Identification of metabolism-associated genes and pathways involved in different stages of clear cell renal cell carcinoma

Cuproptosis-related gene DLAT serves as a prognostic biomarker for immunotherapy in clear cell renal cell carcinoma: multi-database and experimental verification

OBJECTIVE

Renal clear cell carcinoma (ccRCC) is the most common type of renal cancer. Here we aim to explore the prognosis and immunotherapeutic value of copper death-related gene Dihydrolipoamide S-acetyltransferase (DLAT) in ccRCC.

METHODS

The mRNA and protein expressions and methylation level of DLAT, as well as the relation of DLAT to survival prognosis, clinical characteristics, biological function, and immune microenvironment and responses in patients with ccRCC were evaluated using multiple databases. In addition, 75 paired ccRCC tissue samples and 3 kinds of cell lines were tested for experimental validation.

RESULTS

Bioinformatics analysis of multiple databases, qRT-PCR, and western blot verified that DLAT expression in ccRCC was lower than that in paracancerous tissues. Patients with low expression of DLAT had a lower survival rate, worse clinical prognosis, stronger immune cell infiltration and expression of immunosuppressive points, and higher tumor immune dysfunction and exclusion (TIDE) scores.

CONCLUSIONS

DLAT was identified as an independent prognostic factor in ccRCC and was closely related to the prognosis and immune responses of patients with ccRCC.

NUDT1 Could Be a Prognostic Biomarker and Correlated with Immune Infiltration in Clear Cell Renal Cell Carcinoma

Background

Clear cell renal cell carcinoma (ccRCC) is a malignant tumor with high morbidity and mortality. As a member of the Nudix hydrolase superfamily, Nudix (nucleoside diphosphate-linked moiety X)-type motif 1 (NUDT1) is closely related to the occurrence and development of cancer. Our study aims to explore the role of NUDT1 in ccRCC and its relationship with immune infiltration.

Methods

The NUDT1 expression matrix and corresponding clinical information were obtained from The Cancer Genome Atlas (TCGA) database. The expression difference of NUDT1 in ccRCC and its relationship with the clinical characteristics were investigated using R software. Kaplan-Meier (K-M) analysis, univariate Cox regression, multivariate Cox regression, receiver operating characteristic (ROC) curve, and nomogram were utilized to evaluate the survival and prognosis of patients. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were utilized to explore the function of differential genes in low- or high-expression group of NUDT1. TCGA dataset and Tumor IMmune Estimation Resource (TIMER) database were utilized to explore the relationship between NUDT1 and immune infiltration. Finally, TCGA dataset was utilized for gene set enrichment analysis (GSEA).

Results

NUDT1 was not only overexpressed in ccRCC but also significantly correlated with clinicopathological features (P < 0.05). K-M survival analysis showed that upregulated NUDT1 was closely related to the decrease of overall survival (OS) and progression-free survival (PFS) in ccRCC patients. Multivariate Cox regression revealed that NUDT1 was a independent prognostic indicator (HR = 1.437, 95% CI: 1.065-1.939, P=0.018). The ROC curve showed that NUDT1 had a certain accuracy in predicting the outcome of ccRCC patiens. Furthermore, a total of 150 coexpressed genes and 1,886 differentially expressed genes (DEGs) were identified. GO/KEGG and GSEA results suggested that NUDT1 and its DEGs were involved in the immune-related pathways. NUDT1 expression was positively correlated with infiltrating levels of regulatory T cells (Tregs), CD8⁺ T cells, follicular helper T cells, and M0 macrophages. In addition, NUDT1 was positively related to immune checkpoints, such as PD-1, LAG3, CTLA4, and CD70, in ccRCC.

Conclusion

NUDT1 plays a key role in the prognosis and immune cell infiltration of ccRCC patients, indicating its potential use as a prognostic biomarker and therapeutic target.

Identification of a prognostic metabolic gene signature in diffuse large B-cell lymphoma

Diffuse large B-cell lymphoma (DLBCL) is a clinically diverse disease. Given the numerous genetic mutations and variations associated with it, a prognostic gene signature that can be related to the overall survival (OS) is a clinical implication. We used the mRNA expression profiles and clinicopathological data of patients with DLBCL from the Gene Expression Omnibus (GEO) database to identify a metabolism-related gene signature. Using LASSO regression analysis, a novel 13-metabolic gene signature was identified to evaluate prognosis. The information gathered was used to construct the nomogram model to improve risk stratification and quantify risk factors for individual patients. We performed gene set enrichment analysis to identify the enriched signalling axes to further understand the underlying biological pathways. The receiver operating characteristic (ROC) curve revealed a satisfactory performance in the training cohorts. The model also showed clinical benefit when compared to the standard prognostic factors (P < .05) in validation cohorts. This study aimed to combine metabolic dysregulation with clinical features of patients with DLBCL to generate a prognostic model that might not only indicate the value of the metabolic microenvironment for prognostic stratification but also improve the decision-making during individual therapy.

A Three-Metabolic-Genes Risk Score Model Predicts Overall Survival in Clear Cell Renal Cell Carcinoma Patients

Metabolic alterations play crucial roles in carcinogenesis, tumor progression, and prognosis in clear cell renal cell carcinoma (ccRCC). A risk score (RS) model for ccRCC consisting of disease-associated metabolic genes remains unidentified. Here, we utilized gene set enrichment analysis to analyze expression data from normal and tumor groups from the cancer genome atlas. Out of 70 KEGG metabolic pathways, we found seven and two pathways to be significantly enriched in our normal and tumor groups, respectively. We identified 113 genes enriched in these nine pathways. We further filtered 47 prognostic-related metabolic genes and used Least absolute shrinkage and selection operator (LASSO) analysis to construct a three-metabolic-genes RS model composed of ALDH3A2, B3GAT3, and CPT2. We further tested the RS by mapping Kaplan-Meier plots and receiver operating characteristic curves, the results were promising. Additionally, multivariate Cox analysis revealed the RS to be an independent prognostic factor. Thereafter, we considered all the independent factors and constructed a nomogram model, which manifested in better prediction capability. We validated our results using a dataset from ArrayExpress and through qRT-PCR. In summary, our study provided a metabolic gene-based RS model that can be used as a prognostic predictor for patients with ccRCC.

Molecular targeting of renal cell carcinoma by an oral combination

The 5-year survival rate of patients with metastatic renal cell carcinoma (mRCC) is <12% due to treatment failure. Therapeutic strategies that overcome resistance to modestly effective drugs for mRCC, such as sorafenib (SF), could improve outcome in mRCC patients. SF is terminally biotransformed by UDP-glucuronosyltransferase-1A9 (A9) mediated glucuronidation, which inactivates SF. In a clinical-cohort and the TCGA-dataset, A9 transcript and/or protein levels were highly elevated in RCC specimens and predicted metastasis and overall-survival. This suggested that elevated A9 levels even in primary tumors of patients who eventually develop mRCC could be a mechanism for SF failure. 4-methylumbelliferone (MU), a choleretic and antispasmodic drug, downregulated A9 and inhibited SF-glucuronidation in RCC cells. Low-dose SF and MU combinations inhibited growth, motility, invasion and downregulated an invasive signature in RCC cells, patient-derived tumor explants and/or endothelial-RCC cell co-cultures; however, both agents individually were ineffective. A9 overexpression made RCC cells resistant to the combination, while its downregulation sensitized them to SF treatment alone. The combination inhibited kidney tumor growth, angiogenesis and distant metastasis, with no detectable toxicity; A9-overexpressing tumors were resistant to treatment. With effective primary tumor control and abrogation of metastasis in preclinical models, the low-dose SF and MU combinations could be an effective treatment option for mRCC patients. Broadly, our study highlights how targeting specific mechanisms that cause the failure of “old” modestly effective FDA-approved drugs could improve treatment response with minimal alteration in toxicity profile.

Volatilomics Reveals Potential Biomarkers for Identification of Renal Cell Carcinoma: An In Vitro Approach

The identification of noninvasive biomarkers able to detect renal cell carcinoma (RCC) at an early stage remains an unmet clinical need. The recognition that altered metabolism is a core hallmark of cancer boosted metabolomic studies focused in the search for cancer biomarkers. The present work aims to evaluate the performance of the volatile metabolites present in the extracellular medium to discriminate RCC cell lines with distinct histological subtypes (clear cell and papillary) and metastatic potential from non-tumorigenic renal cells. Hence, volatile organic compounds (VOCs) and volatile carbonyl compounds (VCCs) were extracted by headspace solid-phase microextraction (HS-SPME) and analyzed by gas chromatography-mass spectrometry (GC-MS). Multivariate and univariate analysis unveiled a panel of metabolites responsible for the separation between groups, mostly belonging to ketones, alcohols, alkanes and aldehydes classes. Some metabolites were found similarly altered for all RCC cell lines compared to non-tumorigenic cells, namely 2-ethylhexanol, tetradecane, formaldehyde, acetone (increased) and cyclohexanone and acetaldehyde (decreased). Furthermore, significantly altered levels of cyclohexanol, decanal, decane, dodecane and 4-methylbenzaldehyde were observed in all metastatic RCC cell lines when compared with the non-metastatic ones. Moreover, some alterations in the volatile composition were also observed between RCC histological subtypes. Overall, our results demonstrate the potential of volatile profiling for identification of noninvasive candidate biomarkers for early RCC diagnosis.

Transcriptome analysis reveals gender-specific differences in overall metabolic response of male and female patients in lung adenocarcinoma

Background

Evidence from multiple studies suggests metabolic abnormalities play an important role in lung cancer. Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer. The present study aimed to explore differences in the global metabolic response between male and female patients in LUAD and to identify the metabolic genes associated with lung cancer susceptibility.

Methods

Transcriptome and clinical LUAD data were acquired from The Cancer Genome Atlas (TCGA) database. Information on metabolic genes and metabolic subsystems were collected from the Recon3D human metabolic model. Two validation datasets (GSE68465 and GSE72094) were downloaded from the Gene Expression Omnibus (GEO) database. Differential expression analysis, gene set enrichment analysis and protein-protein interaction networks were used to identified key metabolic pathways and genes. Functional experiments were used to verify the effects of genes on proliferation, migration, and invasion in lung cancer cells in vitro.

Results

Samples of tumors and adjacent non-tumor tissue from both male and female patients exhibited distinct global patterns of gene expression. In addition, we found large differences in methionine and cysteine metabolism, pyruvate metabolism, cholesterol metabolism, nicotinamide adenine dinucleotide (NAD) metabolism, and nuclear transport between male and female LUAD patients. We identified 34 metabolic genes associated with lung cancer susceptibility in males and 15 in females. Most of the metabolic cancer-susceptibility genes had high prediction accuracy for lung cancer (AUC > 0.9). Furthermore, both bioinformatics analysis and experimental results showed that TAOK2 was down-regulated and ASAH1 was up-regulated in male tumor tissue and female tumor tissue in LUAD. Functional experiments showed that inhibiting ASAH1 suppressed the proliferation, migration, and invasion of lung cancer cells.

Conclusions

Metabolic cancer-susceptibility genes may be used alone or in combination as diagnostic markers for LUAD. Further studies are required to elucidate the functions of these genes in LUAD.

A cluster of long non-coding RNAs exhibit diagnostic and prognostic values in renal cell carcinoma

Kidney cancer ranked in the top 10 for both men and women in the estimated numbers of new cancer cases in the United States in 2018. Targeted therapies have recently been administered to patients with clear cell renal cell carcinoma (ccRCC), but the overall survival of patients at the terminal stage of the disease has not been as good as expected. It is therefore necessary to uncover efficient biomarkers for early diagnosis, and to clarify the molecular mechanisms underlying ccRCC progression and metastasis. Increased evidence has shown that long non-coding RNAs (lncRNAs) play important roles during tumor progression. In this study, 10 candidate lncRNAs with diagnostic and prognostic values in ccRCC were identified: IGFL2-AS1, AC023043.1, AP000439.2, AC124854.1, AL355102.4, TMEM246-AS1, AL133467.3, ZNF582-AS1, LINC01510 and PSMG3-AS1. Enrichment analysis revealed metabolic and functional pathways, which may be closely associated with kidney cancer tumorigenesis. Six representative processes were summarized, namely glycolysis, amino acid metabolism, lipid synthesis, reductive carboxylation, nucleotide metabolism, transmembrane transport and signal transduction. In combination, the present results provided prognostic and diagnostic biomarkers for ccRCC and might pave the way for targeted intervention and molecular therapies in the future.