TP53 to mediate immune escape in tumor microenvironment: an overview of the research progress.

Increasing evidence suggests that key cancer-causing driver genes continue to exert a sustained influence on the tumor microenvironment (TME), highlighting the importance of immunotherapeutic targeting of gene mutations in governing tumor progression. TP53 is a prominent tumor suppressor that encodes the p53 protein, which controls the initiation and progression of different tumor types. Wild-type p53 maintains cell homeostasis and genomic instability through complex pathways, and mutant p53 (Mut p53) promotes tumor occurrence and development by regulating the TME. To date, it has been wildly considered that TP53 is able to mediate tumor immune escape. Herein, we summarized the relationship between TP53 gene and tumors, discussed the mechanism of Mut p53 mediated tumor immune escape, and summarized the progress of applying p53 protein in immunotherapy. This study will provide a basic basis for further exploration of therapeutic strategies targeting p53 protein.

USP10 promotes the progression of triple-negative breast cancer by enhancing the stability of TCF4 protein.

Investigating the role of ubiquitin-specific peptidase 10 (USP10) in triple-negative breast cancer (TNBC). Analyzed USP10 expression levels in tumors using public databases. Detected USP10 mRNA and protein levels in cell lines. Examined USP10 expression in tumor tissues from breast cancer patients. Conducted USP10 knockdown experiments and analyzed changes in cell proliferation and metastasis. Confirmed protein-protein interactions with USP10 through mass spectrometry, Co-IP, and fluorescence experiments. Assessed impact of USP10 on transcription factor 4 (TCF4) ubiquitination and validated TCF4's influence on TNBC cells. We initially identified a pronounced overexpression of USP10 across multiple tumor types, including TNBC. Subsequently, we observed a conspicuous upregulation of USP10 expression levels in breast cancer cell lines compared to normal breast epithelial cells. However, upon subsequent depletion of USP10 within cellular contexts, we noted a substantial attenuation of malignant proliferation and metastatic potential in TNBC cells. In subsequent experimental analyses, we elucidated the physical interaction between USP10 and the transcription factor TCF4, whereby USP10 facilitated the deubiquitination modification of TCF4, consequently promoting its protein stability and contributing to the initiation and progression of TNBC. Collectively, this study demonstrates that USP10 facilitated the deubiquitination modification of TCF4, consequently promoting its protein stability and contributing to the initiation and progression of TNBC.

RAP2A promotes apoptosis resistance of hepatocellular carcinoma cells via the mTOR pathway.

Hepatocellular carcinoma (HCC) is the most common digestive system cancer. In the current study, we investigated the biological effects of Ras-related protein Rap-2a (RAP2A), a GTPase protein, in HCC tissues and cells. We found that RAP2A was upregulated in HCC tissues and cells. RAP2A knockdown could effectively inhibit the proliferation of HCC cells and weaken its apoptosis resistance. In terms of its action mechanism, RAP2A may be involved in activating the mTOR signaling pathway. Therefore, we believe that RAP2A is abnormally highly expressed in HCC tissues and promotes tumor cell proliferation and apoptosis resistance by activating the mTOR signaling pathway, and it may serve as a potential target for HCC treatment.

GRMDA: Graph Regression for MiRNA-Disease Association Prediction.

Nowadays, as more and more associations between microRNAs (miRNAs) and diseases have been discovered, miRNA has gradually become a hot topic in the biological field. Because of the high consumption of time and money on carrying out biological experiments, computational method which can help scientists choose the most likely associations between miRNAs and diseases for further experimental studies is desperately needed. In this study, we proposed a method of Graph Regression for MiRNA-Disease Association prediction (GRMDA) which combines known miRNA-disease associations, miRNA functional similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity. We used Gaussian interaction profile kernel similarity to supplement the shortage of miRNA functional similarity and disease semantic similarity. Furthermore, the graph regression was synchronously performed in three latent spaces, including association space, miRNA similarity space, and disease similarity space, by using two matrix factorization approaches called Singular Value Decomposition and Partial Least-Squares to extract important related attributes and filter the noise. In the leave-one-out cross validation and five-fold cross validation, GRMDA obtained the AUCs of 0.8272 and 0.8080 ± 0.0024, respectively. Thus, its performance is better than some previous models. In the case study of Lymphoma using the recorded miRNA-disease associations in HMDD V2.0 database, 88% of top 50 predicted miRNAs were verified by experimental literatures. In order to test the performance of GRMDA on new diseases with no known related miRNAs, we took Breast Neoplasms as an example by regarding all the known related miRNAs as unknown ones. We found that 100% of top 50 predicted miRNAs were verified. Moreover, 84% of top 50 predicted miRNAs in case study for Esophageal Neoplasms based on HMDD V1.0 were verified to have known associations. In conclusion, GRMDA is an effective and practical method for miRNA-disease association prediction.