Identification of a 5-Gene Signature Predicting Progression and Prognosis of Clear Cell Renal Cell Carcinoma

ADCY4 promotes brain metastasis in small cell lung cancer and is associated with energy metabolism

Brain metastasis (BMs) in small cell lung cancer (SCLC) has a very poor prognosis. This study combined WGCNA with the mfuzz algorithm to identify potential biomarkers in the peripheral blood of patients with BMs. By comparing the significantly differentially expressed genes present in BMs samples, we identified ADCY4 as a target for further study. Expression of ADCY4 was used to cluster mfuzz expression pattern, and 28 hub genes for functional enrichment. PPI network analysis were obtained by comparing with differentially expressed genes in BMs. GABRE, NFE4 and LMOD2 are highly expressed in patients with BMs and have a good diagnostic effect. Immunoinfiltration analysis showed that SCLC patients with BMs may be associated with memory B cells, Tregs, NK cell activation, macrophage M0 and dendritic cell activation. prophytic was used to investigate the ADCY4-mediated anti-tumor drug response. In conclusion, ADCY4 can be used as a promising candidate biomarker for predicting BMs, molecular and immune features in SCLC. PCR showed that ADCY4 expression was increased in NCI-H209 and NCI-H526 SCLC cell lines. In vitro experiments confirmed that the expression of ADCY4 was significantly decreased after anti-PD1 antibody treatment, while the expression of energy metabolism factors were significantly different. This study reveals a potential mechanism by which ADCY4 mediates poor prognosis through energy metabolism -related pathways in SCLC.

Comprehensive Analysis of N6-Methylandenosine-Related lncRNAs in Clear Cell Renal Cell Carcinoma: A Correlation With Prognosis, Tumor Progression, and Therapeutic Response

This study aims to develop a prognostic signature based on m6A-related lncRNAs for clear cell renal cell carcinoma (ccRCC). Differential expression analysis and Pearson correlation analysis were used to identify m6A-related lncRNAs associated with patient outcomes in The Cancer Genome Atlas (TCGA) database. Our approach led to the development of an m6A-related lncRNA risk score (MRLrisk), formulated using six identified lncRNAs: NFE4, AL008729.2, AL139123.1, LINC02154, AC124854.1 and ARHGAP31-AS1. Higher MRLrisk was identified as a risk factor for patients' prognosis in ccRCC. Furthermore, an MRLrisk-based nomogram was developed and demonstrated as a reliable tool for prognosis prediction in ccRCC. Enrichment analysis and tumor mutation signature studies were conducted to investigate MRLrisk-related biological phenotypes. The tumor immune dysfunction and exclusion (TIDE) score was employed to infer patients' response to immunotherapy, indicating a negative correlation between high MRLrisk and immunotherapy response. Our focus then shifted to LINC02154 for deeper exploration. We assessed LINC02154 expression in 28 ccRCC/normal tissue pairs and 3 ccRCC cell lines through quantitative real-time polymerase chain reaction (qRT-PCR). Functional experiments, including EdU incorporation, flow cytometry and transwell assays, were performed to assess the role of LINC02154 in ccRCC cell functions, discovering that its downregulation hinders cancer cell proliferation and migration. Furthermore, the influence of LINC02154 on ccRCC cells' sensitivity to Sunitinib was explored using CCK-8 assays, demonstrating that decreased LINC02154 expression increases Sunitinib sensitivity. In summary, this study successfully developed an MRLrisk model with significant prognostic value for ccRCC and established LINC02154 as a critical biomarker and prospective therapeutic target in ccRCC management.

Integration of basement membrane-related genes in a risk signature for prognosis in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is characterized by high heterogeneity and recurrence rates, posing significant challenges for stratification and treatment. Basement membrane-related genes (BMGs) play a crucial role in tumor initiation and progression. Clinical and transcriptomic data of ccRCC patients were extracted from TCGA and GEO databases. We employed univariate regression and LASSO-Cox stepwise regression analysis to construct a BMscore model based on BMGs expression level. A nomogram combining clinical features and BMscore was constructed to predict individual survival probabilities. Further enrichment analysis and immune-related analysis were conducted to explore the enriched pathways and immune features associated with BMGs. High-risk individuals predicted by BMscore exhibited poorer overall survival, which was consistent with the validation dataset. BMscore was identified as an independent risk factor for ccRCC. Functional analysis revealed that BMGs were related to cell-matrix and tumor-associated signaling pathways. Immune profiling suggests that BMGs play a key role in immune interactions and the tumor microenvironment. BMGs serve as a novel prognostic predictor for ccRCC and play a role in the immune microenvironment and treatment response. Targeting the BM may represent an alternative therapeutic approach for ccRCC.

A novel DNA methylation signature revealed GDF6 and RCC1 as potential prognostic biomarkers correlated with cell proliferation in clear cell renal cell carcinoma

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) accounts for the majority (80%-90%) of renal cell carcinoma (RCC) patients at the time of diagnosis, and approximately 15% of ccRCC patients will develop distant metastasis or recurrence during their lifetime. Increasing number of studies have revealed that the aberrant DNA methylations is closely correlated with the tumorigenesis in ccRCC.

RESULTS

In this study, we utilized a LASSO (least absolute shrinkage and selection operator) model to identify a combination of 13 probes-based DNA methylation signature that associated with the progression-free survival (PFS) of ccRCC patients. First, differentially methylated regions (CpGs) related to PFS and phenotypes were identified. Next, prognostic DNA methylation probes were selected from the differentially methylated probes (DMPs) and calculated risk scores to stratify patients with ccRCC. The performance of this signature was validated in an independent testing set using various analyses, including Kaplan-Meier analysis for PFS and receiver operating characteristic (ROC) curve analysis. Based on our 13-DNA methylation probes signature, ccRCC patients were successfully stratified into high- and low-risk groups. Combining DNA methylation signature with clinical variables such as T stage, M stage and tumor grade could further improve the accuracy of prediction. Moreover, we highlight two molecular biomarkers (RCC1 and GDF6) corresponding to our probes. Invitro experiments showed that knockdown of RCC1 or GDF6 in ccRCC cell lines reduced cell proliferation, which indicated that both biomarkers are associated with tumorigenesis.

CONCLUSIONS

The 13-probes-based DNA methylation signature has the potential to serve as an independent tool for survival outcome improvement and treatment strategy selection for ccRCC patients. In addition, our findings suggest that RCC1 and GDF6 may serve as promising markers for ccRCC.

Two-Dimensional-PAGE Coupled with nLC-MS/MS-Based Identification of Differentially Expressed Proteins and Tumorigenic Pathways in MCF7 Breast Cancer Cells Transfected for JTB Protein Silencing

The identification of new cancer-associated genes/proteins, the characterization of their expression variation, the interactomics-based assessment of differentially expressed genes/proteins (DEGs/DEPs), and understanding the tumorigenic pathways and biological processes involved in BC genesis and progression are necessary and possible by the rapid and recent advances in bioinformatics and molecular profiling strategies. Taking into account the opinion of other authors, as well as based on our own team's in vitro studies, we suggest that the human jumping translocation breakpoint (hJTB) protein might be considered as a tumor biomarker for BC and should be studied as a target for BC therapy. In this study, we identify DEPs, carcinogenic pathways, and biological processes associated with JTB silencing, using 2D-PAGE coupled with nano-liquid chromatography tandem mass spectrometry (nLC-MS/MS) proteomics applied to a MCF7 breast cancer cell line, for complementing and completing our previous results based on SDS-PAGE, as well as in-solution proteomics of MCF7 cells transfected for JTB downregulation. The functions of significant DEPs are analyzed using GSEA and KEGG analyses. Almost all DEPs exert pro-tumorigenic effects in the JTBlow condition, sustaining the tumor suppressive function of JTB. Thus, the identified DEPs are involved in several signaling and metabolic pathways that play pro-tumorigenic roles: EMT, ERK/MAPK, PI3K/AKT, Wnt/β-catenin, mTOR, C-MYC, NF-κB, IFN-γ and IFN-α responses, UPR, and glycolysis/gluconeogenesis. These pathways sustain cancer cell growth, adhesion, survival, proliferation, invasion, metastasis, resistance to apoptosis, tight junctions and cytoskeleton reorganization, the maintenance of stemness, metabolic reprogramming, survival in a hostile environment, and sustain a poor clinical outcome. In conclusion, JTB silencing might increase the neoplastic phenotype and behavior of the MCF7 BC cell line. The data is available via ProteomeXchange with the identifier PXD046265.

Basement Membrane-Associated lncRNA Risk Model Predicts Prognosis and Guides Clinical Treatment in Clear Cell Renal Cell Carcinoma

The basement membrane (BM) affects the invasion and growth of malignant tumors. The role and mechanism of BM-associated lncRNAs in clear cell renal cell carcinoma (ccRCC) are unknown. In this study, we identified biomarkers of ccRCC and developed a risk model to assess patient prognosis. We downloaded transcripts and clinical data from the Cancer Genome Atlas (TCGA). Differential analysis, co-expression analysis, Cox regression analysis, and lasso regression were used to identify BM-associated prognostic lncRNAs and create a risk prediction model. We evaluated and validated the accuracy of the model using multiple methods and constructed a nomogram to predict the prognosis of ccRCC. GO, KEGG, and immunity analyses were used to explore differences in biological function. We constructed a risk model containing six BM-associated lncRNAs (LINC02154, IGFL2-AS1, NFE4, AC112715.1, AC092535.5, and AC105105.3). The risk model has higher diagnostic efficiency compared to clinical characteristics and can be used to forecast patient prognoses. We used renal cancer cells and tissue microarrays to verify the expression of lncRNAs in the risk model. We found that knocking down LINC02154 and AC112715.1 could inhibit the invasion ability of renal cancer cells. The risk model based on BM-associated lncRNAs can well predict ccRCC and guide clinical treatment.

Expression of gasdermin D in clear cell renal cell carcinoma and its effect on its biological function

Background

Clear cell renal cell carcinoma (ccRCC) is the most common type of renal cell carcinoma, which suffers from the lack of diagnosis and treatment methods, and many patients cannot be diagnosed at first time. Gasdermin D (GSDMD) is involved in inflammatory reactions and pyroptosis and is considered a potential therapeutic target. This paper's aim is to elucidate the expression of GSDMD in clear cell renal cell carcinoma and its value for treatment and prognosis, as well as its impact on the biological function of clear cell renal cell carcinoma.

Method

The Cancer Genome Atlas (TCGA) database was used to compare the expression of GSDMD in tumor and normal tissues, analyze its correlation with cancer stage and overall survival time, and establish receiver operating characteristic (ROC) curve, which was confirmed by the Gene Expression Omnibus (GEO) database and immunohistochemical staining of clinical samples and PCR and Western blotting (WB) of cell lines. The relationship between GSDMD and patient prognosis and staging was analyzed using TCGA database and validated using clinical sample data. Differentially expressed genes (DEGs) and epithelial-mesenchymal transition (EMT)-related genes of GSDMD were screened by TCGA database. Protein-protein interaction (PPI) of GSDMD was constructed by GeneMANIA and STRING, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment were analyzed by the Metascape database. Then, R software was used to analyze the immune cell infiltration, immune microenvironment score, and tumor mutational burden (TMB) analysis of GSDMD high- and low-expression groups in TCGA database. GSDMD lentivirus was used to transfect 769-P cells to construct stable upregulated and downregulated transfected cell lines. PCR was used to verify the expression differences of differentially expressed genes between the high- and low-expression groups of GSDMD; then, MTT, flow apoptosis, and Transwell were used to detect the proliferation, apoptosis, invasion, and migration of the transfected cells.

Results

The results of bioinformatics analysis showed that the expression of GSDMD in clear cell renal cell carcinoma was significantly correlated with patient stage and overall survival, and the tumor with high expression of GSDMD had a worse stage and overall survival. GSDMD has some significance in the diagnosis of ccRCC. The results of EMT correlation analysis and enrichment analysis showed that GSDMD was correlated with genes and pathways related to invasion and metastasis of renal cell carcinoma. The subsequent immune cell infiltration analysis showed that there were many differences in the infiltration of immune cells between the high- and low-expression groups of GSDMD, such as naive B cells. The immune microenvironment score showed that the high-expression group had a lower proportion of stromal cells than the local expression group but had a higher proportion of immune cells. Through TMB, it was shown that the high-expression group had a higher mutation. The expression of GSDMD in renal cell carcinoma by immunohistochemistry and in vitro cell experiments was confirmed. According to the prognostic information of clinical patients, it was found that GSDMD was significantly correlated with TNM stage, Fuhrman grade, lymph node metastasis, gender, and smoking or not, and the prognosis of patients with high expression of GSDMD was worse. After that, we constructed stable transfection cell lines with high expression and knockdown through lentivirus transfection and verified the expression amount of differentially expressed genes by PCR, which is consistent with the results of TCGA database. Then, we confirmed that GSDMD is related to proliferation, invasion, migration, and apoptosis of ccRCC by MTT, flow apoptosis, and Transwell assay. The low expression of GSDMD inhibits the proliferation, invasion, and migration of tumors and enhances apoptosis and vice versa. Therefore, GSDMD can be used as a potential biological marker for the diagnosis and prognosis of ccRCC.

Identification and validation of a DNA methylation-driven gene-based prognostic model for clear cell renal cell carcinoma

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is a malignant tumor with heterogeneous morphology and poor prognosis. This study aimed to establish a DNA methylation (DNAm)-driven gene-based prognostic model for ccRCC.

METHODS

Reduced representation bisulfite sequencing (RRBS) was performed on the DNA extracts from ccRCC patients. We analyzed the RRBS data from 10 pairs of patient samples to screen the candidate CpG sites, then trained and validated an 18-CpG site model, and integrated the clinical characters to establish a Nomogram model for the prognosis or risk evaluation of ccRCC.

RESULTS

We identified 2261 DMRs in the promoter region. After DMR selection, 578 candidates were screened, and was correspondence with 408 CpG dinucleotides in the 450 K array. We collected the DNAm profiles of 478 ccRCC samples from TCGA dataset. Using the training set with 319 samples, a prognostic panel of 18 CpGs was determined by univariate Cox regression, LASSO regression, and multivariate Cox proportional hazards regression analyses. We constructed a prognostic model by combining the clinical signatures. In the test set (159 samples) and whole set (478 samples), the Kaplan-Meier plot showed significant differences; and the ROC curve and survival analyses showed AUC greater than 0.7. The Nomogram integrated with clinicopathological characters and methylation risk score had better performance, and the decision curve analyses also showed a beneficial effect.

CONCLUSIONS

This work provides insight into the role of hypermethylation in ccRCC. The targets identified might serve as biomarkers for early ccRCC diagnosis and prognosis biomarkers for ccRCC. We believe our findings have implications for better risk stratification and personalized management of this disease.

Cuproptosis-related lncRNA signature for prognostic prediction in patients with acute myeloid leukemia

BACKGROUND

Long non-coding RNAs (lncRNAs) have been reported to have a crucial impact on the pathogenesis of acute myeloid leukemia (AML). Cuproptosis, a copper-triggered modality of mitochondrial cell death, might serve as a promising therapeutic target for cancer treatment and clinical outcome prediction. Nevertheless, the role of cuproptosis-related lncRNAs in AML is not fully understood.

METHODS

The RNA sequencing data and demographic characteristics of AML patients were downloaded from The Cancer Genome Atlas database. Pearson correlation analysis, the least absolute shrinkage and selection operator algorithm, and univariable and multivariable Cox regression analyses were applied to identify the cuproptosis-related lncRNA signature and determine its feasibility for AML prognosis prediction. The performance of the proposed signature was evaluated via Kaplan-Meier survival analysis, receiver operating characteristic curves, and principal component analysis. Functional analysis was implemented to uncover the potential prognostic mechanisms. Additionally, quantitative real-time PCR (qRT-PCR) was employed to validate the expression of the prognostic lncRNAs in AML samples.

RESULTS

A signature consisting of seven cuproptosis-related lncRNAs (namely NFE4, LINC00989, LINC02062, AC006460.2, AL353796.1, PSMB8-AS1, and AC000120.1) was proposed. Multivariable cox regression analysis revealed that the proposed signature was an independent prognostic factor for AML. Notably, the nomogram based on this signature showed excellent accuracy in predicting the 1-, 3-, and 5-year survival (area under curve = 0.846, 0.801, and 0.895, respectively). Functional analysis results suggested the existence of a significant association between the prognostic signature and immune-related pathways. The expression pattern of the lncRNAs was validated in AML samples.

CONCLUSION

Collectively, we constructed a prediction model based on seven cuproptosis-related lncRNAs for AML prognosis. The obtained risk score may reveal the immunotherapy response in patients with this disease.

Establishment of an experimental model of normal dog bladder organoid using a three-dimensional culture method

Dog bladder cancer (BC) is mostly muscle-invasive (MI) with poor prognosis, and its pathogenesis is close to human MIBC. Three-dimensional (3D) organoid culture ensures novel knowledge on cancer diseases including BC. Recently, we have established dog BC organoids (BCO) using their urine samples. BCO recapitulated the epithelial structures, characteristics, and drug sensitivity of BC-diseased dogs. However, organoids from dog normal bladder epithelium are not established yet. Therefore, the present study aimed to establish dog normal bladder organoids (NBO) for further understanding the pathogenesis of dog BC and human MIBC. The established NBO underwent various analyzes including cell marker expressions, histopathological structures, cancer-related gene expression patterns, and drug sensitivity. NBO could be produced non-invasively with a continuous culturing and recapitulated the structures and characteristics of the dog's normal bladder mucosal tissues. Different drug sensitivities were observed in each NBO. The analysis of RNA sequencing revealed that several novel genes were changed in NBO compared with BCO. NBO showed a higher expression of p53 and E-cadherin, but a lower expression of MDM2 and Twist1 compared with BCO. These results suggest that NBO could be a promising experimental 3D model for studying the developmental mechanisms of dog BC and human MIBC.

A Novel Machine Learning 13-Gene Signature: Improving Risk Analysis and Survival Prediction for Clear Cell Renal Cell Carcinoma Patients

Simple Summary

Clear cell renal cell carcinoma is a type of kidney cancer which comprises the majority of all renal cell carcinomas. Many efforts have been made to identify biomarkers which could help healthcare professionals better treat this kind of cancer. With extensive public data available, we conducted a machine learning study to determine a gene signature that could indicate patient survival with high accuracy. Through the min-Redundancy and Max-Relevance algorithm we generated a signature of 13 genes highly correlated with patient outcomes. These findings reveal potential strategies for personalized medicine in the clinical practice.

Abstract

Patients with clear cell renal cell carcinoma (ccRCC) have poor survival outcomes, especially if it has metastasized. It is of paramount importance to identify biomarkers in genomic data that could help predict the aggressiveness of ccRCC and its resistance to drugs. Thus, we conducted a study with the aims of evaluating gene signatures and proposing a novel one with higher predictive power and generalization in comparison to the former signatures. Using ccRCC cohorts of the Cancer Genome Atlas (TCGA-KIRC) and International Cancer Genome Consortium (ICGC-RECA), we evaluated linear survival models of Cox regression with 14 signatures and six methods of feature selection, and performed functional analysis and differential gene expression approaches. In this study, we established a 13-gene signature (AR, AL353637.1, DPP6, FOXJ1, GNB3, HHLA2, IL4, LIMCH1, LINC01732, OTX1, SAA1, SEMA3G, ZIC2) whose expression levels are able to predict distinct outcomes of patients with ccRCC. Moreover, we performed a comparison between our signature and others from the literature. The best-performing gene signature was achieved using the ensemble method Min-Redundancy and Max-Relevance (mRMR). This signature comprises unique features in comparison to the others, such as generalization through different cohorts and being functionally enriched in significant pathways: Urothelial Carcinoma, Chronic Kidney disease, and Transitional cell carcinoma, Nephrolithiasis. From the 13 genes in our signature, eight are known to be correlated with ccRCC patient survival and four are immune-related. Our model showed a performance of 0.82 using the Receiver Operator Characteristic (ROC) Area Under Curve (AUC) metric and it generalized well between the cohorts. Our findings revealed two clusters of genes with high expression (SAA1, OTX1, ZIC2, LINC01732, GNB3 and IL4) and low expression (AL353637.1, AR, HHLA2, LIMCH1, SEMA3G, DPP6, and FOXJ1) which are both correlated with poor prognosis. This signature can potentially be used in clinical practice to support patient treatment care and follow-up.

Weighted Gene Coexpression Network Analysis Identifies TBC1D10C as a New Prognostic Biomarker for Breast Cancer

Background

Immune checkpoint inhibitors are a promising therapeutic strategy for breast cancer (BRCA) patients. The tumor microenvironment (TME) can downregulate the immune response to cancer therapy. Our study is aimed at finding a TME-related biomarker to identify patients who might respond to immunotherapy.

Method

We downloaded raw data from several databases including TCGA and MDACC to identify TME hub genes associated with overall survival (OS) and the progression-free interval (PFI) by WGCNA. Correlations between hub genes and either tumor-infiltrating immune cells or immune checkpoints were conducted by ssGSEA.

Result

TME-related green and black modules were selected by WGCNA to further screen hub genes. Random forest and univariate and multivariate Cox regressions were applied to screen hub genes (MYO1G, TBC1D10C, SELPLG, and LRRC15) and construct a nomogram to predict the survival of BRCA patients. The C-index for the nomogram was 0.713. A DCA of the predictive model revealed that the net benefit of the nomogram was significantly higher than others and the calibration curve demonstrated a good performance by the nomogram. Only TBC1D10C was correlated with both OS and the PFI (both p values < 0.05). TBC1D10C also had a high positive association with tumor-infiltrating immune cells and common immune checkpoints (PD-1, CTLA-4, and TIGIT).

Conclusion

We constructed a TME-related gene signature model to predict the survival probability of BRCA patients. We also identified a hub gene, TBC1D10C, which was correlated with both OS and the PFI and had a high positive association with tumor-infiltrating immune cells and common immune checkpoints. TBC1D10C may be a new biomarker to select patients who may benefit from immunotherapy.

Construction and validation of a prognostic model for kidney renal clear cell carcinoma based on podocyte-associated genes

Background

As the most common renal malignancy, kidney renal clear cell carcinoma (KIRC) has a high prevalence and death rate as well as a poor response to treatment. Developing an efficient prognostic model is essential for accurately predicting the outcome and therapeutic benefit of KIRC patients.

Methods

Gene expression profiles of podocyte‐associated genes (PAGs) were obtained from The Cancer Genome Atlas and GEO datasets. Cox regression and Lasso regression analyses were then used for filtering prognosis‐associated PAGs. Risk score (RS) was computed from these genetic characteristics. Kaplan–Meier analysis and receiver operating characteristic (ROC) curves were applied for ascertaining the prognostic value. Stratified analysis was used to sufficiently validate model performance. Concordance index was used to compare the predictive ability of different models. Immuno‐infiltration analysis and immunophenoscore were utilized for the prediction of patient reaction to immune checkpoint inhibitors (ICIs).

Results

WT1, ANLN, CUBN, OSGEP, and RHOA were significantly associated with KIRC prognosis. Prognostic analysis indicated that high‐RS patients have a significantly poorer outcome. Cox regression analysis demonstrated a potential for RS to be an independent prognostic factor. Pathway enrichment results indicated a lower enrichment of cancer‐related biological pathways in the low‐RS subgroup. Immune infiltration analysis and IPS demonstrated greater responsiveness to ICIs in the high RS group.

Conclusions

This podocyte‐associated KIRC prognostic model can effectively predict KIRC prognosis and immunotherapy response, which may help to provide clinicians with more effective treatment strategies.

KRAB-ZFPs and cancer stem cells identity

Studies on carcinogenesis continue to provide new information about different disease-related processes. Among others, much research has focused on the involvement of cancer stem cells (CSCs) in tumor initiation and progression. Studying the similarities and differences between CSCs and physiological stem cells (SCs) allows for a better understanding of cancer biology. Recently, it was shown that stem cell identity is partially governed by the Krϋppel-associated box domain zinc finger proteins (KRAB-ZFPs), the biggest family of transcription regulators. Several KRAB-ZFP factors exert a known effect in tumor cells, acting as tumor suppressor genes (TSGs) or oncogenes, yet their role in CSCs is still poorly characterized. Here, we review recent studies regarding the influence of KRAB-ZFPs and their cofactor protein TRIM28 on CSCs phenotype, stemness features, migration and invasion potential, metastasis, and expression of parental markers.

Prognostic Role of the Ubiquitin Proteasome System in Clear Cell Renal Cell Carcinoma: A Bioinformatic Perspective

Background: Clear cell renal cell carcinoma (ccRCC) is a common malignant tumor of the urinary system. The ubiquitin proteasome system (UPS) plays an important role in the generation, metabolism and survival of tumor. We are aimed to make a comprehensive exploration of the UPS's role in ccRCC with bioinformatic tools, which may contribute to the understanding of UPS in ccRCC, and give insight for further research. Methods: The UPS-related genes (UPSs) were collected by an integrative approach. The expression and clinical data were downloaded from TCGA database. R soft was used to perform the differentially expressed UPSs analysis, functional enrichment analysis. We also estimated prognostic value of each UPS with the help of GEPIA database. Two predicting models were constructed with the differentially expressed UPSs and prognosis-related genes, respectively. The correlations of risk score with clinical characteristics were also evaluated. Data of GSE29609 cohort were obtained from GEO database to validate the prognostic models. Results: We finally identified 91 differentially expressed UPSs, 48 prognosis related genes among them, and constructed a prognostic model with 18 UPSs successfully, the AUC was 0.760. With the help of GEPIA, we found 391 prognosis-related UPSs, accounting for 57.84% of all UPSs. Another prognostic model was constructed with 28 prognosis-related genes of them, and with a better AUC of 0.825. Additionally, our models can also stratify patients into high and low risk groups accurately in GSE29609 cohort. Similar prognostic values of our models were observed in the validated GSE29609 cohort. Conclusions: UPS is dysregulated in ccRCC. UPS related genes have significant prognostic value in ccRCC. Models constructed with UPSs are effective and applicable. An abnormal ubiquitin proteasome system should play an important role in ccRCC and be worthy of further study.

DNA Methylation-Based Panel Predicts Survival of Patients With Clear Cell Renal Cell Carcinoma and Its Correlations With Genomic Metrics and Tumor Immune Cell Infiltration

DNA methylation based prognostic factor for patients with clear cell renal cell carcinoma (ccRCC) remains unclear. In the present study, we identified survival-related DNA methylation sites based on the differentially methylated DNA CpG sites between normal renal tissue and ccRCC. Then, these survival-related DNA methylation sites were included into an elastic net regularized Cox proportional hazards regression (CoxPH) model to build a DNA methylation-based panel, which could stratify patients into different survival groups with excellent accuracies in the training set and test set. External validation suggested that the DNA methylation-based panel could effectively distinguish normal controls from tumor samples and classify patients into metastasis group and non-metastasis group. The nomogram containing DNA methylation-based panel was reliable in clinical settings. Higher total mutation number, SCNA level, and MATH score were associated with higher methylation risk. The innate immune, ratio between CD8⁺T cell versus Treg cell as well as Th17 cell versus Th2 cell were significantly decreased in high methylation risk group. In inclusion, we developed a DNA methylation-based panel which might be independent prognostic factor in ccRCC. Patients with higher methylation risk were associated genomic alteration and poor immune microenvironment.

Construction of a 13-Gene Signature as a Novel Prognostic Marker for Patients with Clear Cell Renal Cell Carcinoma and the Role of XCR1 in Cell Proliferation

Objective

The tumor microenvironment plays a key role in regulating tumor progression. This research aimed to develop the biomarker related to tumor microenvironment in clear cell renal cell carcinoma (ccRCC).

Methods

The ESTIMATE algorithm was used to evaluate the immune score of ccRCC cases from The Cancer Genome Atlas (TCGA). Differentially expressed genes between high and low immune scores were identified and a 13-gene signature was constructed by the LASSO Cox regression model to predict overall survival (OS) for ccRCC cases in TCGA or International Cancer Genome Consortium (ICGC) project. The immune cell fractions were calculated by the TIMER algorithm. Cell viability and gene expression were determined by CCK-8 and qRT-PCR, respectively.

Results

The OS of patients with high immune scores was worse than that of patients with low immune scores. The OS between ccRCC patients from TCGA or ICGC cohort in high- and low-risk groups stratified by the gene signature was significantly different. Subgroup analysis also showed a robust prognostic ability of the gene signature. Multivariate Cox regression analysis demonstrated that this gene signature was an independent prognostic factor. The nomogram that integrated the gene signature and three clinicopathological risk factors had a favorably predictive ability in predicting 3, 5 and 10 year survival. Moreover, the high-risk group had a significantly higher abundance of B cell, T cell, CD4, neutrophil and DC infiltration. Among 13 genes, X-C motif chemokine receptor1 (XCR1) was upregulated in ccRCC cells and exerted an inhibitory effect on cell proliferation.

Conclusion

This study constructs a 13-gene signature as a novel prognostic marker to predict the survival of ccRCC patients and XCR1 may serve as a therapeutic target.

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

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

A cluster of metabolism-related genes predict prognosis and progression of clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) has long been considered as a metabolic disease characterized by metabolic reprogramming due to the abnormal accumulation of lipid droplets in the cytoplasm. However, the prognostic value of metabolism-related genes in ccRCC remains unclear. In our study, we investigated the associations between metabolism-related gene profile and prognosis of ccRCC patients in the Cancer Genome Atlas (TCGA) database. Importantly, we first constructed a metabolism-related prognostic model based on ten genes (ALDH6A1, FBP1, HAO2, TYMP, PSAT1, IL4I1, P4HA3, HK3, CPT1B, and CYP26A1) using Lasso cox regression analysis. The Kaplan–Meier analysis revealed that our model efficiently predicts prognosis in TCGA_KIRC Cohort and the clinical proteomic tumor analysis consortium (CPTAC_ccRCC) Cohort. Using time-dependent ROC analysis, we showed the model has optimal performance in predicting long-term survival. Besides, the multivariate Cox regression analysis demonstrated our model is an independent prognostic factor. The risk score calculated for each patient was significantly associated with various clinicopathological parameters. Notably, the gene set enrichment analysis indicated that fatty acid metabolism was enriched considerably in low-risk patients. In contrast, the high-risk patients were more associated with non-metabolic pathways. In summary, our study provides novel insight into metabolism-related genes’ roles in ccRCC.

Development and validation of an immune prognostic classifier for clear cell renal cell carcinoma

BACKGROUND

Tumor-infiltrating immune cells are indispensable to the progression and prognosis of clear cell renal cell carcinoma (ccRCC).

OBJECTIVE

The aim of this study was to explore the clinical implications of immune cell infiltrates in ccRCC.

METHODS

The Cancer Genome Atlas (TCGA) database (N= 515) and E-MTAB-1980 cohort of patients (N= 101) were adopted to estimate the prognostic value of immune cell infiltration. Twenty-four types of immune cells were evaluated using single-sample gene set enrichment analysis. Cox regression analyses were conducted to develop an immune risk score.

RESULTS

Survival analyses revealed that 13 genes significantly associated with the overall survival (OS). Furthermore, multivariate Cox analysis identified an immune risk score on the basis of mast cells, natural killer CD56bright cells, T helper 17 (Th17) cells, and Th2 cells. The immune risk score was associated with OS, with hazard ratios of 2.72 (95% CI 2.17-3.40) and 3.24 (95% CI 1.64-6.44) in TCGA and E-MTAB-1980 datasets, respectively. This immune risk score was significantly correlated with some immunotherapy-related biomarkers.

CONCLUSIONS

We profiled a prognostic signature and established an immune risk score model for ccRCC, which could provide novel predictive markers for patients with ccRCC and an indicator for immunotherapy response measurement.

Identification and Comprehensive Validation of a DNA Methylation-Driven Gene-Based Prognostic Model for Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most prevalent renal malignancy in adults with generally poor prognosis. This study aimed to establish a DNA methylation-driven gene-based prognostic model for ccRCC. We collected DNA methylation and gene expression profiles of over 1500 ccRCC samples from The Cancer Genome Atlas (TCGA) dataset, four Gene Expression Omnibus (GEO) datasets, the Genotype-Tissue Expression (GTEx) dataset, and cancer cell lines from Cancer Cell Line Encyclopedia database and performed comprehensive bioinformatics analysis. As a result, a total of 31 differentially expressed methylation-driven genes (DEMDGs) were identified. After univariate Cox regression, least absolute shrinkage and selection operator, and multivariate Cox regression analyses, four (NFE2L3, HHLA2, IFI16, and ZNF582) were finally selected to construct a risk score prognostic model. The high-risk group demonstrated significantly poor prognosis than the low-risk group did in TCGA training (hazard ratio [HR] = 3.533, p < 0.001), TCGA internal, and GEO external validation datasets. Furthermore, the nomogram, including the prognostic model and clinical factors, showed promising prognostic value (HR = 5.756, p < 0.001, and area under the curve at 1 year = 0.856). In addition, the model was found to be significantly associated with drug sensitivity of eight targeted agents. These findings provided a novel and reliable four DEMDG-based prognostic model for ccRCC.

Development and validation of an integrative methylation signature and nomogram for predicting survival in clear cell renal cell carcinoma

Background

Growing evidence has shown that genetic or epigenetic alterations are highly involved in the initiation and progression of renal cell carcinoma (RCC). This study aimed to find prognostic methylation markers in clear cell RCC (ccRCC).

Methods

In this study, we developed and confirmed an integrated and comprehensive methylation signature by integrating DNA methylation, gene expression, and The Cancer Genome Atlas (TCGA) survival data. First, the methylation signature was found and checked based on data analysis of published datasets. Then, independent predictive factors were selected using the Cox proportional model and incorporated into the nomogram. Finally, the predictive nomogram was derived and validated using a concordance index and calibration plots.

Results

A series of differentially expressed and methylated genes were identified. After intersection analysis, Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, protein-protein interaction (PPI) analysis, and correlation analysis, FCGR1A, F2, and NOD2 were established as a predictive signature. According to the Kaplan-Meier survival analysis, the risk score system based on the predictive signature was able to stratify the patients into high- and low-risk groups with significantly different overall survival. The receiver operating characteristic (ROC) analysis further showed that the predictive signature yielded high sensitivity and specificity in predicting the prognosis outcome of ccRCC patients. Moreover, univariate and multivariate Cox regression analysis confirmed that the three-gene methylation signature was an independent prognostic factor in ccRCC. Finally, a nomogram comprising the predictive signature and several independent variables were constructed and proved to effectively predict ccRCC patient survival.

Conclusions

The three-gene methylation signature was revealed to be a potential novel and independent adverse predictor of prognosis for ccRCC patients and may serve as a promising marker for treatment management and survival outcome improvement. However, substantial validation experiments are required to characterize the molecular background of the predictive signature.

Construction and validation of a seven-gene signature for predicting overall survival in patients with kidney renal clear cell carcinoma via an integrated bioinformatics analysis

Kidney renal clear cell carcinoma (KIRC) remains a significant challenge worldwide because of its poor prognosis and high mortality rate, and accurate prognostic gene signatures are urgently required for individual therapy. This study aimed to construct and validate a seven-gene signature for predicting overall survival (OS) in patients with KIRC. The mRNA expression profile and clinical data of patients with KIRC were obtained from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC). Prognosis-associated genes were identified, and a prognostic gene signature was constructed. Then, the prognostic efficiency of the gene signature was assessed. The results obtained using data from the TCGA were validated using those from the ICGC and other online databases. Gene set enrichment analyses (GSEA) were performed to explore potential molecular mechanisms. A seven-gene signature (PODXL, SLC16A12, ZIC2, ATP2B3, KRT75, C20orf141, and CHGA) was constructed, and it was found to be effective in classifying KIRC patients into high- and low-risk groups, with significantly different survival based on the TCGA and ICGC validation data set. Cox regression analysis revealed that the seven-gene signature had an independent prognostic value. Then, we established a nomogram, including the seven-gene signature, which had a significant clinical net benefit. Interestingly, the seven-gene signature had a good performance in distinguishing KIRC from normal tissues. GSEA revealed that several oncological signatures and GO terms were enriched. This study developed a novel seven-gene signature and nomogram for predicting the OS of patients with KIRC, which may be helpful for clinicians in establishing individualized treatments.