The role of long noncoding RNAs in liquid-liquid phase separation

Subcellular Drug Distribution: Exploring Organelle-Specific Characteristics for Enhanced Therapeutic Efficacy

The efficacy and potential toxicity of drug treatments depends on the drug concentration at its site of action, intricately linked to its distribution within diverse organelles of mammalian cells. These organelles, including the nucleus, endosome, lysosome, mitochondria, endoplasmic reticulum, Golgi apparatus, lipid droplets, exosomes, and membrane-less structures, create distinct sub-compartments within the cell, each with unique biological features. Certain structures within these sub-compartments possess the ability to selectively accumulate or exclude drugs based on their physicochemical attributes, directly impacting drug efficacy. Under pathological conditions, such as cancer, many cells undergo dynamic alterations in subcellular organelles, leading to changes in the active concentration of drugs. A mechanistic and quantitative understanding of how organelle characteristics and abundance alter drug partition coefficients is crucial. This review explores biological factors and physicochemical properties influencing subcellular drug distribution, alongside strategies for modulation to enhance efficacy. Additionally, we discuss physiologically based computational models for subcellular drug distribution, providing a quantifiable means to simulate and predict drug distribution at the subcellular level, with the potential to optimize drug development strategies.

Utilizing Liquid-liquid phase separation-related lncRNAs to predict the prognosis and treatment response of PCa

BACKGROUND

Studies have indicated a close association between genes linked to liquid-liquid phase separation (LLPS) and the progression of prostate cancer (PCa). However, the interplay among long non-coding RNAs (lncRNAs) linked to LLPS in PCa remains elusive. Therefore, we constructed a prediction model based on LLPS-related LncRNA in PCa to explore its relationship with the prognosis and drug treatment of PCa.

METHODS

We obtained clinical and sequencing data from TCGA and LLPS genes from the Phase Separation Protein Database. By analyzing the differential expression of LLPS-related genes and lncRNAs in prostate cancer, and using Poisson correlation, we identified LLPS-related lncRNAs. Prognostic LLPS-lncRNAs were found through prognostic correlation analysis and included in a Cox model to compute regression coefficients. Patients were scored and divided into high- and low-risk groups. Independent prognostic factors were integrated into a prognostic nomogram with risk and Gleason scores. We also conducted drug sensitivity analyses, GSEA, and validated the impact of key lncRNAs through functional experiments.

RESULTS

Our study identified five LLPS-associated lncRNAs that are of prognostic importance. And found notable disparities in biochemical recurrence rates and survival outcomes between these risk groups, with the low-risk cohort exhibiting superior prognostic indicators. Moreover, our prediction nomogram demonstrated robust predictive accuracy and significant clinical utility. Furthermore, our model exhibited promising capabilities in forecasting patient sensitivity to various conventional therapeutic drugs, thereby highlighting its potential in personalized treatment strategies. GSEA showed that these lncRNAs may influence PCa prognosis and sensitivity to therapeutic agents by affecting pathways such as cell cycle. Knockdown of AC009812.4 could inhibit the ability of PCa cells to proliferate, migrate and invade, and compare to paracancerous tissue, AC009812.4 in PCa tissue has significantly higher expression.

CONCLUSION

Our research uncovers the prognostic significance of lncRNAs associated with LLPS in PCa and established a model exhibiting excellent predictive accuracy for prognosis. Those lncRNAs may influence progress of PCa as well as sensitivity to therapy drugs through pathways such as cell cycle.