III KEYNOTE-604 Study. J Clin oncology: official J Am Soc Clin Oncol. 2020;38:2369\u0026ndash;79. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1200/JCO.20.00793\u003c/span\u003e\u003cspan address=\"10.1200/JCO.20.00793\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGadgeel S, et al. Updated Analysis From KEYNOTE-189: Pembrolizumab or Placebo Plus Pemetrexed and Platinum for Previously Untreated Metastatic Nonsquamous Non-Small-Cell Lung Cancer. J Clin oncology: official J Am Soc Clin Oncol. 2020;38:1505\u0026ndash;17. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1200/JCO.19.03136\u003c/span\u003e\u003cspan address=\"10.1200/JCO.19.03136\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJassem J, et al. Updated Overall Survival Analysis From IMpower110: Atezolizumab Versus Platinum-Based Chemotherapy in Treatment-Naive Programmed Death-Ligand 1-Selected NSCLC. J Thorac oncology: official publication Int Association Study Lung Cancer. 2021;16:1872\u0026ndash;82. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jtho.2021.06.019\u003c/span\u003e\u003cspan address=\"10.1016/j.jtho.2021.06.019\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePassaro A, Brahmer J, Antonia S, Mok T, Peters S. Managing Resistance to Immune Checkpoint Inhibitors in Lung Cancer: Treatment and Novel Strategies. J Clin oncology: official J Am Soc Clin Oncol. 2022;40:598\u0026ndash;610. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1200/JCO.21.01845\u003c/span\u003e\u003cspan address=\"10.1200/JCO.21.01845\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi R, et al. APOBEC-mediated mutagenesis is a favorable predictor of prognosis and immunotherapy for bladder cancer patients: evidence from pan-cancer analysis and multiple databases. Theranostics. 2022;12:4181\u0026ndash;99. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.7150/thno.73235\u003c/span\u003e\u003cspan address=\"10.7150/thno.73235\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLahiri A, et al. Lung cancer immunotherapy: progress, pitfalls, and promises. Mol Cancer. 2023;22:40. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12943-023-01740-y\u003c/span\u003e\u003cspan address=\"10.1186/s12943-023-01740-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu S, et al. Combination strategies to maximize the benefits of cancer immunotherapy. J Hematol Oncol. 2021;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13045-021-01164-5\u003c/span\u003e\u003cspan address=\"10.1186/s13045-021-01164-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAgarwal P, Le DT, Boland PM. Immunotherapy in colorectal cancer. Adv Cancer Res. 2021;151:137\u0026ndash;96. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/bs.acr.2021.03.002\u003c/span\u003e\u003cspan address=\"10.1016/bs.acr.2021.03.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLorentzen CL, Haanen JB, Met O, Svane IM. Clinical advances and ongoing trials on mRNA vaccines for cancer treatment. Lancet Oncol. 2022;23:e450\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S1470-2045(22)00372-2\u003c/span\u003e\u003cspan address=\"10.1016/S1470-2045(22)00372-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun S, et al. Development and validation of an immune-related prognostic signature in lung adenocarcinoma. Cancer Med. 2020;9:5960\u0026ndash;75. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/cam4.3240\u003c/span\u003e\u003cspan address=\"10.1002/cam4.3240\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu M, et al. Clinical Significance and Immunometabolism Landscapes of a Novel Recurrence-Associated Lipid Metabolism Signature In Early-Stage Lung Adenocarcinoma: A Comprehensive Analysis. Front Immunol. 2022;13:783495. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fimmu.2022.783495\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2022.783495\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSorin M, et al. Single-cell spatial landscapes of the lung tumour immune microenvironment. Nature. 2023;614:548\u0026ndash;54. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41586-022-05672-3\u003c/span\u003e\u003cspan address=\"10.1038/s41586-022-05672-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZuo S, Wei M, Wang S, Dong J, Wei J. Pan-Cancer Analysis of Immune Cell Infiltration Identifies a Prognostic Immune-Cell Characteristic Score (ICCS) in Lung Adenocarcinoma. Front Immunol. 2020;11:1218. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fimmu.2020.01218\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2020.01218\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShinohara S, et al. New evaluation of the tumor immune microenvironment of non-small cell lung cancer and its association with prognosis. J Immunother Cancer. 2022;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/jitc-2021-003765\u003c/span\u003e\u003cspan address=\"10.1136/jitc-2021-003765\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLinde IL et al. Neutrophil-activating therapy for the treatment of cancer. \u003cem\u003eCancer cell\u003c/em\u003e 41, 356\u0026ndash;372 e310, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ccell.2023.01.002\u003c/span\u003e\u003cspan address=\"10.1016/j.ccell.2023.01.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaijens ST, Vledder A, de Bruyn M, Nijman HW. Tumor-infiltrating lymphocytes in the immunotherapy era. Cell Mol Immunol. 2021;18:842\u0026ndash;59. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41423-020-00565-9\u003c/span\u003e\u003cspan address=\"10.1038/s41423-020-00565-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiao Y et al. Cathepsin C promotes breast cancer lung metastasis by modulating neutrophil infiltration and neutrophil extracellular trap formation. \u003cem\u003eCancer cell\u003c/em\u003e 39, 423\u0026ndash;437 e427, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ccell.2020.12.012\u003c/span\u003e\u003cspan address=\"10.1016/j.ccell.2020.12.012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGajewski TF, Schreiber H, Fu YX. Innate and adaptive immune cells in the tumor microenvironment. Nat Immunol. 2013;14:1014\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/ni.2703\u003c/span\u003e\u003cspan address=\"10.1038/ni.2703\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\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":"Disulfidptosis, Lung adenocarcinoma, Pentose phosphate pathway, Tumor immune microenvironment, Prognosis, Immunotherapy","lastPublishedDoi":"10.21203/rs.3.rs-5051024/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5051024/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDisulfidptosis is a form of cell death, where generation of nicotinamide adenine dinucleotide phosphate (NADPH) through the pentose phosphate pathway (PPP) play an important role. The discovery of disulfidptosis provides new in-sights into lung adenocarcinoma (LUAD) therapeutics.\u003c/p\u003e\u003ch2\u003eResearch Design and Methods:\u003c/h2\u003e \u003cp\u003eDisulfidptosis regulators (DSRs) was used to identify subgroups. Meanwhile, WGCNA and single-cell analysis were performed to identify genes related to disulfidptosis and PPP (DPRGs). To determine the risk signature, clinical features were analyzed, as well as prognostic pre-dictive ability, tumor immune microenvironment (TIME), immunotherapeutic response and drug sensitivity. Finally, the results were experimentally verified \u003cem\u003ein vitro and vivo.\u003c/em\u003e\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe identified two DSR and DPRG clusters associated with distinct immune profiles involved in regulating different biological processes. The risk signature was effective in assessing LUAD prognosis in patients. It showed a strong correlation with TIME and could predict the immunotherapy response. After LRRC61 knockdown, the proliferation, migration and anti-apoptotic ability of LUAD cells were significantly reduced. Moreover, the xenograft tumors showed tumour growth was promoted when overexpressing LRRC61.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eWe analyzed DSRs and DPRGs in LUAD and developed an evaluation system that assesses the risk and guides the clinical application of drugs, including chemotherapeutic and immunotherapeutic agents.\u003c/p\u003e","manuscriptTitle":"Disulfidptosis and pentose phosphate pathway-associated prognosis signature guides immunotherapy for lung adenocarcinoma patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-22 08:52:42","doi":"10.21203/rs.3.rs-5051024/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":"fc596010-742e-43fa-9a19-4e7cc7fded63","owner":[],"postedDate":"October 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-24T05:53:40+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-22 08:52:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5051024","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5051024","identity":"rs-5051024","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}