Integrative Epigenetic and Gene Expression Analysis of Renal Tumor Progression to Metastasis

Construction of a 12-Gene Prognostic Risk Model and Tumor Immune Microenvironment Analysis Based on the Clear Cell Renal Cell Carcinoma Model

OBJECTIVES

Accurate survival predictions and early interventional therapy are crucial for people with clear cell renal cell carcinoma (ccRCC).

METHODS

In this retrospective study, we identified differentially expressed immune-related (DE-IRGs) and oncogenic (DE-OGs) genes from The Cancer Genome Atlas (TCGA) dataset to construct a prognostic risk model using univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analysis. We compared the immunogenomic characterization between the high- and low-risk patients in the TCGA and the PUCH cohort, including the immune cell infiltration level, immune score, immune checkpoint, and T-effector cell- and interferon (IFN)-γ-related gene expression.

RESULTS

A prognostic risk model was constructed based on 9 DE-IRGs and 3 DE-OGs and validated in the training and testing TCGA datasets. The high-risk group exhibited significantly poor overall survival compared with the low-risk group in the training (P < 0.0001), testing (P = 0.016), and total (P < 0.0001) datasets. The prognostic risk model provided accurate predictive value for ccRCC prognosis in all datasets. Decision curve analysis revealed that the nomogram showed the best net benefit for the 1-, 3-, and 5-year risk predictions. Immunogenomic analyses of the TCGA and PUCH cohorts showed higher immune cell infiltration levels, immune scores, immune checkpoint, and T-effector cell- and IFN-γ-related cytotoxic gene expression in the high-risk group than in the low-risk group.

CONCLUSION

The 12-gene prognostic risk model can reliably predict overall survival outcomes and is strongly associated with the tumor immune microenvironment of ccRCC.

Genetic and Epigenetic Characteristics in Isolated Pancreatic Metastases of Clear-Cell Renal Cell Carcinoma

Isolated pancreatic metastases of renal cell carcinoma (IsPMRCC) are a rare manifestation of metastatic, clear-cell renal cell carcinoma (RCC) in which distant metastases occur exclusively in the pancreas. In addition to the main symptom of the isolated occurrence of pancreatic metastases, the entity surprises with additional clinical peculiarities: (a) the unusually long interval of about 9 years between the primary RCC and the onset of pancreatic metastases; (b) multiple pancreatic metastases occurring in 36% of cases; (c) favourable treatment outcomes with a 75% 5-year survival rate; and (d) volume and growth-rate dependent risk factors generally accepted to be relevant for overall survival in metastatic surgery are insignificant in isPMRCC. The genetic and epigenetic causes of exclusive pancreatic involvement have not yet been investigated and are currently unknown. Conversely, according to the few available data in the literature, the following genetic and epigenetic peculiarities can already be identified as the cause of the protracted course: 1. high genetic stability of the tumour cell clones in both the primary tumour and the pancreatic metastases; 2. a low frequency of copy number variants associated with aggressiveness, such as 9p, 14q and 4q loss; 3. in the chromatin-modifying genes, a decreased rate of PAB1 (3%) and an increased rate of PBRM1 (77%) defects are seen, a profile associated with a favourable course; 4. an increased incidence of KDM5C mutations, which, in common with increased PBRM1 alterations, is also associated with a favourable outcome; and 5. angiogenetic biomarkers are increased in tumour tissue, while inflammatory biomarkers are decreased, which explains the good response to TKI therapy and lack of sensitivity to IT.

Abnormally elevated EZH2-mediated H3K27me3 enhances osteogenesis in aortic valve interstitial cells by inhibiting SOCS3 expression

BACKGROUND AND AIMS

The osteogenic transition of aortic valve interstitial cells (AVICs) plays a critical role for the progression of calcific aortic valve disease (CAVD). Enhancer of zeste homolog 2 (EZH2) is an important methyltransferase for histone H3 Lys27 (H3K27) that has been found to be involved in osteogenesis. Here, we investigated the effect and mechanism of EZH2 in CAVD progression.

METHODS

High throughout mRNA sequencing, qRT-PCR and immunoblot were performed to screen differentially expressed genes in non-CAVD and CAVD aortic valves. To investigate the role of EZH2 and SOCS3 in osteogenesis, AVICs were treated with siRNA, adenovirus and specific inhibitors, then osteogenic markers and mineralized deposits were examined. In vivo, the morphology and function of aortic valves were investigated by HE stain and echocardiography in ApoE^-/- mice fed a long-term western diet (WD).

RESULTS

We discovered that EZH2 was upregulated and SOCS3 was downregulated in calcified aortic valves. In AVICs, inhibition or silencing of EZH2 attenuated the osteogenic responses. On the other hand, demethylases inhibitor (GSK-J4) enhanced osteogenic transition of AVICs. Moreover, SOCS3 knockdown enhanced the expression of osteogenic markers, while SOCS3 overexpression suppressed osteogenesis and calcification. The chromatin immunoprecipitation and restored experiments indicated that EZH2 directly targeted SOCS3 to promote osteogenic responses of AVICs. In vivo, treatment with EZH2 inhibitor through intraperitoneal injection attenuated aortic valve thickening, calcification and dysfunction induced by WD.

CONCLUSIONS

Collectively, we found that EZH2-mediated H3K27me3 enhanced osteogenesis and microcalcification of AVICs via inhibiting SOCS3 expression, which provides potential targets for future therapeutic interventions of CAVD.

SPTBN1 abrogates renal clear cell carcinoma progression via glycolysis reprogramming in a GPT2-dependent manner

BACKGROUND

Renal clear cell carcinoma (ccRCC) is the most prevalent tumors worldwide. Discovering effective biomarkers is essential to monitor the prognosis and provide alternative clinical options. SPTBN1 is implicated in various cancerous processes. However, its role in ccRCC remains unelucidated. This study intends to explore the biological function and mechanism of SPTBN1 in ccRCC.

METHODS

Single-cell and bulk RNA-seq, tissue microarray, real-time quantitative PCR, and western blotting were applied to verify the expression and predictive value of SPTBN1 in ccRCC. Gain or loss of functional ccRCC cell line models were constructed, and in vitro and in vivo assays were performed to elucidate its tumorigenic phenotypes. Actinomycin D experiment, RNA immunoprecipitation (RIP), specific inhibitors, and rescue experiments were carried out to define the molecular mechanisms.

RESULTS

SPTBN1 was down-regulated in ccRCC and knockdown of SPTBN1 displayed a remarkably oncogenic role both in vitro and in vivo; while overexpressing SPTBN1 reversed this effect. SPTBN1 mediated ccRCC progression via the pathway of glutamate pyruvate transaminase 2 (GPT2)-dependent glycolysis. The expression of GPT2 was significantly negatively correlated with that of SPTBN1. As an RNA binding protein SPTBN1, regulated the mRNA stability of GPT2.

CONCLUSION

Our research demonstrated that SPTBN1 is significantly down-regulated in ccRCC. SPTBN1 knockdown promotes ccRCC progression via activating GPT2-dependent glycolysis. SPTBN1 may serve as a therapeutic target for the treatment of ccRCC.

Comprehensive analysis of FRAS1/FREM family as potential biomarkers and therapeutic targets in renal clear cell carcinoma

Background: FRAS1 (Fraser syndrome protein 1), together with FREM1 (the Fras1-related extracellular matrix proteins 1) and FREM2, belonging to the FRAS1/FREM extracellular matrix protein family, are considered to play essential roles in renal organogenesis and cancer progression. However, their roles in kidney renal clear cell carcinoma (KIRC) remain to be elucidated.

Methods: FRAS1/FREM RNA expression analysis was performed using TCGA/GTEx databases, and valided using GEO databases and real-time PCR. Protein expression was peformed using CPTAC databases. Herein, we employed an array of bioinformatics methods and online databases to explore the potential oncogenic roles of FRAS1/FREM in KIRC.

Results: We found that FRAS1, FREM1 and FREM2 genes and proteins expression levels were significantly decreased in KIRC tissues than in normal tissues. Decreased FRAS1/FREM expression levels were significantly associated with advanced clinicopathological parameters (pathological stage, grade and tumor metastasis status). Notably, the patients with decreased FRAS1/FREM2 expression showed a high propensity for metastasis and poor prognosis. FRAS1/FREM were correlated with various immune infiltrating cells, especially CD4+ T cells and its corresponding subsets (Th1, Th2, Tfh and Tregs). FRAS1 and FREM2 had association with DNA methylation and their single CpG methylation levels were associated with prognosis. Moreover, FRAS1/FREM might exert antitumor effects by functioning in key oncogenic signalling pathways and metabolic pathways. Drug sensitivity analysis indicated that high FRAS1 and FREM2 expression can be a reliable predictor of targeted therapeutic drug response, highlighting the potential as anticancer drug targets.

Conclusion: Together, our results indicated that FRAS1/FREM family members could be potential therapeutic targets and valuable prognostic biomarkers of KIRC.

Identification of ST3GAL5 as a prognostic biomarker correlating with CD8+ T cell exhaustion in clear cell renal cell carcinoma

Aberrant sialylation is frequently observed in tumor development, but which sialyltransferases are involved in this event are not well known. Herein, we performed comprehensive analyses on six ST3GAL family members, the α-2,3 sialyltransferases, in clear cell renal cell carcinoma (ccRCC) from public datasets. Only ST3GAL5 was consistently and significantly overexpressed in ccRCC (n = 791 in total), compared with normal kidney tissues. Its overexpression was positively correlated with tumor stage, grade, and the poor prognosis in ccRCC patients. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses indicated the involvement of ST3GAL5 in tumor immunoregulation. Then we revealed that ST3GAL5 expression showed a positive correlation with CD8⁺ T cell infiltration, using multiple tools on TIMER2.0 web server. Notably, ST3GAL5 overexpression was further identified to be associated with expression signature of CD8⁺ T cell exhaustion in ccRCC samples from three datasets (n = 867 in total; r > 0.3, p < 0.001). In our own ccRCC cohort (n = 45), immunohistochemistry and immunofluorescence staining confirmed that ST3GAL5 overexpression was accompanied by high CD8⁺ T cell infiltration with the increased exhaustion markers. Altogether, ST3GAL5 as a promising prognostic biomarker with CD8⁺ T cell exhaustion in ccRCC is indicated.

Breast cancer metastasis: is it a matter of OMICS and proper ex-vivo models?

Genomics has greatly increased the understanding of the study of breast cancer (BC) and has shaped the concept of intra-tumor heterogeneity, currently recognized as a propelling force for cancer progression. In this context, knowledge and understanding of metastatic breast cancer (mBC) has somehow lagged behind that of primary breast cancer. This may be explained by the relative scarcity of matched mBC samples, however it is possible that the mutation spectrum obtained from primary BC does not capture the full complexity of the metastatic disease. Here, we provide a few examples supporting this possibility, from public databases. We evoke the need to perform an integrated multi-OMICS characterization of mBC, to obtain a broad understanding of this complex disease, whose evolution cannot be explained solely by genomics. Pertinent to this, we suggest that rather an infrequent use of Patient-Derived –Tumor-Organoids (PDTOs) may be influenced by assuming that the metastatic conditions of PDTOs growth (mPDTOs) should be similar to those of the tissue of origin. We challenge this view by suggesting that the use of “target-organ inspired” growth conditions for mPDTOs, may better fit the emerging knowledge of metastatic disease. Thus, the integrated use of multi-OMICS and of clinically relevant mPDTOs may allow a further understanding of such disease and foster therapeutically relevant advances. We believe that our points may be valid for other solid cancers.

Integrated bioinformatics analysis to identify the key gene associated with metastatic clear cell renal cell carcinoma

Metastasis of clear cell renal cell carcinoma (ccRCC) is a leading cause of death. The purpose of this research was to investigate the key gene in ccRCC tumor metastasis. Three microarray datasets (GSE22541, GSE85258, and GSE105261), which included primary and metastatic ccRCC tissues, were obtained from the Gene Expression Omnibus (GEO) database. Expression profiling and clinical data of ccRCC were downloaded from The Cancer Genome Atlas (TCGA) dataset. A total of 20 overlapping differentially expressed genes (DEGs) were identified using the R limma package. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated that the DEGs were mainly enriched in tumor metastasis-related pathways. Gene expression analysis and survival analysis in the GEPIA2 database further identified the key gene HSD11B2. qRT-PCR result manifested that HSD11B2 level was significantly down-regulated in ccRCC tissues compared with adjacent normal tissues. ROC analysis showed that HSD11B2 exhibited good diagnostic efficiency for metastatic and non-metastatic ccRCC. Univariate and multivariate Cox regression analysis showed that HSD11B2 expression was an independent prognostic factor. To establish a nomogram combining HSD11B2 expression and clinical factors, and a new method for predicting the survival probability of ccRCC patients. Gene Set Enrichment Analysis (GSEA) enrichment results showed that low expression of HSD11B2 was mainly enriched in tumor signaling pathways and immune-related pathways. Immune analysis revealed a significant correlation between HSD11B2 and tumor immune infiltrates in ccRCC. This study suggests that HSD11B2 can serve as a potential biomarker and therapeutic target for ccRCC metastasis.

Exploring the synergistic effects of cabozantinib and a programmed cell death protein 1 inhibitor in metastatic renal cell carcinoma with machine learning

Clinical evidence supports the combination of cabozantinib with an immune checkpoint inhibitor for the treatment of metastatic clear cell renal cell carcinoma (mccRCC) and suggests a synergistic antitumour activity of this combination. Nevertheless, the biological basis of this synergy is not fully characterized. We studied the mechanisms underpinning the potential synergism of cabozantinib combined with a PD1 inhibitor in mccRCC and delved into cabozantinib monotherapy properties supporting its role to partner these combinations. To model physiological drug action, we used a machine learning-based technology known as Therapeutic Performance Mapping Systems, applying two approaches: Artificial Neural Networks and Sampling Methods. We found that the combined therapy was predicted to exert a wide therapeutic action in the tumour and the microenvironment. Cabozantinib may enhance the effects of PD1 inhibitors on immunosurveillance by modulating the innate and adaptive immune system, through the inhibition of VEGF-VEGFR and Gas6-AXL/TYRO3/MER (TAM) axes, while the PD1 inhibitors may boost the antiangiogenic and pro-apoptotic effects of cabozantinib by modulating angiogenesis and T-cell cytotoxicity. Cabozantinib alone was predicted to restore cellular adhesion and hamper tumour proliferation and invasion. These data provide a biological rationale and further support for cabozantinib plus PD1 inhibitor combination and may guide future nonclinical and clinical research.

Key genes associated with prognosis and metastasis of clear cell renal cell carcinoma

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is a tumor that frequently shows the hematogenous pathway and tends to be resistant to radiotherapy and chemotherapy. However, the exact mechanism of ccRCC metastasis remains unknown.

METHODS

Differentially expressed genes (DEGs) of three gene expression profiles (GSE85258, GSE105288 and GSE22541) downloaded from the Gene Expression Omnibus (GEO) database were analyzed by GEO2R analysis, and co-expressed DEGs among the datasets were identified using a Venn drawing tool. The co-expressed DEGs were investigated using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, and hub genes were determined based on the protein-protein interaction network established by STRING. After survival analysis performed on UALCAN website, possible key genes were selected and verified in ccRCC cell lines and ccRCC tissues (n = 44). Statistical analysis was conducted using GraphPad Prism (Version 8.1.1).

RESULTS

A total of 104 co-expressed DEGs were identified in the three datasets. Pathway analysis revealed that these genes were enriched in the extracellular matrix (ECM)-receptor interaction, protein digestion and absorption and focal adhesion. Survival analysis on 17 hub genes revealed that four key genes with a significant impact on survival: procollagen C-endopeptidase enhancer (PCOLCE), prolyl 4-hydroxylase subunit beta (P4HB), collagen type VI alpha 2 (COL6A2) and collagen type VI alpha 3 (COL6A3). Patients with higher expression of these key genes had worse survival than those with lower expression. In vitro experiments revealed that the mRNA expression levels of PCOLCE, P4HB and COL6A2 were three times higher and that of COL6A3 mRNA was 16 times higher in the metastatic ccRCC cell line Caki-1 than the corresponding primary cell line Caki-2. Immunohistochemistry revealed higher expression of the proteins encoded by these four genes in metastatic ccRCC compared with tumors from the corresponding primary sites, with statistical significance.

CONCLUSION

PCOLCE, P4HB, COL6A2 and COL6A3 are upregulated in metastatic ccRCC and might be related to poor prognosis and distant metastases.

The TGF-β/HDAC7 axis suppresses TCA cycle metabolism in renal cancer

Mounting evidence points to alterations in mitochondrial metabolism in renal cell carcinoma (RCC). However, the mechanisms that regulate the TCA cycle in RCC remain uncharacterized. Here, we demonstrate that loss of TCA cycle enzyme expression is retained in RCC metastatic tissues. Moreover, proteomic analysis demonstrates that reduced TCA cycle enzyme expression is far more pronounced in RCC relative to other tumor types. Loss of TCA cycle enzyme expression is correlated with reduced expression of the transcription factor PGC-1α, which is also lost in RCC tissues. PGC-1α reexpression in RCC cells restores the expression of TCA cycle enzymes in vitro and in vivo and leads to enhanced glucose carbon incorporation into TCA cycle intermediates. Mechanistically, TGF-β signaling, in concert with histone deacetylase 7 (HDAC7), suppresses TCA cycle enzyme expression. Our studies show that pharmacologic inhibition of TGF-β restores the expression of TCA cycle enzymes and suppresses tumor growth in an orthotopic model of RCC. Taken together, this investigation reveals a potentially novel role for the TGF-β/HDAC7 axis in global suppression of TCA cycle enzymes in RCC and provides insight into the molecular basis of altered mitochondrial metabolism in this malignancy.

A five-gene methylation signature predicts overall survival of patients with clear cell renal cell carcinoma

BACKGROUND

In this study, we aimed to screen methylation signatures associated with the prognosis of patients with clear cell renal cell carcinoma (ccRCC).

METHODS

Gene expression and methylation profiles of ccRCC patients were downloaded from publicly available databases, and differentially expressed genes (DEGs)-differentially methylated genes (DMGs) were obtained. Subsequently, gene set enrichment and transcription factor (TF) regulatory network analyses were performed. In addition, a prognostic model was constructed and the relationship between disease progression and immunity was analyzed.

RESULTS

A total of 23 common DEGs-DMGs were analyzed, among which 14 DEGs-DMGs were obtained with a cutoff value of PCC < 0 and p < 0.05. The enrichment analysis showed that the 14 DEGs-DMGs were enriched in three GO terms and three KEGG pathways. In addition, a total of six TFs were shown to be associated with the 14 DEGs-DMGs, including RP58, SOX9, NF-κB65, ATF6, OCT, and IK2. A prognostic model using five optimized DEGs-DMGs which efficiently predicted survival was constructed and validated using the GSE105288 dataset. Additionally, four types of immune cells (NK cells, macrophages, neutrophils, and cancer-associated fibroblasts), as well as ESTIMATE, immune, and stromal scores were found to be significantly correlated with ccRCC progression (normal, primary, and metastasis) in addition to the five optimized DEGs-DMGs.

CONCLUSION

A five-gene methylation signature with the predictive ability for ccRCC prognosis was investigated in this study, consisting of CCNB2, CDKN1C, CTSH, E2F2, and ERMP1. In addition, potential targets for methylation-mediated immunotherapy were highlighted.

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.

The CSRNP Gene Family Serves as a Prognostic Biomarker in Clear Cell Renal Cell Carcinoma

The cysteine-serine-rich nuclear protein (CSRNP) family has prognostic value for various cancers. However, the association between this proteins and prognosis of clear cell renal cell carcinoma (ccRCC) remains unclear. This study aimed to determine the prognostic value of the CSRNP family for patients with ccRCC. Therefore, the gene expression profiling interactive analysis database was used to analyze the mRNA expression of CSRNP family members (CSRNPs) in relation with survival. Combined and independent prognostic values of CSRNPs were evaluated using SurvExpress and multivariate Cox regression analyses, respectively. Potential signaling pathways impacted by CSRNPs were evaluated using Metascape. Associations between the CSRNP family and immunocyte infiltration were determined from single-sample gene set enrichment analysis. Both cBioPortal and MethSurv were used to explore whether genomic and epidemic alterations might influence prognosis. We found that when both CSRNP1 and CSRNP3 had a low expression, patients with ccRCC had a worse overall survival (OS). Therefore, a prognostic signature was constructed as follows: risk score = -0.224 × exp_{mRNA of CSRNP1} + 0.820 × exp_{mRNA of CSRNP2} - 1.428 × exp_{mRNA of CSRNP3} . We found that OS was worse in patients from the high- than from the low-risk groups (AUC = 0.69). Moreover, this signature was an independent predictor after adjusting for clinical features. Functional enrichment analysis positively associated CSRNPs with the acute inflammatory response and humoral immune response pathways. This was validated by correlating each CSRNP with 28 types of immunocytes in tumor and normal tissues. A higher expression of CSRNP1 and CSRNP3 was associated with a better prognosis in both the high- and low-mutant burden groups. Cg19538674, cg07772537, and cg07811002 of CSRNP1, CSRNP2, and CSRNP3, respectively, were the predominant DNA methylation sites affecting OS. The CSRNP gene family signature may serve as a prognostic biomarker for predicting OS in patients with ccRCC. The association between CSRNPs and immune infiltration might offer future clinical treatment options.

Diseasome and comorbidities complexities of SARS-CoV-2 infection with common malignant diseases

With the increasing number of immunoinflammatory complexities, cancer patients have a higher risk of serious disease outcomes and mortality with SARS-CoV-2 infection which is still not clear. In this study, we aimed to identify infectome, diseasome and comorbidities between COVID-19 and cancer via comprehensive bioinformatics analysis to identify the synergistic severity of the cancer patient for SARS-CoV-2 infection. We utilized transcriptomic datasets of SARS-CoV-2 and different cancers from Gene Expression Omnibus and Array Express Database to develop a bioinformatics pipeline and software tools to analyze a large set of transcriptomic data and identify the pathobiological relationships between the disease conditions. Our bioinformatics approach revealed commonly dysregulated genes (MARCO, VCAN, ACTB, LGALS1, HMOX1, TIMP1, OAS2, GAPDH, MSH3, FN1, NPC2, JUND, CHI3L1, GPNMB, SYTL2, CASP1, S100A8, MYO10, IGFBP3, APCDD1, COL6A3, FABP5, PRDX3, CLEC1B, DDIT4, CXCL10 and CXCL8), common gene ontology (GO), molecular pathways between SARS-CoV-2 infections and cancers. This work also shows the synergistic complexities of SARS-CoV-2 infections for cancer patients through the gene set enrichment and semantic similarity. These results highlighted the immune systems, cell activation and cytokine production GO pathways that were observed in SARS-CoV-2 infections as well as breast, lungs, colon, kidney and thyroid cancers. This work also revealed ribosome biogenesis, wnt signaling pathway, ribosome, chemokine and cytokine pathways that are commonly deregulated in cancers and COVID-19. Thus, our bioinformatics approach and tools revealed interconnections in terms of significant genes, GO, pathways between SARS-CoV-2 infections and malignant tumors.

Hypoxia associated multi-omics molecular landscape of tumor tissue in patients with hepatocellular carcinoma

The present study was designed to update the knowledge about hypoxia-related multi-omic molecular landscape in hepatocellular carcinoma (HCC) tissues. Large-size HCC datasets from multiple centers were collected. The hypoxia exposure of tumor tissue from patients in 10 HCC cohorts was estimated using a novel HCC-specific hypoxia score system constructed in our previous study. A comprehensive bioinformatical analysis was conducted to compare hypoxia-associated multi-omic molecular features in patients with a high hypoxia score to a low hypoxia score. We found that patients with different exposure to hypoxia differed significantly in transcriptomic, genomic, epigenomic, and proteomic alterations, including differences in mRNA, microRNA (miR), and long non-coding RNA (lncRNA) expression, differences in copy number alterations (CNAs), differences in DNA methylation levels, differences in RNA alternative splicing events, and differences in protein levels. HCC survival- associated molecular events were identified. The potential correlation between molecular features related to hypoxia has also been explored, and various networks have been constructed. We revealed a particularly comprehensive hypoxia-related molecular landscape in tumor tissues that provided novel evidence and perspectives to explain the role of hypoxia in HCC. Clinically, the data obtained from the present study may enable the development of individualized treatment or management strategies for HCC patients with different levels of hypoxia exposure.

BUB1B Overexpression Is an Independent Prognostic Marker and Associated with CD44, p53, and PD-L1 in Renal Cell Carcinoma

INTRODUCTION

BUB1 mitotic checkpoint serine/threonine kinase B encoded by BUB1B gene is a member of the spindle assembly checkpoint family. Several reports have demonstrated that overexpression of BUB1B is associated with cancer progression and prognosis.

OBJECTIVE

This study aims to clarify the expression and function of BUB1B in renal cell carcinoma (RCC).

METHODS

The expression of BUB1B was determined using immunohistochemistry and bioinformatics analysis in RCC. The effects of BUB1B knockdown on cell growth and invasion were evaluated. We analyzed the interaction between BUB1B, cancer stem cell markers, p53, and PD-L1 in RCC.

RESULTS

In 121 cases of RCC, immunohistochemistry showed that 30 (25%) of the RCC cases were positive for BUB1B. High BUB1B expression was significantly correlated with high nuclear grade, T stage, and M stage. A Kaplan-Meier analysis showed that the high expression of BUB1B was associated with poor overall survival after nephrectomy. High BUB1B expression was associated with CD44, p53, and PD-L1 in RCC. Knockdown of BUB1B suppressed cell growth and invasion in RCC cell lines. Knockdown of BUB1B also suppressed the expression of CD44 and increased the expression of phospho-p53 (Ser15). In silico analysis showed that BUB1B was associated with inflamed CD8+, exhausted T-cell signature, IFN-γ signature, and the response to nivolumab.

CONCLUSION

These results suggest that BUB1B plays an oncogenic role and may be a promising predictive biomarker for survival in RCC.

Epigenome screening highlights that JMJD6 confers an epigenetic vulnerability and mediates sunitinib sensitivity in renal cell carcinoma

Aberrant epigenetic reprogramming represents a hallmark of renal cell carcinoma (RCC) tumorigenesis and progression. Whether there existed other epigenetic vulnerabilities that could serve as therapeutic targets remained unclear and promising. Here, we combined the clustered regularly interspaced short palindromic repeats functional screening results and multiple RCC datasets to identify JMJD6 as the potent target in RCC. JMJD6 expression correlated with poor survival outcomes of RCC patients and promoted RCC progression in vitro and in vivo. Mechanistically, aberrant p300 led to high JMJD6 expression, which activated a series of oncogenic crosstalk. Particularly, high-throughput sequencing data revealed that JMJD6 could assemble super-enhancers to drive a list of identity genes in kidney cancer, including VEGFA, β-catenin, and SRC. Moreover, this JMJD6-mediated oncogenic effect could be suppressed by a novel JMJD6 inhibitor (SKLB325), which was further demonstrated in RCC cells, patient-derived organoid models, and in vivo. Given the probable overlapped crosstalk between JMJD6 signature and tyrosine kinase inhibitors downstream targets, targeting JMJD6 sensitized RCC to sunitinib and was synergistic when they were combined together. Collectively, this study indicated that targeting JMJD6 was an effective approach to treat RCC patients.

Downregulated METTL14 accumulates BPTF that reinforces super-enhancers and distal lung metastasis via glycolytic reprogramming in renal cell carcinoma

Background: Methyltransferase-like 14 (METTL14) participates in tumorigenesis in several malignancies, but how METTL14 mediates the metastasis of renal cell carcinoma (RCC) has never been reported. Methods: Western blotting, quantitative real-time PCR, and immunohistochemistry were used to determine the mRNA and protein levels of relevant genes. Methylated RNA immunoprecipitation sequencing and RNA sequencing were utilized to screen potential targets of METTL14. Chromatin immunoprecipitation sequencing and assay for transposase-accessible chromatin sequencing were performed to investigate epigenetic alterations. The biological roles and mechanisms of METTL14/BPTF in promoting lung metastasis were confirmed in vitro and in vivo using cell lines, patient samples, xenograft models, and organoids. Results: Utilizing the TCGA-KIRC and Ruijin-RCC datasets, we found low expression of METTL14 in mRCC samples, which predicted poor prognosis. METTL14 deficiency promoted RCC metastasis in vitro and in vivo. Mechanistically, METTL14-mediated m⁶A modification negatively regulated the mRNA stability of bromodomain PHD finger transcription factor (BPTF) and depended on BPTF to drive lung metastasis. Accumulated BPTF in METTL14-deficient cells remodeled the enhancer landscape to reinforce several oncogenic crosstalk. Particularly, BPTF constituted super-enhancers that activate downstream targets like enolase 2 and SRC proto-oncogene nonreceptor tyrosine kinase, leading to glycolytic reprogramming of METTL14^-/- cells. Finally, we determined the efficacy of the BPTF inhibitor AU1 in suppressing mRCC of patient-derived cells, mRCC-derived organoids (MDOs), and orthotopic xenograft models. Conclusions: Our study is the first to investigate the essential role of m⁶A modification and the METTL14/BPTF axis in the epigenetic and metabolic remodeling of mRCC, highlighting AU1 as a vital therapeutic candidate.

Identification of novel prognostic biomarkers in renal cell carcinoma

Objective: To identify novel prognostic biomarkers in renal cell carcinoma (RCC).

Results: 12 coding genes and one miRNA were finally identified as prognostic biomarkers. All of them were related to a poor prognosis. Lower expression levels of the coding genes were observed in higher clinical stages. Prognostic signatures including 7 biomarkers were identified. Patients in the high-risk group had worse survival than those in the low-risk group. The areas under the curves in different years indicated that it was a valuable signature in prognosis. It was found that elevated WDR72 inhibited the survival and invasion of 786-O and 769P cells in vitro.

Conclusions: Thirteen prognostic biomarkers of RCC were identified. Among them, 7 biomarkers comprised a signature to evaluate the RCC prognosis. WDR72 was a cancer suppressor and a potential therapeutic target in RCC.

Methods: Differentially expressed genes/miRNAs (DEGs/DEMs) and prognosis-related genes/miRNAs were acquired from public database. Prognostic biomarkers were identified by overlapping the significant DEGs/DEMs and prognosis-related genes/miRNAs. The associations between these biomarkers and the clinical stages were analyzed. All of these prognostic biomarkers were further investigated with multi-variable Cox regression. Finally, the inhibitory effect of WDR72 on the growth and invasion of RCC cells was studied.

Identification of PDE7B as a Potential Core Gene Involved in the Metastasis of Clear Cell Renal Cell Carcinoma

Background

Metastasis is the main cause of treatment failure in various cancer, including ccRCC. However, the key genes involved in ccRCC metastasis remain largely unknown.

Purpose

The identification of the aberrant gene expression patterns associated with metastatic traits is of great clinical significance. The aim of this study was to investigate the clinical significance and function of PDE7B in ccRCC.

Materials and Methods

Expression profiling data for patient-matched primary and metastatic ccRCC tumors were obtained from GEO Dataset. Limma package was used to identify differentially expressed genes (DEGs) between the metastatic and the primary groups. Gene Ontology, Kyoto Encyclopedia of Genes Genomes (KEGG), and PPI network analysis were used to study the interacting activities and the interconnection of the DEGs. CCK-8 assays and Transwell assays were performed to detect the proliferation and migration of renal cancer cells.

Results

We obtained 163 DEGs, including 132 that were upregulated and 31 that were downregulated in metastatic ccRCC tissues. Both Gene Ontology function and KEGG pathway analysis showed that DEGs were involved in extracellular matrix (ECM) organization and cell adhesion. After utilizing PPI network to explore the interconnection among the DEGs, 22 genes were selected as the hub genes. Subsequently, survival analysis revealed that seven hub genes (SFN, NKX2-1, HP, MAPT, EPHA4, KCNAB1, and PDE7B) were significantly associated with overall survival disease-specific survival, and progression-free interval in ccRCC. Moreover, the low expression of PDE7B was found in clinical ccRCC samples and correlated with TNM stage and histologic grade. We further showed that knockdown of PDE7B increased cell growth and migration of renal cancer cells.

Conclusion

Our results implicated that PDE7B may play a key role in the development of metastatic RCC.

The pattern and prognostic relevance of immune activity scores and tumor-infiltrating immune cells in metastatic clear cell renal cell carcinoma: Evidence from multiple datasets

PURPOSE

To investigate the immune activity scores (IAS) and tumor-infiltrating immune cells (TIIC) in metastatic clear cell renal cell carcinoma (mccRCC) patients and to explore their patterns and potential prognostic values.

METHODS

The gene expression profiles and clinical information of ccRCC patients from multiple Gene Expression Omnibus (GEO) datasets and TCGA were used as study cohorts. Overall, 3 sets of 69 variables associated with tumor-immune interactions were collected from several tumor immunophenotype analysis websites. Least absolute shrinkage and selection operator (LASSO) and area under receiver operating characteristic (AUC) analyses were performed to establish and evaluate the predictive models.

RESULTS

Several TIIC and IAS variables are significantly different between patients and between different sites within the same patient. The AUC of the multivariable logistic models based on IAS and the two TIIC groups is 0.705 (95%CI 0.643-0.766), 0.719 (95%CI 0.650-0.788), and 0.685 (95%CI 0.623-0.747), respectively. The AUC of the LASSO model is 0.715 (95%CI 0.652-0.777). Certain subtypes identified by the consensus clustering method show a favorable OS (log-rank, p < 0.01) in both nonmetastatic and metastatic ccRCC patients.

CONCLUSION

IAS and TIIC could vary between patients and different sites within the same patient, and distinct patterns of these variables could correlate with clinical features. Heterogeneity might exist in the biological process of metastasis. LASSO logistic regression reveals that the infiltration of two TIICs would be a predictor of metastatic ccRCC. Last, certain subtypes may have a better prognosis in both ccRCC and mccRCC patients.

PRDM16 suppresses HIF-targeted gene expression in kidney cancer

Analysis of transcriptomic data demonstrates extensive epigenetic gene silencing of the transcription factor PRDM16 in renal cancer. We show that restoration of PRDM16 in RCC cells suppresses in vivo tumor growth. RNaseq analysis reveals that PRDM16 imparts a predominantly repressive effect on the RCC transcriptome including suppression of the gene encoding semaphorin 5B (SEMA5B). SEMA5B is a HIF target gene highly expressed in RCC that promotes in vivo tumor growth. Functional studies demonstrate that PRDM16's repressive properties, mediated by physical interaction with the transcriptional corepressors C-terminal binding proteins (CtBP1/2), are required for suppression of both SEMA5B expression and in vivo tumor growth. Finally, we show that reconstitution of RCC cells with a PRDM16 mutant unable to bind CtBPs nullifies PRDM16's effects on both SEMA5B repression and tumor growth suppression. Collectively, our data uncover a novel epigenetic basis by which HIF target gene expression is amplified in kidney cancer and a new mechanism by which PRDM16 exerts its tumor suppressive effects.

Overexpression of LINC00160 predicts poor outcome and promotes progression of clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal carcinoma and exhibits a high risk of invasion and metastasis. It is urgent to uncover a novel biomarker and clarify the underlying mechanism for ccRCC progression and metastasis. Although accumulating research has demonstrated that long non-coding RNAs (lncRNAs) play crucial roles in tumor progression, numerous lncRNAs in ccRCC are largely unknown. Therefore, we screened the differentially expressed lncRNAs among several GEO datasets and chose LNC00160 for further investigation. LNC00160 was significantly upregulated in ccRCC and high expression predicted poor prognosis; higher expression of LNC00160 was associated with advanced clinic pathological parameters in TCGA_KIRC Cohort. Knockdown of LNC00160 suppressed malignancy of ccRCC in vitro and in vivo. Correlation analysis and gene set enrichment analysis (GSEA) revealed that LNC00160 might be associated with Wnt signaling pathway, mTOR signaling pathway, fatty acid metabolism and cell cycle. In conclusion, our results demonstrated that LNC00160 acted as an oncogenic gene and a specific prognostic indicator for patients with ccRCC, and that LNC00160 might be a targeted intervention for ccRCC patients in the future.

TMSB10 acts as a biomarker and promotes progression of clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is one of the most common urological malignancies. Identifying novel biomarkers and investigating the underlying mechanism of ccRCC development will be crucial to the management and treatment of ccRCC in patients. Thymosin b10 (TMSB10), a member of the thymosin family, is involved in various physiological processes, including tissue regeneration and inflammatory regulation. Moreover, it has been found to be upregulated in many types of carcinoma. However, its roles in ccRCC remain to be elucidated. The present study aimed to explore the expression of TMSB10 in ccRCC through mining The Cancer Genome Atlas (TCGA) and Oncomine databases, and to investigate the association between TMSB10 expression and clinicopathological factors. Furthermore, immunohistochemistry assays and western blotting were conducted to verify TMSB10 expression levels in human ccRCC tissues and cell lines. Functional analyses were also performed to identify the roles of TMSB10 in vitro. The results revealed that TMSB10 was significantly upregulated in RCC tissues and cell lines. The expression of TMSB10 was closely associated with various clinicopathological parameters. In addition, high expression of TMSB10 predicted poor clinical outcome. The receiver operating characteristic curve revealed that TMSB10 could sufficiently distinguish the tumor from normal kidney (area under the curve = 0.9543, P<0.0001). Furthermore, knockdown of TMSB10 impaired the proliferation of ccRCC cells, and attenuated cell and invasion in vitro. In addition, TMSB10 knockdown downregulated reduced the phosphorylation of PI3K and the expression of vascular endothelial growth factor. In conclusion, the present study demonstrated that high expression of TMSB10 could serve as a useful diagnostic and prognostic biomarker and a potential therapeutic target for ccRCC.

Targeting the Immune system and Epigenetic Landscape of Urological Tumors

In the last years, we have witnessed remarkable advances in targeted therapies for cancer patients. There is a growing effort to either replace or reduce the dose of unspecific, systemic (chemo)therapies, given the associated short- and long-term side effects, by introducing more specific targeted therapies as single or combination agents. Due to the well-known implications of the immune system and epigenetic landscape in modulating cancer development, both have been explored as potential targets in several malignancies, including those affecting the genitourinary tract. As the immune system function is also epigenetically regulated, there is rationale for combining both strategies. However, this is still rather underexplored, namely in urological tumors. We aim to briefly review the use of immune therapies in prostate, kidney, bladder, and testicular cancer, and further describe studies providing supporting evidence on their combination with epigenetic-based therapies.

PGC1α suppresses kidney cancer progression by inhibiting collagen-induced SNAIL expression

The transcriptional events that promote invasive and metastatic phenotypes in renal cell carcinoma (RCC) remain poorly understood. Here we report that the decreased expression of peroxisome proliferator-activated receptor gamma, coactivator 1 alpha (PGC1α) and the increased expression of several genes encoding collagen family members are associated with RCC tumor progression. PGC1α restoration attenuates invasive phenotypes and suppresses tumor progression in vivo. In contrast, collagens produced by RCC cells promote invasive and migratory phenotypes. PGC1α restoration suppresses the expression of collagens and tumor phenotypes via the induction of miR-29a. Furthermore, decreased collagens via the PGC1α/miR-29a axis suppresses collagen-mediated activation of discoidin domain receptor 1 (DDR1)/ERK signaling. In turn, the suppression of collagen/DDR1 signaling by PGC1α leads to decreased levels of the known EMT regulators SNAIL1 and 2. Collectively, our results demonstrate a novel role for PGC1α in the regulation of proinvasive SNAIL proteins.

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.

Identification of potential core genes in metastatic renal cell carcinoma using bioinformatics analysis

Most cases of mRCC without an early finding are not candidates for curative therapies, which may be one of the reasons for the poor patient prognosis. Therefore, candidate markers to diagnose the disease and treatment with high efficiency are urgently demanded. Three datasets of mRNA microarray have been assessed to discover DEGs between tissues from metastatic RCC and RCC. 111 DEGs in total were identified according to the expression profile result of genes in the database of GEO. Enrichment analyses for GO and KEGG have been conducted to reveal the interacting activities in the DEGs. A network of PPI has been established to reveal the interconnection among the DEGs, and we selected 10 hub genes. Subsequently, the disease-free survival rate and total survival rate analysis for the hub genes have been carried out with the method of Kaplan-Meier curve. RCC patients with CDH11, COL3A1, COL5A1, COL5A2, COL6A3 and COL11A1 alteration showed worse overall survival. Nonetheless, RCC patients with CDH11, COL3A1, COL5A1, COL5A2 and COL11A1 alteration showed worse disease-free survival. In the Jones Renal dataset, mRNA levels of 10 hub genes were associated with metastasis, and the gene expression level in patients with mRCC was higher than that in patients without metastasis. COL5A1, COL6A3 and COL11A1 expression levels were remarkably related to RCC patient survival rate using UALCAN. COL5A1, COL6A3 and COL11A1 were positively correlated with each other in RCC. These genes have been recognized as genes with clinical relevance, revealing that they might have important roles in carcinogenesis or development of mRCC.

DNA methylation-based classification and identification of renal cell carcinoma prognosis-subgroups

BACKGROUND

Renal cell carcinoma (RCC) is the most common kidney cancer and includes several molecular and histological subtypes with different clinical characteristics. The combination of DNA methylation and gene expression data can improve the classification of tumor heterogeneity, by incorporating differences at the epigenetic level and clinical features.

METHODS

In this study, we identified the prognostic methylation and constructed specific prognosis-subgroups based on the DNA methylation spectrum of RCC from the TCGA database.

RESULTS

Significant differences in DNA methylation profiles among the seven subgroups were revealed by consistent clustering using 3389 CpGs that indicated that were significant differences in prognosis. The specific DNA methylation patterns reflected differentially in the clinical index, including TNM classification, pathological grade, clinical stage, and age. In addition, 437 CpGs corresponding to 477 genes of 151 samples were identified as specific hyper/hypomethylation sites for each specific subgroup. A total of 277 and 212 genes corresponding to DNA methylation at promoter sites were enriched in transcription factor of GKLF and RREB-1, respectively. Finally, Bayesian network classifier with specific methylation sites was constructed and was used to verify the test set of prognoses into DNA methylation subgroups, which was found to be consistent with the classification results of the train set. DNA methylation-based classification can be used to identify the distinct subtypes of renal cell carcinoma.

CONCLUSIONS

This study shows that DNA methylation-based classification is highly relevant for future diagnosis and treatment of renal cell carcinoma as it identifies the prognostic value of each epigenetic subtype.

ESRRG promoter hypermethylation as a diagnostic and prognostic biomarker in laryngeal squamous cell carcinoma

Background

Estrogen‐related receptor gamma (ESRRG) has been identified as a tumor suppressor gene in several cancers. We aimed to evaluate ESRRG promoter methylation in laryngeal squamous cell carcinoma (LSCC) and its relative clinical value in LSCC.

Methods

Bisulfite pyrosequencing assays were performed on 91 pairs of tumor and paracancer tissues from LSCC patients in China. The diagnostic value and overall survival (OS) were analyzed descriptively by receiver operating characteristic (ROC) curves and the Kaplan‐Meier methods, respectively.

Results

The ESRRG promoter was more frequently hypermethylated in tumor tissues than in adjacent tissues (P < 0.01). ESRRG promoter methylation was significantly increased in advanced T stage tumors (P < 0.01) and advanced clinical stage patients (P < 0.01). Moreover, the area under the ROC curve (AUC) value (0.81) indicated high discrimination accuracy. Furthermore, ESRRG hypermethylation was associated with poor OS, as confirmed by Kaplan‐Meier survival curves (P < 0.01).

Conclusion

Our study indicated that ESRRG promoter hypermethylation contributed to LSCC‐related risks, primarily tumor progression and survival prognosis, in patients. ESRRG promoter methylation could, therefore, be a diagnostic and prognostic biomarker in LSCC.