Characterization of a Pyroptosis-Related lncRNA signature to evaluate immune features and predict prognosis in Lower-grade glioma

Characterization of a Pyroptosis-Related lncRNA signature to evaluate immune features and predict prognosis in Lower-grade 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 Characterization of a Pyroptosis-Related lncRNA signature to evaluate immune features and predict prognosis in Lower-grade glioma Yi Chen, Qiang Liu, Qing Yu, Rui Sui, Ji Shi, Haiyang Liang, Jia Liu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4581543/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 Lower-grade gliomas (LGGs), primarily affecting younger populations, pose a unique challenge in oncological treatment due to their diverse genetic and molecular profiles. Pyroptosis, a specialized form of programmed cell death different from apoptosis, plays a crucial role in cancer pathogenesis by causing cell lysis and inflammation, thereby affecting tumor behavior. This study focuses on the prognostic importance of pyroptosis-related long non-coding RNAs (lncRNAs) in LGGs, aiming to provide new perspectives for individualized therapy. The research involved bioinformatic and statistical analyses of data from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). It includes the collection of RNA-seq expression data from TCGA and CGGA databases, identification of prognostic pyroptosis-related lncRNAs through Pearson correlation and Cox regression analysis, and construction of a Prognostic LncRNA Pyroptosis Score (PLPS) using LASSO regression. The study also encompasses functional enrichment analysis using GO and GSEA, immune characteristics evaluation using various algorithms, and analysis of the genetic landscape in different PLPS subgroups. The study identified 18 pyroptosis-related lncRNAs with significant prognostic value in LGG patients. From these, a PLPS was developed, based on 8 selected lncRNAs, to predict the overall survival of LGG patients. Patients were classified into low-risk and high-risk groups according to the PLPS, allowing an evaluation of their prognoses and clinical molecular features. The study also investigated the immune infiltration status and genomic variations of these patients. The research demonstrated the potential of the identified lncRNAs as biomarkers for personalized treatment strategies in LGG. The findings revealed a complex interaction between pyroptosis, lncRNAs, and tumor biology in LGGs, highlighting the importance of pyroptosis in tumor progression. This study not only contributes significantly to our understanding of LGG pathogenesis and treatment but also opens new pathways for developing targeted therapies based on individual molecular profiles. The results underscore the potential for more effective, personalized treatment approaches in oncology, particularly in the context of LGG. Lower-grade gliomas pyroptosis pyroptosis-related lncRNA prognostic signature immune infiltration LASSO Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Glioma is a brain and spinal cord tumor that originates in the glial cells, which serve as the supportive tissue [1-3]. Usually, grade 2 and 3 gliomas categorized as lower-grade gliomas (LGGs) since they are rarer than grade 4 gliomas, glioblastoma (GBM) [4]. Although LGG is rarer than the grade GBM, its incidence among younger people is increasing [5]. Diagnosis of LGGs primarily involves imaging techniques like magnetic resonance imaging (MRI), which can identify the tumor's location and characteristics [6]. However, definitive diagnosis often requires a biopsy to determine the specific type and grade of the tumor [7]. The standard treatment approach for LGGs generally includes surgery, chemotherapy and radiation therapy. The main objective of surgery is to remove as much of the tumor as feasible [8]. However, due to the infiltrative nature of gliomas, it is challenging to remove them entirely without damaging normal brain tissue [9]. Radiation therapy and chemotherapy are used to control tumor growth and manage symptoms [10]. The limitations of current treatment methods include the difficulty in completely eradicating the tumor, potential side effects, and the risk of the tumor evolving into a higher grade [11]. Moreover, the effectiveness of treatment varies widely among individuals due to the genetic and molecular diversity of LGGs [12, 13]. Molecular biology and genetics advances provide insights into LGGs, leading to more personalized and targeted treatment approaches [14]. Hence, it is critically important to identify reliable biomarkers which can forecast the prognosis for LGGs patients and to discover new potential therapeutic targets for the treatment of LGGs. Pyroptosis, a unique kind of programmed cell death that differs from apoptosis, is marked by its inflammatory properties and shows its significance in immune responses and disease pathogenesis, including cancer [15]. In tumors and gliomas, pyroptosis can exhibit dual roles [16]. For one thing, it can inhibit tumor growth by inducing cancer cells death and stimulating anti-tumor immunity [17]. For example, in certain types of gliomas, triggering pyroptosis within cancer cells can impede tumor advancement and enhance patient outcomes [18]. For another, the inflammatory environment created by pyroptosis can also promote tumor growth and metastasis in some contexts, making its role in cancer complex and context-dependent [19]. The mechanism of pyroptosis is largely mediated by the gasdermin superfamily, particularly Gasdermin D (GSDMD), which plays a central role [20-22]. In tumors, including gliomas, the role of the gasdermin superfamily is emerging as a significant area of research [23, 24]. For instance, activation of pyroptosis through the gasdermin pathway in tumor cells has been proposed as a therapeutic strategy, as it can lead to the direct cancer cells death and the potential enhancement of anti-tumor immunity [25]. However, the exact role and therapeutic potential of gasdermins in gliomas, especially in LGG, is unclear. It remains an area of ongoing investigation, with the possibility of targeting these pathways to represent a novel approach to cancer therapy. Long non-coding RNAs (lncRNAs), generally exceeding 200 nucleotides in length, do not translate into proteins but are involved in controlling numerous cellular functions via various mechanisms [26, 27]. In tumors, lncRNAs can act as oncogenes or suppressors, influencing cancer cell proliferation, apoptosis, metastasis, and drug resistance [28]. Several lncRNAs have been identified in glioma as key tumor proliferation and invasion regulators [29, 30]. For instance, the lncRNA H19 has demonstrated its ability to enhance the proliferation and invasion of glioma cells by influencing the miR-675/IGF1R axis [31]. Similarly, the lncRNA NEAT1, known for its increased expression in gliomas, contributes to tumor progression by the Wnt/β-catenin pathway [32-34]. These lncRNAs often exert their effects by interacting with other molecules, including microRNAs and proteins, affecting various signaling pathways in tumor biology [35]. LncRNAs are important players in the complex regulation of pyroptosis because they broadly influence the transcriptional properties of various cellular processes [36]. One instance is the lncRNA X inactive-specific transcript, which has been identified as inhibiting pyroptosis in non-small cell lung cancer by suppressing the SOD2/ROS pathway [37-39]. Also, lncRNAs can perform as competing endogenous RNAs or circular RNAs to initiate pyroptosis in the tumor microenvironment (TME) [40]. Although there has been some research on the role of lncRNA in LGG, the relationship between lncRNA and cell pyroptosis in LGG has not yet been mentioned. Further studying their complex effects will help discover implications for translation into clinical applications. In this study, we used bioinformatics and statistical methods to analyze data from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) to explore the prognostic significance of pyroptosis-related lncRNAs in LGG patients. Our findings revealed that 18 of these lncRNAs were prognostically significant in LGG patients from TCGA and CGGA. Subsequently, we developed a novel Pyroptosis-related LncRNA Prognostic Signature (PLPS) depended on 8 lncRNAs' predictive power for overall survival (OS) in LGG patients. LGG patients were then categorized into low-risk and high-risk groups according to the PLPS. We observed that patients in these groups exhibited different prognoses, clinical molecular characteristics, levels of immune infiltration, and genomic alterations. This study underscores the potential of pyroptosis-related lncRNAs as innovative biomarkers for predicting prognosis and tailoring treatment in LGG, offering new perspectives for individualized treatment approaches based on specific molecular and immunological profiles. Materials and Methods Data Collection The RNA-seq expression data for lncRNA and mRNA, normalized using Fragments Per Kilobase of exon model per Million mapped reads (FPKM), along with the clinical information of the patients included in the study, were sourced from TCGA (https://portal.gdc.cancer.gov/) and the CGGA (http://www.cgga.org.cn/). 476 and 161 LGG patients were enrolled in the TCGA and CGGA, respectively. Detailed clinicopathological and molecular characteristics of the samples used in this study are provided in Supplementary Table S1. Identification of Prognostic Pyroptosis-Related lncRNAs The geneset related to pyroptosis, encompassing 45 genes, was obtained from the Molecular Signatures Database (https://www.gsea-msigdb.org/gsea/msigdb/index.jsp), and is detailed in Supplementary Table S2. We analyzed the expression data of 12,083 lncRNAs from the TCGA and 12,091 lncRNAs from the CGGA. Initially, a Pearson correlation analysis was conducted between the pyroptosis-related genes and lncRNAs to identify pyroptosis-related lncRNAs, using criteria of R > 0.4 or R < -0.4 and P < 0.05. This was followed by univariate Cox regression analysis to ascertain their prognostic significance, with a significance threshold set at P < 0.001. Pyroptosis-related lncRNAs identified in both the TCGA and CGGA cohorts were considered valid for further study. Construction of PLPS The identified prognostic pyroptosis-related lncRNAs were integrated into an elastic net regularization technique, specifically the least absolute shrinkage and selection operator (LASSO) regression. This analysis was conducted within the TCGA dataset using the "glmnet" R package [41]. The Pyroptosis-Related lncRNA Prognostic Signature (PLPS) was then established by selecting the optimal penalty parameter λ, which corresponded with the minimum error observed in a 10-fold cross-validation process. The risk score for each patient was calculated using the following algorithm: In this algorithm, "Gene(i)" represents the expression value of each included lncRNA, and "Coef(i)" is the corresponding coefficient assigned to it. LGG patients in the study were stratified into two subgroups — low-risk and high-risk — based on the median value of their calculated risk scores. Utilizing the R programming language, principal component analysis (PCA) was executed to assess and compare the genome-wide expression patterns between these two subgroups. Functional Enrichment Analysis Correlation analysis between the PLPS and gene expression profiles was carried out using data from both the TCGA and CGGA datasets. The top 400 genes most closely correlated with PLPS were identified and listed in Supplementary Table S2 for further analysis. To delve into the biological processes these genes are involved in, Gene Ontology (GO) analysis was conducted using the DAVID (Database for Annotation, Visualization and Integrated Discovery) online tools (https://david.ncifcrf.gov/). This analysis focused on the top 400 genes most significantly associated with PLPS. Additionally, Gene Set Enrichment Analysis (GSEA) was employed to determine if the identified gene sets exhibited statistically significant differences between the low-risk and high-risk groups. The statistical significance of these differences was evaluated using the normalized enrichment score (NES) and the P value. Evaluation of the Immune Characteristics The immune scores, stromal scores, ESTIMATE scores, and tumor purity for each patient with LGGs were determined using the ESTIMATE algorithm. This calculation was performed with the help of the "estimate" R package. [41]. Single-sample Gene Set Enrichment Analysis (ssGSEA) and the Tumor Immune Estimation Resource (TIMER) were employed to quantify the immune infiltration enrichment scores for various immune cells and immune-related functions. The gene sets used for this analysis, which are annotated for reference, can be found in Supplementary Table S3. Genetic Landscape of Different PLPS Subgroups Somatic mutation and copy number variation (CNV) data for LGG patients were sourced from the TCGA database. The mutation types and frequencies in both the low-risk and high-risk groups were analyzed and visualized using an oncoplot created with the maftools package. The relationship between CNV and PLPS was assessed using GISTIC 2.0 software. Additionally, the tumor mutation burden (TMB) for each patient was calculated as mutations per megabase (mut/Mb) utilizing maftools. Statistical analysis Statistical analyses and data visualization were executed using GraphPad Prism 7, SPSS 19, and R software 4.0.3. Student’s t-tests were used for assessing differences in expression, while Pearson correlation was employed to examine linear relationships. The association between the PLPS and clinicopathological factors was determined using the chi-square test. Kaplan–Meier survival analysis assessed survival distributions, with the log-rank test evaluating significance between stratified groups. Univariate and multivariate Cox regression analyses identified independent prognostic factors. Time-dependent receiver operating characteristic (ROC) curve analysis was utilized to assess the predictive accuracy of PLPS. The R packages used in this study included corrplot, ggplot2, glmnet, pheatmap, maftools, and pca. A P-value of less than 0.05 was considered statistically significant. Results Identification of Prognostic Pyroptosis-Related lncRNAs in LGG Patients As the methodology is shown in Figure 1A, in this study, we initially matched the ENSEMBL IDs with the lncRNA annotation file, identifying 12,083 lncRNAs in the TCGA and 12,091 lncRNAs in the CGGA. Additionally, we compiled a pyroptosis-related gene set comprising 45 genes from the Molecular Signatures Database. A lncRNA was classified as pyroptosis-related if its expression showed a notable correlation with one or more pyroptosis-related genes (R > 0.4 or R < -0.4, P < 0.05). In the TCGA cohort, 396 lncRNAs were significantly correlated with pyroptosis-related genes. Utilizing univariate Cox regression analysis for prognosis, we selected 48 lncRNAs from this group. Similarly, in the CGGA dataset, we identified 73 lncRNAs following the same criteria. Eventually, 18 lncRNAs common to both datasets were recognized as key pyroptosis-related lncRNAs. Figure 1B illustrates the correlations between these 18 lncRNAs and the pyroptosis-related genes in the TCGA. Construction of the PLPS Here, the PLPS was established to predict OS in LGG patients. This was achieved by incorporating 18 pyroptosis-related prognostic lncRNAs into a LASSO regression model, leading to the selection of 8 pivotal lncRNAs for the PLPS construction. These lncRNAs included AL359643.3, AC025171.3, AC010319.4, CYTOR, NEAT1, LINC02381, LIN00641, and LINC00672 (Figure 2A-B). The coefficient values related to this analysis are displayed in Figure 2C. Further analysis entailed categorizing LGG patients into high and low-expression groups. This division was based on the median expression values of the 8 identified lncRNAs within the TCGA. Survival analysis presented that high AL359643.3, AC025171.3, AC010319.4, CYTOR, NEAT1, and LINC02381 expression levels were meaningfully associated with shorter OS (Figure 2D-I). In contrast, high expression of LIN00641 and LINC00672 was linked to a better prognosis (Figure 2J-K). These findings were corroborated in the CGGA cohort (Figure S1). The PLPS for each LGG patient was then calculated according to the coefficients and expression levels of these 8 lncRNAs in the TCGA cohort. The results provide a nuanced understanding of how individual lncRNAs within the PLPS contribute to patient prognosis. The distinct survival outcomes associated with high expression levels of specific lncRNAs highlight their potential as biomarkers for stratifying patients into different risk categories, thereby aiding in personalized treatment planning and prognosis estimation in LGG. The association between PLPS and clinicopathological features in LGG patients Patients were separated into a high-risk group (n=238 in TCGA, n=80 in CGGA) and a low-risk (n=238 in TCGA, n=81 in CGGA) group with the median risk value. To identify the difference between two groups, we performed PCA based on the genome expression data in both TCGA and CGGA datasets. The analysis revealed that despite some overlap, patients classified as high or low risk tended to cluster in separate directions (Figure 3A-B). Furthermore, the heat map with lncRNAs expression and other clinical features (MGMT status, 1p/19q codeletion, IDH1 status, age, gender, subtype) of the TCGA dataset revealed the expression of AL359643.3, AC025171.3, AC010319.4, CYTOR, NEAT1, LINC02381 upregulated with increasing risk score while the expression of LIN00641, LINC00672 downregulated with increasing risk score. In addition, we found that MGMT unmethylated status, 1p/19q non-codeletion, IDH1 wildtype, advanced age, mesenchymal subtype, and grade 3 patients were greatly enriched in the high-risk class by using the chi-square test (Figure 3C). Additionally, the association between the PLPS and various clinicopathological factors was examined using a t-test. The findings indicated that the risk score was notably higher in patients aged over 45, those with WHO grade 3 gliomas, IDH1 wildtype, MGMT promoter methylation, 1p19q non-codeletion, and in those with gliomas of the mesenchymal and classical subtypes (Figure 3D-I). In contrast, the risk score was unrelated to gender (Figure S2). Similarly, the risk score showed consistent trends in the CCGA dataset, but no significant elevations were observed in MGMT unmethylated and classical subtype gliomas (Figure 3J-O). This comprehensive analysis underscores the potential of PLPS as a tool for assessing the prognosis of LGG patients, correlating genomic data with clinicopathological features to facilitate more tailored therapeutic approaches. Prognostic Validity of the PLPS for LGG The prognostic significance of the PLPS was additionally assessed by employing the log-rank test and Kaplan-Meier analysis in both the TCGA and CGGA datasets, comparing the low-risk and high-risk groups. The findings revealed that LGG patients with lower risk scores had notably better prognoses than those with higher risk scores (Figure 4A-B, P<0.05). The distribution of risk scores and survival status is illustrated in Figures 4C-D, indicating a concentration of living patients in the low-risk group. Furthermore, the ROC curve analysis demonstrated that PLPS had a significant potential to predict overall survival in both the TCGA cohort (1-year AUC = 0.846, 2-year AUC = 0.841, 3-year OS = 0.769, Figure 4E) and CGGA cohort (1-year AUC = 0.811, 2-year AUC = 0.828, 3-year OS = 0.844, Figure 4F). Stratification analysis within the TCGA dataset revealed that PLPS maintained its predictive ability across various subgroups. For grade 2 and 3 gliomas, a higher risk score was correlated with a poorer prognosis (Figure 4G-H, P<0.05). When dividing patients into younger (age <45 years) and older (age ≥45 years) groups, the prognostic value of the risk score remained consistent (Figure 4I-J, P<0.05). Classification based on three important molecular markers – IDH1 mutation, MGMT promoter status, and 1p/19q codeletion – showed that a lower risk score correlated with longer OS in all subgroups except for the 1p/19q non-codeletion cohort, where the trend was similar despite the P value being 0.062 (Figure 4K-P). In patients who received radiotherapy and chemotherapy, the high-risk group exhibited reduced OS compared to the low-risk group (Figure 4Q-R), a finding consistent with results from the CGGA dataset (Figure S3). These findings suggest that PLPS is a robust tool for accurately identifying LGG patients with unfavorable prognoses, regardless of their clinical, pathological, molecular, and treatment characteristics. PLPS was an independent prognostic indicator for LGG patients To determine whether the PLPS acts as an independent prognostic factor for LGG patients, univariate and multivariate Cox regression analyses were performed. In the TCGA dataset, the univariate Cox analysis revealed significant associations of age, tumor grade, radiotherapy, IDH1 mutation, and MGMT promoter status with OS. The multivariate Cox regression further confirmed that a high-risk score (multivariate: HR: 1.702, CI: 1.117–2.594, P=0.013) independently predicted poorer prognosis in LGG patients (Figure 5A). This finding was corroborated by the CGGA dataset, which also identified PLPS as an independent risk factor for OS in LGGs (multivariate: HR: 1.713, CI: 1.275–2.301, P<0.001, Figure 5B). Time-dependent ROC curves were then employed to compare the prognostic predictive ability of PLPS against other independent predictors such as age, WHO grade, and IDH1 status within both TCGA and CGGA. The 1-, 2-, and 3-year ROC curves indicated that the risk score based on pyroptosis-related lncRNAs had higher prediction accuracy than age, WHO grade, and IDH1 status (Figure 5C-H). These results suggest that PLPS is an independent indicator and could be valuable in clinical prognosis evaluation of LGG patients. Correlation of the PLPS With the Immune Landscape of LGG Microenvironment To elucidate the biological functions and signaling pathways associated with PLPS in LGG, GO analysis was conducted using DAVID online tools, focusing on the top 400 genes most correlated with PLPS in the TCGA dataset. The analysis revealed that these genes predominantly participate in immune-related biological processes, like immune response, interferon-gamma-mediated signaling pathway, antigen processing and presentation (Figure 6A). Similarly, GSEA indicated crucial enrichment of immune-related biological processes in the high-risk group, including activation of immune response, T cell receptor signaling pathway, type I interferon production, and B cell activation (Figure 6B). Further exploration of the correlation between PLPS and the immune landscape of the LGG microenvironment was conducted using the TCGA dataset. The risk score proved a significant positive correlation with the immune score (R=0.553, P<0.001), stromal score (R=0.603, P<0.001), ESTIMATE score (R=0.592, P<0.001), and a negative correlation with tumor purity (R=-0.605, P<0.001) (Figures 6C-F). The analysis of immune cell infiltration, conducted through ssGSEA and TIMER algorithms, indicated a significant enrichment of immune cell types, including macrophages, activated CD4 and CD8 T cells, myeloid dendritic cells, and activated B cells, within the high-risk group (Figures 6G-H). This group also said great correlations with most immune-related functions (P<0.001, Figure 6I). The expression of several immune checkpoints, including CD274 (PD-L1), CD80, CD44, CD48, CTLA4, LAG3, PDCD1, NRP1, CD276, and BTLA, was notably higher in the high-risk group compared to the low-risk group in the TCGA (P<0.001, Figure 6J). The aforementioned findings were further validated in the CGGA dataset (Figure S4). Overall, these results indicate a strong association between PLPS and immune infiltration, with the high-risk group demonstrating highly activated immune characteristics. This could potentially make PLPS a useful biomarker for predicting the response to immune checkpoint inhibitor therapies. Genomic profiles in different PLPS groups of LGG patients To delve deeper into the molecular differences between high-risk and low-risk LGG groups, analyses of somatic mutations, copy number alterations (CNA), and TMB were conducted using the TCGA database. The analysis began with identifying the top 20 genes with the highest mutation rates in LGG patients. It was found that IDH1 mutations were the most frequent in both high-risk and low-risk groups. However, IDH1, CIC, and NOTCH1 mutations were significantly more prevalent in low-risk patients, whereas TP53, ATRX, TTN, and EGFR mutations were observed more frequently in high-risk gliomas (Figure 7A). The study then explored somatic CNAs, revealing different chromosomal alteration patterns between low and high-risk LGGs. Amplification of Chr 7 and deletion of Chr 10 were notably more common in high-risk LGGs. Conversely, the incidence of 1p/19q codeletion, a hallmark of oligodendroglioma, decreased with higher risk scores (Figure 7B). GISTIC 2.0 analysis, comparing the lower and upper quartile groups, identified focal amplifications and deletions. High-risk cases showed focal amplification peaks at regions like PIK3C2B (1q32.1), PDGFRA (4q12), EGFR (7p11.2), CDK4 (12q14.1), and a focal deletion peak at 9p21.3 (CDKN2A, CDKN2B). Significant amplification peaks were also observed at 2p24.2, 7q34, 8q24.13, 11q23.3, and 19p13.3, with frequent deletions at 2q37.3, 4q34.3, 10q26.3, and 19q13.42 (Figure 7C, Supplementary Table S4). TMB analysis revealed that high-risk patients had significantly higher TMB than low-risk patients (P<0.001, Figure 7D), and a positive correlation was observed between TMB and PLPS (Figure 7E). Kaplan–Meier survival analysis showed that patients with high TMB and high-risk scores had the worst prognosis, while those with low TMB and low-risk scores had the highest survival rate (Figure 7F). These findings suggest that TMB, along with PLPS, could be an important factor in understanding the prognosis of LGG patients. Discussion Glioma is known as the "brain killer". It has infiltrative growth characteristics and is difficult to completely eradicate on a large scale. LGG include primary and secondary neuroepithelial tumors. For grade II tumors, these tumors are often incurable, but the average survival time is greater than 5 years [42]. In the past, comprehensive treatment consisting of surgery, radiotherapy, and chemotherapy was the troika for the treatment of glioma [43]. At present, low-grade gliomas are mainly treated with surgery, and targeted drugs are added to molecular diagnosis and treatment [44]. LncRNAs have been identified as key regulators in several aspects of glioma behavior, like proliferation, aggression, metastasis, and drug resistance [45]. Many studies have explored the function of lncRNAs in the treatment of low-grade glioma. One such example is the lncRNA brain cytoplasmic RNA 1, which functions as a tumor suppressor and holds potential for use in the diagnosis and treatment of glioma [46]. Another study has identified a specific panel of lncRNAs that show prognostic potential in gliomas. These lncRNAs could be instrumental in the future for differentiating between glioma patients with favorable and unfavorable prognoses [47]. Furthermore, lncRNAs are implicated in the tumorigenesis and progression of gliomas. Their expression levels have been found to correlate with various clinical aspects, such as tumor grade, survival rates, treatment responses, and overall prognosis [48]. These examples provide a glimpse into the ongoing research and potential applications of lncRNAs in the treatment and understanding of low-grade glioma. Moreover, one such mechanism that has garnered significant interest is pyroptosis. Unlike apoptosis, which is often dysregulated in cancer cells, pyroptosis can be triggered in cells that have become resistant to apoptosis, making it a promising alternative pathway for inducing tumor cell death. The feasibility of leveraging pyroptosis in glioma treatment has been demonstrated in many studies. For instance, in one study, researchers found that activating the pyroptosis pathway in glioma cells led to significant tumor regression [18]. This was achieved by using compounds that specifically triggered gasdermin proteins, the key effectors of pyroptosis, leading to the rupture of glioma cell membranes and subsequent cell death. Importantly, this approach not only reduced tumor size but also elicited an immune response, helping to clear tumor cells more effectively [49]. The potential of pyroptosis in treating gliomas, particularly LGGs, lies in its ability to target tumor cells while potentially engaging the immune system. This dual action could provide a more comprehensive approach to glioma treatment, addressing not only the direct elimination of tumor cells but also harnessing the body's immune response to fight the disease. As research progresses, harnessing pyroptosis in glioma therapy may offer a new avenue for treating this challenging and diverse group of brain tumors. Herein, we found18 pyroptosis-related lncRNAs had prognostic value in TCGA and CGGA LGG patients. This study proposes new immune checkpoints for the current urgent need to screen and study LGG. In medical diagnosis prediction, the goal is often to identify the most relevant factors that contribute to a particular health outcome or disease from a vast array of clinical and molecular data [50]. LASSO regression, a type of linear regression analysis, is particularly valued for its ability to enhance the accuracy and interpretability of predictive models, especially in scenarios with a large number of potential predictors and relatively fewer observations [51]. This is where LASSO regression shines. It not only helps in fitting a model that predicts the outcome based on various predictors but also performs variable selection [52]. This regularization technique reduces the model complexity, mitigates overfitting, and enhances model interpretability by retaining only the most significant variables. PLPS is an example of a predictive model in oncology, specifically designed for LGG, that harnesses the power of LASSO regression. PLPS provides a more precise prognostic tool tailored to the individual characteristics of LGG patients. This precision is partly due to the LASSO regression’s ability to identify the most relevant lncRNAs from a large dataset, which are crucial in the pathology of LGG. The model aids clinicians in making more informed decisions regarding treatment strategies, by providing insights into the prognosis depended on the molecular profile of the tumor. The development of PLPS represents a step forward in personalized medicine, where treatments and prognostic tools are tailored to the individual characteristics of each patient’s disease, thus potentially improving outcomes. In our study, we separated LGG patients into low-risk and high-risk categories by PLPS, revealing significant differences in prognosis, clinical molecular characteristics, immune infiltration, and genomic variation between the two groups. Additionally, we confirmed the prognostic validity of the PLPS for LGG and established that PLPS is an independent prognostic indicator for LGG patients. The relevance between pyroptosis and immune infiltration is a burgeoning area of interest in the treatment of glioma [53]. When glioma cells undergo pyroptosis, they release damage-associated molecular patterns and cytokines, which can attract immune cells to the tumor site [54]. This influx of immune cells constitutes immune infiltration [55]. The presence of these immune cells in the tumor microenvironment is critical, as they can recognize and eliminate cancer cells, thus bolstering the body's anti-tumor response. The joint application of therapies that promote pyroptosis and those that enhance immune infiltration presents a synergistic approach in glioma treatment [56]. By inducing pyroptosis, the tumor becomes more visible to the immune system, potentially overcoming the immunosuppressive environment often found in brain tumors [16]. Concurrently, therapies that boost immune infiltration, such as checkpoint inhibitors or adoptive cell transfer, can capitalize on the pro-inflammatory effects of pyroptosis to enhance their efficacy [57]. This integrated strategy addresses two fundamental challenges in glioma treatment, one is the resistance of glioma cells to conventional therapies and the tumor's ability to evade immune surveillance [58]. By simultaneously inducing pyroptosis and promoting immune infiltration, there is potential not only for more effective tumor destruction but also for initiating long-lasting immune responses against glioma cells [16]. Our study found that PLPS is associated with immune infiltration, and high-risk groups exhibit highly activated immune signatures, which may be potential biomarkers for predicting response to immune checkpoint inhibitor treatment. The study opens up new possibilities for developing more effective and targeted treatments for glioma, raising hope for improved outcomes for patients battling this aggressive cancer. Conclusion In conclusion, our research underscores the potential of pyroptosis-related lncRNAs as innovative biomarkers for the prognosis and personalized treatment of LGG. Through bioinformatic and statistical analysis of the TCGA and CGGA datasets, we identified 18 pyroptosis-related lncRNAs with prognostic significance in LGG patients. We developed a PLPS using 8 of these lncRNAs to predict OS in LGG patients. This PLPS enabled the stratification of LGG patients into distinct low-risk and high-risk groups, revealing differences in their prognoses, clinical molecular features, immune infiltration status, and genomic variations. Our study provides valuable insights into tailoring therapeutic strategies based on individual molecular and immunological characteristics, highlights the importance of pyroptosis-related lncRNAs in the context of LGG, and greatly facilitates the study of pyroptosis in glioma, laying the foundation for future potential therapeutic advances. Declarations Authors’ Contributions Y.C., Q.L., and Q.Y. contributed equally to this work and should be considered joint first authors. Y.C., Q.L., and H.X. designed the study, wrote, and revised the manuscript. R.S., J.S., H.L., J.L., and X.Z. substantially contributed to the planning and editing of this work. H.X. and H.P. supervised the work and revised the article for critical revision of important intellectual content. All authors read, edited, and approved the final manuscript for publication. Author Disclosure Statement The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding This work was supported by grants from the National Natural Science Foundation of China (82203682), the Natural Science Foundation of Liaoning Province (2023-BS-046) and the Science and Technology Planning Project of Shenyang (20-205-4-003). Data Availability Statement Data will be made available on request. References Tan, A.C., et al., Management of glioblastoma: State of the art and future directions. CA Cancer J Clin, 2020. 70 (4): p. 299-312. Ostrom, Q.T., et al., CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2013-2017. Neuro Oncol, 2020. 22 (12 Suppl 2): p. iv1-iv96. 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Mu, M., et al., LncRNA BCYRN1 inhibits glioma tumorigenesis by competitively binding with miR-619-5p to regulate CUEDC2 expression and the PTEN/AKT/p21 pathway. Oncogene, 2020. 39 (45): p. 6879-6892. Reon, B.J., et al., Expression of lncRNAs in Low-Grade Gliomas and Glioblastoma Multiforme: An In Silico Analysis. PLoS Med, 2016. 13 (12): p. e1002192. Zhang, Y., et al., Long non-coding RNAs as epigenetic mediator and predictor of glioma progression, invasiveness, and prognosis. Semin Cancer Biol, 2022. 83 : p. 536-542. Loveless, R., R. Bloomquist, and Y. Teng, Pyroptosis at the forefront of anticancer immunity. J Exp Clin Cancer Res, 2021. 40 (1): p. 264. Vasan, R.S., Biomarkers of cardiovascular disease: molecular basis and practical considerations. Circulation, 2006. 113 (19): p. 2335-62. Wang, Q., et al., Nomogram established on account of Lasso-Cox regression for predicting recurrence in patients with early-stage hepatocellular carcinoma. Front Immunol, 2022. 13 : p. 1019638. Li, Y., F. Lu, and Y. Yin, Applying logistic LASSO regression for the diagnosis of atypical Crohn's disease. Sci Rep, 2022. 12 (1): p. 11340. Cao, K., et al., Necroptosis-related lncRNAs: establishment of a gene module and distinction between the cold and hot tumors in glioma. Front Oncol, 2023. 13 : p. 1087117. Wei, X., et al., Role of pyroptosis in inflammation and cancer. Cell Mol Immunol, 2022. 19 (9): p. 971-992. Banchereau, J. and R.M. Steinman, Dendritic cells and the control of immunity. Nature, 1998. 392 (6673): p. 245-52. Zeng, Y., et al., Optimization of cancer immunotherapy through pyroptosis: A pyroptosis-related signature predicts survival benefit and potential synergy for immunotherapy in glioma. Front Immunol, 2022. 13 : p. 961933. Guo, Z.S., et al., Oncolytic Immunotherapy: Conceptual Evolution, Current Strategies, and Future Perspectives. Front Immunol, 2017. 8 : p. 555. Majc, B., et al., Immunotherapy of Glioblastoma: Current Strategies and Challenges in Tumor Model Development. Cells, 2021. 10 (2). Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigure0114.docx YiSupplementaryTableS1.csv YiSupplementaryTableS2.xlsx YiSupplementaryTableS3.xlsx YiSupplementaryTableS4.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4581543","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":330021403,"identity":"1a661201-43a4-4ffe-8146-77c4cc3e3f5a","order_by":0,"name":"Yi Chen","email":"","orcid":"","institution":"Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Chen","suffix":""},{"id":330021405,"identity":"2532e58e-298e-4e6a-a88f-660f64314029","order_by":1,"name":"Qiang Liu","email":"","orcid":"","institution":"Cancer Hospital of China Medical University, Cancer Hospital of Dalian University 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patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB.\u003c/strong\u003e Heat map of the correlations between the 18 identified pyroptosis-related lncRNAs and pyroptosis-related genes within the TCGA dataset.\u003c/p\u003e","description":"","filename":"Picture1.png","url":"https://assets-eu.researchsquare.com/files/rs-4581543/v1/10f9f548dedea636bbdd24bd.png"},{"id":60945287,"identity":"21c576f9-f8b0-46f1-b5e5-de831558c2b6","added_by":"auto","created_at":"2024-07-23 22:21:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":494665,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction and Validation of the PLPS for Predicting Overall Survival in LGG Patients.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA-B.\u003c/strong\u003e LASSO regression model used for their identification.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC.\u003c/strong\u003e The coefficient values for each lncRNA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD-I.\u003c/strong\u003e The association with overall survival in LGG patients in TCGA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJ-K.\u003c/strong\u003e Prognostic Implications of LIN00641 and LINC00672.\u003c/p\u003e","description":"","filename":"Picture2.png","url":"https://assets-eu.researchsquare.com/files/rs-4581543/v1/f531d4f73c0d09e3e4ad82d1.png"},{"id":60944664,"identity":"14d269d5-52b8-4053-b557-05bb86ac58b2","added_by":"auto","created_at":"2024-07-23 22:13:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":468795,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation of PLPS with Clinicopathological Features in LGG Patients.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA-B.\u003c/strong\u003e Distribution of high and low-risk LGG patients based on PLPS in PCA plots.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC.\u003c/strong\u003e Heatmap showing the association between lncRNA expression, risk scores, and clinical features in the TCGA dataset.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD-I.\u003c/strong\u003e Analysis of risk scores in relation to various clinicopathological factors including age, WHO grade, IDH1 status, MGMT promoter methylation, and 1p19q co-deletion in TCGA and\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJ-O.\u003c/strong\u003e Analysis of risk scores in relation to various clinicopathological factors in CGGA datasets.\u003c/p\u003e","description":"","filename":"Picture3.png","url":"https://assets-eu.researchsquare.com/files/rs-4581543/v1/b25957017eade0422a2ac208.png"},{"id":60944657,"identity":"babb4bc2-d320-4ae5-83ac-aac4c480aed2","added_by":"auto","created_at":"2024-07-23 22:13:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":493414,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation of PLPS with Clinicopathological Characteristics in LGG Patients.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA-B.\u003c/strong\u003e Kaplan-Meier survival curves for the low-risk and high-risk groups in the TCGA and CGGA cohorts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC-D.\u003c/strong\u003e Distribution of risk scores and survival statuses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eE-F.\u003c/strong\u003e PLPS's accuracy in predicting overall survival.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eG-R.\u003c/strong\u003e Detailed analyses of risk scores in relation to key clinicopathological factors across TCGA datasets.\u003c/p\u003e","description":"","filename":"Picture4.png","url":"https://assets-eu.researchsquare.com/files/rs-4581543/v1/6ae727925932d16f1f4b6daf.png"},{"id":60945288,"identity":"14558747-6fda-4d28-b0e7-35ea672a9606","added_by":"auto","created_at":"2024-07-23 22:21:04","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":397320,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEvaluating the Independent Prognostic Value of PLPS in LGG.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA-B.\u003c/strong\u003e Univariate and multivariate Cox regression analyses from both TCGA and CGGA datasets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC-H.\u003c/strong\u003e Comparation of predictive accuracy of PLPS with other independent predictors.\u003c/p\u003e","description":"","filename":"Picture5.png","url":"https://assets-eu.researchsquare.com/files/rs-4581543/v1/12bf09e8b721612212340a5f.png"},{"id":60945289,"identity":"218af3e2-0acc-4ca1-8836-e1597e0f7b8d","added_by":"auto","created_at":"2024-07-23 22:21:04","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":769826,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation of PLPS with Immune Landscape and Signaling Pathways in LGG.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003e GO analysis conducted using DAVID online tools.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB.\u003c/strong\u003e GSEA of significant enrichment of immune-related biological processes in the high-risk group\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC-F.\u003c/strong\u003e Correlation of the PLPS risk score with immune score, stromal score, and ESTIMATE score, and a negative correlation with tumor purity in the TCGA dataset.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eG-H.\u003c/strong\u003e Infiltration of various immune cells estimated by ssGSEA and TIMER algorithms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eI.\u003c/strong\u003e Risk score of immune related gene in LGG.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJ.\u003c/strong\u003e Gene expression of immune checkpoints in TCGA.\u003c/p\u003e","description":"","filename":"Picture6.png","url":"https://assets-eu.researchsquare.com/files/rs-4581543/v1/f268f47beaf4129fdefd99f0.png"},{"id":60944658,"identity":"bae55745-c14b-4210-b58a-cd0708e95e45","added_by":"auto","created_at":"2024-07-23 22:13:04","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":726682,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMolecular Differences and Prognostic Implications in High and Low Risk LGGs Based on PLPS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003e Somatic Mutation Profiles in High and Low-Risk LGG Patients Based on PLPS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB.\u003c/strong\u003e Somatic copy number alterations in LGGs between high and low-risk groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC.\u003c/strong\u003e Focal Amplifications and Deletions in High-Risk LGG Patients Identified by GISTIC 2.0 Analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD.\u003c/strong\u003e Significant difference in TMB between high and low-risk LGG patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eE.\u003c/strong\u003e Relationship between tumor mutation burden and PLPS risk score.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eF.\u003c/strong\u003e Kaplan-Meier curves for the prognostic implications of combining TMB and PLPS risk scores in LGG patients.\u003c/p\u003e","description":"","filename":"Picture7.png","url":"https://assets-eu.researchsquare.com/files/rs-4581543/v1/90f6668a729132bd954aa6c6.png"},{"id":64321106,"identity":"dff2aa2d-c4cb-4396-9f31-4995d9161af8","added_by":"auto","created_at":"2024-09-11 15:26:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4259886,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4581543/v1/933d635f-46c1-4c14-83a4-5de2c654e3d1.pdf"},{"id":60944662,"identity":"f7255f66-c7f0-4050-bf36-26d7bde4c445","added_by":"auto","created_at":"2024-07-23 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22:13:04","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":9310,"visible":true,"origin":"","legend":"","description":"","filename":"YiSupplementaryTableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4581543/v1/e8b576f4673eca6b95c1fddc.xlsx"},{"id":60944659,"identity":"e3aba3c8-b8c6-4a29-9774-c1713c023a07","added_by":"auto","created_at":"2024-07-23 22:13:04","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":33603,"visible":true,"origin":"","legend":"","description":"","filename":"YiSupplementaryTableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4581543/v1/ec158ae06c434abdfb357436.xlsx"},{"id":60944660,"identity":"33c48bf3-9671-48d3-b46b-4f8d314e4d4c","added_by":"auto","created_at":"2024-07-23 22:13:04","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":17772,"visible":true,"origin":"","legend":"","description":"","filename":"YiSupplementaryTableS4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4581543/v1/1550548ada9e3c156ee7dcfa.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Characterization of a Pyroptosis-Related lncRNA signature to evaluate immune features and predict prognosis in Lower-grade glioma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlioma is a brain and spinal cord tumor that originates in the glial cells, which serve as the supportive tissue\u0026nbsp;[1-3]. Usually, grade 2 and 3 gliomas categorized as lower-grade gliomas (LGGs) since they are rarer than grade 4 gliomas, glioblastoma (GBM)\u0026nbsp;[4]. Although LGG is rarer than the grade GBM, its incidence among younger people is increasing\u0026nbsp;[5]. Diagnosis of LGGs primarily involves imaging techniques like magnetic resonance imaging (MRI), which can identify the tumor\u0026apos;s location and characteristics\u0026nbsp;[6]. However, definitive diagnosis often requires a biopsy to determine the specific type and grade of the tumor\u0026nbsp;[7].\u0026nbsp;The standard treatment approach for LGGs generally includes surgery, chemotherapy and radiation therapy. The main objective of surgery is to remove as much of the tumor as feasible\u0026nbsp;[8]. However, due to the infiltrative nature of gliomas, it is challenging to remove them entirely without damaging normal brain tissue\u0026nbsp;[9]. Radiation therapy and chemotherapy are used to control tumor growth and manage symptoms\u0026nbsp;[10]. The limitations of current treatment methods include the difficulty in completely eradicating the tumor, potential side effects, and the risk of the tumor evolving into a higher grade\u0026nbsp;[11].\u0026nbsp;Moreover, the effectiveness of treatment varies widely among individuals due to the genetic and molecular diversity of LGGs\u0026nbsp;[12, 13].\u0026nbsp;Molecular biology and genetics advances provide insights into LGGs, leading to more personalized and targeted treatment approaches\u0026nbsp;[14].\u0026nbsp;Hence, it is critically important to identify reliable biomarkers which can forecast the prognosis for LGGs patients and to discover new potential therapeutic targets for the treatment of LGGs.\u003c/p\u003e\n\u003cp\u003ePyroptosis, a unique kind of programmed cell death that differs from apoptosis, is marked by its inflammatory properties and shows its significance in immune responses and disease pathogenesis, including cancer\u0026nbsp;[15]. In tumors and gliomas, pyroptosis can exhibit dual roles\u0026nbsp;[16]. For one thing, it can inhibit tumor growth by inducing cancer cells death and stimulating anti-tumor immunity\u0026nbsp;[17]. For example, in certain types of gliomas, triggering pyroptosis within cancer cells can impede tumor advancement and enhance patient outcomes\u0026nbsp;[18]. For another, the inflammatory environment created by pyroptosis can also promote tumor growth and metastasis in some contexts, making its role in cancer complex and context-dependent\u0026nbsp;[19]. The mechanism of pyroptosis is largely mediated by the gasdermin superfamily, particularly Gasdermin D (GSDMD), which plays a central role\u0026nbsp;[20-22]. In tumors, including gliomas, the role of the gasdermin superfamily is emerging as a significant area of research\u0026nbsp;[23, 24]. For instance, activation of pyroptosis through the gasdermin pathway in tumor cells has been proposed as a therapeutic strategy, as it can lead to the direct cancer cells death and the potential enhancement of anti-tumor immunity\u0026nbsp;[25]. However, the exact role and therapeutic potential of gasdermins in gliomas, especially in LGG, is unclear. It remains an area of ongoing investigation, with the possibility of targeting these pathways to represent a novel approach to cancer therapy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLong non-coding RNAs (lncRNAs), generally exceeding 200 nucleotides in length, do not translate into proteins but are involved in controlling numerous cellular functions via various mechanisms\u0026nbsp;[26, 27]. In tumors, lncRNAs can act as oncogenes or suppressors, influencing cancer cell proliferation, apoptosis, metastasis, and drug resistance\u0026nbsp;[28]. Several lncRNAs have been identified in glioma as key tumor proliferation and invasion regulators\u0026nbsp;[29, 30]. For instance, the lncRNA H19 has demonstrated its ability to enhance the proliferation and invasion of glioma cells by influencing the miR-675/IGF1R axis\u0026nbsp;[31]. Similarly, the lncRNA NEAT1, known for its increased expression in gliomas, contributes to tumor progression by the Wnt/\u0026beta;-catenin pathway\u0026nbsp;[32-34]. These lncRNAs often exert their effects by interacting with other molecules, including microRNAs and proteins, affecting various signaling pathways in tumor biology\u0026nbsp;[35]. LncRNAs are important players in the complex regulation of pyroptosis because they broadly influence the transcriptional properties of various cellular processes\u0026nbsp;[36]. One instance is the lncRNA X inactive-specific transcript, which has been identified as inhibiting pyroptosis in non-small cell lung cancer by suppressing the SOD2/ROS pathway\u0026nbsp;[37-39]. Also, lncRNAs can perform as competing endogenous RNAs or circular RNAs to initiate pyroptosis in the tumor microenvironment (TME)\u0026nbsp;[40]. Although there has been some research on the role of lncRNA in LGG, the relationship between lncRNA and cell pyroptosis in LGG has not yet been mentioned. Further studying their complex effects will help discover implications for translation into clinical applications.\u003c/p\u003e\n\u003cp\u003eIn this study, we used bioinformatics and statistical methods to analyze data from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) to explore the prognostic significance of pyroptosis-related lncRNAs in LGG patients. Our findings revealed that 18 of these lncRNAs were prognostically significant in LGG patients from TCGA and CGGA. Subsequently, we developed a novel Pyroptosis-related LncRNA Prognostic Signature (PLPS) depended on 8 lncRNAs\u0026apos; predictive power for overall survival (OS) in LGG patients. LGG patients were then categorized into low-risk and high-risk groups according to the PLPS. We observed that patients in these groups exhibited different prognoses, clinical molecular characteristics, levels of immune infiltration, and genomic alterations. This study underscores the potential of pyroptosis-related lncRNAs as innovative biomarkers for predicting prognosis and tailoring treatment in LGG, offering new perspectives for individualized treatment approaches based on specific molecular and immunological profiles.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eData Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe RNA-seq expression data for lncRNA and mRNA, normalized using Fragments Per Kilobase of exon model per Million mapped reads (FPKM), along with the clinical information of the patients included in the study, were sourced from TCGA (https://portal.gdc.cancer.gov/) and the CGGA (http://www.cgga.org.cn/). 476 and 161 LGG patients were enrolled in the TCGA and CGGA, respectively. Detailed clinicopathological and molecular characteristics of the samples used in this study are provided in Supplementary Table S1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of Prognostic Pyroptosis-Related lncRNAs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe geneset related to pyroptosis, encompassing 45 genes, was obtained from the Molecular Signatures Database (https://www.gsea-msigdb.org/gsea/msigdb/index.jsp), and is detailed in Supplementary Table S2. We analyzed the expression data of 12,083 lncRNAs from the TCGA and 12,091 lncRNAs from the CGGA. Initially, a Pearson correlation analysis was conducted between the pyroptosis-related genes and lncRNAs to identify pyroptosis-related lncRNAs, using criteria of R \u0026gt; 0.4 or R \u0026lt; -0.4 and P \u0026lt; 0.05. This was followed by univariate Cox regression analysis to ascertain their prognostic significance, with a significance threshold set at P \u0026lt; 0.001. Pyroptosis-related lncRNAs identified in both the TCGA and CGGA cohorts were considered valid for further study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of PLPS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe identified prognostic pyroptosis-related lncRNAs were integrated into an elastic net regularization technique, specifically the least absolute shrinkage and selection operator (LASSO) regression. This analysis was conducted within the TCGA dataset using the \"glmnet\" R package [41]. The Pyroptosis-Related lncRNA Prognostic Signature (PLPS) was then established by selecting the optimal penalty parameter λ, which corresponded with the minimum error observed in a 10-fold cross-validation process. The risk score for each patient was calculated using the following algorithm:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eIn this algorithm, \"Gene(i)\" represents the expression value of each included lncRNA, and \"Coef(i)\" is the corresponding coefficient assigned to it. LGG patients in the study were stratified into two subgroups — low-risk and high-risk — based on the median value of their calculated risk scores. Utilizing the R programming language, principal component analysis (PCA) was executed to assess and compare the genome-wide expression patterns between these two subgroups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional Enrichment Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrelation analysis between the PLPS and gene expression profiles was carried out using data from both the TCGA and CGGA datasets. The top 400 genes most closely correlated with PLPS were identified and listed in Supplementary Table S2 for further analysis. To delve into the biological processes these genes are involved in, Gene Ontology (GO) analysis was conducted using the DAVID (Database for Annotation, Visualization and Integrated Discovery) online tools (https://david.ncifcrf.gov/). This analysis focused on the top 400 genes most significantly associated with PLPS. Additionally, Gene Set Enrichment Analysis (GSEA) was employed to determine if the identified gene sets exhibited statistically significant differences between the low-risk and high-risk groups. The statistical significance of these differences was evaluated using the normalized enrichment score (NES) and the P value.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEvaluation of the Immune Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe immune scores, stromal scores, ESTIMATE scores, and tumor purity for each patient with LGGs were determined using the ESTIMATE algorithm. This calculation was performed with the help of the \"estimate\" R package.\u0026nbsp;[41]. Single-sample Gene Set Enrichment Analysis (ssGSEA) and the Tumor Immune Estimation Resource (TIMER) were employed to quantify the immune infiltration enrichment scores for various immune cells and immune-related functions.\u0026nbsp;The gene sets used for this analysis, which are annotated for reference, can be found in Supplementary Table S3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenetic Landscape of Different PLPS Subgroups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSomatic mutation and copy number variation (CNV) data for LGG patients were sourced from the TCGA database. The mutation types and frequencies in both the low-risk and high-risk groups were analyzed and visualized using an oncoplot created with the maftools package. The relationship between CNV and PLPS was assessed using GISTIC 2.0 software. Additionally, the tumor mutation burden (TMB) for each patient was calculated as mutations per megabase (mut/Mb) utilizing maftools.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analyses and data visualization were executed using GraphPad Prism 7, SPSS 19, and R software 4.0.3. Student’s t-tests were used for assessing differences in expression, while Pearson correlation was employed to examine linear relationships. The association between the PLPS and clinicopathological factors was determined using the chi-square test. Kaplan–Meier survival analysis assessed survival distributions, with the log-rank test evaluating significance between stratified groups. Univariate and multivariate Cox regression analyses identified independent prognostic factors. Time-dependent receiver operating characteristic (ROC) curve analysis was utilized to assess the predictive accuracy of PLPS. The R packages used in this study included corrplot, ggplot2, glmnet, pheatmap, maftools, and pca. A P-value of less than 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eIdentification of Prognostic Pyroptosis-Related lncRNAs in LGG Patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs the methodology is shown in Figure 1A, in this study, we initially matched the ENSEMBL IDs with the lncRNA annotation file, identifying 12,083 lncRNAs in the TCGA and 12,091 lncRNAs in the CGGA. Additionally, we compiled a pyroptosis-related gene set comprising 45 genes from the Molecular Signatures Database. A lncRNA was classified as pyroptosis-related if its expression showed a notable correlation with one or more pyroptosis-related genes (R \u0026gt; 0.4 or R \u0026lt; -0.4, P \u0026lt; 0.05). In the TCGA cohort, 396 lncRNAs were significantly correlated with pyroptosis-related genes. Utilizing univariate Cox regression analysis for prognosis, we selected 48 lncRNAs from this group. Similarly, in the CGGA dataset, we identified 73 lncRNAs following the same criteria. Eventually, 18 lncRNAs common to both datasets were recognized as key pyroptosis-related lncRNAs. Figure 1B illustrates the correlations between these 18 lncRNAs and the pyroptosis-related genes in the TCGA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of the PLPS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHere, the PLPS was established to predict OS in LGG patients. This was achieved by incorporating 18 pyroptosis-related prognostic lncRNAs into a LASSO regression model, leading to the selection of 8 pivotal lncRNAs for the PLPS construction. These lncRNAs included AL359643.3, AC025171.3, AC010319.4, CYTOR, NEAT1, LINC02381, LIN00641, and LINC00672 (Figure 2A-B). The coefficient values related to this analysis are displayed in Figure 2C. Further analysis entailed categorizing LGG patients into high and low-expression groups. This division was based on the median expression values of the 8 identified lncRNAs within the TCGA. Survival analysis presented that high AL359643.3, AC025171.3, AC010319.4, CYTOR, NEAT1, and LINC02381 expression levels were meaningfully associated with shorter OS (Figure 2D-I). In contrast, high expression of LIN00641 and LINC00672 was linked to a better prognosis (Figure 2J-K). These findings were corroborated in the CGGA cohort (Figure S1). The PLPS for each LGG patient was then calculated according to the coefficients and expression levels of these 8 lncRNAs in the TCGA cohort. The results provide a nuanced understanding of how individual lncRNAs within the PLPS contribute to patient prognosis. The distinct survival outcomes associated with high expression levels of specific lncRNAs highlight their potential as biomarkers for stratifying patients into different risk categories, thereby aiding in personalized treatment planning and prognosis estimation in LGG.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe association between PLPS and clinicopathological features in LGG patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients were separated into a high-risk group (n=238 in TCGA, n=80 in CGGA) and a low-risk (n=238 in TCGA, n=81 in CGGA) group with the median risk value. To identify the difference between two groups, we performed PCA based on the genome expression data in both TCGA and CGGA datasets. The analysis revealed that despite some overlap, patients classified as high or low risk tended to cluster in separate directions (Figure 3A-B).\u0026nbsp;Furthermore, the heat map with lncRNAs expression and other clinical features (MGMT status, 1p/19q codeletion, IDH1 status, age, gender, subtype) of the TCGA dataset revealed the expression of AL359643.3, AC025171.3, AC010319.4, CYTOR, NEAT1, LINC02381 upregulated with increasing risk score while the expression of LIN00641, LINC00672 downregulated with increasing risk score. In addition, we found that MGMT unmethylated status, 1p/19q non-codeletion, IDH1 wildtype, advanced age, mesenchymal subtype, and grade 3 patients were greatly enriched in the high-risk class by using the chi-square test (Figure 3C). Additionally, the association between the PLPS and various clinicopathological factors was examined using a t-test. The findings indicated that the risk score was notably higher in patients aged over 45, those with WHO grade 3 gliomas, IDH1 wildtype, MGMT promoter methylation, 1p19q non-codeletion, and in those with gliomas of the mesenchymal and classical subtypes (Figure 3D-I). In contrast, the risk score was unrelated to gender (Figure S2). Similarly, the risk score showed consistent trends in the CCGA dataset, but no significant elevations were observed in MGMT unmethylated and classical subtype gliomas (Figure 3J-O). This comprehensive analysis underscores the potential of PLPS as a tool for assessing the prognosis of LGG patients, correlating genomic data with clinicopathological features to facilitate more tailored therapeutic approaches.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrognostic Validity of the PLPS for LGG\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe prognostic significance of the PLPS was additionally assessed by employing the log-rank test and Kaplan-Meier analysis in both the TCGA and CGGA datasets, comparing the low-risk and high-risk groups. The findings revealed that LGG patients with lower risk scores had notably better prognoses than those with higher risk scores (Figure 4A-B, P\u0026lt;0.05). The distribution of risk scores and survival status is illustrated in Figures 4C-D, indicating a concentration of living patients in the low-risk group. Furthermore, the ROC curve analysis demonstrated that PLPS had a significant potential to predict overall survival in both the TCGA cohort (1-year AUC = 0.846, 2-year AUC = 0.841, 3-year OS = 0.769, Figure 4E) and CGGA cohort (1-year AUC = 0.811, 2-year AUC = 0.828, 3-year OS = 0.844, Figure 4F). Stratification analysis within the TCGA dataset revealed that PLPS maintained its predictive ability across various subgroups. For grade 2 and 3 gliomas, a higher risk score was correlated with a poorer prognosis (Figure 4G-H, P\u0026lt;0.05). When dividing patients into younger (age \u0026lt;45 years) and older (age \u0026ge;45 years) groups, the prognostic value of the risk score remained consistent (Figure 4I-J, P\u0026lt;0.05). Classification based on three important molecular markers \u0026ndash; IDH1 mutation, MGMT promoter status, and 1p/19q codeletion \u0026ndash; showed that a lower risk score correlated with longer OS in all subgroups except for the 1p/19q non-codeletion cohort, where the trend was similar despite the P value being 0.062 (Figure 4K-P). In patients who received radiotherapy and chemotherapy, the high-risk group exhibited reduced OS compared to the low-risk group (Figure 4Q-R), a finding consistent with results from the CGGA dataset (Figure S3). These findings suggest that PLPS is a robust tool for accurately identifying LGG patients with unfavorable prognoses, regardless of their clinical, pathological, molecular, and treatment characteristics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePLPS was an independent prognostic indicator for LGG patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo determine whether the PLPS acts as an independent prognostic factor for LGG patients, univariate and multivariate Cox regression analyses were performed. In the TCGA dataset, the univariate Cox analysis revealed significant associations of age, tumor grade, radiotherapy, IDH1 mutation, and MGMT promoter status with OS. The multivariate Cox regression further confirmed that a high-risk score (multivariate: HR: 1.702, CI: 1.117\u0026ndash;2.594, P=0.013) independently predicted poorer prognosis in LGG patients (Figure 5A). This finding was corroborated by the CGGA dataset, which also identified PLPS as an independent risk factor for OS in LGGs (multivariate: HR: 1.713, CI: 1.275\u0026ndash;2.301, P\u0026lt;0.001, Figure 5B). Time-dependent ROC curves were then employed to compare the prognostic predictive ability of PLPS against other independent predictors such as age, WHO grade, and IDH1 status within both TCGA and CGGA. The 1-, 2-, and 3-year ROC curves indicated that the risk score based on pyroptosis-related lncRNAs had higher prediction accuracy than age, WHO grade, and IDH1 status (Figure 5C-H). These results suggest that PLPS is an independent indicator and could be valuable in clinical prognosis evaluation of LGG patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation of the PLPS With the Immune Landscape of LGG Microenvironment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo elucidate the biological functions and signaling pathways associated with PLPS in LGG, GO analysis was conducted using DAVID online tools, focusing on the top 400 genes most correlated with PLPS in the TCGA dataset. The analysis revealed that these genes predominantly participate in immune-related biological processes, like immune response, interferon-gamma-mediated signaling pathway, antigen processing and presentation (Figure 6A). Similarly, GSEA indicated crucial enrichment of immune-related biological processes in the high-risk group, including activation of immune response, T cell receptor signaling pathway, type I interferon production, and B cell activation (Figure 6B). Further exploration of the correlation between PLPS and the immune landscape of the LGG microenvironment was conducted using the TCGA dataset. The risk score proved a significant positive correlation with the immune score (R=0.553, P\u0026lt;0.001), stromal score (R=0.603, P\u0026lt;0.001), ESTIMATE score (R=0.592, P\u0026lt;0.001), and a negative correlation with tumor purity (R=-0.605, P\u0026lt;0.001) (Figures 6C-F). The analysis of immune cell infiltration, conducted through ssGSEA and TIMER algorithms, indicated a significant enrichment of immune cell types, including macrophages, activated CD4 and CD8 T cells, myeloid dendritic cells, and activated B cells, within the high-risk group (Figures 6G-H). This group also said great correlations with most immune-related functions (P\u0026lt;0.001, Figure 6I). The expression of several immune checkpoints, including CD274 (PD-L1), CD80, CD44, CD48, CTLA4, LAG3, PDCD1, NRP1, CD276, and BTLA, was notably higher in the high-risk group compared to the low-risk group in the TCGA (P\u0026lt;0.001, Figure 6J). The aforementioned findings were further validated in the CGGA dataset (Figure S4). Overall, these results indicate a strong association between PLPS and immune infiltration, with the high-risk group demonstrating highly activated immune characteristics. This could potentially make PLPS a useful biomarker for predicting the response to immune checkpoint inhibitor therapies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenomic profiles in different PLPS groups of LGG patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo delve deeper into the molecular differences between high-risk and low-risk LGG groups, analyses of somatic mutations, copy number alterations (CNA), and TMB were conducted using the TCGA database. The analysis began with identifying the top 20 genes with the highest mutation rates in LGG patients. It was found that IDH1 mutations were the most frequent in both high-risk and low-risk groups. However, IDH1, CIC, and NOTCH1 mutations were significantly more prevalent in low-risk patients, whereas TP53, ATRX, TTN, and EGFR mutations were observed more frequently in high-risk gliomas (Figure 7A). The study then explored somatic CNAs, revealing different chromosomal alteration patterns between low and high-risk LGGs. Amplification of Chr 7 and deletion of Chr 10 were notably more common in high-risk LGGs. Conversely, the incidence of 1p/19q codeletion, a hallmark of oligodendroglioma, decreased with higher risk scores (Figure 7B). GISTIC 2.0 analysis, comparing the lower and upper quartile groups, identified focal amplifications and deletions. High-risk cases showed focal amplification peaks at regions like PIK3C2B (1q32.1), PDGFRA (4q12), EGFR (7p11.2), CDK4 (12q14.1), and a focal deletion peak at 9p21.3 (CDKN2A, CDKN2B). Significant amplification peaks were also observed at 2p24.2, 7q34, 8q24.13, 11q23.3, and 19p13.3, with frequent deletions at 2q37.3, 4q34.3, 10q26.3, and 19q13.42 (Figure 7C, Supplementary Table S4). TMB analysis revealed that high-risk patients had significantly higher TMB than low-risk patients (P\u0026lt;0.001, Figure 7D), and a positive correlation was observed between TMB and PLPS (Figure 7E). Kaplan\u0026ndash;Meier survival analysis showed that patients with high TMB and high-risk scores had the worst prognosis, while those with low TMB and low-risk scores had the highest survival rate (Figure 7F). These findings suggest that TMB, along with PLPS, could be an important factor in understanding the prognosis of LGG patients.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eGlioma is known as the \u0026quot;brain killer\u0026quot;. It has infiltrative growth characteristics and is difficult to completely eradicate on a large scale. LGG include primary and secondary neuroepithelial tumors. For grade II tumors, these tumors are often incurable, but the average survival time is greater than 5 years\u0026nbsp;[42].\u0026nbsp;In the past, comprehensive treatment consisting of surgery, radiotherapy, and chemotherapy was the troika for the treatment of glioma\u0026nbsp;[43]. At present, low-grade gliomas are mainly treated with surgery, and targeted drugs are added to molecular diagnosis and treatment\u0026nbsp;[44]. LncRNAs have been identified as key regulators in several aspects of glioma behavior, like proliferation, aggression, metastasis, and drug resistance\u0026nbsp;[45]. Many studies have explored the function of lncRNAs in the treatment of low-grade glioma. One such example is the lncRNA brain cytoplasmic RNA 1, which functions as a tumor suppressor and holds potential for use in the diagnosis and treatment of glioma\u0026nbsp;[46]. Another study has identified a specific panel of lncRNAs that show prognostic potential in gliomas. These lncRNAs could be instrumental in the future for differentiating between glioma patients with favorable and unfavorable prognoses\u0026nbsp;[47]. Furthermore, lncRNAs are implicated in the tumorigenesis and progression of gliomas. Their expression levels have been found to correlate with various clinical aspects, such as tumor grade, survival rates, treatment responses, and overall prognosis\u0026nbsp;[48]. These examples provide a glimpse into the ongoing research and potential applications of lncRNAs in the treatment and understanding of low-grade glioma. Moreover, one such mechanism that has garnered significant interest is pyroptosis. Unlike apoptosis, which is often dysregulated in cancer cells, pyroptosis can be triggered in cells that have become resistant to apoptosis, making it a promising alternative pathway for inducing tumor cell death. The feasibility of leveraging pyroptosis in glioma treatment has been demonstrated in many studies. For instance, in one study, researchers found that activating the pyroptosis pathway in glioma cells led to significant tumor regression\u0026nbsp;[18]. This was achieved by using compounds that specifically triggered gasdermin proteins, the key effectors of pyroptosis, leading to the rupture of glioma cell membranes and subsequent cell death. Importantly, this approach not only reduced tumor size but also elicited an immune response, helping to clear tumor cells more effectively\u0026nbsp;[49]. The potential of pyroptosis in treating gliomas, particularly LGGs, lies in its ability to target tumor cells while potentially engaging the immune system. This dual action could provide a more comprehensive approach to glioma treatment, addressing not only the direct elimination of tumor cells but also harnessing the body\u0026apos;s immune response to fight the disease. As research progresses, harnessing pyroptosis in glioma therapy may offer a new avenue for treating this challenging and diverse group of brain tumors. Herein, we found18 pyroptosis-related lncRNAs had prognostic value in TCGA and CGGA LGG patients. This study proposes new immune checkpoints for the current urgent need to screen and study LGG.\u003c/p\u003e\n\u003cp\u003eIn medical diagnosis prediction, the goal is often to identify the most relevant factors that contribute to a particular health outcome or disease from a vast array of clinical and molecular data\u0026nbsp;[50]. LASSO regression, a type of linear regression analysis, is particularly valued for its ability to enhance the accuracy and interpretability of predictive models, especially in scenarios with a large number of potential predictors and relatively fewer observations\u0026nbsp;[51].\u0026nbsp;This is where LASSO regression shines. It not only helps in fitting a model that predicts the outcome based on various predictors but also performs variable selection\u0026nbsp;[52].\u0026nbsp;This regularization technique reduces the model complexity, mitigates overfitting, and enhances model interpretability by retaining only the most significant variables. PLPS is an example of a predictive model in oncology, specifically designed for LGG, that harnesses the power of LASSO regression. PLPS provides a more precise prognostic tool tailored to the individual characteristics of LGG patients. This precision is partly due to the LASSO regression\u0026rsquo;s ability to identify the most relevant lncRNAs from a large dataset, which are crucial in the pathology of LGG. The model aids clinicians in making more informed decisions regarding treatment strategies, by providing insights into the prognosis depended on the molecular profile of the tumor. The development of PLPS represents a step forward in personalized medicine, where treatments and prognostic tools are tailored to the individual characteristics of each patient\u0026rsquo;s disease, thus potentially improving outcomes. In our study, we separated LGG patients into low-risk and high-risk categories by PLPS, revealing significant differences in prognosis, clinical molecular characteristics, immune infiltration, and genomic variation between the two groups. Additionally, we confirmed the prognostic validity of the PLPS for LGG and established that PLPS is an independent prognostic indicator for LGG patients.\u003c/p\u003e\n\u003cp\u003eThe relevance between pyroptosis and immune infiltration is a burgeoning area of interest in the treatment of glioma [53]. When glioma cells undergo pyroptosis, they release damage-associated molecular patterns and cytokines, which can attract immune cells to the tumor site [54]. This influx of immune cells constitutes immune infiltration [55]. The presence of these immune cells in the tumor microenvironment is critical, as they can recognize and eliminate cancer cells, thus bolstering the body\u0026apos;s anti-tumor response. The joint application of therapies that promote pyroptosis and those that enhance immune infiltration presents a synergistic approach in glioma treatment [56]. By inducing pyroptosis, the tumor becomes more visible to the immune system, potentially overcoming the immunosuppressive environment often found in brain tumors [16]. Concurrently, therapies that boost immune infiltration, such as checkpoint inhibitors or adoptive cell transfer, can capitalize on the pro-inflammatory effects of pyroptosis to enhance their efficacy [57]. This integrated strategy addresses two fundamental challenges in glioma treatment, one is the resistance of glioma cells to conventional therapies and the tumor\u0026apos;s ability to evade immune surveillance [58]. By simultaneously inducing pyroptosis and promoting immune infiltration, there is potential not only for more effective tumor destruction but also for initiating long-lasting immune responses against glioma cells [16]. Our study found that PLPS is associated with immune infiltration, and high-risk groups exhibit highly activated immune signatures, which may be potential biomarkers for predicting response to immune checkpoint inhibitor treatment. The study opens up new possibilities for developing more effective and targeted treatments for glioma, raising hope for improved outcomes for patients battling this aggressive cancer.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our research underscores the potential of pyroptosis-related lncRNAs as innovative biomarkers for the prognosis and personalized treatment of LGG. Through bioinformatic and statistical analysis of the TCGA and CGGA datasets, we identified 18 pyroptosis-related lncRNAs with prognostic significance in LGG patients. We developed a PLPS using 8 of these lncRNAs to predict OS in LGG patients. This PLPS enabled the stratification of LGG patients into distinct low-risk and high-risk groups, revealing differences in their prognoses, clinical molecular features, immune infiltration status, and genomic variations. Our study provides valuable insights into tailoring therapeutic strategies based on individual molecular and immunological characteristics, highlights the importance of pyroptosis-related lncRNAs in the context of LGG, and greatly facilitates the study of pyroptosis in glioma, laying the foundation for future potential therapeutic advances.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eY.C., Q.L., and Q.Y. contributed equally to this work and should be considered joint first authors. Y.C., Q.L., and H.X. designed the study, wrote, and revised the manuscript. R.S., J.S., H.L., J.L., and X.Z. substantially contributed to the planning and editing of this work. H.X. and H.P. supervised the work and revised the article for critical revision of important intellectual content. All authors read, edited, and approved the final manuscript for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Disclosure Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from the National Natural Science Foundation of China (82203682), the Natural Science Foundation of Liaoning Province (2023-BS-046) and the Science and Technology Planning Project of Shenyang (20-205-4-003).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be made available on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTan, A.C., et al., \u003cem\u003eManagement of glioblastoma: State of the art and future directions.\u003c/em\u003e CA Cancer J Clin, 2020. \u003cstrong\u003e70\u003c/strong\u003e(4): p. 299-312.\u003c/li\u003e\n\u003cli\u003eOstrom, Q.T., et al., \u003cem\u003eCBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2013-2017.\u003c/em\u003e Neuro Oncol, 2020. \u003cstrong\u003e22\u003c/strong\u003e(12 Suppl 2): p. iv1-iv96.\u003c/li\u003e\n\u003cli\u003eJiang, T., et al., \u003cem\u003eCGCG clinical practice guidelines for the management of adult diffuse gliomas.\u003c/em\u003e Cancer Lett, 2016. \u003cstrong\u003e375\u003c/strong\u003e(2): p. 263-273.\u003c/li\u003e\n\u003cli\u003eGittleman, H., A.E. 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Pyroptosis, a specialized form of programmed cell death different from apoptosis, plays a crucial role in cancer pathogenesis by causing cell lysis and inflammation, thereby affecting tumor behavior. This study focuses on the prognostic importance of pyroptosis-related long non-coding RNAs (lncRNAs) in LGGs, aiming to provide new perspectives for individualized therapy.\u003cstrong\u003e \u003c/strong\u003eThe research involved bioinformatic and statistical analyses of data from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). It includes the collection of RNA-seq expression data from TCGA and CGGA databases, identification of prognostic pyroptosis-related lncRNAs through Pearson correlation and Cox regression analysis, and construction of a Prognostic LncRNA Pyroptosis Score (PLPS) using LASSO regression. The study also encompasses functional enrichment analysis using GO and GSEA, immune characteristics evaluation using various algorithms, and analysis of the genetic landscape in different PLPS subgroups. The study identified 18 pyroptosis-related lncRNAs with significant prognostic value in LGG patients. From these, a PLPS was developed, based on 8 selected lncRNAs, to predict the overall survival of LGG patients. Patients were classified into low-risk and high-risk groups according to the PLPS, allowing an evaluation of their prognoses and clinical molecular features. The study also investigated the immune infiltration status and genomic variations of these patients. The research demonstrated the potential of the identified lncRNAs as biomarkers for personalized treatment strategies in LGG. The findings revealed a complex interaction between pyroptosis, lncRNAs, and tumor biology in LGGs, highlighting the importance of pyroptosis in tumor progression. This study not only contributes significantly to our understanding of LGG pathogenesis and treatment but also opens new pathways for developing targeted therapies based on individual molecular profiles. The results underscore the potential for more effective, personalized treatment approaches in oncology, particularly in the context of LGG.\u003c/p\u003e","manuscriptTitle":"Characterization of a Pyroptosis-Related lncRNA signature to evaluate immune features and predict prognosis in Lower-grade glioma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-23 22:12:59","doi":"10.21203/rs.3.rs-4581543/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":"a05dd56c-4e33-41da-b8e9-1f4961fa4d5d","owner":[],"postedDate":"July 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-09-11T15:18:34+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-23 22:12:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4581543","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4581543","identity":"rs-4581543","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}