Down-regulation of ROBO2 expression in prostate cancers

Robo2 promotes osteoblast differentiation and mineralization through autophagy and is activated by parathyroid hormone induction

BACKGROUND

As a systemic skeletal disorder, osteoporosis can increase fracture risk. This study wants to discuss the mechanism of osteoporosis and find possible molecular therapy. Bone morphogenetic protein 2 (BMP2) was utilized to stimulate MC3T3-E1 to establish a cellular osteoporosis model in vitro.

METHODS

Initially, the viability of BMP2-induced MC3T3-E1 was assessed with a Cell counting kit-8 (CCK-8) assay. By real-time quantitative PCR (RT-qPCR) and western blot, Robo2 expression after roundabout (Robo) silencing or overexpression was estimated. Besides, alkaline phosphatase (ALP) expression, mineralization level and LC3II green fluorescent protein (GFP) expression were evaluated using ALP assay, Alizarin red staining and immunofluorescence staining, separately. Additionally, the expression of proteins related to osteoblast differentiation and autophagy was analyzed by RT-qPCR and western blot. Then, following autophagy inhibitor 3-methyladenine (3-MA) treatment, osteoblast differentiation and mineralization were measured again.

RESULTS

MC3T3-E1 cells were differentiated into osteoblasts under BMP2 induction and Robo2 expression was greatly ascended. After Robo2 silencing, Robo2 expression was markedly diminished. ALP activity and mineralization level in BMP2-induced MC3T3-E1 cells were declined after depleting Robo2. Robo2 expression was conspicuously enhanced after overexpressing Robo2. Robo2 overexpression promoted the differentiation and mineralization of BMP2-induced MC3T3-E1 cells. Rescue experiments revealed that Robo2 silence and its overexpression could regulate the autophagy of BMP2-stimulated MC3T3-E1 cells. After 3-MA treatment, the increased ALP activity and mineralization level of BMP2-induced MC3T3-E1 cells with Robo2 upregulation were reduced. Furthermore, parathyroid hormone 1-34 (PTH1-34) treatment enhanced the expression of ALP, Robo2, LC3II and Beclin-1 and reduced the levels of LC3I and p62 of MC3T3-E1 cells concentration-dependently.

CONCLUSION

Collectively, Robo2, which was activated by PTH1-34, promoted osteoblast differentiation and mineralization through autophagy.

A new immune-related gene signature predicts the prognosis and immune escape of bladder cancer

BACKGROUND

The biological roles of immune-related genes (IRGs) in bladder cancer (BC) need to be further elucidated.

OBJECTIVE

To elucidate the predictive value of IRGs for prognosis and immune escape in BC.

METHODS

We comprehensively analyzed the transcriptomic and clinical information of 430 cases, including 19 normal and 411 BC patients from the TCGA database, and verified 165 BC cases in the GSE13507 dataset. The risk model was constructed based on IRGs by applying LASSO Cox regression and exploring the relationship between the risk score and prognosis, gene mutations, and immune escape in BC patients.

RESULTS

We identified 4 survival-related genes (PSMC1, RAC3, ROBO2 and ITGB3) among 6,196 IRGs in both the TCGA and GES13507 datasets,, which were used to establish a gene risk model by applying LASSO Cox regression. The results showed that the high-risk (HR) group was closely associated with poor survival or advanced pathological stage of BC. Furthermore, the risk score was found to be an independent risk factor for prognosis of BC patients. In addition, high-risk individuals showed a greater prevalence of TP53 mutations lower CD8+ T-cell and NK cell infiltration, higher Treg cell infiltration, higher expression of PD-L1, and higher immune exclusion scores than those in the low-risk (LR) group. Finally, the experimental verification shows that the model construction gene, especially PMSC1, plays an important role in the growth and metastasis of bladder cancer.

CONCLUSIONS

These evidences revealed the vital role of IRGs in predicting prognosis, TP53 mutation and immune escape in BC patients.

ROBO1 protein expression is independently associated with biochemical recurrence in prostate cancer patients who underwent radical prostatectomy in Asian patients

Background

The purpose of this study is to investigate the correlation between ROBO1 expression and prostate cancer aggressiveness.

Methods

ROBO1 expression was evaluated in normal prostate epithelial cells (PrEC) and different prostate cancer cell lines by Western blot analysis. The migration and invasion of native and ROBO1 knockdown cells were evaluated using migration chambers and a Matrigel-coated membrane, respectively. Samples from 145 patients who underwent radical prostatectomy between June 2000 and June 2008, were retrieved from the paraffin files for tissue microarray (TMA) with immunohistochemical analysis. Biochemical recurrence (BCR)-free survival curves were estimated using the Kaplan-Meier and Cox regression methods in two groups of patients classified according to the degree of ROBO1 expression (low or high expression).

Results

ROBO1 is highly expressed in the prostate cancer cell lines. All ROBO1 knockdown cells (PC3, 22Rv1 and DU 145) showed markedly decreased migration and invasiveness compared to native cells. In 145 patients with radical prostatectomy, the Kaplan-Meier curves and log-rank test for BCR-free survival stratified by ROBO1 expression in organ-confined (pT2) or not (pT3), showed significant differences in 10-year survival between the ROBO1 high and low expression groups (87.2% versus 52.6% in pT2; P=0.047, 51.0% versus 36.9% in pT3; P=0.033). The multivariable-adjusted model showed a markedly increased hazard ratio (HR) in patients with high ROBO1 expression compared to the patients with low ROBO1expression in every model.

Conclusions

ROBO1 may play an important role in the migration and invasion of prostate cancer cells, and was independently associated with BCR.

Discovery of Cancer-Specific and Independent Prognostic Gene Subsets of the Slit-Robo Family Using TCGA-PANCAN Datasets

The Slit-Robo family of axon guidance molecules works in concert, playing important roles in organ development and cancer. Expressions of individual Slit-Robo genes have been used in calculating univariable hazard ratios (HR_uni) for predicting cancer prognosis in the literature. However, Slit-Robo members do not act independently; hence, hazard ratios from multivariable Cox regression (HR_multi) on the whole gene set can further lead to identification of cancer-specific, novel, and independent prognostic gene pairs or modules. Herein, we obtained mRNA expressions of the Slit-Robo family consisting of four Robos (ROBO1/2/3/4) and three Slits (SLIT1/2/3), along with four types of survival outcome across cancers found in the Cancer Genome Atlas (TCGA). We used cluster heat maps to visualize closely associated pairs/modules of prognostic genes across 33 different cancers. We found a smaller number of significant genes in HR_multi than in HR_uni, suggesting that the former analysis was less redundant. High ROBO4 expression emerged as relatively protective within the family, in both types of HR analyses. Multivariable Cox regression, on the other hand, revealed significantly more HR signatures containing Slit-Robo pairs acting in opposing directions than those containing Slit-Slit or Robo-Robo pairs for disease-specific survival. Furthermore, we discovered, through the online app SmulTCan's lasso regression, Slit-Robo gene subsets that significantly differentiated between high- versus low-risk prognosis patient groups, particularly for renal cancers and low-grade glioma. The statistical pipeline reported herein can help test independent and significant pairs/modules within a codependent gene family for cancer prognostication, and thus should also prove useful in personalized/precision medicine research.

ROBO2 hampers malignant biological behavior and predicts a better prognosis in pancreatic adenocarcinoma

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is a fatalmalignant cancer with extremely poor prognosis and high mortality. Genome wide studies show that Slit/Robo signaling pathway takes a major effect in the oncogenesis and progression of pancreatic cancer. However, the function and mechanism of ROBO2 in the development of PDAC remains unclear.

METHODS

In present study, we use Western blot and real-time polymerase chain reaction (RT-PCR) to detect the expression of ROBO2 in pancreatic cell lines. Cell proliferation,Transwellmigration and invasion were conducted inAsPC-1, MIA PaCa-2 and PANC-1cell lines. RNA sequencing, bioinformatics analysisand Western blot were used to explore its mechanism and potential target molecules. The expression of ROBO2 in 95 tumor tissues was detected by immunohistochemistry.

RESULTS

ROBO2 expression was downregulated in PDAC cell lines and tissue samples. A high expression of ROBO2 was associated with better prognosis. Upregulation of ROBO2 inhibited PDAC cell proliferation, migration, and invasion. However, we found theoppositeresults in the ROBO2 downregulation group. In addition, the function of ROBO2 on cell proliferation was further affirmed by the animal model. Finally, the results of RNA sequencing indicated that ROBO2 partly promoted the antitumor activity by inhibiting ECM1 in PDAC.

CONCLUSIONS

Our work suggests that ROBO2 inhibits tumor progression in PDAC and may serve as a predictive biomarker and therapeutic target in PDAC.

High ROBO3 expression predicts poor survival in non-M3 acute myeloid leukemia

Roundabout guidance receptor proteins are crucial components of the SLIT/ROBO signaling pathway. This pathway is important for the nervous system and in embryonic development. Recently, increasing evidence has shown that roundabout guidance receptor proteins and the SLIT/ROBO signaling pathway also participate in tumorigenesis. Here, by analyzing transcriptome data from the TCGA and GEO databases, we found that ROBO3 is highly expressed in non-M3 acute myeloid leukemia. High ROBO3 expression was associated with increased age at diagnosis and poorer risk classification (both P < 0.01). Patients with high ROBO3 expression had higher rates of TP53 and RUNX1 mutations (both P < 0.05). Significantly worse overall survival and event-free survival were observed in high ROBO3 expression patients compared with low ROBO3 expression patients (OS: P = 0.004; EFS: P= 0.012). High ROBO3 expression was also associated with poorer overall survival and event-free survival in a subgroup of patients who received intensive chemotherapy (OS: P = 0.024; EFS: P = 0.040). Moreover, multivariate analysis indicated that high ROBO3 expression was an independent risk factor for poor overall survival in non-M3 acute myeloid leukemia patients who are younger than 60 and received intensive chemotherapy during remission induction. Bioinformatics analysis by Kyoto Encyclopedia of Genes and Genomes and Gene Ontology revealed that high ROBO3 expression significantly altered cell adhesion and extracellular matrix-related pathways (adjusted P < 0.05). Taken together, the data demonstrate that ROBO3 is upregulated in non-M3 acute myeloid leukemia and may be a potent biomarker of inferior prognosis.

Alcohol Intake and Alcohol-SNP Interactions Associated with Prostate Cancer Aggressiveness

Excessive alcohol intake is a well-known modifiable risk factor for many cancers. It is still unclear whether genetic variants or single nucleotide polymorphisms (SNPs) can modify alcohol intake's impact on prostate cancer (PCa) aggressiveness. The objective is to test the alcohol-SNP interactions of the 7501 SNPs in the four pathways (angiogenesis, mitochondria, miRNA, and androgen metabolism-related pathways) associated with PCa aggressiveness. We evaluated the impacts of three excessive alcohol intake behaviors in 3306 PCa patients with European ancestry from the PCa Consortium. We tested the alcohol-SNP interactions using logistic models with the discovery-validation study design. All three excessive alcohol intake behaviors were not significantly associated with PCa aggressiveness. However, the interactions of excessive alcohol intake and three SNPs (rs13107662 [CAMK2D, p = 6.2 × 10-6], rs9907521 [PRKCA, p = 7.1 × 10-5], and rs11925452 [ROBO1, p = 8.2 × 10-4]) were significantly associated with PCa aggressiveness. These alcohol-SNP interactions revealed contrasting effects of excessive alcohol intake on PCa aggressiveness according to the genotypes in the identified SNPs. We identified PCa patients with the rs13107662 (CAMK2D) AA genotype, the rs11925452 (ROBO1) AA genotype, and the rs9907521 (PRKCA) AG genotype were more vulnerable to excessive alcohol intake for developing aggressive PCa. Our findings support that the impact of excessive alcohol intake on PCa aggressiveness was varied by the selected genetic profiles.

Integration of single nucleotide variants and whole-genome DNA methylation profiles for classification of rheumatoid arthritis cases from controls

This study evaluated the use of multiomics data for classification accuracy of rheumatoid arthritis (RA). Three approaches were used and compared in terms of prediction accuracy: (1) whole-genome prediction (WGP) using SNP marker information only, (2) whole-methylome prediction (WMP) using methylation profiles only, and (3) whole-genome/methylome prediction (WGMP) with combining both omics layers. The number of SNP and of methylation sites varied in each scenario, with either 1, 10, or 50% of these preselected based on four approaches: randomly, evenly spaced, lowest p value (genome-wide association or epigenome-wide association study), and estimated effect size using a Bayesian ridge regression (BRR) model. To remove effects of high levels of pairwise linkage disequilibrium (LD), SNPs were also preselected with an LD-pruning method. Five Bayesian regression models were studied for classification, including BRR, Bayes-A, Bayes-B, Bayes-C, and the Bayesian LASSO. Adjusting methylation profiles for cellular heterogeneity within whole blood samples had a detrimental effect on the classification ability of the models. Overall, WGMP using Bayes-B model has the best performance. In particular, selecting SNPs based on LD-pruning with 1% of the methylation sites selected based on BRR included in the model, and fitting the most significant SNP as a fixed effect was the best method for predicting disease risk with a classification accuracy of 0.975. Our results showed that multiomics data can be used to effectively predict the risk of RA and identify cases in early stages to prevent or alter disease progression via appropriate interventions.

'Multi-omic' data analysis using O-miner

Innovations in -omics technologies have driven advances in biomedical research. However, integrating and analysing the large volumes of data generated from different high-throughput -omics technologies remain a significant challenge to basic and clinical scientists without bioinformatics skills or access to bioinformatics support. To address this demand, we have significantly updated our previous O-miner analytical suite, to incorporate several new features and data types to provide an efficient and easy-to-use Web tool for the automated analysis of data from '-omics' technologies. Created from a biologist's perspective, this tool allows for the automated analysis of large and complex transcriptomic, genomic and methylomic data sets, together with biological/clinical information, to identify significantly altered pathways and prioritize novel biomarkers/targets for biological validation. Our resource can be used to analyse both in-house data and the huge amount of publicly available information from array and sequencing platforms. Multiple data sets can be easily combined, allowing for meta-analyses. Here, we describe the analytical pipelines currently available in O-miner and present examples of use to demonstrate its utility and relevance in maximizing research output. O-miner Web server is free to use and is available at http://www.o-miner.org.

Integrating of genomic and transcriptomic profiles for the prognostic assessment of breast cancer

PURPOSE

To evaluate the prognostic effect of the integration of genomic and transcriptomic profiles in breast cancer.

METHODS

Eight hundred and ten samples from the Cancer Genome Atlas (TCGA) data sets were randomly divided into the training set (540 subjects) and validation set (270 subjects). We first selected single-nucleotide polymorphism (SNPs) and genes associated with breast cancer prognosis in the training set to construct the prognostic prediction model, and then replicated the prediction efficiency in the validation set.

RESULTS

Four SNPs and three genes associated with the prognosis of breast cancer in the training set were included in the prognostic model. Patients were divided into the high-risk group and low-risk group based on the four SNPs and three genes signature-based genetic prognostic index. High-risk patients showed a significant worse overall survival [Hazard Ratio (HR) 9.43, 95% confidence interval (CI) 3.81-23.33, P < 0.001] than the low-risk group. Compared to the model constructed with only gene expression, the C statistics for the signature-based genetic prognostic index [area under curves (AUC) = 0.79, 95% CI 0.72-0.86] showed a significant increase (P < 0.001). Additionally, we further replicated the prognostic prediction model in the validation set as patients in the high-risk group also showed a significantly worse overall survival (HR 4.55, 95% CI 1.50-13.88, P < 0.001), and the C statistics for the signature-based genetic prognostic index was 0.76 (95% CI 0.65-0.86). The following time-dependent ROC revealed that the mean of AUCs were 0.839 and 0.748 in the training set and the validation set, respectively.

CONCLUSIONS

Our findings suggested that integrating genomic and transcriptomic profiles could greatly improve the predictive efficiency of the prognosis of breast cancer patients.

Genomic sequencing identifies a few mutations driving the independent origin of primary liver tumors in a chronic hepatitis murine model

With the development of high-throughput genomic analysis, sequencing a mouse primary cancer model provides a new opportunity to understand fundamental mechanisms of tumorigenesis and progression. Here, we characterized the genomic variations in a hepatitis-related primary hepatocellular carcinoma (HCC) mouse model. A total of 12 tumor sections and four adjacent non-tumor tissues from four mice were used for whole exome and/or whole genome sequencing and validation of genotyping. The functions of the mutated genes in tumorigenesis were studied by analyzing their mutation frequency and expression in clinical HCC samples. A total of 46 single nucleotide variations (SNVs) were detected within coding regions. All SNVs were only validated in the sequencing samples, except the Hras mutation, which was shared by three tumors in the M1 mouse. However, the mutated allele frequency varied from high (0.4) to low (0.1), and low frequency (0.1-0.2) mutations existed in almost every tumor. Together with a diploid karyotype and an equal distribution pattern of these SNVs within the tumor, these results suggest the existence of subclones within tumors. A total of 26 mutated genes were mapped to 17 terms describing different molecular and cellular functions. All 41 human homologs of the mutated genes were mutated in the clinical samples, and some mutations were associated with clinical outcomes, suggesting a high probability of cancer driver genes in the spontaneous tumors of the mouse model. Genomic sequencing shows that a few mutations can drive the independent origin of primary liver tumors and reveals high heterogeneity among tumors in the early stage of hepatitis-related primary hepatocellular carcinoma.

Intra-tumor heterogeneity in TP53 null High Grade Serous Ovarian Carcinoma progression

Background

High grade serous ovarian cancer is characterised by high initial response to chemotherapy but poor outcome in the long term due to acquired resistance. One of the main genetic features of this disease is TP53 mutation. The majority of TP53 mutated tumors harbor missense mutations in this gene, correlated with p53 accumulation. TP53 null tumors constitute a specific subgroup characterised by nonsense, frameshift or splice-site mutations associated to complete absence of p53 expression. Different studies show that this kind of tumors may have a worse prognosis than other TP53 mutated HGSC.

Methods

In this study, we sought to characterise the intra-tumor heterogeneity of a TP53 null HGSC consisting of six primary tumor samples, two intra-pelvic and four extra-pelvic recurrences using exome sequencing and comparative genome hybridisation.

Results

Significant heterogeneity was found among the different tumor samples, both at the mutational and copy number levels. Exome sequencing identified 102 variants, of which only 42 were common to all three samples; whereas 7 of the 18 copy number changes found by CGH analysis were presented in all samples. Sanger validation of 20 variants found by exome sequencing in additional regions of the primary tumor and the recurrence allowed us to establish a sequence of the tumor clonal evolution, identifying those populations that most likely gave rise to recurrences and genes potentially involved in this process, like GPNMB and TFDP1. Using functional annotation and network analysis, we identified those biological functions most significantly altered in this tumor. Remarkably, unexpected functions such as microtubule-based movement and lipid metabolism emerged as important for tumor development and progression, suggesting its potential interest as therapeutic targets.

Conclusions

Altogether, our results shed light on the clonal evolution of the distinct tumor regions identifying the most aggressive subpopulations and at least some of the genes that may be implicated in its progression and recurrence, and highlights the importance of considering intra-tumor heterogeneity when carrying out genetic and genomic studies, especially when these are aimed to diagnostic procedures or to uncover possible therapeutic strategies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-015-1952-z) contains supplementary material, which is available to authorized users.