Glycolysis-Based Genes Are Potential Biomarkers in Thyroid Cancer

While increased glycolysis has been identified as a cancer marker and attracted much attention in thyroid cancer (THCA), the prognostic role of it remains to be further elucidated. Here we aimed to determine a specific glycolysis-associated risk model to predict THCA patients' survival. We also explored the interaction between this signature and tumor immune microenvironment and performed drug screening to identify specific drugs targeting the glycolysis-associated signature. Six genes (CHST6, POM121C, PPFIA4, STC1, TGFBI, and FBP2) comprised the specific model, which was an independent prognostic indicator in THCA patients determined by univariate, LASSO and multivariate Cox regression analyses. The receiver operating characteristic (ROC) curve analysis confirmed the excellent clinical performance of the prognostic signature. According to the specific gene signature, patients were categorized into high- and low-risk subgroups. The high-risk group was characterized by decreased immune score and elevated tumor purity, as well as worser survival prognosis compared to the low-risk group. We also validated the expression of these genes in clinical samples and in-vitro experiments. Lastly, we identified potential drugs targeting the glycolysis-associated signature. The derived glycolysis-related signature is an independent prognostic biomarker for THCA patients and might be used as an efficacy of biomarker for drug-sensitivity prediction.

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Identification and Validation of a Novel Clinical Signature to Predict the Prognosis in Confirmed Coronavirus Disease 2019 Patients

Clinical Infectious Diseases ◽

10.1093/cid/ciaa793 ◽

2020 ◽

Cited By ~ 1

Author(s):

Shangrong Wu ◽

Zhiguo Du ◽

Sanying Shen ◽

Bo Zhang ◽

Hong Yang ◽

...

Keyword(s):

Risk Model ◽

Cox Regression ◽

Prognostic Biomarker ◽

Risk Group ◽

Test Group ◽

Training Group ◽

Low Risk ◽

Rank Test ◽

Log Rank Test ◽

Disease Prognosis

Abstract Background Our aim in this study was to identify a prognostic biomarker to predict the disease prognosis and reduce the mortality rate of coronavirus disease 2019 (COVID-19), which has caused a worldwide pandemic. Methods COVID-19 patients were randomly divided into training and test groups. Univariate and multivariate Cox regression analyses were performed to identify the disease prognosis signature, which was selected to establish a risk model in the training group. The disease prognosis signature of COVID-19 was validated in the test group. Results The signature of COVID-19 was combined with the following 5 indicators: neutrophil count, lymphocyte count, procalcitonin, age, and C-reactive protein. The signature stratified patients into high- and low-risk groups with significantly relevant disease prognosis (log-rank test, P < .001) in the training group. The survival analysis indicated that the high-risk group displayed substantially lower survival probability than the low-risk group (log-rank test, P < .001). The area under the receiver operating characteristic (ROC) curve showed that the signature of COVID-19 displayed the highest predictive accuracy regarding disease prognosis, which was 0.955 in the training group and 0.945 in the test group. The ROC analysis of both groups demonstrated that the predictive ability of the signature surpassed the use of each of the 5 indicators alone. Conclusions The signature of COVID-19 presents a novel predictor and prognostic biomarker for closely monitoring patients and providing timely treatment for those who are severely or critically ill.

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Identification and Validation of Autophaggen-based Nomograms to Predict the Prognostic Value of Patients with Cervical Cancer

10.20944/preprints202008.0476.v1 ◽

2020 ◽

Author(s):

JinQun Jiang ◽

HongYan Xu ◽

PingShen Zhao ◽

Hai Lu

Keyword(s):

Cervical Cancer ◽

High Risk ◽

Risk Score ◽

Risk Model ◽

Cox Regression ◽

Risk Group ◽

Low Risk ◽

Enrichment Score ◽

Training Set ◽

Autophagy Gene

Cervical cancer is a common malignancy in women and has a poor prognosis.More and more studies have shown that autophagy disorder is closely related to the occurrence of tumors. However, the prognostic role of autophagy gene in cervical cancer is still unclear. In this study, we constructed the risk signatures of autophagy related genes to predict the prognosis of cervical cancer. The expression profiles and clinical information of autophagy gene sets were downloaded from the TCGA and GES52903 queues as training sets and validation sets. The cervical normal tissue expression profile data from UCSC XENA website is GTEx data as a supplement to TCGA normal cervical tissue. Univariate COX regression analysis of 17 different autophagy genes with the Consensus approach tumor samples from the TCGA is divided into six subtypes, and the clinical traits in the six subtypes have different distribution, with further then absolute shrinkage and selection operator (LASSO) and multiariable COX regression method finally got seven autophagy genetic risk model is constructed, in the training set, the survival rate of high risk group is lower than the low risk group (p < 0.0001), the validation set,The AUC area of the receiver operating characteristic (ROC) curve, the training set is 0.894, and the verification set is 0.736. We find that the high and low risk score is closely related to the TMN stage (All P is less than 0.05).The nomogram shows that the risk score combined with other indicators such as age, G,T,M, and N better predicts 1-year, 2-year, 3-year survival, and the DCA curve shows that the risk model combined with other indicators produces better clinical efficacy.Then immune cells in 28 in the enrichment score, there were statistically significant differences, high and low risk most GSEA enrichment analysis, the main enrichment in G2 / M checkpoint high-risk score, Genes defining epithelial and mesenchymal transition, raised in response to the low oxygen levels (hypoxia) gene, gene is important to the mitotic spindle assembly, these are closely related with the occurrence of tumor . In conclusion, our constructed autophagy risk signature may be a prognostic tool for cervical cancer.

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Identification of Novel Tumor Microenvironment-Related Long Noncoding RNAs to Determine the Prognosis and Response to Immunotherapy of Hepatocellular Carcinoma Patients

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.781307 ◽

2021 ◽

Vol 8 ◽

Author(s):

Shenglan Huang ◽

Jian Zhang ◽

Xiaolan Lai ◽

Lingling Zhuang ◽

Jianbing Wu

Keyword(s):

Hepatocellular Carcinoma ◽

High Risk ◽

Tumor Microenvironment ◽

Immune Cells ◽

Risk Model ◽

Cox Regression ◽

Risk Group ◽

Low Risk ◽

Kaplan Meier ◽

Treatment Responses

Introduction: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with poor prognosis. The tumor microenvironment (TME) plays a vital role in HCC progression. Thus, this research was designed to analyze the correlation between the TME and the prognosis of HCC patients and to construct a TME-related long noncoding RNA (lncRNA) signature to determine HCC patients’ prognosis and response to immunotherapy.Methods: We assessed the stromal–immune–estimate scores within the HCC microenvironment using the ESTIMATE (Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression Data) algorithm based on The Cancer Genome Atlas database, and their associations with survival and clinicopathological parameters were also analyzed. Thereafter, differentially expressed lncRNAs were filtered out according to the immune and stromal scores. Cox regression analysis was performed to build a TME-related lncRNA risk signature. Kaplan–Meier analysis was used to explore the prognostic value of the risk signature. Furthermore, we explored the biological functions and immune microenvironment features in the high- and low-risk groups. Lastly, we probed the association of the risk model with treatment responses to immune checkpoint inhibitors (ICIs) in HCC.Results: The stromal, immune, and estimate scores were obtained utilizing the ESTIMATE algorithm for patients with HCC. Kaplan–Meier analysis showed that high scores were significantly correlated with better prognosis in HCC patients. Six TME-related lncRNAs were screened to construct the prognostic model. The Kaplan–Meier curves suggested that HCC patients with low risk had better prognosis than those with high risk. Receiver operating characteristic (ROC) curve and Cox regression analyses indicated that the risk model could predict HCC survival exactly and independently. Functional enrichment analysis revealed that some tumor- and immune-related pathways were activated in the high-risk group. We also revealed that some immune cells, which were important in enhancing immune responses toward cancer, were significantly increased in the low-risk group. In addition, there was a close correlation between ICIs and the risk signature, which can be used to predict the treatment responses of HCC patients.Conclusion: We analyzed the influence of the stromal, immune, and estimate scores on the prognosis of HCC patients. A novel TME-related lncRNA risk model was established, which could be effectively applied as an independent prognostic biomarker and predictor of ICIs for HCC patients.

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A Novel Prognostic Cancer-related lncRNA Signature in Papillary Renal Cell Carcinoma

10.21203/rs.3.rs-576394/v1 ◽

2021 ◽

Author(s):

Binghai Chen ◽

Di Dong ◽

Qin Yao ◽

Yuanzhang Zou ◽

Wei Hu

Keyword(s):

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Renal Cell ◽

High Efficiency ◽

Risk Model ◽

Cox Regression ◽

Clinical Feature ◽

Roc Curve Analysis ◽

Significant Difference

Abstract BackgroundPapillary renal cell carcinoma (pRCC) ranks second in renal cell carcinoma and the prognosis of pRCC remains poor. Here, we aimed to screen and identify a novel prognostic cancer-related lncRNA signature in pRCC. MethodsThe RNA-seq profile and clinical feature of pRCC cases were downloaded from TCGA database. Significant cancer-related lncRNAs were obtained from the Immlnc database. Differentially expressed cancer-related lncRNAs (DECRLs) in pRCC were screened for further analysis. Cox regression report was implemented to identify prognostic cancer-related lncRNAs and establish a prognostic risk model, and ROC curve analysis was used to evaluate its precision. The correlation between RP11-63A11.1 and clinical characteristics was further analyzed. Finally, the expression level and role of RP11-63A11.1 were studied in vitro. ResultsA total of 367 DECRLs were finally screened and 26 prognostic cancer-related lncRNAs were identified. Among them, ten lncRNAs (RP11-573D15.8, LINC01317, RNF144A-AS1, TFAP2A-AS1, LINC00702, GAS6-AS1, RP11-400K9.4, LUCAT1, RP11-63A11.1, and RP11-156L14.1) were independently connected with prognosis of pRCC. These ten lncRNAs were incorporated into a prognostic risk model. In accordance with the median value of the riskscore, pRCC cases were separated into high and low risk groups. Survival analysis indicated that there was a significant difference on overall survival (OS) rate between the two groups. The area under curve (AUC) in different years indicated that the model was of high efficiency in prognosis prediction. RP11-63A11.1 was mainly expressed in renal tissues and it correlated with the tumor stage, T, M, N classifications, OS, PFS, and DSS of pRCC patients. Consistent with the expression in pRCC tissue samples, RP11-63A11.1 was also downregulated in pRCC cells. More importantly, upregulation of RP11-63A11.1 attenuated cell survival and induced apoptosis. ConclusionsTen cancer-related lncRNAs were incorporated into a powerful model for prognosis evaluation. RP11-63A11.1 functioned as a cancer suppressor in pRCC and it might be a potential therapeutic target for treating pRCC.

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Construction of a Prognostic Model Related to Ferroptosis and Iron-Metabolism and the Relationship with the Immune Microenvironment of Gastric Cancer

10.21203/rs.3.rs-708494/v1 ◽

2021 ◽

Author(s):

Fang Wen ◽

Xiaoxue Chen ◽

Wenjie Huang ◽

Shuai Ruan ◽

Suping Gu ◽

...

Keyword(s):

Gastric Cancer ◽

High Risk ◽

Iron Metabolism ◽

Risk Model ◽

Cox Regression ◽

Risk Group ◽

Risk Groups ◽

High Risk Group ◽

Low Risk ◽

Immune Microenvironment

Abstract Background: The diagnosis rate and mortality of gastric cancer (GC) are among the highest in the global, so it is of great significance to predict the survival time of GC patients. Ferroptosis and iron-metabolism make a critical impact on tumor development and are closely linked to the treatment of cancer and the prognosis of patients. However, the predictive value of the genes involved in ferroptosis and iron-metabolism in GC and their effects on immune microenvironment remain to be further clarified.Methods: In this study, the RNA sequence information and general clinical indicators of GC patients were acquired from the public databases. We first systematically screen out 134 DEGs and 13 PRGs related to ferroptosis and iron-metabolism. Then, we identified six PRDEGs (GLS2, MTF1, SLC1A5, SP1, NOX4, and ZFP36) based on the LASSO-penalized Cox regression analysis. The 6-gene prognostic risk model was established in the TCGA cohort and the GC patients were separated into the high- and the low-risk groups through the risk score median value. GEO cohort was used for verification. The expression of PRDEGs was verified by quantitative QPCR.Results: Our study demonstrated that patients in the low-risk group had a higher survival probability compared with those in high-risk group. In addition, univariate and multivariate Cox regression analyses confirmed that the risk score was an independent prediction parameter. The ROC curve analysis and nomogram manifested that the risk model had the high predictive ability and was more sensitive than general clinical features. Furthermore, compared with the high-risk group, the low-risk group had higher TMB and a longer 5-year survival period. In the immune microenvironment of GC, there were also differences in immune function and highly infiltrated immune cells between the two risk groups.Conclusions: The prognostic risk model based on the six genes associated with ferroptosis and iron-metabolism has a good performance for predicting the prognosis of patients with GC. The treatment of cancer by inducing tumor ferroptosis or mediating tumor iron-metabolism, especially combined with immunotherapy, provides a new possibility for individualized treatment of GC patients.

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Identification and validation of hub genes predicting prognosis of hepatocellular carcinoma

Digestive Surgery ◽

10.1159/000520893 ◽

2021 ◽

Author(s):

Ziyan Chen ◽

Haitao Yu ◽

Lijun Wu ◽

Sina Zhang ◽

Zhihui Lin ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Model ◽

Risk Model ◽

Cox Regression ◽

Risk Group ◽

Histological Grade ◽

High Risk Group ◽

Low Risk ◽

Hub Genes ◽

Cox Regression Analysis

Introduction: Selecting the hub genes associated with hepatocellular carcinoma (HCC) to construct a COX regression model for predicting prognosis in HCC patients. Methods: Using HCC patient data from the ICGC and TCGA databases, screened for 40 core genes highly correlated with histological grade of HCC. Univariate and multivariate COX regression analysis were performed on the genes highly associated with HCC prognosis and the model was established. The expression of those genes was measured by immunohistochemistry in 110 HCC patients who underwent the surgery in The First Affiliated Hospital of Wenzhou Medical University. The survival of HCC patients was analyzed by the Kaplan-Meier method. Results: Eight genes (CDC45, CENPA, MCM10, MELK, CDC20, ASF1B, FANCD2 and NCAPH) were correlated with prognosis, and the same result was observed in 110 HCC patients. Using the regression model, the HCC patients in the training set were classified as high- and low-risk groups. The overall survival (OS) of patients in the high-risk group was shorter than that in the low-risk group, the same results were obtained in verification set. Conclusion: This study found that the risk model according to these eight genes can be used as a predictor of prognosis in HCC. These genes may become alternative biomarkers and therapeutic targets and provide new therapeutic strategies for HCC.

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Construction of a Prognostic Immune-Related LncRNA Risk Model for Lung Adenocarcinoma

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.648806 ◽

2021 ◽

Vol 9 ◽

Author(s):

Yue Li ◽

Ruoyi Shen ◽

Anqi Wang ◽

Jian Zhao ◽

Jieqi Zhou ◽

...

Keyword(s):

High Risk ◽

Differentially Expressed Genes ◽

Risk Model ◽

Cox Regression ◽

Risk Group ◽

Enrichment Analysis ◽

Risk Groups ◽

Low Risk ◽

Differentially Expressed ◽

Cox Regression Analysis

BackgroundLung adenocarcinoma (LUAD) originates mainly from the mucous epithelium and glandular epithelium of the bronchi. It is the most common pathologic subtype of non-small cell lung cancer (NSCLC). At present, there is still a lack of clear criteria to predict the efficacy of immunotherapy. The 5-year survival rate for LUAD patients remains low.MethodsAll data were downloaded from The Cancer Genome Atlas (TCGA) database. We used Gene Set Enrichment Analysis (GSEA) database to obtain immune-related mRNAs. Immune-related lncRNAs were acquired by using the correlation test of the immune-related genes with R version 3.6.3 (Pearson correlation coefficient cor = 0.5, P < 0.05). The TCGA-LUAD dataset was divided into the testing set and the training set randomly. Based on the training set to perform univariate and multivariate Cox regression analyses, we screened prognostic immune-related lncRNAs and given a risk score to each sample. Samples were divided into the high-risk group and the low-risk group according to the median risk score. By the combination of Kaplan–Meier (KM) survival curve, the receiver operating characteristic (ROC) (AUC) curve, the independent risk factor analysis, and the clinical data of the samples, we assessed the accuracy of the risk model. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed on the differentially expressed mRNAs between the high-risk group and the low-risk group. The differentially expressed genes related to immune response between two risk groups were analyzed to evaluate the role of the model in predicting the efficacy and effects of immunotherapy. In order to explain the internal mechanism of the risk model in predicting the efficacy of immunotherapy, we analyzed the differentially expressed genes related to epithelial-mesenchymal transition (EMT) between two risk groups. We extracted RNA from normal bronchial epithelial cell and LUAD cells and verified the expression level of lncRNAs in the risk model by a quantitative real-time polymerase chain reaction (qRT-PCR) test. We compared our risk model with other published prognostic signatures with data from an independent cohort. We transfected LUAD cell with siRNA-LINC0253. Western blot analysis was performed to observed change of EMT-related marker in protein level.ResultsThrough univariate Cox regression analysis, 24 immune-related lncRNAs were found to be strongly associated with the survival of the TCGA-LUAD dataset. Utilizing multivariate Cox regression analysis, 10 lncRNAs were selected to establish the risk model. The K-M survival curves and the ROC (AUC) curves proved that the risk model has a fine predictive effect. The GO enrichment analysis indicated that the effect of the differentially expressed genes between high-risk and low-risk groups is mainly involved in immune response and intercellular interaction. The KEGG enrichment analysis indicated that the differentially expressed genes between high-risk and low-risk groups are mainly involved in endocytosis and the MAPK signaling pathway. The expression of genes related to the efficacy of immunotherapy was significantly different between the two groups. A qRT-PCR test verified the expression level of lncRNAs in LUAD cells in the risk model. The AUC of ROC of 5 years in the independent validation dataset showed that this model had superior accuracy. Western blot analysis verified the change of EMT-related marker in protein level.ConclusionThe immune lncRNA risk model established by us could better predict the prognosis of patients with LUAD.

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Identification of Five Immune-Related lncRNAs Predicting Survival and Tumor Microenvironment Characteristics in Breast Cancer

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/6676692 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Ran Xiao ◽

Meng Yang ◽

Yuanyuan Tan ◽

Rumeng Ding ◽

Duolu Li

Keyword(s):

Breast Cancer ◽

High Risk ◽

Tumor Microenvironment ◽

Risk Model ◽

Risk Group ◽

Risk Groups ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Clinical Samples ◽

Cox Regression Analysis

A common cancer in females, breast cancer (BRCA) mortality has been recently reduced; however, the prognosis of BRCA patients remains poor. This study attempted to develop prognostic immune-related long noncoding RNAs (lncRNAs) for BRCA and identify the effects of these lncRNAs on the tumor microenvironment (TME). Gene expression data from The Cancer Genome Atlas (TCGA) database were collected in order to select differentially expressed lncRNAs. Immune-related lncRNAs were downloaded from the ImmLnc database, where 316 immune-related lncRNAs were identified, 12 of which were found to be significantly related to the prognosis of BRCA patients. Multivariate cox regression analysis was then applied to construct prognostic immune-related lncRNAs as the risk model, including C6orf99, LINC00987, SIAH2-AS1, LINC01010, and ELOVL2-AS1. High-risk and low-risk groups were distinguished according to the median of immune-related risk scores. Accordingly, the overall survival (OS) in the high-risk group was observed to be shorter than that in the low-risk group. qRT-PCR analysis demonstrated that lncRNA expression levels in BRCA cell lines were in basic agreement with predictions except for LINC00987. By validating numerous clinical samples, lncRNA C6orf99 was shown to be highly expressed in the advanced stage, while LINC01010 and SIAH2-AS1 decreased in the advanced T-stage and M-stage. Moreover, the expression of LINC0098 was found to be significantly decreased among the groups (>50 years old). Gene set enrichment analysis (GSEA) was applied to analyze the cancer hallmarks and immunological characteristics of the high-risk and low-risk groups. Importantly, the TIMER database demonstrated that this immune-related lncRNA risk model for breast cancer is related to the infiltration of immune cells. In conclusion, the results indicated that five immune-related lncRNAs could be used as a prognostic model and may even accelerate immunotherapy for BRCA patients.

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Identification and Validation of Autophagy-Related Gene Nomograms to Predict the Prognostic Value of Patients with Cervical Cancer

Journal of Oncology ◽

10.1155/2021/5583400 ◽

2021 ◽

Vol 2021 ◽

pp. 1-17

Author(s):

Jinqun Jiang ◽

HongYan Xu ◽

YiHao Wang ◽

Hai Lu

Keyword(s):

Cervical Cancer ◽

Risk Model ◽

Cox Regression ◽

Risk Group ◽

Low Risk ◽

Enrichment Score ◽

Cervical Tissue ◽

Training Set ◽

Mesenchymal Transition ◽

Normal Cervical Tissue

Autophagy is a process of engulfing one’s own cytoplasmic proteins or organelles and coating them into vesicles, fusing with lysosomes to form autophagic lysosomes, and degrading the contents it encapsulates. Increasing studies have shown that autophagy disorders are closely related to the occurrence of tumors. However, the prognostic role of autophagy genes in cervical cancer is still unclear. In this study, we constructed risk signatures of autophagy-related genes (ARGs) to predict the prognosis of cervical cancer. The expression profiles and clinical information of autophagy gene sets were downloaded from TCGA and GSE52903 queues as training and validation sets. The normal cervical tissue expression profile data from the UCSC XENA website (obtained from GTEx) were used as a supplement to the TCGA normal cervical tissue. Univariate COX regression analysis of 17 different autophagy genes was performed with the consensus approach. Tumor samples from TCGA were divided into six subtypes, and the clinical traits of the six subtypes had different distributions. Further absolute shrinkage and selection operator (LASSO) and multivariable COX regression yielded an autophagy genetic risk model consisting of eight genes. In the training set, the survival rate of the high-risk group was lower than that of the low-risk group ( p < 0.0001). In the validation set, the AUC area of the receiver operating characteristic (ROC) curve was 0.772 for the training set and 0.889 for the verification set. We found that high and low risk scores were closely related to TNM stage ( p < 0.05). The nomogram shows that the risk score combined with other indicators, such as G, T, M, and N, better predicts 1-, 3-, and 5-year survival rates. Decline curve analysis (DCA) shows that the risk model combined with other indicators produces better clinical efficacy. Immune cells with an enrichment score of 28 showed statistically significant differences related to high and low risk. GSEA enrichment analysis showed the main enrichment being in KRAS activation, genes defining epithelial and mesenchymal transition (EMT), raised in response to the low oxygen level (hypoxia) gene and NF-kB in response to TNF. These pathways are closely related to the occurrence of tumors. Our constructed autophagy risk signature may be a prognostic tool for cervical cancer.

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A novel pyroptosis-related gene signature predicts the prognosis of glioma through immune infiltration

BMC Cancer ◽

10.1186/s12885-021-09046-2 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Moxuan Zhang ◽

Yanhao Cheng ◽

Zhengchun Xue ◽

Qiang Sun ◽

Jian Zhang

Keyword(s):

High Risk ◽

Risk Model ◽

Cox Regression ◽

Validation Cohort ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

Related Gene ◽

Cox Regression Analysis ◽

Genome Atlas

Abstract Background Glioma is the most common primary intracranial tumour and has a very poor prognosis. Pyroptosis, also known as inflammatory necrosis, is a type of programmed cell death that was discovered in recent years. The expression and role of pyroptosis-related genes in gliomas are still unclear. Methods In this study, we analysed the RNA-seq and clinical information of glioma patients from The Cancer Genome Atlas (TCGA) database and Chinese Glioma Genome Atlas (CGGA) database. To investigate the prognosis and immune microenvironment of pyroptosis-related genes in gliomas, we constructed a risk model based on the TCGA cohort. The patients in the CGGA cohort were used as the validation cohort. Results In this study, we identified 34 pyroptosis-related differentially expressed genes (DEGs) in glioma. By clustering these DEGs, all glioma cases can be divided into two clusters. Survival analysis showed that the overall survival time of Cluster 1 was significantly higher than that of Cluster 2. Using the TCGA cohort as the training set, a 10-gene risk model was constructed through univariate Cox regression analysis and LASSO Cox regression analysis. According to the risk score, gliomas were divided into high-risk and low-risk groups. Survival analysis showed that the low-risk group had a longer survival time than the high-risk group. The above results were verified in the CGGA validation cohort. To verify that the risk model was independent of other clinical features, the distribution and the Kaplan-Meier survival curves associated with risk scores were performed. Combined with the characteristics of the clinical cases, the risk score was found to be an independent factor predicting the overall survival of patients with glioma. The analysis of single sample Gene Set Enrichment Analysis (ssGSEA) showed that compared with the low-risk group, the high-risk group had immune cell and immune pathway activities that were significantly upregulated. Conclusion We established 10 pyroptosis-related gene markers that can be used as independent clinical predictors and provide a potential mechanism for the treatment of glioma.

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