A robust six-miRNA prognostic signature for head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC) remains a major health problem worldwide. We aimed to identify a robust microRNA (miRNA)-based signature for predicting HNSCC prognosis. The miRNA expression profiles of HNSCC were obtained from The Cancer Genome Atlas (TCGA) database. The TCGA HNSCC cohort was randomly divided into the discovery and validation cohort. A miRNA-based prognostic signature was built up based on TGCA discovery cohort, and then further validated. The downstream targets of prognostic miRNAs were subjected to functional enrichment analyses. The role of miR-1229-3p, a prognosis-related miRNA, in tumorigenesis of HNSCC was further evaluated. A total of 305 significantly differentially expressed miRNAs were found between HNSCC samples and normal tissues. A six-miRNA prognostic signature was constructed, which exhibited a strong association with overall survival (OS) in the TCGA discovery cohort. In addition, these findings were successfully confirmed in TCGA validation cohort and our own independent cohort. The miRNA-based signature was demonstrated as an independent prognostic indicator for HNSCC. A risk signature-based nomogram model was constructed and showed good performance for predicting the OS for HNSCC. The functional analyses revealed that the downstream targets of these prognostic miRNAs were closely linked to cancer progression. Mechanistically, in vitro analysis revealed that miR-1229-3p played a tumor promoting role in HNSCC. In conclusion, our study has developed a robust miRNA-based signature for predicting the prognosis of HNSCC with high accuracy, which will contribute to improve the therapeutic outcome.     

Identification of a 4-miRNA signature as a potential prognostic biomarker for pancreatic adenocarcinoma

An microRNA (miRNA) signature to predict the clinical outcome of pancreatic adenocarcinoma (PAAD) is still lacking. In the current study, we aimed at identifying and evaluating a prognostic miRNA signature for patients with PAAD. The miRNA expression profile and the clinical information regarding patients with PAAD were recruited from The Cancer Genome Atlas database. Differentially expressed miRNAs were identified between normal and tumor samples. By means of survival analysis, a 4-miRNA signature for predicting patients' with PAAD overall survival (OS) was constructed. Receiver operating characteristic (ROC) analysis was applied to determine the efficiency of survival prediction. Furthermore, the biological function of the predicted miRNAs was evaluated using a bioinformatics approach. Four (hsa-mir-126, hsa-mir-3613, hsa-mir-424, and hsa-mir-4772) out of 17 differentially expressed miRNAs were associated to the OS of patients with PAAD. Moreover, the area under the curve (AUC) of the constructed 4-miRNA signature associated to patients' with PAAD 2-year survival was 0.789. The multivariate Cox's proportional hazards regression model suggested that this 4-miRNA signature was an independent prognostic factor of other clinical parameters in patients with PAAD. Further pathway enrichment analyses revealed that the miRNAs in the 4-miRNA signature might regulate genes that affect focal adhesion, Wnt signaling pathway, and PI3K-Akt signaling pathway. Thus, these findings indicated that the 4-miRNA signature might be an effective independent prognostic biomarker in the prediction of PAAD patients' survival.     

Single-cell sequencing analysis and transcriptome analysis constructed the macrophage related gene-related signature in lung adenocarcinoma and verified by an independent cohort

BackgroundRecent studies have emphasized the close relationship between macrophages and tumor immunity, and the prognosis of lung adenocarcinoma (LUAD) patients is intimately linked to this. Nonetheless, the prognostic signature and classification of different immune patterns in LUAD patients based on the macrophages is largely unexplored.MethodsTwo sc-RNAseq datasets of LUAD patients were collected and reprocessed. The differentially expressed genes (DEGs) related to macrophages between LUAD tissues and normal lung tissues were then identified. Based upon the above genes, three distinct immune patterns in the TCGA-LUAD cohort were identified. The ssGSEA and CIBERSORT were applied for immune profiling and characterization of different subtypes. A four-gene prognostic signature for LUAD patients was established based on the DEGs between the subtypes using stepwise multi-Cox regression. TCGA-LUAD cohort was used as training set. Five GEO-LUAD datasets and an independent cohort containing 112 LUAD samples were used for validation. TIDE (tumor immune dysfunction and exclusion) and drug sensitivity analyses were also performed.ResultsMacrophage-related differentially expressed genes were found out using the publicly available scRNA-seq data of LUAD. Three different immune patterns which were proved to have distinct immune infiltration characteristics in the TCGA-LUAD cohort were recognized based on the above macrophage-related genes. Thereafter, 174 DEGs among the above three different immune patterns were figured out; on the basis of this, a four-gene prognostic signature was constructed. This signature distinguished the prognosis of LUAD patients well in various GSE datasets as well as our independent cohort. Further analyses revealed that patients which had a higher risk score also accompanied with a lower immune infiltration level and a worse response to several immunotherapy biomarkers.ConclusionThis study highlighted that macrophage were significantly associated with TME diversity and complexity. The four-gene prognostic signature could be used for predicting outcomes and immune landscapes for patients with LUAD.     

A metabolism-relevant signature as a predictor for prognosis and therapeutic response in pancreatic cancer

Although several altered metabolic genes have been identified to be involved in the tumorigenesis and advance of pancreatic cancer (PC), their prognostic values remained unclear. The purpose of this study was to explore new targets and establish a metabolic signature to predict prognosis and chemotherapy response for optimal individualized treatment. The expression data of PC patients from two independent cohorts and metabolism-related genes from KEGG were utilized and analyzed for the establishment of the signature via lasso regression. Then, the differentially expressed candidate genes were further confirmed via online data mining platform and qRT-PCR of clinical specimens. Then, the analyses of gene set enrichment, mutation, and chemotherapeutic response were performed via R package. As results showed, 109 differentially expressed metabolic genes were screened out in PC. Then a metabolism-related five-gene signature comprising B3GNT3, BCAT1, KYNU, LDHA, and TYMS was constructed and showed excellent ability for predicting survival. A novel nomogram coordinating the metabolic signature and other independent prognostic parameters was developed and showed better predictive power in predicting survival. In addition, this metabolic signature was significantly involved in the activation of multiple oncological pathways and regulation of the tumor immune microenvironment. The patients with high risk scores had higher tumor mutation burdens and were prone to be more sensitive to chemotherapy. In summary, our work identified a new metabolic signature and established a superior prognostic nomogram which may supply more indications to explore novel strategies for diagnosis and treatment.     

Establishment and Validation of an Interferon-Stimulated Genes (ISGs) Prognostic Signature in Pan-cancer Patients: A Multicenter,Real-world Study

Our study aims at developing an interferon-stimulated genes (ISGs) signature that could predict overall survival (OS) in cancer patients, which enrolled a total of 5643 pan-cancer patients. Linear models for microarray data method analysis were conducted to identify the differentially expressed prognostic genes in the global ISGs family. Time-dependent receiver operating characteristic (ROC) and Kaplan-Meier survival analysis were used to test the efficiency of a multi-gene signature in predicting the prognosis of pan-cancer patients. The prognostic performance and potential biological function of gene signature were verified by quantitative real-time PCR in a pan-cancer independent cohort. Three ISGs genes were finally identified to build a classifier, a specific risk score formula, with which patients were classified into the low- or high-risk groups. Time-dependent ROC analyses proved prognostic accuracy. Then, its prognostic value was validated in seven external validation series. A nomogram was constructed to guide the individualized treatment of patients with lung adenocarcinoma. Biological pathway and tumor immune infiltration analysis showed that the signature might cause poor prognosis by blocking NK cell activation. Finally, the signature in our centers was confirmed by real-time quantitative PCR. A robust ISGs-related feature was discovered to effectively classify pan-cancer patients into subgroups with different OS.     

Identification of a universal 6-lncRNA prognostic signature for three pathologic subtypes of renal cell carcinoma

Renal cell carcinoma (RCC) is the most common adult renal epithelial cancer susceptible to metastasis and patients with irresectable RCC always have a poor prognosis. Long noncoding RNAs (lncRNAs) have recently been documented as having critical roles in the etiology of RCC. Nevertheless, the prognostic significance of lncRNA-based signature for outcome prediction in patients with RCC has not been well investigated. Therefore, it is essential to identify a lncRNA-based signature for predicting RCC prognosis. In the current study, we comprehensively analyzed the RNA sequencing data of the three main pathological subtypes of RCC (kidney renal clear cell carcinoma [KIRC], kidney renal papillary cell carcinoma [KIRP], and kidney chromophobe carcinoma [KICH]) from The Cancer Genome Atlas (TCGA) database, and identified a 6-lncRNA prognostic signature with the help of a step-wise multivariate Cox regression model. The 6-lncRNA signature stratified the patients into low- and high-risk groups with significantly different prognosis. Multivariate Cox regression analysis showed that predictive value of the 6-lncRNA signature was independent of other clinical or pathological factors in the entire cohort and in each cohort of RCC subtypes. In addition, the three independent prognostic clinical factors (including age, pathologic stage III, and stage IV) was also stratified into low- and high-risk groups basis on the risk score, and the stratification analyses demonstrated that the high-risk score was a poor prognostic factor. In conclusion, these findings indicate that the 6-lncRNA signature is a novel prognostic biomarker for all three subtypes of RCC, and can increase the accuracy of predicting overall survival.     

A 4-microRNA signature for survival prognosis in pediatric and adolescent acute myeloid leukemia

Acute myeloid leukemia (AML) is a hematologic malignancy with significant molecular heterogeneity. MicroRNAs (miRNAs) play a critical role in AML diagnosis, pathogenesis, and prognosis of AML. Little has been done to identify a miRNA signature in pediatric and adolescent patients for predicting overall survival. This study aims to identify a panel of miRNA signature that could predict the prognosis of all younger AML patients with all subtypes of AML by analyzing data from The Cancer Genome Atlas (TCGA). A total of 229 patients under 23 years with miRNA data and corresponding clinical data from TCGA database were enrolled in this study. Through conducting multivariate analysis in the training test, it was identified that the high expression of hsa-miR-509 and hsa-miR-542 were independent poor prognostic factors, whereas that of hsa-miR-146a and hsa-miR-3667 had a trend to be favorable factors. A 4-miRNA signature was constructed by these miRNAs considering the weight of each. In testing group and all 229 patients’ cohort as well as 59 cytogenetically normal AML (CN-AML) patients’ cohort, higher risk score was associated with shorter overall survival (OS). All results were confidential by using powerful statistical analysis. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analysis were carried out to further develop leukemia-relevant mechanisms supporting the model. The results indicate that the 4-miRNA-based signature is a reliable prognostic biomarker for pediatric and adolescent AML patients.     

Comprehensive molecular analysis of a four-pyroptosis-gene signature with prognosis and immune landscape in lung adenocarcinoma

Pyroptosis plays an important role in tumor immunity. However, the biological behavior and prognostic significance of pyroptosis remain unclear. We identified 41 pyroptosis regulators differently expressed in lung adenocarcinoma (LUAD). All cases of LUAD can be classified into two molecular subtypes using unsupervised clustering algorithm. Using multiple analyses, a four-pyroptosis-gene signature was constructed, and all LUAD patients were categorized as low-risk or high-risk with a longer overall survival (OS) time in the low-risk group(P < 0.001). This signature had power prognosis and stratification which was validated by six independent datasets and clinical subtypes. Besides, this signature showed distinct clinical outcomes, immune landscapes in different risk groups. Moreover, the low-risk group had a higher response against immunotherapy with a lower TIDE score. Importantly, this signature surpassed other biomarkers (TIDE, TMB, PD-L1) in predicting prognosis. Overall, the current study might help with precise prognostic prediction and crucial treatment strategies, eventually promoting tailored therapy for LUAD patients.     

Profiles of immune infiltration in lung adenocarcinoma and their clinical significant: A gene-expression–based retrospective study

Lung cancer is one of the fatal tumors. The tumor microenvironment plays a key role in regulating tumor progression. To figure out the role of tumor microenvironment in lung adenocarcinoma (LUAD), ESTIMATE algorithm was used to evaluate the immune scores in LUAD. Patients with low immune scores had a worse overall survival (OS) compared with high immune scores. Using RNA-Seq data of 489 patients in The Cancer Genome Atlas (TCGA), differentially expressed genes (DEGs) were identified between high- and low-immune score groups. Based on the DEGs, nine-gene signature was constructed by the least absolute shrinkage and selection operator Cox regression model in TCGA set. The signature demonstrated significant prognostic value in both TCGA and Gene Expression Omnibus database. Multivariate Cox regression analyses indicated that nine-genes signature was an independent prognostic factor. Subgroup analysis also revealed a robust prognostic ability of nine-gene signature. A nomogram with a C-index of 0.722 had a favorable power for predicting 3-, 5-, and 10-year survival for clinical use by integrating nine-gene signature and other clinical features. Co-expression and functional enrichment analysis showed that nine-gene signature was significantly associated with immune response and provided potential profound molecules for revealing the mechanism of tumor initiation and progression. In conclusion, we revealed the significance of immune infiltration and built a novel nine-gene signature as a reliable prognostic factor for patients with LUAD.     

Construction and validation of a TP53-associated immune prognostic model for gastric cancer

Increasing evidence indicates that TP53 mutation impacts the patients' prognosis by regulating the gastric cancer (GC) immunophenotype. An immune prognostic signature (IPS) was constructed based on TP53 status. The effects of the IPS on the immune microenvironment of GC were analyzed. We also constructed a nomogram integrating the IPS and other clinical factors. An IPS was constructed in the TCGA cohort and validated in the meta-GEO cohort. TP53 mutation resulted in the downregulation of the immune response in GC. Concretely, high-risk patients were characterized by increased monocyte, macrophage M0 and T cell follicular helper infiltration; increased stromal score, ESTIMATE score and immune score; higher TIM3 and BTLA expression; and decreased dendritic cell and T cell CD4 memory-activated infiltration and tumor purity. The nomogram also showed good predictive performance. These results suggest that the IPS is an effective prognostic indicator for GC patients, which might provide a theoretical foundation for immunotherapy.     

Ferroptosis-related gene signature correlates with the tumor immune features and predicts the prognosis of glioma patients

Background: Glioma is a malignant intracranial tumor and the most fatal cancer. The role of ferroptosis in the clinical progression of gliomas is unclear.Method: Univariate and least absolute shrinkage and selection operator (Lasso) Cox regression methods were used to develop a ferroptosis-related signature (FRSig) using a cohort of glioma patients from the Chinese Glioma Genome Atlas (CGGA), and was validated using an independent cohort of glioma patients from The Cancer Genome Atlas (TCGA). A single-sample gene set enrichment analysis (ssGSEA) was used to calculate levels of the immune infiltration. Multivariate Cox regression was used to determine the independent prognostic role of clinicopathological factors and to establish a nomogram model for clinical application.Results: We analyzed the correlations between the clinicopathological features and ferroptosis-related gene (FRG) expression and established an FRSig to calculate the risk score for individual glioma patients. Patients were stratified into two subgroups with distinct clinical outcomes. Immune cell infiltration in the glioma microenvironment and immune-related indexes were identified that significantly correlated with the FRSig, the tumor mutation burden (TMB), copy number alteration (CNA), and immune checkpoint expression was also significantly positively correlated with the FRSig score. Ultimately, an FRSig-based nomogram model was constructed using the independent prognostic factors age, World Health Organization (WHO) grade, and FRSig score.Conclusion: We established the FRSig to assess the prognosis of glioma patients. The FRSig also represented the glioma microenvironment status. Our FRSig will contribute to improve patient management and individualized therapy by offering a molecular biomarker signature for precise treatment.     

A dual immune signature of CD8+ T cells and MMP9 improves the survival of patients with hepatocellular carcinoma

The 5-year survival of hepatocellular carcinoma (HCC) is difficult due to the high recurrence rate and metastasis. Tumor infiltrating immune cells (TICs) and immune-related genes (IRGs) bring hope to improve survival and treatment of HCC patients. However, there are problems in predicting immune signatures and identifying novel therapeutic targets. In the study, the CIBERSORT algorithm was used to evaluate 22 immune cell infiltration patterns in gene expression omnibus (GEO) and the cancer genome atlas (TCGA) data. Eight immune cells were found to have significant infiltration differences between the tumor and normal groups. The CD8+ T cells immune signature was constructed by least absolute shrinkage and selection operator (LASSO) algorithm. The high infiltration level of CD8+ T cells could significantly improve survival of patients. The weighted gene co-expression network analysis (WGCNA) algorithm identified MMP9 was closely related to the overall survival of HCC patients. K-M survival and tROC analysis confirmed that MMP9 had an excellent prognostic prediction. Cox regression showed that a dual immune signature of CD8+ T cells and MMP9 was independent survival factor in HCC. Therefore, a dual prognostic immune signature could improve the survival of patient and may provide a new strategy for the immunotherapy of HCC.     

A novel tailored immune gene pairs signature for overall survival prediction in lower-grade gliomas

Lower-grade gliomas (LGGs) have a good prognosis with a wide range of overall survival (OS) outcomes. An accurate prognostic system can better predict survival time. An RNA-Sequencing (RNA-seq) prognostic signature showed a better predictive power than clinical predictor models. A signature constructed using gene pairs can transcend changes from biological heterogeneity, technical biases, and different measurement platforms. RNA-seq coupled with corresponding clinical information were extracted from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). Immune-related gene pairs (IRGPs) were used to establish a prognostic signature through univariate and multivariate Cox proportional hazards regression. Weighted gene co-expression network analysis (WGCNA) was used to evaluate module eigengenes correlating with immune cell infiltration and to construct gene co-expression networks. Samples in the training and testing cohorts were dichotomized into high- and low-risk groups. Risk score was identified as an independent predictor, and exhibited a closed relationship with prognosis. WGCNA presented a gene set that was positively correlated with age, WHO grade, isocitrate dehydrogenase (IDH) mutation status, 1p/19 codeletion, risk score, and immune cell infiltrations (CD4 T cells, B cells, dendritic cells, and macrophages). A nomogram comprising of age, WHO grade, 1p/19q codeletion, and three gene pairs (BIRC5|SSTR2, BMP2|TNFRSF12A, and NRG3|TGFB2) was established as a tool for predicting OS. The IPGPs signature, which is associated with immune cell infiltration, is a novel tailored tool for individual-level prediction.     

Integrated analysis of gene expression and DNA methylation datasets identified key genes and a 6-gene prognostic signature for primary lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the main subtype of non-small cell lung cancer with a poor survival prognosis. In our study, gene expression, DNA methylation, and clinicopathological data of primary LUAD were utilized to identify potential prognostic markers for LUAD, which were recruited from The Cancer Genome Atlas (TCGA) database. Univariate regression analysis showed that there were 21 methylation-associated DEGs related to overall survival (OS), including 9 down- and 12 up-regulated genes. The 12 up-regulated genes with hypomethylation may be risky genes, whereas the other 9 down-regulated genes with hypermethylation might be protective genes. By using the Step-wise multivariate Cox analysis, a methylation-associated 6-gene (consisting of CCL20, F2, GNPNAT1, NT5E, B3GALT2, and VSIG2) prognostic signature was constructed and the risk score based on this gene signature classified patients into high- or low-risk groups. Patients of the high-risk group had shorter OS than those of the low-risk group in both the training and validation cohort. Multivariate Cox analysis and the stratified analysis revealed that the risk score was an independent prognostic factor for LUAD patients. The methylation-associated gene signature may serve as a prognostic factor for LUAD patients and the represent hypermethylated or hypomethylated genes might be potential targets for LUAD therapy.     

Construction and validation of a seven-gene signature for predicting overall survival in patients with kidney renal clear cell carcinoma via an integrated bioinformatics analysis

ABSTRACT

Kidney renal clear cell carcinoma (KIRC) remains a significant challenge worldwide because of its poor prognosis and high mortality rate, and accurate prognostic gene signatures are urgently required for individual therapy. This study aimed to construct and validate a seven-gene signature for predicting overall survival (OS) in patients with KIRC. The mRNA expression profile and clinical data of patients with KIRC were obtained from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC). Prognosis-associated genes were identified, and a prognostic gene signature was constructed. Then, the prognostic efficiency of the gene signature was assessed. The results obtained using data from the TCGA were validated using those from the ICGC and other online databases. Gene set enrichment analyses (GSEA) were performed to explore potential molecular mechanisms. A seven-gene signature (PODXL, SLC16A12, ZIC2, ATP2B3, KRT75, C20orf141, and CHGA) was constructed, and it was found to be effective in classifying KIRC patients into high- and low-risk groups, with significantly different survival based on the TCGA and ICGC validation data set. Cox regression analysis revealed that the seven-gene signature had an independent prognostic value. Then, we established a nomogram, including the seven-gene signature, which had a significant clinical net benefit. Interestingly, the seven-gene signature had a good performance in distinguishing KIRC from normal tissues. GSEA revealed that several oncological signatures and GO terms were enriched. This study developed a novel seven-gene signature and nomogram for predicting the OS of patients with KIRC, which may be helpful for clinicians in establishing individualized treatments.     

Identification of a prognostic gene signature based on an immunogenomic landscape analysis of bladder cancer

Cancer immune plays a critical role in cancer progression. Tumour immunology and immunotherapy are one of the exciting areas in bladder cancer research. In this study, we aimed to develop an immune‐related gene signature to improve the prognostic prediction of bladder cancer. Firstly, we identified 392 differentially expressed immune‐related genes (IRGs) based on TCGA and ImmPort databases. Functional enrichment analysis revealed that these genes were enriched in inflammatory and immune‐related pathways, including in ‘regulation of signaling receptor activity’, ‘cytokine‐cytokine receptor interaction’ and ‘GPCR ligand binding’. Then, we separated all samples in TCGA data set into the training cohort and the testing cohort in a ratio of 3:1 randomly. Data set {"type":"entrez-geo","attrs":{"text":"GSE13507","term_id":"13507"}}GSE13507 was set as the validation cohort. We constructed a prognostic six‐IRG signature with LASSO Cox regression in the training cohort, including AHNAK, OAS1, APOBEC3H, SCG2, CTSE and KIR2DS4. Six IRGs reflected the microenvironment of bladder cancer, especially immune cell infiltration. The prognostic value of six‐IRG signature was further validated in the testing cohort and the validation cohort. The results of multivariable Cox regression and subgroup analysis revealed that six‐IRG signature was a clinically independent prognostic factor for bladder cancer patients. Further, we constructed a nomogram based on six‐IRG signature and other clinicopathological risk factors, and it performed well in predict patients'' survival. Finally, we found six‐IRG signature showed significant difference in different molecular subtypes of bladder cancer. In conclusions, our research provided a novel immune‐related gene signature to estimate prognosis for patients'' survival with bladder cancer.     

Identification of an extracellular vesicle-related gene signature in the prediction of pancreatic cancer clinical prognosis

Although extracellular vesicles (EVs) in body fluid have been considered to be ideal biomarkers for cancer diagnosis and prognosis, it is still difficult to distinguish EVs derived from tumor tissue and normal tissue. Therefore, the prognostic value of tumor-specific EVs was evaluated through related molecules in pancreatic tumor tissue. NA sequencing data of pancreatic adenocarcinoma (PAAD) were acquired from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC). EV-related genes in pancreatic cancer were obtained from exoRBase. Protein–protein interaction (PPI) network analysis was used to identify modules related to clinical stage. CIBERSORT was used to assess the abundance of immune and non-immune cells in the tumor microenvironment. A total of 12 PPI modules were identified, and the 3-PPI-MOD was identified based on the randomForest package. The genes of this model are involved in DNA damage and repair and cell membrane-related pathways. The independent external verification cohorts showed that the 3-PPI-MOD can significantly classify patient prognosis. Moreover, compared with the model constructed by pure gene expression, the 3-PPI-MOD showed better prognostic value. The expression of genes in the 3-PPI-MOD had a significant positive correlation with immune cells. Genes related to the hypoxia pathway were significantly enriched in the high-risk tumors predicted by the 3-PPI-MOD. External databases were used to verify the gene expression in the 3-PPI-MOD. The 3-PPI-MOD had satisfactory predictive performance and could be used as a prognostic predictive biomarker for pancreatic cancer.     

Genome‐wide methylomic analyses identify prognostic epigenetic signature in lower grade glioma

Glioma is the most malignant and aggressive type of brain tumour with high heterogeneity and mortality. Although some clinicopathological factors have been identified as prognostic biomarkers, the individual variants and risk stratification in patients with lower grade glioma (LGG) have not been fully elucidated. The primary aim of this study was to identify an efficient DNA methylation combination biomarker for risk stratification and prognosis in LGG. We conducted a retrospective cohort study by analysing whole genome DNA methylation data of 646 patients with LGG from the TCGA and GEO database. Cox proportional hazard analysis was carried out to screen and construct biomarker model that predicted overall survival (OS). The Kaplan‐Meier survival curves and time‐dependent ROC were constructed to prove the efficiency of the signature. Then, another independent cohort was used to further validate the finding. A two‐CpG site DNA methylation signature was identified by multivariate Cox proportional hazard analysis. Further analysis indicated that the signature was an independent survival predictor from other clinical factors and exhibited higher predictive accuracy compared with known biomarkers. This signature was significantly correlated with immune‐checkpoint blockade, immunotherapy‐related signatures and ferroptosis regulator genes. The expression pattern and functional analysis showed that these two genes corresponding with two methylation sites contained in the model were correlated with immune infiltration level, and involved in MAPK and Rap1 signalling pathway. The signature may contribute to improve the risk stratification of patients and provide a more accurate assessment for precision medicine in the clinic.