Construction and Validation of a Necroptosis-Related lncRNA Signature in Prognosis and Immune Microenvironment for Glioma

Background Glioma is the most common primary brain tumor, representing approximately 80.8% of malignant tumors. Necroptosis triggers and enhances antitumor immunity and is expected to be a new target for tumor immunotherapy. The effectiveness of necroptosis-related lncRNAs as potential therapeutic targets for glioma has not been elucidated. Methods We acquired RNA-seq data sets from LGG and GBM samples, and the corresponding clinical characteristic information is from TCGA. Normal brain tissue data is from GTEX. Based on TCGA and GTEx, we used univariate Cox regression to sort out survival-related lncRNAs. Lasso regression models were then built. Then, we performed a separate Kaplan-Meier analysis of the lncRNAs used for modeling. We validated different risk groups via OS, DFS, enrichment analysis, comprehensive immune analysis, and drug sensitivity. Results We constructed a 12 prognostic lncRNAs model after bioinformatic analysis. Subsequently, the risk score of every glioma patient was calculated based on correlation coefficients and expression levels, and the patients were split into low- and high-risk groups according to the median value of the risk score. A nomogram was established for every glioma patient to predict prognosis. Besides, we found significant differences in OS, DFS, immune infiltration and checkpoints, and immune therapy between different risk subgroups. Conclusion Predictive models of 12 necroptosis-related lncRNAs can facilitate the assessment of the prognosis and molecular characteristics of glioma patients and improve treatment modalities.


Introduction
Glioma is the most common primary brain tumor, representing approximately 80.8% of malignant tumors [1]. Current research has made significant progress in the treatment of glioma surgery, radiotherapy, and chemotherapy. Still, the limitations of the current therapy of glioma, including the impact on patients' neurological function, inferior quality of life, and heavy burden on patients' families, cannot be ignored [2]. Although immunotherapy has made considerable progress as a new treatment for malignancies, the 5-year overall survival (OS) for glioma remains below 35% without significant improvement [1]. It urges us to explore more precise and tolerable therapies for glioma. e utility of necroptosis in cancer is complex. On the one hand, the expression of key regulators in the necroptosis pathway is generally downregulated in cancer cells, indicating that cancer cells may escape necroptosis and survive. On the other hand, the expression levels of key regulators are instead elevated in certain types of cancer. Necroptosis has been reported to induce an inflammatory response, promote cancer metastasis, and produce an immunosuppressive tumor microenvironment [3,4]. Necroptosis has also been found to play a crucial role in neuroinflammation and degenerative lesions of the central nervous system (CNS). Necroptosis can cause a vigorous inflammatory reaction that can dramatically alter the local tissue environment and mediate the pathogenesis of CNS disease [5]. As a form of programmed death that overcomes resistance to apoptosis, necroptosis triggers and enhances antitumor immunity and is expected to be a new target for tumor immunotherapy.
Long noncoding RNA (lncRNA) is a class of RNA molecules with a transcription length of over 200 nt. ey do not encode proteins but participate in protein-coding gene regulation in the form of RNA. LncRNA plays an important role in dose compensation effect, epigenetic regulation, cell cycle regulation, and cell differentiation regulation [6]. Previous studies have shown that the p53-inducible lncRNA TRINGS protects cancer cells from necroptosis induced by glucose starvation [7]. is indicates that there is a relationship between lncRNA and necroptosis. Recent research has shown that lncRNA plays an integral role in glioma proliferation, angiogenesis, stem cells, and drug resistance [8]. LncRNA regulates the malignant phenotype of glioma. LncRNA can act as a molecular signaling mediator, regulating the expression of specific genes and corresponding signaling pathways, such as CRNDE-mTOR signaling [9] and the TALC-cMet pathway [10]. Most of the glioma-related lncRNAs serve as "miRNA sponges" to inhibit miRNA activity (e.g., miR-128-3p/GREM1 [11], miR-619-5p/CUEDC2 [12], miR-494-3p/PRMT1 [13], and miR-106b-5p/TUSC2 [14]. is suggests that the function of lncRNA cannot be negligible in glioma. Necroptosis-related lncRNA has also been found to have prognostic value and a correlation with prognosis and therapeutic targets, and immune analysis in a variety of tumors, for instance, gastric cancer [15], stomach adenocarcinoma [16], breast cancer [17], and lung adenocarcinoma [18]. erefore, our exploration of the function of necroptosis-associated lncRNA in glioma is of significance. e effectiveness of necroptosis-related lncRNAs as potential therapeutic targets for glioma has not been elucidated. Studies should be made to figure out the relation between them to provide new ideas for molecular biology diagnosis and treatment targets.

Datasets for Glioma Patients.
We acquired RNA-seq data sets (HTSeq-Counts and HTSeq-FPKM) of Lower Grade Glioma (LGG) and Glioblastoma Multiforme (GBM) samples, and the corresponding clinical characteristic information is from e Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/). Normal brain tissue data is from Genotype-Tissue Expression Project (GTEX, https:// www.gtexportal.org/home/index.html) database. TCGA data is downloaded for the training group. We use R (4.1.2) software and data. table, dplyr, and tidyr R packages to synthesize the data matrix and perform the analysis.

Acquisition of Necroptosis-Related Genes and lncRNAs.
We combined the published literature, the Gene Set Enrichment Analysis (GSEA, https://www.gsea-msigdb.org/ gsea/index.jsp) data, and the use of the KEGGREST R package to download all genes of the necroptosis pathway on KEGG (https://www.genome.jp/kegg/) to obtain 159 necroptosis-related genes (supplementary materials). We then used the limma R package to identify differentially expressed lncRNAs (Log2 fold change (FC) > 1, false discovery rate (FDR) < 0.05). e selected differentially expressed mRNAs were then subjected to KEGG and GO analysis to explore their functional clustering. Correlation analysis of these differential lncRNAs and necroptosis-related genes has proceeded, yielding necroptosis-associated lncRNA with Pearson correlation coefficients >0.5 and p < 0.001.

Establishment and Validation of Prognostic Model.
Based on TCGA and GTEx, we used univariate Cox proportional hazards regression analysis to sort out survivalrelated lncRNAs in necroptosis-related lncRNAs (p < 0.05). Lasso regression models were then built using survivalrelated lncRNAs, and 1000 iterations were performed to acquire a robust model. We select lncRNAs related to progression based on the penalty parameter (λ). We performed a separate Kaplan-Meier analysis of the lncRNAs used for modeling. e screened lncRNAs were used for the multivariate Cox regression model. Risk scores were calculated for the prognostic models. We used the following formula to calculate the risk score: where the coef (lncRNA k ) was the short form of the coefficient of lncRNAs correlated with survival in the Cox model and exp (lncRNA k ) was the expression of lncRNAs. And then, high-and low-risk groups are established based on the median risk score. To assess the significance of the prognostic model, we used the Kaplan-Meier method to generate survival curves for overall survival (OS) and disease-free survival (DFS). We then combined clinical information using age (≥65, <65), gender (male, female), tumor grade (II, III, IV), IDH status (mutation, mild), MGMT status, and risk score to generate univariate and multivariate forest plots and heat maps for determining the applicability of the prognostic model to the clinic. en, the receiver operating curves (ROC) of 1, 3, and 5 years were used to test the predictive ability of the prognostic model ('survivalROC'package).

Nomogram and Calibration.
e age, gender, tumor grade, IDH status, MGMT status, and risk score were used to set up the nomogram. e Hosmer-Lemeshow test was used to generate correction curves to test whether the predicted results matched the actual.

Gene Set Enrichment Analysis.
We used GSEA software and the KEGG gene set to select significantly enriched pathways in high-and low-risk groups. e screening criteria were p < 0.05 and FDR < 0.05.

Immune Infiltration Analysis and Immune Checkpoints.
To explore the relationship between the prognostic model and the immune microenvironment features, we calculated the immune infiltration statuses between the different groups with the application of TIMER (https://timer. cistrome.org/). Wilcoxon signed-rank test, limma, scales, ggplot2, ggpubr, and ggtext R packages were performed to analyze differences in immune infiltrating cells between high-and low-risk groups. In addition, we made comparisons about TME scores and immune checkpoint activation between low-and high-risk groups by the ggpubr R package. Besides, the immune and stromal scores were analyzed between two different risk subgroups by the ESTIMATE R package. In addition, tumor immune dysfunction and exclusion (TIDE) (https://tide.dfci.harvard.edu) indicates that a higher score corresponds to worse immunotherapy.

Investigation of Drug Sensitivity.
We explored the correlation between 138 kinds of drugs and the subgroups identified with prognostic signature genes using the "pRRophetic" package in R to explore the therapeutic response of necroptosis-related lncRNAs, with their drug sensitivity determined by the half-maximal inhibitory concentration (IC50) of glioma patients.

Statistical Analysis.
e R software 4.12 and its corresponding packages were utilized for statistical analyses.

Identification of Necroptosis-Related lncRNAs in Patients with Glioma.
e detailed process is shown in Figure 1. From TCGA and GTEx matrix, we obtained 1152 normal samples and 667 tumor samples. According to the expression of 159 necroptosis-related genes ( Table 1) and 48 differentially expressed mRNAs between normal and tumor samples (Figure 2(a)), GO results showed that differentially expressed necroptosis-associated mRNAs are mainly clustered in response to the virus, type 1 interferon signaling pathway, endosomal membrane, and cytokine receptor binding (Figure 2(b)). KEGG pathway analysis revealed that mRNAs were mainly enriched in necroptosis, influenza A, NOD-like receptors, COVID-19, and hepatitis B and C signaling pathways (Figure 2(c)). We finally got 354 necroptosis-related lncRNAs (Pearson correlation coefficients >0.5 and p < 0.001), including 32 downregulated lncRNAs and 322 upregulated lncRNAs. e correlation between necroptosis genes and necroptosis-related lncRNAs is shown in Table S1. ese will contribute to investigating the role and mechanisms of necroptosis-related lncRNAs in glioma and other related diseases.

Construction of a Prognostic Model according to
Necroptosis-Related lncRNAs in Glioma Patients. Using the univariate Cox regression analysis, we screened 225 necroptosis-related prognostic lncRNAs ( Figure S1), which were significantly correlated with OS from 354 necroptosis-related lncRNAs in the whole TCGA set (Table S1). To avoid overfitting and improve the accuracy of the prognostic signature, we performed the LASSOpenalized Cox analysis on these lncRNAs. We acquired 29 lncRNAs related to necroptosis in glioma when the firstrank value of Log(λ) was the minimum likelihood of deviance bias (Figures 2(d)-2(e)). Finally, 12 lncRNAs were identified after multivariate Cox regression (Figure 2(f )), and seven lncRNAs were regulated positively by necroptosis genes. Subsequently, the risk score of every glioma patient was calculated based on correlation coefficients calculated, and the patients were split into low-and high-risk groups according to the median value of the risk score. e risk score was calculated as follows:   Figure S2. e survival analysis shows that the low-risk group has longer OS than the high-risk group. ere was a statistical difference in the survival curve between the low-risk and high-risk groups (p < 0.001). In addition, we performed PCA on the entire gene expression profiles, 159 necroptosis genes, 354 necroptosis-related lncRNAs, and a risk model classified by the 12 necroptosis-related lncRNAs to detect differences between high and low-risk groups. According to the results of the risk model, there is a discrepancy in the distribution of low-and high-risk groups (Figure 3(c)).

Assessment of the Necroptosis-Related lncRNA Model and Clinical Features of Glioma Patients.
To determine whether the predictive signature is independent prognostic factors for patients with glioma, Cox regression analysis was performed on the entire set. Univariate Cox regression analysis showed that age, grade, and risk score were notably associated with the OS in glioma patients. e HR of the risk score and 95% confidence interval (CI) were 1.064 and 1.054-1.074 (p < 0.001, Figure 4(a)). Multivariate Cox regression analysis (Figure 4(b)) also showed that age, IDH status, grading, and risk score were significantly associated with the OS in glioma patients.
e results of IDH status were contrary to the age, grading, and risk score. e HR of risk score was 1.027, and the 95% CI was 1.011-1.043 (p < 0.001). To identify false positives, we also performed ROC analysis for clinical features and the risk score. e AUC of the risk score was also higher than the AUCs of other clinicopathological characteristics, showing that the prognostic risk model was relatively reliable (Figure 4(c)). Heatmaps have valuable data visualization capabilities. We plotted a heatmap for age, gender, grade, risk, survival station, and other common clinicopathological features, describing the overall distribution of clinical information and lncRNA expression in 667 patients in TCGA (Figure 4(d)). Besides, we also explored the differences  Journal of Oncology between high-risk and low-risk patients in different clinicopathological subtypes ( Figure S3).

Validation of the Prognostic Model for OS and DFS in TCGA.
To test the predictive competence of the prognostic model, we used the uniform formula to calculate risk scores for every patient in TCGA for overall survival and diseasefree survival. We randomly and equally divided all glioma patients in the study into cohort 1 and cohort 2. Besides, we downloaded DFS information from cbioportal (https:// www.cbioportal.org/) for a portion of glioma patients (n � 131) in TCGA. We divided these patients into high-and low-risk groups using the calculations of the previous model and denoted all patients as cohort 3.  4 5 6 7 8 9 10 11 12 13 14 15 16 17 18   disease-free survival. Survival analysis ( Figure 5(e)) showed that it is consistent with the results of the TCGA training set. Significant differences display between low-and high-risk groups. e low-risk group has a longer OS than the highrisk group. To test the sensitivity and specificity of the predictive model, we used time-dependent receiver operating characteristics (ROC) along with the area under the ROC curve (AUC) to determine the outcome. As shown in Figure 5(f ), the 1-, 3-, and 5-year AUCs of the TCGA cohorts 1 and 2 were 0.824, 0.943, and 0.956. Similarly, the AUCs for DFS were 0.853, 0.645, and 0.794, respectively. is suggests that our prognostic model is approaching clinical reality in terms of OS and DFS.

Construction and Calibration of the Nomogram.
We predicted the prognostic model's 1-, 3-and 5-year OS probability by constructing a nomogram containing risk classes and clinical risk factors. Based on clinical characteristics, including age, gender, MGMT methylation, IDH status, WHO grade, subtype, and risk score, the nomogram was established (Figure 6(a)). Additionally, the OS and model prediction rates for years 1, 3, and 5 achieve satisfactory agreement in the calibration curves for TCGA glioma patients (Figure 6(b)).

Investigation of the Immune Factors Based on Prognostic
Models. We further analyzed the activity and enrichment of multiple immune cells, immune pathways, and functions based on the prognostic model. ere are significant differences in the expression levels of immune indicators between the low-and high-risk groups. Vioplot indicated more immune cells in the immune microenvironment of the highrisk group, such as CD8+ T cells, monocytes, and macrophages (Figure 7(a)). We next conducted a study of immune   Journal of Oncology function between low-andhigh-risk groups. Immune processes are more aggressive in the high-risk group, e.g., APC coinhibition, APC costimulation, cytolytic activity, and inflammation-promoting (Figure 7(b)). Most immune checkpoints also displayed better activation in the high-risk group. is suggests using appropriate immune checkpoint inhibitors for the high-risk group (Figure 6(c)). en, the high-risk subgroup in TCGA shows significantly higher scores in immune, stromal, and ESTIMATE scores (Figures 7(d)-7(e)). In addition, the CAF, Exclusion, and MDSC scores were higher in the high-risk subgroup (Figures 8(a)-8(c)), while dysfunction, IFNG, Merck18, TAM M2, and TIDE scores were higher in the low-risk group (Figures 8(d)-8(h)).

Drug Filtering for Necroptosis-Related lncRNA Prognostic
Model and Environment Analysis. To investigate potential drug targeting in the prognostic model for glioma patients' treatment, we estimated treatment response by half-maximal inhibitory concentration (IC50). We screened 138 drugs with IC50s that differed significantly between the two groups. e IC50 of Imatinib in the high-risk was higher, while the IC50 of Cisplatin, Docetaxel, Paclitaxel, and Sunitinib was higher in the low-risk group (Figures 9(a)-9(e)). In addition, the top5 KEGG enrichment results of the high-risk and low-risk subgroups were shown in
In this study, we obtained 354 differentially expressed necroptosis-related lncRNAs. 12 necroptosis-related lncRNAs highly associated with OS in glioma patients were identified by lasso and univariate and multifactorial Cox regression, and risk prognostic models were constructed by risk score (i.e., AC025857.2, AC092718.4, AL513534.1, AC083864.2, ZNF236-DT, AC099850.3, AL590094.1, AC010226.1, POLR2J4, AC023024.1, SLC25A21-AS1, and AC109439.2). In these lncRNAs, AC092718.4 has been reported to be highly correlated with ovarian cancer as a predictive signature [22]. AC099850.3 has been found to promote proliferation and invasion in hepatocellular carcinoma via the PRR11/PI3K/AKT pathway and is also a major participant in prognostic models for squamous cell carcinoma of the tongue and non-small-cell lung cancer [23][24][25]. High expression of AL590094.1 has been found to be a risk factor for patients with clear cell renal cell carcinoma [26]. AC010226.1 as an m6-related lncRNA could be a new therapeutic target for squamous cell carcinoma of the head and neck [27]. POLR2J4 functioned as an oncogene in colorectal through the microRNA-203a-3p.1 and CREB1 axis and is highly expressed in hepatocellular carcinomas [28,29]. SLC25A21-AS1 as ferroptosis-related lncRNA mediated prognosis associated with immune landscapes and radiotherapy responses in glioma, which may shed some light on our study [30]. AC109439.2 had the potential to be used as an adjunct biomarker for TNM staging and more accurate segmentation of esophageal squamous cell carcinoma patients [31]. Five remaining lncRNAs are reported for the first time (i. e., AC025857. 2    ZNF236-DT is a divergent transcript of its neighboring protein-coding gene ZNF236, located on chromosome 18. AC023024.1 is involved in the degradation process of misfolded proteins in the endoplasmic reticulum and has a role in inflammation control [32]. All 12 lncRNAs can be used as diagnostic and prognostic biomarkers for glioma and function as targets for immunotherapy. Meanwhile, we need further basic experiments to verify their functionality. Our method for screening lncRNAs has been validated, and the model validation approach is common practice with reliable results. e results showed that the low-risk group had a longer OS than the high-risk group and were consistent with clinical reality, indicating that our prognostic model was accurate.
GSEA-GO shows high expression of cranial nerve morphogenesis, deoxyribose phosphate metabolism, anaphylatoxin one rich granules, anaphylatoxin one rich granules lumen, and vesicle lumen, and low expression of retrograde transport endosome to golgi, ubiquitin ligase substrate adaptor activity, torc1 signaling, peptidyl lysine demethylation, and cytoplasmic microtubule organization. GSEA-KEGG indicates high expression of a signaling pathway, that is, systemic lupus erythematosus, n glycan biosynthesis, amino sugar, and nucleotide sugar metabolism, cell cycle and glutathione metabolism, and low expression of a signaling pathway, that is, wnt, inositol phosphate metabolism, butanoate metabolism, long-term depression, and taste transduction. ese signaling pathways and biological processes may inspire future exploration of glioma formation and treatment mechanisms.
Immunotherapy is currently used in many tumors but is still being explored for gliomas as immune surveillance in the CNS is more complex [33]. At the same time, the CNS has a unique immune microenvironment and has long been considered an immune-privileged site, which has caused some disturbance in the immunotherapy of gliomas. In addition, a study has shown that standard therapies for glioma such as surgery, radiotherapy, temozolomide chemotherapy, and glucocorticoids may all be immunosuppressive, further highlighting the desirability of developing treatment options that target the immune response [34]. Vaccine therapy, oncolytic virus therapy, immune checkpoint inhibitors, and chimeric antigen receptor (CAR) t-cell therapy are the immunotherapeutic modalities currently being investigated in glioma. Current vaccine approaches that may take advantage of the adaptive immune system include rindopepimut, a peptide vaccine against epidermal growth factor receptor (EGFR) variant III [35]. Dendritic cell-(DC-) based vaccines that use autologous tumor tissue to generate tumor antigens have also been developed, such as DCVax-L [36]. A recombinant lysozyme poliovirus PVSRIPO that activates antitumor immune response has improved OS in glioma patients in a trial [37]. Tests primarily targeting PD-1/PD-L1 or CTLA-4 immune checkpoint inhibitors have been conducted in glioma [38]. Recent studies show that GD2-CAR-T cells are effective in treating diffuse midline gliomas with h3k27 m mutations [39]. e efficacy of these immunotherapeutic strategies for glioma has not been fully demonstrated, and their authenticity and efficacy are open to question. population of glioma patients, a sample of nearly two thousand is not fully representative of the overall population.

Conclusion
Predictive models of 12 necroptosis-related lncRNAs can facilitate the assessment of the prognosis and molecular characteristics of glioma patients and improve treatment modalities, which can be further applied in the clinic.
Data Availability e data that support the findings of this study are available at the TCGA (https://tcga-data.nci.nih.gov/tcga/) and GTEx.

Conflicts of Interest
e authors declare that they have no conflicts of interest.