Bioinformatic Profiling of Prognosis-Related Genes in Malignant Glioma Microenvironment

Comprehensive analysis of lncRNA-miRNA-mRNA ceRNA network in ischemic stroke

Objective

Competitive endogenous RNA (ceRNA) networks have uncovered a novel mode of RNA interaction, and are implicated in various biological processes and the pathogenesis of IS. This study aimed to explore the potential mechanisms underlying the ceRNA network in IS.

Methods

Four public datasets containing lncRNA and mRNA (GSE22255 and GSE16561) and miRNA (GSE55937 and GSE43618) expression profiles from the GEO database were systematically analyzed to explore the role of RNAs in ischemic stroke (IS). Differentially expressed mRNAs (DEmRNAs), lncRNAs (DElncRNAs), and miRNAs (DEmiRNAs) between IS and normal control samples were identified. LncRNA-miRNA and miRNA-mRNA interactions were predicted, and the competing endogenous RNA (ceRNA) regulatory network was constructed using the Cytoscape software. The correlation between the RNAs in the ceRNA network and the clinical features of the samples was evaluated. Finally, principal component analysis was performed on the RNAs that constitute the ceRNA regulatory network, and their differential expression and principal component relationships among different types of samples were observed.

Results

A total of 224 DEmRNAs, 7 DEmiRNAs, and four DElncRNAs related to IS in four datasets were identified. Then, through target gene prediction, a lncRNA-miRNA-mRNA ceRNA network that contained 3 DElncRNAs, 2 DEmiRNAs, and 24 DEmRNAs was constructed. Correlations of the clinical characteristics showed that PART1 and SERPINH1 were related to clinical diseases, WNK1 was related to lifestyle, and seven RNAs were related to age. PCA results indicate that three principal components of PC1, PC2, and PC3 can clearly distinguish between control and IS samples.

Conclusion

Overall, we constructed a ceRNA network in IS, which could offer insights into the molecular mechanism and potential prognostic biomarkers for further research.

Tripartite motif protein 6 promotes hepatocellular carcinoma progression via multiple pathways

Background/aim

Hepatocellular carcinoma (HCC) ranks among the most prevalent malignancies worldwide and the third leading cause of cancer-related death. The TRIM (tripartite motif-containing) protein family members had been reported to be involved in carcinogenesis and tumor progression. Here we aimed to explore the expression profile of TRIM6 in HCC and investigate its clinical significance as well as underlying mechanisms.

Materials and methods

We retrospectively enrolled 138 HCC patients that underwent surgical resection in our hospital and tested protein expression level of TRIM6 through immunohistochemical staining. The correlation between TRIM6 and patients' characteristics was assessed by Chi-square test. Log-rank test and Cox hazard regression test were conducted for univariate and multivariate survival analyses, respectively. Two human HCC cell lines, Huh7 and Hep3B, were subjected for knockdown and overexpression assays, followed by phonotype tests including proliferation and invasion. Nude mice were used to generate xenograft model to validate our findings in vivo.

Results

TRIM6 was highly expressed in HCC specimen compared to nontumorous liver tissues. Higher TRIM6 expression was correlated with larger tumor size, later tumor stage, and worse prognosis. According to the cellular experiments, TRIM6-knockdown resulted in decreased expression of cyclin B1, c-Myc, Snail, MMP2, and VEGF-A. Consistently, TRIM6-knockdown led to impaired HCC proliferation, invasion, and angiogenesis. In contrast, TRIM6 overexpression showed opposite effects. Finally, the oncogenic role of TRIM6 in HCC was validated by in vivo mice experiments.

Conclusion

TRIM6 can serve as a novel prognostic factor for HCC, which functions by multiple signaling pathways.

High SERPINH1 expression predicts poor prognosis in lung adenocarcinoma

Background

Serpine Protease Inhibitorclade H1 (SERPINH1) is abnormally expressed in a variety of tumor tissues and is linked to the biological processes of tumorigenesis, migration, invasion, and metastasis. SERPINH1 expression and prognosis in malignant tumors, such as gastric, colorectal, and breast cancers, have previously been studied, but the gene has not yet been investigated in lung adenocarcinoma (LUAD) in terms of prognosis and the potential mechanisms of action.

Methods

SERPINH1 was identified as an independent prognostic factor for LUAD in The Cancer Genome Atlas (TCGA) cohort and Affiliated Hospital of Nantong University (NTU) cohort (the LUAD data set) by univariate and multivariate Cox regression analyses. Additionally, we performed immunohistochemical staining to analyze the expression of SERPINH1 in LUAD and normal lung tissue. Based on the TCGA database, we analyzed the correlation of this gene with the tumor mutation burden (TMB), tumor microenvironment, immune infiltration, immune checkpoints, and anti-tumor drugs using the R language-related R package.

Results

SERPINH1 was highly expressed in LUAD tissue. Kaplan-Meier survival curves in both the TCGA cohort and the NTU cohort showed that the SERPINH1 low-expression group had a higher survival rate than the high-expression group. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses of the SERPINH1 co-expressed genes revealed that the gene was associated with the extracellular matrix and cell proliferation and migration. The analysis of SERPINH1 and the TMB revealed a superior survival advantage for patients with high TMB and high SERPINH1 expression, and worse survival for those with low TMB and high SERPINH1 expression. The analysis of the tumor microenvironment (TME) and immune infiltration revealed that the high and low expression of SERPINH1 was associated with different immune infiltration characteristics. The analysis of the immune checkpoints and anti-tumor drugs showed that immunotherapy and anti-neoplastic treatment were more efficacious in the high SERPINH1 expression group than the low SERPINH1 expression group.

Conclusions

Using LUAD tissues and clinical samples, we showed that SERPINH1 can be used as a prognostic biomarker for LUAD. Our findings provide a new approach and strategy for the clinical treatment of LUAD patients.

Exploring the Correlation of Abnormal CXCL9 Expression with Immune Infiltration in Glioma and Patient Prognosis Based on TCGA and GTEx Databases

In this study, we intend to identify key immune-related genes (IRGs) in gliomas using the TCGA and GTEx databases. Following collection of the RNA-seq data of lower-grade glioma (LGG) and glioblastoma (GBM) patients from the TCGA and GTEx databases, the differentially expressed IRGs (DE-IRGs) were screened. The ESTIMATE algorithm was utilized to evaluate StromalScore and ImmuneScore of LGG and GBM samples and a multifactorial Cox risk model was constructed to identify the related risk genes. The core IRGs of LGG and GBM were screened through a PPI network, followed by exploration of their correlation with glioma prognosis. The relationship between IRGs and immune cells in LGG and GBM was detected. In vitro assays were performed to detect the effect of CXCL9 on glioma cell development. We screened 403 and 492 DE-IRGs in LGG and GBM. StromalScore and ImmuneScore were related to overall survival in LGG, but not in GBM. CXCL9 was identified as a core gene in LGG and GBM and shared association with the prognosis of gliomas. Furthermore, a correlation was found between CXCL9 and immune infiltration of LGG and GBM. Glioma cell proliferation and invasion could be inhibited by silencing of CXCL9. Overall, CXCL9 is correlated to the prognosis of glioma patients and may accelerate glioma development via immune regulation.

Identification of a Fibroblast-Related Prognostic Model in Glioma Based on Bioinformatics Methods

Background: Glioma is the most common primary tumor of the central nervous system with a high lethality rate. This study aims to mine fibroblast-related genes with prognostic value and construct a corresponding prognostic model. Methods: A glioma-related TCGA (The Cancer Genome Atlas) cohort and a CGGA (Chinese Glioma Genome Atlas) cohort were incorporated into this study. Variance expression profiling was executed via the “limma” R package. The “clusterProfiler” R package was applied to perform a GO (Gene Ontology) analysis. The Kaplan–Meier (K–M) curve, LASSO regression analysis, and Cox analyses were implemented to determine the prognostic genes. A fibroblast-related risk model was created and affirmed by independent cohorts. We derived enriched pathways between the fibroblast-related high- and low-risk subgroups using gene set variation analysis (GSEA). The immune infiltration cell and the stromal cell were calculated using the microenvironment cell populations-counter (MCP-counter) method, and the immunotherapy response was assessed with the SubMap algorithm. The chemotherapy sensitivity was estimated using the “pRRophetic” R package. Results: A total of 93 differentially expressed fibroblast-related genes (DEFRGs) were uncovered in glioma. Seven prognostic genes were filtered out to create a fibroblast-related gene signature in the TCGA-glioma cohort training set. We then affirmed the fibroblast-related risk model via TCGA-glioma cohort and CGGA-glioma cohort testing sets. The Cox regression analysis proved that the fibroblast-related risk score was an independent prognostic predictor in prediction of the overall survival of glioma patients. The fibroblast-related gene signature revealed by the GSEA was applicable to the immune-relevant pathways. The MCP-counter algorithm results pointed to significant distinctions in the tumor microenvironment between fibroblast-related high- and low-risk subgroups. The SubMap analysis proved that the fibroblast-related risk score could predict the clinical sensitivity of immunotherapy. The chemotherapy sensitivity analysis indicated that low-risk patients were more sensitive to multiple chemotherapeutic drugs. Conclusion: Our study identified prognostic fibroblast-related genes and generated a novel risk signature that could evaluate the prognosis of glioma and offer a theoretical basis for clinical glioma therapy.

Identification of a novel lipid metabolism-related gene signature for predicting colorectal cancer survival

Colorectal cancer (CRC) is a common malignant tumor worldwide. Lipid metabolism is a prerequisite for the growth, proliferation and invasion of cancer cells. However, the lipid metabolism-related gene signature and its underlying molecular mechanisms remain unclear. The aim of this study was to establish a lipid metabolism signature risk model for survival prediction in CRC and to investigate the effect of gene signature on the immune microenvironment. Lipid metabolism-mediated genes (LMGs) were obtained from the Molecular Signatures Database. The consensus molecular subtypes were established using "ConsensusClusterPlus" based on LMGs and the cancer genome atlas (TCGA) data. The risk model was established using univariate and multivariate Cox regression with TCGA database and independently validated in the international cancer genome consortium (ICGC) datasets. Immune infiltration in the risk model was developed using CIBERSORT and xCell analyses. A total of 267 differentially expressed genes (DEGs) were identified between subtype 1 and subtype 2 from consensus molecular subtypes, including 153 upregulated DEGs and 114 downregulated DEGs. 21 DEGs associated with overall survival (OS) were selected using univariate Cox regression analysis. Furthermore, a prognostic risk model was constructed using the risk coefficients and gene expression of eleven-gene signature. Patients with a high-risk score had poorer OS compared with patients in the low-risk score group (p = 3.36e-07) in the TCGA cohort and the validationdatasets (p = 4.03e-05). Analysis of immune infiltration identified multiple T cells were associated with better prognosis in the low-risk group, including Th2 cells (p = 0.0208), regulatory T cells (p = 0.0425), and gammadelta T cells (p = 0.0112). A nomogram integrating the risk model and clinical characteristics was further developed to predict the prognosis of patients with CRC. In conclusion, our study revealed that the expression of lipid-metabolism genes were correlated with the immune microenvironment. The eleven-gene signature might be useful for prediction the prognosis of CRC patients.

Prognostic and immunological role of SERPINH1 in pan-cancer

Background: The SERPINH1 gene plays a vital part in tumorigenesis and development, whereas its potential as an immunotherapy target is still unknown. Hence, this research aimed to probe the roles of SERPINH1 in human tumors.

Method: Using The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx) database, Oncomine, and SangerBox software, the pan-cancer expression of SERPINH1 and its correlation were systematically analyzed. SERPINH1 protein information was detected by the Human Protein Atlas (HPA) database and STRING database. The genomic alterations of SERPINH1 were studied using the c-BioPortal database. The influence of SERPINH1 on prognosis was analyzed using Kaplan–Meier plotter. The R package “clusterProfiler” was used for enrichment analysis to detect the role of SERPINH1. The TIMER2 database was used to further analyze the correlation between the immune cell infiltration score of TCGA samples and the expression of SERPINH1.

Results: SERPINH1 overexpression was related to worse survival status in pan-cancer. In addition, high expression of SERPINH1 was positively associated with tumor stage and poor prognosis. Moreover, SERPINH1 played an important role in tumor microenvironment and immune regulation. Our study revealed that SERPINH1 expression has a strong correlation with immune cell filtration, immune regulation, chemokines, and immune checkpoints.

Conclusion: Our research found that SERPINH1 was a risk factor and predictor of poor prognosis in various tumors. High expression of SERPINH1 may contribute to tumor immune-suppressive status. Also, SERPINH1 may become a potential immunotherapy target in pan-cancer.

CD93 is Associated with Glioma-related Malignant Processes and Immunosuppressive Cell Infiltration as an Inspiring Biomarker of Survivance

Previous reports have confirmed the significance of CD93 in the progression of multiple tumors; however, there are few studies examining its immune properties for gliomas. Here, we methodically investigated the pathophysiological characteristics and clinical manifestations of gliomas. Six hundred ninety-nine glioma patients in TCGA along with 325 glioma patients in CGGA were correspondingly collected for training and validating. We analyzed and visualized total statistics using RStudio. One-way ANOVA and Student's t-test were used to assess groups' differences. All differences were considered statistically significant at the level of P < 0.05. CD93 markedly upregulated among HGG, MGMT promoter unmethylated subforms, IDH wild forms, 1p19q non-codeletion subforms, and mesenchyme type gliomas. ROC analysis illustrated the favorable applicability of CD93 in estimating mesenchyme subform. Kaplan-Meier curves together with multivariable Cox analyses upon survivance identified high-expression CD93 as a distinct prognostic variable for glioma patients. GO analysis of CD93 documented its predominant part in glioma-related immunobiological processes and inflammation responses. We examined the associations of CD93 with immune-related meta-genes, and CD93 positively correlated with HCK, LCK, MHC I, MHC II, STAT1 and IFN, while adverse with IgG. Association analyses between CD93 and gliomas-infiltrating immunocytes indicated that the infiltrating degrees of most immunocytes exhibited positive correlations with CD93, particularly these immunosuppressive subsets such as TAM, Treg, and MDSCs. CD93 is markedly associated with adverse pathology types, unfavorable survival, and immunosuppressive immunocytes infiltration among gliomas, thus identifying CD93 as a practicable marker and a promising target for glioma-based precise diagnosis and therapeutic strategies.

Development and validation of a leukocyte-associated immunoglobulin-like receptor-1 prognostic signature for lower-grade gliomas

OBJECTIVE

Leukocyte-associated immunoglobulin-like receptor-1 (LAIR-1), is an immunosuppressive receptor, widely expressed by immune cells, but the part of LAIR-1 in glioma progression remains unclear. The purpose of this study was to explore the relationship between LAIR-1 expression and the development of lower-grade glioma (LGG) using publicly available data sets.

METHODS

We took advantage of The Cancer Genome Atlas (TCGA) to analyze the expression of LAIR-1 in patients with LGG. Second, Kaplan-Meier methods and univariate and multivariate Cox regression analyses were used to examine the clinical significance of LAIR-1 expression in combination with CGGA databases, and then receiver operating characteristic curve analysis was used to verify the prognostic utility of LAIR-1. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene set enrichment analysis (GSEA) were used to explore the function of LAIR-1. Analysis of the correlation with immune infiltration was conducted using the ESTIMATE algorithm and single sample gene set enrichment analysis.

RESULTS

Our results showed that LAIR-1 expression to be positively correlated with malignant clinicopathologic features of LGG. Univariate analysis and multivariate analysis revealed that overexpression of LAIR-1 was correlated with a worse prognosis in patients. A nomogram model combining LAIR-1 was more useful in guiding clinical diagnosis, and functional enrichment analysis showed that malignant development of glioma was closely affiliated with the tumor immune microenvironment.

CONCLUSION

These results indicate that LAIR 1 is a latent marker for determining the prognosis of LGG patients. LAIR 1 may also participate a critical part in TIME of LGG by regulating the infiltration of immune cells, suggesting that LAIR 1 might be used as a therapeutic target to regulate the antitumor immune response.

Evaluation of JUN, FN1 and LAMB1 polymorphisms in pterygium in a Chinese Han population

PURPOSE

To explore the underlying molecular mechanism of pterygium and identify the key genes regulating the development of pterygium.

METHODS

Differentially expressed mRNAs were obtained from the Gene Expression Omnibus (GEO) database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using the DAVID (http://david.abcc.ncifcrf.gov/). The differential expressions of hub genes were verified using the reverse transcription-real-time fluorescent quantitative PCR (RT-qPCR). The function of the hub genes was further confirmed based on associations between the single nucleotide polymorphisms (SNPs) in hub genes and pterygium. The genotyping results were analyzed using SNPStats online software in five gene models, including codominant, dominant, recessive, overdominant, and log-additive. Five gene models were analyzed using SNPStats.

RESULTS

We found that 240 genes were significantly differentially expressed. Functional enrichment analysis showed that focal adhesion pathway is extremely meaningful, among which JUN, FN1, and LAMB1 were verified to significantly differentially express in pterygium (P = 0.0011, P = 0.0018, and P = 0.0050, respectively). However, the all nine candidate SNPs (rs11688, rs3748814 in JUN; rs1263, rs1132741, rs1250259 in FN1; rs20556, rs35710474, rs25659, rs4320486 in LAMB1), were not statistically associated with pterygium.

CONCLUSION

Our results demonstrated that JUN, FN1, and LAMB1 polymorphisms were not associated with susceptibility to pterygium in Chinese Han population. Considering the fact that these three genes are differentially expressed in pterygium, further research is needed to explain its involvement in pterygium.

Tumor antigens and immune subtypes of glioblastoma: the fundamentals of mRNA vaccine and individualized immunotherapy development

PURPOSE

Glioblastoma (GBM) is the most common primary brain tumor in adults and is notorious for its lethality. Given its limited therapeutic measures and high heterogeneity, the development of new individualized therapies is important. mRNA vaccines have exhibited promising performance in a variety of solid tumors, those designed for glioblastoma (GBM) need further development. The aim of this study is to explore tumor antigens for the development of mRNA vaccines against GBM and to identify potential immune subtypes of GBM to identify the patients suitable for different immunotherapies.

METHODS

RNA-seq data and the clinical information of 143 GBM patients was extracted from the TCGA database; microarray data and the clinical information of 181 GBM patients was obtained from the REMBRANDT cohort. A GBM immunotherapy cohort of 17 patients was obtained from a previous literature. GEPIA2, cBioPortal, and TIMER2 were used to identify the potential tumor antigens. Immune subtypes and gene modules were identified using consensus clustering; immune landscape was constructed using graph-learning-based dimensionality reduction analysis.

RESULTS

Nine potential tumor antigens associated with poor prognosis and infiltration of antigen-presenting cells were identified in GBM: ADAMTSL4, COL6A1, CTSL, CYTH4, EGFLAM, LILRB2, MPZL2, SAA2, and LSP1. Four robust immune subtypes and seven functional gene modules were identified and validated in an independent cohort. Immune subtypes had different cellular and molecular characteristics, with IS1, an immune cold phenotype; IS2, an immune hot and immunosuppressive phenotype; IS3, a relatively immune cold phenotype, second only to IS1; IS4, having a moderate tumor immune microenvironment. Immune landscape revealed the immune distribution of the GBM patients. Additionally, the potential value of immune subtypes for individualized immunotherapy was demonstrated in a GBM immunotherapy cohort.

CONCLUSIONS

ADAMTSL4, COL6A1, CTSL, CYTH4, EGFLAM, LILRB2, MPZL2, SAA2, and LSP1 are the candidate tumor antigens for mRNA vaccine development in GBM, and IS1 GBM patients are best suited for mRNA vaccination, IS2 patients are best suited for immune checkpoint inhibitor. This study provides a theoretical framework for GBM mRNA vaccine development and individualized immunotherapy strategies.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1186/s40537-022-00643-x.

Immune Infiltration-Related Signature Predicts Risk Stratification and Immunotherapy Efficacy in Grade II and III Gliomas

Background: Tumor microenvironment, especially infiltrating immune cell, is crucial for solid tumors including glioma. However, the hub genes as well as their effects on patient prognosis and immunotherapy efficacy remain obscure.

Methods: We employed a total of 952 lower grade glioma (LGG) patients from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases, and 24 samples in our hospital for subsequent analyses. Abundances of immune infiltrates were evaluated using CIBERSORT and ImmuCellAI. Their correlations with prognosis were assessed by log-rank test. Immune infiltration-related hub genes were obtained from overlapped differential expressed genes (DEGs) in various subsets of survival-related immune cell types. The risk signature was constructed by Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analysis. The functional analyses were estimated by GVSA and Gene Set Enrichment Analysis (GSEA) algorithms. And protein–protein interaction enrichment analysis was carried out with the Metascape database integrating STRING, BioGrid, OmniPath, and InWeb_IM.

Results: Among the 21 infiltrates, the abundances of five immune infiltrates were correlated with overall survival (OS) in LGG patients. Higher abundances of naïve CD4+ T cells (p = 0.002), activated mast cells (p = 0.015), and monocytes (p = 0.014) were correlated with better prognosis, while higher abundances of resting memory CD4+ T cells (p = 0.015) and M1 macrophages (p = 0.020) correlated with poorer OS. We finally obtained 44 hub genes and constructed an immune infiltration-related signature (IIRS). The IIRS correlates with clinicopathological characteristics and exhibited potential power in predicting the immunotherapy efficacy. The IRRS correlates with cancer related pathways, especially “epithelial-mesenchymal transition (EMT),” and cytotoxic T lymphocytes.

Conclusion: Our study constructed and validated a novel signature for risk stratification and prediction of immunotherapy response in grade II and III gliomas, which was closely associated with glioma immune microenvironment and could serve as a promising prognostic biomarker for glioma patients.

MYD88 Is a Potential Prognostic Gene and Immune Signature of Tumor Microenvironment for Gliomas

Purpose

To explore the profiles of immune and stromal components of the tumor microenvironment (TME) and their related key genes in gliomas.

Methods

We applied bioinformatic techniques to identify the core gene that participated in the regulation of the TME of the gliomas. And immunohistochemistry staining was used to calculate the gene expressions in clinical cases.

Results

The CIBERSORT and ESTIMATE were used to figure out the composition of TME in 698 glioma cases from The Cancer Genome Atlas (TCGA) database. Differential expression analysis identified 2103 genes between the high and the low-score group. Then the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, univariate Cox regression analysis, and protein–protein interaction (PPI) network construction were conducted based on these genes. MYD88 was identified as the key gene by the combination univariate Cox and PPI analysis. Furthermore, MYD88 expression was significantly associated with the overall survival and WHO grade of glioma patients. The genes in the high-expression MYD88 group were mainly in immune-related pathways in the Gene Set Enrichment Analysis (GSEA). We found that macrophage M2 accounted for the largest portion with an average of 27.6% in the glioma TIICs and was associated with high expression of MYD88. The results were verified in CGGA database and clinical cases in our hospital. Furthermore, we also found the MYD88 expression was higher in IDH1 wild types. The methylation rate was lower in high grade gliomas.

Conclusion

MYD88 had predictive prognostic value in glioma patients by influencing TIICs dysregulation especially the M2-type macrophages.