Clinicopathological and survival analysis of clinically advanced papillary and chromophobe renal cell carcinoma

Overexpression of cyclin F/CCNF as an independent prognostic factor for poor survival in clear cell renal cell carcinoma

Cyclin F (encoded by CCNF gene) has been reported to be implicated in the pathobiology of several human cancers. However, its potential clinical significance in clear cell renal cell carcinoma (ccRCC) remains unknown. The present study aimed to evaluate the potential significance of cyclin F, assessed by immunohistochemical (IHC) staining and molecular (bioinformatics) techniques, as a prognostic marker in ccRCC in relation to clinicopathological features and outcomes. IHC staining was performed using two independent ccRCC tissue array cohorts, herein called tissue macroarray (TMA)_1 and tissue microarray (TMA)_2, composed of 108 ccRCCs and 37 histologically normal tissues adjacent to the tumor (NAT) and 192 ccRCCs and 16 normal kidney samples, respectively. The mRNA expression data were obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) public datasets, followed by bioinformatics analysis of biological mechanisms underlying prognosis. The relationship between immune cell infiltration level and CCNF expression in ccRCC was investigated using the Tumor Immune Estimation Resource 2.0 (TIMER2) and Gene Expression Profiling Interactive Analysis 2 (GEPIA2). Cyclin F expression was significantly elevated in ccRCC lesions compared to both NAT and normal renal tissues. Likewise, CCNF mRNA was markedly increased in ccRCCs relative to non-cancerous tissues. In all analyzed cohorts, tumors with features of more aggressive behavior were more likely to display cyclin F/CCNF-high expression than low. Furthermore, patients with high cyclin F/CCNF expression had shorter overall survival (OS) times than those with low expression. In addition, multivariable analysis revealed that cyclin F/CCNF-high expression was an independent prognostic factor for poor OS in ccRCC. Enrichment analysis for mechanistically relevant processes showed that CCNF and its highly correlated genes initiate the signaling pathways that eventually result in uncontrolled cell proliferation. CCNF expression was also correlated with immune cell infiltration and caused poor outcomes depending on the abundance of tumor-infiltrating immune cells in ccRCC. Our findings suggest that cyclin F/CCNF expression is likely to have an essential role in ccRCC pathobiology through regulating multiple oncogenic signaling pathways and affecting the tumor immune microenvironment and may serve as prognostic biomarker and promising therapeutic target in ccRCC.

Multi-omics and immunogenomics analysis revealed PFKFB3 as a targetable hallmark and mediates sunitinib resistance in papillary renal cell carcinoma: in silico study with laboratory verification

Glycolysis-related metabolic reprogramming is a central hallmark of human cancers, especially in renal cell carcinoma. However, the regulatory function of glycolytic signature in papillary RCC has not been well elucidated. In the present study, the glycolysis-immune predictive signature was constructed and validated using WGCNA, glycolysis-immune clustering analysis. PPI network of DEGs was constructed and visualized. Functional enrichments and patients' overall survival were analyzed. QRT-PCR experiments were performed to detect hub genes' expression and distribution, siRNA technology was used to silence targeted genes; cell proliferation and migration assays were applied to evaluate the biological function. Glucose concentration, lactate secretion, and ATP production were measured. Glycolysis-Immune Related Prognostic Index (GIRPI) was constructed and combined analyzed with single-cell RNA-seq. High-GIRPI signature predicted significantly poorer outcomes and relevant clinical features of pRCC patients. Moreover, GIRPI also participated in several pathways, which affected tumor immune microenvironment and provided potential therapeutic strategy. As a key glycolysis regulator, PFKFB3 could promote renal cancer cell proliferation and migration in vitro. Blocking of PFKFB3 by selective inhibitor PFK-015 or glycolytic inhibitor 2-DG significantly restrained renal cancer cells' neoplastic potential. PFK-015 and sunitinib could synergistically inhibit pRCC cells proliferation. Glycolysis-Immune Risk Signature is closely associated with pRCC prognosis, progression, immune infiltration, and therapeutic response. PFKFB3 may serve as a pivotal glycolysis regulator and mediates Sunitinib resistance in pRCC patients.

Identification of Prognostic Markers of DNA Damage and Oxidative Stress in Diagnosing Papillary Renal Cell Carcinoma Based on High-Throughput Bioinformatics Screening

Purpose

Papillary renal cell carcinoma (pRCC) is the second most common histological subtype of adult kidney tumors, with a poor prognosis due to limited understanding of the disease mechanism. Herein, we have performed high-throughput bioinformatic screening to explore and identify potential biomarkers of DNA damage and oxidative stress for pRCC.

Methods

RNA sequencing data related to pRCC were downloaded from the TCGA database, and differentially expressed genes (DEG) were identified by a wide variety of clustering and classification algorithms, including self-organized maps (SOM), artificial neural networks (ANN), support vector machines (SVM), fuzzy logic, and hyphenated techniques such as neuro-fuzzy networks. Then DAVID and STRING online biological information tools were used to analyze functional enrichment of the regulatory networks of DEG and construct a protein-protein interaction (PPI) network, and then the Cytoscape software was used to identify hub genes. The importance of key genes was assessed by the analysis of the Kaplan-Meier survival curves using the R software. Lastly, we have analyzed the expression of hub genes of DNA damage and oxidative stress (BDKRB1, NMUR2, PMCH, and SAA1) in pRCC tissues and adjacent normal tissues, as well as the relationship between the expression of hub genes in pRCC tissues and pathological characteristics and prognosis of pRCC patients.

Results

A total of 1,992 DEGs for pRCC were identified, with 1,142 upregulated ones and 850 downregulated ones. The DEGs were significantly enriched in activities including DNA damage and oxidative stress, chemical synaptic transmission, an integral component of the membrane, calcium ion binding, and neuroactive ligand-receptor interaction. cytoHubba in the Cytoscape software was used to determine the top 10 hub genes in the PPI network as BDKRB2, NMUR2, NMU, BDKRB1, LPAR5, KNG1, LPAR3, SAA1, MCHR1, PMCH, and NCAPH. Furthermore, the expression level of hub genes BDKRB1, NMUR2, PMCH, and SAA1 in pRCC tissues was significantly higher than that in the adjacent normal tissues. Meanwhile, the expression level of hub genes BDKRB1, NMUR2, PMCH, and SAA1 in pRCC tissues was significantly positively correlated with tumor stage, lymph node metastasis, and the histopathology grade of pRCC. In addition, high expression levels of hub genes BDKRB1, NMUR2, PMCH, and SAA1 were associated with a poor prognosis for patients with pRCC. Univariate and multivariate analyses showed that the expression of hub genes BDKRB1, NMUR2, PMCH, and SAA1 were independent risk factors for the prognosis of patients with pRCC.

Conclusion

The results of this analysis suggested that BDKRB1, NMUR2, PMCH, and SAA1 might be potential prognostic biomarkers and novel therapeutic targets for pRCC.

A novel nomogram and risk classification system predicting the overall survival of patients with papillary renal cell carcinoma after nephrectomy: A population-based study

Background

Papillary renal cell carcinoma (pRCC) is the largest histologic subtype of non-clear-cell RCC. To date, there is no reliable nomogram to predict the prognosis of patients with pRCC after nephrectomy. We aimed to first establish an effective nomogram to predict the overall survival (OS) of patients with pRCC after nephrectomy.

Methods

A total of 3,528 eligible patients with pRCC after nephrectomy were identified from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015. The patients were randomized into the training cohort (n = 2,472) and the validation cohort (n = 1,056) at a 7:3 ratio. In total, 122 real-world samples from our institute (titled the AHMU-pRCC cohort) were used as the external validation cohort. Univariate and subsequent multivariate Cox regression analyses were conducted to identify OS-related prognostic factors, which were further used to establish a prognostic nomogram for predicting 1-, 3-, and 5-year OS probabilities. The performance of the nomogram was evaluated by using the concordance index (C-index), receiver operating characteristic curve (ROC), calibration plot, and decision curve analysis (DCA).

Results

Multivariate Cox analysis showed that age, race, marital status, TNM stage, tumor size, and surgery were significant OS-related prognostic factors. A prognostic model consisting of these clinical parameters was developed and virtualized by a nomogram. High C-index and area under the ROC curve (AUC) values of the nomogram at 1, 3, and 5 years were found in the training, validation, and AHMU-pRCC cohorts. The calibration plot and DCA also showed that the nomogram had a satisfactory clinical application value. A risk classification system was established to risk-stratify patients with pRCC.

Conclusion

Based on a large cohort from the public SEER database, a reliable nomogram predicting the OS of patients with pRCC after nephrectomy was constructed, which could optimize the survival assessment and clinical treatment.

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

Background

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

Methods

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

Results

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

Conclusions

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

Prognostic value of CDCA3 in kidney renal papillary cell carcinoma

Kidney renal papillary cell carcinoma (KIRP) is a type of low-grade malignant renal cell carcinoma. Huge challenges remain in the treatment of KIRP. Cell division cycle associated 3 (CDCA3) participates in human physiological and pathological processes. However, its role in KIRP has not been established. Here, we evaluated the prognostic value of CDCA3 in KIRP using a comprehensive bioinformatics approach. Data for CDCA3 expression in KIRP were obtained from online database. Different expression genes between high and low CDCA3 expression groups were identified and evaluated by performing Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. A gene set enrichment analysis was performed to elucidate the function and pathway differences between the different. Differences in immune cell infiltration between low and high CDCA3 expression groups were analyzed by a single-sample GSEA method for immune cells. A protein-protein interaction network was generated and hub genes were identified. UALCAN was used to analyze associations between the mRNA expression levels of CDCA3 in KIRP tissues with clinicopathologic parameters. The diagnostic efficacy of CDCA3 for KIRP was analyzed by ROC analysis. Logistic regression was used to analyze relationships between the clinicopathological characteristics and CDCA3 expression. Our results indicated that high CDCA3 mRNA expression is significantly associated with some clinicopathologic parameters in KIRP patients High CDCA3 mRNA expression associated with a shorter overall survival, progression-free interval, and disease-special survival. Taken together, CDCA3 is a potential target for the development of anti-KIRP therapeutics and is an efficient prognostic marker.

Integrated Analysis of the Prognosis-Associated RNA-Binding Protein Genes and Candidate Drugs in Renal Papillary Cell Carcinoma

RNA-binding proteins (RBPs) play significant roles in various cancer types. However, the functions of RBPs have not been clarified in renal papillary cell carcinoma (pRCC). In this study, we identified 31 downregulated and 89 upregulated differentially expressed RBPs on the basis of the cancer genome atlas (TCGA) database and performed functional enrichment analyses. Subsequently, through univariate Cox, random survival forest, and multivariate Cox regression analysis, six RBPs of SNRPN, RRS1, INTS8, RBPMS2, IGF2BP3, and PIH1D2 were screened out, and the prognostic model was then established. Further analyses revealed that the high-risk group had poor overall survival. The area under the curve values were 0.87 and 0.75 at 3 years and 0.78 and 0.69 at 5 years in the training set and test set, respectively. We then plotted a nomogram on the basis of the six RBPs and tumor stage with the substantiation in the TCGA cohort. Moreover, we selected two intersectant RBPs and evaluate their biological effects by GSEA and predicted three drugs, including STOCK1N-28457, pyrimethamine, and trapidil by using the Connectivity Map. Our research provided a novel insight into pRCC and improved the determination of prognosis and individualized therapeutic strategies.

Comprehensive subgroup analyses of survival outcomes between clear cell renal cell adenocarcinoma and papillary renal cell adenocarcinoma

To comprehensively compare the survival outcomes of clear cell renal cell carcinoma (ccRCC) and papillary renal cell carcinoma (pRCC), the study cohort included ccRCC and pRCC patients in 2004–2017 from the Surveillance, Epidemiology, and End Results (SEER) database, which comprises 18 registries. Primary outcomes including overall mortality (OM) and cancer‐specific mortality (CSM) were evaluated. Subgroup analyses were conducted for different ages, race, and disease stages. A total of 112,270 cases were eligible for the current analysis, including 92,209 cases of ccRCC and 20,061 cases of pRCC. Univariate analyses suggested that pRCC has a more favorable outcome than ccRCC in terms of CSM (HR: 0.72, 95% CI: 0.68–0.75, p < 0.001) and OM (HR: 0.90, 95% CI: 0.88–0.93, p < 0.001). Multivariate‐adjusted HRs suggested that pRCC has worse survival outcomes than ccRCC (adjusted HR: 1.08 for CSM and 1.05 for OM, both p < 0.05). Subgroup analyses showed that pRCC had a significantly poorer prognosis than ccRCC among patients ≤45 years old (HR_CSM: 1.59, 95% CI: 1.31–1.93, p < 0.001; HR_OM: 1.63, 95% CI: 1.40–1.90, p < 0.001). Among patients with distant metastasis, those with pRCC had a higher risk of CSM and OM than those with ccRCC (HR_CSM: 1.28, 95% CI: 1.19–1.39, p < 0.001; HR_OM: 1.30, 95% CI: 1.21–1.40, p < 0.001). Propensity score analyses for patients ≤45 years old and those with metastasis showed similar results. The lack of information on pRCC subtypes in the SEER database was a limitation. In conclusion, pRCC has poorer survival outcomes than ccRCC among patients younger than 45 years old and patients with distant metastasis.