Network-based metabolic characterization of renal cell carcinoma

Applications of genome-scale metabolic models to the study of human diseases: A systematic review

BACKGROUND AND OBJECTIVES

Genome-scale metabolic networks (GEMs) represent a valuable modeling and computational tool in the broad field of systems biology. Their ability to integrate constraints and high-throughput biological data enables the study of intricate metabolic aspects and processes of different cell types and conditions. The past decade has witnessed an increasing number and variety of applications of GEMs for the study of human diseases, along with a huge effort aimed at the reconstruction, integration and analysis of a high number of organisms. This paper presents a systematic review of the scientific literature, to pursue several important questions about the application of constraint-based modeling in the investigation of human diseases. Hopefully, this paper will provide a useful reference for researchers interested in the application of modeling and computational tools for the investigation of metabolic-related human diseases.

METHODS

This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Elsevier Scopus®, National Library of Medicine PubMed® and Clarivate Web of Science™ databases were enquired, resulting in 566 scientific articles. After applying exclusion and eligibility criteria, a total of 169 papers were selected and individually examined.

RESULTS

The reviewed papers offer a thorough and up-to-date picture of the latest modeling and computational approaches, based on genome-scale metabolic models, that can be leveraged for the investigation of a large variety of human diseases. The numerous studies have been categorized according to the clinical research area involved in the examined disease. Furthermore, the paper discusses the most typical approaches employed to derive clinically-relevant information using the computational models.

CONCLUSIONS

The number of scientific papers, utilizing GEM-based approaches for the investigation of human diseases, suggests an increasing interest in these types of approaches; hopefully, the present review will represent a useful reference for scientists interested in applying computational modeling approaches to investigate the aetiopathology of human diseases; we also hope that this work will foster the development of novel applications and methods for the discovery of clinically-relevant insights on metabolic-related diseases.

Nutritional deuterium depletion and health: a scoping review

INTRODUCTION

Large variations in fatty and amino acid natural 2H/1H ratios in reference with solvent water point to the active involvement of compartmental, inter- and intramolecular deuterium disequilibrium in adaptive biology. Yet, the human deutenome is an untapped area of energy metabolism and health in humans.

OBJECTIVES

The purpose of this scoping review is to examine health effects through deuterium homeostasis using deuterium-depleted water and/or a deuterium-depleted diet. We also aim to reveal health effects of nutritional, metabolic and exercise ketosis, i.e. complete mitochondrial fatty acid oxidation with the production of deuterium depleted (deupleted) metabolic water.

METHODS

A protocol process approach was used to retrieve current research in deuterium depletion according to the preferred reporting items protocol for systematic reviews and meta-analyses, extension for scoping reviews with checklist (PRISMA-ScR).

RESULTS

Fifteen research articles were used. All retrieved articles were heterogenous in nature and additional themes did not evolve. Deuterium depletion was found to have beneficial health effects in the following conditions: cancer prevention, cancer treatment, depression, diabetes, long-term memory, anti-aging, and sports performance. Deutenomics is actively pursued in drug research and there are biomarker roles attributed to large natural variations with adaptive significance in biology.

CONCLUSION

Even with limited data, consistent deuterium depletion can be seen across all conditions reviewed. More randomized control trials are recommended to confirm cause and effect for translationally and clinically informed integrative nutrition-based medical interventions.

In silico prediction of drug-induced nephrotoxicity: current progress and pitfalls

INTRODUCTION

Due to its role in absorption and metabolism, the kidney is an important target for drug toxicity. Drug-induced nephrotoxicity (DIN) presents a significant challenge in clinical practice and drug development. Conventional methods for assessing nephrotoxicity have limitations, highlighting the need for innovative approaches. In recent years, in silico methods have emerged as promising tools for predicting DIN.

AREAS COVERED

A literature search was performed using PubMed and Web of Science, from 2013 to February 2023 for this review. This review provides an overview of the current progress and pitfalls in the in silico prediction of DIN, which discusses the principles and methodologies of computational models.

EXPERT OPINION

Despite significant advancements, this review identified issues accentuates the pivotal imperatives of data fidelity, model optimization, interdisciplinary collaboration, and mechanistic comprehension in sculpting the vista of DIN prediction. Integration of multiple data sources and collaboration between disciplines are essential for improving predictive models. Ultimately, a holistic approach combining computational, experimental, and clinical methods will enhance our understanding and management of DIN.

Metabolomic landscape of renal cell carcinoma in von Hippel-Lindau syndrome in a Chinese cohort

Von Hippel-Lindau (VHL) syndrome is a rare autosomal dominant disorder, where renal cell carcinoma (RCC) serves as a significant cause of mortality. We collected peripheral blood from 61 VHL-RCC patients and 31 healthy individuals, along with 19 paired RCC tumor and adjacent non-malignant samples. Using liquid chromatography-mass spectrometry, we identified 238 plasma and 241 tissue differentially abundant metabolites (DAMs), highlighting key pathways such as arginine and proline metabolism. The top 10 of the 23 DAMs, common to both plasma and tissue, were instrumental in constructing a high-performance diagnostic model. These DAMs demonstrated significant correlations with VHL gene mutation types. Cox regression analysis revealed that plasma levels of N2,N2-dimethylguanosine were associated with the timing of RCC onset in VHL patients, acting as an independent predictive factor. This study enhances diagnostic accuracy for this rare condition and opens new avenues for exploring metabolic mechanisms of the disease and potential therapeutic directions.

Influence of Dietary Polyunsaturated Fatty Acid Intake on Potential Lipid Metabolite Diagnostic Markers in Renal Cell Carcinoma: A Case-Control Study

Non-invasive diagnostics are crucial for the timely detection of renal cell carcinoma (RCC), significantly improving survival rates. Despite advancements, specific lipid markers for RCC remain unidentified. We aimed to discover and validate potent plasma markers and their association with dietary fats. Using lipid metabolite quantification, machine-learning algorithms, and marker validation, we identified RCC diagnostic markers in studies involving 60 RCC and 167 healthy controls (HC), as well as 27 RCC and 74 HC, by analyzing their correlation with dietary fats. RCC was associated with altered metabolism in amino acids, glycerophospholipids, and glutathione. We validated seven markers (l-tryptophan, various lysophosphatidylcholines [LysoPCs], decanoylcarnitine, and l-glutamic acid), achieving a 96.9% AUC, effectively distinguishing RCC from HC. Decreased decanoylcarnitine, due to reduced carnitine palmitoyltransferase 1 (CPT1) activity, was identified as affecting RCC risk. High intake of polyunsaturated fatty acids (PUFAs) was negatively correlated with LysoPC (18:1) and LysoPC (18:2), influencing RCC risk. We validated seven potential markers for RCC diagnosis, highlighting the influence of high PUFA intake on LysoPC levels and its impact on RCC occurrence via CPT1 downregulation. These insights support the efficient and accurate diagnosis of RCC, thereby facilitating risk mitigation and improving patient outcomes.

Abnormal expression of FOXM1 in carcinogenesis of renal cell carcinoma: From experimental findings to clinical applications

Renal cell carcinoma (RCC) is a prevalent malignancy within the genitourinary system. At present, patients with high-grade or advanced RCC continue to have a bleak prognosis. Mounting research have emphasized the significant involvement of Forkhead box M1 (FOXM1) in RCC development and progression. Therefore, it is imperative to consolidate the existing evidence regarding the contributions of FOXM1 to RCC tumorigenesis through a comprehensive review. This study elucidated the essential functions of FOXM1 in promoting RCC growth, invasion, and metastasis by regulating cell cycle progression, DNA repair, angiogenesis, and epithelial-mesenchymal transition (EMT). Also, FOXM1 might serve as a novel diagnostic and prognostic biomarker as well as a therapeutic target for RCC. Clinical findings demonstrated that the expression of FOXM1 was markedly upregulated in RCC samples, while a high level of FOXM1 was found to be associated with a poor overall survival rate of RCC. Furthermore, it is worth noting that FOXM1 may have a significant impact on the resistance of renal cell carcinoma (RCC) to radiotherapy. This observation suggests that inhibiting FOXM1 could be a promising strategy to impede the progression of RCC and enhance its sensitivity to radiotherapy. The present review highlighted the pivotal role of FOXM1 in RCC development. FOXM1 has the capacity to emerge as not only a valuable diagnostic and prognostic tool but also a viable therapeutic option for unresectable RCC.

Identification of key genes and signalling pathways in clear cell renal cell carcinoma: An integrated bioinformatics approach

BACKGROUND

Clear cell Renal Cell Carcinoma (ccRCC) is one of the most prevalent types of kidney cancer. Unravelling the genes responsible for driving cellular changes and the transformation of cells in ccRCC pathogenesis is a complex process.

OBJECTIVE

In this study, twelve microarray ccRCC datasets were chosen from the gene expression omnibus (GEO) database and subjected to integrated analysis.

METHODS

Through GEO2R analysis, 179 common differentially expressed genes (DEGs) were identified among the datasets. The common DEGs were subjected to functional enrichment analysis using ToppFun followed by construction of protein-protein interaction network (PPIN) using Cytoscape. Clusters within the DEGs PPIN were identified using the Molecular Complex Detection (MCODE) Cytoscape plugin. To identify the hub genes, the centrality parameters degree, betweenness, and closeness scores were calculated for each DEGs in the PPIN. Additionally, Gene Expression Profiling Interactive Analysis (GEPIA) was utilized to validate the relative expression levels of hub genes in the normal and ccRCC tissues.

RESULTS

The common DEGs were highly enriched in Hypoxia-inducible factor (HIF) signalling and metabolic reprogramming pathways. VEGFA, CAV1, LOX, CCND1, PLG, EGF, SLC2A1, and ENO2 were identified as hub genes.

CONCLUSION

Among 8 hub genes, only the expression levels of VEGFA, LOX, CCND1, and EGF showed a unique expression pattern exclusively in ccRCC on compared to other type of cancers.

Disturbances in Nitric Oxide Cycle and Related Molecular Pathways in Clear Cell Renal Cell Carcinoma

It is important to note that maintaining adequate levels of nitric oxide (NO), the turnover, and the oxidation level of nitrogen are essential for the optimal progression of cellular processes, and alterations in the NO cycle indicate a crucial step in the onset and progression of multiple diseases. Cellular accumulation of NO and reactive nitrogen species in many types of tumour cells is expressed by an increased susceptibility to oxidative stress in the tumour microenvironment. Clear cell renal cell carcinoma (ccRCC) is a progressive metabolic disease in which tumour cells can adapt to metabolic reprogramming to enhance NO production in the tumour space. Understanding the factors governing NO biosynthesis metabolites in ccRCC represents a relevant, valuable approach to studying NO-based anticancer therapy. Exploring the molecular processes mediated by NO, related disturbances in molecular pathways, and NO-mediated signalling pathways in ccRCC could have significant therapeutic implications in managing and treating this condition.

KL is a favorable prognostic factor related immune for clear cell renal cell carcinoma

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is a prevalent cancer in adult urology, often leading to metastasis and poor prognosis. Recently, advances in tumor immunology and aging research have opened up new possibilities for the treatment of kidney cancer. Therefore, the identification of potential targets and prognostic biomarkers for immunotherapy has become increasingly urgent.

METHODS

Using GSE168845 data, we identified immune-aging-associated differentially expressed genes (IAR-DEGs) by intersecting differentially expressed immune-related genes and aging-related genes. The prognostic value of IAR-DEGs was determined via univariate and multivariate Cox regression analysis, revealing KL as an independent prognostic factor for ccRCC. We also investigated the correlation between KL and various immune-related factors, including immune cell infiltration, immune score, immune checkpoint, MSI, and TIED score. To confirm the expression of KL in ccRCC, we conducted qRT-PCR assays on both ccRCC cell lines and clinical tissue samples, and compared KL expression levels between normal kidney cell line (HK-2) and ACHN, a ccRCC cell line. Finally, we assessed KL protein expression levels in tissues using immunohistochemistry (IHC).

RESULTS

In this study, we utilized Venn diagram analysis to identify 17 co-expressed immune-aging related DEGs from GSE168845, import database, and MSigDB database. GO and KEGG analysis revealed that the functions of the 17 IAR-DEGs were primarily related to "aging". Univariate and multivariate Cox analysis validated these 17 genes, and KL was determined to be an independent prognostic factor for ccRCC. The downregulation of KL was observed in ccRCC tissues and was negatively associated with T stage, M stage, pathological stage, and histologic grade (p < 0.05). This downregulation indicated disease deterioration and a shortened overall survival period. Our calibration curves and nomogram demonstrated the excellent predictive potential of KL. GSEA analysis showed that KL gene mediated immune and aging-related pathways, and was significantly correlated with immune infiltration and MS and TIED score. More research has revealed a significant reduction in KL mRNA expression levels in human renal cancer cells, particularly in ccRCC tissues compared to adjacent normal kidney tissues. Moreover, immunohistochemistry data have demonstrated a marked decrease in KL protein expression levels in ccRCC cells when compared to adjacent normal tissues.

CONCLUSIONS

KL was a potential aging-related target for immunotherapy and valid prognostic biomarker for ccRCC patients.

A pilot radiometabolomics integration study for the characterization of renal oncocytic neoplasia

Differentiating benign renal oncocytic tumors and malignant renal cell carcinoma (RCC) on imaging and histopathology is a critical problem that presents an everyday clinical challenge. This manuscript aims to demonstrate a novel methodology integrating metabolomics with radiomics features (RF) to differentiate between benign oncocytic neoplasia and malignant renal tumors. For this purpose, thirty-three renal tumors (14 renal oncocytic tumors and 19 RCC) were prospectively collected and histopathologically characterised. Matrix-assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI) was used to extract metabolomics data, while RF were extracted from CT scans of the same tumors. Statistical integration was used to generate multilevel network communities of -omics features. Metabolites and RF critical for the differentiation between the two groups (delta centrality > 0.1) were used for pathway enrichment analysis and machine learning classifier (XGboost) development. Receiver operating characteristics (ROC) curves and areas under the curve (AUC) were used to assess classifier performance. Radiometabolomics analysis demonstrated differential network node configuration between benign and malignant renal tumors. Fourteen nodes (6 RF and 8 metabolites) were crucial in distinguishing between the two groups. The combined radiometabolomics model achieved an AUC of 86.4%, whereas metabolomics-only and radiomics-only classifiers achieved AUC of 72.7% and 68.2%, respectively. Analysis of significant metabolite nodes identified three distinct tumour clusters (malignant, benign, and mixed) and differentially enriched metabolic pathways. In conclusion, radiometabolomics integration has been presented as an approach to evaluate disease entities. In our case study, the method identified RF and metabolites important in differentiating between benign oncocytic neoplasia and malignant renal tumors, highlighting pathways differentially expressed between the two groups. Key metabolites and RF identified by radiometabolomics can be used to improve the identification and differentiation between renal neoplasms.

Immune-pyroptosis-related genes predict the prognosis of kidney renal clear cell carcinoma

BACKGROUND

Kidney renal clear cell carcinoma (KIRC) is a common cancer of the adult urological system. Recent developments in tumor immunology and pyroptosis biology have provided new directions for kidney cancer treatment. Therefore, there is an urgent need to identify potential targets and prognostic biomarkers for the combination of immunotherapy and pyroptosis-targeted therapy.

METHODS

The expression of immune-pyroptosis-related differentially expressed genes (IPR-DEGs) between KIRC and healthy tissues was examined using the Gene Expression Omnibus datasets. The GSE168845 dataset was selected for subsequent analyses. Data of 1793 human immune-related genes were downloaded from the ImmPort database (https://www.immport.org./home), while those of 33 pyroptosis-related genes were extracted from previous reviews. The independent prognostic value of IPR-DEGs was determined using differential expression, prognostic, and univariate and multivariate Cox regression analyses. The GSE53757 dataset was used to further verify the GSDMB and PYCARD levels. In our cohorts, the association among DEGs and clinicopathological features and overall survival was analyzed. The least absolute shrinkage and selection operator Cox regression model was established to evaluate the correlation of IPR-DEGs with the immune score, immune checkpoint gene expression, and one-class logistic regression (OCLR) score. KIRC cells and clinical tissue samples were subjected to quantitative real-time polymerase chain reaction to examine the GSDMB and PYCARD mRNA levels. The GSDMB and PYCARD levels in a healthy kidney cell line (HK-2 cells) and two KIRC cell lines (786-O and Caki-1 cells) were verified. The tissue levels of GSDMB and PYCARD were evaluated using immunohistochemical analysis. GSDMB and PYCARD were knocked down in 786-O cells using short-interfering RNA. Cell proliferation was examined using the cell counting kit-8 assay. Cell migration was measured by transwell migration assays RESULTS: GSDMB and PYCARD were determined to be IPR-DEGs with independent prognostic values. A risk prognostic model based on GSDMB and PYCARD was successfully established. In the GSE53757 dataset, the GSDMB and PYCARD levels in KIRC tissues were significantly higher than those in healthy tissues. The GSDMB and PYCARD expression was related to T stage and OS in our cohort. The GSDMB and PYCARD levels were significantly correlated with the immune score, immune checkpoint gene expression, and OCLR score. The results of experimental studies were consistent with those of bioinformatics analysis. The GSDMB and PYCARD levels in KIRC cells were significantly upregulated when compared with those in healthy kidney cells. Consistently, GSDMB and PYCARD in KIRC tissues were significantly upregulated when compared with those in adjacent healthy kidney tissues. GSDMB and PYCARD knockdown significantly decreased 786-O cell proliferation (p < 0.05). Transwell migration result reflects that silencing GSDMB and PYCARD inhibited 786-O cell migration (p < 0.05) .

CONCLUSIONS

GSDMB and PYCARD are potential targets and effective prognostic biomarkers for the combination of immunotherapy and pyroptosis-targeted therapy in KIRC.

Network diffusion-based approach for survival prediction and identification of biomarkers using multi-omics data of papillary renal cell carcinoma

Identification of cancer subtypes based on molecular knowledge is crucial for improving the patient diagnosis, prognosis, and treatment. In this work, we integrated copy number variations (CNVs) and transcriptomic data of Kidney Papillary Renal Cell Carcinoma (KIRP) using a network diffusion strategy to stratify cancers into clinically and biologically relevant subtypes. We constructed GeneNet, a KIRP specific gene expression network from RNA-seq data. The copy number variation data was projected onto GeneNet and propagated on the network for clustering. We identified robust subtypes that are biologically informative and significantly associated with patient survival, tumor stage and clinical subtypes of KIRP. We performed a Singular Value Decomposition (SVD) analysis of KIRP subtypes, which revealed the genes/silent players related to poor survival. A differential gene expression analysis between subtypes showed that genes related to immune, extracellular matrix organization, and genomic instability are upregulated in the poor survival group. Overall, the network-based approach revealed the molecular subtypes of KIRP and captured the relationship between gene expression and CNVs. This framework can be further expanded to integrate other omics data.

Blood Plasma Metabolome Profiling at Different Stages of Renal Cell Carcinoma

Early diagnostics significantly improves the survival of patients with renal cell carcinoma (RCC), which is the prevailing type of adult kidney cancer. However, the absence of clinically obvious symptoms and effective screening strategies at the early stages result to disease progression and survival rate reducing. The study was focused on revealing of potential low molecular biomarkers for early-stage RCC. The untargeted direct injection mass spectrometry-based metabolite profiling of blood plasma samples from 51 non-cancer volunteers (control) and 78 patients with different RCC subtypes and stages (early stages of clear cell RCC (ccRCC), papillary RCC (pRCC), chromophobe RCC (chrRCC) and advanced stages of ccRCC) was performed. Comparative analysis of the blood plasma metabolites between the control and cancer groups provided the detection of metabolites associated with different tumor stages. The designed model based on the revealed metabolites demonstrated high diagnostic power and accuracy. Overall, using the metabolomics approach the study revealed the metabolites demonstrating a high value for design of plasma-based test to improve early ccRCC diagnosis.

Dynamic partitioning of branched-chain amino acids-derived nitrogen supports renal cancer progression

Metabolic reprogramming is critical for tumor initiation and progression. However, the exact impact of specific metabolic changes on cancer progression is poorly understood. Here, we integrate multimodal analyses of primary and metastatic clonally-related clear cell renal cancer cells (ccRCC) grown in physiological media to identify key stage-specific metabolic vulnerabilities. We show that a VHL loss-dependent reprogramming of branched-chain amino acid catabolism sustains the de novo biosynthesis of aspartate and arginine enabling tumor cells with the flexibility of partitioning the nitrogen of the amino acids depending on their needs. Importantly, we identify the epigenetic reactivation of argininosuccinate synthase (ASS1), a urea cycle enzyme suppressed in primary ccRCC, as a crucial event for metastatic renal cancer cells to acquire the capability to generate arginine, invade in vitro and metastasize in vivo. Overall, our study uncovers a mechanism of metabolic flexibility occurring during ccRCC progression, paving the way for the development of novel stage-specific therapies.

Identify Biomarkers and Design Effective Multi-Target Drugs in Ovarian Cancer: Hit Network-Target Sets Model Optimizing

Epithelial ovarian cancer (EOC) is highly aggressive with poor patient outcomes, and a deeper understanding of ovarian cancer tumorigenesis could help guide future treatment development. We proposed an optimized hit network-target sets model to systematically characterize the underlying pathological mechanisms and intra-tumoral heterogeneity in human ovarian cancer. Using TCGA data, we constructed an epithelial ovarian cancer regulatory network in this study. We use three distinct methods to produce different HNSs for identification of the driver genes/nodes, core modules, and core genes/nodes. Following the creation of the optimized HNS (OHNS) by the integration of DN (driver nodes), CM (core module), and CN (core nodes), the effectiveness of various HNSs was assessed based on the significance of the network topology, control potential, and clinical value. Immunohistochemical (IHC), qRT-PCR, and Western blotting were adopted to measure the expression of hub genes and proteins involved in epithelial ovarian cancer (EOC). We discovered that the OHNS has two key advantages: the network's central location and controllability. It also plays a significant role in the illness network due to its wide range of capabilities. The OHNS and clinical samples revealed the endometrial cancer signaling, and the PI3K/AKT, NER, and BMP pathways. MUC16, FOXA1, FBXL2, ARID1A, COX15, COX17, SCO1, SCO2, NDUFA4L2, NDUFA, and PTEN hub genes were predicted and may serve as potential candidates for new treatments and biomarkers for EOC. This research can aid in better capturing the disease progression, the creation of potent multi-target medications, and the direction of the therapeutic community in the optimization of effective treatment regimens by various research objectives in cancer treatment.

Altered Plasma, Urine, and Tissue Profiles of Sulfatides and Sphingomyelins in Patients with Renal Cell Carcinoma

Simple Summary

Renal cell carcinoma (RCC) is among the most common cancer types in both men and women, and its early detection significantly improves survival. Minimally-invasive blood- or urine-based tests may increase the RCC detection rate, especially before patients develop symptoms. Here, we report significant changes in concentrations of sulfatides and sphingomyelins in plasma and urine in RCC patients compared to healthy controls. For the first time, we present findings that similar alterations appear in the lipid profiles of body fluids and tissues in patients. We observe gradual changes in sulfatide and sphingomyelin concentrations with increasing tumor stage and grade. We built binary classifiers that detect RCC based on plasma and urine lipidome dysregulations, and we show that the plasma lipidome alterations enable distinguishing between early-stage RCC and controls. Our results demonstrate the considerable potential of lipid screening in biofluids for RCC detection and monitoring in clinical settings.

Abstract

Purpose: RCC, the most common type of kidney cancer, is associated with high mortality. A non-invasive diagnostic test remains unavailable due to the lack of RCC-specific biomarkers in body fluids. We have previously described a significantly altered profile of sulfatides in RCC tumor tissues, motivating us to investigate whether these alterations are reflected in collectible body fluids and whether they can enable RCC detection. Methods: We collected and further analyzed 143 plasma, 100 urine, and 154 tissue samples from 155 kidney cancer patients, together with 207 plasma and 70 urine samples from 214 healthy controls. Results: For the first time, we show elevated concentrations of lactosylsulfatides and decreased levels of sulfatides with hydroxylated fatty acyls in body fluids of RCC patients compared to controls. These alterations are emphasized in patients with the advanced tumor stage. Classification models are able to distinguish between controls and patients with RCC. In the case of all plasma samples, the AUC for the testing set was 0.903 (0.844–0.954), while for urine samples it was 0.867 (0.763–0.953). The models are able to efficiently detect patients with early- and late-stage RCC based on plasma samples as well. The test set sensitivities were 80.6% and 90%, and AUC values were 0.899 (0.832–0.952) and 0.981 (0.956–0.998), respectively. Conclusion: Similar trends in body fluids and tissues indicate that RCC influences lipid metabolism, and highlight the potential of the studied lipids for minimally-invasive cancer detection, including patients with early tumor stages, as demonstrated by the predictive ability of the applied classification models.

Overview of Methionine Adenosyltransferase 2A (MAT2A) as an Anticancer Target: Structure, Function, and Inhibitors

Methionine adenosyltransferase 2A (MAT2A) is a rate-limiting enzyme in the methionine cycle that primarily catalyzes the synthesis of S-adenosylmethionine (SAM) from methionine and adenosine triphosphate (ATP). MAT2A has been recognized as a therapeutic target for the treatment of cancers. Recently, a few MAT2A inhibitors have been reported, and three entered clinical trials to treat solid tumorsor lymphoma with MTAP loss. This review aims to summarize the current understanding of the roles of MAT2A in cancer and the discovery of MAT2A inhibitors. Furthermore, a perspective on the use of MAT2A inhibitors for the treatment of cancer is also discussed. We hope to provide guidance for future drug design and optimization via analysis of the binding modes of known MAT2A inhibitors.

Two-dimensional correlated spectroscopy distinguishes clear cell renal cell carcinoma from other kidney neoplasms and non-cancer kidney

Background

Routinely used clinical scanners, such as computed tomography (CT), magnetic resonance imaging (MRI) and ultrasound (US), are unable to distinguish between aggressive and indolent tumor subtypes in masses localized to the kidney, often leading to surgical overtreatment. The results of the current investigation demonstrate that chemical differences, detected in human kidney biopsies using two-dimensional COrrelated SpectroscopY (2D L-COSY) and evaluated using multivariate statistical analysis, can distinguish these subtypes.

Methods

One hundred and twenty-six biopsy samples from patients with a confirmed enhancing kidney mass on abdominal imaging were analyzed as part of the training set. A further forty-three samples were used for model validation. In patients undergoing radical nephrectomy, biopsies of non-cancer kidney cortical tissue were also collected as a non-cancer control group. Spectroscopy data were analyzed using multivariate statistical analysis, including principal component analysis (PCA) and orthogonal projection to latent structures with discriminant analysis (OPLS-DA), to identify biomarkers in kidney cancer tissue that was also classified using the gold-standard of histopathology.

Results

The data analysis methodology showed good separation between clear cell renal cell carcinoma (ccRCC) versus non-clear cell RCC (non-ccRCC) and non-cancer cortical tissue from the kidneys of tumor-bearing patients. Variable Importance for the Projection (VIP) values, and OPLS-DA loadings plots were used to identify chemical species that correlated significantly with the histopathological classification. Model validation resulted in the correct classification of 37/43 biopsy samples, which included the correct classification of 15/17 ccRCC biopsies, achieving an overall predictive accuracy of 86%, Those chemical markers with a VIP value >1.2 were further analyzed using univariate statistical analysis. A subgroup analysis of 47 tumor tissues arising from T1 tumors revealed distinct separation between ccRCC and non-ccRCC tissues.

Conclusions

This study provides metabolic insights that could have future diagnostic and/or clinical value. The results of this work demonstrate a clear separation between clear cell and non-ccRCC and non-cancer kidney tissue from tumor-bearing patients. The clinical translation of these results will now require the development of a one-dimensional (1D) magnetic resonance spectroscopy (MRS) protocol, for the kidney, using an in vivo clinical MRI scanner.

Expression of GOT2 Is Epigenetically Regulated by DNA Methylation and Correlates with Immune Infiltrates in Clear-Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (KIRC) is the most common and highly malignant pathological type of kidney cancer, characterized by a profound metabolism dysregulation. As part of aspartate biosynthesis, mitochondrial GOT2 (glutamic-oxaloacetic transaminase 2) is essential for regulating cellular energy production and biosynthesis, linking multiple pathways. Nevertheless, the expression profile and prognostic significance of GOT2 in KIRC remain unclear. This study comprehensively analyzed the transcriptional levels, epigenetic regulation, correlation with immune infiltration, and prognosis of GOT2 in KIRC using rigorous bioinformatics analysis. We discovered that the expression levels of both mRNA and protein of GOT2 were remarkably decreased in KIRC tissues in comparison with normal tissues and were also significantly related to the clinical features and prognosis of KIRC. Remarkably, low GOT2 expression was positively associated with poorer overall survival (OS) and disease-free survival (DFS). Further analysis revealed that GOT2 downregulation is driven by DNA methylation in the promoter-related CpG islands. Finally, we also shed light on the influence of GOT2 expression in immune cell infiltration, suggesting that GOT2 may be a potential prognostic marker and therapeutic target for KIRC patients.

Molecular Subtypes Based on Genomic and Transcriptomic Features Correlate with the Responsiveness to Immune Checkpoint Inhibitors in Metastatic Clear Cell Renal Cell Carcinoma

Simple Summary

Immune checkpoint inhibitors (ICIs), such as programmed cell death protein 1 (PD-1) blockade, have proven to be the most effective agents for the management of many cancer types. Although ICIs are the current standard of care for treating metastatic clear cell renal cell carcinoma (ccRCC), 40–60% of patients still have intrinsic resistance to ICIs across multiple clinical trials. Therefore, identifying optimal biomarkers that can predict either responders or non-responders to ICIs has been of tremendous importance. Here, we generated targeted sequencing and whole transcriptomic sequencing of 60 patients with metastatic ccRCC treated with ICIs. Moreover, transcriptomic analysis was integrated to identify molecular subtypes using a total of 177 tumor samples by merging our data and published data derived from the CheckMate 025 trial. Our results show that these molecular subtypes are associated with specific genomic alterations, distinct molecular pathways, and differential clinical outcomes in patients with metastatic ccRCC treated with ICIs.

Abstract

Clear cell renal cell carcinoma (ccRCC) has been reported to be highly immune to and infiltrated by T cells and has angiogenesis features, but the effect of given features on clinical outcomes followed by immune checkpoint inhibitors (ICIs) in ccRCC has not been fully characterized. Currently, loss of function mutation in PBRM1, a PBAF-complex gene frequently mutated in ccRCC, is associated with clinical benefit from ICIs, and is considered as a predictive biomarker for response to anti-PD-1 therapy. However, functional mechanisms of PBRM1 mutation regarding immunotherapy responsiveness are still poorly understood. Here, we performed targeted sequencing (n = 60) and whole transcriptomic sequencing (WTS) (n = 61) of patients with metastatic ccRCC treated by ICIs. By integrating WTS data from the CheckMate 025 trial, we obtained WTS data of 177 tumors and finally identified three molecular subtypes that are characterized by distinct molecular phenotypes and frequency of PBRM1 mutations. Patient clustered subtypes 1 and 3 demonstrated worse responses and survival after ICIs treatment, with a low proportion of PBRM1 mutation and angiogenesis-poor, but were immune-rich and cell-cycle enriched. Notably, patients clustered in the subtype 2 showed a better response and survival after ICIs treatment, with enrichment of PBRM1 mutation and metabolic programs and a low exhausted immune phenotype. Further analysis of the subtype 2 population demonstrated that GATM (glycine amidinotransferase), as a novel gene associated with PBRM1 mutation, plays a pivotal role in ccRCC by using a cell culture model, revealing tumor, suppressive-like features in reducing proliferation and migration. In summary, we identified that metastatic ccRCC treated by ICIs have distinct genomic and transcriptomic features that may account for their responsiveness to ICIs. We also revealed that the novel gene GATM can be a potential tumor suppressor and/or can be associated with therapeutic efficacy in metastatic ccRCC treated by ICIs.

Exploring Histological Similarities Across Cancers From a Deep Learning Perspective

Histopathology image analysis is widely accepted as a gold standard for cancer diagnosis. The Cancer Genome Atlas (TCGA) contains large repositories of histopathology whole slide images spanning several organs and subtypes. However, not much work has gone into analyzing all the organs and subtypes and their similarities. Our work attempts to bridge this gap by training deep learning models to classify cancer vs. normal patches for 11 subtypes spanning seven organs (9,792 tissue slides) to achieve high classification performance. We used these models to investigate their performances in the test set of other organs (cross-organ inference). We found that every model had a good cross-organ inference accuracy when tested on breast, colorectal, and liver cancers. Further, high accuracy is observed between models trained on the cancer subtypes originating from the same organ (kidney and lung). We also validated these performances by showing the separability of cancer and normal samples in a high-dimensional feature space. We further hypothesized that the high cross-organ inferences are due to shared tumor morphologies among organs. We validated the hypothesis by showing the overlap in the Gradient-weighted Class Activation Mapping (GradCAM) visualizations and similarities in the distributions of nuclei features present within the high-attention regions.

Identification of the Expression and Clinical Significance of E2F Family in Clear Cell Renal Cell Carcinoma

Background

Multiple studies have identified that E2F transcriptions act as important regulators for the tumorigenesis and progression of several human cancers. However, little is known about the function of E2Fs in clear cell renal cell carcinoma (ccRCC).

Methods

We firstly investigated the expression levels, genetic alteration, and biological function of E2Fs in patients with ccRCC and the connections between the immune cell infiltration and the overall survivals of ccRCC patients with the E2Fs expression levels based on UALCAN, The Cancer Genome Atlas database, Gene Expression Profiling Interactive Analysis, TIMER, STRING, GSCALite and cBioPortal databases.

Results

Results revealed that the expression levels of E2F1/2/3/4/6/7/8 were markedly upregulated in patients with ccRCC, while the expression of E2F5 displayed an opposite trend. We also experimentally validated the overexpression of E2F3/4/7 in human ccRCC tissues and ccRCC cell lines. Furthermore, the high E2F1/2/3/4/7/8 expression levels were clearly associated with worse pathological characteristics of ccRCC, including high pathological stage, poor molecular subtypes and high tumor grade. Meanwhile, high expression levels of E2F1/2/4/7/8 were evidently associated with worse overall survivals (OSs) and progression-free survivals (PFSs) of patients harboring ccRCC. Univariate and multivariate analyses illustrated that the expressions of E2F4/5/7 were independent factors associated with the OSs and PFSs of patients with ccRCC. Meanwhile, the mutations in E2Fs were also significantly related to poor OSs and PFSs of patients with ccRCC. Mechanically, the E2Fs genes synergistically promoted the progression of ccRCC by accelerating the cell cycle and inhibiting DNA damage response and apoptosis after performing the protein structure, functional enrichment, and PPI network analyses. In addition, E2Fs genes were also significantly associated with tumor immune cells infiltration and the drug sensitivity in ccRCC.

Conclusion

As a result, E2F4/7 were highly expressed in ccRCC and significantly associated with worse pathological characteristics of ccRCC, including high pathological stage, poor molecular subtypes and high tumor grade, tumor immune cell infiltration, and drug sensitivity, consequently translating into poor OSs and PFSs of patients with ccRCC. Our results indicated that E2F4/7 could be potential biomarkers and therapeutic targets of ccRCC patients.

Identification of a Novel Defined Immune-Autophagy-Related Gene Signature Associated With Clinical and Prognostic Features of Kidney Renal Clear Cell Carcinoma

Background: As a common cancer of the urinary system in adults, renal clear cell carcinoma is metastatic in 30% of patients, and 1-2 years after diagnosis, 60% of patients die. At present, the rapid development of tumor immunology and autophagy had brought new directions to the treatment of renal cancer. Therefore, it was extremely urgent to find potential targets and prognostic biomarkers for immunotherapy combined with autophagy. Methods: Through GSE168845, immune-related genes, autophagy-related genes, and immune-autophagy-related differentially expressed genes (IAR-DEGs) were identified. Independent prognostic value of IAR-DEGs was determined by differential expression analysis, prognostic analysis, and univariate and multivariate Cox regression analyses. Then, the lasso Cox regression model was established to evaluate the correlation of IAR-DEGs with the immune score, immune checkpoint, iron death, methylation, and one-class logistic regression (OCLR) score. Results: In this study, it was found that CANX, BID, NAMPT, and BIRC5 were immune-autophagy-related genes with independent prognostic value, and the risk prognostic model based on them was well constructed. Further analysis showed that CANX, BID, NAMPT, and BIRC5 were significantly correlated with the immune score, immune checkpoint, iron death, methylation, and OCLR score. Further experimental results were consistent with the bioinformatics analysis. Conclusion: CANX, BID, NAMPT, and BIRC5 were potential targets and effective prognostic biomarkers for immunotherapy combined with autophagy in kidney renal clear cell carcinoma.

The role of redox homeostasis biomarkers in clear cell renal cell carcinoma development and progression

The clear cell renal cell carcinoma (ccRCC) is the most frequent and the most aggresive subtype of renal cell carcinoma usually detected at an already advanced stage. It might even be observed as a metabolic disease since complex molecular changes and disturbed redox homeostasis are its hallmark. As certain changes are characteristic for tumorigenesis, while some other for metastatic disease, the identification of metabolic modifications could also point out the stage of tumor progression. Hypoxia inducible factor, as a factor regulating transcription of genes encoding glycolytic enzymes, as well as controlling lipid accumulation, has a particular place in ccRCC development. Additionaly, disturbed redox homeostasis induces the Keap1/Nrf2 pathway which further modulates the synthesis of phase-II detoxifying metabolism enzymes. The upregulation of glutathione transferases, Pi class especially, inhibits kinase-dependent apoptosis that is essential in tumor progression. Furthermore, hydrogen peroxide (H2O2) acts as a signaling molecule conveying redox signals, while superoxide dismutase, as well as glutathione peroxidase are enzymes involved in its production and degradation. Hence, the activity of these enzymes impacts hydrogen peroxide levels and consequentially the ability of ccRCC cells to evade negative effect of reactive oxygen species.

Genome-Scale Metabolic Modeling Enables In-Depth Understanding of Big Data

Genome-scale metabolic models (GEMs) enable the mathematical simulation of the metabolism of archaea, bacteria, and eukaryotic organisms. GEMs quantitatively define a relationship between genotype and phenotype by contextualizing different types of Big Data (e.g., genomics, metabolomics, and transcriptomics). In this review, we analyze the available Big Data useful for metabolic modeling and compile the available GEM reconstruction tools that integrate Big Data. We also discuss recent applications in industry and research that include predicting phenotypes, elucidating metabolic pathways, producing industry-relevant chemicals, identifying drug targets, and generating knowledge to better understand host-associated diseases. In addition to the up-to-date review of GEMs currently available, we assessed a plethora of tools for developing new GEMs that include macromolecular expression and dynamic resolution. Finally, we provide a perspective in emerging areas, such as annotation, data managing, and machine learning, in which GEMs will play a key role in the further utilization of Big Data.

Fibrometabolism-An emerging therapeutic frontier in pulmonary fibrosis

Fibrosis is the final pathological outcome and major cause of morbidity and mortality in many common and chronic inflammatory, immune-mediated, and metabolic diseases. Despite the growing incidence of fibrotic diseases and extensive research efforts, there remains a lack of effective therapies that improve survival. The application of omics technologies has revolutionized our approach to identifying previously unknown therapeutic targets and potential disease biomarkers. The application of metabolomics, in particular, has improved our understanding of disease pathomechanisms and garnered a wave of scientific interest in the role of metabolism in the biology of myofibroblasts, the key effector cells of the fibrogenic response. Emerging evidence suggests that alterations in metabolism not only are a feature of but also may play an influential role in the pathogenesis of fibrosis, most notably in idiopathic pulmonary fibrosis (IPF), the most rapidly progressive and fatal of all fibrotic conditions. This review will detail the role of key metabolic pathways, their alterations in myofibroblasts, and the potential this new knowledge offers for the development of antifibrotic therapeutic strategies.

Involvement of Tricarboxylic Acid Cycle Metabolites in Kidney Diseases

Mitochondria are complex organelles that orchestrate several functions in the cell. The primary function recognized is energy production; however, other functions involve the communication with the rest of the cell through reactive oxygen species (ROS), calcium influx, mitochondrial DNA (mtDNA), adenosine triphosphate (ATP) levels, cytochrome c release, and also through tricarboxylic acid (TCA) metabolites. Kidney function highly depends on mitochondria; hence mitochondrial dysfunction is associated with kidney diseases. In addition to oxidative phosphorylation impairment, other mitochondrial abnormalities have been described in kidney diseases, such as induction of mitophagy, intrinsic pathway of apoptosis, and releasing molecules to communicate to the rest of the cell. The TCA cycle is a metabolic pathway whose primary function is to generate electrons to feed the electron transport system (ETS) to drives energy production. However, TCA cycle metabolites can also release from mitochondria or produced in the cytosol to exert different functions and modify cell behavior. Here we review the involvement of some of the functions of TCA metabolites in kidney diseases.

Machine Learning-Enabled Renal Cell Carcinoma Status Prediction Using Multiplatform Urine-Based Metabolomics

Renal cell carcinoma (RCC) is diagnosed through expensive cross-sectional imaging, frequently followed by renal mass biopsy, which is not only invasive but also prone to sampling errors. Hence, there is a critical need for a noninvasive diagnostic assay. RCC exhibits altered cellular metabolism combined with the close proximity of the tumor(s) to the urine in the kidney, suggesting that urine metabolomic profiling is an excellent choice for assay development. Here, we acquired liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) data followed by the use of machine learning (ML) to discover candidate metabolomic panels for RCC. The study cohort consisted of 105 RCC patients and 179 controls separated into two subcohorts: the model cohort and the test cohort. Univariate, wrapper, and embedded methods were used to select discriminatory features using the model cohort. Three ML techniques, each with different induction biases, were used for training and hyperparameter tuning. Assessment of RCC status prediction was evaluated using the test cohort with the selected biomarkers and the optimally tuned ML algorithms. A seven-metabolite panel predicted RCC in the test cohort with 88% accuracy, 94% sensitivity, 85% specificity, and 0.98 AUC. Metabolomics Workbench Study IDs are ST001705 and ST001706.

Clinicopathologic features of TDO2 overexpression in renal cell carcinoma

BACKGROUND

Tryptophan 2,3-dioxygenase (TDO2) is the primary enzyme catabolizing tryptophan. Several lines of evidence revealed that overexpression of TDO2 is involved in anoikis resistance, spheroid formation, proliferation, and invasion and correlates with poor prognosis in some cancers. The aim of this research was to uncover the expression and biofunction of TDO2 in renal cell carcinoma (RCC).

METHODS

To show the expression of TDO2 in RCC, we performed qRT-PCR and immunohistochemistry in integration with TCGA data analysis. The interaction of TDO2 with PD-L1, CD44, PTEN, and TDO2 expression was evaluated. We explored proliferation, colony formation, and invasion in RCC cells line affected by knockdown of TDO2.

RESULTS

RNA-Seq and immunohistochemical analysis showed that TDO2 expression was upregulated in RCC tissues and was associated with advanced disease and poor survival of RCC patients. Furthermore, TDO2 was co-expressed with PD-L1 and CD44. In silico analysis and in vitro knockout of PTEN in RCC cell lines revealed the ability of PTEN to regulate the expression of TDO2. Knockdown of TDO2 suppressed the proliferation and invasion of RCC cells.

CONCLUSION

Our results suggest that TDO2 might have an important role in disease progression and could be a promising marker for targeted therapy in RCC. (199 words).

The Association of Polymorphisms in Nrf2 and Genes Involved in Redox Homeostasis in the Development and Progression of Clear Cell Renal Cell Carcinoma

Deleterious effects of SNPs found in genes encoding transcriptional factors, as well as antioxidant and detoxification enzymes, are disputable; however, their functional significance seems to modify the risk for clear cell renal cell carcinoma (ccRCC) development and progression. We investigated the effect of specific Nrf2, SOD2, GPX1 gene variants and GSTP1ABCD haplotype on ccRCC risk and prognosis and evaluated the association between GSTP1 and regulatory (JNK1/2) and executor (caspase-3) apoptotic molecule expression in ccRCC tissue samples and the presence of GSTP1 : JNK1/2 protein : protein interactions. Genotyping was performed in 223 ccRCC patients and 336 matched controls by PCR-CTTP and qPCR. Protein expression was analyzed using immunoblot, while the existence of GSTP1 : JNK1 protein : protein interactions was investigated by immunoprecipitation experiments. An increased risk of ccRCC development was found among carriers of variant genotypes of both SOD2 rs4880 and GSTP1 rs1695 polymorphisms. Nrf2 rs6721961 genetic polymorphism in combination with both rs4880 and rs1695 showed higher ccRCC risk as well. Haplotype analysis revealed significant risk of ccRCC development in carriers of the GSTP1C haplotype. Furthermore, GSTP1 variant forms seem to affect the overall survival in ccRCC patients, and the proposed molecular mechanism underlying the GSTP1 prognostic role might be the presence of GSTP1 : JNK1/2 protein : protein interactions.

Indication of high lipid content in epithelial-mesenchymal transitions of breast tissues

The epithelial-mesenchymal transition (EMT) is a crucial process in cancer progression and metastasis. Study of metabolic changes during the EMT process is important in seeking to understand the biochemical changes associated with cancer progression, not least in scoping for therapeutic strategies aimed at targeting EMT. Due to the potential for high sensitivity and specificity, Raman spectroscopy was used here to study the metabolic changes associated with EMT in human breast cancer tissue. For Raman spectroscopy measurements, tissue from 23 patients were collected, comprising non-lesional, EMT and non-EMT formalin-fixed and paraffin embedded breast cancer samples. Analysis was made in the fingerprint Raman spectra region (600-1800 cm-1) best associated with cancer progression biochemical changes in lipid, protein and nucleic acids. The ANOVA test followed by the Tukey's multiple comparisons test were conducted to see if there existed differences between non-lesional, EMT and non-EMT breast tissue for Raman spectroscopy measurements. Results revealed that significant differences were evident in terms of intensity between the non-lesional and EMT samples, as well as the EMT and non-EMT samples. Multivariate analysis involving independent component analysis, Principal component analysis and non-negative least square were used to analyse the Raman spectra data. The results show significant differences between EMT and non-EMT cancers in lipid, protein, and nucleic acids. This study demonstrated the capability of Raman spectroscopy supported by multivariate analysis in analysing metabolic changes in EMT breast cancer tissue.

The Large Scale Structure of Human Metabolism Reveals Resilience via Extensive Signaling Crosstalk

Metabolism is loosely defined as the set of physical and chemical interactions associated with the processes responsible for sustaining life. Two evident features arise whenever one looks at metabolism: first, metabolism is conformed as a very complex and intertwined construct of the many associated biomolecular processes. Second, metabolism is characterized by a high degree of stability reflected by the organisms resilience to either environmental changes or pathogenic conditions. Here we will investigate the relationship between these two features. By having access to the full set of human metabolic interactions as reported in the highly curated KEGG database, we built an integrated human metabolic network comprising metabolic, transcriptional regulation, and protein-protein interaction networks. We hypothesized that a metabolic process may exhibit resilience if it can recover from perturbations at the pathway level; in other words, metabolic resilience could be due to pathway crosstalk which may implicate that a metabolic process could proceed even when a perturbation has occurred. By analyzing the topological structure of the integrated network, as well as the hierarchical structure of its main modules or subnetworks, we observed that behind biological resilience lies an intricate communication structure at the topological and functional level with pathway crosstalk as the main component. The present findings, alongside the advent of large biomolecular databases, such as KEGG may allow the study of the consequences of this redundancy and resilience for the study of healthy and pathological phenotypes with many potential applications in biomedical science.

Deuterium Depletion Inhibits Cell Proliferation, RNA and Nuclear Membrane Turnover to Enhance Survival in Pancreatic Cancer

The effects of deuterium-depleted water (DDW) containing deuterium (D) at a concentration of 25 parts per million (ppm), 50 ppm, 105 ppm and the control at 150 ppm were monitored in MIA-PaCa-2 pancreatic cancer cells by the real-time cell impedance detection xCELLigence method. The data revealed that lower deuterium concentrations corresponded to lower MiA PaCa-2 growth rate. Nuclear membrane turnover and nucleic acid synthesis rate at different D-concentrations were determined by targeted [1,2-13C2]-D-glucose fate associations. The data showed severely decreased oxidative pentose cycling, RNA ribose 13C labeling from [1,2-13C2]-D-glucose and nuclear membrane lignoceric (C24:0) acid turnover. Here, we treated advanced pancreatic cancer patients with DDW as an extra-mitochondrial deuterium-depleting strategy and evaluated overall patient survival. Eighty-six (36 male and 50 female) pancreatic adenocarcinoma patients were treated with conventional chemotherapy and natural water (control, 30 patients) or 85 ppm DDW (56 patients), which was gradually decreased to preparations with 65 ppm and 45 ppm deuterium content for each 1 to 3 months treatment period. Patient survival curves were calculated by the Kaplan-Meier method and Pearson correlation was taken between medial survival time (MST) and DDW treatment in pancreatic cancer patients. The MST for patients consuming DDW treatment (n = 56) was 19.6 months in comparison with the 6.36 months' MST achieved with chemotherapy alone (n = 30). There was a strong, statistically significant Pearson correlation (r = 0.504, p < 0.001) between survival time and length and frequency of DDW treatment.

Transcriptome Analysis of Ovarian and Uterine Clear Cell Malignancies

PURPOSE

Ovarian and uterine clear cell carcinomas (CCCs) are rare but associated with poor prognosis. This study explored RNA transcription patterns characteristic of these tumors.

EXPERIMENTAL DESIGN

RNA sequencing (RNA-seq) of 11 ovarian CCCs and five uterine CCCs was performed and compared to publicly available data from high grade serous ovarian cancers (HGSOCs). Ingenuity Pathway Analyses were performed. CIBERSORT analyses estimated relative fractions of 22 immune cell types in each RNA-seq sample. Sequencing data was correlated with PD-L1 immunohistochemical expression.

RESULTS

RNA-seq revealed 1,613 downregulated and 1,212 upregulated genes (corrected p < 0.05, |FC |≥10) in ovarian CCC versus HGSOC. Two subgroups were identified in the ovarian CCC, characterized by ethnicity and expression differences in ARID1A. There were 3,252 differentially expressed genes between PD-L1+/- ovarian CCCs, revealing immune response, cell death, and DNA repair networks, negatively correlated with PD-L1 expression, whereas cellular proliferation networks positively correlated with expression. In clear cell ovarian versus clear cell uterine cancer, 1,607 genes were significantly upregulated, and 109 genes were significantly downregulated (corrected p < 0.05, |FC|≥10). Comparative pathway analysis of late and early stage ovarian CCCs revealed unique metabolic and PTEN pathways, whereas uterine CCCs had unique Wnt/Ca+, estrogen receptor, and CCR5 signaling. CIBERSORT analysis revealed that activated mast cells and regulatory T cell populations were relatively enriched in uterine CCCs. The PD-L1+ ovarian CCCs had enriched resting NK cells and memory B cell populations, while PD-L1- had enriched CD8 T-cells, monocytes, eosinophils, and activated dendritic cells.

CONCLUSIONS

Unique transcriptional expression profiles distinguish clear cell uterine and ovarian cancers from each other and from other more common histologic subtypes. These insights may aid in devising novel therapeutics.

MicroRNA-1291-5p Sensitizes Pancreatic Carcinoma Cells to Arginine Deprivation and Chemotherapy through the Regulation of Arginolysis and Glycolysis

Cancer cells are dysregulated and addicted to continuous supply and metabolism of nutritional glucose and amino acids (e.g., arginine) to drive the synthesis of critical macromolecules for uncontrolled growth. Recent studies have revealed that genome-derived microRNA (miRNA or miR)-1291-5p (miR-1291-5p or miR-1291) may modulate the expression of argininosuccinate synthase (ASS1) and glucose transporter protein type 1 (GLUT1). We also developed a novel approach to produce recombinant miR-1291 agents for research, which are distinguished from conventional chemo-engineered miRNA mimics. Herein, we firstly demonstrated that bioengineered miR-1291 agent was selectively processed to high levels of target miR-1291-5p in human pancreatic cancer (PC) cells. After the suppression of ASS1 protein levels, miR-1291 perturbed arginine homeostasis and preferably sensitized ASS1-abundant L3.3 cells to arginine deprivation therapy. In addition, miR-1291 treatment reduced the protein levels of GLUT1 in both AsPC-1 and PANC-1 cells, leading to a lower glucose uptake (deceased > 40%) and glycolysis capacity (reduced approximately 50%). As a result, miR-1291 largely improved cisplatin efficacy in the inhibition of PC cell viability. Our results demonstrated that miR-1291 was effective to sensitize PC cells to arginine deprivation treatment and chemotherapy through targeting ASS1- and GLUT1-mediated arginolysis and glycolysis, respectively, which may provide insights into understanding miRNA signaling underlying cancer cell metabolism and development of new strategies for the treatment of lethal PC. SIGNIFICANCE STATEMENT: Many anticancer drugs in clinical use and under investigation exert pharmacological effects or improve efficacy of coadministered medications by targeting cancer cell metabolism. Using new recombinant miR-1291 agent, we revealed that miR-1291 acts as a metabolism modulator in pancreatic carcinoma cells through the regulation of argininosuccinate synthase- and glucose transporter protein type 1-mediated arginolysis and glycolysis. Consequently, miR-1291 effectively enhanced the efficacy of arginine deprivation (pegylated arginine deiminase) and chemotherapy (cisplatin), offering new insights into development of rational combination therapies.

[Advancement of E2F1 in Common Tumors]

细胞周期相关转录因子E2F1（E2F transcription factor 1）是细胞周期相关转录因子E2F家族成员之一，主要参与包括细胞周期进展、DNA修复、DNA复制、细胞分化，增殖和凋亡等多种细胞过程。E2F1在全身多种肿瘤组织和细胞中呈高表达，起着促癌基因的作用，E2F1表达上调与肿瘤的发生、发展、转移及预后密切相关。因此，E2F1有望成为肿瘤治疗的新靶点。本文就E2F1在目前常见肿瘤中的最新研究进展做一综述。