Gene expression profiling of clear cell renal cell carcinoma: gene identification and prognostic classification

Insulin receptor expression to predict resistance to axitinib and elucidation of the underlying molecular mechanism in metastatic renal cell carcinoma

BACKGROUND

The study aimed to examine the significance of insulin receptor (INSR) expression in predicting resistance to axitinib in clear cell renal cell carcinoma (ccRCC).

METHODS

Clinicopathological data were collected from 36 consecutive patients with metastatic RCC who received axitinib. Thirty-three primary tumours were obtained for immunohistochemistry. Patient-derived xenograft (PDX) models were created by transplanting primary tumours into immunodeficient mice, establishing axitinib-resistant PDX models. RCC cell lines were co-cultured with human renal glomerular endothelial cells (HGECs) treated with siRNA of INSR (HGEC-siINSR). Gene expression alteration was analysed using microarray.

RESULTS

The patients with low INSR expression who received axitinib had a poorer outcome. Multivariate analysis showed that INSR expression was the independent predictor of progression-free survival. INSR expression decreased in axitinib-resistant PDX tumours. RCC cell lines showed upregulated interferon responses and highly increased interferon-β levels by co-culturing with HGEC-siINSR. HGECs showed decreased INSR and increased interferon-β after axitinib administration. RCC cell lines co-cultured with HGEC-siINSR showed high programmed death-ligand 1 (PD-L1) expression, which increased after interferon-β administration.

CONCLUSIONS

Decreased INSR in RCC could be a biomarker to predict axitinib resistance. Regarding the resistant mechanism, vascular endothelial cells with decreased INSR in RCC may secrete interferon-β and induce PD-L1.

Epigenetic and transcriptomic characterization reveals progression markers and essential pathways in clear cell renal cell carcinoma

Identifying tumor-cell-specific markers and elucidating their epigenetic regulation and spatial heterogeneity provides mechanistic insights into cancer etiology. Here, we perform snRNA-seq and snATAC-seq in 34 and 28 human clear cell renal cell carcinoma (ccRCC) specimens, respectively, with matched bulk proteogenomics data. By identifying 20 tumor-specific markers through a multi-omics tiered approach, we reveal an association between higher ceruloplasmin (CP) expression and reduced survival. CP knockdown, combined with spatial transcriptomics, suggests a role for CP in regulating hyalinized stroma and tumor-stroma interactions in ccRCC. Intratumoral heterogeneity analysis portrays tumor cell-intrinsic inflammation and epithelial-mesenchymal transition (EMT) as two distinguishing features of tumor subpopulations. Finally, BAP1 mutations are associated with widespread reduction of chromatin accessibility, while PBRM1 mutations generally increase accessibility, with the former affecting five times more accessible peaks than the latter. These integrated analyses reveal the cellular architecture of ccRCC, providing insights into key markers and pathways in ccRCC tumorigenesis.

Biomarkers in Urological Cancers

Urological tumours have become one of the most common cancers in the last decade. It is important to apply an approach that evaluates many factors related to the patient and the disease carefully to minimize cancer-associated morbidity and mortality. The clinical use of cancer biomarkers is a valuable part of the clinical management of urological cancers. These biomarkers may lead to optimized detection, treatment, and follow-up of urological cancers. With the development of molecular research, newly developed biomarkers and next-generation sequencing have also contributed to patient management. In this chapter, we will present biomarkers in the most common urological cancers under subheadings of bladder cancer, prostate cancer, kidney cancer, and testicular cancer. Additionally, due to the development that occurred in the next-generation sequencing (NGS), all the above-mentioned malignancies are evaluated with regard to NGS.

The Uniqueness of Clear Cell Renal Cell Carcinoma: Summary of the Process and Abnormality of Glucose Metabolism and Lipid Metabolism in ccRCC

Kidney cancer is a cancer with an increasing incidence in recent years. Clear cell renal cell carcinoma (ccRCC) accounts for up to 80% of all kidney cancers. The understanding of the pathogenesis, tumor progression, and metastasis of renal carcinoma is not yet perfect. Kidney cancer has some characteristics that distinguish it from other cancers, and the metabolic aspect is the most obvious. The specificity of glucose and lipid metabolism in kidney cancer cells has also led to its being studied as a metabolic disease. As the most common type of kidney cancer, ccRCC has many characteristics that represent the specificity of kidney cancer. There are features that we are very concerned about, including the presence of lipid droplets in cells and the obesity paradox. These two points are closely related to glucose metabolism and lipid metabolism. Therefore, we hope to explore whether metabolic changes affect the occurrence and development of kidney cancer by looking for evidence of changes on expression at the genomic and protein levels in glucose metabolism and lipid metabolism in ccRCC. We begin with the representative phenomenon of abnormal cancer metabolism: the Warburg effect, through the collection of popular metabolic pathways and related genes in the last decade, as well as some research hotspots, including the role of ferroptosis and glutamine in cancer, systematically elaborated the factors affecting the incidence and metastasis of kidney cancer. This review also identifies the similarities and differences between kidney cancer and other cancers in order to lay a theoretical foundation and provide a valid hypothesis for future research.

Stratification of patients with clear cell renal cell carcinoma to facilitate drug repositioning

Clear cell renal cell carcinoma (ccRCC) is the most common histological type of kidney cancer and has high heterogeneity. Stratification of ccRCC is important since distinct subtypes differ in prognosis and treatment. Here, we applied a systems biology approach to stratify ccRCC into three molecular subtypes with different mRNA expression patterns and prognosis of patients. Further, we developed a set of biomarkers that could robustly classify the patients into each of the three subtypes and predict the prognosis of patients. Then, we reconstructed subtype-specific metabolic models and performed essential gene analysis to identify the potential drug targets. We identified four drug targets, including SOAT1, CRLS1, and ACACB, essential in all the three subtypes and GPD2, exclusively essential to subtype 1. Finally, we repositioned mitotane, an FDA-approved SOAT1 inhibitor, to treat ccRCC and showed that it decreased tumor cell viability and inhibited tumor cell growth based on in vitro experiments.

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Three consistent molecular ccRCC subtypes were found to guide patients' prognoses

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REOs-based biomarker was developed to robustly classify patients at individual level

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SOAT1 is identified as a common drug target for all ccRCC subtypes

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Mitotane was repositioned treatment of ccRCC via inhibiting SOAT1

Lymphoblastoid cell line with B1 cell characteristics established from a chronic lymphocytic leukemia clone by in vitro EBV infection

Chronic lymphocytic leukemia (CLL) cells express the receptor for Epstein-Barr virus (EBV) and can be infected in vitro. Infected cells do not express the growth-promoting set of EBV-encoded genes and therefore they do not yield LCLs, in most experiments. With exceptional clones, lines were obtained however. We describe a new line, HG3, established by in vitro EBV-infection from an IGHV1–2 unmutated CLL patient clone. All cells expressed EBNA-2 and LMP-1, the EBV-encoded genes pivotal for transformation. The karyotype, FISH cytogenetics and SNP-array profile of the line and the patient's ex vivo clone showed biallelic 13q14 deletions with genomic loss of DLEU7, miR15a/miR16–1, the two micro-RNAs that are deleted in 50% of CLL cases. Further features of CLL cells were: expression of CD5/CD20/CD27/CD43 and release of IgM natural antibodies reacting with oxLDL-like epitopes on apoptotic cells (cf. stereotyped subset-1). Comparison with two LCLs established from normal B cells showed 32 genes expressed at higher levels (> 2-fold). Among these were LHX2 and LILRA. These genes may play a role in the development of the disease. LHX2 expression was shown in self-renewing multipotent hematopoietic stem cells, and LILRA4 codes for a receptor for bone marrow stromal cell antigen-2 that contributes to B cell development. Twenty-four genes were expressed at lower levels, among these PARD3 that is essential for asymmetric cell division. These genes may contribute to establish precursors of CLL clones by regulation of cellular phenotype in the hematopoietic compartment. Expression of CD5/CD20/CD27/CD43 and spontaneous production of natural antibodies may identify the CLL cell as a self-renewing B1 lymphocyte.

The Role of Artificial Intelligence in the Diagnosis and Prognosis of Renal Cell Tumors

The increasing availability of molecular data provided by next-generation sequencing (NGS) techniques is allowing improvement in the possibilities of diagnosis and prognosis in renal cancer. Reliable and accurate predictors based on selected gene panels are urgently needed for better stratification of renal cell carcinoma (RCC) patients in order to define a personalized treatment plan. Artificial intelligence (AI) algorithms are currently in development for this purpose. Here, we reviewed studies that developed predictors based on AI algorithms for diagnosis and prognosis in renal cancer and we compared them with non-AI-based predictors. Comparing study results, it emerges that the AI prediction performance is good and slightly better than non-AI-based ones. However, there have been only minor improvements in AI predictors in terms of accuracy and the area under the receiver operating curve (AUC) over the last decade and the number of genes used had little influence on these indices. Furthermore, we highlight that different studies having the same goal obtain similar performance despite the fact they use different discriminating genes. This is surprising because genes related to the diagnosis or prognosis are expected to be tumor-specific and independent of selection methods and algorithms. The performance of these predictors will be better with the improvement in the learning methods, as the number of cases increases and by using different types of input data (e.g., non-coding RNAs, proteomic and metabolic). This will allow for more precise identification, classification and staging of cancerous lesions which will be less affected by interpathologist variability.

ADFP promotes cell proliferation in lung adenocarcinoma via Akt phosphorylation

Previously, we identified differentially expressed proteins, including ADFP, between lung adenocarcinoma (LAC) tissue and paired normal bronchioloalveolar epithelium. In this study, we investigated the role of ADFP in LAC. ADFP levels in the serum of patients with lung cancer and benign diseases were measured by enzyme‐linked immunosorbent assays (ELISA). shRNA was used to knock‐down or overexpress ADFP in A549 and NCI‐H1299 cells. The biological function of ADFP and its underlying mechanisms was evaluated in vivo and in vitro. ADFP was highly expressed in the serum of lung cancer patients, especially those with LAC. ADFP promoted cell proliferation and up‐regulated the p‐Akt/Akt ratio in A549 and NCI‐H1299 cells in vitro. Furthermore, in nude mice, ADFP promoted tumour formation with high levels of p‐Akt/Akt, Ki67 and proliferating cell nuclear antigen (PCNA). Similar to the effect of ADFP knock‐down, MK‐2206 (a phosphorylation inhibitor of Akt) reduced A549 and NCI‐H1299 cell proliferation. In ADFP‐overexpressing A549 and NCI‐H1299 cells, proliferation was suppressed by MK‐2206 and returned to the control level. ADFP did not regulate invasion, migration or adhesion in LAC cells. Together, these results suggest that ADFP promotes LAC cell proliferation in vitro and in vivo by increasing Akt phosphorylation level.

Potential Links between YB-1 and Fatty Acid Synthesis in Clear Cell Renal Carcinoma

According to the National Institutes of Health, clear cell renal cell carcinoma (ccRCC) is the most common type of Renal Cell Carcinoma (RCC), making up approximately 75% of total renal carcinoma cases. Clear cell Renal Cell Carcinoma is characterized by a significant accumulation of lipids in the cytoplasm, which allows light from microscopes to pass through giving them a "clear" phenotype. Many of these lipids are in the form of fatty acids, both free and incorporated into lipid droplets. RCC is typically associated with a poor prognosis due to the lack of specific symptoms. Some symptoms include blood in urine, fever, lump on the side, weight loss, fatigue, to name a few; all of which can be associated with non-specific, non-cancerous, health conditions that contribute to difficult diagnosis. Treatment of RCC has typically been centered around radical nephrectomy as the standard of care, but due to the potentially small size of lesions and the possibility of causing surgically induced chronic kidney disease, treatments have shifted to more cautious, less invasive approaches. These approaches include active surveillance, nephron-sparing surgery, and other minimally invasive techniques like cryotherapy and renal ablation. Although these techniques have had the desired effect of reducing the number of surgeries, there is still considerable potential for renal impairment and the chance that tumors can grow out of control without surgery. With the difficulty that surrounds the treatment of ccRCC and its considerably high mortality rate amongst urological cancers, it is important to look for novel approaches to improve patient outcomes. This review looks at available literature and our data that suggests the lipogenic enzyme stearoyl-CoA desaturase may be more beneficial to patient survival than once thought. As our understanding of the importance of lipids in cell metabolism and longevity matures, it is important to present new perspectives that present a new understanding of ccRCC and the role of lipids in survival mechanisms engaged by transformed cells during cancer progression. In this review, we provide evidence that pharmacological inhibition of lipid desaturation in renal cancer patients is not without risk, and that the presence of unsaturated fatty acids may be a beneficial factor in patient outcomes. Although more direct experimental evidence is needed to make definitive conclusions, it is clear that the work reviewed herein should challenge our current understanding of cancer biology and may inform novel approaches to the diagnosis and treatment of ccRCC.

An optimal prognostic model based on gene expression for clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most prevalent type of RCC; however, prognostic prediction tools for ccRCC are scant. Developing mRNA or long non-coding RNA (lncRNA)-based risk assessment tools may improve the prognosis in patients with ccRCC. RNA-sequencing and prognostic data from patients with ccRCC were downloaded from The Cancer Genome Atlas and the European Bioinformatics Institute Array database at the National Center for Biotechnology Information. Differentially expressed (DE) RNAs (DERs) and prognostic DERs were screened between less favorable and favorable prognoses using the limma package in R 3.4.1, and analyzed using univariate and multivariate Cox regression analyses, respectively. Risk score models were constructed using optimal combinations of DEmRNAs and DElncRNAs identified using the Least Absolute Shrinkage And Selection Operator Cox regression model of the penalized package. Associations between risk score models and overall survival time were evaluated. Independent prognostic clinical factors were screened using univariate and multivariate Cox regression analyses, and nomogram models were constructed. Gene Ontology biological processes and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were conducted using the clusterProfiler package in R3.4.1. A total of 451 DERs were identified, including 404 mRNAs and 47 lncRNAs, between less favorable and favorable prognoses, and 269 DERs, including 233 mRNAs and 36 lncRNAs, were identified as independent prognostic factors. Optimal combinations including 10 DEmRNAs or 10 DElncRNAs were screened using four risk score models based on the status or expression levels of the 10 DEmRNAs or 10 DElncRNAs. The model based on the expression levels of the 10 DEmRNAs had the highest prognostic power. These prognostic DEmRNAs may be involved in biological processes associated with the inflammatory response, complement and coagulation cascades and neuroactive ligand-receptor interaction pathways. The present validated risk assessment tool based on the expression levels of these 10 DEmRNAs may help to identify patients with ccRCC at a high risk of mortality. These 10 DEmRNAs in optimal combinations may serve as prognostic biomarkers and help to elucidate the pathogenesis of ccRCC.

Classification of clear cell renal cell carcinoma based on PKM alternative splicing

Clear cell renal cell carcinoma (ccRCC) accounts for 70-80% of kidney cancer diagnoses and displays high molecular and histologic heterogeneity. Hence, it is necessary to reveal the underlying molecular mechanisms involved in progression of ccRCC to better stratify the patients and design effective treatment strategies. Here, we analyzed the survival outcome of ccRCC patients as a consequence of the differential expression of four transcript isoforms of the pyruvate kinase muscle type (PKM). We first extracted a classification biomarker consisting of eight gene pairs whose within-sample relative expression orderings (REOs) could be used to robustly classify the patients into two groups with distinct molecular characteristics and survival outcomes. Next, we validated our findings in a validation cohort and an independent Japanese ccRCC cohort. We finally performed drug repositioning analysis based on transcriptomic expression profiles of drug-perturbed cancer cell lines and proposed that paracetamol, nizatidine, dimethadione and conessine can be repurposed to treat the patients in one of the subtype of ccRCC whereas chenodeoxycholic acid, fenoterol and hexylcaine can be repurposed to treat the patients in the other subtype.

Integrative analysis of cross-modal features for the prognosis prediction of clear cell renal cell carcinoma

Motivation

As a highly heterogeneous disease, clear cell renal cell carcinoma (ccRCC) has quite variable clinical behaviors. The prognostic biomarkers play a crucial role in stratifying patients suffering from ccRCC to avoid over- and under-treatment. Researches based on hand-crafted features and single-modal data have been widely conducted to predict the prognosis of ccRCC. However, these experience-dependent methods, neglecting the synergy among multimodal data, have limited capacity to perform accurate prediction. Inspired by complementary information among multimodal data and the successful application of convolutional neural networks (CNNs) in medical image analysis, a novel framework was proposed to improve prediction performance.

Results

We proposed a cross-modal feature-based integrative framework, in which deep features extracted from computed tomography/histopathological images by using CNNs were combined with eigengenes generated from functional genomic data, to construct a prognostic model for ccRCC. Results showed that our proposed model can stratify high- and low-risk subgroups with significant difference (P-value < 0.05) and outperform the predictive performance of those models based on single-modality features in the independent testing cohort [C-index, 0.808 (0.728–0.888)]. In addition, we also explored the relationship between deep image features and eigengenes, and make an attempt to explain deep image features from the view of genomic data. Notably, the integrative framework is available to the task of prognosis prediction of other cancer with matched multimodal data.

Availability and implementation

https://github.com/zhang-de-lab/zhang-lab? from=singlemessage

Supplementary information

Supplementary data are available at Bioinformatics online.

Systematic review and REMARK scoring of renal cell carcinoma prognostic circulating biomarker manuscripts

Background

No validated molecular biomarkers exist to help guide prognosis of renal cell carcinoma (RCC) patients. We seek to evaluate the quality of published prognostic circulating RCC biomarker manuscripts using the Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK) guidelines.

Methods

The phrase “(renal cell carcinoma OR renal cancer OR kidney cancer OR kidney carcinoma) AND circulating AND (biomarkers OR cell free DNA OR tumor DNA OR methylated cell free DNA OR methylated tumor DNA)” was searched in Embase, Medline and PubMed March 2018. Relevant manuscripts were scored using 48 REMARK sub-criteria for a maximal score of 20 points.

Results

The search identified 535 publications: 33 were manuscripts of primary research and were analyzed. The mean REMARK score was 10.6 (range 6.42–14.2). All manuscripts stated their biomarker, study objectives and method of case selection. The lowest scoring criteria: time lapse between storage of blood/serum and marker assay (n = 2) and lack of flow diagram (n = 2). REMARK scores were significantly higher in publications stating adherence to REMARK guidelines (p = 0.0307) and reporting statistically significant results (p = 0.0318).

Conclusions

Most RCC prognostic biomarker manuscripts poorly adhere to the REMARK guidelines. Better designed studies and appropriate reporting are required to address this urgent unmet need.

Assessment of biochemical recurrence of prostate cancer (Review)

The assessment of the risk of biochemical recurrence (BCR) is critical in the management of males with prostate cancer (PC). Over the past decades, a comprehensive effort has been focusing on improving risk stratification; a variety of models have been constructed using PC-associated pathological features and molecular alterations occurring at the genome, protein and RNA level. Alterations in RNA expression (lncRNA, miRNA and mRNA) constitute the largest proportion of the biomarkers of BCR. In this article, we systemically review RNA-based BCR biomarkers reported in PubMed according to the PRISMA guidelines. Individual miRNAs, mRNAs, lncRNAs and multi-gene panels, including the commercially available signatures, Oncotype DX and Prolaris, will be discussed; details related to cohort size, hazard ratio and 95% confidence intervals will be provided. Mechanistically, these individual biomarkers affect multiple pathways critical to tumorigenesis and progression, including epithelial-mesenchymal transition (EMT), phosphatase and tensin homolog (PTEN), Wnt, growth factor receptor, cell proliferation, immune checkpoints and others. This variety in the mechanisms involved not only validates their associations with BCR, but also highlights the need for the coverage of multiple pathways in order to effectively stratify the risk of BCR. Updates of novel biomarkers and their mechanistic insights are considered, which suggests new avenues to pursue in the prediction of BCR. Additionally, the management of patients with BCR and the potential utility of the stratification of the risk of BCR in salvage treatment decision making for these patients are briefly covered. Limitations will also be discussed.

miR-22 Regulates Invasion, Gene Expression and Predicts Overall Survival in Patients with Clear Cell Renal Cell Carcinoma

Background:

Clear cell renal cell carcinoma (ccRCC) is molecularly diverse and distinct molecular subtypes show different clinical outcomes. MicroRNAs (miRNAs) are essential components of gene regulatory networks and play a crucial role in progression of many cancer types including ccRCC.

Objective:

Identify prognostic miRNAs and determine the role of miR-22 in ccRCC.

Methods:

Hierarchical clustering was done in R using gene expression profiles of over 450 ccRCC cases in The Cancer Genome Atlas (TCGA). Kaplan-Meier analysis was performed to identify prognostic miRNAs in the TCGA dataset. RNA-Seq was performed to identify miR-22 target genes in primary ccRCC cells and Matrigel invasion assay was performed to assess the effects of miR-22 overexpression on cell invasion.

Results:

Hierarchical clustering analysis using 2,621 prognostic genes previously identified by our group demonstrated that ccRCC patients with longer overall survival expressed lower levels of genes promoting proliferation or immune responses, while better maintaining gene expression associated with cortical differentiation and cell adhesion. Targets of 26 miRNAs were significantly enriched in the 2,621 prognostic genes and these miRNAs were prognostic by themselves. MiR-22 was associated with poor overall survival in the TCGA dataset. Overexpression of miR-22 promoted invasion of primary ccRCC cells in vitro and modulated transcriptional programs implicated in cancer progression including DNA repair, cell proliferation and invasion.

Conclusions:

Our results suggest that ccRCCs with differential clinical outcomes have distinct transcriptomes for which miRNAs could serve as master regulators. MiR-22, as a master regulator, promotes ccRCC progression at least in part by enhancing cell invasion.

Brain metastases

An estimated 20% of all patients with cancer will develop brain metastases, with the majority of brain metastases occurring in those with lung, breast and colorectal cancers, melanoma or renal cell carcinoma. Brain metastases are thought to occur via seeding of circulating tumour cells into the brain microvasculature; within this unique microenvironment, tumour growth is promoted and the penetration of systemic medical therapies is limited. Development of brain metastases remains a substantial contributor to overall cancer mortality in patients with advanced-stage cancer because prognosis remains poor despite multimodal treatments and advances in systemic therapies, which include a combination of surgery, radiotherapy, chemotherapy, immunotherapy and targeted therapies. Thus, interest abounds in understanding the mechanisms that drive brain metastases so that they can be targeted with preventive therapeutic strategies and in understanding the molecular characteristics of brain metastases relative to the primary tumour so that they can inform targeted therapy selection. Increased molecular understanding of the disease will also drive continued development of novel immunotherapies and targeted therapies that have higher bioavailability beyond the blood-tumour barrier and drive advances in radiotherapies and minimally invasive surgical techniques. As these discoveries and innovations move from the realm of basic science to preclinical and clinical applications, future outcomes for patients with brain metastases are almost certain to improve.

Comprehensive assessment gene signatures for clear cell renal cell carcinoma prognosis

There are many prognostic gene signature models in clear cell renal cell carcinoma (ccRCC). However, different results from various methods and samples are hard to contribute to clinical practice. It is necessary to develop a robust gene signature for improving clinical practice in ccRCC.A method was proposed to integrate least absolute shrinkage and selection operator and multiple Cox regression to obtain mRNA and microRNA signature from the cancer genomic atlas database for predicting prognosis of ccRCC. The gene signature model consisted by 5 mRNAs and 1 microRNA was identified. Prognosis index (PI) model was constructed from RNA expression and median value of PI is used to classified patients into high- and low-risk groups.The results showed that high-risk patients showed significantly decrease survival comparison with low-risk groups [hazard ratio (HR) =7.13, 95% confidence interval = 3.71-13.70, P < .001]. As the gene signature was mainly consisted by mRNA, the validation data can use transcriptomic data to verify. For comparison of the performance with previous works, other gene signature models and 4 datasets of ccRCC were retrieved from publications and public database. For estimating PI in each model, 3 indicators including HR, concordance index , and the area under the curve of receiver operating characteristic for 3 years were calculated across 4 independent datasets.The comparison results showed that the integrative model from our study was more robust than other models via comprehensive analysis. These findings provide some genes for further study their functions and mechanisms in ccRCC tumorigenesis and malignance, and may be useful for effective clinical decision making of ccRCC patients.

The roles of metallothioneins in carcinogenesis

Metallothioneins (MTs) are small cysteine-rich proteins that play important roles in metal homeostasis and protection against heavy metal toxicity, DNA damage, and oxidative stress. In humans, MTs have four main isoforms (MT1, MT2, MT3, and MT4) that are encoded by genes located on chromosome 16q13. MT1 comprises eight known functional (sub)isoforms (MT1A, MT1B, MT1E, MT1F, MT1G, MT1H, MT1M, and MT1X). Emerging evidence shows that MTs play a pivotal role in tumor formation, progression, and drug resistance. However, the expression of MTs is not universal in all human tumors and may depend on the type and differentiation status of tumors, as well as other environmental stimuli or gene mutations. More importantly, the differential expression of particular MT isoforms can be utilized for tumor diagnosis and therapy. This review summarizes the recent knowledge on the functions and mechanisms of MTs in carcinogenesis and describes the differential expression and regulation of MT isoforms in various malignant tumors. The roles of MTs in tumor growth, differentiation, angiogenesis, metastasis, microenvironment remodeling, immune escape, and drug resistance are also discussed. Finally, this review highlights the potential of MTs as biomarkers for cancer diagnosis and prognosis and introduces some current applications of targeting MT isoforms in cancer therapy. The knowledge on the MTs may provide new insights for treating cancer and bring hope for the elimination of cancer.

Differential gene expression profiling of matched primary renal cell carcinoma and metastases reveals upregulation of extracellular matrix genes

Background

The majority of renal cell carcinoma (RCC) studies analyze primary tumors, and the corresponding results are extrapolated to metastatic RCC tumors. However, it is unknown if gene expression profiles from primary RCC tumors differs from patient-matched metastatic tumors. Thus, we sought to identify differentially expressed genes between patient-matched primary and metastatic RCC tumors in order to understand the molecular mechanisms underlying the development of RCC metastases.

Patients and methods

We compared gene expression profiles between patient-matched primary and metastatic RCC tumors using a two-stage design. First, we used Affymetrix microarrays on 15 pairs of primary RCC [14 clear cell RCC (ccRCC), 1 papillary] tumors and patient-matched pulmonary metastases. Second, we used a custom NanoString panel to validate seven candidate genes in an independent cohort of 114 ccRCC patients. Differential gene expression was evaluated using a mixed effect linear model; a random effect denoting patient was included to account for the paired data. Third, The Cancer Genome Atlas (TCGA) data were used to evaluate associations with metastasis-free and overall survival in primary ccRCC tumors.

Results

We identified and validated up regulation of seven genes functionally involved in the formation of the extracellular matrix (ECM): DCN, SLIT2, LUM, LAMA2, ADAMTS12, CEACAM6 and LMO3. In primary ccRCC, CEACAM6 and LUM were significantly associated with metastasis-free and overall survival (P < 0.01).

Conclusions

We evaluated gene expression profiles using the largest set to date, to our knowledge, of patient-matched primary and metastatic ccRCC tumors and identified up regulation of ECM genes in metastases. Our study implicates up regulation of ECM genes as a critical molecular event leading to visceral, bone and soft tissue metastases in ccRCC.

An insight into the complex roles of metallothioneins in malignant diseases with emphasis on (sub)isoforms/isoforms and epigenetics phenomena

Metallothioneins (MTs) belong to a group of small cysteine-rich proteins that are ubiquitous throughout all kingdoms. The main function of MTs is scavenging of free radicals and detoxification and homeostating of heavy metals. In humans, 16 genes localized on chromosome 16 have been identified to encode four MT isoforms labelled by numbers (MT-1-MT-4). MT-2, MT-3 and MT-4 proteins are encoded by a single gene. MT-1 comprises many (sub)isoforms. The known active MT-1 genes are MT-1A, -1B, -1E, -1F, -1G, -1H, -1M and -1X. The rest of the MT-1 genes (MT-1C, -1D, -1I, -1J and -1L) are pseudogenes. The expression and localization of individual MT (sub)isoforms and pseudogenes vary at intra-cellular level and in individual tissues. Changes in MT expression are associated with the process of carcinogenesis of various types of human malignancies, or with a more aggressive phenotype and therapeutic resistance. Hence, MT (sub)isoform profiling status could be utilized for diagnostics and therapy of tumour diseases. This review aims on a comprehensive summary of methods for analysis of MTs at (sub)isoforms levels, their expression in single tumour diseases and strategies how this knowledge can be utilized in anticancer therapy.

Identification and Characterization of Renal Cell Carcinoma Gene Markers

Microarray gene expression profiling has been used to distinguish histological subtypes of renal cell carcinoma (RCC), and consequently to identify specific tumor markers. The analytical procedures currently in use find sets of genes whose average differential expression across the two categories differ significantly. In general each of the markers thus identified does not distinguish tumor from normal with 100% accuracy, although the group as a whole might be able to do so. For the purpose of developing a widely used economically viable diagnostic signature, however, large groups of genes are not likely to be useful. Here we use two different methods, one a support vector machine variant, and the other an exhaustive search, to reanalyze data previously generated in our Lab (Lenburg et al. 2003). We identify 158 genes, each having an expression level that is higher (lower) in every tumor sample than in any normal sample, and each having a minimum differential expression across the two categories at a significance of 0.01. The set is highly enriched in cancer related genes (p = 1.6 × 10–12), containing 43 genes previously associated with either RCC or other types of cancer. Many of the biomarkers appear to be associated with the central alterations known to be required for cancer transformation. These include the oncogenes JAZF1, AXL, ABL2; tumor suppressors RASD1, PTPRO, TFAP2A, CDKN1C; and genes involved in proteolysis or cell-adhesion such as WASF2, and PAPPA.

Molecular Subtypes Improve Prognostic Value of International Metastatic Renal Cell Carcinoma Database Consortium Prognostic Model

INTRODUCTION

Gene-expression signatures for prognosis have been reported in localized renal cell carcinoma (RCC). The aim of this study was to test the predictive power of two different signatures, ClearCode34, a 34-gene signature model [Eur Urol 2014;66:77-84], and an 8-gene signature model [Eur Urol 2015;67:17-20], in the setting of systemic therapy for metastatic disease.

MATERIALS AND METHODS

Metastatic RCC (mRCC) patients from five institutions who were part of TCGA were identified and clinical data were retrieved. We trained and implemented each gene model as described by the original study. The latter was demonstrated by faithful regeneration of a figure and results from the original study. mRCC patients were dichotomized to good or poor prognostic risk groups using each gene model. Cox proportional hazard regression and concordance index (C-Index) analysis were used to investigate an association between each prognostic risk model and overall survival (OS) from first-line therapy.

RESULTS

Overall, 54 patients were included in the final analysis. The primary endpoint was OS. Applying the ClearCode34 model, median survival for the low-risk-ccA (n = 17)-and the high-risk-ccB (n = 37)-subtypes were 27.6 and 22.3 months (hazard ratio (HR): 2.33; p = .039), respectively. ClearCode34 ccA/ccB and International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) classifications appear to represent distinct risk criteria in mRCC, and we observed no significant overlap in classification (p > .05, chi-square test). On multivariable analyses and adjusting for IMDC groups, ccB remained independently associated with a worse OS (p = .044); the joint model of ccA/ccB and IMDC was significantly more accurate in predicting OS than a model with IMDC alone (p = .045, F-test). This was also observed in C-Index analysis; a model with both ccA and ccB subtypes had higher accuracy (C-Index 0.63, 95% confidence interval [CI] = 0.51-0.75) and 95% CIs of the C-Index that did not include the null value of 0.5 in contrast to a model with IMDC alone (0.60, CI = 0.47-0.72). The 8-gene signature molecular subtype model was a weak but insignificant predictor of survival in this cohort (p = .13). A model that included both the 8-gene signature and IMDC (C-Index 0.62, CI = 0.49-0.76) was more prognostic than IMDC alone but did not reach significance, as the 95% CI included the null value of 0.5. These two genomic signatures share no genes in common and are enriched in different biological pathways. The ClearCode34 included genes ARNT and EPAS1 (also known as HIF2a), which are involved in regulation of gene expression by hypoxia-inducible factor.

CONCLUSION

The ClearCode34 but not the 8-gene molecular model improved the prognostic predictive power of the IMDC model in this cohort of 54 patients with metastatic clear cell RCC. The Oncologist 2017;22:286-292 IMPLICATIONS FOR PRACTICE: The clinical and laboratory factors included in the International Metastatic Renal Cell Carcinoma Database Consortium model provide prognostic information in metastatic renal cell carcinoma (mRCC). The present study shows that genomic signatures, originally validated in localized RCC, may add further complementary prognostic information in the metastatic setting. This study may provide new insights into the molecular basis of certain mRCC subgroups. The integration of clinical and molecular data has the potential to redefine mRCC classification, enhance the understanding of mRCC biology, and potentially predict response to treatment in the future.

Epigallocatechin-3-gallate Sensitizes Human 786-O Renal Cell Carcinoma Cells to TRAIL-Induced Apoptosis

Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) is a promising anticancer agent. Epigallocatechin-3-gallate (EGCG) is a polyphenolic constituent of green tea. In this study, potentiating effect of EGCG on TRAIL-induced apoptosis human renal carcinoma cell line 786-O which is relatively resistant to TRAIL was examined, and the possible mechanism was investigated. Here, we show that co-treatment with EGCG and TRAIL induced significantly more profound apoptosis in 786-O cells. Treatment of 786-O cells with EGCG and TRAIL downregulated c-FLIP, Mcl-1, and Bcl-2 proteins in a caspase-dependent pathway. Moreover, we found that pretreatment with NAC markedly inhibited the expression levels of c-FLIP, Mcl-1, and Bcl-2 downregulated by the combinatory treatment, suggesting that the regulating effect of EGCG on these above apoptosis-relevant molecules was partially mediated by generation of ROS. Taken together, the present study demonstrates that EGCG sensitizes human 786-O renal cell carcinoma cells to TRAIL-induced apoptosis by downregulation of c-FLIP, Mcl-1, and Bcl-2.

Major Action of Endogenous Lysyl Oxidase in Clear Cell Renal Cell Carcinoma Progression and Collagen Stiffness Revealed by Primary Cell Cultures

Human clear cell renal cell carcinoma (ccRCC) is therapy resistant; therefore, it is worthwhile studying in depth the molecular aspects of its progression. In ccRCC the biallelic inactivation of the VHL gene leads to stabilization of hypoxia-inducible factors (HIFs). Among the targets of HIF-1α transcriptional activity is the LOX gene, which codes for the inactive proenzyme (Pro-Lox) from which, after extracellular secretion and proteolysis, derives the active enzyme (Lox) and the propeptide (Lox-PP). By increasing stiffness of extracellular matrix by collagen crosslinking, Lox promotes tumor progression and metastasis. Lox and Lox-PP can reenter the cells where Lox promotes cell proliferation and invasion, whereas Lox-PP acts as tumor suppressor because of its Ras recision and apoptotic activity. Few data are available concerning LOX in ccRCC. Using an in vitro model of ccRCC primary cell cultures, we performed, for the first time in ccRCC, a detailed study of endogenous LOX and also investigated their transcriptomic profile. We found that endogenous LOX is overexpressed in ccRCC, is involved in a positive-regulative loop with HIF-1α, and has a major action on ccRCC progression through cellular adhesion, migration, and collagen matrix stiffness increment; however, the oncosuppressive action of Lox-PP was not found to prevail. These findings may suggest translational approaches for new therapeutic strategies in ccRCC.

Cooperative Effect of miR-141-3p and miR-145-5p in the Regulation of Targets in Clear Cell Renal Cell Carcinoma

Background

Due to the poor prognosis for advanced renal cell carcinoma (RCC), there is an urgent need for new therapeutic targets and for prognostic markers to identify high risk tumors. MicroRNAs (miRNAs) are frequently dysregulated in tumors, play a crucial role during carcinogenesis and therefore might be promising new biomarkers. In previous studies, we identified miR-141-3p and miR-145-5p to be downregulated in clear cell RCC (ccRCC). Our objective was to investigate the functional association of these miRNAs, focusing on the cooperative regulation of new specific targets and their role in ccRCC progression.

Methods

The effect of miR-141-3p and miR-145-5p on cell migration was examined by overexpression in 786-O cells. New targets of both miRNAs were identified by miRWalk, validated in 786-O and ACHN cells and additionally characterized in ccRCC tissue on mRNA and protein level.

Results

In functional analysis, a tumor suppressive effect of miR-141-3p and miR-145-5p by decreasing migration and invasion of RCC cells could be shown. Furthermore, co-overexpression of the miRNAs seemed to result in an increased inhibition of cell migration. Both miRNAs were recognized as post-transcriptional regulators of the targets EAPP, HS6ST2, LOX, TGFB2 and VRK2. Additionally, they showed a cooperative effect again as demonstrated by a significantly increased inhibition of HS6ST2 and LOX expression after simultaneous overexpression of both miRNAs. In ccRCC tissue, LOX mRNA expression was strongly increased compared to normal tissue, allowing also to distinguish between non-metastatic and already metastasized primary tumors. Finally, in subsequent tissue microarray analysis LOX protein expression showed a prognostic relevance for the overall survival of ccRCC patients.

Conclusion

These results illustrate a jointly strengthening effect of the dysregulated miR-141-3p and miR-145-5p in various tumor associated processes. Focusing on the cooperative effect of miRNAs provides new opportunities for the development of therapeutic strategies and offers novel prognostic and diagnostic capabilities.

Renal Cell Carcinoma: A Study through NMR-Based Metabolomics Combined with Transcriptomics

Renal cell carcinoma (RCC) is a heterogeneous cancer often showing late symptoms. Until now, some candidate protein markers have been proposed for its diagnosis. Metabolomics approaches have been applied, predominantly using Mass Spectrometry (MS), while Nuclear Magnetic Resonance (NMR)-based studies remain limited. There is no study about RCC integrating NMR-based metabolomics with transcriptomics. In this work, ¹H-NMR spectroscopy combined with multivariate statistics was applied on urine samples, collected from 40 patients with clear cell RCC (ccRCC) before nephrectomy and 29 healthy controls; nine out of 40 patients also provided samples one-month after nephrectomy. We observed increases of creatine, alanine, lactate and pyruvate, and decreases of hippurate, citrate, and betaine in all ccRCC patients. A network analysis connected most of these metabolites with glomerular injury, renal inflammation and renal necrosis/cell death. Interestingly, intersecting metabolites with transcriptomic data from CD133+/CD24+ tumoral renal stem cells isolated from ccRCC patients, we found that both genes and metabolites differentially regulated in ccRCC patients belonged to HIF-α signaling, methionine and choline degradation, and acetyl-CoA biosynthesis. Moreover, when comparing urinary metabolome of ccRCC patients after nephrectomy, some processes, such as the glomerular injury, renal hypertrophy, renal necrosis/cell death and renal proliferation, were no more represented.

A Molecular Model for Predicting Overall Survival in Patients with Metastatic Clear Cell Renal Carcinoma: Results from CALGB 90206 (Alliance)

BACKGROUND

Prognosis associated with metastatic renal cell carcinoma (mRCC) can vary widely.

METHODS

This study used pretreatment nephrectomy specimens from a randomized phase III trial. Expression levels of candidate genes were determined from archival tumors using the OpenArray® platform for TaqMan® RT-qPCR. The dataset was randomly divided at 2:1 ratio into training (n = 221) and testing (n = 103) sets to develop a multigene prognostic signature.

FINDINGS

Gene expressions were measured in 324 patients. In the training set, multiple models testing 424 candidate genes identified a prognostic signature containing 8 genes plus MSKCC clinical risk factors. In the testing set, the time dependent (td) AUC for a prognostic model containing the 8 genes with and without MSKCC risk factors were 0.72 and 0.69, respectively. The tdAUC for the clinical risk factors alone was 0.61. Additional primary mRCCs from patients with mRCC (n = 12) were sampled in multiple sites and standard deviations of gene expressions within a tumor were used as a measure of heterogeneity. All 8 genes in the final prognostic model met our criteria for minimal heterogeneity.

CONCLUSIONS

A molecular prognostic signature based on 8 genes was developed and is ready for external validation in this patient population and other related settings such as nonmetastatic RCC.

Three Dimensional Culture of Human Renal Cell Carcinoma Organoids

Renal cell carcinomas arise from the nephron but are heterogeneous in disease biology, clinical behavior, prognosis, and response to systemic therapy. Development of patient-specific in vitro models that efficiently and faithfully reproduce the in vivo phenotype may provide a means to develop personalized therapies for this diverse carcinoma. Studies to maintain and model tumor phenotypes in vitro were conducted with emerging three-dimensional culture techniques and natural scaffolding materials. Human renal cell carcinomas were individually characterized by histology, immunohistochemistry, and quantitative PCR to establish the characteristics of each tumor. Isolated cells were cultured on renal extracellular matrix and compared to a novel polysaccharide scaffold to assess cell-scaffold interactions, development of organoids, and maintenance of gene expression signatures over time in culture. Renal cell carcinomas cultured on renal extracellular matrix repopulated tubules or vessel lumens in renal pyramids and medullary rays, but cells were not observed in glomeruli or outer cortical regions of the scaffold. In the polysaccharide scaffold, renal cell carcinomas formed aggregates that were loosely attached to the scaffold or free-floating within the matrix. Molecular analysis of cell-scaffold constructs including immunohistochemistry and quantitative PCR demonstrated that individual tumor phenotypes could be sustained for up to 21 days in culture on both scaffolds, and in comparison to outcomes in two-dimensional monolayer cultures. The use of three-dimensional scaffolds to engineer a personalized in vitro renal cell carcinoma model provides opportunities to advance understanding of this disease.

Expression of pre-selected TMEMs with predicted ER localization as potential classifiers of ccRCC tumors

Background

VHL inactivation is the most established molecular characteristic of clear cell renal cell carcinoma (ccRCC), with only a few additional genes implicated in development of this kidney tumor. In recently published ccRCC gene expression meta-analysis study we identified a number of deregulated genes with limited information available concerning their biological role, represented by gene transcripts belonging to transmembrane proteins family (TMEMs). TMEMs are predicted to be components of cellular membranes, such as mitochondrial membranes, ER, lysosomes and Golgi apparatus. Interestingly, the function of majority of TMEMs remains unclear. Here, we analyzed expression of ten TMEM genes in the context of ccRCC progression and development, and characterized these proteins bioinformatically.

Methods

The expression of ten TMEMs (RTP3, SLC35G2, TMEM30B, TMEM45A, TMEM45B, TMEM61, TMEM72, TMEM116, TMEM207 and TMEM213) was measured by qPCR. T-test, Pearson correlation, univariate and multivariate logistic and Cox regression were used in statistical analysis. The topology of studied proteins was predicted with Metaserver, together with PSORTII, Pfam and Localizome tools.

Results

We observed significant deregulation of expression of 10 analyzed TMEMs in ccRCC tumors. Cluster analysis of expression data suggested the down-regulation of all tested TMEMs to be a descriptor of the most advanced tumors. Logistic and Cox regression potentially linked TMEM expression to clinical parameters such as: metastasis, Fuhrman grade and overall survival. Topology predictions classified majority of analyzed TMEMs as type 3 and type 1 transmembrane proteins, with predicted localization mainly in ER.

Conclusions

The massive down-regulation of expression of TMEM family members suggests their importance in the pathogenesis of ccRCC and the bioinformatic analysis of TMEM topology implies a significant involvement of ER proteins in ccRCC pathology.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-015-1530-4) contains supplementary material, which is available to authorized users.

Acute myocardial infarction activates distinct inflammation and proliferation pathways in circulating monocytes, prior to recruitment, and identified through conserved transcriptional responses in mice and humans

AIMS

Monocytes play critical roles in tissue injury and repair following acute myocardial infarction (AMI). Specifically targeting inflammatory monocytes in experimental models leads to reduced infarct size and improved healing. However, data from humans are sparse, and it remains unclear whether monocytes play an equally important role in humans. The aim of this study was to investigate whether the monocyte response following AMI is conserved between humans and mice and interrogate patterns of gene expression to identify regulated functions.

METHODS AND RESULTS

Thirty patients (AMI) and 24 control patients (stable coronary atherosclerosis) were enrolled. Female C57BL/6J mice (n = 6/group) underwent AMI by surgical coronary ligation. Myocardial injury was quantified by magnetic resonance imaging (human) and echocardiography (mice). Peripheral monocytes were isolated at presentation and at 48 h. RNA from separated monocytes was hybridized to Illumina beadchips. Acute myocardial infarction resulted in a significant peripheral monocytosis in both species that positively correlated with the extent of myocardial injury. Analysis of the monocyte transcriptome following AMI demonstrated significant conservation and identified inflammation and mitosis as central processes to this response. These findings were validated in both species.

CONCLUSIONS

Our findings show that the monocyte transcriptome is conserved between mice and humans following AMI. Patterns of gene expression associated with inflammation and proliferation appear to be switched on prior to their infiltration of injured myocardium suggesting that the specific targeting of inflammatory and proliferative processes in these immune cells in humans are possible therapeutic strategies. Importantly, they could be effective in the hours after AMI.

Tumour and patient factors in renal cell carcinoma-towards personalized therapy

Renal cell carcinoma (RCC) comprises a heterogeneous group of histologically and molecularly distinct tumour subtypes. Current targeted therapies have improved survival in patients with advanced disease but complete response occurs rarely, if at all. The genomic characterization of RCC is central to the development of novel targeted therapies. Large-scale studies employing multiple 'omics' platforms have led to the identification of key driver genes and commonly altered pathways. Specific molecular alterations and signatures that correlate with tumour phenotype and clinical outcome have been identified and can be harnessed for patient management and counselling. RCC seems to be a remarkably diverse malignancy with significant intratumour and intertumour genetic heterogeneity. The tumour microenvironment is increasingly recognized as a vital regulator of RCC tumour biology. Patient factors, including immune response and drug metabolism, vary widely, which can lead to widely divergent responses to drug therapy. Intratumour heterogeneity poses a significant challenge to the development of personalized therapies in RCC as a single biopsy might not accurately represent the clonal population ultimately responsible for aggressive biologic behaviour. On the other hand, the diversity of genomic alterations in RCC could also afford opportunities for targeting unique pathways based on analysis of an individual tumour's molecular composition.

Predictive molecular biomarkers to guide clinical decision making in kidney cancer: current progress and future challenges

Although the past decade has seen a surfeit of new targeted therapies for renal cell carcinoma (RCC), no predictive molecular biomarker is currently used in routine clinical practice to guide personalized therapy as a companion diagnostic. Many putative biomarkers have been suggested, but none have undergone rigorous validation. There have been considerable advances in the biological understanding of RCC in recent years, with the development of accompanying molecular diagnostics that with additional validation, may be helpful for routine clinical decision making. In this review, we summarize the current understanding of predictive biomarkers in RCC management and also highlight upcoming developments of interest in biomarker research for personalizing RCC diagnostics and therapeutics.

The developing story of Sprouty and cancer

Sprouty proteins are evolutionarily conserved modulators of MAPK/ERK pathway. Through interacting with an increasing number of effectors, mediators, and regulators with ultimate influence on multiple targets within or beyond ERK, Sprouty orchestrates a complex, multilayered regulatory system and mediates a crosstalk among different signaling pathways for a coordinated cellular response. As such, Sprouty has been implicated in various developmental and physiological processes. Evidence shows that ERK is aberrantly activated in malignant conditions. Accordingly, Sprouty deregulation has been reported in different cancer types and shown to impact cancer development, progression, and metastasis. In this article, we have tried to provide an overview of the current knowledge about the Sprouty physiology and its regulatory functions in health, as well as an updated review of the Sprouty status in cancer. Putative implications of Sprouty in cancer biology, their clinical relevance, and their proposed applications are also revisited. As a developing story, however, role of Sprouty in cancer remains to be further elucidated.

Sprouty 1 predicts prognosis in human epithelial ovarian cancer

Sprouty proteins are evolutionary-conserved modulators of receptor tyrosine kinase (RTK) signaling. We have previously reported inverse correlation of the Sprouty 1 (Spry1) protein expression with ovarian cancer cell proliferation, migration, invasion and survival. In the present study, the expression status of Spry1 protein and its clinical relevance in patients with epithelial ovarian cancer were explored. Matched tumor and normal tissue samples from 100 patients with epithelial ovarian cancer were immunohistochemically stained for Spry1. Expression of ERK, p-ERK, Ki67, FGF-2, VEGF and IL-6 and their correlation with Spry1 were also evaluated. In addition, correlation between Spry1 and clinicopathological characteristics and predictive significance of Spry1 for overall survival (OS) and disease-free survival (DFS) were analysed. Our data indicated that Spry1 was significantly downregulated in tumor tissues (p=0.004). Spry1 showed significant inverse correlation with p-ERK/ERK (p=0.045), Ki67 (p=0.010), disease stage (p=0.029), tumor grade (p=0.037), recurrence (p=0.001) and lymphovascular invasion (p=0.042). It was revealed that Spry1 low-expressing patients had significantly poorer OS (p=0.010) and DFS (p=0.012) than those with high expression of Spry1. Multivariate analysis showed that high Spry1 (p=0.030), low stage (p=0.048) and no residual tumor (p=0.007) were independent prognostic factors for a better OS, among which high Spry1 (p=0.035) and low stage (p=0.035) remained as independent predictors of DFS, too. We also found that the expression of Spry1 significantly correlates with the expression of Spry2 (p<0.001), but not that of Spry4. In conclusion, we report for the first time to our knowledge that Spry1 protein is downregulated in human epithelial ovarian cancer. Spry1 expression significantly impacts tumor behavior and shows predictive value as an independent prognostic factor for survival and recurrence.

Analysis and interpretation of transcriptomic data obtained from extended Warburg effect genes in patients with clear cell renal cell carcinoma

Background

Many cancers adopt a metabolism that is characterized by the well-known Warburg effect (aerobic glycolysis). Recently, numerous attempts have been made to treat cancer by targeting one or more gene products involved in this pathway without notable success. This work outlines a transcriptomic approach to identify genes that are highly perturbed in clear cell renal cell carcinoma (CCRCC).

Methods

We developed a model of the extended Warburg effect and outlined the model using Cytoscape. Following this, gene expression fold changes (FCs) for tumor and adjacent normal tissue from patients with CCRCC (GSE6344) were mapped on to the network. Gene expression values with FCs of greater than two were considered as potential targets for treatment of CCRCC.

Results

The Cytoscape network includes glycolysis, gluconeogenesis, the pentose phosphate pathway (PPP), the TCA cycle, the serine/glycine pathway, and partial glutaminolysis and fatty acid synthesis pathways. Gene expression FCs for nine of the 10 CCRCC patients in the GSE6344 data set were consistent with a shift to aerobic glycolysis. Genes involved in glycolysis and the synthesis and transport of lactate were over-expressed, as was the gene that codes for the kinase that inhibits the conversion of pyruvate to acetyl-CoA. Interestingly, genes that code for unique proteins involved in gluconeogenesis were strongly under-expressed as was also the case for the serine/glycine pathway. These latter two results suggest that the role attributed to the M2 isoform of pyruvate kinase (PKM2), frequently the principal isoform of PK present in cancer: i.e. causing a buildup of glucose metabolites that are shunted into branch pathways for synthesis of key biomolecules, may not be operative in CCRCC. The fact that there was no increase in the expression FC of any gene in the PPP is consistent with this hypothesis. Literature protein data generally support the transcriptomic findings.

Conclusions

A number of key genes have been identified that could serve as valid targets for anti-cancer pharmaceutical agents. Genes that are highly over-expressed include ENO2, HK2, PFKP, SLC2A3, PDK1, and SLC16A1. Genes that are highly under-expressed include ALDOB, PKLR, PFKFB2, G6PC, PCK1, FBP1, PC, and SUCLG1.

Frontiers in clinical and molecular diagnostics and staging of metastatic clear cell renal cell carcinoma

The last few years have brought advances in the understanding of the molecular biology of metastatic clear cell renal cell carcinoma (RCC). Both preclinical research and clinical trials brought together results from the latest advancements in RCC diagnostic and staging. Understanding of the complex molecular alterations involved in the development and progression of RCC enables development of immunohistochemical and genetic diagnostic tools and is also opening the doors for experimental targeted therapies. At the same time, improvements of medical and molecular imaging improves the sensitivity and specificity of metastatic disease diagnosis. Moreover, independent validation of molecular profiles across high-throughput platforms, methods, laboratories and cancer populations has recently been successfully performed in RCC. Generation of informative, clinical diagnostic tools is likely to contribute to development of novel personalized diagnostic and treatment protocols and ensure prolonged survival of RCC patient in the near future.

A multigene assay identifying distinct prognostic subtypes of clear cell renal cell carcinoma with differential response to tyrosine kinase inhibition

Patients with clear cell renal cell carcinoma (ccRCC) have divergent survival outcomes and therapeutic responses, which may be determined by underlying molecular diversity. We aimed to develop a practical molecular assay that can identify subtypes with differential prognosis and response to targeted therapy. Whole-genome expression analysis of formalin-fixed paraffin-embedded (FFPE) material from 55 ccRCC patients was performed and two molecular subtypes with differential clinical outcomes were identified by hierarchical clustering. An eight-gene quantitative polymerase chain reaction assay for classification into two subtypes was developed for FFPE material. The primary objective was to assess assay performance by correlating ccRCC prognostic subtypes to cancer-specific survival (CSS) and, for patients receiving targeted therapy, radiologic response. In three validation cohorts, patients could be distinguished into prognostic subtypes with differential CSS (Singapore General Hospital FFPE cohort: n = 224; p = 1.48 × 10(-8); the Cancer Genome Atlas RNA-Sequencing cohort: n = 419; p = 3.06 × 10(-7); Van Andel Research Institute microarray cohort: n=174; p=0.00743). For 48 patients receiving tyrosine kinase inhibitor (TKI) treatment, the prognostic classification was associated with radiologic response to treatment (p = 5.96 × 10(-4)) and prolonged survival on TKI treatment (p=0.019). The multigene assay can classify ccRCCs into clinical prognostic subtypes, which may be predictive of response in patients receiving TKI therapy.

Loss of Sprouty2 in human high-grade serous ovarian carcinomas promotes EGF-induced E-cadherin down-regulation and cell invasion

Sprouty (SPRY) proteins are well-characterized factors that inhibit receptor tyrosine kinase signaling. Our Human Exonic Evidence-Based Oligonucleotide (HEEBO) microarray results showed that the mRNA levels of SPRY2, but not of SPRY1 or SPRY4, are down-regulated in high-grade serous ovarian carcinoma (HGSC) tissues and epithelial ovarian cancer (EOC) cell lines. Molecular inversion probe (MIP) copy number analysis showed the deletion of the SPRY2 locus in HGSC. Overexpression of SPRY2 reduced EGF-induced cell invasion by attenuating EGF-induced E-cadherin down-regulation. Moreover, a positive correlation between SPRY2 and E-cadherin protein levels was observed in HGSC tissues. This study reveals the loss of SPRY2 in HGSC and indicates an important tumor-suppressive role for SPRY2 in mediating the stimulatory effect of EGF on human EOC progression.

Whole-Transcriptome profiling of formalin-fixed, paraffin-embedded renal cell carcinoma by RNA-seq

Background

Formalin-fixed paraffin-embedded (FFPE) tissue samples are routinely archived in the course of patient care and can be linked to clinical outcomes with long-term follow-up. However, FFPE tissues have degraded RNA which poses challenges for analyzing gene expression. Next-generation sequencing (NGS) is rapidly becoming accepted as an effective tool for measuring gene expressions for research and clinical use. However, the feasibility of NGS has not been firmly established when using FFPE tissue.

Results

We optimized strategies for whole transcriptome sequencing (RNA-seq) using FFPE tissue. Ribosomal RNA (rRNA) was successfully depleted by competitive hybridization using the Ribo-zero™ Kit (Epicentre Biotechnologies), and rRNA sequence content was less than one percent for each library. Gene expression measured by FFPE RNA-seq was compared to two different standards: RNA-seq from fresh frozen (FF) tissue and quantitative PCR (qPCR). Both FF and FFPE tumors were sequenced on an Illumina Genome Analyzer IIX with an average of 10 million reads. The distribution of FPKMs (fragments per kilobase of exon per million fragments mapped) and number of detected genes were similar between FFPE and FF. RNA-seq expressions from FF and FFPE samples from the same renal cell carcinoma (RCC) correlated highly (r = 0.919 for tumor 1 and r = 0.954 for tumor 2). On hierarchical cluster analysis, samples clustered by patient identity rather than method of preservation. TaqMan qPCR of 424 RCC-related genes correlated highly with FFPE RNA-seq expressions (r = 0.775 for FFPE tumor 1, r = 0.803 for FFPE tumor 2). Expression fold changes were considered, to assess biologic relevance of gene expressions. Expression fold changes between FFPE tumors (tumor 1/tumor 2) correlated well when comparing qPCR and RNA-seq (r = 0.890). Expression fold changes between tumors from different risk groups (our high risk RCC/The Cancer Genome Atlas, TCGA, low risk RCC) also correlated well when comparing RNA-seq from FF and FFPE tumors (r = 0.887).

Conclusions

FFPE RNA-seq provides reliable genes expression data, comparable to that obtained from fresh frozen tissue. It represents a useful tool for discovery and validation of biomarkers.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-1087) contains supplementary material, which is available to authorized users.

Classification models for clear cell renal carcinoma stage progression, based on tumor RNAseq expression trained supervised machine learning algorithms

Background

Clear-cell Renal Cell Carcinoma (ccRCC) is the most- prevalent, chemotherapy resistant and lethal adult kidney cancer. There is a need for novel diagnostic and prognostic biomarkers for ccRCC, due to its heterogeneous molecular profiles and asymptomatic early stage. This study aims to develop classification models to distinguish early stage and late stage of ccRCC based on gene expression profiles. We employed supervised learning algorithms- J48, Random Forest, SMO and Naïve Bayes; with enriched model learning by fast correlation based feature selection to develop classification models trained on sequencing based gene expression data of RNAseq experiments, obtained from The Cancer Genome Atlas.

Results

Different models developed in the study were evaluated on the basis of 10 fold cross validations and independent dataset testing. Random Forest based prediction model performed best amongst the models developed in the study, with a sensitivity of 89%, accuracy of 77% and area under Receivers Operating Curve of 0.8.

Conclusions

We anticipate that the prioritized subset of 62 genes and prediction models developed in this study will aid experimental oncologists to expedite understanding of the molecular mechanisms of stage progression and discovery of prognostic factors for ccRCC tumors.

3D texture analysis in renal cell carcinoma tissue image grading

One of the most significant processes in cancer cell and tissue image analysis is the efficient extraction of features for grading purposes. This research applied two types of three-dimensional texture analysis methods to the extraction of feature values from renal cell carcinoma tissue images, and then evaluated the validity of the methods statistically through grade classification. First, we used a confocal laser scanning microscope to obtain image slices of four grades of renal cell carcinoma, which were then reconstructed into 3D volumes. Next, we extracted quantitative values using a 3D gray level cooccurrence matrix (GLCM) and a 3D wavelet based on two types of basis functions. To evaluate their validity, we predefined 6 different statistical classifiers and applied these to the extracted feature sets. In the grade classification results, 3D Haar wavelet texture features combined with principal component analysis showed the best discrimination results. Classification using 3D wavelet texture features was significantly better than 3D GLCM, suggesting that the former has potential for use in a computer-based grading system.

GPC3 reduces cell proliferation in renal carcinoma cell lines

BACKGROUND

Glypican 3 (GPC3) is a member of the family of glypican heparan sulfate proteoglycans (HSPGs). The GPC3 gene may play a role in controlling cell migration, negatively regulating cell growth and inducing apoptosis. GPC3 is downregulated in several cancers, which can result in uncontrolled cell growth and can also contribute to the malignant phenotype of some tumors. The purpose of this study was to analyze the mechanism of action of the GPC3 gene in clear cell renal cell carcinoma.

METHODS

Five clear cell renal cell carcinoma cell lines and carcinoma samples were used to analyze GPC3 mRNA expression (qRT-PCR). Then, representative cell lines, one primary renal carcinoma (786-O) and one metastatic renal carcinoma (ACHN), were chosen to carry out functional studies. We constructed a GPC3 expression vector and transfected the renal carcinoma cell lines, 786-O and ACHN. GPC3 overexpression was analyzed using qRT-PCR and immunocytochemistry. We evaluated cell proliferation using MTT and colony formation assays. Flow cytometry was used to evaluate apoptosis and perform cell cycle analyses.

RESULTS

We observed that GPC3 is downregulated in clear cell renal cell carcinoma samples and cell lines compared with normal renal samples. GPC3 mRNA expression and protein levels in 786-O and ACHN cell lines increased after transfection with the GPC3 expression construct, and the cell proliferation rate decreased in both cell lines following overexpression of GPC3. Further, apoptosis was not induced in the renal cell carcinoma cell lines overexpressing GPC3, and there was an increase in the cell population during the G1 phase in the cell cycle.

CONCLUSION

We suggest that the GPC3 gene reduces the rate of cell proliferation through cell cycle arrest during the G1 phase in renal cell carcinoma.

Genomic Analysis as the First Step toward Personalized Treatment in Renal Cell Carcinoma

Drug resistance mechanisms in renal cell carcinoma (RCC) still remain elusive. Although most patients initially respond to targeted therapy, acquired resistance can still develop eventually. Most of the patients suffer from intrinsic (genetic) resistance as well, suggesting that there is substantial need to broaden our knowledge in the field of RCC genetics. As molecular abnormalities occur for various reasons, ranging from single nucleotide polymorphisms to large chromosomal defects, conducting whole-genome association studies using high-throughput techniques seems inevitable. In principle, data obtained via genome-wide research should be continued and performed on a large scale for the purposes of drug development and identification of biological pathways underlying cancerogenesis. Genetic alterations are mostly unique for each histological RCC subtype. According to recently published data, RCC is a highly heterogeneous tumor. In this paper, the authors discuss the following: (1) current state-of-the-art knowledge on the potential biomarkers of RCC subtypes; (2) significant obstacles encountered in the translational research on RCC; and (3) recent molecular findings that may have a crucial impact on future therapeutic approaches.

The expression of the Sprouty 1 protein inversely correlates with growth, proliferation, migration and invasion of ovarian cancer cells

Background

Our recent study on a panel of human ovarian cancer cells revealed that SKOV-3 cells barely express the Sprouty isoform 1 (Spry1) while 1A9 cells maintain it at a level similar to normal ovarian cells. Here we investigated the functional outcomes of induced alterations in the expression of Spry1 in the two cell lines in vitro.

Methods

Using the Spry1 specific plasmid and siRNA, the expression of Spry1 was induced and conversely silenced in SKOV-3 and 1A9 cells, respectively. The functional outcome was investigated by means of proliferation, MTT, scratch-wound, migration and invasion assays and selection of the stable clones. Mechanism of the effect was explored by Western blot.

Results

In the Spry1-transfected SKOV-3 cells, a significant reduction in growth and proliferation was evident. Stable clones of the Spry1-transfected SKOV-3 were almost undetectable after day 14. The number of migrated and invaded cells and the percentage of the scratch closure were significantly lower in the Spry1-transfected group. Spry1 silencing in 1A9 cells, on the other hand, led to a significant increase in cell growth and proliferation. The number of migrated and invaded cells and the percentage of the scratch closure significantly increased in Spry1-silenced 1A9 group. Mechanistically, overexpression of Bax, activation of caspases 3, 7, 8 and 9, cleavage of PARP and attenuation of Bcl-2 and Bcl-xl were observed along with reduced activation of Erk and Akt and increased amount and activity of PTEN in the Spry1-transfected SKOV-3 cells.

Conclusions

Here, we report the inverse correlation between the expression of Spry1 and growth, proliferation, invasion and migration of ovarian cancer cells.

Computational repositioning and preclinical validation of pentamidine for renal cell cancer

Although early stages of clear cell renal cell carcinoma (ccRCC) are curable, survival outcome for metastatic ccRCC remains poor. We previously established a highly accurate signature of differentially expressed genes that distinguish ccRCC from normal kidney. The purpose of this study was to apply a new individualized bioinformatics analysis (IBA) strategy to these transcriptome data in conjunction with Gene Set Enrichment Analysis of the Connectivity Map (C-MAP) database to identify and reposition FDA-approved drugs for anticancer therapy. Here, we demonstrate that one of the drugs predicted to revert the RCC gene signature toward normal kidney, pentamidine, is effective against RCC cells in culture and in a RCC xenograft model. ccRCC-specific gene expression signatures of individual patients were used to query the C-MAP software. Eight drugs with negative correlation and P-value <0.05 were analyzed for efficacy against RCC in vitro and in vivo. Our data demonstrate consistency across most patients with ccRCC for the set of high-scoring drugs. Most of the selected high-scoring drugs potently induce apoptosis in RCC cells. Several drugs also demonstrate selectivity for Von Hippel-Lindau negative RCC cells. Most importantly, at least one of these drugs, pentamidine, slows tumor growth in the 786-O human ccRCC xenograft mouse model. Our findings suggest that pentamidine might be a new therapeutic agent to be combined with current standard-of-care regimens for patients with metastatic ccRCC and support our notion that IBA combined with C-MAP analysis enables repurposing of FDA-approved drugs for potential anti-RCC therapy.