Identification of Survival-Specific Genes in Clear Cell Renal Cell Carcinoma Using a Customized Next-Generation Sequencing Gene Panel

Sex-specific survival gene mutations are discovered as clinical predictors of clear cell renal cell carcinoma

Although sex differences have been reported in patients with clear cell renal cell carcinoma (ccRCC), biological sex has not received clinical attention and genetic differences between sexes are poorly understood. This study aims to identify sex-specific gene mutations and explore their clinical significance in ccRCC. We used data from The Cancer Genome Atlas-Kidney Renal Clear Cell Carcinoma (TCGA-KIRC), The Renal Cell Cancer-European Union (RECA-EU) and Korean-KIRC. A total of 68 sex-related genes were selected from TCGA-KIRC through machine learning, and 23 sex-specific genes were identified through verification using the three databases. Survival differences according to sex were identified in nine genes (ACSS3, ALG13, ASXL3, BAP1, JADE3, KDM5C, KDM6A, NCOR1P1, and ZNF449). Female-specific survival differences were found in BAP1 in overall survival (OS) (TCGA-KIRC, p = 0.004; RECA-EU, p = 0.002; and Korean-KIRC, p = 0.003) and disease-free survival (DFS) (TCGA-KIRC, p = 0.001 and Korean-KIRC, p = 0.000004), and NCOR1P1 in DFS (TCGA-KIRC, p = 0.046 and RECA-EU, p = 0.00003). Male-specific survival differences were found in ASXL3 (OS, p = 0.017 in TCGA-KIRC; and OS, p = 0.005 in RECA-EU) and KDM5C (OS, p = 0.009 in RECA-EU; and DFS, p = 0.016 in Korean-KIRC). These results suggest that biological sex may be an important predictor and sex-specific tailored treatment may improve patient care in ccRCC.

Discovery and Validation of Survival-Specific Genes in Papillary Renal Cell Carcinoma Using a Customized Next-Generation Sequencing Gene Panel

PURPOSE

Papillary renal cell carcinoma (PRCC), the second most common kidney cancer, is morphologically, genetically, and molecularly heterogeneous with diverse clinical manifestations. Genetic variations of PRCC and their association with survival are not yet well-understood. This study aimed to identify and validate survival-specific genes in PRCC and explore their clinical utility.

MATERIALS AND METHODS

Using machine learning, 293 patients from the Cancer Genome Atlas-Kidney Renal Papillary Cell Carcinoma (TCGA-KIRP) database were analyzed to derive genes associated with survival. To validate these genes, DNAs were extracted from the tissues of 60 Korean PRCC patients. Next generation sequencing was conducted using a customized PRCC gene panel of 202 genes, including 171 survival-specific genes. Kaplan-Meier and Log-rank tests were used for survival analysis. Fisher's exact test was performed to assess the clinical utility of variant genes.

RESULTS

A total of 40 survival-specific genes were identified in the TCGA-KIRP database through machine learning and statistical analysis. Of them, 10 (BAP1, BRAF, CFDP1, EGFR, ITM2B, JAK1, NODAL, PCSK2, SPATA13, and SYT5) were validated in the Korean-KIRP database. Among these survival gene signatures, three genes (BAP1, PCSK2, and SPATA13) showed survival specificity in both overall survival (OS) (p = 0.00004, p = 1.38 × 10-7, and p = 0.026, respectively) and disease-free survival (DFS) (p = 0.00002, p = 1.21 × 10-7, and p = 0.036, respectively). Notably, the PCSK2 mutation demonstrated survival specificity uniquely in both the TCGA-KIRP (OS: p = 0.010 and DFS: p = 0.301) and Korean-KIRP (OS: p = 1.38 × 10-7 and DFS: p = 1.21 × 10-7) databases.

CONCLUSIONS

We discovered and verified genes specific for the survival of PRCC patients in the TCGA-KIRP and Korean-KIRP databases. The survival gene signature, including PCSK2 commonly obtained from the 40 gene signature of TCGA and the 10 gene signature of the Korean database, is expected to provide insight into predicting the survival of PRCC patients and developing new treatment.

USP40 promotes hepatocellular carcinoma cell proliferation, migration and stemness by deubiquitinating and stabilizing Claudin1

BACKGROUND

Hepatocellular carcinoma (HCC) is a prevalent malignant tumor that poses a major threat to people's lives and health. Previous studies have found that multiple deubiquitinating enzymes are involved in the pathogenesis of HCC. The purpose of this work was to elucidate the function and mechanism of the deubiquitinating enzyme USP40 in HCC progression.

METHODS

The expression of USP40 in human HCC tissues and HCC cell lines was investigated using RT-qPCR, western blotting and immunohistochemistry (IHC). Both in vitro and in vivo experiments were conducted to determine the crucial role of USP40 in HCC progression. The interaction between USP40 and Claudin1 was identified by immunofluorescence, co-immunoprecipitation and ubiquitination assays.

RESULTS

We discovered that USP40 is elevated in HCC tissues and predicts poor prognosis in HCC patients. USP40 knockdown inhibits HCC cell proliferation, migration and stemness, whereas USP40 overexpression shows the opposite impact. Furthermore, we confirmed that Claudin1 is a downstream gene of USP40. Mechanistically, USP40 interacts with Claudin1 and inhibits its polyubiquitination to stabilize Claudin1 protein.

CONCLUSIONS

Our study reveals that USP40 enhances HCC malignant development by deubiquitinating and stabilizing Claudin1, suggesting that targeting USP40 may be a novel approach for HCC therapy.

NLRP2 in health and disease

NLR family pyrin domain containing 2 (NLRP2) is a novel member of the Nod-like receptor (NLR) family. However, our understanding of NLRP2 has long been ambiguous. NLRP2 may have a role in the innate immune response, but its 'specific' functions remain controversial. Although NLRP2 can initiate inflammasome and promote inflammation, it can also downregulate inflammatory signals. Additionally, NLRP2 has been reported to function in the reproductive system and shows high expression in the placenta. However, the exact role of NLRP2 in the reproductive system is unclear. Here, we highlight the most current progress on NLRP2 in inflammasome activation, effector function and regulation of nuclear factor-κB. And we discuss functions of NLRP2 in inflammatory diseases, reproductive disorders and the potential implication of NLRP2 in human diseases.

[Establishment of a mutation prediction model for evaluating the efficacy of immunotherapy in renal carcinoma]

OBJECTIVE

To establish a mutation prediction model for efficacy assessment, the genomic sequencing data of renal cancer patients from the MSKCC (Memorial Sloan Kettering Cancer Center) pan-cancer immunotherapy cohort was used.

METHODS

The genomic sequencing data of 121 clear cell renal cell carcinoma patients treated with immune checkpoint inhibitors (ICI) in the MSKCC pan-cancer immunotherapy cohort were obtained from cBioPortal database (<a href="http://www.cbioportal.org/" target="_blank">http://www.cbioportal.org/</a>) and they were analyzed by univariate and multivariate Cox regression analysis to identify mutated genes associated with ICI treatment efficacy, and we constructed a comprehensive prediction model for drug efficacy of ICI based on mutated genes using nomogram. Survival analysis and time-dependent receiver operator characteristic curves were performed to assess the prognostic value of the model. Transcriptome and genomic sequencing data of 538 renal cell carcinoma patients were obtained from the TCGA database (<a href="https://portal.gdc.cancer.gov/" target="_blank">https://portal.gdc.cancer.gov/</a>). Gene set enrichment analysis was used to identify the potential functions of the mutated genes enrolled in the nomogram.

RESULTS

We used multivariate Cox regression analysis and identified mutations in PBRM1 and ARID1A were associated with treatment outcomes in the patients with renal cancer in the MSKCC pan-cancer immunotherapy cohort. Based on this, we established an efficacy prediction model including age, gender, treatment type, tumor mutational burden (TMB), PBRM1 and ARID1A mutation status (HR=4.33, 95%CI: 1.42-13.23, P=0.01, 1-year survival AUC=0.700, 2-year survival AUC=0.825, 3-year survival AUC=0.776). The validation (HR=2.72, 95%CI: 1.12-6.64, P=0.027, 1-year survival AUC=0.694, 2-year survival AUC=0.709, 3-year survival AUC=0.609) and combination (HR=2.20, 95%CI: 1.14-4.26, P=0.019, 1-year survival AUC=0.613, 2-year survival AUC=0.687, 3-year survival AUC=0.526) sets confirmed these results. Gene set enrichment analysis indicated that PBRM1 was involved in positive regulation of epithelial cell differentiation, regulation of the T cell differentiation and regulation of humoral immune response. In addition, ARID1A was involved in regulation of the T cell activation, positive regulation of T cell mediated cyto-toxicity and positive regulation of immune effector process.

CONCLUSION

PBRM1 and ARID1A mutations can be used as potential biomarkers for the evaluation of renal cancer immunotherapy efficacy. The efficacy prediction model established based on the mutation status of the above two genes can be used to screen renal cancer patients who are more suitable for ICI immunotherapy.