Dual roles of ß-arrestin 1 in mediating cell metabolism and proliferation in gastric cancer.

ß-Arrestin 1 (ARRB1) has been recognized as a multifunctional adaptor protein in the last decade, beyond its original role in desensitizing G protein-coupled receptor signaling. Here, we identify that ARRB1 plays essential roles in mediating gastric cancer (GC) cell metabolism and proliferation, by combining cohort analysis and functional investigation using patient-derived preclinical models. Overexpression of ARRB1 was associated with poor outcome of GC patients and knockdown of ARRB1 impaired cell proliferation both ex vivo and in vivo. Intriguingly, ARRB1 depicted diverse subcellular localizations during a passage of organoid cultures (7 d) to exert dual functions. Further analysis revealed that nuclear ARRB1 binds with transcription factor E2F1 triggering up-regulation of proliferative genes, while cytoplasmic ARRB1 modulates metabolic flux by binding with the pyruvate kinase M2 isoform (PKM2) and hindering PKM2 tetramerization, which reduces pyruvate kinase activity and leads to cellular metabolism shifts from oxidative phosphorylation to aerobic glycolysis. As ARRB1 localization was shown mostly in the cytoplasm in human GC samples, therapeutic potential of the ARRB1-PKM2 axis was tested, and we found tumor proliferation could be attenuated by the PKM2 activator DASA-58, especially in ARRB1high organoids. Together, the data in our study highlight a spatiotemporally dependent role of ARRB1 in mediating GC cell metabolism and proliferation and implies reactivating PKM2 may be a promising therapeutic strategy in a subset of GC patients.

Construction of a 12-Gene Prognostic Risk Model and Tumor Immune Microenvironment Analysis Based on the Clear Cell Renal Cell Carcinoma Model.

OBJECTIVES: Accurate survival predictions and early interventional therapy are crucial for people with clear cell renal cell carcinoma (ccRCC). METHODS: In this retrospective study, we identified differentially expressed immune-related (DE-IRGs) and oncogenic (DE-OGs) genes from The Cancer Genome Atlas (TCGA) dataset to construct a prognostic risk model using univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analysis. We compared the immunogenomic characterization between the high- and low-risk patients in the TCGA and the PUCH cohort, including the immune cell infiltration level, immune score, immune checkpoint, and T-effector cell- and interferon (IFN)-γ-related gene expression. RESULTS: A prognostic risk model was constructed based on 9 DE-IRGs and 3 DE-OGs and validated in the training and testing TCGA datasets. The high-risk group exhibited significantly poor overall survival compared with the low-risk group in the training (P < 0.0001), testing (P = 0.016), and total (P < 0.0001) datasets. The prognostic risk model provided accurate predictive value for ccRCC prognosis in all datasets. Decision curve analysis revealed that the nomogram showed the best net benefit for the 1-, 3-, and 5-year risk predictions. Immunogenomic analyses of the TCGA and PUCH cohorts showed higher immune cell infiltration levels, immune scores, immune checkpoint, and T-effector cell- and IFN-γ-related cytotoxic gene expression in the high-risk group than in the low-risk group. CONCLUSION: The 12-gene prognostic risk model can reliably predict overall survival outcomes and is strongly associated with the tumor immune microenvironment of ccRCC.

Deep-learning based approach to identify substrates of human E3 ubiquitin ligases and deubiquitinases.

E3 ubiquitin ligases (E3s) and deubiquitinating enzymes (DUBs) play key roles in protein degradation. However, a large number of E3 substrate interactions (ESIs) and DUB substrate interactions (DSIs) remain elusive. Here, we present DeepUSI, a deep learning-based framework to identify ESIs and DSIs using the rich information present in protein sequences. Utilizing the collected golden standard dataset, key hyperparameters in the process of model training, including the ones relevant to data sampling and number of epochs, have been systematically assessed. The performance of DeepUSI was thoroughly evaluated by multiple metrics, based on internal and external validation. Application of DeepUSI to cancer-associated E3 and DUB genes identified a list of druggable substrates with functional implications, warranting further investigation. Together, DeepUSI presents a new framework for predicting substrates of E3 ubiquitin ligases and deubiquitinates.