Prognosis Analysis and Validation of m6A Signature and Tumor Immune Microenvironment in Glioma

Establishment of a nomogram with EMP3 for predicting clinical outcomes in patients with glioma: A bi-center study

Aim

To demonstrate the clinical value of epithelial membrane protein 3 (EMP3) with bioinformatic analysis and clinical data, and then to establish a practical nomogram predictive model with bicenter validation.

Methods

The data from CGGA and TCGA database were used to analyze the expression of EMP3 and its correlation with clinical prognosis. Then, we analyzed EMP3 expression in samples from 179 glioma patients from 2013 to 2017. Univariate and multivariate cox regression were used to predict the prognosis with multiple factors. Finally, a nomogram to predict poor outcomes was formulated. The accuracy and discrimination of nomograms were determined with ROC curve and calibration curve in training and validation cohorts.

Results

EMP3 was significantly higher in higher‐grade glioma and predicted poor prognosis. In multivariate analysis, high expression of EMP3 (HR = 2.842, 95% CI 1.984–4.071), WHO grade (HR = 1.991, 95% CI 1.235–3.212), and IDH1 mutant (HR = 0.503, 95% CI 0.344–0.737) were included. The nomogram was constructed based on the above features, which represented great predictive value in clinical outcomes.

Conclusion

This study demonstrated EMP3 as a novel predictor for clinical progression and clinical outcomes in glioma. Moreover, the nomogram with EMP3 expression represented a practical approach to provide individualized risk assessment for glioma patients.

Dissecting Prognosis Modules and Biomarkers in Glioblastoma Based on Weighted Gene Co-Expression Network Analysis

Introduction

As one of the most prevalent and malignant brain cancers, glioblastoma multiforme (GBM) presents a poor prognosis and the molecular mechanisms remain poorly understood. Consequently, molecular research, including various biomarkers, is essential to exploit the occurrence and development of glioma.

Methods

Weighted gene co-expression network analysis (WGCNA) was used to construct gene co-expression modules and networks based on the Chinese Glioma Genome Atlas (CGGA) glioblastoma specimens. Then, protein–protein interaction (PPI) and gene ontology (GO) analyses were performed to mine hub genes. RT-PCR and immunohistochemistry were employed to examine the expression level of GRPR, CXCL5, and CXCL11 in glioma patients.

Results

We confirmed two gene modules by protein–protein interaction networks. Functional enrichment analysis was performed to identify the significance of gene modules. Prognostic biomarkers GRPR, CXCL5, and CXCL11 related to the survival time of GBM samples were mined in The Cancer Genome Atlas (TCGA) dataset. qRT-PCR revealed that GRPR, CXCL5, and CXCL11 led to a significant increase in GBM sample compared to control.

Conclusion

In this study, we developed and confirmed three mRNA signatures (GRPR, CXCL5, and CXCL11) for evaluating overall survival in GBM patients. Our research assists in existing understanding of GBM diagnosis and prognosis.

Decreased m6A Modification of CD34/CD276(B7-H3) Leads to Immune Escape in Colon Cancer

Previous studies have reported that m6a modification promotes tumor immune escape by affecting tumor microenvironment (TME). Due to the complexity of TME, a single biomarker is insufficient to describe the complex biological characteristics of tumor and its microenvironment. Therefore, it is more meaningful to explore a group of effective biomarkers reflecting different characteristics of cancer to evaluate the biological characteristics of solid tumors. Here, the immune gene CD34/CD276 with different m6A peak was obtained by m6A sequencing (MeRIP-seq) of colon cancer (CRC)clinical samples and combined with MsIgDB database, which was used to perform cluster analysis on TCGA-COAD level 3 data. The CD34/CD276 as a molecular marker for CRC prognosis was confirmed by survival analysis and immunohistochemical assay. Further bioinformatics analysis was carried out to analyze the molecular mechanism of CD34/CD276 affecting the TME through m6a-dependent down-regulation and ultimately promoting immune escape of CRC.

M5C regulator-mediated methylation modification patterns and tumor microenvironment infiltration characterization in lung adenocarcinoma

Background

In recent years, immunotherapy has made great progress, and the regulatory role of epigenetics has been verified. However, the role of 5-methylcytosine (m⁵C) in the tumor microenvironment (TME) and immunotherapy response remains unclear.

Methods

Based on 11 m⁵C regulators, we evaluated the m⁵C modification patterns of 572 lung adenocarcinoma (LUAD) patients. The m⁵C score was constructed by principal component analysis (PCA) algorithms in order to quantify the m⁵C modification pattern of individual LUAD patients.

Results

Two m⁵C methylation modification patterns were identified according to 11 m⁵C regulators. The two patterns had a remarkably distinct TME immune cell infiltration characterization. Next, 226 differentially expressed genes (DEGs) related to the m⁵C phenotype were screened. Patients were divided into three different gene cluster subtypes based on these genes, which had different TME immune cell infiltration and prognosis characteristics. The m⁵C score was constructed to quantify the m⁵C modification pattern of individual LUAD patients. We found that the high m⁵C score group had a better prognosis. The role of the m⁵C score in predicting prognosis was also verified in the dataset GSE31210.

Conclusions

Our study revealed that m⁵C modification played a significant role in TME regulation of LUAD. Investigation of the m⁵C regulation mode may have some implications for tumor immunotherapy in the future.

Integrated Analysis Reveals Prognostic Value and Immune Correlates of CD86 Expression in Lower Grade Glioma

Background

CD86 has great potential to be a new target of immunotherapy by regulating cancer immune response. However, it remains unclear whether CD86 is a friend or foe in lower-grade glioma (LGG).

Methods

The prognostic value of CD86 expression in pan-cancer was analyzed using Cox regression and Kaplan-Meier analysis with data from the cancer genome atlas (TCGA). Cancer types where CD86 showed prognostic value in overall survival and disease-specific survival were identified for further analyses. The Chinese Glioma Genome Atlas (CGGA) dataset were utilized for external validation. Quantitative real-time PCR (qRT-PCR), Western blot (WB), and Immunohistochemistry (IHC) were conducted for further validation using surgical samples from Jiangsu Province hospital. The correlations between CD86 expression and tumor immunity were analyzed using the Estimation of Stromal and Immune cells in Malignant Tumours using Expression data (ESTIMATE) algorithm, Tumor IMmune Estimation Resource (TIMER) database, and expressions of immune checkpoint molecules. Gene Set Enrichment Analysis (GSEA) was performed using clusterprofiler r package to reveal potential pathways.

Results

Pan-cancer survival analysis established CD86 expression as an unfavorable prognostic factor in tumor progression and survival for LGG. CD86 expression between Grade-II and Grade-III LGG was validated using qRT-PCR and WB. Additionally, CD86 expression in LGG with unmethylated O(6)-methylguanine-DNA-methyltransferase (MGMT) promoter was significantly higher than those with methylated MGMT (P<0.05), while in LGG with codeletion of 1p/19q it was significantly downregulated as opposed to those with non-codeletion (P<2.2*10-16). IHC staining validated that CD86 expression was correlated with MGMT status and X1p/19q subtypes, which was independent of tumor grade. Multivariate regression validated that CD86 expression acts as an unfavorable prognostic factor independent of clinicopathological factors in overall survival of LGG patients. Analysis of tumor immunity and GSEA revealed pivotal role of CD86 in immune response for LGG.

Conclusions

Integrated analysis shows that CD86 is an unfavorable prognostic biomarker in LGG patients. Targeting CD86 may become a novel approach for immunotherapy of LGG.

Identification and validation of a five-lncRNA prognostic signature related to Glioma using bioinformatics analysis

Background

To accurately predict the prognosis of glioma patients.

Methods

A total of 541 samples from the TCGA cohort, 181 observations from the CGGA database and 91 samples from our cohort were included in our study. Long non-coding RNAs (LncRNAs) associated with glioma WHO grade were evaluated by weighted gene co-expression network analysis (WGCNA). Five lncRNA features were selected out to construct prognostic signatures based on the Cox regression model.

Results

By weighted gene co-expression network analysis (WGCNA), 14 lncRNAs related to glioma grade were identified. Using univariate and multivariate Cox analysis, five lncRNAs (CYTOR, MIR155HG, LINC00641, AC120036.4 and PWAR6) were selected to develop the prognostic signature. The Kaplan-Meier curve depicted that the patients in high risk group had poor prognosis in all cohorts. The areas under the receiver operating characteristic curve of the signature in predicting the survival of glioma patients at 1, 3, and 5 years were 0.84, 0.92, 0.90 in the CGGA cohort; 0.8, 0.85 and 0.77 in the TCGA set and 0.72, 0.90 and 0.86 in our own cohort. Multivariate Cox analysis demonstrated that the five-lncRNA signature was an independent prognostic indicator in the three sets (CGGA set: HR = 2.002, p < 0.001; TCGA set: HR = 1.243, p = 0.007; Our cohort: HR = 4.457, p = 0.008, respectively). A nomogram including the lncRNAs signature and clinical covariates was constructed and demonstrated high predictive accuracy in predicting 1-, 3- and 5-year survival probability of glioma patients.

Conclusion

We established a five-lncRNA signature as a potentially reliable tool for survival prediction of glioma patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-07972-9.