Competing risks data analysis with high-dimensional covariates: an application in bladder cancer

Alternative splicing regulation and its therapeutic potential in bladder cancer

Bladder cancer is one of the leading causes of mortality globally. The development of bladder cancer is closely associated with alternative splicing, which regulates human gene expression and enhances the diversity of functional proteins. Alternative splicing is a distinctive feature of bladder cancer, and as such, it may hold promise as a therapeutic target. This review aims to comprehensively discuss the current knowledge of alternative splicing in the context of bladder cancer. We review the process of alternative splicing and its regulation in bladder cancer. Moreover, we emphasize the significance of abnormal alternative splicing and splicing factor irregularities during bladder cancer progression. Finally, we explore the impact of alternative splicing on bladder cancer drug resistance and the potential of alternative splicing as a therapeutic target.

Tutorial on survival modeling with applications to omics data

MOTIVATION

Identification of genomic, molecular and clinical markers prognostic of patient survival is important for developing personalized disease prevention, diagnostic and treatment approaches. Modern omics technologies have made it possible to investigate the prognostic impact of markers at multiple molecular levels, including genomics, epigenomics, transcriptomics, proteomics and metabolomics, and how these potential risk factors complement clinical characterization of patient outcomes for survival prognosis. However, the massive sizes of the omics datasets, along with their correlation structures, pose challenges for studying relationships between the molecular information and patients' survival outcomes.

RESULTS

We present a general workflow for survival analysis that is applicable to high-dimensional omics data as inputs when identifying survival-associated features and validating survival models. In particular, we focus on the commonly used Cox-type penalized regressions and hierarchical Bayesian models for feature selection in survival analysis, which are especially useful for high-dimensional data, but the framework is applicable more generally.

AVAILABILITY AND IMPLEMENTATION

A step-by-step R tutorial using The Cancer Genome Atlas survival and omics data for the execution and evaluation of survival models has been made available at https://ocbe-uio.github.io/survomics.

High-dimensional feature selection in competing risks modeling: A stable approach using a split-and-merge ensemble algorithm

Variable selection is critical in competing risks regression with high-dimensional data. Although penalized variable selection methods and other machine learning-based approaches have been developed, many of these methods often suffer from instability in practice. This paper proposes a novel method named Random Approximate Elastic Net (RAEN). Under the proportional subdistribution hazards model, RAEN provides a stable and generalizable solution to the large-p-small-n variable selection problem for competing risks data. Our general framework allows the proposed algorithm to be applicable to other time-to-event regression models, including competing risks quantile regression and accelerated failure time models. We show that variable selection and parameter estimation improved markedly using the new computationally intensive algorithm through extensive simulations. A user-friendly R package RAEN is developed for public use. We also apply our method to a cancer study to identify influential genes associated with the death or progression from bladder cancer.

Identification of salivary autoantibodies as biomarkers of oral cancer with immunoglobulin A enrichment combined with affinity mass spectrometry

Globally, oral cavity squamous cell carcinoma (OSCC) is one of the most common fatal illnesses. Its high mortality is ascribed to the fact that the disease is often diagnosed at a late stage, which indicates an urgent need for approaches for the early detection of OSCC. The use of salivary autoantibodies (autoAbs) as OSCC biomarkers has numerous advantages such as easy access to saliva samples and efficient detection of autoAbs using well-established secondary reagents. To improve OSCC screening, we identified OSCC-associated autoAbs with the enrichment of salivary autoAbs combined with affinity mass spectrometry (MS). The salivary IgA of healthy individuals and OSCC patients was purified with peptide M-conjugated beads and then applied to immunoprecipitated antigens (Ags) in OSCC cells. Using tandem MS analysis and spectral counting-based quantitation, the level of 10 Ags increased in the OSCC group compared with the control group. Moreover, salivary levels of autoAbs to the 10 Ags were determined by a multiplexed bead-based immunoassay. Among them, seven were significantly higher in early-stage OSCC patients than in healthy individuals. A marker panel consisting of autoAbs to LMAN2, PTGR1, RAB13, and UQCRC2 was further developed to improve the early diagnosis of OSCC.

Fast Lasso-type safe screening for Fine-Gray competing risks model with ultrahigh dimensional covariates

The Fine-Gray proportional sub-distribution hazards (PSH) model is among the most popular regression model for competing risks time-to-event data. This article develops a fast safe feature elimination method, named PSH-SAFE, for fitting the penalized Fine-Gray PSH model with a Lasso (or adaptive Lasso) penalty. Our PSH-SAFE procedure is straightforward to implement, fast, and scales well to ultrahigh dimensional data. We also show that as a feature screening procedure, PSH-SAFE is safe in a sense that the eliminated features are guaranteed to be inactive features in the original Lasso (or adaptive Lasso) estimator for the penalized PSH model. We evaluate the performance of the PSH-SAFE procedure in terms of computational efficiency, screening efficiency and safety, run-time, and prediction accuracy on multiple simulated datasets and a real bladder cancer data. Our empirical results show that the PSH-SAFE procedure possesses desirable screening efficiency and safety properties and can offer substantially improved computational efficiency as well as similar or better prediction performance in comparison to their baseline competitors.

Overexpressed SNRPB/D1/D3/E/F/G correlate with poor survival and immune infiltration in hepatocellular carcinoma

BACKGROUND

Prior reports have indicated that the abnormal expression of small nuclear ribonucleoproteins (snRNPs) genes is related to malignant tumors. However, in hepatocellular carcinoma (HCC), the precise role of snRNPs is not well understood. Therefore, the purpose of this study was to evaluate the prognostic roles of SNRPB/D1/D2/D3/E/F/G and their correlation to immune infiltration in HCC.

METHODS

The study was carried out via the following databases, software, and experimental validation: ONCOMINE, GEPIA2, UALCAN, The Cancer Genome Atlas, Gene Expression Omnibus, ArrayExpress, Kaplan-Meier plotter, cBioPortal, STRING, DAVID 6.8, TIMER, Cytoscape software, and immunohistochemistry experiments.

RESULTS

Overexpressed SNRPB/D1/D2/D3/E/F/G proteins were found in HCC tissues. The transcription levels of 7 snRNPs genes were related to the TP53 mutation and tumor grades. SNRPB/D1/D2/D3/F/G expression was significantly correlated with cancer staging, whereas SNRPE was not. Moreover, Kaplan-Meier survival analysis showed that upregulation of SNRPB/D1/D2/E/G was relevant to worse OS in HCC patients, especially in patients with alcohol consumption and those without viral hepatitis. Multivariate Cox regression analysis indicated that expression of SNRPB/D1/D3/E/F/G were independent prognostic factors for unfavorable OS in HCC. In addition, a high mutation rate of snRNPs genes (44%) was also found in HCC. The mRNA expression levels of snRNPs were meaningfully and positively related to six types of infiltrating immune cells (B cells, CD4⁺ T cells, CD8⁺ T cells, neutrophil, macrophage, and dendritic cells). Also, SNRPB/D1/G genes were significantly associated with molecular markers of various immune cells in HCC.

CONCLUSIONS

SNRPB/D1/D3/E/F/G are potential prognostic biomarkers for a short OS in HCC, and SNRPB/D1/G were novel immune therapy targets in HCC patients.

SNRPD1/E/F/G Serve as Potential Prognostic Biomarkers in Lung Adenocarcinoma

Objectives: Sm proteins (SNRPB/D1/D2/D3/E/F/G), involved in pre-mRNA splicing, were previously reported in the tumorigenesis of several cancers. However, their specific role in lung adenocarcinoma (LUAD) remains obscure. Our study aims to feature abnormal expressions and mutations of genes for Sm proteins and assess their potential as therapeutic targets via integrated bioinformatics analysis.

Methods: In this research, we explored the expression pattern and prognostic worth of genes for Sm proteins in LUAD across TCGA, GEO, UALCAN, Oncomine, Metascape, David 6.8, and Kaplan-Meier Plotter, and confirmed its independent prognostic value via univariate and multivariate cox regression analysis. Meanwhile, their expression patterns were validated by RT-qPCR. Gene mutations and co-expression of genes for Sm proteins were analyzed by the cBioPortal database. The PPI network for Sm proteins in LUAD was visualized by the STRING and Cytoscape. The correlations between genes for Sm proteins and immune infiltration were analyzed by using the “GSVA” R package.

Results: Sm proteins genes were found upregulated expression in both LUAD tissues and LUAD cell lines. Moreover, highly expressed mRNA levels for Sm proteins were strongly associated with short survival time in LUAD. Genes for Sm proteins were positively connected with the infiltration of Th2 cells, but negatively connected with the infiltration of mast cells, Th1 cells, and NK cells. Importantly, Cox regression analysis showed that high SNRPD1/E/F/G expression were independent risk factors for the overall survival of LUAD.

Conclusion: Our study showed that SNRPD1/E/F/G could independently predict the prognostic outcome of LUAD and was correlated with immune infiltration. Also, this report laid the foundation for additional exploration on the potential treatment target’s role of SNRPD1/E/F/G in LUAD.

Regularized Weighted Nonparametric Likelihood Approach for High-Dimension Sparse Subdistribution Hazards Model for Competing Risk Data

Variable selection and penalized regression models in high-dimension settings have become an increasingly important topic in many disciplines. For instance, omics data are generated in biomedical researches that may be associated with survival of patients and suggest insights into disease dynamics to identify patients with worse prognosis and to improve the therapy. Analysis of high-dimensional time-to-event data in the presence of competing risks requires special modeling techniques. So far, some attempts have been made to variable selection in low- and high-dimension competing risk setting using partial likelihood-based procedures. In this paper, a weighted likelihood-based penalized approach is extended for direct variable selection under the subdistribution hazards model for high-dimensional competing risk data. The proposed method which considers a larger class of semiparametric regression models for the subdistribution allows for taking into account time-varying effects and is of particular importance, because the proportional hazards assumption may not be valid in general, especially in the high-dimension setting. Also, this model relaxes from the constraint of the ability to simultaneously model multiple cumulative incidence functions using the Fine and Gray approach. The performance/effectiveness of several penalties including minimax concave penalty (MCP); adaptive LASSO and smoothly clipped absolute deviation (SCAD) as well as their L₂ counterparts were investigated through simulation studies in terms of sensitivity/specificity. The results revealed that sensitivity of all penalties were comparable, but the MCP and MCP-L₂ penalties outperformed the other methods in term of selecting less noninformative variables. The practical use of the model was investigated through the analysis of genomic competing risk data obtained from patients with bladder cancer and six genes of CDC20, NCF2, SMARCAD1, RTN4, ETFDH, and SON were identified using all the methods and were significantly correlated with the subdistribution.

Prostaglandin Reductase 1 as a Potential Therapeutic Target for Cancer Therapy

Altered tumor metabolism is a hallmark of cancer and targeting tumor metabolism has been considered as an attractive strategy for cancer therapy. Prostaglandin Reductase 1 (PTGR1) is a rate-limiting enzyme involved in the arachidonic acid metabolism pathway and mainly responsible for the deactivation of some eicosanoids, including prostaglandins and leukotriene B4. A growing evidence suggested that PTGR1 plays a significant role in cancer and has emerged as a novel target for cancer therapeutics. In this review, we summarize the progress made in recent years toward the understanding of PTGR1 function and structure, highlight the roles of PTGR1 in cancer, and describe potential inhibitors of PTGR1. Finally, we provide some thoughts on future directions that might facilitate the PTGR1 research and therapeutics development.

HELLS serves as a poor prognostic biomarker and its downregulation reserves the malignant phenotype in pancreatic cancer

BACKGROUND

SMARCAs, belonged to SWI/SNF2 subfamilies, are critical to cellular processes due to their modulation of chromatin remodeling processes. Although SMARCAs are implicated in the tumor progression of various cancer types, our understanding of how those members affect pancreatic carcinogenesis is quite limited and improving this requires bioinformatics analysis and biology approaches.

METHODS

To address this issue, we investigated the transcriptional and survival data of SMARCAs in patients with pancreatic cancer using ONCOMINE, GEPIA, Human Protein Atlas, and Kaplan-Meier plotter. We further verified the effect of significant biomarker on pancreatic cancer in vitro through functional experiment.

RESULTS

The Kaplan-Meier curve and log-rank test analyses showed a positive correlation between SMARCA1/2/3/SMARCAD1 and patients' overall survival (OS). On the other hand, mRNA expression of SMARCA6 (also known as HELLS) showed a negative correlation with OS. Meanwhile, no significant correlation was found between SMARCA4/5/SMARCAL1 and tumor stages and OS. The knockdown of HELLS impaired the colony formation ability, and inhibited pancreatic cancer cell proliferation by arresting cells at S phase.

CONCLUSIONS

Data mining analysis and cell function research demonstrated that HELLS played oncogenic roles in the development and progression of pancreatic cancer, and serve as a poor prognostic biomarker for pancreatic cancer. Our work laid a foundation for further clinical applications of HELLS in pancreatic cancer.

Pan-Cancer Metastasis Prediction Based on Graph Deep Learning Method

Tumor metastasis is the major cause of mortality from cancer. From this perspective, detecting cancer gene expression and transcriptome changes is important for exploring tumor metastasis molecular mechanisms and cellular events. Precisely estimating a patient’s cancer state and prognosis is the key challenge to develop a patient’s therapeutic schedule. In the recent years, a variety of machine learning techniques widely contributed to analyzing real-world gene expression data and predicting tumor outcomes. In this area, data mining and machine learning techniques have widely contributed to gene expression data analysis by supplying computational models to support decision-making on real-world data. Nevertheless, limitation of real-world data extremely restricted model predictive performance, and the complexity of data makes it difficult to extract vital features. Besides these, the efficacy of standard machine learning pipelines is far from being satisfactory despite the fact that diverse feature selection strategy had been applied. To address these problems, we developed directed relation-graph convolutional network to provide an advanced feature extraction strategy. We first constructed gene regulation network and extracted gene expression features based on relational graph convolutional network method. The high-dimensional features of each sample were regarded as an image pixel, and convolutional neural network was implemented to predict the risk of metastasis for each patient. Ten cross-validations on 1,779 cases from The Cancer Genome Atlas show that our model’s performance (area under the curve, AUC = 0.837; area under precision recall curve, AUPRC = 0.717) outstands that of an existing network-based method (AUC = 0.707, AUPRC = 0.555).

Variable selection methods for predicting clinical outcomes following allogeneic hematopoietic cell transplantation

Allogeneic hematopoietic cell transplantation (allo-HCT) is a potentially curative procedure for a large number of diseases. However, the greatest barriers to the success of allo-HCT are relapse and graft-versus-host-disease (GVHD). Many studies have examined the reconstitution of the immune system after allo-HCT and searched for factors associated with clinical outcome. Serum biomarkers have also been studied to predict the incidence and prognosis of GVHD. However, the use of multiparametric immunophenotyping has been less extensively explored: studies usually focus on preselected and predefined cell phenotypes and so do not fully exploit the richness of flow cytometry data. Here we aimed to identify cell phenotypes present 30 days after allo-HCT that are associated with clinical outcomes in 37 patients participating in a trial relating to the prevention of GVHD, derived from 82 flow cytometry markers and 13 clinical variables. To do this we applied variable selection methods in a competing risks modeling framework, and identified specific subsets of T, B, and NK cells associated with relapse. Our study demonstrates the value of variable selection methods for mining rich, high dimensional clinical data and identifying potentially unexplored cell subpopulations of interest.

Comparison of Variable Selection Methods for Time-to-Event Data in High-Dimensional Settings

Over the last decades, molecular signatures have become increasingly important in oncology and are opening up a new area of personalized medicine. Nevertheless, biological relevance and statistical tools necessary for the development of these signatures have been called into question in the literature. Here, we investigate six typical selection methods for high-dimensional settings and survival endpoints, including LASSO and some of its extensions, component-wise boosting, and random survival forests (RSF). A resampling algorithm based on data splitting was used on nine high-dimensional simulated datasets to assess selection stability on training sets and the intersection between selection methods. Prognostic performances were evaluated on respective validation sets. Finally, one application on a real breast cancer dataset has been proposed. The false discovery rate (FDR) was high for each selection method, and the intersection between lists of predictors was very poor. RSF selects many more variables than the other methods and thus becomes less efficient on validation sets. Due to the complex correlation structure in genomic data, stability in the selection procedure is generally poor for selected predictors, but can be improved with a higher training sample size. In a very high-dimensional setting, we recommend the LASSO-pcvl method since it outperforms other methods by reducing the number of selected genes and minimizing FDR in most scenarios. Nevertheless, this method still gives a high rate of false positives. Further work is thus necessary to propose new methods to overcome this issue where numerous predictors are present. Pluridisciplinary discussion between clinicians and statisticians is necessary to ensure both statistical and biological relevance of the predictors included in molecular signatures.

High expression of eukaryotic initiation factor 3M predicts poor prognosis in colon adenocarcinoma patients

Eukaryotic initiation factor 3 subunit M (EIF3M) is required for key steps in the initiation of protein synthesis, and dysregulation of EIF3M is associated with tumorigenesis. This study aimed to explore the clinicopathological and prognostic role of EIF3M in patients with colon adenocarcinoma. A total of 82 pathology specimens, 20 freeze-thawed tumors and 80 healthy controls were used to investigate the expression of EIF3M in colon adenocarcinoma through immunohistochemistry, western blotting, RT-qPCR and ELISA. In addition, Kaplan-Meier curves and Cox regression analysis were used to analyze overall survival (OS) and disease-free survival (DFS). Furthermore, the Oncomine database was used for analyzing EIF3M expression. The positive rate of EIF3M in colon adenocarcinoma was higher compared with that in normal colon tissues (62.20% vs. 29.27%; P<0.001). The mean score of EIF3M was also higher in colon adenocarcinoma compared with normal colon tissue (17.28±10.05 vs. 6.53±4.87; P<0.001). The levels of EIF3M expression in freeze-thawed tumors and serum from 20 patients with colon adenocarcinoma were higher than those in normal tissues and serum from healthy controls, respectively (P<0.001). In addition, positive expression of EIF3M was associated with tumor size (P=0.002) and Dukes' stage (P<0.001). In multivariate Cox regression analysis, EIF3M expression was an independent prognostic factor for OS (P=0.003) and DFS (P=0.001). Oncomine database analysis showed a higher expression of EIF3M expression in colon adenocarcinoma compared with normal colon tissues, colon squamous cell carcinomas or gastrointestinal stromal tumors. In conclusion, EIF3M expression was associated with tumor size and Dukes' stage in colon adenocarcinoma. Hence, EIF3M is a potential prognostic indicator for colon adenocarcinoma.

Variable selection in competing risks models based on quantile regression

The proportional subdistribution hazard regression model has been widely used by clinical researchers for analyzing competing risks data. It is well known that quantile regression provides a more comprehensive alternative to model how covariates influence not only the location but also the entire conditional distribution. In this paper, we develop variable selection procedures based on penalized estimating equations for competing risks quantile regression. Asymptotic properties of the proposed estimators including consistency and oracle properties are established. Monte Carlo simulation studies are conducted, confirming that the proposed methods are efficient. A bone marrow transplant data set is analyzed to demonstrate our methodologies.

SMARCAD1 Promotes Pancreatic Cancer Cell Growth and Metastasis through Wnt/β-catenin-Mediated EMT

Pancreatic cancer (PC) is one of the most lethal diseases, characterized by early metastasis and high mortality. Subunits of the SWI/SNF complex have been identified in many studies as the regulators of tumor progression, but the role of SMARCAD1, one member of the SWI/SNF family, in pancreatic cancer has not been elucidated. Based on analysis of GEO database and immunohistochemical detection of patient-derived pancreatic cancer tissues, we found that SMARCAD1 is more highly expressed in pancreatic cancer tissues and that its expression level negatively correlates with patients' survival time. With further investigation, it shows that SMARCAD1 promotes the proliferation, migration, invasion of pancreatic cancer cells. Mechanistically, we first demonstrate that SMARCAD1 induces EMT via activating Wnt/β-catenin signaling pathway in pancreatic cancer. Our results provide the role and potential mechanism of SMARCAD1 in pancreatic cancer, which may prove useful marker for diagnostic or therapeutic applications of PC disease.

Bias Due to Confounders for the Exposure-Competing Risk Relationship

BACKGROUND

Epidemiologic studies that aim to estimate a causal effect of an exposure on a particular event of interest may be complicated by the existence of competing events that preclude the occurrence of the primary event. Recently, many articles have been published in the epidemiologic literature demonstrating the need for appropriate models to accommodate competing risks when they are present. However, there has been little attention to variable selection for confounder control in competing risk analyses.

METHODS

We employ simulation to demonstrate the bias in two variable selection strategies include covariates that are associated with the exposure and (1) which change the cause-specific hazard of any of the outcomes; or (2) which change the cause-specific hazard of the specific event of interest.

RESULTS

We demonstrated minimal to no bias in estimators adjusted for confounders of exposure and either the event of interest or the competing event, but bias of varying magnitude in almost all estimators adjusted only for confounders of exposure and the primary outcome.

DISCUSSION

When estimating causal effects for which there are competing risks, the analysis should control for confounders of both the exposure-primary outcome effect and of the exposure-competing outcome effect.

Comparison of variable selection methods for high-dimensional survival data with competing events

BACKGROUND

In the era of personalized medicine, it's primordial to identify gene signatures for each event type in the context of competing risks in order to improve risk stratification and treatment strategy. Until recently, little attention was paid to the performance of high-dimensional selection in deriving molecular signatures in this context. In this paper, we investigate the performance of two selection methods developed in the framework of high-dimensional data and competing risks: Random survival forest and a boosting approach for fitting proportional subdistribution hazards models.

METHODS

Using data from bladder cancer patients (GSE5479) and simulated datasets, stability and prognosis performance of the two methods were evaluated using a resampling strategy. For each sample, the data set was split into 100 training and validation sets. Molecular signatures were developed in the training sets by the two selection methods and then applied on the corresponding validation sets.

RESULTS

Random survival forest and boosting approach have comparable performance for the prediction of survival data, with few selected genes in common. Nevertheless, many different sets of genes are identified by the resampling approach, with a very small frequency of genes occurrence among the signatures. Also, the smaller the training sample size, the lower is the stability of the signatures.

CONCLUSION

Random survival forest and boosting approach give good predictive performance but gene signatures are very unstable. Further works are needed to propose adequate strategies for the analysis of high-dimensional data in the context of competing risks.

Ptgr1 expression is regulated by NRF2 in rat hepatocarcinogenesis and promotes cell proliferation and resistance to oxidative stress

Prostaglandin reductase-1 (Ptgr1) is an alkenal/one oxidoreductase that is involved in the catabolism of eicosanoids and lipid peroxidation such as 4-hydroxynonenal (4-HNE). Recently, we reported that Ptgr1 is overexpressed in human clinical and experimentally induced samples of hepatocellular carcinoma (HCC). However, how the expression of this gene is regulated and its role in carcinogenesis are not yet known. Here, we studied parameters associated with antioxidant responses and the mechanisms underlying the induction of Ptgr1 expression by the activation of Nuclear Factor (erythroid-derived-2)-like-2 (NRF2). For these experiments, we used two protocols of induced hepatocarcinogenesis in rats. Furthermore, we determined the effect of PTGR1 on cell proliferation and resistance to oxidative stress in cell cultures of the epithelial liver cell line, C9. Ptgr1 was overexpressed during the early phase in altered hepatocyte foci, and this high level of expression was maintained in persistent nodules until tumors developed. Ptgr1 expression was regulated by NRF2, which bound to an antioxidant response element at -653bp in the rat Ptgr1 gene. The activation of NRF2 induced the activation of an antioxidant response that included effects on proteins such as glutamate-cysteine ligase, catalytic subunit, NAD(P)H dehydrogenase quinone-1 (NQO1) and glutathione-S-transferase-P (GSTP1). These effects may have produced a reduced status that was associated with a high proliferation rate in experimental tumors. Indeed, when Ptgr1 was stably expressed, we observed a reduction in the time required for proliferation and a protective effect against hydrogen peroxide- and 4-HNE-induced cell death. These data were consistent with data showing colocalization between PTGR1 and 4-HNE protein adducts in liver nodules. These findings suggest that Ptgr1 and antioxidant responses act as a metabolic adaptation and could contribute to proliferation and cell-death evasion in liver tumor cells. Furthermore, these data indicate that Ptgr1 could be used to design early diagnostic tools or targeted therapies for HCC.

High Expression of PTGR1 Promotes NSCLC Cell Growth via Positive Regulation of Cyclin-Dependent Protein Kinase Complex

Lung cancer has been the most common cancer and the main cause of cancer-related deaths worldwide for several decades. PTGR1 (prostaglandin reductase 1), as a bifunctional enzyme, has been involved in the occurrence and progression of cancer. However, its impact on human lung cancer is rarely reported. In this study, we found that PTGR1 was overexpressed in lung cancer based on the analyses of Oncomine. Moreover, lentivirus-mediated shRNA knockdown of PTGR1 reduced cell viability in human lung carcinoma cells 95D and A549 by MTT and colony formation assay. PTGR1 depletion led to G2/M phase cell cycle arrest and increased the proportion of apoptotic cells in 95D cells by flow cytometry. Furthermore, silencing PTGR1 in 95D cells resulted in decreased levels of cyclin-dependent protein kinase complex (CDK1, CDK2, cyclin A2, and cyclin B1) by western blotting and then PTGR1 is positively correlated with cyclin-dependent protein by using the data mining of the Oncomine database. Therefore, our findings suggest that PTGR1 may play a role in lung carcinogenesis through regulating cell proliferation and is a potential new therapeutic strategy for lung cancer.

Knockdown of prostaglandin reductase 1 (PTGR1) suppresses prostate cancer cell proliferation by inducing cell cycle arrest and apoptosis

Chemoresistance is a serious problem for the treatment of androgen-independent prostate cancer (PC). The underlying molecular mechanisms by which androgen-independent PC cells acquire the capacity to proliferate remain largely unclear. The aim of this study was to investigate the biological role of prostaglandin reductase 1 (PTGR1) in prostate cancer. Data from the Oncomine database showed that PTGR1 is commonly upregulated in PC tissue in comparison to corresponding normal controls. Two PTGR1-specific short hairpin RNA (shRNA) sequences were used to block the expression of PTGR1 via a lentivirus-mediated system in the androgen-independent PC cell lines DU145 and PC 3. Functional analysis revealed that knockdown of PTGR1 significantly inhibited proliferation and colony formation by PC cells. The inhibition of cell proliferation was related to arrest of the cell cycle in the G0/G1 phase and increased apoptosis in response to PTGR1 knockdown as indicated by flow cytometry. PTGR1 silencing was found to mechanically enhance the expression of p21, caspase 3, and cleaved PARP and to decrease the level of cyclin D1. In conclusion, PTGR1 plays an essential role in PC cells and may be a potential therapeutic target for PC.

Altered primary chromatin structures and their implications in cancer development

BACKGROUND

Cancer development is a complex process involving both genetic and epigenetic changes. Genetic changes in oncogenes and tumor-suppressor genes are generally considered as primary causes, since these genes may directly regulate cellular growth. In addition, it has been found that changes in epigenetic factors, through mutation or altered gene expression, may contribute to cancer development. In the nucleus of eukaryotic cells DNA and histone proteins form a structure called chromatin which consists of nucleosomes that, like beads on a string, are aligned along the DNA strand. Modifications in chromatin structure are essential for cell type-specific activation or repression of gene transcription, as well as other processes such as DNA repair, DNA replication and chromosome segregation. Alterations in epigenetic factors involved in chromatin dynamics may accelerate cell cycle progression and, ultimately, result in malignant transformation. Abnormal expression of remodeler and modifier enzymes, as well as histone variants, may confer to cancer cells the ability to reprogram their genomes and to yield, maintain or exacerbate malignant hallmarks. At the end, genetic and epigenetic alterations that are encountered in cancer cells may culminate in chromatin changes that may, by altering the quantity and quality of gene expression, promote cancer development.

METHODS

During the last decade a vast number of studies has uncovered epigenetic abnormalities that are associated with the (anomalous) packaging and remodeling of chromatin in cancer genomes. In this review I will focus on recently published work dealing with alterations in the primary structure of chromatin resulting from imprecise arrangements of nucleosomes along DNA, and its functional implications for cancer development.

CONCLUSIONS

The primary chromatin structure is regulated by a variety of epigenetic mechanisms that may be deregulated through gene mutations and/or gene expression alterations. In recent years, it has become evident that changes in chromatin structure may coincide with the occurrence of cancer hallmarks. The functional interrelationships between such epigenetic alterations and cancer development are just becoming manifest and, therefore, the oncology community should continue to explore the molecular mechanisms governing the primary chromatin structure, both in normal and in cancer cells, in order to improve future approaches for cancer detection, prevention and therapy, as also for circumventing drug resistance.

Effect of prostaglandin reductase 1 (PTGR1) on gastric carcinoma using lentivirus-mediated system

Gastric carcinoma is a digestive related malignant tumor with poor diagnosis and prognosis for advanced patients. PTGR1 (prostaglandin reductase 1), as a potential cancer biomarker, has not been reported in gastric carcinoma occurrence. To investigate the role of PTGR1 on gastric carcinoma cells, human PTGR1 was efficiently silenced by lentivirus-mediated system in MGC-803 cells confirmed by quantitative real-time PCR (qRT-PCR) and western blot. Then cell proliferation, colony formation and cell cycle were determined after knockdown of PTGR1 by MTT assay, colony assay and flow cytometry, respectively and data suggested that PTGR1 down regulated MGC-803 cells significantly suppressed the proliferation and colony formation ability and induced cell cycle arrest in the G0/G1 phase compared to controls (P < 0.001). Further investigation demonstrated knockdown of PTGR1 influenced cell proliferation and cell cycle via activating p21 and p53 signaling pathway described by Western blot assay. Our findings indicate that PTGR1 may be an oncogene in human gastric carcinoma and identified as a diagnosis and prognosis target for gastric carcinoma.