Exploring Heterogeneity Across Diverse Regulated-Cell Death Patterns in Glioma

Exploring Heterogeneity Across Diverse Regulated-Cell Death Patterns in Glioma | 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 Exploring Heterogeneity Across Diverse Regulated-Cell Death Patterns in Glioma Zhipeng Jiang, Hailong Huang, Zihan Wang, Youwei Guo, Wen Yin, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4321362/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 Glioma stands out as the most malignant ailment affecting the central nervous system. Regulated cell death, orchestrated by a multitude of genes, serves as a pivotal determinant in shaping cellular destiny and significantly contributes to tumor advancement. However, there is a dearth of literature delving into the evolution of glioma disease through the prism of cell death patterns. Hence, our objective is to delve into the pertinent molecular mechanisms underlying glioma, with a specific focus on the potential role of regulated cell death. Results Different patterns of regulated cell death collectively contribute to the progression of glioma. Clusters characterized by relatively specific high expression of alkalosis and netotic cell death exhibit relatively malignant clinical features. Through differential gene screening, we constructed a prognostic signature consisting of genes such as TIMP1. This model demonstrates good prognostic predictive ability, with its scoring reflecting the progression of glioma. Finally, experimental validation of TIMP1 confirms its involvement in the progression of malignant cells. Conclusion These findings provide new insights into understanding the relationship between regulated cell death and glioma development and identify novel biomarkers may help to guiding precise treatments to glioma. Glioma Regulated cell death Prognostic signature TIMP1 Multi-omic analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Background Glioma represents a significant disease burden among patients with neurological tumors worldwide [ 1 ]. The latest WHO Classification Criteria introduced in 2021 integrate both histological features and molecular phenotypes of gliomas, resulting in subtypes that offer enhanced diagnostic precision and more accurate prognostic implications [ 2 ]. Among gliomas, IDH wild-type glioblastoma is the most aggressive subtype with the poorest prognosis. Despite the implementation of standard surgical procedures combined with chemoradiotherapy, the overall survival rarely exceeds 5 years. Current research efforts in glioma treatment primarily focus on monoclonal antibodies and targeted drugs; however, the outcomes have been largely unsatisfactory [ 3 ]. Given the absence of effective targeted therapies for glioma, there is an urgent need to explore novel strategies aimed at improving glioma prognosis. Predictive models could potentially serve as valuable tools for both prognostic assessment and molecular drug prediction in glioma management. Regulated cell death (RCD) refers to the spontaneous death of cells regulated by multiple genes, occurring in both normal and tumor cells [ 4 ]. 12 types of cell death have been identified as relevant in breast cancer, including apoptosis, pyroptosis, ferroptosis, autophagy, necroptosis, cuproptosis, parthanatos, entotic cell death, netotic cell death (NETosis), lysosome-dependent cell death, oxeiptosis, and alkaliptosis [ 5 ]. Apoptosis is the most common type of RCD, which is usually triggered by the caspase family [ 6 ]. Distinguished with apoptosis, necroptosis is an inflammatory RCD that is activated by factors such as TNF [ 7 ]. Pyroptosis is another prominent form of RCD induced by inflammatory caspase [ 8 ]. Ferroptosis and cuproptosis are metabolic dysplastic cell death caused by abnormal accumulation of iron or copper [ 9 , 10 ]. Autophagy is an LC3 subfamily-related degradation pathway and is accompanied by the accumulation of autophagic vacuoles [ 11 , 12 ]. Lysosome-dependent cell death can mediate RCD through lysosomal membrane permeabilization and release of contained hydrolase [ 13 ]. Pathanatos is an RCD that does not depend on caspase, which is mainly due to the large amount of polyADP ribose produced by PARP-1 activation. After intracellular metabolic disorders, RCD-inducing factors are released from the mitochondria into the nucleus, chromatin agglutinates, and large fragments of DNA are produced, eventually leading to Parthanatos [ 14 ]. Entotic cell death is an RCD triggered by competition and phagocytosis between neighboring cells of the same kind, which is characterized mainly by the rearrangement of cell adhesion molecules and cytoskeleton [ 15 ]. Netotic cell death is a form of RCD driven by neutrophil-derived extracellular traps, which is regulated by NADPH oxidase-mediated ROS production and histone citrullination [ 16 ]. Oxeiptosis is another novel type of caspase-independent RCD induced by oxygen radicals, which is driven by the KEAP1-PGAM5-AIFM1 pathway [ 17 ]. Alkaliptosis is a PH-dependent RCD driven by cytoplasmic alkalosis, and changes in carbonic anhydrase 9 can regulate the occurrence of alkaliptosis [ 18 ]. Immunogenic cell death is a form of cell death triggered by the release of damage-associated molecular patterns during immune responses [ 19 ]. MPT-driven necrosis is a form of cell death triggered by severe oxidative stress or Ca2 + overload [ 20 ]. The newly discovered disulfidptosis is a cell death type caused by the abnormal enrichment of disulfide bonds, which is closely related to a variety of tumors [ 21 – 23 ]. Various types of RCD mechanisms are essential for maintaining normal cellular functions and tissue homeostasis in response to internal or external stimuli [ 24 ]. The various RCD pathways do not function independently in regulating cell growth and death; instead, they interact with each other, forming an interconnected network. Dysfunction or failure of multiple pathways can contribute to the occurrence and development of diseases. In this study, we have taken the RCD pathways as a starting point to explore the relationship between various RCD pathways and gliomas. Through clustering analysis of RCD, we have identified the most suitable prognostic evaluation model and validated it across multiple dimensions. These methods are beneficial for a deeper exploration of the molecular mechanisms underlying glioma progression. Methods Data download and pre-processing The RCD-related gene sets, comprising crucial genes regulating fifteen different RCD patterns, were constructed based on review articles and manually collated [ 5 , 14 ]. Ultimately, 15 RCD pathways were included in the followed analysis. Transcriptome and clinical data of glioma patients were downloaded from The Cancer Genome Atlas (TCGA, https://tcga-data.nci.nih.gov/tcga/ ) via UCSC Xena (( http://xena.ucsc.edu/public/ ), Glioma Longitudinal AnalySiS (GLASS, https://glassconsortium.org/ ) Consortium and Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/ ) databases (ID: GSE55918). RNA-Seq data was processed in the form of TPM (transcripts per million) while microarray data were SCAN normalized. Both datasets were log2 converted for further analysis. Single cell RNA-seq data was obtained from Synapse ( https://www.synapse.org/ ) database (ID: syn22257780). Spatial transcriptome data was obtained from Data dryad (https://datadryad.org/stash/dataset/doi: 10.5061/dryad.h70rxwdmj ). The flowchart of this study was shown in (Fig. 1 ). Unsupervised clustering of RCD pathway-based scores The pathway scores corresponding to different RCD patterns were computed through gene set variation analysis (GSVA) utilizing the R package ‘GSVA’ with the ‘ssgsea’ method. Subsequently, consensus clustering was conducted on these scores for each patient using the R package ‘ConsensusClusterPlus’. This approach aimed to elucidate pathway-based RCD subtypes. Survival analysis The R package ‘survival’ and ‘survminer’ were used to perform univariate and multivariate cox regression. Kaplan-Meier curves were plotted using ‘ggsurvplot’. R package ‘timeROC’ Receiver was employed to conduct Receiver Operating Characteristic (ROC) analyses. The nomogram was built using R package ‘rms’ using diverse clinical features. Functional enrichment analysis Functional difference between clusters were investigated using GSVA with hallmark gene sets downloaded from Molecular Signature Database ( https://www.gseamsigdb.org/gsea/msigdb/index.jsp ). Differential expression analysis and construction of prognostic signature Differential expressed genes (DEGs) were detected utilizing the R package ‘DESeq2’. Genes meeting the following criteria were deemed significant: 1) p-value 1.5. The intersection of these DEGs was then utilized to establish the prognostic linear model. A variety of machine learning algorithms were combined to construct the model. The combination yielding the highest C-index was chosen as the final model. The RCD-related score (RRScore) was calculated using the formula below: $$RRScore= \beta i\text{*}Ei$$ \(\beta i\) indicates coefficient calculated by the corresponding algorithms. \(Ei\) indicates the normalized expression of each selected genes. Patients within each dataset were stratified into high-RRScore and low-RRScore groups based on the median value of RRScore. Immune related analysis Multi-algorithms including CIBERSORT, EPIC, xCELL, MCPCounter, QUANTISEQ were applied to measure the immune infiltration level of bulk samples. ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumours using Expression data) algorithm was employed to evaluate the immune and stromal components of tumors, from which can we calculate the tumor purity. The prediction of immunotherapy response between groups charactered by RCD-related score was conducted by applying Tumor Immune Dysfunction and Exclusion (TIDE) algorithm. Genomic analysis Single nucleotide variation of glioma patients was analyzed via R package ‘maftools’. Tumor mutation burden (TMB) and mutant-allele tumor heterogeneity (MATH) were calculated using ‘maftools’ as well. Copy number variation (CNV) of glioma patients were analyzed using ‘GISTIC2.0’. Drug susceptibility prediction analysis The prediction of drug sensitivity was performed using R package ‘oncoPredict’, which output the predictive value on behalf of half maximal inhibitory concentration (IC50) using data from Genomics of Drug Sensitivity in Cancer (GDSC, https://www.cancerrxgene.org ) database. Single cell and spatial RNA-seq data processing The preprocessing of scRNAseq data, cell clustering and dimension reduction were performed using the R package ‘Seurat’. Cell annotation of which was downloaded from the corresponding dataset. The RRScore was calculated using ‘AddModuleScore’ function. The spatial transcriptome data was normalized using ‘SCTransform’ algorithm. The specific molecular components of the Hypoxia and pEMT modules were referenced from previously published literature (see Supplementary Table 1) [ 25 ]. Immunohistochemical analysis of glioma tissues Paraffin-embedded tissue microarrays were obtained from the Department of Neurosurgery at Xiangya Hospital. Tumor tissues from glioma patients who underwent initial radical resection (n = 83) and adjacent non-tumor tissues (n = 12) were included in this study. The human tissue consent procedures and protocols were approved by the Ethics Committee of Xiangya Hospital (Approval No. 202404081). Immunohistochemical (IHC) analysis was performed using primary antibodies against TIMP1 (dilution 1:2000, Proteintech, China). The IHC score was calculated: Total score = Percentage score (≤ 25%, 1; 26–50%, 2; 51–75%, 3; and > 75%, 4) × Intensity score (no staining, 0; light brown, 1; moderate brown, 2; and dark brown, 3). Malignant phenotype experiments of tumor cells Si-RNA was procured from RiboBio Corporation. The glioma cell line U251 underwent treatment with si-RNA for 24–48 hours to assess its efficacy in interfering with RNA expression, followed by subsequent malignant phenotype experiments. Cell proliferation assays comprised CCK-8 and colony formation assays. Post-digestion, cells were evenly seeded at a density of 2000 cells per well in a 96-well plate. CCK-8 solution was added at 24-hour intervals, and changes in cell quantity were quantified using instrumentation. In parallel, cells were plated at 1000 cells per well in a 6-well plate, and after 1–2 weeks, they were fixed and stained with crystal violet for colony enumeration, allowing for evaluation of differences in colony formation. For Transwell assays, cells were digested and seeded at a density of 20,000 cells per unit in chambers coated with Matrigel. Serum-free medium was utilized for the upper chamber, while serum-containing medium was added to the lower chamber. Following 48–72 hours of incubation, cell migration was assessed through appropriate staining and photography to discern disparities in invasion capability. Upon reaching confluence in the 6-well plate, tumor cells were subjected to a scratch assay using a 200 µl pipette tip. After removal of detached cells, images were captured under a microscope at 0-hour and 48-hour to quantify differences in cell migration distance. Results Exploring RCD pathway heterogeneity in glioma To characterize the heterogeneity of cellular death pathways in gliomas, we utilized GSVA to estimate the enrichment scores for RCD pathways in each sample within the TCGA cohort. Through clustering analysis, we determined that the optimal number of clusters was 4 (Fig. 2 A and S1 A-G). Subsequently, based on the enrichment scores of the RCD pathways, we classified 667 glioma tumors into 4 distinct heterogeneous subtypes (Fig. 2 B). Cluster 1 exhibited relatively specific high expression of alkaliptosis and netotic cell death, while Cluster 2 showed relatively specific high expression of ferroptosis and entotic cell death. Clusters 3 and 4 displayed distinct expression characteristics in Mpt-driven necrosis, disulfidepotosis, parthanatos, autophagy, and cuproptosis. Moreover, compared to the other three clusters, Cluster 1 demonstrated the poorest clinical prognosis, with the lowest overall survival and disease-specific survival rates (Fig. 2 C,D). Subsequently, we conducted functional enrichment analysis for the four clusters. In the HALLMARKS database, patients in Cluster 1 were found to be more enriched in Mtorc1 signaling, reactive oxygen species pathway, hypoxia, EMT, and DNA repair. In the Reactome database, cases in Cluster 1 showed greater enrichment in events such as Cyclin A B1 B2 Associated Events During G2 M Transition, Unwinding of DNA, and release of apoptotic factors from the mitochondria, among others (Fig. 2 E, S1 H). Clinical and immune profiling of four clusters In the subset of grade 2 gliomas with relatively favorable prognosis, Cluster 3 and Cluster 4 show significant representation, with Cluster 3 being predominant. Notably, in Grade 4 gliomas, Cluster 1, associated with the poorest prognosis, holds the largest share, reaching 80%. Among other clinical features, Cluster 1 consistently dominates in clinical phenotypes associated with poorer prognosis, such as glioblastoma, IDH wild-type, 1p/19q non-codeletion, MGMT unmethylated, and others (Fig. 3 A,B and S2 A-F). Subsequently, we utilized the CIBERSORT algorithm to calculate the proportions of immune cells in each cluster. The results revealed variations in several cell populations, including macrophage M2, monocytes, T cells CD4 memory resting, and plasma cells (Fig. 3 C and S2 G). Furthermore, utilizing four distinct algorithms, we evaluated the differences in cellular composition within the microenvironment of each cluster. In the EPIC algorithm, Cluster 1 exhibited the highest relative expression of Cancer-Associated Fibroblasts (CAFs) and endothelial cells. According to the xCELL algorithm, Cluster 1 showed elevated expression of astrocytes and CD4 + memory T cells. The MCPcounter algorithm indicated a relatively higher presence of endothelial cells and fibroblasts in Cluster 1. In the QUANTISEQ algorithm, Cluster 1 demonstrated a higher relative presence of macrophages and T cells (Fig. 3 D). Additionally, we utilized the ESTIMATE algorithm to compute the tumor immune-related scores. Compared to the clusters with better prognosis, namely Cluster 3 and Cluster 4, Cluster 1 exhibits higher ESTIMATE Score, Immune Score, and Stromal Score. Moreover, Cluster 1 has the lowest tumor purity (Fig. 3 E,F). Genetic landscape analysis of four clusters We proceeded to elucidate the genetic variation characteristics of the tumors. Initially, we computed the MATH scores for the four clusters, revealing that Cluster 1 exhibits the lowest MATH score among them (Fig. 4 A). Conversely, Cluster 1 demonstrates the highest TMB score (Fig. 4 B). Subsequent scrutiny of the prognostic implications of these clusters indicated that the ClusterA-TMB high group is associated with the most unfavorable prognosis, while the ClusterNonA-TMBlow group exhibits the most favorable survival outcomes (Fig. 4 C). Within the glioma patient cohort from TCGA, approximately 91.19% (611/670) displayed genetic mutations. Notably, IDH1 mutations were most prevalent (60%), with Cluster 1 predominantly devoid of IDH1 mutations. Additional mutations encompassed TP53 (44%), ATRX (29%), CIC (16%), and others (Fig. 4 D). Lastly, we provided an account of gene amplifications or deletions across chromosomal positions. Cluster 1 exhibited heightened mutation rates across all chromosomes compared to the other three clusters, with amplifications predominantly localized to chr4, chr7, and chr12, while deletions were principally concentrated on chr1, chr4, and chr9 (Fig. 4 E-H). Exploring prognostic signatures across four clusters To further explore the association of genes with prognosis across different glioma clusters, we initially conducted a comprehensive analysis of gene expression differences between Cluster 1, indicative of the poorest prognosis, and the other three clusters (Fig. 5 A-C). The Principal Component Analysis (PCA) plot demonstrated distinct and independent patient distribution landscapes across the four clusters (Fig. 5 D). Subsequently, we identified a set of differentially expressed genes, comprising 234 commonly upregulated genes and 75 commonly downregulated genes (Fig. 5 E,F). Leveraging these 309 differential genes, we employed a range of sophisticated machine learning algorithms to develop a robust prognostic model for gliomas, utilizing the TCGA dataset for training and CGGA325 and CGGA693 datasets for validation. Among the diverse algorithms evaluated, the CoxBoost + SuperPC combination demonstrated superior performance, leading us to select 12 genes for the construction of the RCD-related prognostic signature (Fig. 5 G,H). Comprehensive prognostic evaluation of RCD-related signature We conducted a comprehensive evaluation of the prognostic significance of the RCD-related model built upon the 12 identified genes. In the TCGA training set, patients classified as RRScore-high group exhibited markedly lower average survival rates compared to their low-risk counterparts, with all areas under the curve (AUC) values surpassing 0.85 (Fig. 6 A). The CGGA325 and CGGA693 validation sets further confirmed the shorter overall survival in the RRScore-high group, corroborated by AUC values indicating robust prognostic assessment (Fig. 6 B,C). Expanding the validation scope, we incorporated the GSE55918 and GLASS datasets as additional verification sets. In these datasets, patients in the RRScore-high group consistently displayed significantly reduced survival times relative to those in the RRScore-low group, as elucidated by Kaplan-Meier curves highlighting distinct prognostic disparities (Fig. 6 F,G). Furthermore, we performed univariate and multivariate risk assessments for the prognostic signature. The outcomes identified age, grade, IDH status, 1p/19q codeletion, and riskscore as independent prognostic factors (Fig. 6 D,E). Integrating the riskscore with all variables, we developed a nomogram to quantitatively depict the prognostic index associated with glioma patients (Fig. 6 H). The study findings underscored a c-index of 0.871, underscoring its robust capacity for prognostic prediction (Fig. 6 I). Exploring the clinical implications of RCD-related signature in glioma The prognostically relevant genes exhibit positive correlations with alkaliptosis, apoptosis, ferroptosis, and various other forms of cell death, while showing negative correlations with MPT-driven necrosis and parthanatos. Notably, PTPRT displays a contrasting trend to the associations (Fig. 7 A). We further explored the potential clinical implications of the signature in terms of therapeutic response. Through drug sensitivity correlation analysis, we identified 13 compounds as potential therapeutic agents for gliomas. Compounds such as Dasatinib and Gemcitabine demonstrated high IC50 values in the RRScore-low group, whereas Daporinad and Lapatinib exhibited elevated IC50 values in the RRScore-high group (Fig. 7 B). The relationships among glioma prognostic genes, RCD, and predicted drug pathways are depicted in Fig. 7 C. Additionally, the RRScore-high group demonstrated relatively lower Microsatellite Instability (MSI) scores but higher Exclusion scores, IFNG scores, and TIDE scores (Fig. 7 D). Lastly, we conducted an analysis of the correlation with immune modulators, encompassing co-stimulators, co-inhibitors, ligands, cell adhesion molecules, receptors, antigen presentation components, and others. Most of the immunomodulators in the high RRScore group exhibited relatively high expression levels, with only a few exceptions such as VTCN1 and IL12A, which showed no significant correlation (Fig. 7 E). Single cell analysis of RCD-related signature We conducted an in-depth analysis at the single-cell level to explore programmed cell death and its potential impact on glioma tumor progression. Cells with high riskscore predominantly clustered within malignant cells, with a subset also distributed among immune cells in CPTAC dataset (Fig. 8 A). Employing published datasets consisting of both LGG and GBM samples [ 26 ], we observed a lower average communication number in the high-risk group, indicating potential differences in cell communication (Fig. 8 B) upon stratifying patients based on their scores. Notably, interaction strength was relatively higher in malignant cells of the high-risk group, while myeloid cells exhibited stronger interactions in the low-risk group (Fig. 8 C). Subsequent analysis revealed signaling alterations between different cell types, including pathway changes in PTN, MK, EGF, MIF, and PSAP in various malignant cell subtypes, along with alterations in the CCL pathway in myeloid cells (Fig. 8 D). Comparative analysis of relative information flow between high and low-risk groups highlighted predominant alterations in pathways like ncWNT, CHEMERIN, CD137, and VEGF in the high-risk group, whereas changes in CX3C, CSF3, EDN, and PROS pathways were notable in the low-risk group (Fig. 8 E). Furthermore, we compared signaling pathway alterations within different cell types in each group. In the high-risk group, pathways such as CCL3, CCL5, IGF, and TGFB were significantly upregulated (Fig. 8 F). Lastly, we identified potential target genes for myeloid cells, including SPP1, HMOX1, SOCS3, CXCL2, and NUPR1, which could serve as targets for therapeutic interventions (Fig. 8 G). Spatial characterization of RCD-related signature We explored the spatial characteristics of the RCD-related signature within the glioma microenvironment. We computed RRScore for various cell types and observed that diff.-like and prolif.stem-like cancer cells exhibited the highest RRScore across all cell types, while stem-like malignant cells and oligodendrocytes displayed the lowest RRScore (Fig. 9 A). Spatial cell type deconvolution analysis revealed distinct spatial distributions of transcriptome-defined cell types in glioma. Cells with high RRS in the diff.-like cancer subtype displayed unique spatial niches compared to those with low RRS, forming a hypoxic, EMT-high tumor core. This core, reminiscent of a histologically defined pseudopalisading zone [ 27 ], exhibited regulatory cell death patterns. Notably, myeloid cells were excluded from the tumor core in diff.-like RRS-high cells, despite having a similar spatial distribution overall (Fig. 9 B-D). malignant phenotype experiments of tumor cells We initially performed TIMP1 staining on pathological specimens from glioma patients, revealing a relative increase in TIMP1 protein expression in high-grade gliomas (Fig. 10 A). Subsequently, we conducted cellular experiments to validate these findings. Upon downregulating TIMP1 expression (Fig. 10 B), a notable decrease in cell proliferation capacity was observed (Fig. 10 C,D). Furthermore, results from colony formation assays indicated a reduction in both the size and number of cell colonies following TIMP1 knockdown (Fig. 10 E). Similarly, findings from Transwell and scratch assays demonstrated that TIMP1 promotes the migration and invasion capabilities of glioma cells (Fig. 10 F). Discussion The cellular death mechanisms play a crucial role in maintaining tissue homeostasis under normal physiological conditions and are equally significant in the pathogenesis of various diseases [ 28 ]. Glioma, as a highly invasive malignant tumor, has garnered significant clinical attention due to its formidable prognosis, presenting substantial challenges in diagnosis and treatment [ 29 ]. In this study, we undertook a thorough analysis, employing distinct pathways associated with RCD, to systematically explore the interplay between cell death mechanisms and glioma. For the first time, our findings indicate that various forms of cell death collectively may impact the prognosis of glioma. Notably, we identified prognostically relevant genes such as TIMP1, suggesting their potential involvement in regulating the progression of malignant cells through the modulation of multiple cell death pathways. Our research not only advances our understanding of glioma pathophysiology but also provides a novel perspective with potential clinical significance, particularly in terms of guiding treatment strategies and prognostic assessment. Glioma patients were stratified into four prognostically relevant subtypes based on RCD-pathways. Cluster1, associated with the poorest prognosis, exhibited a specific death pathway known as alkaliptosis. Alkaliptosis, initially identified in studies involving JTC801, an opioid analgesic, conducted on pancreatic cancer cells, denotes a mode of cell death triggered by pH-dependent alkaliptosis [ 18 , 30 ]. Although research on alkaliptosis in the context of glioma has been limited, our study marks the first report establishing a correlation between alkaliptosis and the prognosis of glioma patients. Interestingly, among Cluster1 patients, another relatively specific form of cell death is NETosis. NETosis is a cellular death process occurring predominantely in inflammatory cells, epithelial cells, or tumor cells under conditions of inflammatory stress [ 31 ]. It is often accompanied by the release of associated inflammatory factors, potentially contributing to ischemia-reperfusion injury and the progression of tumors [ 32 , 33 ]. Existing literature suggests that NETosis can awaken nearby dormant tumor cells, stimulating their proliferation and thereby contributing to cancer relapse and progression. This observation may elucidate the relatively poorer prognosis observed in Cluster4, characterized by a high enrichment of NETosis compared to the pathway-similar Cluster3. The two subtypes associated with the poorest prognosis, Cluster1 and Cluster2, exhibit enrichment in multiple similar death pathways. These include apoptosis, oxeipotosis, pyroptosis, immunogenic cell death, lysosome-dependent cell death, necroptosis, and ferroptosis. These death pathways have been extensively reported to contribute to the malignant progression of cells across various cancers.[ 14 ] Notably, Cluster 2 presents a relatively unique death pathway known as entotic cell death. Entotic cell death is a form of endocytic cell death where tumor cells engulf and eliminate neighboring cells of the same type [ 34 ]. This process often occurs under conditions such as abnormal proliferation or glucose deprivation, potentially facilitating nutrient acquisition for tumor growth while also risking the removal of tumor cells by nearby normal cells [ 35 , 36 ]. Its ambiguous role in glioma warrants further exploration. Tumor immunity and molecular alterations serve as critical prognostic indications in cancer. Cluster 1, characterized by poorer clinical features (higher grade, glioblastoma subtype, IDH mutation status, etc.), demonstrates higher immune-related scores (ESTIMATE score, Immune score, and Stromal score), and lower tumor purity. Utilizing algorithms for tumor microenvironment cell infiltration, we observe an enrichment of CAFs, M2 macrophages, astrocytes, among others, in Cluster 1. These cell types have been previously implicated in the progression of glioma [ 37 ]. Additionally, Cluster 1 exhibits a higher TMB score, indicating a greater number of genetic mutations within the tumor. High TMB often p often correlates with a poorer prognosis, consistent with our findings [ 38 ]. In the specific molecular mutation landscape, we noted that Cluster 1, associated with the worst prognosis, frequently lacks mutations in IDH1 and ATRX but harbors mutations in genes such as EGFR. These molecular alterations are essential clinical markers for poor prognosis [ 39 ]. Interestingly, the best prognosis in Cluster 3 is associated with mutations in CIC. CIC mutations are often associated with the co-deletion of 1p/19q and are commonly observed in oligodendrogliomas [ 40 ]. This observation aligns with our previous pathological subtype correlation analysis. Deep machine learning facilitates the selection of the most suitable prognostic signature from numerous candidate molecules [ 41 ]. We utilized a combination of various algorithms to compute and screened a glioma prognostic signature comprising 12 genes. Our signature demonstrates relatively accurate prognostic evaluation across five databases: TCGA, CGGA325, CGGA693, GSE55918 and GLASS. Assessing treatment responses in tumors, including targeted drugs and immune checkpoint blockade therapy, is crucial for understanding tumor prognosis. Dasatinib, a multi-target kinase inhibitor, holds potential for inhibiting all members of the Src kinase family (SFK) and BCR-Abl [ 42 ]. However, literature reports limited efficacy of this drug in the treatment of recurrent glioblastomas, possibly attributed to specific molecular alterations or the complexity of the blood-brain barrier. Our study predicts relatively higher IC50 values of these drugs in the RRScore-low group, suggesting potential characteristics of drug resistance in malignant tumors. Conversely, drugs with relatively higher IC50 values in the RRScore-high group may represent promising treatment options for gliomas. Nevertheless, the choice of any drug is a result of computational analysis using bioinformatics methods and requires validation for clinical value in glioma patients. Tumor cells have the capability to activate immune checkpoint molecules in immune cells, promoting programmed cell death and subsequent immune evasion [ 43 ]. Our results demonstrate that the high RRSscore group exhibits elevated expression levels of most immune checkpoint molecules, indicating their potential as therapeutic targets for future glioma treatments. In contrast to patients in the RRScore-high group, those in the RRScore-low group demonstrate a higher frequency of cell-to-cell interactions, potentially indicating increased engagement of immune cells. The robust cellular interactions between malignant cells and immune cells play a crucial role in tumor progression and metastasis [ 44 ]. Our research reveals a significant upregulation of the TGF-β and BMP pathways in the cell communication signaling between malignant cells and myeloid immune cells. TGF-β protein and BMP are members of the TGF superfamily and are secreted by tumor cells, exerting their effects on immune cells such as macrophages within the tumor microenvironment. This interaction contributes to the resistance to immune therapy [ 45 ]. Such phenomena may also contribute to the poorer prognosis observed in patients categorized in the RRScore-high group. In spatial transcriptomic analysis, the subset with RRScore-high in the diff-like category exhibits a spatial distribution on histopathology slides reminiscent of hypoxia and pMET. Hypoxic conditions can induce the activation of hypoxia-inducible factors (HIF1α, etc.), which mediate tumor growth, invasion, immune suppression, and drug resistance [ 46 ]. On the other hand, pEMT represents a module within the epithelial-mesenchymal transition process and plays a pro-carcinogenic role across various tumors, including gliomas [ 25 ]. The spatial distribution patterns of these two pathways delineate areas within glioma tissue characterized by heightened malignancy levels. This underscores that the signature derived from our RCD screening accurately reflect the benign or malignant status of cells. This observation aligns with our previous GSVA hallmarks pathway analysis. While a portion of our findings has been validated through cellular molecular experiments, it is crucial to further validate them with a larger cohort of clinical patients. Additionally, exploring the mechanistic interactions between molecules and multiple pathways of programmed cell death regulation is essential. Therefore, we intend to conduct additional validation in multicenter follow-up studies with higher quality and larger sample sizes. Furthermore, we plan to perform additional molecular experiments in the future to enhance our understanding of the underlying mechanisms. In conclusion, our study highlights the collective influence of various forms of RCD on the progression of gliomas. Our signature, established based on RCD pathways, demonstrates relatively accurate prognostic prediction capabilities. This underscores the importance of considering diverse cell death mechanisms in assessing glioma prognosis and suggests the potential clinical utility of our signature in improving prognostic evaluation for glioma patients. Conclusions Our study unveils the heterogeneous landscape of RCD pathways in glioma, shedding light on their molecular diversity and clinical relevance. Through rigorous computational analyses, we identified four distinct glioma clusters, each characterized by unique RCD pathway enrichments and clinical phenotypes. Notably, Cluster 1 emerged as particularly ominous, associated with aggressive clinical features and dismal prognosis. Furthermore, we developed a robust RCD-related prognostic signature comprising 12 genes, capable of accurately stratifying glioma patients into high and low-risk groups with significant prognostic disparities. Importantly, this signature demonstrated consistent predictive power across diverse validation datasets, highlighting its clinical utility in guiding personalized treatment strategies. Our findings also uncover potential therapeutic implications, identifying promising drug candidates and highlighting avenues for targeted intervention. Additionally, single-cell analysis reveals spatial and signaling alterations associated with high-risk glioma subtypes, offering insights into tumor heterogeneity and microenvironmental dynamics. In conclusion, our study provides a comprehensive understanding of RCD pathways in gliomas, offering valuable insights into their molecular landscape, clinical implications, and therapeutic opportunities. These findings pave the way for precision medicine approaches and the development of novel therapeutic strategies, ultimately improving patient outcomes in this challenging disease. Abbreviations RCD Regulated cell death NETosis Netotic cell death TCGA The Cancer Genome Atlas CGGA Chinese Glioma Genome Atlas GLASS Glioma Longitudinal AnalySIS GEO Consortium and Gene Expression Omnibus GSVA Gene Set Variation Analysis GBM Glioblastoma Multiforme ROC Receiver Operating Characteristic GSEA Gene Set Enrichment Analysis DEGs Differential Expressed Genes RRScore RCD Related Score ESTIMATE Estimation of STromal and Immune cells in MAlignant Tumours using Expression data TIDE Tumor Immune Dysfunction and Exclusion TMB Tumor Mutation Burden MATH Mutant allele Tumor Heterogeneity CNV Copy Number Variation TCGA The Cancer Genome Atlas TMB Tumor Mutation Burden CAFs Cancer Associated Fibroblasts PCA Principal Component Analysis AUC Areas Under the Curve Declarations Ethics approval and consent to participate Human tissue samples used in this study were obtained from partially excised tissues after surgery, written informed consent has been obtained from the patients to publish this paper. The human tissue consent procedures and protocols were approved by the Ethics Committee of Xiangya Hospital (Approval No. 202404081). Availability of data and materials The data source of the manuscript is from the public database. The details were displayed in the materials and methods section. Other data could contact the corresponding authors. Competing interests The authors declare they have no conflicts of interest. Funding This study was supported by the National Natural Science Foundation of China (81472355), and Provincial Natural Science Foundation of Hunan (2022JJ30931). Authors' contributions CR and XJ conceived the study. ZJ, HH, ZW, WG, WY, HH, QC, LW and WL collected and analyzed data and visualized figures. ZJ, HH and ZW wrote the manuscript. All authors reviewed and approved the submitted manuscript. Acknowledgments We sincerely thank the CGGA, TCGA, GEO, SYNAPSE and CPTAC databases for freely providing the transcriptomic information of glioma samples. 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Mol Cancer. 2021;20(1):131. Cortez MA, Masrorpour F, Ivan C, Zhang J, Younes AI, Lu Y, Estecio MR, Barsoumian HB, Menon H, Caetano MDS, et al. Bone morphogenetic protein 7 promotes resistance to immunotherapy. Nat Commun. 2020;11(1):4840. Yin J, Ge X, Shi Z, Yu C, Lu C, Wei Y, Zeng A, Wang X, Yan W, Zhang J, et al. Extracellular vesicles derived from hypoxic glioma stem-like cells confer temozolomide resistance on glioblastoma by delivering mir-30b-3p. Theranostics. 2021;11(4):1763–79. Additional Declarations No competing interests reported. Supplementary Files supplementaryfile.docx TableS1Themodulegenes..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. 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(B) Heatmap visualization of expression levels in 15 RCD-associated pathways. (C-D) Kaplan-Meier (KM) curves for patients’ OS and DSS between four clusters. (E) Enrichment analysis conducted on four clusters.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4321362/v1/9ba6da0c2624519e77d22092.png"},{"id":55784381,"identity":"230ab27b-30c3-4a80-8c37-99ffb33a773f","added_by":"auto","created_at":"2024-05-03 06:25:26","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":746802,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eClinical and immune profiling of four clusters.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A-B) The clinical and pathological characteristics of the four clusters. (C) Estimated proportion of four clusters. (D) Multiple algorithms used to assess the cellular composition of the four clusters. (E) The immune-related scores of the four clusters. (F) The tumor purity of the four clusters.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4321362/v1/e9603623e46ad2e0adba1d4b.png"},{"id":55784379,"identity":"74495ac1-870c-4b5c-8d11-8d3ddad8ae5b","added_by":"auto","created_at":"2024-05-03 06:25:26","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":448794,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGenetic landscape analysis of four clusters.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The MATH score of the four clusters. (B) The TMB score of four clusters. (C) KM curves for patients’ OS. (E) The molecule mutation landscape of the four clusters. (E-F) The mutation landscape on 23 pairs of chromosomes in the four clusters.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4321362/v1/3926206d3e42964316038ef9.png"},{"id":55784008,"identity":"28fa9cef-f6c9-470b-be41-c506e9517afc","added_by":"auto","created_at":"2024-05-03 06:17:26","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1138053,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExploring prognostic signatures across four clusters.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A-C) The volcano plot illustrates the screening of differentially expressed genes. (D) The PCA plot illustrates the distribution characteristics of the four patient clusters. (E-F) Selecting the subset of differentially expressed genes that are commonly upregulated or downregulated. (G) Construction a prognostic signature using machine learning techniques. (H) The genes involved in the construction of the signature.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4321362/v1/2aba68bbb205fd679676df72.png"},{"id":55784015,"identity":"1161665a-1811-4472-b013-6070717fae79","added_by":"auto","created_at":"2024-05-03 06:17:26","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":675392,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComprehensive prognostic evaluation of RCD-related signature.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The prognostic evaluation of RCD-related signature in TCGA set. (B-C) The prognostic evaluation of RCD-related signature in two test sets. (D-E) The prognostic evaluation of RCD-related signature in two validation sets. (F-G) Univariate and multivariate Cox regression for glioma prognostic indicators. (H) Constructed a nomogram for predicting prognosis. (I) The calibration curve of the nomogram.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4321362/v1/d40ce7ba4711819d9b1403bf.png"},{"id":55784016,"identity":"0b83be51-8b2d-4b76-b6b7-ec763eca2936","added_by":"auto","created_at":"2024-05-03 06:17:26","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":569998,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExploring the clinical implications of RCD-related signature in glioma.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The correlation heatmap between genes and RCD pathways. (B) The boxplots of the drugs. (C) The correlation between gene, RCD pathways, target and drugs. (D) The relationship between immune therapy-related score and RRS score. (E) The relationship between immune modulator and RRS score.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-4321362/v1/988aa9f7870ef5b9d03edc4f.png"},{"id":55784014,"identity":"01326471-6ad2-4595-8c8e-15ef9aee57d9","added_by":"auto","created_at":"2024-05-03 06:17:26","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":909334,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle cell analysis of RCD-related signature.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The single-cell distribution characteristics in CPTAC. (B) Classifying glioma patients from the SYNAPSE dataset based on the RRS score. (C) Analysis of differential interaction number and strength. (D) The signaling changes of different cell clusters. (E-F) The signaling changes of different RRScore groups. (G) The predicted target genes in single cell analysis.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-4321362/v1/462fb48d9b43cbcc4ae29f26.png"},{"id":55784012,"identity":"e10d9af8-b970-475b-9b09-5510973a4c13","added_by":"auto","created_at":"2024-05-03 06:17:26","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":6239101,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe spatial characterization of RCD-related signature.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The RRS score in multiple cell clusters. (B) The hypoxia and pEMT spatial characterization. (C-D) The RRS score distribution characterization in glioma samples.\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-4321362/v1/292712a32acf71099c903e77.png"},{"id":55784007,"identity":"fecea0c5-09ce-4e24-9d6c-d636a5aecb7e","added_by":"auto","created_at":"2024-05-03 06:17:26","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":5533021,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMalignant phenotype experiments of tumor cells.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) IHC staining of TIMP1 in glioma patients. (B) qPCR demonstrate downregulation of TIMP1. (C-D) The CCK-8 assay and colony formation reveal a decrease in the proliferation capacity of cells following TIMP1 downregulation. (E) The scratch assay demonstrates a reduction in the migration ability of cells after TIMP1 knockdown. (F) The Transwell assay reveals a decrease in the invasive capability of cells following TIMP1 knockdown (*\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.005).\u003c/p\u003e","description":"","filename":"Figure10.png","url":"https://assets-eu.researchsquare.com/files/rs-4321362/v1/8bd4d68386f09ffe30592176.png"},{"id":55803299,"identity":"5ff1fd4f-1557-4e4e-8b8f-dfd57c8f15dc","added_by":"auto","created_at":"2024-05-03 13:42:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6710699,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4321362/v1/63417990-c784-4f16-8630-e6524729ae41.pdf"},{"id":55784010,"identity":"15bd735e-196c-4b46-a92c-7d69ddf69f09","added_by":"auto","created_at":"2024-05-03 06:17:26","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3435929,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"supplementaryfile.docx","url":"https://assets-eu.researchsquare.com/files/rs-4321362/v1/0e9c7b9fe23eec4760eca436.docx"},{"id":55784004,"identity":"a0d3faff-1dd7-4037-a446-ab4eb8a57900","added_by":"auto","created_at":"2024-05-03 06:17:26","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":23513,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1Themodulegenes..docx","url":"https://assets-eu.researchsquare.com/files/rs-4321362/v1/5556a82ba7a7ef71bacb0f48.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring Heterogeneity Across Diverse Regulated-Cell Death Patterns in Glioma","fulltext":[{"header":"Background","content":"\u003cp\u003eGlioma represents a significant disease burden among patients with neurological tumors worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The latest WHO Classification Criteria introduced in 2021 integrate both histological features and molecular phenotypes of gliomas, resulting in subtypes that offer enhanced diagnostic precision and more accurate prognostic implications [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Among gliomas, IDH wild-type glioblastoma is the most aggressive subtype with the poorest prognosis. Despite the implementation of standard surgical procedures combined with chemoradiotherapy, the overall survival rarely exceeds 5 years. Current research efforts in glioma treatment primarily focus on monoclonal antibodies and targeted drugs; however, the outcomes have been largely unsatisfactory [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Given the absence of effective targeted therapies for glioma, there is an urgent need to explore novel strategies aimed at improving glioma prognosis. Predictive models could potentially serve as valuable tools for both prognostic assessment and molecular drug prediction in glioma management.\u003c/p\u003e \u003cp\u003eRegulated cell death (RCD) refers to the spontaneous death of cells regulated by multiple genes, occurring in both normal and tumor cells [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. 12 types of cell death have been identified as relevant in breast cancer, including apoptosis, pyroptosis, ferroptosis, autophagy, necroptosis, cuproptosis, parthanatos, entotic cell death, netotic cell death (NETosis), lysosome-dependent cell death, oxeiptosis, and alkaliptosis [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Apoptosis is the most common type of RCD, which is usually triggered by the caspase family [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Distinguished with apoptosis, necroptosis is an inflammatory RCD that is activated by factors such as TNF [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Pyroptosis is another prominent form of RCD induced by inflammatory caspase [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Ferroptosis and cuproptosis are metabolic dysplastic cell death caused by abnormal accumulation of iron or copper [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Autophagy is an LC3 subfamily-related degradation pathway and is accompanied by the accumulation of autophagic vacuoles [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Lysosome-dependent cell death can mediate RCD through lysosomal membrane permeabilization and release of contained hydrolase [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Pathanatos is an RCD that does not depend on caspase, which is mainly due to the large amount of polyADP ribose produced by PARP-1 activation. After intracellular metabolic disorders, RCD-inducing factors are released from the mitochondria into the nucleus, chromatin agglutinates, and large fragments of DNA are produced, eventually leading to Parthanatos [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Entotic cell death is an RCD triggered by competition and phagocytosis between neighboring cells of the same kind, which is characterized mainly by the rearrangement of cell adhesion molecules and cytoskeleton [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Netotic cell death is a form of RCD driven by neutrophil-derived extracellular traps, which is regulated by NADPH oxidase-mediated ROS production and histone citrullination [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Oxeiptosis is another novel type of caspase-independent RCD induced by oxygen radicals, which is driven by the KEAP1-PGAM5-AIFM1 pathway [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Alkaliptosis is a PH-dependent RCD driven by cytoplasmic alkalosis, and changes in carbonic anhydrase 9 can regulate the occurrence of alkaliptosis [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Immunogenic cell death is a form of cell death triggered by the release of damage-associated molecular patterns during immune responses [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. MPT-driven necrosis is a form of cell death triggered by severe oxidative stress or Ca2\u0026thinsp;+\u0026thinsp;overload [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The newly discovered disulfidptosis is a cell death type caused by the abnormal enrichment of disulfide bonds, which is closely related to a variety of tumors [\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eVarious types of RCD mechanisms are essential for maintaining normal cellular functions and tissue homeostasis in response to internal or external stimuli [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The various RCD pathways do not function independently in regulating cell growth and death; instead, they interact with each other, forming an interconnected network. Dysfunction or failure of multiple pathways can contribute to the occurrence and development of diseases.\u003c/p\u003e \u003cp\u003eIn this study, we have taken the RCD pathways as a starting point to explore the relationship between various RCD pathways and gliomas. Through clustering analysis of RCD, we have identified the most suitable prognostic evaluation model and validated it across multiple dimensions. These methods are beneficial for a deeper exploration of the molecular mechanisms underlying glioma progression.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eData download and pre-processing\u003c/p\u003e \u003cp\u003eThe RCD-related gene sets, comprising crucial genes regulating fifteen different RCD patterns, were constructed based on review articles and manually collated [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Ultimately, 15 RCD pathways were included in the followed analysis.\u003c/p\u003e \u003cp\u003eTranscriptome and clinical data of glioma patients were downloaded from The Cancer Genome Atlas (TCGA, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tcga-data.nci.nih.gov/tcga/\u003c/span\u003e\u003cspan address=\"https://tcga-data.nci.nih.gov/tcga/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) via UCSC Xena ((\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://xena.ucsc.edu/public/\u003c/span\u003e\u003cspan address=\"http://xena.ucsc.edu/public/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), Glioma Longitudinal AnalySiS (GLASS, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://glassconsortium.org/\u003c/span\u003e\u003cspan address=\"https://glassconsortium.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) Consortium and Gene Expression Omnibus (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) databases (ID: GSE55918). RNA-Seq data was processed in the form of TPM (transcripts per million) while microarray data were SCAN normalized. Both datasets were log2 converted for further analysis. Single cell RNA-seq data was obtained from Synapse (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.synapse.org/\u003c/span\u003e\u003cspan address=\"https://www.synapse.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database (ID: syn22257780). Spatial transcriptome data was obtained from Data dryad (https://datadryad.org/stash/dataset/doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5061/dryad.h70rxwdmj\u003c/span\u003e\u003cspan address=\"10.5061/dryad.h70rxwdmj\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The flowchart of this study was shown in (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUnsupervised clustering of RCD pathway-based scores\u003c/p\u003e \u003cp\u003eThe pathway scores corresponding to different RCD patterns were computed through gene set variation analysis (GSVA) utilizing the R package \u0026lsquo;GSVA\u0026rsquo; with the \u0026lsquo;ssgsea\u0026rsquo; method. Subsequently, consensus clustering was conducted on these scores for each patient using the R package \u0026lsquo;ConsensusClusterPlus\u0026rsquo;. This approach aimed to elucidate pathway-based RCD subtypes.\u003c/p\u003e \u003cp\u003eSurvival analysis\u003c/p\u003e \u003cp\u003eThe R package \u0026lsquo;survival\u0026rsquo; and \u0026lsquo;survminer\u0026rsquo; were used to perform univariate and multivariate cox regression. Kaplan-Meier curves were plotted using \u0026lsquo;ggsurvplot\u0026rsquo;. R package \u0026lsquo;timeROC\u0026rsquo; Receiver was employed to conduct Receiver Operating Characteristic (ROC) analyses. The nomogram was built using R package \u0026lsquo;rms\u0026rsquo; using diverse clinical features.\u003c/p\u003e \u003cp\u003eFunctional enrichment analysis\u003c/p\u003e \u003cp\u003eFunctional difference between clusters were investigated using GSVA with hallmark gene sets downloaded from Molecular Signature Database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gseamsigdb.org/gsea/msigdb/index.jsp\u003c/span\u003e\u003cspan address=\"https://www.gseamsigdb.org/gsea/msigdb/index.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDifferential expression analysis and construction of prognostic signature\u003c/p\u003e \u003cp\u003eDifferential expressed genes (DEGs) were detected utilizing the R package \u0026lsquo;DESeq2\u0026rsquo;. Genes meeting the following criteria were deemed significant: 1) p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05; 2) |log2FC|\u0026gt;1.5. The intersection of these DEGs was then utilized to establish the prognostic linear model. A variety of machine learning algorithms were combined to construct the model. The combination yielding the highest C-index was chosen as the final model. The RCD-related score (RRScore) was calculated using the formula below:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$RRScore= \\beta i\\text{*}Ei$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\beta i\\)\u003c/span\u003e \u003c/span\u003eindicates coefficient calculated by the corresponding algorithms. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(Ei\\)\u003c/span\u003e\u003c/span\u003e indicates the normalized expression of each selected genes. Patients within each dataset were stratified into high-RRScore and low-RRScore groups based on the median value of RRScore.\u003c/p\u003e \u003cp\u003eImmune related analysis\u003c/p\u003e \u003cp\u003eMulti-algorithms including CIBERSORT, EPIC, xCELL, MCPCounter, QUANTISEQ were applied to measure the immune infiltration level of bulk samples. ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumours using Expression data) algorithm was employed to evaluate the immune and stromal components of tumors, from which can we calculate the tumor purity. The prediction of immunotherapy response between groups charactered by RCD-related score was conducted by applying Tumor Immune Dysfunction and Exclusion (TIDE) algorithm.\u003c/p\u003e \u003cp\u003eGenomic analysis\u003c/p\u003e \u003cp\u003eSingle nucleotide variation of glioma patients was analyzed via R package \u0026lsquo;maftools\u0026rsquo;. Tumor mutation burden (TMB) and mutant-allele tumor heterogeneity (MATH) were calculated using \u0026lsquo;maftools\u0026rsquo; as well. Copy number variation (CNV) of glioma patients were analyzed using \u0026lsquo;GISTIC2.0\u0026rsquo;.\u003c/p\u003e \u003cp\u003eDrug susceptibility prediction analysis\u003c/p\u003e \u003cp\u003eThe prediction of drug sensitivity was performed using R package \u0026lsquo;oncoPredict\u0026rsquo;, which output the predictive value on behalf of half maximal inhibitory concentration (IC50) using data from Genomics of Drug Sensitivity in Cancer (GDSC, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cancerrxgene.org\u003c/span\u003e\u003cspan address=\"https://www.cancerrxgene.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database.\u003c/p\u003e \u003cp\u003eSingle cell and spatial RNA-seq data processing\u003c/p\u003e \u003cp\u003eThe preprocessing of scRNAseq data, cell clustering and dimension reduction were performed using the R package \u0026lsquo;Seurat\u0026rsquo;. Cell annotation of which was downloaded from the corresponding dataset. The RRScore was calculated using \u0026lsquo;AddModuleScore\u0026rsquo; function.\u003c/p\u003e \u003cp\u003eThe spatial transcriptome data was normalized using \u0026lsquo;SCTransform\u0026rsquo; algorithm. The specific molecular components of the Hypoxia and pEMT modules were referenced from previously published literature (see Supplementary Table\u0026nbsp;1) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eImmunohistochemical analysis of glioma tissues\u003c/p\u003e \u003cp\u003eParaffin-embedded tissue microarrays were obtained from the Department of Neurosurgery at Xiangya Hospital. Tumor tissues from glioma patients who underwent initial radical resection (n\u0026thinsp;=\u0026thinsp;83) and adjacent non-tumor tissues (n\u0026thinsp;=\u0026thinsp;12) were included in this study. The human tissue consent procedures and protocols were approved by the Ethics Committee of Xiangya Hospital (Approval No. 202404081). Immunohistochemical (IHC) analysis was performed using primary antibodies against TIMP1 (dilution 1:2000, Proteintech, China). The IHC score was calculated: Total score\u0026thinsp;=\u0026thinsp;Percentage score (\u0026le;\u0026thinsp;25%, 1; 26\u0026ndash;50%, 2; 51\u0026ndash;75%, 3; and \u0026gt;\u0026thinsp;75%, 4) \u0026times; Intensity score (no staining, 0; light brown, 1; moderate brown, 2; and dark brown, 3).\u003c/p\u003e \u003cp\u003eMalignant phenotype experiments of tumor cells\u003c/p\u003e \u003cp\u003eSi-RNA was procured from RiboBio Corporation. The glioma cell line U251 underwent treatment with si-RNA for 24\u0026ndash;48 hours to assess its efficacy in interfering with RNA expression, followed by subsequent malignant phenotype experiments.\u003c/p\u003e \u003cp\u003eCell proliferation assays comprised CCK-8 and colony formation assays. Post-digestion, cells were evenly seeded at a density of 2000 cells per well in a 96-well plate. CCK-8 solution was added at 24-hour intervals, and changes in cell quantity were quantified using instrumentation. In parallel, cells were plated at 1000 cells per well in a 6-well plate, and after 1\u0026ndash;2 weeks, they were fixed and stained with crystal violet for colony enumeration, allowing for evaluation of differences in colony formation.\u003c/p\u003e \u003cp\u003eFor Transwell assays, cells were digested and seeded at a density of 20,000 cells per unit in chambers coated with Matrigel. Serum-free medium was utilized for the upper chamber, while serum-containing medium was added to the lower chamber. Following 48\u0026ndash;72 hours of incubation, cell migration was assessed through appropriate staining and photography to discern disparities in invasion capability.\u003c/p\u003e \u003cp\u003eUpon reaching confluence in the 6-well plate, tumor cells were subjected to a scratch assay using a 200 \u0026micro;l pipette tip. After removal of detached cells, images were captured under a microscope at 0-hour and 48-hour to quantify differences in cell migration distance.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eExploring RCD pathway heterogeneity in glioma\u003c/p\u003e \u003cp\u003eTo characterize the heterogeneity of cellular death pathways in gliomas, we utilized GSVA to estimate the enrichment scores for RCD pathways in each sample within the TCGA cohort. Through clustering analysis, we determined that the optimal number of clusters was 4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA-G). Subsequently, based on the enrichment scores of the RCD pathways, we classified 667 glioma tumors into 4 distinct heterogeneous subtypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Cluster 1 exhibited relatively specific high expression of alkaliptosis and netotic cell death, while Cluster 2 showed relatively specific high expression of ferroptosis and entotic cell death. Clusters 3 and 4 displayed distinct expression characteristics in Mpt-driven necrosis, disulfidepotosis, parthanatos, autophagy, and cuproptosis. Moreover, compared to the other three clusters, Cluster 1 demonstrated the poorest clinical prognosis, with the lowest overall survival and disease-specific survival rates (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC,D). Subsequently, we conducted functional enrichment analysis for the four clusters. In the HALLMARKS database, patients in Cluster 1 were found to be more enriched in Mtorc1 signaling, reactive oxygen species pathway, hypoxia, EMT, and DNA repair. In the Reactome database, cases in Cluster 1 showed greater enrichment in events such as Cyclin A B1 B2 Associated Events During G2 M Transition, Unwinding of DNA, and release of apoptotic factors from the mitochondria, among others (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003eS1\u003c/span\u003eH).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eClinical and immune profiling of four clusters\u003c/p\u003e \u003cp\u003eIn the subset of grade 2 gliomas with relatively favorable prognosis, Cluster 3 and Cluster 4 show significant representation, with Cluster 3 being predominant. Notably, in Grade 4 gliomas, Cluster 1, associated with the poorest prognosis, holds the largest share, reaching 80%. Among other clinical features, Cluster 1 consistently dominates in clinical phenotypes associated with poorer prognosis, such as glioblastoma, IDH wild-type, 1p/19q non-codeletion, MGMT unmethylated, and others (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eA,B and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA-F).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSubsequently, we utilized the CIBERSORT algorithm to calculate the proportions of immune cells in each cluster. The results revealed variations in several cell populations, including macrophage M2, monocytes, T cells CD4 memory resting, and plasma cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eC and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003eS2\u003c/span\u003eG). Furthermore, utilizing four distinct algorithms, we evaluated the differences in cellular composition within the microenvironment of each cluster. In the EPIC algorithm, Cluster 1 exhibited the highest relative expression of Cancer-Associated Fibroblasts (CAFs) and endothelial cells. According to the xCELL algorithm, Cluster 1 showed elevated expression of astrocytes and CD4\u0026thinsp;+\u0026thinsp;memory T cells. The MCPcounter algorithm indicated a relatively higher presence of endothelial cells and fibroblasts in Cluster 1. In the QUANTISEQ algorithm, Cluster 1 demonstrated a higher relative presence of macrophages and T cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eAdditionally, we utilized the ESTIMATE algorithm to compute the tumor immune-related scores. Compared to the clusters with better prognosis, namely Cluster 3 and Cluster 4, Cluster 1 exhibits higher ESTIMATE Score, Immune Score, and Stromal Score. Moreover, Cluster 1 has the lowest tumor purity (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eE,F).\u003c/p\u003e \u003cp\u003eGenetic landscape analysis of four clusters\u003c/p\u003e \u003cp\u003eWe proceeded to elucidate the genetic variation characteristics of the tumors. Initially, we computed the MATH scores for the four clusters, revealing that Cluster 1 exhibits the lowest MATH score among them (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Conversely, Cluster 1 demonstrates the highest TMB score (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Subsequent scrutiny of the prognostic implications of these clusters indicated that the ClusterA-TMB high group is associated with the most unfavorable prognosis, while the ClusterNonA-TMBlow group exhibits the most favorable survival outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Within the glioma patient cohort from TCGA, approximately 91.19% (611/670) displayed genetic mutations. Notably, IDH1 mutations were most prevalent (60%), with Cluster 1 predominantly devoid of IDH1 mutations. Additional mutations encompassed TP53 (44%), ATRX (29%), CIC (16%), and others (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Lastly, we provided an account of gene amplifications or deletions across chromosomal positions. Cluster 1 exhibited heightened mutation rates across all chromosomes compared to the other three clusters, with amplifications predominantly localized to chr4, chr7, and chr12, while deletions were principally concentrated on chr1, chr4, and chr9 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eE-H).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eExploring prognostic signatures across four clusters\u003c/p\u003e \u003cp\u003eTo further explore the association of genes with prognosis across different glioma clusters, we initially conducted a comprehensive analysis of gene expression differences between Cluster 1, indicative of the poorest prognosis, and the other three clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-C). The Principal Component Analysis (PCA) plot demonstrated distinct and independent patient distribution landscapes across the four clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). Subsequently, we identified a set of differentially expressed genes, comprising 234 commonly upregulated genes and 75 commonly downregulated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003eE,F). Leveraging these 309 differential genes, we employed a range of sophisticated machine learning algorithms to develop a robust prognostic model for gliomas, utilizing the TCGA dataset for training and CGGA325 and CGGA693 datasets for validation. Among the diverse algorithms evaluated, the CoxBoost\u0026thinsp;+\u0026thinsp;SuperPC combination demonstrated superior performance, leading us to select 12 genes for the construction of the RCD-related prognostic signature (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003eG,H).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eComprehensive prognostic evaluation of RCD-related signature\u003c/p\u003e \u003cp\u003eWe conducted a comprehensive evaluation of the prognostic significance of the RCD-related model built upon the 12 identified genes. In the TCGA training set, patients classified as RRScore-high group exhibited markedly lower average survival rates compared to their low-risk counterparts, with all areas under the curve (AUC) values surpassing 0.85 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). The CGGA325 and CGGA693 validation sets further confirmed the shorter overall survival in the RRScore-high group, corroborated by AUC values indicating robust prognostic assessment (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003eB,C). Expanding the validation scope, we incorporated the GSE55918 and GLASS datasets as additional verification sets. In these datasets, patients in the RRScore-high group consistently displayed significantly reduced survival times relative to those in the RRScore-low group, as elucidated by Kaplan-Meier curves highlighting distinct prognostic disparities (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003eF,G). Furthermore, we performed univariate and multivariate risk assessments for the prognostic signature. The outcomes identified age, grade, IDH status, 1p/19q codeletion, and riskscore as independent prognostic factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003eD,E). Integrating the riskscore with all variables, we developed a nomogram to quantitatively depict the prognostic index associated with glioma patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003eH). The study findings underscored a c-index of 0.871, underscoring its robust capacity for prognostic prediction (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003eI).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eExploring the clinical implications of RCD-related signature in glioma\u003c/p\u003e \u003cp\u003eThe prognostically relevant genes exhibit positive correlations with alkaliptosis, apoptosis, ferroptosis, and various other forms of cell death, while showing negative correlations with MPT-driven necrosis and parthanatos. Notably, PTPRT displays a contrasting trend to the associations (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). We further explored the potential clinical implications of the signature in terms of therapeutic response. Through drug sensitivity correlation analysis, we identified 13 compounds as potential therapeutic agents for gliomas. Compounds such as Dasatinib and Gemcitabine demonstrated high IC50 values in the RRScore-low group, whereas Daporinad and Lapatinib exhibited elevated IC50 values in the RRScore-high group (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). The relationships among glioma prognostic genes, RCD, and predicted drug pathways are depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003eC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAdditionally, the RRScore-high group demonstrated relatively lower Microsatellite Instability (MSI) scores but higher Exclusion scores, IFNG scores, and TIDE scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). Lastly, we conducted an analysis of the correlation with immune modulators, encompassing co-stimulators, co-inhibitors, ligands, cell adhesion molecules, receptors, antigen presentation components, and others. Most of the immunomodulators in the high RRScore group exhibited relatively high expression levels, with only a few exceptions such as VTCN1 and IL12A, which showed no significant correlation (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003eSingle cell analysis of RCD-related signature\u003c/p\u003e \u003cp\u003eWe conducted an in-depth analysis at the single-cell level to explore programmed cell death and its potential impact on glioma tumor progression. Cells with high riskscore predominantly clustered within malignant cells, with a subset also distributed among immune cells in CPTAC dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). Employing published datasets consisting of both LGG and GBM samples [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], we observed a lower average communication number in the high-risk group, indicating potential differences in cell communication (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e8\u003c/span\u003eB) upon stratifying patients based on their scores. Notably, interaction strength was relatively higher in malignant cells of the high-risk group, while myeloid cells exhibited stronger interactions in the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e8\u003c/span\u003eC). Subsequent analysis revealed signaling alterations between different cell types, including pathway changes in PTN, MK, EGF, MIF, and PSAP in various malignant cell subtypes, along with alterations in the CCL pathway in myeloid cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e8\u003c/span\u003eD). Comparative analysis of relative information flow between high and low-risk groups highlighted predominant alterations in pathways like ncWNT, CHEMERIN, CD137, and VEGF in the high-risk group, whereas changes in CX3C, CSF3, EDN, and PROS pathways were notable in the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e8\u003c/span\u003eE). Furthermore, we compared signaling pathway alterations within different cell types in each group. In the high-risk group, pathways such as CCL3, CCL5, IGF, and TGFB were significantly upregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e8\u003c/span\u003eF). Lastly, we identified potential target genes for myeloid cells, including SPP1, HMOX1, SOCS3, CXCL2, and NUPR1, which could serve as targets for therapeutic interventions (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e8\u003c/span\u003eG).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSpatial characterization of RCD-related signature\u003c/p\u003e \u003cp\u003eWe explored the spatial characteristics of the RCD-related signature within the glioma microenvironment. We computed RRScore for various cell types and observed that diff.-like and prolif.stem-like cancer cells exhibited the highest RRScore across all cell types, while stem-like malignant cells and oligodendrocytes displayed the lowest RRScore (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e9\u003c/span\u003eA). Spatial cell type deconvolution analysis revealed distinct spatial distributions of transcriptome-defined cell types in glioma. Cells with high RRS in the diff.-like cancer subtype displayed unique spatial niches compared to those with low RRS, forming a hypoxic, EMT-high tumor core. This core, reminiscent of a histologically defined pseudopalisading zone [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], exhibited regulatory cell death patterns. Notably, myeloid cells were excluded from the tumor core in diff.-like RRS-high cells, despite having a similar spatial distribution overall (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e9\u003c/span\u003eB-D).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003emalignant phenotype experiments of tumor cells\u003c/p\u003e \u003cp\u003eWe initially performed TIMP1 staining on pathological specimens from glioma patients, revealing a relative increase in TIMP1 protein expression in high-grade gliomas (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e10\u003c/span\u003eA). Subsequently, we conducted cellular experiments to validate these findings. Upon downregulating TIMP1 expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e10\u003c/span\u003eB), a notable decrease in cell proliferation capacity was observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e10\u003c/span\u003eC,D). Furthermore, results from colony formation assays indicated a reduction in both the size and number of cell colonies following TIMP1 knockdown (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e10\u003c/span\u003eE). Similarly, findings from Transwell and scratch assays demonstrated that TIMP1 promotes the migration and invasion capabilities of glioma cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e10\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe cellular death mechanisms play a crucial role in maintaining tissue homeostasis under normal physiological conditions and are equally significant in the pathogenesis of various diseases [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Glioma, as a highly invasive malignant tumor, has garnered significant clinical attention due to its formidable prognosis, presenting substantial challenges in diagnosis and treatment [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In this study, we undertook a thorough analysis, employing distinct pathways associated with RCD, to systematically explore the interplay between cell death mechanisms and glioma. For the first time, our findings indicate that various forms of cell death collectively may impact the prognosis of glioma. Notably, we identified prognostically relevant genes such as TIMP1, suggesting their potential involvement in regulating the progression of malignant cells through the modulation of multiple cell death pathways. Our research not only advances our understanding of glioma pathophysiology but also provides a novel perspective with potential clinical significance, particularly in terms of guiding treatment strategies and prognostic assessment.\u003c/p\u003e \u003cp\u003eGlioma patients were stratified into four prognostically relevant subtypes based on RCD-pathways. Cluster1, associated with the poorest prognosis, exhibited a specific death pathway known as alkaliptosis. Alkaliptosis, initially identified in studies involving JTC801, an opioid analgesic, conducted on pancreatic cancer cells, denotes a mode of cell death triggered by pH-dependent alkaliptosis [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Although research on alkaliptosis in the context of glioma has been limited, our study marks the first report establishing a correlation between alkaliptosis and the prognosis of glioma patients. Interestingly, among Cluster1 patients, another relatively specific form of cell death is NETosis. NETosis is a cellular death process occurring predominantely in inflammatory cells, epithelial cells, or tumor cells under conditions of inflammatory stress [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. It is often accompanied by the release of associated inflammatory factors, potentially contributing to ischemia-reperfusion injury and the progression of tumors [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Existing literature suggests that NETosis can awaken nearby dormant tumor cells, stimulating their proliferation and thereby contributing to cancer relapse and progression. This observation may elucidate the relatively poorer prognosis observed in Cluster4, characterized by a high enrichment of NETosis compared to the pathway-similar Cluster3. The two subtypes associated with the poorest prognosis, Cluster1 and Cluster2, exhibit enrichment in multiple similar death pathways. These include apoptosis, oxeipotosis, pyroptosis, immunogenic cell death, lysosome-dependent cell death, necroptosis, and ferroptosis. These death pathways have been extensively reported to contribute to the malignant progression of cells across various cancers.[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] Notably, Cluster 2 presents a relatively unique death pathway known as entotic cell death. Entotic cell death is a form of endocytic cell death where tumor cells engulf and eliminate neighboring cells of the same type [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. This process often occurs under conditions such as abnormal proliferation or glucose deprivation, potentially facilitating nutrient acquisition for tumor growth while also risking the removal of tumor cells by nearby normal cells [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Its ambiguous role in glioma warrants further exploration.\u003c/p\u003e \u003cp\u003eTumor immunity and molecular alterations serve as critical prognostic indications in cancer. Cluster 1, characterized by poorer clinical features (higher grade, glioblastoma subtype, IDH mutation status, etc.), demonstrates higher immune-related scores (ESTIMATE score, Immune score, and Stromal score), and lower tumor purity. Utilizing algorithms for tumor microenvironment cell infiltration, we observe an enrichment of CAFs, M2 macrophages, astrocytes, among others, in Cluster 1. These cell types have been previously implicated in the progression of glioma [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Additionally, Cluster 1 exhibits a higher TMB score, indicating a greater number of genetic mutations within the tumor. High TMB often p often correlates with a poorer prognosis, consistent with our findings [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In the specific molecular mutation landscape, we noted that Cluster 1, associated with the worst prognosis, frequently lacks mutations in IDH1 and ATRX but harbors mutations in genes such as EGFR. These molecular alterations are essential clinical markers for poor prognosis [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Interestingly, the best prognosis in Cluster 3 is associated with mutations in CIC. CIC mutations are often associated with the co-deletion of 1p/19q and are commonly observed in oligodendrogliomas [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. This observation aligns with our previous pathological subtype correlation analysis.\u003c/p\u003e \u003cp\u003eDeep machine learning facilitates the selection of the most suitable prognostic signature from numerous candidate molecules [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. We utilized a combination of various algorithms to compute and screened a glioma prognostic signature comprising 12 genes. Our signature demonstrates relatively accurate prognostic evaluation across five databases: TCGA, CGGA325, CGGA693, GSE55918 and GLASS. Assessing treatment responses in tumors, including targeted drugs and immune checkpoint blockade therapy, is crucial for understanding tumor prognosis. Dasatinib, a multi-target kinase inhibitor, holds potential for inhibiting all members of the Src kinase family (SFK) and BCR-Abl [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. However, literature reports limited efficacy of this drug in the treatment of recurrent glioblastomas, possibly attributed to specific molecular alterations or the complexity of the blood-brain barrier. Our study predicts relatively higher IC50 values of these drugs in the RRScore-low group, suggesting potential characteristics of drug resistance in malignant tumors. Conversely, drugs with relatively higher IC50 values in the RRScore-high group may represent promising treatment options for gliomas. Nevertheless, the choice of any drug is a result of computational analysis using bioinformatics methods and requires validation for clinical value in glioma patients. Tumor cells have the capability to activate immune checkpoint molecules in immune cells, promoting programmed cell death and subsequent immune evasion [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Our results demonstrate that the high RRSscore group exhibits elevated expression levels of most immune checkpoint molecules, indicating their potential as therapeutic targets for future glioma treatments.\u003c/p\u003e \u003cp\u003eIn contrast to patients in the RRScore-high group, those in the RRScore-low group demonstrate a higher frequency of cell-to-cell interactions, potentially indicating increased engagement of immune cells. The robust cellular interactions between malignant cells and immune cells play a crucial role in tumor progression and metastasis [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Our research reveals a significant upregulation of the TGF-β and BMP pathways in the cell communication signaling between malignant cells and myeloid immune cells. TGF-β protein and BMP are members of the TGF superfamily and are secreted by tumor cells, exerting their effects on immune cells such as macrophages within the tumor microenvironment. This interaction contributes to the resistance to immune therapy [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Such phenomena may also contribute to the poorer prognosis observed in patients categorized in the RRScore-high group.\u003c/p\u003e \u003cp\u003eIn spatial transcriptomic analysis, the subset with RRScore-high in the diff-like category exhibits a spatial distribution on histopathology slides reminiscent of hypoxia and pMET. Hypoxic conditions can induce the activation of hypoxia-inducible factors (HIF1α, etc.), which mediate tumor growth, invasion, immune suppression, and drug resistance [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. On the other hand, pEMT represents a module within the epithelial-mesenchymal transition process and plays a pro-carcinogenic role across various tumors, including gliomas [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The spatial distribution patterns of these two pathways delineate areas within glioma tissue characterized by heightened malignancy levels. This underscores that the signature derived from our RCD screening accurately reflect the benign or malignant status of cells. This observation aligns with our previous GSVA hallmarks pathway analysis.\u003c/p\u003e \u003cp\u003eWhile a portion of our findings has been validated through cellular molecular experiments, it is crucial to further validate them with a larger cohort of clinical patients. Additionally, exploring the mechanistic interactions between molecules and multiple pathways of programmed cell death regulation is essential. Therefore, we intend to conduct additional validation in multicenter follow-up studies with higher quality and larger sample sizes. Furthermore, we plan to perform additional molecular experiments in the future to enhance our understanding of the underlying mechanisms.\u003c/p\u003e \u003cp\u003eIn conclusion, our study highlights the collective influence of various forms of RCD on the progression of gliomas. Our signature, established based on RCD pathways, demonstrates relatively accurate prognostic prediction capabilities. This underscores the importance of considering diverse cell death mechanisms in assessing glioma prognosis and suggests the potential clinical utility of our signature in improving prognostic evaluation for glioma patients.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur study unveils the heterogeneous landscape of RCD pathways in glioma, shedding light on their molecular diversity and clinical relevance. Through rigorous computational analyses, we identified four distinct glioma clusters, each characterized by unique RCD pathway enrichments and clinical phenotypes. Notably, Cluster 1 emerged as particularly ominous, associated with aggressive clinical features and dismal prognosis. Furthermore, we developed a robust RCD-related prognostic signature comprising 12 genes, capable of accurately stratifying glioma patients into high and low-risk groups with significant prognostic disparities. Importantly, this signature demonstrated consistent predictive power across diverse validation datasets, highlighting its clinical utility in guiding personalized treatment strategies. Our findings also uncover potential therapeutic implications, identifying promising drug candidates and highlighting avenues for targeted intervention. Additionally, single-cell analysis reveals spatial and signaling alterations associated with high-risk glioma subtypes, offering insights into tumor heterogeneity and microenvironmental dynamics.\u003c/p\u003e\n\u003cp\u003eIn conclusion, our study provides a comprehensive understanding of RCD pathways in gliomas, offering valuable insights into their molecular landscape, clinical implications, and therapeutic opportunities. These findings pave the way for precision medicine approaches and the development of novel therapeutic strategies, ultimately improving patient outcomes in this challenging disease.\u003c/p\u003e\n"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.869801084990957%\" valign=\"top\"\u003e\n \u003cp\u003eRCD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"76.13019891500905%\" valign=\"top\"\u003e\n \u003cp\u003eRegulated cell death\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.869801084990957%\" valign=\"top\"\u003e\n \u003cp\u003eNETosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"76.13019891500905%\" valign=\"top\"\u003e\n \u003cp\u003eNetotic cell death\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.869801084990957%\" valign=\"top\"\u003e\n \u003cp\u003eTCGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"76.13019891500905%\" valign=\"top\"\u003e\n \u003cp\u003eThe Cancer Genome Atlas\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.869801084990957%\" valign=\"top\"\u003e\n \u003cp\u003eCGGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"76.13019891500905%\" valign=\"top\"\u003e\n \u003cp\u003eChinese Glioma Genome Atlas\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.869801084990957%\" valign=\"top\"\u003e\n \u003cp\u003eGLASS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"76.13019891500905%\" valign=\"top\"\u003e\n \u003cp\u003eGlioma Longitudinal AnalySIS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.869801084990957%\" valign=\"top\"\u003e\n \u003cp\u003eGEO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"76.13019891500905%\" valign=\"top\"\u003e\n \u003cp\u003eConsortium and Gene Expression Omnibus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.869801084990957%\" valign=\"top\"\u003e\n \u003cp\u003eGSVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"76.13019891500905%\" valign=\"top\"\u003e\n \u003cp\u003eGene Set Variation Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.869801084990957%\" valign=\"top\"\u003e\n \u003cp\u003eGBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"76.13019891500905%\" valign=\"top\"\u003e\n \u003cp\u003eGlioblastoma Multiforme\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.869801084990957%\" valign=\"top\"\u003e\n \u003cp\u003eROC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"76.13019891500905%\" valign=\"top\"\u003e\n \u003cp\u003eReceiver Operating Characteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.869801084990957%\" valign=\"top\"\u003e\n \u003cp\u003eGSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"76.13019891500905%\" valign=\"top\"\u003e\n \u003cp\u003eGene Set Enrichment Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.869801084990957%\" valign=\"top\"\u003e\n \u003cp\u003eDEGs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"76.13019891500905%\" valign=\"top\"\u003e\n \u003cp\u003eDifferential Expressed Genes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.869801084990957%\" valign=\"top\"\u003e\n \u003cp\u003eRRScore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"76.13019891500905%\" valign=\"top\"\u003e\n \u003cp\u003eRCD Related Score\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.869801084990957%\" valign=\"top\"\u003e\n \u003cp\u003eESTIMATE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"76.13019891500905%\" valign=\"top\"\u003e\n \u003cp\u003eEstimation of STromal and Immune cells in MAlignant Tumours using Expression data\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.869801084990957%\" valign=\"top\"\u003e\n \u003cp\u003eTIDE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"76.13019891500905%\" valign=\"top\"\u003e\n \u003cp\u003eTumor Immune Dysfunction and Exclusion\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.869801084990957%\" valign=\"top\"\u003e\n \u003cp\u003eTMB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"76.13019891500905%\" valign=\"top\"\u003e\n \u003cp\u003eTumor Mutation Burden\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.869801084990957%\" valign=\"top\"\u003e\n \u003cp\u003eMATH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"76.13019891500905%\" valign=\"top\"\u003e\n \u003cp\u003eMutant allele Tumor Heterogeneity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.869801084990957%\" valign=\"top\"\u003e\n \u003cp\u003eCNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"76.13019891500905%\" valign=\"top\"\u003e\n \u003cp\u003eCopy Number Variation\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.869801084990957%\" valign=\"top\"\u003e\n \u003cp\u003eTCGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"76.13019891500905%\" valign=\"top\"\u003e\n \u003cp\u003eThe Cancer Genome Atlas\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.869801084990957%\" valign=\"top\"\u003e\n \u003cp\u003eTMB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"76.13019891500905%\" valign=\"top\"\u003e\n \u003cp\u003eTumor Mutation Burden\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.869801084990957%\" valign=\"top\"\u003e\n \u003cp\u003eCAFs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"76.13019891500905%\" valign=\"top\"\u003e\n \u003cp\u003eCancer Associated Fibroblasts\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.869801084990957%\" valign=\"top\"\u003e\n \u003cp\u003ePCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"76.13019891500905%\" valign=\"top\"\u003e\n \u003cp\u003ePrincipal Component Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.869801084990957%\" valign=\"top\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"76.13019891500905%\" valign=\"top\"\u003e\n \u003cp\u003eAreas Under the Curve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHuman tissue samples used in this study were obtained from partially excised tissues after surgery, written informed consent has been obtained from the patients to publish this paper. The human tissue consent procedures and protocols were approved by the Ethics Committee of Xiangya Hospital (Approval No. 202404081). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data source of the manuscript is from the public database. The details were displayed in the materials and methods section. Other data could contact the corresponding authors.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare they have no conflicts of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Natural Science Foundation of China (81472355), and Provincial Natural Science Foundation of Hunan (2022JJ30931).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCR and XJ conceived the study. ZJ, HH, ZW, WG, WY, HH, QC, LW and WL collected and analyzed data and visualized figures. ZJ, HH and ZW wrote the manuscript. All authors reviewed and approved the submitted manuscript.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely thank the CGGA, TCGA, GEO, SYNAPSE and CPTAC databases for freely providing the transcriptomic information of glioma samples.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eOstrom QT, Patil N, Cioffi G, Waite K, Kruchko C, Barnholtz-Sloan JS. Cbtrus statistical report: Primary brain and other central nervous system tumors diagnosed in the united states in 2013\u0026ndash;2017. Neuro Oncol. 2020;22(12 Suppl 2):iv1\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLouis DN, Perry A, Wesseling P, Brat DJ, Cree IA, Figarella-Branger D, Hawkins C, Ng HK, Pfister SM, Reifenberger G, et al. The 2021 who classification of tumors of the central nervous system: A summary. 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Theranostics. 2021;11(4):1763\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Glioma, Regulated cell death, Prognostic signature, TIMP1, Multi-omic analysis","lastPublishedDoi":"10.21203/rs.3.rs-4321362/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4321362/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eGlioma stands out as the most malignant ailment affecting the central nervous system. Regulated cell death, orchestrated by a multitude of genes, serves as a pivotal determinant in shaping cellular destiny and significantly contributes to tumor advancement. However, there is a dearth of literature delving into the evolution of glioma disease through the prism of cell death patterns. Hence, our objective is to delve into the pertinent molecular mechanisms underlying glioma, with a specific focus on the potential role of regulated cell death.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eDifferent patterns of regulated cell death collectively contribute to the progression of glioma. Clusters characterized by relatively specific high expression of alkalosis and netotic cell death exhibit relatively malignant clinical features. Through differential gene screening, we constructed a prognostic signature consisting of genes such as TIMP1. This model demonstrates good prognostic predictive ability, with its scoring reflecting the progression of glioma. Finally, experimental validation of TIMP1 confirms its involvement in the progression of malignant cells.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThese findings provide new insights into understanding the relationship between regulated cell death and glioma development and identify novel biomarkers may help to guiding precise treatments to glioma.\u003c/p\u003e","manuscriptTitle":"Exploring Heterogeneity Across Diverse Regulated-Cell Death Patterns in Glioma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-03 06:17:21","doi":"10.21203/rs.3.rs-4321362/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":"ca941121-6957-4ef4-b49b-6a659926d00f","owner":[],"postedDate":"May 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-03T13:33:57+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-03 06:17:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4321362","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4321362","identity":"rs-4321362","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}