Identification of the dopamine transporter SLC6A3 as a biomarker for patients with renal cell carcinoma

Identification of novel biomarkers to distinguish clear cell and non-clear cell renal cell carcinoma using bioinformatics and machine learning

Renal cell carcinoma (RCC), accounting for 90% of all kidney cancer, is categorized into clear cell RCC (ccRCC) and non-clear cell RCC (non-ccRCC) for treatment based on the current NCCN Guidelines. Thus, the classification will be associated with therapeutic implications. This study aims to identify novel biomarkers to differentiate ccRCC from non-ccRCC using bioinformatics and machine learning. The gene expression profiles of ccRCC and non-ccRCC subtypes (including papillary RCC (pRCC) and chromophobe RCC (chRCC)), were obtained from TCGA. Differential expression genes (DEGs) were identified, and specific DEGs for ccRCC and non-ccRCC were explored using a Venn diagram. Gene Ontology and pathway enrichment analysis were performed using DAVID. The top ten expressed genes in ccRCC were then selected for machine learning analysis. Feature selection was operated to identify a minimum highly effective gene set for constructing a predictive model. The expression of best-performing gene set was validated on tissue samples from RCC patients using immunohistochemistry techniques. Subsequently, machine learning models for diagnosing RCC were developed using H-scores. There were 910, 415, and 835 genes significantly specific for DEGs in ccRCC, pRCC, and chRCC, respectively. Specific DEGs in ccRCC enriched in PD-1 signaling, immune system, and cytokine signaling in the immune system, whereas TCA cycle and respiratory, signaling by insulin receptor, and metabolism were enriched in chRCC. Feature selection based on Decision Tree Classifier revealed that the model with two genes, including NDUFA4L2 and DAT, had an accuracy of 98.89%. Supervised classification models based on H-score of NDUFA4L2, and DAT revealed that Decision Tree models showed the best performance with 82% accuracy and 0.9 AUC. NDUFA4L2 expression was associated with lymphovascular invasion, pathologic stage and pT stage in ccRCC. Using integrated bioinformatics and machine learning analysis, NDUFA4L2 and DAT were identified as novel biomarkers to differential diagnosis ccRCC from non-ccRCC.

Non-SMC condensin I complex subunit H participates in anti-programmed cell death-1 resistance of clear cell renal cell carcinomas

Non-SMC condensin I complex subunit H (NCAPH) is reported to play an important role and be a poor prognostic factor in various cancers. However, the function and regulatory mechanism of NCAPH in clear cell renal cell carcinoma (ccRCC) remain unknown. The roles of NCAPH on ccRCC growth were detected in vitro and in vivo assays. The regulatory mechanism of NCAPH was explored by immunoprecipitation assay, ubiquitination assay, ChIP assay, RIP assay, luciferase reporter assay and RNA pull-down assay. The role of NCAPH in immunoregulation also was explored by flow cytometry, T cell-mediated tumour cell killing assay and immune-competent mouse model. In this research, we displayed that NCAPH was upregulated in ccRCC and patients with elevated NCAPH expression had an undesirable prognosis. Functionally, NCAPH depletion restrained ccRCC growth in vitro and in vivo. The elevated NCAPH was attributed to FOXP3-mediated transcription, FUS-mediated transcription splicing and METTL3-mediated m6A modification. Moreover, YTHDC1 promoted NCAPH mRNA nuclear export, and IGF2BP3 enhanced NCAPH mRNA stability in an m6A-dependent manner. NCAPH increased PD-L1 expression by inhibiting the degradation of β-catenin in ccRCC cells, which further facilitated aerobic glycolysis and immune tolerance of ccRCC. Collectively, our findings display the vital function of NCAPH in ccRCC and uncover that NCAPH may be regarded as a potential therapeutic target to reverse the immune tolerance of ccRCC.

Up-regulation of SLC27A2 suppresses the proliferation and invasion of renal cancer by down-regulating CDK3-mediated EMT

Clear cell renal cell carcinoma (ccRCC) is one of the most common malignant tumors of the urinary system. Distant metastasis is the leading cause of poor prognosis in ccRCC. However, ccRCC is found poorly responsitive to radiotherapy and chemotherapy. Effective therapeutic strategies for its metastasis remain scarce. We analyzed clinical samples and public database, for differential expression of SLC27A2 and further explored its relationship with clinical prognosis. Biochemistry and functional experiments were carried out to study the potential mechanisms of SLC27A2, CDK3, and EMT. SLC27A2 was significantly downregulated in clinical specimens and renal cancer cell lines and predicted poor prognosis. We found that specific upregulation of SLC27A2 could significantly inhibited the proliferation, migration, and invasion of renal cancer cell lines. SLC27A2 could also influence the Epithelial-mesenchymal transition (EMT) signaling pathway, linked to the progression and metastasis of renal cancer. Using whole transcriptome sequencing of SLC27A2, CDK3 was identified as a regulatory SLC27A2 target. In terms of mechanism, SLC27A2 may further inhibit the epithelial-to-mesenchymal transition by negatively regulating CDK3. Our work suggests that functional inhibition of SLC27A2-CDK3-EMT axis may be an attractive therapeutic target for metastasis of ccRCC.

Bioinformatics Study Identified EGF as a Crucial Gene in Papillary Renal Cell Cancer

Background

Due to a lack of knowledge of the disease process, papillary renal cell carcinoma (PRCC) has a dismal outlook. This research was aimed at uncovering the possible biomarkers and the underlying principles in PRCC using a bioinformatics method.

Methods

We searched the Gene Expression Omnibus (GEO) datasets to obtain the GSE11151 and GSE15641 gene expression profiles of PRCC. We used the R package limma to identify the differentially expressed genes (DEGs). The online tool DAVID and ClusterProfiler package in R software were used to analyze Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway dominance, respectively. The STRING database was utilized to construct the PPI network of DEGs. Using the Cytoscape technology, a protein-protein interaction (PPI) network that associated with DEGs was created, and the hub genes were identified using the Cytoscape plug-in CytoHubba. The hub genes were subjected to a Kaplan-Meier analysis to identify their correlations with survival rates.

Results

From the selected datasets, a total of 240 common DEGs were identified in the PRCC, including 50 upregulated genes and 190 downregulated regulated genes. Renal growth, external exosome, binding of heparin, and metabolic processes were all substantially associated with DEGs. The CytoHubba plug-in-based analysis identified the 10 hub genes (ALB, KNG1, C3, CXCL12, EGF, TIMP1, VCAN, PLG, LAMC1, and CASR) from the original PPI network. The higher expression group of EGF was associated with poor outcome in patients with PRCC.

Conclusions

We revealed important genes and proposed biological pathways that may be implicated in the formation of PRCC. EGF might be a predictive biomarker for PRCC and therefore should be investigated as a novel treatment strategy.

Screening and Validation of a Carvacrol-Targeting Viability-Regulating Protein, SLC6A3, in Liver Hepatocellular Carcinoma

Background

Liver hepatocellular carcinoma (LIHC) is the second leading cause of tumor-related death in the world. Carvacrol was also found to inhibit multiple cancer types. Here, we proposed that Carvacrol inhibited LIHC.

Methods

We used MTT assay to determine the inhibition of Carvacrol on LIHC cells. BATMAN-TCM was used to predict targets of Carvacrol. These targets were further screened by their survival association and expression in cancer using TCGA data. The bioinformatic screened candidates were further validated in in vitro experiments and clinical samples. Finally, docking models of the interaction of Carvacrol and target protein were conducted.

Results

Carvacrol inhibited the viability of LIHC cell lines. 40 target genes of Carvacrol were predicted, 8 of them associated with survival. 4 genes were found differentially expressed in LIHC vs. normal liver. Among these genes, the expression of SLC6A3 and SCN4A was found affected by Carvacrol in LIHC cells, but only SLC6A3 correlated with the viability inhibition of Carvacrol on LIHC cell lines. A docking model of the interaction of Carvacrol and SLC6A3 was established with a good binding affinity. SLC6A3 knockdown and expression revealed that SLC6A3 promoted the viability of LIHC cells.

Conclusion

Carvacrol inhibited the viability of LIHC cells by downregulating SLC6A3.

SPINKs in Tumors: Potential Therapeutic Targets

The serine protease inhibitor Kazal type (SPINK) family includes SPINK1-14 and is the largest branch in the serine protease inhibitor family. SPINKs play an important role in pancreatic physiology and disease, sperm maturation and capacitation, Nager syndrome, inflammation and the skin barrier. Evidence shows that the unregulated expression of SPINK1, 2, 4, 5, 6, 7, and 13 is closely related to human tumors. Different SPINKs exhibit various regulatory modes in different tumors and can be used as tumor prognostic markers. This article reviews the role of SPINK1, 2, 4, 5, 6, 7, and 13 in different human cancer processes and helps to identify new cancer treatment targets.

Association of a Novel Prognosis Model with Tumor Mutation Burden and Tumor-Infiltrating Immune Cells in Thyroid Carcinoma

Although immunotherapy has recently demonstrated a substantial promise in treating advanced thyroid carcinoma (THCA), it is not appropriate for all THCA patients. As a result, this study aims to identify biomarkers for predicting immunotherapy efficacy and prognosis in THCA patients based on a constructed prognostic model. The transcriptomic and corresponding clinical data of THCA patients were obtained from the Cancer Genome Atlas (TCGA) database. We identified differentially expressed genes (DEGs) between THCA and normal samples and performed an intersection analysis of DEGs with immune-related genes (IRGs) downloaded from the ImmPort database. Functional enrichment analysis was performed on the chosen immune-related DEGs. Subsequently, Cox and LASSO regression analyses were conducted to obtain three hub immune-related DEGs, including PPBP, SEMA6B, and GCGR. Following that, a prognostic risk model was established and validated based on PPBP, SEMA6B, and GCGR genes to predict immunotherapy efficacy and THCA prognosis. Finally, we investigated the association between the constructed risk model and tumor mutational burden (TMB), abundance of tumor-infiltrating immune cells (TICs) as well as immunotherapeutic targets (PDL-1, PD-1, and CTLA4) in THCA. THCA patients in the high-risk score (RS) group showed higher TMB levels and worse prognosis than the low RS group. Patients in the high-RS group had higher proportions of monocytes, M2 macrophages, and activated dendritic cells, whereas those in the low-RS group exhibited higher numbers of M1 macrophages and dendritic resting cells. Our data implied that the constructed THCA prognostic model was sound and we concluded that the THCA patients having high TMB and low PD-L1 expression levels might respond poorly to immunotherapy. Taken together, we constructed a novel prognostic model for THCA patients to predict their prognosis and immunotherapy efficacy, providing a viable option for the future management of THCA patients in the clinic.

Identification of adhesion-associated extracellular matrix component thrombospondin 3 as a prognostic signature for clear cell renal cell carcinoma

PURPOSE

Clear cell renal cell carcinoma (ccRCC) is a highly aggressive disease, and approximately 30% of patients are diagnosed at the metastatic stage. Even with targeted therapies, the prognosis of advanced ccRCC is poor. The aim of this study was to investigate clinical prognosis signatures by analyzing the ccRCC datasets in The Cancer Genome Atlas (TCGA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and the function of thrombospondin 3 (THBS3) in ccRCC.

MATERIALS AND METHODS

We analyzed the ccRCC datasets in TCGA and CPTAC to search for extracellular matrix (ECM)-related and adhesion-associated genes, and conducted overall survival, Cox, and receiver operating characteristic analyses. We also performed CCK8, colony formation, and transwell assays to compared the proliferation and migration ability of THBS3 knockout cells with those of cells without THBS3 knockout.

RESULTS

Comprehensive bioinformatics analysis revealed that THBS3 is a novel candidate oncogene that is overexpressed in ccRCC tumor tissue and that its elevated expression indicates poor prognosis. Our study also showed that knockdown of THBS3 inhibits proliferation, colony formation, and migration of ccRCC cells.

CONCLUSIONS

In summary, our data have revealed that THBS3 is upregulated in cancer tissues and could be used as a novel prognostic marker for ccRCC. Our findings thus offer theoretical support with bioinformatics analyses to the study of ECM and adhesion proteins in ccRCC, which may provide a new perspective for the clinical management of ccRCC.

Identification of C3 and FN1 as potential biomarkers associated with progression and prognosis for clear cell renal cell carcinoma

Background

Clear cell renal cell carcinoma (ccRCC) is one of the most lethal urological malignancies, but the pathogenesis and prognosis of ccRCC remain obscure, which need to be better understand.

Methods

Differentially expressed genes were identified and function enrichment analyses were performed using three publicly available ccRCC gene expression profiles downloaded from the Gene Expression Omnibus database. The protein-protein interaction and the competing endogenous RNA (ceRNA) networks were visualized by Cytoscape. Multivariate Cox analysis was used to predict an optimal risk mode, and the survival analysis was performed with the Kaplan-Meier curve and log-rank test. Protein expression data were downloaded from Clinical Proteomic Tumor Analysis Consortium database and Human Protein Atlas database, and the clinical information as well as the corresponding lncRNA and miRNA expression data were obtained via The Cancer Genome Atlas database. The co-expressed genes and potential function of candidate genes were explored using data exacted from the Cancer Cell Line Encyclopedia database.

Results

Of the 1044 differentially expressed genes shared across the three datasets, 461 were upregulated, and 583 were downregulated, which significantly enriched in multiple immunoregulatory-related biological process and tumor-associated pathways, such as HIF-1, PI3K-AKT, P53 and Rap1 signaling pathways. In the most significant module, 36 hub genes were identified and were predominantly enriched in inflammatory response and immune and biotic stimulus pathways. Survival analysis and validation of the hub genes at the mRNA and protein expression levels suggested that these genes, particularly complement component 3 (C3) and fibronectin 1 (FN1), were primarily responsible for ccRCC tumorigenesis and progression. Increased expression of C3 or FN1 was also associated with advanced clinical stage, high pathological grade, and poor survival in patients with ccRCC. Univariate and multivariate Cox regression analysis qualified the expression levels of the two genes as candidate biomarkers for predicting poor survival. FN1 was potentially regulated by miR-429, miR-216b and miR-217, and constructed a bridge to C3 and C3AR1 in the ceRNA network, indicating a critical position of FN1.

Conclusions

The biomarkers C3 and FN1 could provide theoretical support for the development of a novel prognostic tool to advance ccRCC diagnosis and targeted therapy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08818-0.

Silencing circular RNA circ_0054537 and upregulating microRNA-640 suppress malignant progression of renal cell carcinoma via regulating neuronal pentraxin-2 (NPTX2)

Hsa_circ_0054537 (circ_0054537) is a novel tumor-related circular RNA in renal cell carcinoma (RCC), and we intended to ascertain its dysregulation and functions in RCC malignant progression, as well as the underlying mechanism via serving as competing endogenous RNA (ceRNA). In this research, using real-time quantitative PCR, we found circ_0054537 was upregulated in RCC tissues and cells, and distributed throughout the cytoplasm. Then, functional effects of circ_0054537 in RCC were detected using cell counting kit-8, transwell, flow cytometry and glycolysis stress test and adenosine Triphosphate (ATP) assays. The results uncovered that circ_0054537 knockdown inhibited cell proliferation, migration, invasion, autophagy and glycolysis, but promoted apoptosis in RCC cells. Notably, circ_0054537 was identified as a ceRNA for microRNA (miR)-640, and miR-640 could target neuronal pentraxin-2 (NPTX2), as evidenced by dual-luciferase reporter assay and RNA immunoprecipitation assay. Besides, miR-640 downregulation or NPTX2 overexpression partly overturned the tumor suppressor function of circ_0054537 silence and miR-640 overexpression in RCC cells. Additionally, RCC cell growth in vivo was retarded by circ_0054537 silence. In conclusion, circ_0054537/miR-640/NPTX2 ceRNA pathway regulated RCC malignant progression in vitro and curbed RCC tumor growth in vivo, which could be a potential diagnosis and therapeutic target of RCC.

Common pathways and functional profiles reveal underlying patterns in Breast, Kidney and Lung cancers

Background

Cancer is a major health problem which presents a high heterogeneity. In this work we explore omics data from Breast, Kidney and Lung cancers at different levels as signalling pathways, functions and miRNAs, as part of the CAMDA 2019 Hi-Res Cancer Data Integration Challenge. Our goal is to find common functional patterns which give rise to the generic microenvironment in these cancers and contribute to a better understanding of cancer pathogenesis and a possible clinical translation down further studies.

Results

After a tumor versus normal tissue comparison of the signaling pathways and cell functions, we found 828 subpathways, 912 Gene Ontology terms and 91 Uniprot keywords commonly significant to the three studied tumors. Such features interestingly show the power to classify tumor samples into subgroups with different survival times, and predict tumor state and tissue of origin through machine learning techniques. We also found cancer-specific alternative activation subpathways, such as the ones activating STAT5A in ErbB signaling pathway. miRNAs evaluation show the role of miRNAs, such as mir-184 and mir-206, as regulators of many cancer pathways and their value in prognoses.

Conclusions

The study of the common functional and pathway activities of different cancers is an interesting approach to understand molecular mechanisms of the tumoral process regardless of their tissue of origin. The existence of platforms as the CAMDA challenges provide the opportunity to share knowledge and improve future scientific research and clinical practice.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13062-021-00293-8.

Systematic expression analysis of m 6 A RNA methyltransferases in clear cell renal cell carcinoma

Objectives

To investigate the regulation of the N‐6‐methyladenosine (m⁶A) methyltransferases METTL3, METTL14, WTAP, KIAA1429, and METTL4, referred to as “m⁶A writers,” in clear cell renal cell carcinoma (ccRCC), and other RCC subtypes in respect of the potential prognostic value.

Patients and methods

Tissue samples were collected within the framework of the Biobank at the Center for Integrated Oncology Bonn. The expression of the methyltransferases was systematically determined in clear cell renal carcinoma (ccRCC) on the RNA (real‐time PCR) and protein level (immunohistochemistry). Additionally, protein expression of the m⁶A writers was further investigated in papillary RCC, chromophobe RCC, sarcomatoid RCC, oncocytoma, and normal renal tissue (immunohistochemistry).

Results

The expression of all m⁶A‐methyltransferases was significantly downregulated in ccRCC compared to benign renal tissue. Low m⁶A‐methyltransferase levels were correlated with higher histological grade, advanced pT‐stage, pN‐stage, and metastatic disease. Reduced m⁶A‐methyltransferase expression was associated with shorter overall survival.

Conclusion

In conclusion, m⁶A‐methyltransferases are dysregulated in ccRCC and might act as tumor suppressor genes, which could be of particular importance for future diagnostic and therapeutic options.

SLC6A3 as a potential circulating biomarker for gastric cancer detection and progression monitoring

BACKGROUND

Gastric cancer is a malignant tumor originating from the gastric mucosal epithelium, with no obvious symptoms at the early stage. The dopamine transporter gene (SLC6A3) is involved in the metabolism of dopamine and catecholamine and is a potential gene for Parkinson's disease and alcoholism. But the role of SLC6A3 in gastric cancer is still unknown. The aim of our study is to investigate the potential diagnostic value of SLC6A3 on gastric cancer.

METHODS

Quantitative real-time PCR (RT-qPCR) was used to detect the expression of SLC6A3 in clinical samples and cells. A total of 246 samples were enrolled in this study (26 pairs of tissue samples; Serum of 113 patients with gastric cancer, 51 polyps patients and 56 healthy controls). The diagnostic value of SLC6A3 was evaluated by the ROC curve and analyzed the changes of SLC6A3 expression before and after surgery. The prognostic value, interacting proteins and related pathways of SLC6A3 were evaluated by TCGA analysis in UALCAN database (http://ualcan.path.uab.edu/).

RESULTS

The expression level of SLC6A3 in gastric cancer was significantly higher than that in controls. Further, the proportion under the ROC curve (AUC) for SLC6A3, CEA and CA19-9 was 0.818 (95 % confidence interval [CI]: 0.754 to 0.883, P < 0.001), and the expression level of SLC6A3 in the serum of patients with gastric cancer decreased significantly after surgery (P < 0.001). Bioinformatic enrichment analysis of SLC6A3 displayed the relevant metabolic pathways involved in its interacting proteins.

CONCLUSION

SLC6A3 is involved in the occurrence and development of gastric cancer and can be used as a potential diagnostic indicator for gastric cancer.

N6 -Methyladenosine (m6 A) readers are dysregulated in renal cell carcinoma

N⁶ -Methyladenosine (m⁶ A) is the most common modification of messenger RNA (mRNA) in mammals. It critically influences RNA metabolism and plays an essential role in virtually all types of bioprocesses including gene expression, tissue development, self-renewal and differentiation of stem cells, stress response and circadian clock control. It plays a crucial role in carcinogenesis and could be used as a prognostic and a diagnostic tool and as a target for new anticancer therapies. m⁶ A modification is dynamically and reversibly regulated by three types of proteins. Methyltransferases, so-called "writers" add a methyl group to the adenosine, which can be removed by demethylases, also called "erasers." m⁶ A-specific RNA-binding proteins, from here on referred to as "readers," preferentially bind to the m⁶ A site and mediate biological functions, such as translation, splicing or decay of RNA. In this study, we examined the expression of the six m⁶ A readers HNRNPA2B1, HNRNPC, YTHDC1 and YTHDF1-3 in clear cell renal carcinoma (ccRCC). We show that on mRNA level the expression of all six m⁶ A readers is significantly downregulated compared to normal renal tissue and on protein level five out of six readers are dysregulated. Lower levels of some m⁶ A readers are correlated with advanced stage and grade as well as associated with a shorter overall, progression-free and cancer-specific survival. In summary, we could show that m⁶ A readers are dysregulated in ccRCC and might therefore act as a tumor marker, could give further information on the individual prognosis and be a target of innovative cancer therapy.

Overexpression of NPTX2 Promotes Malignant Phenotype of Epithelial Ovarian Carcinoma via IL6-JAK2/STAT3 Signaling Pathway Under Hypoxia

Background

Epithelial ovarian cancer (EOC) is the main subtype of ovarian cancer and shows an aggressive phenotype and poor prognosis. Neuronal pentraxin II (NPTX2) is a member of the neuronal pentraxin family and plays a contradictory role in different tumors. However, there has been no report about the possible role and effect of NPTX2 in EOC.

Methods

Bioinformatics analysis, qPCR, western blotting and immunohistochemistry were used to detect the expression of NPTX2 in EOC. Lentivirus-based transfection for NPTX2 overexpression or knockdown was performed on the EOC cell lines A2780, HEY, SKOV3 and OVCAR-3. The effect of NPTX2 on the malignant phenotype of EOC was examined through methods of MTS assay, Edu assay, transwell assay, western blotting analysis, qPCR analysis, luciferase reporter assay and xenograft experiment.

Results

EOC tissues showed higher NPTX2 expression than the normal tissues with poor prognosis. NPTX2 overexpression can promote the proliferation, invasion, migration and tumorigenesis of EOC via IL6-JAK2/STAT3 signaling pathway. Moreover, hypoxia-inducible factor-1(HIF-1) can promote the transcription and expression of NPTX2 under the hypoxic environment. NPTX2 knockdown abolished the hypoxia-induced malignant phenotypes in ECO.

Conclusions

The above results suggest that NPTX2 may play a novel role in ovarian cancer's malignant phenotype and act as a promising treatment target for EOC molecular therapy.

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.

Glycosylation and raft endocytosis in cancer

Changes in glycosylation on proteins or lipids are one of the hallmarks of tumorigenesis. In many cases, it is still not understood how glycan information is translated into biological function. In this review, we discuss at the example of specific cancer-related glycoproteins how their endocytic uptake into eukaryotic cells is tuned by carbohydrate modifications. For this, we not only focus on overall uptake rates, but also illustrate how different uptake processes-dependent or not on the conventional clathrin machinery-are used under given glycosylation conditions. Furthermore, we discuss the role of certain sugar-binding proteins, termed galectins, to tune glycoprotein uptake by inducing their crosslinking into lattices, or by co-clustering them with glycolipids into raft-type membrane nanodomains from which the so-called clathrin-independent carriers (CLICs) are formed for glycoprotein internalization into cells. The latter process has been termed glycolipid-lectin (GL-Lect) hypothesis, which operates in a complementary manner to the clathrin pathway and galectin lattices.

Lipid chaperones and associated diseases: a group of chaperonopathies defining a new nosological entity with implications for medical research and practice

Fatty acid-binding proteins (FABPs) are lipid chaperones assisting in the trafficking of long-chain fatty acids with functions in various cell compartments, including oxidation, signaling, gene-transcription regulation, and storage. The various known FABP isoforms display distinctive tissue distribution, but some are active in more than one tissue. Quantitative and/or qualitative changes of FABPs are associated with pathological conditions. Increased circulating levels of FABPs are biomarkers of disorders such as obesity, insulin resistance, cardiovascular disease, and cancer. Deregulated expression and malfunction of FABPs can result from genetic alterations or posttranslational modifications and can be pathogenic. We have assembled the disorders with abnormal FABPs as chaperonopathies in a distinct nosological entity. This entity is similar but separate from that encompassing the chaperonopathies pertaining to protein chaperones. In this review, we discuss the role of FABPs in the pathogenesis of metabolic syndrome, cancer, and neurological diseases. We highlight the opportunities for improving diagnosis and treatment that open by encompassing all these pathological conditions within of a coherent nosological group, focusing on abnormal lipid chaperones as biomarkers of disease and etiological-pathogenic factors.

MIXnorm: normalizing RNA-seq data from formalin-fixed paraffin-embedded samples

MOTIVATION

Recent studies have shown that RNA-sequencing (RNA-seq) can be used to measure mRNA of sufficient quality extracted from formalin-fixed paraffin-embedded (FFPE) tissues to provide whole-genome transcriptome analysis. However, little attention has been given to the normalization of FFPE RNA-seq data, a key step that adjusts for unwanted biological and technical effects that can bias the signal of interest. Existing methods, developed based on fresh-frozen or similar-type samples, may cause suboptimal performance.

RESULTS

We proposed a new normalization method, labeled MIXnorm, for FFPE RNA-seq data. MIXnorm relies on a two-component mixture model, which models non-expressed genes by zero-inflated Poisson distributions and models expressed genes by truncated normal distributions. To obtain maximum likelihood estimates, we developed a nested EM algorithm, in which closed-form updates are available in each iteration. By eliminating the need for numerical optimization in the M-step, the algorithm is easy to implement and computationally efficient. We evaluated MIXnorm through simulations and cancer studies. MIXnorm makes a significant improvement over commonly used methods for RNA-seq expression data.

AVAILABILITY AND IMPLEMENTATION

R code available at https://github.com/S-YIN/MIXnorm.

CONTACT

swang@smu.edu.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Profiles of overall survival-related gene expression-based risk signature and their prognostic implications in clear cell renal cell carcinoma

The present work aimed to evaluate the prognostic value of overall survival (OS)-related genes in clear cell renal cell carcinoma (ccRCC) and to develop a nomogram for clinical use. Transcriptome data from The Cancer Genome Atlas (TCGA) were collected to screen differentially expressed genes (DEGs) between ccRCC patients with OS > 5 years (149 patients) and those with <1 year (52 patients). In TCGA training set (265 patients), seven DEGs (cytochrome P450 family 3 subfamily A member 7 (CYP3A7), contactin-associated protein family member 5 (CNTNAP5), adenylate cyclase 2 (ADCY2), TOX high mobility group box family member 3 (TOX3), plasminogen (PLG), enamelin (ENAM), and collagen type VII α 1 chain (COL7A1)) were further selected to build a prognostic risk signature by the least absolute shrinkage and selection operator (LASSO) Cox regression model. Survival analysis confirmed that the OS in the high-risk group was dramatically shorter than their low-risk counterparts. Next, univariate and multivariate Cox regression revealed the seven genes-based risk score, age, and Tumor, lymph Node, and Metastasis staging system (TNM) stage were independent prognostic factors to OS, based on which a novel nomogram was constructed and validated in both TCGA validation set (265 patients) and the International Cancer Genome Consortium cohort (ICGC, 84 patients). A decent predictive performance of the nomogram was observed, the C-indices and corresponding 95% confidence intervals of TCGA training set, validation set, and ICGC cohort were 0.78 (0.74–0.82), 0.75 (0.70–0.80), and 0.70 (0.60–0.80), respectively. Moreover, the calibration plots of 3- and 5 years survival probability indicated favorable curve-fitting performance in the above three groups. In conclusion, the proposed seven genes signature-based nomogram is a promising and robust tool for predicting the OS of ccRCC, which may help tailor individualized therapeutic strategies.

The Basic Characteristics of the Pentraxin Family and Their Functions in Tumor Progression

The pentraxin is a superfamily of proteins with the same domain known as the pentraxin domain at C-terminal. This family has two subgroups, namely; short pentraxins (C-reactive protein and serum amyloid P component) and long pentraxins (neuronal pentraxin 1, neuronal pentraxin 2, neuronal pentraxin receptor, pentraxin 3 and pentraxin 4). Each group shares a similar structure with the pentameric complexes arranged in a discoid shape. Previous studies revealed the functions of different pentraxin family members. Most of them are associated with human innate immunity. Inflammation has commonly been associated with tumor progression, implying that the pentraxin family might also participate in tumor progression. Therefore, we reviewed the basic characteristics and functions of the pentraxin family and their role in tumor progression.

The Landscape of Iron Metabolism-Related and Methylated Genes in the Prognosis Prediction of Clear Cell Renal Cell Carcinoma

Background: Clear cell renal cell carcinoma (ccRCC) is characteristics of resistance to chemotherapy and radiotherapy. The prognosis of ccRCC was dismay with immense diversity. Iron metabolism disturbance is a common phenomenon in ccRCC. The purpose of our study is to identify and validate the candidate prognostic gene signature of iron metabolism and methylation closely related to the poor prognosis of ccRCC through comprehensive bioinformatics analysis in The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases.

Methods: The prognostic iron metabolism-related genes were screened according to the overlapping differentially expressed genes (DEGs) from the TCGA database. We built a prognostic model using risk score method to predict OS, each ccRCC patient's risk score was calculated, and the resulting score can divide these patients into two categories according to the cut-point risk score. The prognostic significance of the hub genes was further evaluated with the Kaplan-Meier (KM) survival and Receiver Operating Characteristic (ROC) curve analysis. Univariate and multivariate Cox regression analysis was implemented to evaluate the impact of each variable on OS. Furthermore, the prediction power of the 25 gene signatures has been validated using an independent ccRCC cohort from the GEO database. The Gene Set Enrichment Analysis (GSEA) identified the characteristics of hub related oncogenes. Finally, we utilize Weighted Gene Co-expression Network Analysis (WGCNA) to investigate the co-expression network based on these DEGs.

Results: In this study, we identified and validated 25 iron metabolism-related and methylated genes as the prognostic signatures, which differentiated ccRCC patients into high and low risk subgroups. The KM analysis showed that the survival rate of the high-risk patients was significantly lower than that of the low-risk patients. The risk score calculated with 25 gene signatures could largely predict OS and DFS for 1, 3, and 5 years in patients with ccRCC.

Conclusions: Taken together, we identified the key iron metabolism-related and methylated genes for ccRCC through a comprehensive bioinformatics analysis. This study provides a reliable and robust gene signature for the prognostic predictor of ccRCC patients and maybe provides a promising treatment strategy for this lethal disease.

Antiparkinson Drug Benztropine Suppresses Tumor Growth, Circulating Tumor Cells, and Metastasis by Acting on SLC6A3/DAT and Reducing STAT3

Tumor growth, progression, and therapy resistance are crucial factors in the prognosis of cancer. The properties of three-dimensional (3D) tumor-like organoids (tumoroids) more closely resemble in vivo tumors compared to two-dimensionally cultured cells and are therefore effectively used for assays and drug screening. We here established a repurposed drug for novel anticancer research and therapeutics using a 3D tumoroid-based screening system. We screened six pharmacologically active compounds by using an original tumoroid-based multiplex phenotypic screening system with a matrix metalloproteinase 9 (MMP9) promoter-driven fluorescence reporter for the evaluation of both tumoroid formation and progression. The antiparkinson drug benztropine was the most effective compound uncovered by the screen. Benztropine significantly inhibited in vitro tumoroid formation, cancer cell survival, and MMP9 promoter activity. Benztropine also reduced the activity of oncogenic signaling transducers and trans-activators for MMP9, including STAT3, NF-κB, and β-catenin, and the properties of cancer stem cells/cancer-initiating cells. Benztropine and GBR-12935 directly targeted the dopamine transporter DAT/SLC6A3, whose genetic alterations such as amplification were correlated with poor prognosis for cancer patients. Benztropine also inhibited the tumor growth, circulating tumor cell (CTC) number, and rate of metastasis in a tumor allograft model in mice. In conclusion, we propose the repurposing of benztropine for anticancer research and therapeutics that can suppress tumor progression, CTC, and metastasis of aggressive cancers by reducing key pro-tumorigenic factors.

Deletion of Von Hippel-Lindau Interferes with Hyper Osmolality Induced Gene Expression and Induces an Unfavorable Gene Expression Pattern

Loss of von Hippel–Lindau (VHL) protein function can be found in more than 90% of patients with clear cell renal carcinoma (ccRCC). Mice lacking Vhl function in the kidneys have urine concentration defects due to postulated reduction of the hyperosmotic gradient. Hyperosmolality is a kidney-specific microenvironment and induces a unique gene expression pattern. This gene expression pattern is inversely regulated in patients with ccRCC with consequences for cancer-specific survival. Within this study, we tested the hypothesis if Vhl function influences the hyperosmolality induced changes in gene expression. We made use of the Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/Cas9 technology to inhibit functional Vhl expression in murine collecting duct cell line. Loss of Vhl function induced morphological changes within the cells similar to epithelial to mesenchymal transition like phenotype. Vhl-deficient cells migrated faster and proliferated slower compared to control cells. Gene expression profiling showed significant changes in gene expression patterns in Vhl-deficient cells compared to control cells. Several genes with unfavorable outcomes showed induced and genes with favorable outcomes for patients with renal cancer reduced gene expression level. Under hyperosmotic condition, the expression of several hyperosmolality induced genes, with favorable prognostic value, was downregulated in cells that do not express functional Vhl. Taken together, this study shows that Vhl interferes with hyperosmotic signaling pathway and hyperosmolality affected pathways might represent new promising targets.

Evaluation of kininogen 1, osteopontin and α-1-antitrypsin in plasma, bronchoalveolar lavage fluid and urine for lung squamous cell carcinoma diagnosis

Lung squamous cell carcinoma (LUSC) progression is accompanied by changes in protein levels that may be reflected in body fluids, such as plasma, bronchoalveolar lavage fluid (BALF) and urine. Certain proteins present in these biofluids can facilitate lung cancer diagnosis. Kininogen 1 (KNG1), osteopontin (OPN) and α-1-antitrypsin (AAT) are associated with tumorigenesis. The present study aimed to explore the combined monitoring of plasma, urine and BALF to gain insight into LUSC by monitoring the levels of the above three protein using ELISA. LUSC (n=31) and healthy controls with benign lung diseases (n=20) were enrolled in the study. KNG1 levels in plasma, BALF and urine were significantly higher in patients with LUSC patients than in controls (P<0.0001, P<0.0001 and P=0.0010, respectively). OPN was upregulated in the plasma and BALF of patients with LUSC relative to controls (P=0.0107 and P=0.0004, respectively), whereas its levels in the urine of healthy controls were significantly higher (P=0.0088). Patients with LUSC had higher AAT levels in plasma, BALF and urine compared with those of the controls (P=0.0022, P=0.0014 and P=0.0005, respectively). Receiver operating characteristic analysis showed an area under the curve (AUC) of 0.81 for KNG1 in plasma, 0.91 in BALF and 0.81 in urine. The AUC for OPN was 0.71 in plasma, 0.83 in BALF and 0.75 in urine. The AUC for AAT was 0.74 in plasma, 0.74 in BALF and 0.86 in urine. Immunohistochemical staining in 20 paired LUSC and adjacent normal tissues showed that KNG1, OPN and AAT levels were higher in LUSC tissues. Therefore, our results showed that KNG1, OPN and AAT in biofluids might be useful for the diagnosis of LUSC. These markers in urine and BALF may be better than in plasma for detecting LUSC.

Limited utility of qPCR-based detection of tumor-specific circulating mRNAs in whole blood from clear cell renal cell carcinoma patients

Background

RNA sequencing data is providing abundant information about the levels of dysregulation of genes in various tumors. These data, as well as data based on older microarray technologies have enabled the identification of many genes which are upregulated in clear cell renal cell carcinoma (ccRCC) compared to matched normal tissue. Here we use RNA sequencing data in order to construct a panel of highly overexpressed genes in ccRCC so as to evaluate their RNA levels in whole blood and determine any diagnostic potential of these levels for renal cell carcinoma patients.

Methods

A bioinformatics analysis with Python was performed using TCGA, GEO and other databases to identify genes which are upregulated in ccRCC while being absent in the blood of healthy individuals. Quantitative Real Time PCR (RT-qPCR) was subsequently used to measure the levels of candidate genes in whole blood (PAX gene) of 16 ccRCC patients versus 11 healthy individuals. PCR results were processed in qBase and GraphPadPrism and statistics was done with Mann-Whitney U test.

Results

While most analyzed genes were either undetectable or did not show any dysregulated expression, two genes, CDK18 and CCND1, were paradoxically downregulated in the blood of ccRCC patients compared to healthy controls. Furthermore, LOX showed a tendency towards upregulation in metastatic ccRCC samples compared to non-metastatic.

Conclusions

This analysis illustrates the difficulty of detecting tumor regulated genes in blood and the possible influence of interference from expression in blood cells even for genes conditionally absent in normal blood. Testing in plasma samples indicated that tumor specific mRNAs were not detectable. While CDK18, CCND1 and LOX mRNAs might carry biomarker potential, this would require validation in an independent, larger patient cohort.

Construction of a novel gene-based model for prognosis prediction of clear cell renal cell carcinoma

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) comprises the majority of kidney cancer death worldwide, whose incidence and mortality are not promising. Identifying ideal biomarkers to construct a more accurate prognostic model than conventional clinical parameters is crucial.

METHODS

Raw count of RNA-sequencing data and clinicopathological data were acquired from The Cancer Genome Atlas (TCGA). Tumor samples were divided into two sets. Differentially expressed genes (DEGs) were screened in the whole set and prognosis-related genes were identified from the training set. Their common genes were used in LASSO and best subset regression which were performed to identify the best prognostic 5 genes. The gene-based risk score was developed based on the Cox coefficient of the individual gene. Time-dependent receiver operating characteristic (ROC) and Kaplan-Meier (KM) survival analysis were used to assess its prognostic power. GSE29609 dataset from GEO (Gene Expression Omnibus) database was used to validate the signature. Univariate and multivariate Cox regression were performed to screen independent prognostic parameters to construct a nomogram. The predictive power of the nomogram was revealed by time-dependent ROC curves and the calibration plot and verified in the validation set. Finally, Functional enrichment analysis of DEGs and 5 novel genes were performed to suggest the potential biological pathways.

RESULTS

PADI1, ATP6V0D2, DPP6, C9orf135 and PLG were screened to be significantly related to the prognosis of ccRCC patients. The risk score effectively stratified the patients into high-risk group with poor overall survival (OS) based on survival analysis. AJCC-stage, age, recurrence and risk score were regarded as independent prognostic parameters by Cox regression analysis and were used to construct a nomogram. Time-dependent ROC curves showed the nomogram performed best in 1-, 3- and 5-year survival predictions compared with AJCC-stage and risk score in validation sets. The calibration plot showed good agreement of the nomogram between predicted and observed outcomes. Functional enrichment analysis suggested several enriched biological pathways related to cancer.

CONCLUSIONS

In our study, we constructed a gene-based model integrating clinical prognostic parameters to predict prognosis of ccRCC well, which might provide a reliable prognosis assessment tool for clinician and aid treatment decision-making in the clinic.

Sertraline/ICG-loaded liposome for dual-modality imaging and effective chemo-photothermal combination therapy against metastatic clear cell renal cell carcinoma

A large number of chemotherapeutic drugs, utilized in the treatment of advanced metastatic clear cell renal cell carcinoma, are typically prone to poor biocompatibility, lack of targeting specificity, and high toxicity, which mostly leads to unsatisfactory clinical outcomes. As a new drug delivery pathway, nanoliposomes have the advantages of simplifying metabolism, reducing drug side-effects, and providing specific targeting, which can potentially improve the therapeutic effect toward tumor therapy. In this study, a clinically integrated nanoliposome containing Sertraline Hydrochloride and indocyanine green (ICG), here named as Ser/ICG@Lip, was successfully synthesized by film-dispersion and hydration-sonication methods. The photoacoustic imaging and near-infrared fluorescence imaging capabilities of this novel nanoliposome were validated in vitro. The high encapsulation rate of Sertraline Hydrochloride and ICG ensured the safety and therapeutic efficacy of the particle. Moreover, our results suggest that chemo-photothermal combination therapy can be more effective than single photothermal or chemotherapy treatments against malignant tumor cells. This is the first study introducing Sertraline Hydrochloride as a liposome-encapsulated chemotherapeutic agent, containing photothermal capabilities, for the treatment of metastatic renal clear cell cancer cells. This novel drug system has potential to evolve into an alternate treatment method for metastatic clear cell renal cell carcinoma.

Contribution and Expression of Organic Cation Transporters and Aquaporin Water Channels in Renal Cancer

The body homeostasis is maintained mainly by the function of the kidneys, which regulate salt and water balance and excretion of metabolism waste products and xenobiotics. This important renal function is determined by the action of many transport systems, which are specifically expressed in the different parts of the nephron, the functional unit of the kidneys. These transport systems are involved, for example, in the reabsorption of sodium, glucose, and other important solutes and peptides from the primary urine. They are also important in the reabsorption of water and thereby production of a concentrated urine. However, several studies have shown the importance of transport systems for different tumor entities. Transport systems, for example, contributed to the proliferation and migration of cancer cells and thereby on tumor progression. They could also serve as drug transporters that could enable drug resistance by outward transport of, for example, chemotherapeutic agents and other drugs. Although many renal transporters have been characterized in detail with respect to the significance for proper kidney function, their role in renal cancer progression is less known. Here, we describe the types of renal cancer and review the studies that analyzed the role of organic cation transporters of the SLC22-family and of the aquaporin water channel family in kidney tumors.

RNA Sequencing of Collecting Duct Renal Cell Carcinoma Suggests an Interaction between miRNA and Target Genes and a Predominance of Deregulated Solute Carrier Genes

Collecting duct carcinoma (CDC) is a rare renal cell carcinoma subtype with a very poor prognosis. There have been only a few studies on gene expression analysis in CDCs. We compared the gene expression profiles of two CDC cases with those of eight normal tissues of renal cell carcinoma patients. At a threshold of |log2fold-change| ≥1, 3349 genes were upregulated and 1947 genes were downregulated in CDCs compared to the normal samples. Pathway analysis of the deregulated genes revealed that cancer pathways and cell cycle pathways were most prominent in CDCs. The most upregulated gene was keratin 17, and the most downregulated gene was cubilin. Among the most downregulated genes were four solute carrier genes (SLC3A1, SLC9A3, SLC26A7, and SLC47A1). The strongest negative correlations between miRNAs and mRNAs were found between the downregulated miR-374b-5p and its upregulated target genes HIST1H3B, HK2, and SLC7A11 and between upregulated miR-26b-5p and its downregulated target genes PPARGC1A, ALDH6A1, and MARC2. An upregulation of HK2 and a downregulation of PPARGC1A, ALDH6A1, and MARC2 were observed at the protein level. Survival analysis of the cancer genome atlas (TCGA) dataset showed for the first time that low gene expression of MARC2, cubilin, and SLC47A1 and high gene expression of KRT17 are associated with poor overall survival in clear cell renal cell carcinoma patients. Altogether, we identified dysregulated protein-coding genes, potential miRNA-target interactions, and prognostic markers that could be associated with CDC.

The Role of Serine Peptidase Inhibitor Kazal Type 13 (SPINK13) as a Clinicopathological and Prognostic Biomarker in Patients with Clear Cell Renal Cell Carcinoma

Background

The serine peptidase inhibitor Kazal type 13 (SPINK13) gene has tumor suppressor activity, but its role in renal cell carcinoma (RCC) remains unknown. This study aimed to investigate mRNA expression of SPINK13 in clear cell renal cell carcinoma (CCRCC) in human tissue and to use bioinformatics data to investigate the role of SPINK13 expression as a clinicopathological and prognostic biomarker for patients with CCRCC.

Material/Methods

Patients with CCRCC (N=533) with available RNA sequence data from The Cancer Genome Atlas (TCGA)-CCRCC database were analyzed with patients who had a tissue diagnosis of CCRCC (N=305) at the Fudan University Shanghai Cancer Center (FUSCC). Differential transcriptional and proteome expression profiles were obtained from the ONCOMINE cancer microarray database, TCGA, and the Human Protein Atlas (HPA) database. Quantitative reverse transcription-polymerase chain reaction (RT-qPCR) measured SPINK13 mRNA expression in 305 samples of CCRCC tissue from the FUSCC. The effects of clinicopathological parameters on progression-free survival (PFS) and overall survival (OS) were analyzed using the Kaplan-Meier and log-rank test.

Results

Transcriptional and proteome expression of SPINK13 were significantly increased CCRCC tissue samples. Increased SPINK13 mRNA expression was significantly associated with reduced PFS and OS in 838 patients with CCRCC patients from the two independent cohorts, the FUSCC and the TCGA-CCRCC cohorts (p<0.01). Gene set enrichment analysis (GSEA) showed that SPINK13 expression was involved in complement, apical junction, epithelial-mesenchymal transition (EMT), glycolysis, hypoxia, and inflammation signaling pathways.

Conclusions

Increased expression of SPINK13 was associated with poor prognosis in patients with CCRCC.

Systematic expression analysis of the mitochondrial respiratory chain protein subunits identifies COX5B as a prognostic marker in clear cell renal cell carcinoma

OBJECTIVE

To analyze the expression of mitochondrial respiratory chain protein subunits in clear cell renal cell carcinoma.

METHODS

Possible prognostic candidates were determined using The Cancer Genome Atlas database (n = 605). The database provided valid messenger ribonucleic acid expression data for 93 genes encoding for the subunits. Selected subunits were further investigated at the messenger ribonucleic acid and protein level by real-time polymerase chain reaction, western blot and immunohistochemistry with the cohorts of the University Hospital Bonn.

RESULTS

The Cancer Genome Atlas messenger ribonucleic acid expression data indicated univariate and multivariate prognostic impact for seven subunits (NDUFS8, NDUFS7, COX5B, COX6B1, SDHD, COX15 and COX19). Using real-time polymerase chain reaction, significant downregulation (P < 0.05, n = 74) could be shown for COX5B, COX6B1, NDUFS7 and NDUF8 in clear cell renal cell carcinoma tissue. Survival analysis of polymerase chain reaction data showed a non-significant relationship (P = 0.067) of high COX5B expression and poor overall survival. Western blot (n = 8) and immunohistochemistry analysis (n = 167) confirmed significant COX5B downregulation on the protein level. Immunohistochemistry analysis identified COX5B as a prognostic marker for overall (P = 0.017) and cancer-specific survival (P = 0.042).

CONCLUSIONS

The present study findings suggest downregulation of additional subunits of mitochondrial respiratory chain proteins in clear cell renal cell carcinoma. Remarkably, COX5B, a subunit of the respiratory chain complex IV, can be identified as a novel prognostic marker.

Role of fatty acid binding proteins (FABPs) in cancer development and progression

Fatty acid binding proteins (FABPs) are small, water soluble proteins that bind long chain fatty acids and other biologically active ligands to facilitate intracellular localization. Twelve FABP family members have been identified to date, with 10 isoforms expressed in humans. Functionally, FABPs are important in fatty acid metabolism and transport, with distinct family members having the capacity to influence gene transcription. Expression of FABPs is usually cell/tissue specific to one predominant FABP family member. Dysregulation of FABP expression can occur through genetic mutation and/or environmental-lifestyle influences. In addition to intracellular function, exogenous, circulating FABP expression can occur and is associated with specific disease states such as insulin resistance. A role for FABPs is increasingly being reported in tumor biology with elevated exogenous FABP expression being associated with tumor progression and invasiveness. However, a less clear role has been appreciated for dysregulated FABP expression during cell transformation and early expansion.

Novel urokinase-plasminogen activator inhibitor SPINK13 inhibits growth and metastasis of hepatocellular carcinoma in vivo

Advanced hepatocellular carcinoma (HCC) is a highly aggressive malignancy that is a serious threat to the public health system of China. Urokinase-plasminogen activator (uPA) can promote the invasive growth and metastasis of HCC cells by activating matrix metalloproteinases (MMPs), leading to the breakage of the extra-cellular matrix. uPA is a promising target for advanced HCC treatment. In this stuy the expression of uPA was examined by quantitative polymerase chain reaction in hepatic cell lines. Protein interaction between uPA and SPINK13 was identified by immunoprecipitation. In vitro biochemical assay was used to examine the inhibitory effect of the SPINK13 on the direct cleaving of the recombinant pro-MMP9 by uPA. The antitumor effect of SPINK13 was examined by transwell assay or the nude mice tumor model.The expression of uPA was much higher in highly aggressive HCC cell lines than in lowly aggressive HCC cell lines or non-tumor hepatic cell lines. SPINK13 interacted with uPA in HCC cells and directly inhibited the cleaving of MMP9 by uPA. Treatment of the recombinant SPINK13 protein inhibited the invasion of HCC cells in several experiments, such as transwell experiments or the intrahepatic growth model. The results of the study indicated that SPINK13 could function as a promising therapeutic approach for patients with advanced HCC.

Key miRNAs and target genes played roles in the development of clear cell renal cell carcinoma

BACKGROUND

Clear cell renal cell carcinoma (CCRCC) is the most aggressive form of renal cell carcinoma (RCC).

OBJECTIVE

This study was aimed to identify the differentially expressed miRNAs and target genes in CCRCC.

METHODS

The miRNA and mRNA next-generation sequencing data were downloaded from The Cancer Genome Atlas (TCGA) dataset. Differential expression analysis was performed, followed by correlation analysis of miRNA-mRNA. Functional enrichment and survival analysis was also performed.

RESULTS

Seven hundred and eighty-seven patients with CCRCC from TCGA data portal were included in this study. A total of 52 differentially expressed miRNAs were identified in CCRCC. Then 2361 differentially expressed genes (DEGs) were identified. Prediction analysis and correlation analysis revealed that 89 miRNA-mRNA pairs were not only predicted by algorithms but also had a significant inverse relationship. Several differentially expressed miRNAs such as hsa-mir-501 and their target genes including AK1, SLC25A15 and PCDHGC3 had a significant prognostic value for CCRCC patients.

CONCLUSIONS

Alterations of differentially expressed miRNAs and target genes may be involved in the development of CCRCC and can be considered as the prognostic markers for CCRCC.

Bioinformatic identification of key genes and analysis of prognostic values in clear cell renal cell carcinoma

The present study aimed to identify new key genes as potential biomarkers for the diagnosis, prognosis or targeted therapy of clear cell renal cell carcinoma (ccRCC). Three expression profiles (GSE36895, GSE46699 and GSE71963) were collected from Gene Expression Omnibus. GEO2R was used to identify differentially expressed genes (DEGs) in ccRCC tissues and normal samples. The Database for Annotation, Visualization and Integrated Discovery was utilized for functional and pathway enrichment analysis. STRING v10.5 and Molecular Complex Detection were used for protein-protein interaction (PPI) network construction and module analysis, respectively. Regulation network analyses were performed with the WebGestal tool. UALCAN web-portal was used for expression validation and survival analysis of hub genes in ccRCC patients from The Cancer Genome Atlas (TCGA). A total of 65 up- and 164 downregulated genes were identified as DEGs. DEGs were enriched with functional terms and pathways compactly related to ccRCC pathogenesis. Seventeen hub genes and one significant module were filtered out and selected from the PPI network. The differential expression of hub genes was verified in TCGA patients. Kaplan-Meier plot showed that high mRNA expression of enolase 2 (ENO2) was associated with short overall survival in ccRCC patients (P=0.023). High mRNA expression of cyclin D1 (CCND1) (P<0.001), fms related tyrosine kinase 1 (FLT1) (P=0.004), plasminogen (PLG) (P<0.001) and von Willebrand factor (VWF) (P=0.008) appeared to serve as favorable factors in survival. These findings indicate that the DEGs may be key genes in ccRCC pathogenesis and five genes, including ENO2, CCND1, PLT1, PLG and VWF, may serve as potential prognostic biomarkers in ccRCC.

Bioinformatic identification of key genes and pathways that may be involved in the pathogenesis of HBV-associated acute liver failure

In order to explore the molecular mechanisms behind the pathogenesis of acute liver failure (ALF) associated with hepatitis B virus (HBV) infection, the present study aimed to identify potential key genes and pathways involved using samples from patients with HBV-associated ALF. The GSE38941 array dataset was downloaded from the Gene Expression Omnibus database, and differentially expressed genes (DEGs) between 10 liver samples from 10 healthy donors and 17 liver specimens from 4 patients with HBV-associated ALF were analyzed using the Linear Models for Microarray Data package. Gene Ontology and KEGG pathway enrichment analyses of the DEGs were performed, followed by functional annotation of the genes and construction of a protein–protein interaction (PPI) network. Subnetwork modules were subsequently identified and analyzed. In total, 3142 DEGs were identified, of which 1755 were upregulated and 1387 were downregulated. The extracellular exosome, immune response, and inflammatory response pathways may potentially be used as biomarkers of ALF pathogenesis. In total, 17 genes (including CCR5, CXCR4, ALB, C3, VGEFA, and IGF1) were identified as hub genes in the PPI network and may therefore be potential marker genes for HBV-associated ALF.

Systematic expression analysis of the mitochondrial complex III subunits identifies UQCRC1 as biomarker in clear cell renal cell carcinoma

Mitochondrial dysfunction is common in cancer, and the mitochondrial electron transport chain is often affected in carcinogenesis. So far, few is known about the expression of the mitochondrial complex III (ubiquinol-cytochrome c reductase complex) subunits in clear cell renal cell carcinoma (ccRCC). In this study, the NextBio database was used to determine an expression profile of the mitochondrial complex III subunits based on published microarray studies. We observed that five out of 11 subunits of the complex III were downregulated in at least three microarray studies. The decreased mRNA expression level of UQCRFS1 and UQCRC1 in ccRCC was confirmed using PCR. Low mRNA levels UQCRC1 were also correlated with a shorter period of cancer-specific and overall survival. Furthermore, UQCRFS1 and UQCRC1 were also decreased in ccRCC on the protein level as determined using Western blotting and immunohistochemistry. UQCRC1 protein expression was also lower in ccRCC than in papillary and chromophobe subtypes. Analyzing gene expression and DNA methylation in The Cancer Genome Atlas cohort revealed an inverse correlation of gene expression and DNA methylation, suggesting that DNA hypermethylation is regulating the expression of UQCRC1 and UQCRFS1. Taken together, our data implicate that dysregulated UQCRC1 and UQCRFS1 are involved in impaired mitochondrial electron transport chain function.

Promoter DNA methylation analysis reveals a novel diagnostic CpG-based biomarker and RAB25 hypermethylation in clear cell renel cell carcinoma

Clear-cell renal cell carcinoma (ccRCC) is a common aggressive urinary malignant tumor that cannot be easily diagnosed at an early stage. The DNA methylation occurs within promoter before precancerous lesion plays a pivotal role that could help us in diagnosing and understanding ccRCC. In this study, based on a whole-genome promoter DNA methylation profiling, we used shrunken centroids classifier method to identify a CpG-based biomarker that is capable of differentiating between ccRCC tumor and adjacent tissues. The biomarker was validated in 19 ccRCCs and three public datasets. We found that both CYP4B1 and RAB25 are downregulated with promoter hypermethylation and CA9 is upregulated with promoter hypomethylation, and we validated their mRNA differential expressions in 19 ccRCCs and 10 GEO datasets. We further confirmed that hypermethylated RAB25 is inversely correlated with its mRNA level. Log-rank test showed that ccRCC patients with low levels of CA9 promoter methylation had a higher survival rate. This reveals clinically a potential biomarker for use in early detection for ccRCC, and provides a better understanding of carcinogenesis.

Systematic Analysis of the Expression of the Mitochondrial ATP Synthase (Complex V) Subunits in Clear Cell Renal Cell Carcinoma

Mitochondrial dysfunction is common in cancer and the mitochondrial electron transport chain is often affected in carcinogenesis. To date, little is known about the expression of the ATP synthase subunits in clear cell renal cell carcinoma (ccRCC). The NextBio database was used to determine an expression profile of the ATP synthase subunits based on published microarray studies. We observed down-regulation of 23 out of 29 subunits of the ATP synthase. Differential expression was validated exemplarily for 12 genes (ATP5A1, ATP5B, ATPAF1, ATP5C1, ATP5D, ATP5O, ATP5F1, ATP5G1, ATP5G2, ATP5G3, ATP5I, ATP5S; screening cohort ccRCC n = 18 and normal renal tissue n = 10) using real-time PCR. Additional eight genes (ATP5A1, ATP5B, ATPAF1, ATP5F1, ATP5G1, ATP5G2, ATP5G3, ATP5S) were internally validated within an enlarged cohort (ccRCC n = 74; normal renal tissue n = 36). Furthermore, down-regulation of ATP5A1, ATPAF1, ATP5G1/G2/G3 was confirmed on the protein level using Western Blot and immunohistochemistry. We observed that altered expression of ATPAF1 and ATP5G1/G2/G3 was correlated with overall survival in patients with ccRCC. In conclusion, down-regulation of many ATP Synthase subunits occurs in ccRCC and is the basis for the reduced activity of the mitochondrial electron chain. Alteration of the expression of ATP5A1, ATPAF1, and ATP5G1/G2/G3 is characteristic for ccRCC and may be prognostic for ccRCC patients' outcome.

Relationship between anti-depressant use and lung cancer survival

OBJECTIVES

In recent years, the anti-cancer properties of several commonly used drugs have been explored, with drugs such as aspirin and beta-blockers associated with improved cancer outcomes. Previous preclinical work demonstrated that tricyclic anti-depressants have antitumor efficacy in lung cancer. Our goal was to examine the association between anti-depressant use and survival in lung cancer.

MATERIALS AND METHODS

We examined the association between use of common anti-depressants and survival in 1,097 lung cancer patients from the NCI-Maryland lung cancer study. The types of anti-depressants included in the study were norepinephrine and dopamine reuptake inhibitors, serotonin reuptake inhibitors, selective serotonin reuptake inhibitors, non-selective serotonin reuptake inhibitors, and tricyclic anti-depressants. Anti-depressant use was extracted from the medical history section of a detailed interviewer-administered questionnaire. Specific use in the three months before a lung cancer diagnosis was determined. Cox portioned hazards modeling was used to estimate the association between anti-depressant use with lung cancer-specific death with adjustment for potential confounding co-factors.

RESULTS

Anti-depressant use was associated with extended lung cancer-specific survival. In an analysis of specific classes of anti-depressant use, NDRIs and TCAs were associated with improved survival. Importantly, the extended survival associated with anti-depressants was maintained after adjustment for the clinical indications for these drugs, suggestive of a direct effect on lung cancer biology.

CONCLUSIONS

Considering the manageable and largely tolerable side effects of anti-depressants, and the low cost of these drugs, these results indicate that evaluation of anti-depressants as adjunct therapeutics with chemotherapy may have a translational effect for lung cancer patients.

Systematic Expression Analysis of Mitochondrial Complex I Identifies NDUFS1 as a Biomarker in Clear-Cell Renal-Cell Carcinoma

INTRODUCTION

Mitochondrial dysfunction is common in cancer, and the mitochondrial electron transport chain is often affected in carcinogenesis. So far, little is known about the expression of the mitochondrial complex I (NADH:ubiquinone oxidoreductase) subunits in clear-cell renal-cell carcinoma (ccRCC).

MATERIALS AND METHODS

An expression profile of the mitochondrial complex I subunits was determined using the NextBio database. Subsequently, the expression of selected subunits was experimentally validated on mRNA (quantitative real-time polymerase chain reaction) and protein (Western blot analysis, immunohistochemistry) level.

RESULTS

We observed that 7 subunits of the complex I were down-regulated in at least 3 microarray studies. Deregulated mRNA expression was confirmed for NDUFA3, NDUFA, NDUFB1, NDUFB9, NDUFS1, NDUFS8, and NDUFV1. Low NDUFS1 mRNA expression was a significant and independent adverse predictor of a shorter overall survival in our mRNA cohort and the ccRCC cohort of The Cancer Genome Atlas project. NDUFS1 expression was furthermore analyzed on the protein level, and a distinct down-regulation was observed in ccRCC as well as in the chromophobe and the sarcomatoid subtype compared to normal renal tissue.

CONCLUSION

Expression alterations occur in only a few subunits of the mitochondrial complex I subunits in ccRCC, and altered mRNA and protein expression levels of NDUFS1 may be useful to distinguish between renal-cell carcinoma and normal renal tissue.

Development and confirmation of potential gene classifiers of human clear cell renal cell carcinoma using next-generation RNA sequencing

OBJECTIVE

A previous study by this group demonstrated the feasibility of RNA sequencing (RNAseq) technology for capturing disease biology of clear cell renal cell carcinoma (ccRCC), and presented initial results for carbonic anhydrase-9 (CA9) and tumor necrosis factor-α-induced protein-6 (TNFAIP6) as possible biomarkers of ccRCC (discovery set) [Eikrem et al. PLoS One 2016;11:e0149743]. To confirm these results, the previous study is expanded, and RNAseq data from additional matched ccRCC and normal renal biopsies are analyzed (confirmation set).

MATERIALS AND METHODS

Two core biopsies from patients (n = 12) undergoing partial or full nephrectomy were obtained with a 16 g needle. RNA sequencing libraries were generated with the Illumina TruSeq^® Access library preparation protocol. Comparative analysis was done using linear modeling (voom/Limma; R Bioconductor).

RESULTS

The formalin-fixed and paraffin-embedded discovery and confirmation data yielded 8957 and 11,047 detected transcripts, respectively. The two data sets shared 1193 of differentially expressed genes with each other. The average expression and the log₂-fold changes of differentially expressed transcripts in both data sets correlated, with R²=.95 and R²=.94, respectively. Among transcripts with the highest fold changes were CA9, neuronal pentraxin-2 and uromodulin. Epithelial-mesenchymal transition was highlighted by differential expression of, for example, transforming growth factor-β₁ and delta-like ligand-4. The diagnostic accuracy of CA9 was 100% and 93.9% when using the discovery set as the training set and the confirmation data as the test set, and vice versa, respectively. These data further support TNFAIP6 as a novel biomarker of ccRCC. TNFAIP6 had combined accuracy of 98.5% in the two data sets.

CONCLUSIONS

This study provides confirmatory data on the potential use of CA9 and TNFAIP6 as biomarkers of ccRCC. Thus, next-generation sequencing expands the clinical application of tissue analyses.