Identification of Prognostic Metabolism-Related Genes in Clear Cell Renal Cell Carcinoma

Immunoglobulin heavy constant gamma 1 silencing decreases tonicity-responsive enhancer-binding protein expression to alleviate diabetic nephropathy

AIMS/INTRODUCTION

The molecular mechanisms of diabetic nephropathy (DN) are poorly identified. However, the advantage of an increasing amount on microarray data of diabetic nephropathy intrigued us to explore the mechanisms based on bioinformatics prediction for diabetic nephropathy.

MATERIALS AND METHODS

Bioinformatics analysis was conducted to screen the hub genes associated with diabetic nephropathy. The average human renal tubular epithelial cells were exposed to high glucose (HG) to generate an in vitro cell model. In addition, a mouse model of diabetic nephropathy was established using a high-fat diet and streptozotocin injection. Finally, the shRNA targeting immunoglobulin heavy constant gamma 1 (IGHG1) was introduced in vitro and in vivo to illustrate its effect on downstream factors and on the development diabetic nephropathy.

RESULTS

Bioinformatics analysis revealed that IGHG1, TRIM11 (tripartite motif protein 11), and TonEBP are highly expressed in diabetic nephropathy. In vitro cell experiments demonstrated that IGHG1 positively regulates the expression of TRIM11 and TonEBP (tonicity-responsive enhancer binding protein) in HK2 cells treated with high glucose. Furthermore, TRIM11 upregulates the expression of TonEBP through activation of the MEK/ERK (mitogen-activated protein kinase/extracellular signal-regulated kinase) signaling pathway in HK2 cells treated with high glucose. In vivo, animal experiments further confirmed that silencing IGHG1 could prevent the occurrence and development of diabetic nephropathy.

CONCLUSION

The silencing of IGHG1 alleviated diabetic nephropathy by inhibiting the TRIM11/MEK/ERK axis and by downregulating TonEBP.

Functional enrichment analysis of LYSET and identification of related hub gene signatures as novel biomarkers to predict prognosis and immune infiltration status of clear cell renal cell carcinoma

PURPOSE

The latest research shows that the lysosomal enzyme trafficking factor (LYSET) encoded by TMEM251 is a key regulator of the amino acid metabolism reprogramming (AAMR) and related pathways significantly correlate with the progression of some tumors. The purpose of this study was to explore the potential pathways of the TMEM251 in clear cell renal cell carcinoma (ccRCC) and establish related predictive models based on the hub genes in these pathways for prognosis and tumor immune microenvironment (TIME).

METHODS

We obtained mRNA expression data and clinical information of ccRCC samples from The Cancer Genome Atlas (TCGA), E-MATE-1980, and immunotherapy cohorts. Single-cell sequencing data (GSE152938) were downloaded from the Gene Expression Omnibus (GEO) database. We explored biological pathways of the LYSET by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of TMEM251-coexpression genes. The correlation of LYSET-related pathways with the prognosis was conducted by Gene Set Variation Analysis (GSVA) and unsupervised cluster analysis. The least absolute shrinkage and selection operator (LASSO) and Cox regression were used to identify hub prognostic genes and construct the risk score. Immune infiltration analysis was conducted by CIBERSORTx and Tumor Immune Estimation Resource (TIMER) databases. The predictive value of the risk score and hub prognostic genes on immunotherapy responsiveness was analyzed through the tumor mutation burden (TMB) score, immune checkpoint expression, and survival analysis. Immunohistochemistry (IHC) was finally used to verify the expressions of hub prognostic genes.

RESULTS

The TMEM251 was found to be significantly correlated with some AAMR pathways. AAGAB, ENTR1, SCYL2, and WDR72 in LYSET-related pathways were finally identified to construct a risk score model. Immune infiltration analysis showed that LYSET-related gene signatures significantly influenced the infiltration of some vital immune cells such as CD4 + cells, NK cells, M2 macrophages, and so on. In addition, the constructed risk score was found to be positively correlated with TMB and some common immune checkpoint expressions. Different predictive values of these signatures for Nivolumab therapy responsiveness were also uncovered in immunotherapy cohorts. Finally, based on single-cell sequencing analysis, the TMEM251 and the hub gene signatures were found to be expressed in tumor cells and some immune cells. Interestingly, IHC verification showed a potential dual role of four hub genes in ccRCC progression.

CONCLUSION

The novel predictive biomarkers we built may benefit clinical decision-making for ccRCC. Our study may provide some evidence that LYSET-related gene signatures could be novel potential targets for treating ccRCC and improving immunotherapy efficacy. Our nomogram might be beneficial to clinical choices, but the results need more experimental verifications in the future.

SLC9A1 Binding mTOR Signaling Pathway-Derived Risk Score Predicting Survival and Immune in Clear Cell Renal Cell Carcinoma

Objective. Clear cell renal cell carcinoma (ccRCC) is one of the common renal cell carcinomas (RCC) with a high risk of recurrence. Considering that SLC9A1 is involved in various cellular physiological processes and probably mediates the course of mTOR signaling in tumors, this study constructed a risk model for SLC9A1 combined with mTOR signaling in ccRCC, aiming at better predicting the prognosis of patients. Methods. ccRCC expression matrices were downloaded from TCGA and ICGC databases to compare the expression of SLC9A1 in TCGA, and qRT-PCR was adopted to validate the SLC9A1 expression in different RCC cells and normal kidney cells. The CIBERSORT and ESTIMATE algorithms were used to assess samples for immunity. mTOR signaling-associated genes were downloaded from the KEGG website, and then the genes were adopted to screen genes associated with SLC9A1 expression and mTOR signaling pathway colleagues, based on which univariate COX regression and lasso regression Cox analyses were conducted to construct a ccRCC prognostic risk model. ROC curves and nomograms were used to assess the validity of the models. Results. ccRCC tumor samples showed lower SLC9A1 expression than normal samples, as also evidenced by qRT-PCR. The SLC9A1 expression was highly correlated with tumor immunity. Totally, 564 key genes associated with both SLC9A1 expression and mTOR signaling were screened out, and the risk model consisting of 11 gene signatures was constructed in ccRCC based on the 564 genes. Since patients at a high risk had poorer survival outcomes, the high-risk group presented poorer immunotherapy outcomes. Moreover, a higher clinical grade of patients suggested a higher risk score. The risk score can serve as one independent prognostic factor for the prognosis prediction of ccRCC patients. Conclusion. An extremely promising prognostic indicator for ccRCC based on SLCA9A1 and mTOR signaling has been constructed to provide reference for clinical treatment.

An oxidative stress-related signature for predicting the prognosis of liver cancer

Introduction: This study aimed to screen for oxidative stress-related genes (OSRGs) and build an oxidative stress-related signature to predict the prognosis of liver cancer. Methods: OSRGs with a protein domain correlation score ≥ 6 were downloaded from the GeneCards database and intersected with The Cancer Genome Atlas (TCGA) data for subsequent analyses. Differential immune cells (DICs) and immune and stromal scores between the normal and tumor samples were determined, followed by unsupervised hierarchical cluster analysis. Immune-related OSRGs were identified using weighted gene co-expression network analysis. An OSRG-related risk signature was then built, and the GSE14520 dataset was used for validation. A nomogram evaluation model was used to predict prognosis. Results: Nine DICs were determined between the normal and tumor groups, and three subtypes were obtained: clusters 1, 2, and 3. Cluster 1 had the best prognosis among the clusters. One hundred thirty-eight immune-related OSRGs were identified, and seven prognosis-related OSRGs were used to build the OSRG score prognostic model. Patients in the high OSRG score group had a poorer prognosis than those in the low OSRG score group. Six immune cell infiltration and enrichment scores of the 16 immune gene sets showed significant differences between the high and low OSRG score groups. Moreover, a nomogram was constructed based on the prognostic signature and clinicopathological features and had a robust predictive performance and high accuracy. Conclusion: The OSRG-related risk signature and the prognostic nomogram accurately predicted patient survival.

Treatment strategies for clear cell renal cell carcinoma: Past, present and future

Clear cell renal cell carcinoma (ccRCC) is the most prevalent histological subtype of kidney cancer, which is prone to metastasis, recurrence, and resistance to radiotherapy and chemotherapy. The burden it places on human health due to its refractory nature and rising incidence rate is substantial. Researchers have recently determined the ccRCC risk factors and optimized the clinical therapy based on the disease's underlying molecular mechanisms. In this paper, we review the established clinical therapies and novel potential therapeutic approaches for ccRCC, and we support the importance of investigating novel therapeutic options in the context of combining established therapies as a research hotspot, with the goal of providing diversified therapeutic options that promise to address the issue of drug resistance, with a view to the early realization of precision medicine and individualized treatment.

Identification of bicalutamide resistance-related genes and prognosis prediction in patients with prostate cancer

Background

Prostate cancer (PCa) is the second most common type of cancer and the fifth leading cause of cancer-related death in men. Androgen deprivation therapy (ADT) has become the first-line therapy for inhibiting PCa progression; however, nearly all patients receiving ADT eventually progress to castrate-resistant prostate cancer. Therefore, this study aimed to identify hub genes related to bicalutamide resistance in PCa and provide new insights into endocrine therapy resistance.

Methods

The data were obtained from public databases. Weighted correlation network analysis was used to identify the gene modules related to bicalutamide resistance, and the relationship between the samples and disease-free survival was analyzed. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed, and hub genes were identified. The LASSO algorithm was used to develop a bicalutamide resistance prognostic model in patients with PCa, which was then verified. Finally, we analyzed the tumor mutational heterogeneity and immune microenvironment in both groups.

Results

Two drug resistance gene modules were identified. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses revealed that both modules are involved in RNA splicing. The protein-protein interaction network identified 10 hub genes in the brown module LUC7L3, SNRNP70, PRPF3, LUC7L, CLASRP, CLK1, CLK2, U2AF1L4, NXF1, and THOC1) and 13 in the yellow module (PNN, PPWD1, SRRM2, DHX35, DMTF1, SALL4, MTA1, HDAC7, PHC1, ACIN1, HNRNPH1, DDX17, and HDAC6). The prognostic model composed of RNF207, REC8, DFNB59, HOXA2, EPOR, PILRB, LSMEM1, TCIRG1, ABTB1, ZNF276, ZNF540, and DPY19L2 could effectively predict patient prognosis. Genomic analysis revealed that the high- and low-risk groups had different mutation maps. Immune infiltration analysis showed a statistically significant difference in immune infiltration between the high- and low-risk groups, and that the high-risk group may benefit from immunotherapy.

Conclusion

In this study, bicalutamide resistance genes and hub genes were identified in PCa, a risk model for predicting the prognosis of patients with PCa was constructed, and the tumor mutation heterogeneity and immune infiltration in high- and low-risk groups were analyzed. These findings offer new insights into ADT resistance targets and prognostic prediction in patients with PCa.

Identification of subtypes of clear cell renal cell carcinoma and construction of a prognostic model based on fatty acid metabolism genes

Background: The effects of fatty acid metabolism in many tumors have been widely reported. Due to the diversity of lipid synthesis, uptake, and transformation in clear cell renal cell carcinoma (ccRCC) cells, many studies have shown that ccRCC is associated with fatty acid metabolism. The study aimed was to explore the impact of fatty acid metabolism genes on the prognosis and immunotherapy of ccRCC.

Methods: Two subtypes were distinguished by unsupervised clustering analysis based on the expression of 309 fatty acid metabolism genes. A prognostic model was constructed by lasso algorithm and multivariate COX regression analysis using fatty acid metabolism genes as the signatures. The tumor microenvironment between subtypes and between risk groups was further analyzed. The International Cancer Genome Consortium cohort was used for external validation of the model.

Results: The analysis showed that subtype B had a poorer prognosis and a higher degree of immune infiltration. The high-risk group had a poorer prognosis and higher tumor microenvironment scores. The nomogram could accurately predict patient survival.

Conclusion: Fatty acid metabolism may affect the prognosis and immune infiltration of patients with ccRCC. The analysis was performed to understand the potential role of fatty acid metabolism genes in the immune infiltration and prognosis of patients. These findings have implications for individualized treatment, prognosis, and immunization for patients with ccRCC.