Identification of high risk anaplastic gliomas by a diagnostic and prognostic signature derived from mRNA expression profiling

Correlation between chromatin epigenetic-related lncRNA signature (CELncSig) and prognosis, immune microenvironment, and immunotherapy in non-small cell lung cancer

Chromatin regulators drive cancer epigenetic changes, and lncRNA can play an important role in epigenetic changes as chromatin regulators. We used univariate Cox, LASSO, and multivariate Cox regression analysis to select epigenetic-associated lncRNA signatures. Twenty-five epigenetic-associated lncRNA signatures (CELncSig) were identified to establish the immune prognostic model. According to Kaplan-Meier analysis, the overall survival of the high-risk group was significantly lower than the low-risk group. Receiver operating characteristic (ROC) curves, C-index, survival curve, nomogram, and principal component analysis (PCA) were performed to validate the risk model. In GO/KEGG analysis, differentially expressed lncRNAs were correlated with the PI3K-Akt pathway, suggesting that they were highly associated with the metastasis of LUAD. Interestingly, in the immune escape analysis, the TIDE score was lower, and the possibility of immune dysfunction is also slighter in the high-risk group, which means they still have the potential to receive immunotherapy. And CELncsig is highly correlated with immune pathways T_cell_co-inhibition and Check-point. Also, the IMvigor210 cohort analysis indicated that our risk-scoring model has significant potential clinical application value in lung cancer immunotherapy. And we also screened out ten potential chemotherapy agents using the 'pRRophetic' package.

IRX1 is a novel gene, overexpressed in high-grade IDH-mutant astrocytomas

BACKGROUND

IDH-mutant astrocytomas include CNS WHO grade 2 (A2), grade 3 (A3) and grade 4 (A4), of which A3 and A4 are high-grade. A3 has a heterogenous clinical outcome that cannot be explained entirely by the existing molecular biomarkers. We comprehensively studied the transcriptome profile of A3 to determine clinical significance.

METHODS

TCGA mRNA-sequencing data of A3 was analyzed to derive differentially expressed genes (DEG), which were short-listed using various approaches. mRNA expression of the short-listed genes was validated using NanoString platform on a uniformly treated and molecularly characterized A3 cohort. Protein expression of one prognostically significant gene, Iroquois-class homeodomain (IRX1) was assessed by immunohistochemistry and correlated with patient survival and tumor recurrence. IRX1 expression was also studied in different grades of astrocytoma. Since DNA methyltransferase 3 alpha (DNMT3A) influences IRX1 expression, its mutations were evaluated in a subset of tumors.

RESULTS

TCGA analysis identified 96 DEG in A3 tumours. 57 genes were short-listed and finally narrowed down to 14 genes. mRNA values of 12/14 genes validated in our cohort. On multiple-variable analysis, IRX1 was the most prognostically relevant gene, with respect to progression free survival of patients. Further, IRX1 immunoexpression was significantly higher in A3 and A4 when compared to A2 and glioblastoma. Higher IRX1 immunoexpression correlated with poor prognosis in patients with A3 tumours. Also, a higher IRX1 expression was associated with DNMT3A mutation.

CONCLUSION

Our study identifies IRX1 as a novel biomarker overexpressed in high-grade IDH-mutant astrocytomas with prognostic significance in A3. DNMT3A mutation probably modulates IRX1 expression.

A novel cuproptosis-related gene signature of prognosis and immune microenvironment in head and neck squamous cell carcinoma cancer

BACKGROUND

Cuproptosis is a novel form of cell death that is highly related to mitochondrial metabolism and mediated by protein lipoacetylation. This study systematically assessed the differential expression and genetic alterations of cuproptosis-related genes (CRGs) in head and neck squamous-cell carcinoma (HNSCC) and constructed CRG risk models to predict survival in patients with HNSCC.

METHODS

We investigated the expression of 19 CRGs in HNSCC and noncancerous tissues, and the relationship between mutation load, immune infiltration, and clinical features was examined based on data from public databases. CRG risk models were constructed by univariate Cox analysis and lasso regression and validated by independent datasets for their accuracy in predicting survival outcomes in patients with HNSCC. The expression distribution of CRGs in HNSCC cells was further explored in the HNSCC single-cell sequencing dataset.

RESULTS

NFE2L2, ATP7A, FDX1, LIAS, DLD, DLAT, PDHB, MTF1 and DBT were highly expressed in noncancerous samples, while GLS, CDKN2A and DLST were highly expressed in HNSCC samples (p < 0.05). Gene copy number variation frequency (CNV) revealed CDKN2A, FDX1 and DLAT copy number deletions and LIPT2 and NFE2L2 copy number increases. Ten CRGs were used to construct a risk model to predict overall survival (OS) in HNSCC, yielding reduced OS in the high-risk group compared to the low-risk group, training group (p = 9.733e - 05), and testing group (p = 0.040). The CRG risk model was significantly correlated with various immune cells, regulatory T cells (Tregs) and memory B cells were significantly negatively correlated (p = 0.027, p = 0.00084), and resting CD4 memory T cells was significantly positively correlated (p = 9e - 04). Most CRGs significantly affected the clinical characteristics of HNSCC. NFE2L2, SLC31A1, PDHA1, CDKN2A and DBT were highly expressed in epithelial cells of HNSCC, while SLC31A1, DBT and NFE2L2 were highly expressed in T cells, and SLC31A1 in B cells. In monocytes, NFE2L2, SLC31A1 and PDHA1 were highly expressed.

CONCLUSION

The CRG risk model can be used as a potential prognostic factor for HNSCC patients and may provide new insights into cancer treatment from the perspective of copper metabolism.

Identification of an immune-related gene signature as a prognostic target and the immune microenvironment for adrenocortical carcinoma

BACKGROUND

Adrenocortical carcinoma (ACC) is a rare endocrine malignancy. Even with complete tumor resection and adjuvant therapies, the prognosis of patients with ACC remains unsatisfactory. In the microtumor environment, the impact of a disordered immune system and abnormal immune responses is enormous. To improve treatment, novel prognostic predictors and treatment targets for ACC need to be identified. Hence, credible prognostic biomarkers of immune-associated genes (IRGs) should be explored and developed.

MATERIAL AND METHODS

We downloaded RNA-sequencing data and clinical data from The Cancer Genome Atlas (TCGA) data set, Genotype-Tissue Expression data set, and Gene Expression Omnibus data set. Gene set enrichment analysis (GSEA) was applied to reveal the potential functions of differentially expressed genes.

RESULTS

GSEA indicated an association between ACC and immune-related functions. We obtained 332 IRGs and constructed a prognostic signature on the strength of 3 IRGs (INHBA, HELLS, and HDAC4) in the training cohort. The high-risk group had significantly poorer overall survival than the low-risk group (p < .001). Multivariate Cox regression was performed with the signature as an independent prognostic indicator for ACC. The testing cohort and the entire TCGA ACC cohort were utilized to validate these findings. Moreover, external validation was conducted in the GSE10927 and GSE19750 cohorts. The tumor-infiltrating immune cells analysis indicated that the quantity of T cells, natural killer cells, macrophage cells, myeloid dendritic cells, and mast cells in the immune microenvironment differed between the low-risk and high-risk groups.

CONCLUSION

Our three-IRG prognostic signature and the three IRGs can be used as prognostic indicators and potential immunotherapeutic targets for ACC. Inhibitors of the three novel IRGs might activate immune cells and play a synergistic role in combination therapy with immunotherapy for ACC in the future.

Promoting Prognostic Model Application: A Review Based on Gliomas

Malignant neoplasms are characterized by poor therapeutic efficacy, high recurrence rate, and extensive metastasis, leading to short survival. Previous methods for grouping prognostic risks are based on anatomic, clinical, and pathological features that exhibit lower distinguishing capability compared with genetic signatures. The update of sequencing techniques and machine learning promotes the genetic panels-based prognostic model development, especially the RNA-panel models. Gliomas harbor the most malignant features and the poorest survival among all tumors. Currently, numerous glioma prognostic models have been reported. We systematically reviewed all 138 machine-learning-based genetic models and proposed novel criteria in assessing their quality. Besides, the biological and clinical significance of some highly overlapped glioma markers in these models were discussed. This study screened out markers with strong prognostic potential and 27 models presenting high quality. Conclusively, we comprehensively reviewed 138 prognostic models combined with glioma genetic panels and presented novel criteria for the development and assessment of clinically important prognostic models. This will guide the genetic models in cancers from laboratory-based research studies to clinical applications and improve glioma patient prognostic management.

Promoting Prognostic Model Application: A Review Based on Gliomas

Malignant neoplasms are characterized by poor therapeutic efficacy, high recurrence rate, and extensive metastasis, leading to short survival. Previous methods for grouping prognostic risks are based on anatomic, clinical, and pathological features that exhibit lower distinguishing capability compared with genetic signatures. The update of sequencing techniques and machine learning promotes the genetic panels-based prognostic model development, especially the RNA-panel models. Gliomas harbor the most malignant features and the poorest survival among all tumors. Currently, numerous glioma prognostic models have been reported. We systematically reviewed all 138 machine-learning-based genetic models and proposed novel criteria in assessing their quality. Besides, the biological and clinical significance of some highly overlapped glioma markers in these models were discussed. This study screened out markers with strong prognostic potential and 27 models presenting high quality. Conclusively, we comprehensively reviewed 138 prognostic models combined with glioma genetic panels and presented novel criteria for the development and assessment of clinically important prognostic models. This will guide the genetic models in cancers from laboratory-based research studies to clinical applications and improve glioma patient prognostic management.

Autophagy-Related Long Non-coding RNA Is a Prognostic Indicator for Bladder Cancer

Bladder cancer (BC) is one of the most common malignant urinary system tumors, and its prognosis is poor. In recent years, autophagy has been closely linked to the development of BC. Therefore, we investigated the potential prognostic role of autophagy-related long non-coding RNA (lncRNA) in patients with BC. We obtained the lncRNA information and autophagy genes, respectively, from The Cancer Genome Atlas (TCGA) data set and the human autophagy database (HADb) and performed a co-expression analysis to identify autophagy gene-associated lncRNAs. Then, we divided the data into training group and testing group. In the training group, 15 autophagy-related lncRNAs were found to have a prognostic value (AC026369.3, USP30-as1, AC007991.2, AC104785.1, AC010503.4, AC037198.1, AC010331.1, AF131215.6, AC084357.2, THUMPD3-AS1, U62317.4, MAN1B1-DTt, AC024060.1, AL662844.4, and AC005229.4). The patients were divided into low-risk group and high-risk group based on the prognostic lncRNAs. The overall survival (OS) time for the high-risk group was shorter than that for the low-risk group [risk ratio (hazard ratio, HR) = 1.08, 95% CI: 1.06-1.10; p < 0.0001]. Using our model, the defined risk value can predict the prognosis of a patient. Next, the model was assessed in the TCGA testing group to further validate these results. A total of 203 patients with BC were recruited to verify the lncRNA characteristics. We divided these patients into high-risk group and low-risk group. The results of testing data set show that the survival time of high-risk patients is shorter than that of low-risk patients. In the training group, the area under the curve (AUC) was more than 0.7, indicating a high level of accuracy. The AUC for a risk model was greater than that for each clinical feature alone, indicating that the risk value of a model was the best indicator for predicting the prognosis. Further training data analysis showed that the gene set was significantly enriched in cancer-related pathways, including actin cytoskeleton regulation and gap junctions. In conclusion, our 15 autophagy-related lncRNAs have a prognostic potential for BC, and may play key roles in the biology of BC.

MiR-330-3p and miR-485-5p as biomarkers for glioblastoma: An integrated bioinformatics and experimental study

Glioblastoma Multiforme (GBM) is the most common, invasive, and malignant primary brain tumor with a poor prognosis and a median survival of 12-15 months. This study tried to identify the most significant miRNA biomarkers in both tissue and serum samples of GBM. GSE25632 was employed from gene expression omnibus and using WGCNA package, association of miRNA networks and clinical data was explored and brown and green modules identified as the most relevant modules. Independently, Limma package was utilized to identify differentially expressed miRNAs (DEMs) in GSE25632 by cutoff logFC > 2 and P.value < 0.05. By merging the results of Limma and WGCNA, the miRNAs that were in brown and green modules and had mentioned cutoff were selected as hub miRNAs. Performing enrichment analysis, Pathways in cancer, Prostate cancer, Glioma, p53 signaling pathway, and Focal adhesion were identified as the most important signaling pathways. Based on miRNA- target genes, has-mir-330-3p and has-mir-485-5p were identified as core miRNAs. The expression level of core miRNAs was validated by GSE90604, GSE42657, and GSE93850. We evaluated the expression level of common target genes of two detected core genes based on GSE77043, GSE42656, GSE22891, GSE15824, and GSE122498. The ability of detected miRNAs to discriminate GBM from healthy controls was assessed by area under the curve (AUC) using the ROC curve analysis. Based on TCGA database, we tested the prognostic significance of miRNAs using overall survival analysis. We evaluated the expression level of the miRNAs in tissue of 83 GBM patients and also non-tumoral adjacent (as control) tissues. We used serum samples of 34 GBM patients to evaluate the expression levels of the hub miRNAs compare to the controls. Our results showed that has-mir-330-3p and has-mir-485-5p could be potential biomarkers in GBM.

A novel gene signature based on five immune checkpoint genes predicts the survival of glioma

BACKGROUND

Glioma is the most common and fatal type of nerve neoplasm in the central nervous system. Several biomarkers have been considered for prognosis prediction, which is not accurate enough. We aimed to carry out a gene signature related to the expression of immune checkpoints which was enough for its performance in prediction.

METHODS

Gene expression of immune checkpoints in TGGA database was filtrated. The 5 selected genes underwent verification by COX and Lasso-COX regression. Next, the selected genes were included to build a novel signature for further analysis.

RESULTS

Patients were sub-grouped into high and low risk according to the novel signature. Immune response, clinicopathologic characters, and survival showed significant differences between those 2 groups. Terms including "naive," "effector," and "IL-4" were screened out by GSEA. The results showed strong relevance between the signature and immune response.

CONCLUSIONS

We constructed a gene signature with 5 immune checkpoints. The signature predicted survival effectively. The novel signature performed more functional than previous biomarkers.

An mRNA characterization model predicting survival in patients with invasive breast cancer based on The Cancer Genome Atlas database

BACKGROUND

Invasive breast cancer is a highly heterogeneous tumor, although there have been many prediction methods for invasive breast cancer risk prediction, the prediction effect is not satisfactory. There is an urgent need to develop a more accurate method to predict the prognosis of patients with invasive breast cancer.

OBJECTIVE

To identify potential mRNAs and construct risk prediction models for invasive breast cancer based on bioinformaticsMETHODS: In this study, we investigated the differences in mRNA expression profiles between invasive breast cancer and normal breast samples, and constructed a risk model for the prediction of prognosis of invasive breast cancer with univariate and multivariate Cox analyses.

RESULTS

We constructed a risk model comprising 8 mRNAs (PAX7, ZIC2, APOA5, TP53AIP1,MYBPH, USP41, DACT2, and POU3F2) for the prediction of invasive breast cancer prognosis. We used the 8-mRNA risk prediction model to divide 1076 samples into high-risk groups and low-risk groups, the Kaplan-Meier curve showed that the high-risk group was closely related to the poor prognosis of overall survival in patients with invasive breast cancer. The receiver operating characteristic curve revealed an area under the curve of 0.773 for the 8 mRNA model at 3-year overall survival, indicating that this model showed good specificity and sensitivity for prediction of prognosis of invasive breast cancer.

CONCLUSIONS

The study provides an effective bioinformatic analysis for the better understanding of the molecular pathogenesis and prognosis risk assessment of invasive breast cancer.

Multiple microarray analyses identify key genes associated with the development of Non-Small Cell Lung Cancer from Chronic Obstructive Pulmonary Disease

Introduction: Chronic obstructive pulmonary disease (COPD) is an independent risk factor of non-small cell lung cancer (NSCLC). This study aimed to analyze the key genes and potential molecular mechanisms that are involved in the development from COPD to NSCLC.

Methods: Expression profiles of COPD and NSCLC in GSE106899, GSE12472, and GSE12428 were downloaded from the Gene Expression Omnibus (GEO) database, followed by identification of the differentially expressed genes (DEGs) between COPD and NSCLC. Based on the identified DEGs, functional pathway enrichment and lung carcinogenesis-related networks analyses were performed and further visualized with Cytoscape software. Then, principal component analysis (PCA), cluster analysis, and support vector machines (SVM) verified the ability of the top modular genes to distinguish COPD from NSCLC. Additionally, the corrections between these key genes and clinical staging of NSCLC were studied using the UALCAN and HPA websites. Finally, a prognostic risk model was constructed based on multivariate Cox regression analysis. Kaplan-Meier survival curves of the top modular genes on the training and verification sets were generated.

Results: A total of 2350, 1914, and 1850 DEGs were obtained from GSE106899, GSE12472, and GSE12428 datasets, respectively. Following analysis of protein-protein interaction networks, the identified modular gene signatures containing H2AFX, MCM2, MCM3, MCM7, POLD1, and RPA1 were identified as markers for discrimination between COPD and NSCLC. The modular gene signatures were mainly enriched in the processes of DNA replication, cell cycle, mismatch repair, and others. Besides, the expression levels of these genes were significantly higher in NSCLC than in COPD, which was further verified by the immunohistochemistry. In addition, the high expression levels of H2AFX, MCM2, MCM7, and POLD1 correlate with poor prognosis of lung adenocarcinoma (LUAD). The Cox regression prognostic risk model showed the similar results and the predictive ability of this model is independent of other clinical variables.

Conclusions: This study revealed several key modules that closely relate to NSCLC with underlying disease COPD, which provide a deeper understanding of the potential mechanisms underlying the malignant development from COPD to NSCLC. This study provides valuable prognostic factors in high-risk lung cancer patients with COPD.

Long Non-coding RNA Expression Profiling Identifies a Four-Long Non-coding RNA Prognostic Signature for Isocitrate Dehydrogenase Mutant Glioma

Background: Isocitrate dehydrogenase (IDH) mutant is one of the most robust and important genetic aberrations in glioma. However, the underlying regulation mechanism of long non-coding RNA (lncRNA) in IDH mutant glioma has not been systematically portrayed.

Methods:In this work, 775 IDH mutant glioma samples with transcriptome data, including 167 samples from the Chinese Glioma Genome Atlas (CGGA) RNAseq dataset, 390 samples from The Cancer Genome Atlas (TCGA) dataset, 79 samples from GSE16011 dataset, and 139 samples from CGGA microarray dataset, were enrolled. R language and GraphPad Prism software were applied for the statistical analysis and graphical work.

Results: By comparing the differentially lncRNA genes between IDH mutant and IDH wild-type glioma samples, a four-lncRNA (JAG1, PVT1, H19, and HAR1A) signature was identified in IDH mutant glioma patients. The signature model was established based on the expression level and the regression coefficient of the four lncRNA genes. IDH mutant glioma samples could be successfully stratified into low-risk and high-risk groups in CGGA RNAseq, TCGA, GSE16011, and CGGA microarray databases. Meanwhile, multivariate Cox analysis showed that the four-lncRNA signature was an independent prognostic biomarker after adjusting for other clinicopathologic factors. Moreover, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses revealed that the immune response and cellular metabolism were significantly associated with the four-lncRNA risk signature.

Conclusion: Taken together, the four-lncRNA risk signature was identified as a novel prognostic marker for IDH mutant glioma patients and may potentially lead to improvements in the lives of glioma patients.

Identification of an immune-related long non-coding RNA signature and nomogram as prognostic target for muscle-invasive bladder cancer

To identify an immune-related prognostic signature based on long non-coding RNAs (lncRNAs) and find immunotherapeutic targets for bladder urothelial carcinoma, we downloaded RNA-sequencing data from The Cancer Genome Atlas (TCGA) dataset. Functional enrichment analysis demonstrated bladder urothelial carcinoma was related to immune-related functions. We obtained 332 immune-related genes and 262 lncRNAs targeting immune-related genes. We constructed a signature based on eight lncRNAs in training cohort. Patients were classified as high-risk and low-risk according to signature risk score. High-risk patients had poor overall survival compared with low-risk patients (P < 0.001). Multivariate Cox regression suggested the signature was an independent prognostic indicator. The findings were further validated in testing, entire TCGA and external validation cohorts. Gene set enrichment analysis indicated significant enrichment of immune-related phenotype in high-risk group. Immunohistochemistry and online analyses validated the functions of 4 key immune-related genes (LIG1, TBX1, CTSG and CXCL12) in bladder urothelial carcinoma. Nomogram proved to be a good classifier for muscle-invasive bladder cancer through combining the signature. In conclusion, our immune-related prognostic signature and nomogram provided prognostic indicators and potential immunotherapeutic targets for muscle-invasive bladder cancer.

IDH mutation-specific radiomic signature in lower-grade gliomas

Unravelling the heterogeneity is the central challenge for glioma precession oncology. In this study, we extracted quantitative image features from T2-weighted MR images and revealed that the isocitrate dehydrogenase (IDH) wild type and mutant lower grade gliomas (LGGs) differed in their expression of 146 radiomic descriptors. The logistic regression model algorithm further reduced these to 86 features. The classification model could discriminate the two types in both the training and validation sets with area under the curve values of 1.0000 and 0.9932, respectively. The transcriptome-radiomic analysis revealed that these features were associated with the immune response, biological adhesion, and several malignant behaviors, all of which are consistent with biological processes that are differentially expressed in IDH wild type and IDH mutant LGGs. Finally, a prognostic signature showed an ability to stratify IDH mutant LGGs into high and low risk groups with distinctive outcomes. By extracting a large number of radiomic features, we identified an IDH mutation-specific radiomic signature with prognostic implications. This radiomic signature may provide a way to non-invasively discriminate lower-grade gliomas as with or without the IDH mutation.

Expression and prognostic value of mRNAs in lower grade glioma with MGMT promoter methylated

Due to the varied overall survival (OS), limited studies focus on the factors that affect the prognosis for lower grade glioma patients (LGGs) with MGMT promoter methylated. A total of 579 samples (TCGA LGGs 456; CGGA LGGs 123) were included to identify potential genes for LGGs with MGMT promoter methylated. All bioinformatics analyses were conducted using SPSS software and GraphPad Prism 6. Based on COX regression analysis, we established a four-gene signature (ALDOC, APOBEC3C, ANXA1 and ARPP21) and divided LGGs into two groups based on median risk score. The OS of LGGs in high risk group was shorter than low risk group (P < 0.0001). Furthermore, the OS in high risk group were shorter than low risk group in Grade II and III, respectively (P = 0.0003; P = 0.0104). It showed that the signature was an independent prognosis factor on multivariate Cox regression analysis (P = 0.033). Patients in high group tended to displayed high grade (GIII), IDH1 wild type and mesenchymal subtype preference. Four-gene signature was discovered for LGGs with MGMT promoter methylated. Our findings suggested that the four genes could serve as prognostic biomarkers.

Identification of N6-methyladenosine-related lncRNAs for patients with primary glioblastoma

To investigate the m⁶a-related long non-coding RNAs (lncRNAs) that may be exploited as potential biomarkers in primary glioblastoma (pGBM), a cohort of 268 glioma samples from GSE16011 dataset was included for discovery. The Chinese Glioma Genome Atlas (CGGA) microarray and RNA sequencing databases were used for validation. Bioinformatic analyses were performed using the R software. The m⁶a-lncRNA co-expression networks were constructed, and four m⁶a-related lncRNAs (MIR9-3HG, LINC00900, MIR155HG, and LINC00515) were identified in pGBM patients on the univariate Cox regression analysis. Patients in the low-risk group had longer overall survival (OS) and progression-free survival (PFS) than those in the high-risk group (P = 0.0025, P = 0.0070). Moreover, the high-risk group displayed older age, isocitrate dehydrogenase (IDH) wild-type, classical and mesenchymal TCGA subtype, and G3 CGGA subtype. Distinct m⁶a status was identified according to histologic grade and two groups (low-risk and high-risk). Functional annotation showed that differentially expressed genes between the two groups were enriched in immune response, apoptosis, cell adhesion, negative regulation of transcription, negative regulation of RNA metabolic process, and regulation of RNA metabolic process. We profiled the m⁶a status in glioma and identified four m⁶a-related prognostic lncRNAs for pGBMs.

Postoperative standard chemoradiotherapy benefits primary glioblastoma patients of all ages

BACKGROUND

Glioblastoma is the most malignant tumor of the central nervous system. Several prediction models have been produced to aid in prognosis assessment. Age, a primary decision factor for prognosis, is associated with increased genomic alterations, however the exact link between increased age and poor prognosis is unknown.

OBJECTIVE

In this study, we aimed to reveal the underlying cause of poor prognosis in elderly patients.

METHODS

This study included data on 616 primary GBM tumor samples from the CGGA and TCGA databases and 41 nontumor brain tissue samples obtained from GSE53890. Hallmarks and clinicopathological characteristics were evaluated in both tumor and nontumor brain tissues. The association between choice of treatment regimen and age was measured using ANOVA and Student's t test.

RESULTS

Age was a robust predictor of poor prognosis in glioma. No age-related hallmarks of cancer were detected, including pathological characteristics or mutations. However, treatment choice was strongly significantly different between old and young patients. Combined chemo-radiation therapy could benefit old and young GBM patients, however, old patients are currently less likely to choose it.

CONCLUSION

The vast divergence in prognosis between young and old GBM patients is largely caused by choice of treatment rather than age-related tumor genomic characteristics. Postoperative standard radio- and chemotherapy provide strong benefits to primary glioblastoma patients of all ages.

Systematic analysis of differentially methylated expressed genes and site-specific methylation as potential prognostic markers in head and neck cancer

Head and neck cancer (HNC) remains one of the most malignant tumors with a significantly high mortality. DNA methylation exerts a vital role in the prognosis of HNC. In this study, we try to screen abnormal differential methylation genes (DMGs) and pathways in Head-Neck Squamous Cell Carcinoma via integral bioinformatics analysis. Data of gene expression microarrays and gene methylation microarrays were obtained from the Cancer Genome Atlas database. Aberrant DMGs were identified by the R Limma package. We conducted the Cox regression analysis to select the prognostic aberrant DMGs and site-specific methylation. Five aberrant DMGs were recognized that significantly correlated with overall survival. The prognostic model was constructed based on five DMGs (PAX9, STK33, GPR150, INSM1, and EPHX3). The five DMG models acted as prognostic biomarkers for HNC. The area under the curve based on the five DMGs predicting 5-year survival is 0.665. Moreover, the correlation between the DMGs/site-specific methylation and gene expression was also explored. The findings demonstrated that the five DMGs can be used as independent prognostic biomarkers for predicting the prognosis of patients with HNC. Our study might lay the groundwork for further mechanism exploration in HNC and may help identify diagnostic biomarkers for early stage HNC.

Prognostic implications of aberrantly expressed methylation‑driven genes in hepatocellular carcinoma: A study based on The Cancer Genome Atlas

RNA‑Sequencing and methylation data for hepatocellular carcinoma (HCC) were downloaded from The Cancer Genome Atlas (TCGA). The aberrantly expressed methylation‑driven genes in HCC and normal tissues were identified using the Limma package and the MethylMix algorithm. The Database for Annotation, Visualization and Integrated Discovery and ConsensusPathDB were used for Gene Ontology (GO) enrichment and pathway analysis. Univariate and multivariate Cox regression analyses were used to construct a prognostic risk model of HCC. Survival curve and receiver operating characteristic (ROC) curves were applied to evaluate the clinical utility of the risk model. A total of 238 methylation‑driven genes were successfully identified from cancer and normal tissues. GO enrichment analysis indicated that these genes functioned in the extracellular space, interfering with lipid metabolism in hepatocytes and regulating adaptive immune responses. In total, 14 relevant pathways were identified. The following prognostic risk model was generated: Risk score=CALML3 (degree of methylation) x (‑4.860) + CCNI2 x (2.071) + TNFRSF12A x (‑3.369) + IFITM1 x (1.203) + ENPP7P13 x (‑1.366) + DDT x (2.139) + RASAL2‑AS1 x (‑1.384) + ANKRD22 x (‑3.215). The median risk score (0.970) derived from this model was set as cutoff value for assigning patients to high‑ or low‑risk group. The 5‑year survival rate was 35.8% [95% confidence interval (CI)=27.1‑47.4%] in the high‑risk group and 61.7% (95% CI=51.4‑74.2%) in the low‑risk group (P<0.0001). The ROC curve showed an area under the curve of 0.742, indicating that this model is appropriate for predicting the survival rate of patients. Furthermore, the methylation and expression levels of two key genes, tumor necrosis factor superfamily member 12A and D‑dopachrome decarboxylase, were significantly associated with prognosis and were correlated with cg00510447, cg26808293, cg11060661 and cg16132339 methylation. In conclusion, a prognostic risk model for HCC is proposed based on the bioinformatic analysis of methylation‑driven genes. The findings of the present study may improve understanding of the pathogenesis and prognosis of HCC.

Combined Therapy Sensitivity Index Based on a 13-Gene Signature Predicts Prognosis for IDH Wild-type and MGMT Promoter Unmethylated Glioblastoma Patients

Glioblastoma (GBM) is one of the lethal tumors with poor prognosis. However, prognostic prediction approaches need to be further explored. Therefore, we developed an evaluation system that could be used for prognostic prediction of GBM patients. Published mRNA expression datasets from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and Chinese Glioma Genome Atlas (CGGA) were analyzed. Quantitative Realtime-PCR of signature genes and molecular aberrations of 178 Xiangya GBM patients were used for confirmation. Gene set enrichment analysis (GSEA) was performed for functional annotation. As a result, we established a 13-gene signature which named Combined Therapy Sensitivity Index (CTSI). Based on a cutoff point, we divided patients into high-risk group and low-risk group. Based on Kaplan-Meier analysis and multivariate Cox regression analysis, we found that patients in the high-risk group had a shorter overall survival time than patients in the low-risk group (p<0.001 in TCGA and CGGA datasets, p=0.047 in GSE4271 dataset, p=0.008 in Xiangya GBM cohort, HR: 1.65-3.42). By comparing the status of IDH mutation, TERT promoter mutation (TERTp-mut) and MGMT promoter methylation, CTSI was predictable in IDH wild-type (IDH-wt)/MGMT promoter unmethylated (MGMTp-unmeth) patients (p=0.037 in IDH-wt/TERTp-mut/MGMTp-unmeth subgroup, HR: 1.98; p=0.032 in IDH-wt/TERTp-wt/MGMTp-unmeth subgroup, HR: 2.09). Based on GESA, the Gene Ontology (GO) gene sets were enriched differently between CTSI high-risk and low-risk groups. Our results showed CTSI risk score can predict the prognosis of IDH-wt/MGMTp-unmeth GBM patients. Based on CTSI, combined with the status of IDH mutation, TERT promoter mutation and MGMT promoter methylation, a stepwise prognosis evaluation system which can provide precise prognosis prediction for GBM patients was established.

A Potential Prognostic Gene Signature for Predicting Survival for Glioblastoma Patients

OBJECTIVE

This study aimed to screen prognostic gene signature of glioblastoma (GBM) to construct prognostic model.

METHODS

Based on the GBM information in the Cancer Genome Atlas (TCGA, training set), prognostic genes (Set X) were screened by Cox regression. Then, the optimized prognostic gene signature (Set Y) was further screened by the Cox-Proportional Hazards (Cox-PH). Next, two prognostic models were constructed: model A was based on the Set Y; model B was based on part of the Set X. The samples were divided into low- and high-risk groups according to the median prognosis index (PI). GBM datasets in Gene Expression Ominous (GEO, GSE13041) and Chinese Glioma Genome Atlas (CGGA) were used as the testing datasets to confirm the prognostic models constructed based on TCGA.

RESULTS

We identified that the prognostic 14-gene signature was significantly associated with the overall survival (OS) in the TCGA. In model A, patients in high- and low-risk groups showed the significantly different OS (P = 7.47 × 10-9, area under curve (AUC) 0.995) and the prognostic ability were also confirmed in testing sets (P=0.0098 and 0.037). The model B in training set was significant but failed in testing sets.

CONCLUSION

The prognostic model which was constructed based on the prognostic 14-gene signature presented a high predictive ability for GBM. The 14-gene signature may have clinical implications in the subclassification of GBM.

A comprehensive review of available omics data resources and molecular profiling for precision glioma studies

Gliomas are the most common and lethal type of primary malignant central nervous system tumors, with an extremely poor prognosis. The latest progression in the technological development of sequencing/microarray and bioinformatics has provided insights into the glioma genome. These technologies have generated large amounts of easily accessible biological omics data, providing an unprecedented opportunity to study glioma formation. According to the 2016 WHO organization classification of brain tumors, gliomas are currently diagnosed with respect to morphological and molecular tumor alterations, especially for isocitrate dehydrogenase and 1p/19q codeletions. In the present study, the comprehensive molecular profiling and available omics data resources for malignant gliomas were reviewed for novel insights into the biology and classification of these tumors. These molecular profiling resources may be useful for improving the understanding of malignant gliomas, and to accelerate the clinical, experimental and epidemiological studies that may lead to improvements in the lives of patients with glioma.

A novel scoring system for gastric cancer risk assessment based on the expression of three CLIP4 DNA methylation-associated genes

Gastric cancer (GC) is the fifth most common cancer and the third leading cause of cancer-associated mortality worldwide. In the current study, comprehensive bioinformatic analyses were performed to develop a novel scoring system for GC risk assessment based on CAP-Gly domain containing linker protein family member 4 (CLIP4) DNA methylation status. Two GC datasets with methylation sequencing information and mRNA expression profiling were downloaded from the The Cancer Genome Atlas and Gene Expression Omnibus databases. Differentially expressed genes (DEGs) between the CLIP4 hypermethylation and CLIP4 hypomethylation groups were screened using the limma package in R 3.3.1, and survival analysis of these DEGs was performed using the survival package. A risk scoring system was established via regression factor-weighted gene expression based on linear combination to screen the most important genes associated with CLIP4 methylation and prognosis. Genes associated with high/low-risk value were selected using the limma package. Functional enrichment analysis of the top 500 DEGs that positively and negatively associated with risk values was performed using DAVID 6.8 online and the gene set enrichment analysis (GSEA) software. In total, 35 genes were identified to be that significantly associated with prognosis and CLIP4 DNA methylation, and three prognostic signature genes, claudin-11 (CLDN11), apolipoprotein D (APOD), and chordin like 1 (CHRDL1), were used to establish a risk assessment system. The prognostic scoring system exhibited efficiency in classifying patients with different prognoses, where the low-risk groups had significantly longer overall survival times than those in the high-risk groups. CLDN11, APOD and CHRDL1 exhibited reduced expression in the hypermethylation and low-risk groups compare with the hypomethylation and high-risk groups, respectively. Multivariate Cox analysis indicated that risk value could be used as an independent prognostic factor. In functional analysis, six functional gene ontology terms and five GSEA pathways were associated with CLDN11, APOD and CHRDL1. The results established the credibility of the scoring system in this study. Additionally, these three genes, which were significantly associated with CLIP4 DNA methylation and GC risk assessment, were identified as potential prognostic biomarkers.

Identification of a DNA Repair-Related Multigene Signature as a Novel Prognostic Predictor of Glioblastoma

BACKGROUND

Glioblastoma (GBM) is an extremely challenging malignancy to treat. Although temozolomide (TMZ) is a standard treatment regimen, many patients with GBM develop chemoresistance. The aim of this study was to identify a DNA repair-related gene signature to better stratify patients treated with TMZ.

METHODS

We selected 89 cases of primary GBM (pGBM) from the Chinese Glioma Genome Atlas RNA-seq dataset as the training cohort, whereas The Cancer Genome Atlas RNA-seq and Gene Set Enrichment (GSE) 16011 mRNA array sets were used as validation cohorts. Regression analysis and linear risk score assessment were performed to build a DNA repair-related signature. We used Kaplan-Meier analysis to evaluate the predictive value of the signature for overall survival (OS) in the different groups. Multivariate Cox regression analysis was used to determine whether the 5-gene signature could independently predict OS.

RESULTS

Using our 5-gene signature panel of APEX1, APRT, PARP2, PMS2L2, and POLR2L, we divided patients with pGBM into high- and low-risk groups. Patients with a low-risk score were predicted to have favorable survival and greater benefit from TMZ therapy compared with patients from the high-risk group (P < 0.05). Moreover, receiver operating characteristic curves showed that the multigene signature was the most sensitive and specific model for survival prediction (P < 0.05).

CONCLUSIONS

Among patients with pGBM, classification based on a risk score determined using a 5-gene panel indicated different OS and reaction to TMZ. The findings in this study demonstrate that this unique 5-gene signature could be a novel model to predict OS and provide accurate therapy for patients with pGBM.

TRIM58/cg26157385 methylation is associated with eight prognostic genes in lung squamous cell carcinoma

The present study aimed to analyze the differentially expressed genes related to the tripartite motif containing 58 (TRIM58)/cg26157385 methylation sites, and consequently to provide theoretical basis for elucidating the influence of TRIM58/cg26157385 methylation on lung cancer prognosis. Methylation-sequencing information, mRNA expression profiling data and clinical data were downloaded from cBioPortal database to screen out candidate genes related to the methylation of TRIM58/cg26157385 in squamous cell lung carcinoma. The differentially expressed genes related to TRIM58 methylation were extracted form both training dataset and validation dataset. Cox regression analysis, risk scoring system construction, correlation analysis between the expression value of genes and clinical information were conducted to reveal TRIM58 methylation-related factors. Additionally, GO function analysis and KEGG pathway enrichment analysis were performed. Based on their expression level and the corresponding survival information for 347 out of 370 samples with squamous cell lung carcinoma, 183 genes significantly associated with prognosis were gained, and the top 8 ones, including alpha-2-macroglobulin-like 1 (A2ML1), cyclin-E1 (CCNE1), COBL, establishment of sister chromatid cohesion N-acetyltransferase 2 (ESCO2), G protein-coupled receptor 115 (GPR115), matrix metalloproteinases 10 (MMP10), OVO homologue-like 1 (OVOL1) and secretoglobin family 1A member 1 (SCGB1A1), were candidate signature genes significantly correlated with TRIM58 methylation. Furthermore, targeted therapy was significantly correlated with prognosis. Functional enrichment analysis demonstrated that the proliferation and differentiation of epidermal cells in lung squamous cell carcinoma patients were abnormal and the homeostasis was disturbed. Eight genes, including A2ML1, CCNE1, COBL, ESCO2, GPR115, MMP10, OVOL1 and SCGB1A1, were significantly related to TRIM58 methylation and treatment of lung squamous cell carcinoma, and may be used as potential prognostic biomarkers. The present study would help to elucidate the influence of TRIM58/cg26157385 methylation on lung cancer prognosis.

Identification of methylation sites and signature genes with prognostic value for luminal breast cancer

Background

Robust and precise molecular prognostic predictors for luminal breast cancer are required. This study aimed to identify key methylation sites in luminal breast cancer, as well as precise molecular tools for predicting prognosis.

Methods

We compared methylation levels of normal and luminal breast cancer samples from The Cancer Genome Atlas dataset. The relationships among differentially methylated sites, corresponding mRNA expression levels and prognosis were further analysed. Differentially expressed genes in normal and cancerous samples were analysed, followed by the identification of prognostic signature genes. Samples were divided into low- and high-risk groups based on the signature genes. Prognoses of low- and high-risk groups were compared. The Gene Expression Omnibus dataset were used to validate signature genes for prognosis prediction. Prognosis of low- and high-risk groups in Luminal A and Luminal B samples from the TCGA and the Metabric cohort dataset were analyzed. We also analysed the correlation between clinical features of low- and high- risk groups as well as their differences in gene expression.

Results

Fourteen methylation sites were considered to be related to luminal breast cancer prognosis because their methylation levels, mRNA expression and prognoses were closely related to each other. The methylation level of SOSTDC1 was used to divide samples into hypo- and hyper-methylation groups. We also identified an mRNA signature, comprising eight transcripts, ESCO2, PACSIN1, CDCA2, PIGR, PTN, RGMA, KLK4 and CENPA, which was used to divide samples into low- and high-risk groups. The low-risk group showed significantly better prognosis than the high-risk group. A correlation analysis revealed that the risk score was an independent prognostic factor. Low- and high- risk groups significantly correlated with the survival ratio in Luminal A samples, but not in Luminal B samples on the basis of the TCGA and the Metabric cohort dataset. Further functional annotation demonstrated that the differentially expressed genes were mainly involved in cell cycle and cancer progression.

Conclusions

We identified several key methylation sites and an mRNA signature for predicting luminal breast cancer prognosis. The signature exhibited effective and precise prediction of prognosis and may serve as a prognostic and diagnostic marker for luminal breast cancer.

Electronic supplementary material

The online version of this article (10.1186/s12885-018-4314-9) contains supplementary material, which is available to authorized users.

A three-gene signature for prognosis in patients with MGMT promoter-methylated glioblastoma

Glioblastoma is the most malignant tumor and has high mortality rate. The methylated prompter of MGMT results in chemotherapy sensitivity for these patients. However, there are still other factors that affected the prognosis for the glioblastoma patients with similar MGMT methylation status. We developed a signature with three genes screened from the whole genome mRNA expression profile from Chinese Glioma Genome Atlas (CGGA) and RNAseq data from The Cancer Genome Atlas (TCGA). Patients with MGMT methylation in low risk group had longer survival than those in high risk group (median overall survival 1074 vs. 372 days; P = 0.0033). Moreover, the prognostic value of the signature was significant difference in cohorts stratified by MGMT methylation and chemotherapy (P=0.0473), while there is no significant difference between low and high risk group or unmethylated MGMT patients without chemotherapy. Multivariate analysis indicated that the risk score was an independent prognosis factor (P = 0.004). In conclusion, our results showed that the signature has prognostic value for patients with MGMT promoter-methylated glioblastomas based on bioinformatics analysis.

A 10‑microRNA prognosis scoring system in esophageal squamous cell carcinoma constructed using bioinformatic methods

MicroRNA (miR) signatures may aid the diagnosis and prediction of cancer; therefore, miRs associated with the prognosis of esophageal squamous cell carcinoma (ESCC) were screened. miR‑sequencing (seq) and mRNA‑seq data from early‑stage ESCC samples were downloaded from The Cancer Genome Atlas (TCGA) database, and samples from subjects with a >6‑month survival time were assessed with Cox regression analysis for prognosis‑associated miRs. A further two miR expression datasets of ESCC samples, GSE43732 and GSE13937, were downloaded from the Gene Expression Omnibus database. Common miRs between prognosis‑associated miRs, and miRs in the GSE43732 and GSE13937, datasets were used for risk score calculations for each sample, and median risk scores were applied for the stratification of low‑ and high‑risk samples. A prognostic scoring system of signature miRs was subsequently constructed and used for survival analysis for low‑ and high‑risk samples. Differentially‑expressed genes (DEGs) corresponding to all miRs were screened and functional annotation was performed. A total of 34 prognostic miRs were screened and a scoring system was created using 10 signature miRs (hsa‑miR‑140, ‑33b, ‑34b, ‑144, ‑486, ‑214, ‑129‑2, ‑374a and ‑412). Using this system, low‑risk samples were identified to be associated with longer survival compared with high‑risk samples in the TCGA and GSE43732 datasets. Age, alcohol and tobacco use, and radiotherapy were prognostic factors for samples with different risk scores and the same clinical features. There were 168 DEGs, and the top 20 risk scores positively‑correlated and the top 20 risk scores negatively‑correlated DEGs were significantly enriched for six and 10 functional terms, respectively. 'Tight junction' and 'melanogenesis' were two significantly enriched pathways of DEGs. miR‑214, miR‑129‑2, miR‑37a and miR‑486 may predict ESCC patient survival, although further studies to validate this hypothesis are required.

A novel gene signature based on five glioblastoma stem-like cell relevant genes predicts the survival of primary glioblastoma

PURPOSE

Primary glioblastoma (pGBM) is the most common and lethal type of neoplasms in the central nervous system, while the existing biomarkers, lacking consideration on the stemness changes of GBM cells, are not specific enough to predict the complex prognosis respectively. We aimed to build a high-efficiency prediction gene signature related to GBM cell stemness and investigate its prognostic value in primary glioblastoma.

METHODS

Differentially expressed genes were screened in GSE23806 database. The selected genes were then verified by univariate Cox regression in 591 patients from four enormous independent databases, including the Chinese Glioma Genome Atlas (CGGA), TCGA, REMBRANDT and GSE16011. Finally, the intersected genes were included to build the gene signature. GO analysis and GSEA were carried out to explore the bioinformatic implication.

RESULTS

The novel five-gene signature was used to identify high- and low-risk groups in the four databases, and the high-risk group showed notably poorer prognosis (P < 0.05). Gene ontology (GO) terms including "immune response", "apoptotic process", and "angiogenesis" were picked out by GO analysis and GSEA, which revealed that the gene signature was highly possibly related to the stemness of GSCs and predicting the prognosis of GBM effectively.

CONCLUSION

We built a gene signature with five glioblastoma stem-like cell (GSC) relevant genes, and predicted the survival in four independent databases effectively, which is possibly related to the stemness of GSCs in pGBM. Several GO terms were investigated to be correlated to the signature. The signature can predict the prognosis of glioblastoma efficiently.

An immune-related lncRNA signature for patients with anaplastic gliomas

We investigated immune-related long non-coding RNAs (lncRNAs) that may be exploited as potential therapeutic targets in anaplastic gliomas. We obtained 572 lncRNAs and 317 immune genes from the Chinese Glioma Genome Atlas microarray and constructed immune-related lncRNAs co-expression networks to identify immune-related lncRNAs. Two additional datasets (GSE16011, REMBRANDT) were used for validation. Gene set enrichment analysis and principal component analysis were used for functional annotation. Immune-lncRNAs co-expression networks were constructed. Nine immune-related lncRNAs (SNHG8, PGM5-AS1, ST20-AS1, LINC00937, AGAP2-AS1, MIR155HG, TUG1, MAPKAPK5-AS1, and HCG18) signature was identified in patients with anaplastic gliomas. Patients in the low-risk group showed longer overall survival (OS) and progression-free survival than those in the high-risk group (P < 0.0001; P < 0.0001). Additionally, patients in the high-risk group displayed no-deletion of chromosomal arms 1p and/or 19q, isocitrate dehydrogenase wild-type, classical and mesenchymal TCGA subtype, G3 CGGA subtype, and lower Karnofsky performance score (KPS). Moreover, the signature was an independent factor and was significantly associated with the OS (P = 0.000, hazard ratio (HR) = 1.434). These findings were further validated in two additional datasets (GSE16011, REMBRANDT). Low-risk and high-risk groups displayed different immune status based on principal components analysis. Our results showed that the nine immune-related lncRNAs signature has prognostic value for anaplastic gliomas.

Molecular mechanisms underlying gliomas and glioblastoma pathogenesis revealed by bioinformatics analysis of microarray data

The aim of this study was to identify key genes associated with gliomas and glioblastoma and to explore the related signaling pathways. Gene expression profiles of three glioma stem cell line samples, three normal astrocyte samples, three astrocyte overexpressing 4 iPSC-inducing and oncogenic factors (myc(T58A), OCT-4, p53DD, and H-Ras(G12V)) samples, three astrocyte overexpressing 7 iPSC-inducing and oncogenic factors (OCT4, H-Ras(G12V), myc(T58A), p53DD, cyclin D1, CDK4(RC24) and hTERT) samples and three glioblastoma cell line samples were downloaded from the ArrayExpress database (accession: E-MTAB-4771). The differentially expressed genes (DEGs) in gliomas and glioblastoma were identified using FDR and t tests, and protein-protein interaction (PPI) networks for these DEGs were constructed using the protein interaction network analysis. The GeneTrail2 1.5 tool was used to identify potentially enriched biological processes among the DEGs using gene ontology (GO) terms and to identify the related pathways using the Kyoto Encyclopedia of Genes and Genomes, Reactome and WikiPathways pathway database. In addition, crucial modules of the constructed PPI networks were identified using the PEWCC1 plug-in, and their topological properties were analyzed using NetworkAnalyzer, both available from Cytoscape. We also constructed microRNA-target gene regulatory network and transcription factor-target gene regulatory network for these DEGs were constructed using the miRNet and binding and expression target analysis. We identified 200 genes that could potentially be involved in the gliomas and glioblastoma. Among them, bioinformatics analysis identified 137 up-regulated and 63 down-regulated DEGs in gliomas and glioblastoma. The significant enriched pathway (PI3K-Akt) for up-regulated genes such as COL4A1, COL4A2, EGFR, FGFR1, LAPR6, MYC, PDGFA, SPP1 were selected as well as significant GO term (ear development) for up-regulated genes such as CELSR1, CHRNA9, DDR1, FGFR1, GLI2, LGR5, SOX2, TSHR were selected, while the significant enriched pathway (amebiasis) for down-regulated gene such as COL3A1, COL5A2, LAMA2 were selected as well as significant GO term (RNA polymerase II core promoter proximal region sequence-specific binding (5) such as MEIS2, MEOX2, NR2E1, PITX2, TFAP2B, ZFPM2 were selected. Importantly, MYC and SOX2 were hub proteins in the up-regulated PPI network, while MET and CDKN2A were hub proteins in the down-regulated PPI network. After network module analysis, MYC, FGFR1 and HOXA10 were selected as the up-regulated coexpressed genes in the gliomas and glioblastoma, while SH3GL3 and SNRPN were selected as the down-regulated coexpressed genes in the gliomas and glioblastoma. MicroRNA hsa-mir-22-3p had a regulatory effect on the most up DEGs, including VSNL1, while hsa-mir-103a-3p had a regulatory effect on the most down DEGs, including DAPK1. Transcription factor EZH2 had a regulatory effect on the both up and down DEGs, including CD9, CHI3L1, MEIS2 and NR2E1. The DEGs, such as MYC, FGFR1, CDKN2A, HOXA10 and MET, may be used for targeted diagnosis and treatment of gliomas and glioblastoma.

Overexpression of Transforming Acidic Coiled Coil‑Containing Protein 3 Reflects Malignant Characteristics and Poor Prognosis of Glioma

Gliomas are malignant primary brain tumors with poor prognosis. Recently, research was indicative of a tight connection between tumor malignancy and genetic alterations. Here, we propose an oncogenic implication of transforming acidic coiled-coil-containing protein 3 (TACC3) in gliomas. By comprehensively analyzing the Chinese glioma genome atlas (CGGA) and publicly available data, we demonstrated that TACC3 were overexpressed along with glioma grade and served as an independent negative prognostic biomarker for glioma patients. Functions’ annotations and gene sets’ enrichment analysis suggested that TACC3 may participate in cell cycle, DNA repair, epithelium-mesenchymal transition and other tumor-related biological processes and molecular pathways. Patients with high TACC3 expression showed CD133⁺ stem cell properties, glioma plasticity and shorter overall survival time under chemo-/radio-therapy. Additionally, a TACC3 associated the miRNA-mRNA network was constructed based on in silico prediction and expression pattern, which provide a foundation for further detection of TACC3-miRNA-mRNA axis function. Collectively, our observations identify TACC3 as an oncogene of tumor malignancy, as well as a prognostic and motoring biomarker for glioma patients.

LncRNA profile study reveals four-lncRNA signature associated with the prognosis of patients with anaplastic gliomas

Anaplastic glioma is Grade III and the median overall survival is about 37.6 months. However, there are still other factors that affect the prognosis for anaplastic glioma patients due to variable overall survival. So we screened four-lncRNA signature (AGAP2-AS1, TPT1-AS1, LINC01198 and MIR155HG) from the lncRNA expression profile from the GSE16011, CGGA and REMBRANDT datasets. The patients in low risk group had longer overall survival than high risk group (median OS 2208.25 vs. 591.30 days; P < 0.0001). Moreover, patients in the low risk group showed similar overall survival to Grade II patients (P = 0.1669), while the high risk group showed significant different to Grade IV (P = 0.0005) with similar trend. So based on the four-lncRNA, the anaplastic gliomas could be divided into grade II-like and grade IV-like groups. On the multivariate analysis, it showed the signature was an independent prognostic factor (P = 0.000). The expression of four lncRNAs in different grades showed that AGAP2-AS1, LINC01198 and MIR155HG were increased with tumor grade, while TPT1-AS1 was decreased. Knockdown of AGAP2-AS1 can inhibit the cell proliferation, migration and invasion, while increase the apoptosis cell rates in vitro. In conclusion, our results showed that the four-lncRNA signature has prognostic value for anaplastic glioma. Moreover, clinicians should conduct corresponding therapies to achieve best treatment with less side effects for two groups patients.

SHOX2 is a Potent Independent Biomarker to Predict Survival of WHO Grade II-III Diffuse Gliomas

Background

Diffuse gliomas, grades II and III, hereafter called lower-grade gliomas (LGG), have variable, difficult to predict clinical courses, resulting in multiple studies to identify prognostic biomarkers. The purpose of this study was to assess expression or methylation of the homeobox family gene SHOX2 as independent markers for LGG survival.

Methods

We downloaded publically available glioma datasets for gene expression and methylation. The Cancer Genome Atlas (TCGA) (LGG, n = 516) was used as a training set, and three other expression datasets (n = 308) and three other methylation datasets (n = 320), were used for validation. We performed Kaplan-Meier survival curves and univariate and multivariate Cox regression model analyses.

Findings

SHOX2 expression and gene body methylation varied among LGG patients and highly significantly predicted poor overall survival. While they were tightly correlated, SHOX2 expression appeared more potent as a prognostic marker and was used for most further studies. The SHOX2 prognostic roles were maintained after analyses by histology subtypes or tumor grade. We found that the combination of SHOX2 expression and IDH genotype status identified a subset of LGG patients with IDH wild-type (IDHwt) and low SHOX2 expression with considerably favorable survival. We further investigated the combination of SHOX2 with other known clinically relevant markers of LGG (TERT expression, 1p/19q chromosome co-deletion, MGMT methylation, ATRX mutation and NES expression). When combined with SHOX2 expression, we identified subsets of LGG patients with significantly favorable survival outcomes, especially in the subgroup with worse prognosis for each individual marker. Finally, multivariate analysis demonstrated that SHOX2 was a potent independent survival marker.

Interpretation

We have identified that SHOX2 expression or methylation are potent independent prognostic indicators for predicting LGG patient survival, and have potential to identify an important subset of LGG patients with IDHwt status with significantly better overall survival. The combination of IDH or other relevant markers with SHOX2 identified LGG subsets with significantly different survival outcomes, and further understanding of these subsets may benefit therapeutic target identification and therapy selections for glioma patients.

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SHOX2 is a potent independent prognostic indicator for grade II and III diffuse gliomas. SHOX2 in combination with IDH has the potential to identify important diffuese gliomas subsets with significantly better survivals. SHOX2 in combination with other markers is potentially useful for identifying distinct prognostic subsets of diffuse gliomas.

Diffuse glioma brain tumors (gliomas encompassing astrocytomas and oligodedrogliomas, grades II and III), have highly variable, difficult to predict clinical courses and a number of specific alterations have been identified that have prognostic or therapeutic implications, whether as single markers or in various combinations. The use of mutation status of the isocitrate dehydrogenase (IDH) enzyme genes has been demonstrated to be a potent prognostic marker greatly improving survival prognosis. SHOX2 methylation was suggested to be associated with lung and breast cancers. In this study we assessed SHOX2 gene methylation and expression as independent markers for diffuse gliomas survival prognosis, by multiple statistical survival analyses of multiple genome-wide datasets. We have identified that SHOX2 is a potent independent prognostic marker, both by itself and in combination with other markers (IDH mutation status, 1p/19q codeltion, ATRX mutation, nestin or TERT expression and MGMT methylation), and potentially useful for refining the molecular classification of diffuse gliomas, and for distinguishing clinically distinct prognostic subgroups of gliomas patients for better therapy selection.