CD70-Targeted Micelles Enhance HIF2α siRNA Delivery and Inhibit Oncogenic Functions in Patient-Derived Clear Cell Renal Carcinoma Cells

Development and implementation of a prognostic model for clear cell renal cell carcinoma based on heterogeneous TLR4 expression

Objective

Clear cell renal cell carcinoma (ccRCC) is the most common subtype among renal cell carcinomas and has the worst prognosis, originating from renal tubular epithelial cells. Toll-like receptor 4 (TLR4) plays a crucial role in ccRCC proliferation, infiltration, and metastasis. The aim of this study was to construct a prognostic scoring model for ccRCC based on TLR4 expression heterogeneity and to explore its association with immune infiltration, thereby providing insights for the treatment and prognostic evaluation of ccRCC.

Methods

Using R software, a differential analysis was conducted on normal samples and ccRCC samples, and in conjunction with the KEGG database, a correlation analysis for the clear cell renal cell carcinoma pathway (hsa05211) was carried out. We observed the expression heterogeneity of TLR4 in the TCGA-KIRC cohort and identified its related differential genes (TRGs). Based on the expression levels of TRGs, consensus clustering was employed to identify TLR4-related subtypes, and further clustering heatmaps, principal component, and single-sample gene set enrichment analyses were conducted. Overlapping differential genes (ODEGs) between subtypes were analysed, and combined with survival data, univariate Cox regression, LASSO, and multivariate Cox regression were used to establish a prognostic risk model for ccRCC. This model was subsequently evaluated through ROC analysis, risk factor correlation analysis, independent prognostic factor analysis, and intergroup differential analysis. The ssGSEA model was employed to explore immune heterogeneity in ccRCC, and the performance of the model in predicting patient prognosis was evaluated using box plots and the oncoPredict software package.

Results

In the TCGA-KIRC cohort, TLR4 expression was notably elevated in ccRCC samples compared to normal samples, correlating with improved survival in the high-expression group. The study identified distinct TLR4-related differential genes and categorized ccRCC into three subtypes with varied survival outcomes. A risk prognosis model based on overlapping differential genes was established, showing significant associations with immune cell infiltration and key immune checkpoints (PD-1, PD-L1, CTLA4). Additionally, drug sensitivity differences were observed between risk groups.

Conclusion

In the TCGA-KIRC cohort, the expression of TLR4 in ccRCC samples exhibited significant heterogeneity. Through clustering analysis, we identified that the primary immune cells across subtypes are myeloid-derived suppressor cells, central memory CD4 T cells, and regulatory T cells. Furthermore, we successfully constructed a prognostic risk model for ccRCC composed of 17 genes. This model provides valuable references for the prognosis prediction and treatment of ccRCC patients.

Nanomedicine in kidney disease

PURPOSE OF REVIEW

The pathophysiological understanding of kidney-related disorders has profoundly increased; however, tissue-specific and cell-specific treatments in this field remain scarce. Advances in nanomedicine enable alteration of pharmacokinetics and targeted treatments improving efficiency and reducing toxicity. This review addresses recent developments of nanocarriers used for various purposes in the broad field of kidney disease, which may pave a path to new therapeutic and diagnostic solutions employing nanomedicine.

RECENT FINDINGS

Controlled delivery of antiproliferative medications enables improved treatment of polycystic kidney disease and fibrosis. Directed anti-inflammatory treatment mitigated glomerulonephritis and tubulointerstitial nephritis. Multiple injury pathways in AKI have been targeted, with therapeutic solutions for oxidative stress, mitochondrial dysfunction, local inflammation and improving self-repair mechanisms. In addition to such treatment development, noninvasive early detection methods (minutes after ischemic insult) have been demonstrated as well. Sustained release of therapies that reduce ischemia-reperfusion injury as well as new aspects for immunosuppression bring hope to improving kidney transplant outcomes. The latest breakthroughs in gene therapy are made achievable by engineering the targeted delivery of nucleic acids for new treatments of kidney disease.

SUMMARY

Recent advances in nanotechnology and pathophysiological understanding of kidney diseases show potential for translatable therapeutic and diagnostic interventions in multiple etiologies of kidney disease.

NanoBeacon.AI: AI-Enhanced Nanodiamond Biosensor for Automated Sensitivity Prediction to Oxidative Phosphorylation Inhibitors

Spalt-like transcription factor 4 (SALL4) is an oncofetal protein that has been identified to drive cancer progression in hepatocellular carcinoma (HCC) and hematological malignancies. Furthermore, a high SALL4 expression level is correlated to poor prognosis in these cancers. However, SALL4 lacks well-structured small-molecule binding pockets, making it difficult to design targeted inhibitors. SALL4-induced expression of oxidative phosphorylation (OXPHOS) genes may serve as a therapeutically targetable vulnerability in HCC through OXPHOS inhibition. Because OXPHOS functions through a set of genes with intertumoral heterogeneous expression, identifying therapeutic sensitivity to OXPHOS inhibitors may not rely on a single clear biomarker. Here, we developed a workflow that utilized molecular beacons, nucleic-acid-based, activatable sensors with high specificity to the target mRNA, delivered by nanodiamonds, to establish an artificial intelligence (AI)-assisted platform for rapid evaluation of patient-specific drug sensitivity. Specifically, when the HCC cells were treated with the nanodiamond-medicated OXPHOS biosensor, high sensitivity and specificity of the sensor allowed for improved identification of OXPHOS expression in cells. Assisted by a trained convolutional neural network, drug sensitivity of cells toward an OXPHOS inhibitor, IACS-010759, could be accurately predicted. AI-assisted OXPHOS drug sensitivity assessment could be accomplished within 1 day, enabling rapid and efficient clinical decision support for HCC treatment. The work proposed here serves as a foundation for the patient-based subtype-specific therapeutic research platform and is well suited for precision medicine.