Metabolomic Profiling in Renal Cell Carcinoma Patients: News and Views

Exploring the prognostic implications of cuproptosis-associated alterations in clear cell renal cell carcinoma via in vitro experiments

This study investigated the impact of novel copper ionophores on the prognosis of clear cell renal cell carcinoma (ccRCC) and the tumor microenvironment (TME). The differential expression of 10 cuproptosis and 40 TME-pathway-related genes were measured in 531 tumor samples and 71 adjacent kidney samples in The Cancer Genome Atlas database. A risk score model was constructed with LASSO cox to predict the prognosis of ccRCC patients. Forest plot and function enrichment were used to study the biological function of the key genes in depth. The study found that the risk score model accurately predicted the prognosis of ccRCC patients. Patients with high scores had higher immune responses with a higher proportion of anti-tumor lymphocytes and a lower proportion of immunosuppressive M2-like macrophages. However, the high-score group also exhibited a higher proportion of T follicular helper cells and regulatory T cells. These results suggest that cuproptosis-based therapy may be worth further investigation for the treatment of ccRCC and TME. Subsequently, by using RNAi, we established the stable depletion models of FDX1 and PDHB in ccRCC cell lines 786-O and ACHN. Through CCK8, colony formation, and Transwell assays, we observed that the knockdown of FDX1 and PDHB could significantly reduce the capabilities of proliferation and migration in ccRCC cells. In conclusion, this study illuminates the potential effectiveness of copper ionophores in the treatment of ccRCC, with higher risk scores correlating with better TME immune responses. It sets the stage for future cuproptosis-based therapy research in ccRCC and other cancers, focusing on copper's role in TME.

Metabolic signature biomarkers for predicting the recurrence of urological cancers

A significant number of patients diagnosed with localized urological cancers experience relapse and disease progression after surgery. Hence, molecular markers for patient risk stratification are needed to improve the current management guidelines. This article critically reviews for the first time, to our knowledge, the promise of metabolomics-based approaches to identify metabolic signatures as candidate prognostic biomarkers to predict recurrences at the time of surgery in prostate cancer (PCa), bladder cancer (BCa), and renal cell carcinoma (RCC). Dysregulations in the levels of several tumoral, circulating, and excreted metabolites have been reported in PCa patients experiencing recurrence within 1.5 to 8 years of follow-up. The combination of these metabolic biomarkers with clinical parameters (e.g., pathological T stage, Gleason score) has shown great potential to improve the predictive ability of PCa recurrence. In contrast, predictive biomarkers of recurrence in BCa and RCC have been poorly explored. Overall, this review highlights the great potential of metabolomics in discovering prognostic biomarkers for a more accurate patient risk stratification in urological cancers.

Distinguishing Renal Cell Carcinoma From Normal Kidney Tissue Using Mass Spectrometry Imaging Combined With Machine Learning

PURPOSE

Accurately distinguishing renal cell carcinoma (RCC) from normal kidney tissue is critical for identifying positive surgical margins (PSMs) during partial and radical nephrectomy, which remains the primary intervention for localized RCC. Techniques that detect PSM with higher accuracy and faster turnaround time than intraoperative frozen section (IFS) analysis can help decrease reoperation rates, relieve patient anxiety and costs, and potentially improve patient outcomes.

MATERIALS AND METHODS

Here, we extended our combined desorption electrospray ionization mass spectrometry imaging (DESI-MSI) and machine learning methodology to identify metabolite and lipid species from tissue surfaces that can distinguish normal tissues from clear cell RCC (ccRCC), papillary RCC (pRCC), and chromophobe RCC (chRCC) tissues.

RESULTS

From 24 normal and 40 renal cancer (23 ccRCC, 13 pRCC, and 4 chRCC) tissues, we developed a multinomial lasso classifier that selects 281 total analytes from over 27,000 detected molecular species that distinguishes all histological subtypes of RCC from normal kidney tissues with 84.5% accuracy. On the basis of independent test data reflecting distinct patient populations, the classifier achieves 85.4% and 91.2% accuracy on a Stanford test set (20 normal and 28 RCC) and a Baylor-UT Austin test set (16 normal and 41 RCC), respectively. The majority of the model's selected features show consistent trends across data sets affirming its stable performance, where the suppression of arachidonic acid metabolism is identified as a shared molecular feature of ccRCC and pRCC.

CONCLUSION

Together, these results indicate that signatures derived from DESI-MSI combined with machine learning may be used to rapidly determine surgical margin status with accuracies that meet or exceed those reported for IFS.

Exploring the potential of treating chronic liver disease targeting the PI3K/Akt pathway and polarization mechanism of macrophages

Chronic liver disease is a significant public health issue that can lead to considerable morbidity and mortality, imposing an enormous burden on healthcare resources. Understanding the mechanisms underlying chronic liver disease pathogenesis and developing effective treatment strategies are urgently needed. In this regard, the activation of liver resident macrophages, namely Kupffer cells, plays a vital role in liver inflammation and fibrosis. Macrophages display remarkable plasticity and can polarize into different phenotypes according to diverse microenvironmental stimuli. The polarization of macrophages into M1 pro-inflammatory or M2 anti-inflammatory phenotypes is regulated by complex signaling pathways such as the PI3K/Akt pathway. This review focuses on investigating the potential of using plant chemicals targeting the PI3K/Akt pathway for treating chronic liver disease while elucidating the polarization mechanism of macrophages under different microenvironments. Studies have demonstrated that inhibiting M1-type macrophage polarization or promoting M2-type polarization can effectively combat chronic liver diseases such as alcoholic liver disease, non-alcoholic fatty liver disease, and liver fibrosis. The PI3K/Akt pathway acts as a pivotal modulator of macrophage survival, migration, proliferation, and their responses to metabolism and inflammatory signals. Activating the PI3K/Akt pathway induces anti-inflammatory cytokine expression, resulting in the promotion of M2-like phenotype to facilitate tissue repair and resolution of inflammation. Conversely, inhibiting PI3K/Akt signaling could enhance the M1-like phenotype, which exacerbates liver damage. Targeting the PI3K/Akt pathway has tremendous potential as a therapeutic strategy for regulating macrophage polarization and activity to treat chronic liver diseases with plant chemicals, providing new avenues for liver disease treatment.

Investigation of VHL gene associated with miR-223 in clear cell renal cell carcinoma

BACKGROUND

Clear cell type renal cell carcinoma (ccRCC) is the most common renal cell carcinoma (RCC). In this study, we examined the expressions of VHL and miR-223 in ccRCC patients׳ tissues to investigate the possible role in the development of ccRCC.

METHODS AND RESULTS

This study collected five expression profiles (GSE36139, GSE3, GSE73731, GSE40435, and GSE26032) from Gene Omnibus Data. Expressions of VHL and miR-223 in paraffinized tumor and normal tissues of 100 Turkish patients' ccRCC tissues were determined by bioinformatic data mining and real-time quantitative polymerase chain reaction (qRT-PCR). The VHL gene was subjected to mutational analysis by DNA sequencing, and pVHL was analyzed using western blotting. Our study's t-test and Pearson correlation analysis showed that VHL gene expression in tumoral tissues with a - 0.39-fold decrease was not significantly lower than normal tissues (p = 0.441), and a 0.97-fold increase miR-223 (p = 0.045) was determined by real-time PCR. Also, as a result of DNA sequence analysis performed in the VHL gene, it was found that 26% of the patients have mutations. The mutations for (VHL):c.60C>A (p.Val20=) and (VHL):c.467delA (p.Tyr156Leu) was detected for the first time in Turkish patients.

CONCLUSIONS

The present study demonstrated that the differences in the expression levels of miR-223 have the potential to be biomarkers to determine the poor prognosis in ccRCC.