Accumulation of fructose 1,6-bisphosphate protects clear cell renal cell carcinoma from oxidative stress

MIOX inhibits autophagy to regulate the ROS -driven inhibition of STAT3/c-Myc-mediated epithelial-mesenchymal transition in clear cell renal cell carcinoma

The specific mechanism of clear cell renal cell carcinoma (ccRCC) progression, a pathological type that accounts for the highest proportion of RCC, remains unclear. In this study, bioinformatics analysis of scRNA-seq dataset in ccRCC revealed that MIOX was a gene specifically down-regulated in tumor epithelial cells of ccRCC. Analysis of the TCGA database further validated the association between decreased MIOX mRNA levels and ccRCC malignant phenotype and poor prognosis. Immunohistochemistry indicated the down-regulation of MIOX in ccRCC tissues compared to paired adjacent renal tissues, with further down-regulation of MIOX in the primary tumors of patients with primary metastasis compared to those without metastasis. Also, patients with low expression of MIOX showed shorter metastasis-free survival (MFS) compared to those with high MIOX expression. In vitro results showed that overexpression of MIOX in ccRCC cells inhibited the proliferation, migration and invasion and promoted apoptosis. Mechanistically, up-regulation of MIOX inhibited autophagy to elevate the levels of ROS, and thus suppressed STAT3/c-Myc-mediated epithelial-mesenchymal transition in ccRCC cells. In vivo data further confirmed that increased MIOX expression suppressed the growth and proliferation of RCC cells and reduced the ability of RCC cells to form metastases in the lung. This study demonstrates that MIOX is an important regulatory molecule of ccRCC, which is conducive to understanding the potential molecular mechanism of ccRCC progression.

Aldolase B attenuates clear cell renal cell carcinoma progression by inhibiting CtBP2

Aldolase B (ALDOB), a glycolytic enzyme, is uniformly depleted in clear cell renal cell carcinoma (ccRCC) tissues. We previously showed that ALDOB inhibited proliferation through a mechanism independent of its enzymatic activity in ccRCC, but the mechanism was not unequivocally identified. We showed that the corepressor C-terminal-binding protein 2 (CtBP2) is a novel ALDOB-interacting protein in ccRCC. The CtBP2-to-ALDOB expression ratio in clinical samples was correlated with the expression of CtBP2 target genes and was associated with shorter survival. ALDOB inhibited CtBP2-mediated repression of multiple cell cycle inhibitor, proapoptotic, and epithelial marker genes. Furthermore, ALDOB overexpression decreased the proliferation and migration of ccRCC cells in an ALDOB-CtBP2 interaction-dependent manner. Mechanistically, our findings showed that ALDOB recruited acireductone dioxygenase 1, which catalyzes the synthesis of an endogenous inhibitor of CtBP2, 4-methylthio 2-oxobutyric acid. ALDOB functions as a scaffold to bring acireductone dioxygenase and CtBP2 in close proximity to potentiate acireductone dioxygenase-mediated inhibition of CtBP2, and this scaffolding effect was independent of ALDOB enzymatic activity. Moreover, increased ALDOB expression inhibited tumor growth in a xenograft model and decreased lung metastasis in vivo. Our findings reveal that ALDOB is a negative regulator of CtBP2 and inhibits tumor growth and metastasis in ccRCC.

Low expression of SLC34A1 is associated with poor prognosis in clear cell renal cell carcinoma

OBJECTIVE

Clear cell renal cell carcinoma (ccRCC) is a malignant renal tumor that is highly prone to metastasis and recurrence. The exact pathogenesis of this cancer is still not well understood. This study aimed to identify novel hub genes in renal clear cell carcinoma and determine their diagnostic and prognostic value.

METHODS

Intersection genes were obtained from multiple databases, and protein-protein interaction analysis and functional enrichment analysis were performed to identify key pathways related to the intersection genes. Hub genes were identified using the cytoHubba plugin in Cytoscape. GEPIA and UALCAN were utilized to observe differences in mRNA and protein expression of hub genes between KIRC and adjacent normal tissues. The Wilcoxon rank sum test was used to analyze hub gene levels between paired KIRC and matched non-cancer samples. IHC results were obtained from the HPA online database, and according to the median gene expression level, they were divided into a high-expression group and a low-expression group. The correlation of these groups with the prognosis of KIRC patients was analyzed. Logistic regression and the Wilcoxon rank sum test were used to test the relationship between SLC34A1 level and clinicopathological features. The diagnostic value of SLC34A1 was evaluated by drawing the receiver operating characteristic (ROC) curve and calculating the area under the curve (AUC). Cox regression analysis was used to analyze the relationship between clinicopathological features, SLC34A1 expression, and KIRC survival rate. LinkedOmics was used to obtain the genes most related to SLC34A1 and their functional enrichment. Genetic mutations and methylation levels of SLC34A1 in KIRC were obtained from the cBioPortal website and the MethSurv website, respectively.

RESULTS

Fifty-eight ccRCC differential genes were identified from six datasets, and they were mainly enriched in 10 functional items and 4 pathways. A total of 5 hub genes were identified. According to the GEPIA database analysis, low expression of SLC34A1, CASR, and ALDOB in tumors led to poor prognosis. Low expression of SLC34A1 mRNA was found to be related to clinicopathological features of patients. SLC34A1 expression in normal tissues could accurately identify tumors (AUC 0.776). SLC34A1 was also found to be an independent predictor of ccRCC in univariate and multivariate Cox analyses. The mutation rate of the SLC34A1 gene was 13%. Eight of the 10 DNA methylated CpG sites were associated with the prognosis of ccRCC. SLC34A1 expression in ccRCC was positively correlated with B cells, eosinophils, neutrophils, T cells, TFH, and Th17 cells, and negatively correlated with Tem, Tgd, and Th2 cells.

CONCLUSION

The expression level of SLC34A1 in KIRC samples was found to be decreased, which predicted a decreased survival rate of KIRC. SLC34A1 may serve as a molecular prognostic marker and therapeutic target for KIRC patients.

Prognostic values of ALDOB expression and 18F-FDG PET/CT in hepatocellular carcinoma

Purpose

The glycolytic enzyme fructose 1,6-bisphosphate aldolase B (ALDOB) is aberrantly expressed and impacts the prognosis in hepatocellular carcinoma (HCC). Hepatic ALDOB loss leads to paradoxical upregulation of glucose metabolism, favoring hepatocellular carcinogenesis. Nevertheless, the relationship between ALDOB expression and ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) uptake, and their effects on HCC prognosis remain unclear. We evaluated whether ALDOB expression is associated with ¹⁸F-FDG uptake and their impacts on HCC prognosis prediction.

Methods

Changes in ALDOB expression levels and the prognostic values in HCC were analyzed using data from The Cancer Genome Atlas (TCGA) database. Ultimately, 34 patients with HCC who underwent ¹⁸F-FDG positron emission tomography/computed tomography (PET/CT) preoperatively were enrolled in this retrospective study. ALDOB expression was determined using immunohistochemistry (IHC) staining, and the maximum standardized uptake value (SUVmax) of HCC was calculated from the ¹⁸F-FDG PET/CT scans. The relationship between ALDOB expression and SUVmax was examined, and their impacts on overall survival were evaluated using Cox proportional hazards models and Kaplan-Meier survival analysis. ALDOB overexpression in HUH7 and 7721 cells was used to analyze its role in tumor metabolism.

Results

According to TCGA database, the ALDOB mRNA level was downregulated in HCC compared to normal tissue, and significantly shortened overall survival in HCC patients. ALDOB protein expression was similarly decreased in IHC findings in HCC than that in adjacent normal tissues (P<0.05) and was significantly associated with tumor size (P<0.001), high tumor-node-metastasis stage (P=0.022), and elevated SUVmax (P=0.009). ALDOB expression in HCC was inversely correlated with SUVmax (r=-0.454; P=0.012), and the optimal SUVmax cutoff value for predicting its expression was 4.15. Prognostically, low ALDOB expression or SUVmax ≥3.9 indicated shorter overall survival time in HCC. Moreover, COX regression analysis suggested high SUVmax as an independent prognostic risk factor for HCC (P=0.036). HCC patients with negative ALDOB expression and positive SUVmax (≥3.9) were correlated with worse prognosis. ALDOB overexpression in HCC cells significantly decreases ¹⁸F-FDG uptake and lactate production.

Conclusion

SUVmax in HCC patients is inversely correlated with ALDOB expression, and ¹⁸F-FDG PET/CT may be useful for ALDOB status prediction. The combined use of ALDOB expression and ¹⁸F-FDG PET/CT data can provide additional information on disease prognosis in HCC patients.

Single-cell transcriptomics reveals common epithelial response patterns in human acute kidney injury

Background

Acute kidney injury (AKI) occurs frequently in critically ill patients and is associated with adverse outcomes. Cellular mechanisms underlying AKI and kidney cell responses to injury remain incompletely understood.

Methods

We performed single-nuclei transcriptomics, bulk transcriptomics, molecular imaging studies, and conventional histology on kidney tissues from 8 individuals with severe AKI (stage 2 or 3 according to Kidney Disease: Improving Global Outcomes (KDIGO) criteria). Specimens were obtained within 1–2 h after individuals had succumbed to critical illness associated with respiratory infections, with 4 of 8 individuals diagnosed with COVID-19. Control kidney tissues were obtained post-mortem or after nephrectomy from individuals without AKI.

Results

High-depth single cell-resolved gene expression data of human kidneys affected by AKI revealed enrichment of novel injury-associated cell states within the major cell types of the tubular epithelium, in particular in proximal tubules, thick ascending limbs, and distal convoluted tubules. Four distinct, hierarchically interconnected injured cell states were distinguishable and characterized by transcriptome patterns associated with oxidative stress, hypoxia, interferon response, and epithelial-to-mesenchymal transition, respectively. Transcriptome differences between individuals with AKI were driven primarily by the cell type-specific abundance of these four injury subtypes rather than by private molecular responses. AKI-associated changes in gene expression between individuals with and without COVID-19 were similar.

Conclusions

The study provides an extensive resource of the cell type-specific transcriptomic responses associated with critical illness-associated AKI in humans, highlighting recurrent disease-associated signatures and inter-individual heterogeneity. Personalized molecular disease assessment in human AKI may foster the development of tailored therapies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-022-01108-9.

Serum metabolomic analysis of men on a low-carbohydrate diet for biochemically recurrent prostate cancer reveals the potential role of ketogenesis to slow tumor growth: a secondary analysis of the CAPS2 diet trial

BACKGROUND

Systemic treatments for prostate cancer (PC) have significant side effects. Thus, newer alternatives with fewer side effects are urgently needed. Animal and human studies suggest the therapeutic potential of low carbohydrate diet (LCD) for PC. To test this possibility, Carbohydrate and Prostate Study 2 (CAPS2) trial was conducted in PC patients with biochemical recurrence (BCR) after local treatment to determine the effect of a 6-month LCD intervention vs. usual care control on PC growth as measured by PSA doubling time (PSADT). We previously reported the LCD intervention led to significant weight loss, higher HDL, and lower triglycerides and HbA1c with a suggested longer PSADT. However, the metabolic basis of these effects are unknown.

METHODS

To identify the potential metabolic basis of effects of LCD on PSADT, serum metabolomic analysis was performed using baseline, month 3, and month 6 banked sera to identify the metabolites significantly altered by LCD and that correlated with varying PSADT.

RESULTS

LCD increased the serum levels of ketone bodies, glycine and hydroxyisocaproic acid. Reciprocally, LCD reduced the serum levels of alanine, cytidine, asymmetric dimethylarginine (ADMA) and 2-oxobutanoate. As high ADMA level is shown to inhibit nitric oxide (NO) signaling and contribute to various cardiovascular diseases, the ADMA repression under LCD may contribute to the LCD-associated health benefit. Regression analysis of the PSADT revealed a correlation between longer PSADT with higher level of 2-hydroxybutyric acids, ketone bodies, citrate and malate. Longer PSADT was also associated with LCD reduced nicotinamide, fructose-1, 6-biphosphate (FBP) and 2-oxobutanoate.

CONCLUSION

These results suggest a potential association of ketogenesis and TCA metabolites with slower PC growth and conversely glycolysis with faster PC growth. The link of high ketone bodies with longer PSADT supports future studies of ketogenic diets to slow PC growth.

Identification of Hub Genes Associated With Clear Cell Renal Cell Carcinoma by Integrated Bioinformatics Analysis

Background

Clear cell renal cell carcinoma (ccRCC) is a common genitourinary cancer type with a high mortality rate. Due to a diverse range of biochemical alterations and a high level of tumor heterogeneity, it is crucial to select highly validated prognostic biomarkers to be able to identify subtypes of ccRCC early and apply precision medicine approaches.

Methods

Transcriptome data of ccRCC and clinical traits of patients were obtained from the GSE126964 dataset of Gene Expression Omnibus and The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) database. Weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) screening were applied to detect common differentially co-expressed genes. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes analysis, survival analysis, prognostic model establishment, and gene set enrichment analysis were also performed. Immunohistochemical analysis results of the expression levels of prognostic genes were obtained from The Human Protein Atlas. Single-gene RNA sequencing data were obtained from the GSE131685 and GSE171306 datasets.

Results

In the present study, a total of 2,492 DEGs identified between ccRCC and healthy controls were filtered, revealing 1,300 upregulated genes and 1,192 downregulated genes. Using WGCNA, the turquoise module was identified to be closely associated with ccRCC. Hub genes were identified using the maximal clique centrality algorithm. After having intersected the hub genes and the DEGs in GSE126964 and TCGA-KIRC dataset, and after performing univariate, least absolute shrinkage and selection operator, and multivariate Cox regression analyses, ALDOB, EFHD1, and ESRRG were identified as significant prognostic factors in patients diagnosed with ccRCC. Single-gene RNA sequencing analysis revealed the expression profile of ALDOB, EFHD1, and ESRRG in different cell types of ccRCC.

Conclusions

The present results demonstrated that ALDOB, EFHD1, and ESRRG may act as potential targets for medical therapy and could serve as diagnostic biomarkers for ccRCC.

Development and Validation of a Hypoxia-Related Signature for Predicting Survival Outcomes in Patients With Bladder Cancer

Objectives

This study aimed to develop and validate a hypoxia signature for predicting survival outcomes in patients with bladder cancer.

Methods

We downloaded the RNA sequence and the clinicopathologic data of the patients with bladder cancer from The Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov/repository?facetTab=files) and the Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/) databases. Hypoxia genes were retrieved from the Molecular Signatures Database (https://www.gsea-msigdb.org/gsea/msigdb/index.jsp). Differentially expressed hypoxia-related genes were screened by univariate Cox regression analysis and Lasso regression analysis. Then, the selected genes constituted the hypoxia signature and were included in multivariate Cox regression to generate the risk scores. After that, we evaluate the predictive performance of this signature by multiple receiver operating characteristic (ROC) curves. The CIBERSORT tool was applied to investigate the relationship between the hypoxia signature and the immune cell infiltration, and the maftool was used to summarize and analyze the mutational data. Gene-set enrichment analysis (GSEA) was used to investigate the related signaling pathways of differentially expressed genes in both risk groups. Furthermore, we developed a model and presented it with a nomogram to predict survival outcomes in patients with bladder cancer.

Results

Eight genes (AKAP12, ALDOB, CASP6, DTNA, HS3ST1, JUN, KDELR3, and STC1) were included in the hypoxia signature. The patients with higher risk scores showed worse overall survival time than the ones with lower risk scores in the training set (TCGA) and two external validation sets (GSE13507 and GSE32548). Immune infiltration analysis showed that two types of immune cells (M0 and M1 macrophages) had a significant infiltration in the high-risk group. Tumor mutation burden (TMB) analysis showed that the risk scores between the wild types and the mutation types of TP53, MUC16, RB1, and FGFR3 were significantly different. Gene-Set Enrichment Analysis (GSEA) showed that immune or cancer-associated pathways belonged to the high-risk groups and metabolism-related signal pathways were enriched into the low-risk group. Finally, we constructed a predictive model with risk score, age, and stage and validated its performance in GEO datasets.

Conclusion

We successfully constructed and validated a novel hypoxia signature in bladder cancer, which could accurately predict patients’ prognosis.

Differential Expression of ADP/ATP Carriers as a Biomarker of Metabolic Remodeling and Survival in Kidney Cancers

ADP/ATP carriers (AACs) are mitochondrial transport proteins playing a strategic role in maintaining the respiratory chain activity, fueling the cell with ATP, and also regulating mitochondrial apoptosis. To understand if AACs might represent a new molecular target for cancer treatment, we evaluated AAC expression levels in cancer/normal tissue pairs available on the Tissue Cancer Genome Atlas database (TCGA), observing that AACs are dysregulated in most of the available samples. It was observed that at least two AACs showed a significant differential expression in all the available kidney cancer/normal tissue pairs. Thus, we investigated AAC expression in the corresponding kidney non-cancer (HK2)/cancer (RCC-Shaw and CaKi-1) cell lines, grown in complete medium or serum starvation, for investigating how metabolic alteration induced by different growth conditions might influence AAC expression and resistance to mitochondrial apoptosis initiators, such as "staurosporine" or the AAC highly selective inhibitor "carboxyatractyloside". Our analyses showed that AAC2 and AAC3 transcripts are more expressed than AAC1 in all the investigated kidney cell lines grown in complete medium, whereas serum starvation causes an increase of at least two AAC transcripts in kidney cancer cell lines compared to non-cancer cells. However, the total AAC protein content is decreased in the investigated cancer cell lines, above all in the serum-free medium. The observed decrease in AAC protein content might be responsible for the decrease of OXPHOS activity and for the observed lowered sensitivity to mitochondrial apoptosis induced by staurosporine or carboxyatractyloside. Notably, the cumulative probability of the survival of kidney cancer patients seriously decreases with the decrease of AAC1 expression in KIRC and KIRP tissues making AAC1 a possible new biomarker of metabolic remodeling and survival in kidney cancers.

Development of cancer prognostic signature based on pan-cancer proteomics

Utilizing genomic data to predict cancer prognosis was insufficient. Proteomics can improve our understanding of the etiology and progression of cancer and improve the assessment of cancer prognosis. And the Clinical Proteomic Tumor Analysis Consortium (CPTAC) has generated extensive proteomics data of the vast majority of tumors. Based on CPTAC, we can perform a proteomic pan-carcinoma analysis. We collected the proteomics data and clinical features of cancer patients from CPTAC. Then, we screened 69 differentially expressed proteins (DEPs) with R software in five cancers: hepatocellular carcinoma (HCC), children’s brain tumor tissue consortium (CBTTC), clear cell renal cell carcinoma (CCRC), lung adenocarcinoma (LUAD) and uterine corpus endometrial carcinoma (UCEC). GO and KEGG analysis were performed to clarify the function of these proteins. We also identified their interactions. The DEPs-based prognostic model for predicting over survival was identified by least absolute shrinkage and selection operator (LASSO)-Cox regression model in training cohort. Then, we used the time-dependent receiver operating characteristics analysis to evaluate the ability of the prognostic model to predict overall survival and validated it in validation cohort. The results showed that the DEPs-based prognostic model could accurately and effectively predict the survival rate of most cancers.

Various forms of HIF-1α protein characterize the clear cell renal cell carcinoma cell lines

Renal cell carcinoma (RCC) represents around 2-3% of all malignancies diagnosed in adult patients. Most frequent (around 70-80% cases) and the most aggressive subtype is clear cell RCC (ccRCC). Mutations in VHL (von Hippel Lindau) gene, characteristic for this cancer type, lead to altered activity of the trimeric VBC (pVHL-elongin B-C) complex and consequently to HIF-1α stabilization. In this study, we present results of exhaustive investigation of HIF-1α alternative transcript variants abundance in A498, CAKI-1, and 786-O ccRCC cell lines. We proved the existence of truncated HIF-1α protein form (HIF1A∆-6) in A498 and HIF1A gene rearrangements in 786-O cell lines. Subsequently, we found that HIF1A∆2-6 was more stable than the full-length HIF-1α. Moreover, the shorter HIF-1α was insensitive for hypoxia and was overaccumulated after proteasome inhibitor treatment indicative of potential diversified roles of full-length and truncated HIF-1α forms in the cell. We also showed that A498, CAKI-1, and 786-O exhibit differential expression of various regulatory genes involved in the control of metabolic processes, that is, glucose and lipid metabolism, and encoding subunits of such machineries like SWI/SNF chromatin remodeling complex. Furthermore, these cell lines exhibited differential responses to axitinib, everolimus, and sunitinib-anticancer drugs-in normoxia and hypoxia as well as various alterations in metabolism-related regulatory processes. Finally, we have shown that overexpression of truncated HIF1A∆2-6 form may affect the protein level of endogenous full-length HIF-1α protein. Thus, our study proves an important role of HIF-1α in the ccRCC development.

Glutathione Metabolism in Renal Cell Carcinoma Progression and Implications for Therapies

A significantly increased level of the reactive oxygen species (ROS) scavenger glutathione (GSH) has been identified as a hallmark of renal cell carcinoma (RCC). The proposed mechanism for increased GSH levels is to counteract damaging ROS to sustain the viability and growth of the malignancy. Here, we review the current knowledge about the three main RCC subtypes, namely clear cell RCC (ccRCC), papillary RCC (pRCC), and chromophobe RCC (chRCC), at the genetic, transcript, protein, and metabolite level and highlight their mutual influence on GSH metabolism. A further discussion addresses the question of how the manipulation of GSH levels can be exploited as a potential treatment strategy for RCC.