An integrated mRNA and microRNA expression signature for glioblastoma multiforme prognosis

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.

PathCNN: interpretable convolutional neural networks for survival prediction and pathway analysis applied to glioblastoma

MOTIVATION

Convolutional neural networks (CNNs) have achieved great success in the areas of image processing and computer vision, handling grid-structured inputs and efficiently capturing local dependencies through multiple levels of abstraction. However, a lack of interpretability remains a key barrier to the adoption of deep neural networks, particularly in predictive modeling of disease outcomes. Moreover, because biological array data are generally represented in a non-grid structured format, CNNs cannot be applied directly.

RESULTS

To address these issues, we propose a novel method, called PathCNN, that constructs an interpretable CNN model on integrated multi-omics data using a newly defined pathway image. PathCNN showed promising predictive performance in differentiating between long-term survival (LTS) and non-LTS when applied to glioblastoma multiforme (GBM). The adoption of a visualization tool coupled with statistical analysis enabled the identification of plausible pathways associated with survival in GBM. In summary, PathCNN demonstrates that CNNs can be effectively applied to multi-omics data in an interpretable manner, resulting in promising predictive power while identifying key biological correlates of disease.

AVAILABILITY AND IMPLEMENTATION

The source code is freely available at: https://github.com/mskspi/PathCNN.

Identification of robust diagnostic and prognostic gene signatures in different grades of gliomas: a retrospective study

Background

Gliomas are the most common primary tumors of the central nervous system. The complexity and heterogeneity of the tumor makes it difficult to obtain good biomarkers for drug development. In this study, through The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA), we analyze the common diagnostic and prognostic moleculer markers in Caucasian and Asian populations, which can be used as drug targets in the future.

Methods

The RNA-seq data from Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) were analyzed to identify signatures. Based on the signatures, the prognosis index (PI) of every patient was constructed to predict the prognostic risk. Also, gene ontology (GO) functional enrichment analysis and KEGG analysis were conducted to investigate the biological functions of these mRNAs. Glioma patients’ data in the CGGA database were introduced to validate the effectiveness of the signatures among Chinese populations. Excluding the previously reported prognostic markers of gliomas from this study, the expression of HSPA5 and MTPN were examined by qRT-PCR and immunohistochemical assay.

Results

In total, 20 mRNAs were finally selected to build PI for patients from TCGA, including 16 high-risk genes and four low-risk genes. For Chinese patients, the log-rank test p values of PI were both less than 0.0001 in two independent datasets. And the AUCs were 0.831 and 0.907 for 3 years of two datasets, respectively. Moreover, among these 20 mRNAs, 10 and 15 mRNAs also had a significant predictive effect via univariate COX analysis in CGGA_693 and CGGA_325, respectively. qRT-PCR and Immunohistochemistry assay indicated that HSPA5 and MTPN over-expressed in Glioma samples compared to normal samples.

Conclusion

The 20-gene signature can forecast the risk of Glioma in TCGA effectively, moreover it can also predict the risks of Chinese patients through validation in the CGGA database. HSPA5 and MTPN are possible biomarkers of gliomas suitable for all populations to improve the prognosis of these patients.

Association of expression of epigenetic molecular factors with DNA methylation and sensitivity to chemotherapeutic agents in cancer cell lines

BACKGROUND

Altered DNA methylation patterns play important roles in cancer development and progression. We examined whether expression levels of genes directly or indirectly involved in DNA methylation and demethylation may be associated with response of cancer cell lines to chemotherapy treatment with a variety of antitumor agents.

RESULTS

We analyzed 72 genes encoding epigenetic factors directly or indirectly involved in DNA methylation and demethylation processes. We examined association of their pretreatment expression levels with methylation beta-values of individual DNA methylation probes, DNA methylation averaged within gene regions, and average epigenome-wide methylation levels. We analyzed data from 645 cancer cell lines and 23 cancer types from the Cancer Cell Line Encyclopedia and Genomics of Drug Sensitivity in Cancer datasets. We observed numerous correlations between expression of genes encoding epigenetic factors and response to chemotherapeutic agents. Expression of genes encoding a variety of epigenetic factors, including KDM2B, DNMT1, EHMT2, SETDB1, EZH2, APOBEC3G, and other genes, was correlated with response to multiple agents. DNA methylation of numerous target probes and gene regions was associated with expression of multiple genes encoding epigenetic factors, underscoring complex regulation of epigenome methylation by multiple intersecting molecular pathways. The genes whose expression was associated with methylation of multiple epigenome targets encode DNA methyltransferases, TET DNA methylcytosine dioxygenases, the methylated DNA-binding protein ZBTB38, KDM2B, SETDB1, and other molecular factors which are involved in diverse epigenetic processes affecting DNA methylation. While baseline DNA methylation of numerous epigenome targets was correlated with cell line response to antitumor agents, the complex relationships between the overlapping effects of each epigenetic factor on methylation of specific targets and the importance of such influences in tumor response to individual agents require further investigation.

CONCLUSIONS

Expression of multiple genes encoding epigenetic factors is associated with drug response and with DNA methylation of numerous epigenome targets that may affect response to therapeutic agents. Our findings suggest complex and interconnected pathways regulating DNA methylation in the epigenome, which may both directly and indirectly affect response to chemotherapy.

Gene signature for prognosis in comparison of pancreatic cancer patients with diabetes and non-diabetes

BACKGROUND

Pancreatic cancer (PC) has much weaker prognosis, which can be divided into diabetes and non-diabetes. PC patients with diabetes mellitus will have more opportunities for physical examination due to diabetes, while pancreatic cancer patients without diabetes tend to have higher risk. Identification of prognostic markers for diabetic and non-diabetic pancreatic cancer can improve the prognosis of patients with both types of pancreatic cancer.

METHODS

Both types of PC patients perform differently at the clinical and molecular levels. The Cancer Genome Atlas (TCGA) is employed in this study. The gene expression of the PC with diabetes and non-diabetes is used for predicting their prognosis by LASSO (Least Absolute Shrinkage and Selection Operator) Cox regression. Furthermore, the results are validated by exchanging gene biomarker with each other and verified by the independent Gene Expression Omnibus (GEO) and the International Cancer Genome Consortium (ICGC). The prognostic index (PI) is generated by a combination of genetic biomarkers that are used to rank the patient's risk ratio. Survival analysis is applied to test significant difference between high-risk group and low-risk group.

RESULTS

An integrated gene prognostic biomarker consisted by 14 low-risk genes and six high-risk genes in PC with non-diabetes. Meanwhile, and another integrated gene prognostic biomarker consisted by five low-risk genes and three high-risk genes in PC with diabetes. Therefore, the prognostic value of gene biomarker in PC with non-diabetes and diabetes are all greater than clinical traits (HR = 1.102, P-value < 0.0001; HR = 1.212, P-value < 0.0001). Gene signature in PC with non-diabetes was validated in two independent datasets.

CONCLUSIONS

The conclusion of this study indicated that the prognostic value of genetic biomarkers in PCs with non-diabetes and diabetes. The gene signature was validated in two independent databases. Therefore, this study is expected to provide a novel gene biomarker for predicting prognosis of PC with non-diabetes and diabetes and improving clinical decision.

Role of alternative splicing signatures in the prognosis of glioblastoma

BACKGROUND

Increasing evidence has validated the crucial role of alternative splicing (AS) in tumors. However, comprehensive investigations on the entirety of AS and their clinical value in glioblastoma (GBM) are lacking.

METHODS

The AS profiles and clinical survival data related to GBM were obtained from The Cancer Genome Atlas database. Univariate and multivariate Cox regression analyses were performed to identify survival-associated AS events. A risk score was calculated, and prognostic signatures were constructed using seven different types of independent prognostic AS events, respectively. The Kaplan-Meier estimator was used to display the survival of GBM patients. The receiver operating characteristic curve was applied to compare the predictive efficacy of each prognostic signature. Enrichment analysis and protein interactive networks were conducted using the gene symbols of the AS events to investigate important processes in GBM. A splicing network between splicing factors and AS events was constructed to display the potential regulatory mechanism in GBM.

RESULTS

A total of 2355 survival-associated AS events were identified. The splicing prognostic model revealed that patients in the high-risk group have worse survival rates than those in the low-risk group. The predictive efficacy of each prognostic model showed satisfactory performance; among these, the Alternate Terminator (AT) model showed the best performance at an area under the curve (AUC) of 0.906. Enrichment analysis uncovered that autophagy was the most enriched process of prognostic AS gene symbols in GBM. The protein network revealed that UBC, VHL, KCTD7, FBXL19, RNF7, and UBE2N were the core genes in GBM. The splicing network showed complex regulatory correlations, among which ELAVL2 and SYNE1_AT_78181 were the most correlated (r = -.506).

CONCLUSIONS

Applying the prognostic signatures constructed by independent AS events shows promise for predicting the survival of GBM patients. A splicing regulatory network might be the potential splicing mechanism in GBM.

Identification of a Specific Gene Module for Predicting Prognosis in Glioblastoma Patients

Introduction: Glioblastoma (GBM) is the most common and malignant variant of intrinsic glial brain tumors. The poor prognosis of GBM has not significantly improved despite the development of innovative diagnostic methods and new therapies. Therefore, further understanding the molecular mechanism that underlies the aggressive behavior of GBM and the identification of appropriate prognostic markers and therapeutic targets is necessary to allow early diagnosis, to develop appropriate therapies and to improve prognoses.

Methods: We used a weighted gene co-expression network analysis (WGCNA) to construct a gene co-expression network with 524 glioblastoma samples from The Cancer Genome Atlas (TCGA). A risk score was then constructed based on four module genes and the patients' overall survival (OS) rate. The prognostic and predictive accuracy of the risk score were verified in the GSE16011 cohort and the REMBRANDT cohort.

Results: We identified a gene module (the green module) related to prognosis. Then, multivariate Cox analysis was performed on 4 hub genes to construct a Cox proportional hazards regression model from 524 glioblastoma patients. A risk score for predicting survival time was calculated with the following formula based on the top four genes in the green module: risk score = (0.00889 × EXP_CLEC5A) + (0.0681 × EXP_FMOD) + (0.1724 × EXP_FKBP9) + (0.1557 × EXP_LGALS8). The 5-year survival rate of the high-risk group (survival rate: 2.7%, 95% CI: 1.2–6.3%) was significantly lower than that of the low-risk group (survival rate: 8.8%, 95% CI: 5.5–14.1%).

Conclusions: This study demonstrated the potential application of a WGCNA-based gene prognostic model for predicting the survival outcome of glioblastoma patients.

A 4-miRNA signature predicts the therapeutic outcome of glioblastoma

Multimodal therapy of glioblastoma (GBM) reveals inter-individual variability in terms of treatment outcome. Here, we examined whether a miRNA signature can be defined for the a priori identification of patients with particularly poor prognosis.

FFPE sections from 36 GBM patients along with overall survival follow-up were collected retrospectively and subjected to miRNA signature identification from microarray data. A risk score based on the expression of the signature miRNAs and cox-proportional hazard coefficients was calculated for each patient followed by validation in a matched GBM subset of TCGA. Genes potentially regulated by the signature miRNAs were identified by a correlation approach followed by pathway analysis.

A prognostic 4-miRNA signature, independent of MGMT promoter methylation, age, and sex, was identified and a risk score was assigned to each patient that allowed defining two groups significantly differing in prognosis (p-value: 0.0001, median survival: 10.6 months and 15.1 months, hazard ratio = 3.8). The signature was technically validated by qRT-PCR and independently validated in an age- and sex-matched subset of standard-of-care treated patients of the TCGA GBM cohort (n=58). Pathway analysis suggested tumorigenesis-associated processes such as immune response, extracellular matrix organization, axon guidance, signalling by NGF, GPCR and Wnt. Here, we describe the identification and independent validation of a 4-miRNA signature that allows stratification of GBM patients into different prognostic groups in combination with one defined threshold and set of coefficients that could be utilized as diagnostic tool to identify GBM patients for improved and/or alternative treatment approaches.

Analysis of the cancer genome atlas (TCGA) database identifies an inverse relationship between interleukin-13 receptor α1 and α2 gene expression and poor prognosis and drug resistance in subjects with glioblastoma multiforme

Glioblastoma multiforme (GBM) is the most common primary brain tumor in adults. A variety of targeted agents are being tested in the clinic including cancer vaccines, immunotoxins, antibodies and T cell immunotherapy for GBM. We have previously reported that IL-13 receptor subunits α1 and α2 of IL-13R complex are overexpressed in GBM. We are investigating the significance of IL-13Rα1 and α2 expression in GBM tumors. In order to elucidate a possible relationship between IL-13Rα1 and α2 expression with severity and prognoses of subjects with GBM, we analyzed gene expression (by microarray) and clinical data available at the public The Cancer Genome Atlas (TCGA) database (Currently known as Global Data Commons). More than 40% of GBM samples were highly positive for IL-13Rα2 mRNA (Log2 ≥ 2) while only less than 16% samples were highly positive for IL-13Rα1 mRNA. Subjects with high IL-13Rα1 and α2 mRNA expressing tumors were associated with a significantly lower survival rate irrespective of their treatment compared to subjects with IL-13Rα1 and α2 mRNA negative tumors. We further observed that IL-13Rα2 gene expression is associated with GBM resistance to temozolomide (TMZ) chemotherapy. The expression of IL-13Rα2 gene did not seem to correlate with the expression of genes for other chains involved in the formation of IL-13R complex (IL-13Rα1 or IL-4Rα) in GBM. However, a positive correlation was observed between IL-4Rα and IL-13Rα1 gene expression. The microarray data of IL-13Rα2 gene expression was verified by RNA-Seq data. In depth analysis of TCGA data revealed that immunosuppressive genes (such as FMOD, CCL2, OSM, etc.) were highly expressed in IL-13Rα2 positive tumors, but not in IL-13Rα2 negative tumors. These results indicate a direct correlation between high level of IL-13R mRNA expression and poor patient prognosis and that immunosuppressive genes associated with IL-13Rα2 may play a role in tumor progression. These findings have important implications in understanding the role of IL-13R in the pathogenesis of GBM and potentially other cancers.

Downregulation of SYT7 inhibits glioblastoma growth by promoting cellular apoptosis

Synaptotagmin‑7 (SYT7) is a member of the synaptotagmin gene family, and encodes a protein that mediates the calcium‑dependent regulation of membrane trafficking during synaptic transmission. A previous study demonstrated that the expression of SYT7 is associated with prostate cancer and serves an important role in development of prostate cancer. However, the roles of SYT7 in the progression of glioma remain unknown. In the present study, reverse transcription‑quantitative polymerase chain reaction (RT‑qPCR) analysis demonstrated that SYT7 was expressed in three human glioma cell lines. Western blotting and RT‑qPCR analysis demonstrated the knockdown efficiency of SYT7 shRNA in 293T cells and U87MG cells. Celigo Image Cytometer Analysis, a caspase‑3/7 assay, flow cytometry and an MTT assay demonstrated that the proliferation of U87MG cells was inhibited as SYT7 was downregulated by a lentiviral vector expressing SYT7 shRNA, via the promotion of cellular apoptosis. The results of the present study demonstrated that the downregulation of SYT7 inhibited glioblastoma growth by promoting cellular apoptosis, and that SYT7 may therefore be a potential target for glioma intervention.

Application of a co‑expression network for the analysis of aggressive and non‑aggressive breast cancer cell lines to predict the clinical outcome of patients

Breast cancer metastasis is a demanding problem in clinical treatment of patients with breast cancer. It is necessary to examine the mechanisms of metastasis for developing therapies. Classification of the aggressiveness of breast cancer is an important issue in biological study and for clinical decisions. Although aggressive and non‑aggressive breast cancer cells can be easily distinguished among different cell lines, it is very difficult to distinguish in clinical practice. The aim of the current study was to use the gene expression analysis from breast cancer cell lines to predict clinical outcomes of patients with breast cancer. Weighted gene co‑expression network analysis (WGCNA) is a powerful method to account for correlations between genes and extract co‑expressed modules of genes from large expression datasets. Therefore, WGCNA was applied to explore the differences in sub‑networks between aggressive and non‑aggressive breast cancer cell lines. The greatest difference topological overlap networks in both groups include potential information to understand the mechanisms of aggressiveness. The results show that the blue and red modules were significantly associated with the biological processes of aggressiveness. The sub‑network, which consisted of TMEM47, GJC1, ANXA3, TWIST1 and C19orf33 in the blue module, was associated with an aggressive phenotype. The sub‑network of LOC100653217, CXCL12, SULF1, DOK5 and DKK3 in the red module was associated with a non‑aggressive phenotype. In order to validate the hazard ratio of these genes, the prognostic index was constructed to integrate them and examined using data from the Cancer Genomic Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Patients with breast cancer from TCGA in the high‑risk group had a significantly shorter overall survival time compared with patients in the low‑risk group (hazard ratio=1.231, 95% confidence interval=1.058‑1.433, P=0.0071, by the Wald test). A similar result was produced from the GEO database. The findings may provide a novel strategy for measuring cancer aggressiveness in patients with breast cancer.

An integrated lncRNA, microRNA and mRNA signature to improve prognosis prediction of colorectal cancer

Although the outcome of patients with colorectal cancer (CRC) has improved significantly, prognosis evaluation still presents challenges due to the disease heterogeneity. Increasing evidences revealed the close correlation between aberrant expression of certain RNAs and the prognosis. We envisioned that combined multiple types of RNAs into a single classifier could improve postoperative risk classification and add prognostic value to the current stage system. Firstly, differentially expressed RNAs including mRNAs, miRNAs and lncRNAs were identified by two different algorithms. Then survival and LASSO analysis was conducted to screen survival-related DERs and build a multi-RNA-based classifier for CRC patient stratification. The prognostic value of the classifier was self-validated in the TCGA CRC cohort and further validated in an external independent set. Finally, survival receiver operating characteristic analysis was used to assess the performance of prognostic prediction. We found that the multi-RNA-based classifier consisted by 12 mRNAs, 1miRNA and 1 lncRNA, which could divide the patients into high and low risk groups with significantly different overall survival (training set: HR 2.54, 95%CI 1.67-3.87, p<0.0001; internal testing set: HR 2.54, 95%CI 1.67-3.87, p<0.0001; validation set: HR 5.02, 95% CI 2.2–11.6; p=0·0002). In addition, the classifier is not only independent of clinical features but also with a similar prognostic ability to the well-established TNM stage (AUC of ROC 0.83 versus 0.74, 95% CI = 0.608-0.824, P =0.0878). Furthermore, combination of the multi-RNA-based classifier with clinical features was a more powerful predictor of prognosis than either of the two parameters alone. In conclusion, the multi-RNA-based classifier may have important clinical implications in the selection of patients with CRC who are at high risk of mortality and add prognostic value to the current stage system.

MicroRNA signatures predict prognosis of patients with glioblastoma multiforme through the Cancer Genome Atlas

MicroRNAs (miRNAs) play major roles in various biological processes and have been implicated in the pathogenesis and malignant progression of glioblastoma multiforme (GBM). The aim of this study was to assess the predictive values of miRNAs for overall survival (OS) of patients with GBM. MiRNA expression profiles and clinical information of 563 GBM patients were obtained from the Cancer Genome Atlas. The most significantly altered miRNAs were identified and miRNA expression profiles were performed, through principal component analysis, the least absolute shrinkage and selection operator method. The survival analysis was performed using the Cox regression models. Additionally, receiver operating characteristic (ROC) analysis was used to assess the performance of survival prediction. We used the bioinformatics tools to establish the miRNA signature for biological relevance assessment. A linear prognostic model of three miRNAs was developed and the patients were divided into high risk and low risk groups based this model. The area under the ROC curve (AUC) for the three miRNA signature predicting 5-year survival was 0.894 (95%CI, 0.789-1.000) in the testing set and0.841 (95%CI, 0.689-0.993) in all GBM patients. High risk patients had significantly shorter OS than patients with low risk (P< 0.001). The results from this study support a three miRNA signature for outcome prediction of GBM. These results provided a new prospect for prognostic biomarker of GBM.

An Integrative Model of miRNA and mRNA Expression Signature for Patients of Breast Invasive Carcinoma with Radiotherapy Prognosis

Radiotherapy is widely used in breast cancer treatment. The radiotherapy for breast invasive carcinoma (BRCA) presents challenges with the complex clinical factors, and too many genes have been found to be associated with BRCA radiotherapy prognosis. The aim of this study was to construct an integrative model to combine the clinical data and RNA expression data (including microRNA and mRNA) to predict the survival durations of BRCA patients with radiotherapy. Also, the authors try to find the key regulation pairs between mRNA and miRNA from prediction. They collected mRNA and microRNA expression profiles and gathered the corresponding clinical data of 73 BRCA patients with radiotherapy from The Cancer Genome Atlas (TCGA). According to an integrative model from univariate Cox regression between RNA expression and patient survival, they classified the patients with radiotherapy into low-risk and high-risk groups. The results showed that nine mRNAs were considered as protective genes and five miRNAs and eight mRNAs were considered as high-risk genes. Moreover, the high-risk group has a significantly shorter survival time in comparison with the low-risk group by the log-rank test (p = 0.0039). The reliability of the gene signature was validated by an independent data set from the Gene Expression Omnibus (GEO). Furthermore, three pairs of miRNA-mRNA, closely associated to survival, were identified. These findings and method may prove valuable for improving the clinical management of BRCA patients with radiotherapy.

Up-regulation of miR-370-3p restores glioblastoma multiforme sensitivity to temozolomide by influencing MGMT expression

MicroRNAs (miRNA) are believed to play an important role in glioblastoma multiforme (GBM)chemotherapy. Our study aims to investigate potential miRNA biomarkers in GBM. Sixty GBM patients, which were given temozolomide (TMZ) chemotherapy and recurrent radiotherapy, were recruited. miRNA array was performed in cancerous and in paired normal tissues. Microarray results were further validated by a quantitative real-time PCR in selected tissues and GBM cell lines. TMZ resistance cells were developed and cell proliferation along with colony formation assays was determined. Our study employed H2AX formation and flow cytometry to analyse the role of miRNA in DNA damage and apoptosis. Our study illustrated 16 miRNA in which 9 were up-regulated and 7 down-regulated. and their differential expression were demonstrated in a recurrent GBM tissue. Among them, miRNA-370-3p demonstrated the highest level of down- regulation in tissues and in TMZ resistance cells. miRNA-370-3p mimic increased its expression and sensitivity of GBM cells to TMZ by suppressing the self-reparative ability of tumour cell DNA. O⁶-methylguanine-DNA methyltransferase (MGMT) was identified as the direct target gene of miR-370-3p, and it was found to be inversely correlated with miR-370-3p expression in tissue samples obtained. Thus, our study demonstrated a critical clinical role of an up-regulated miR-370-3p expression in glioblastoma multiforme chemotherapy sensitivity.

An integrated model of clinical information and gene expression for prediction of survival in ovarian cancer patients

Accumulating evidence shows that clinical factors alone are not adequate for predicting the survival of patients with ovarian cancer (OvCa), and many genes have been found to be associated with OvCa prognosis. The objective of this study was to develop a model that integrates clinical information and a gene signature to predict the survival durations of patients diagnosed with OvCa. We constructed mRNA and microRNA expression profiles and gathered the corresponding clinical data of 552 OvCa patients and 8 normal controls from The Cancer Genome Atlas. Using univariate Cox regression followed by a permutation test, elastic net-regulated Cox regression, and ridge regression, we generated a prognosis index consisting of 2 clinical variables, 7 protective mRNAs, 12 risky mRNAs, and 1 protective microRNA. The area under the curve of the receiver operating characteristic of the integrated clinical-and-gene model was 0.756, larger than that of the clinical-alone model (0.686) or the gene-alone model (0.703). OvCa patients in the high-risk group had a significantly shorter overall survival time compared with patients in the low-risk group (hazard ratio = 8.374, 95% confidence interval = 4.444-15.780, P = 4.90 × 10(-11), by the Wald test). The reliability of the gene signature was confirmed by a public external data set from the Gene Expression Omnibus. Our conclusions that we have identified an integrated clinical-and-gene model superior to the traditional clinical-alone model in ascertaining the survival prognosis of patients with OvCa. Our findings may prove valuable for improving the clinical management of OvCa.

MicroRNA‑566 modulates vascular endothelial growth factor by targeting Von Hippel‑Landau in human glioblastoma in vitro and in vivo

MicroRNAs (miRNAs) are able to function as either oncogenes or tumor suppressor genes in tumorigenesis, and have been proposed as novel targets for anticancer treatment. It has previously been suggested that miRNAs have important roles in the initiation and progression of glioblastoma; however, the effects of miR‑566 in glioblastoma are currently unclear. The present study aimed to demonstrate that miR-566 can modulate vascular endothelial growth factor (VEGF) by targeting Von Hippel‑Lindau (VHL) in glioblastoma in vitro and in vivo by inhibiting the expression of miR-566. Glioblastoma is a highly vascularized tumor, which exhibits increased expression of angiogenic factors, including VEGF, which are crucial in the process of glioblastoma angiogenesis. Existing research has demonstrated that VHL is a tumor suppressor gene that is associated with various tumors. In addition, VHL is able to regulate the expression of VEGF by promoting the degradation of hypoxia‑inducible factor‑1α via ubiquitination. It has been predicted, using bioinformatics, that the VHL gene is regulated by miR‑566. Therefore, the present study hypothesized that miR‑566 may regulate VEGF expression by targeting VHL during the angiogenic process of glioblastoma multiforme. The results of the present study demonstrated that inhibition of miR‑566 expression increases the expression levels of VHL, decreases the expression levels of VEGF, and inhibits the invasive and migratory abilities of glioblastoma. In addition, VHL was identified as a functional target of miR‑566.

Next generation sequencing profiling identifies miR-574-3p and miR-660-5p as potential novel prognostic markers for breast cancer

Background

Prognostication of Breast Cancer (BC) relies largely on traditional clinical factors and biomarkers such as hormone or growth factor receptors. Due to their suboptimal specificities, it is challenging to accurately identify the subset of patients who are likely to undergo recurrence and there remains a major need for markers of higher utility to guide therapeutic decisions. MicroRNAs (miRNAs) are small non-coding RNAs that function as post-transcriptional regulators of gene expression and have shown promise as potential prognostic markers in several cancer types including BC.

Results

In our study, we sequenced miRNAs from 104 BC samples and 11 apparently healthy normal (reduction mammoplasty) breast tissues. We used Case–control (CC) and Case-only (CO) statistical paradigm to identify prognostic markers. Cox-proportional hazards regression model was employed and risk score analysis was performed to identify miRNA signature independent of potential confounders. Representative miRNAs were validated using qRT-PCR. Gene targets for prognostic miRNAs were identified using in silico predictions and in-house BC transcriptome dataset. Gene ontology terms were identified using DAVID bioinformatics v6.7. A total of 1,423 miRNAs were captured. In the CC approach, 126 miRNAs were retained with predetermined criteria for good read counts, from which 80 miRNAs were differentially expressed. Of these, four and two miRNAs were significant for Overall Survival (OS) and Recurrence Free Survival (RFS), respectively. In the CO approach, from 147 miRNAs retained after filtering, 11 and 4 miRNAs were significant for OS and RFS, respectively. In both the approaches, the risk scores were significant after adjusting for potential confounders. The miRNAs associated with OS identified in our cohort were validated using an external dataset from The Cancer Genome Atlas (TCGA) project. Targets for the identified miRNAs were enriched for cell proliferation, invasion and migration.

Conclusions

The study identified twelve non-redundant miRNAs associated with OS and/or RFS. These signatures include those that were reported by others in BC or other cancers. Importantly we report for the first time two new candidate miRNAs (miR-574-3p and miR-660-5p) as promising prognostic markers. Independent validation of signatures (for OS) using an external dataset from TCGA further strengthened the study findings.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1899-0) contains supplementary material, which is available to authorized users.

Differential expression of microRNAs in postoperative radiotherapy sensitive and resistant patients with glioblastoma multiforme

Glioblastoma multiforme (GBM) is the most malignant primary brain tumor and more resistant to radiotherapy. However, hetero-radiosensitivity occurs in different patients. MicroRNAs (miRNAs) play important roles in the initiation and progression of a multitude of tumors. The study aims to examine the different microRNAs expression profiles of postoperative radiotherapy sensitive and resistant patients with GBM, to make an inquiry about their potential role and discover a certain set of radio-sensitivity markers. Three paired samples from six GBM patients who had only been treated with postoperative radiotherapy were selected, and then, they were divided into radiotherapy sensitive group and resistant group according to their overall survivals, local recurrence rates, and Karnofsky Performance Scale scores. Expression profiles of miRNAs in these two groups were determined by the method of microarray assay. Comparing with resistant patients, 13 miRNAs were significantly upregulated and 10 miRNAs were greatly downregulated in sensitive group. Among them, four miRNAs were validated by quantitative RT-PCR. The differentially expressed miRNAs and their putative target genes were revealed by bioformatic analysis to play a role in cell signaling, proliferation, aging, and death. High-enrichment pathway analysis identified that some classical pathways participated in numerous metabolic processes, especially in cell cycle regulation, such as mTOR, MAPK, TGF-beta, and PI3K-Akt signaling pathways. Our research will contribute to identifying clinical diagnostic markers and therapeutic targets in the treatment of GBM by postoperative radiotherapy.

Patient-specific driver gene prediction and risk assessment through integrated network analysis of cancer omics profiles

Extensive and multi-dimensional data sets generated from recent cancer omics profiling projects have presented new challenges and opportunities for unraveling the complexity of cancer genome landscapes. In particular, distinguishing the unique complement of genes that drive tumorigenesis in each patient from a sea of passenger mutations is necessary for translating the full benefit of cancer genome sequencing into the clinic. We address this need by presenting a data integration framework (OncoIMPACT) to nominate patient-specific driver genes based on their phenotypic impact. Extensive in silico and in vitro validation helped establish OncoIMPACT's robustness, improved precision over competing approaches and verifiable patient and cell line specific predictions (2/2 and 6/7 true positives and negatives, respectively). In particular, we computationally predicted and experimentally validated the gene TRIM24 as a putative novel amplified driver in a melanoma patient. Applying OncoIMPACT to more than 1000 tumor samples, we generated patient-specific driver gene lists in five different cancer types to identify modes of synergistic action. We also provide the first demonstration that computationally derived driver mutation signatures can be overall superior to single gene and gene expression based signatures in enabling patient stratification and prognostication. Source code and executables for OncoIMPACT are freely available from http://sourceforge.net/projects/oncoimpact.