Mutations in DNA binding domain of p53 impede RSL1D1-p53 interaction to escape from degradation in human colorectal cancer cells

The importance of protein domain mutations in cancer therapy

Cancer is a complex disease that is caused by multiple genetic factors. Researchers have been studying protein domain mutations to understand how they affect the progression and treatment of cancer. These mutations can significantly impact the development and spread of cancer by changing the protein structure, function, and signalling pathways. As a result, there is a growing interest in how these mutations can be used as prognostic indicators for cancer prognosis. Recent studies have shown that protein domain mutations can provide valuable information about the severity of the disease and the patient's response to treatment. They may also be used to predict the response and resistance to targeted therapy in cancer treatment. The clinical implications of protein domain mutations in cancer are significant, and they are regarded as essential biomarkers in oncology. However, additional techniques and approaches are required to characterize changes in protein domains and predict their functional effects. Machine learning and other computational tools offer promising solutions to this challenge, enabling the prediction of the impact of mutations on protein structure and function. Such predictions can aid in the clinical interpretation of genetic information. Furthermore, the development of genome editing tools like CRISPR/Cas9 has made it possible to validate the functional significance of mutants more efficiently and accurately. In conclusion, protein domain mutations hold great promise as prognostic and predictive biomarkers in cancer. Overall, considerable research is still needed to better define genetic and molecular heterogeneity and to resolve the challenges that remain, so that their full potential can be realized.

Potential prognostic biomarkers of hepatocellular carcinoma based on 4D label-free quantitative proteomics analysis pilot investigation

BACKGROUND

Hepatocellular carcinoma carries a poor prognosis and poses a serious threat to global health. Currently, there are few potential prognostic biomarkers available for the prognosis of hepatocellular carcinoma.

METHODS

This pilot study used 4D label-free quantitative proteomics to compare the proteomes of hepatocellular carcinoma and adjacent non-tumor tissue. A total of 66,075 peptides, 6363 identified proteins, and 772 differentially expressed proteins were identified in specimens from three hepatocellular carcinoma patients. Through functional enrichment analysis of differentially expressed proteins by Gene Ontology, KEGG pathway, and protein domain, we identified proteins with similar functions.

RESULTS

Twelve differentially expressed proteins (RPL17, RPL27, RPL27A, RPS5, RPS16, RSL1D1, DDX18, RRP12, TARS2, YARS2, MARS2, and NARS1) were selected for identification and validation by parallel reaction monitoring. Subsequent Western blotting confirmed overexpression of RPL27, RPS16, and TARS2 in hepatocellular carcinoma compared to non-tumor tissue in 16 pairs of clinical samples. Analysis of The Cancer Genome Atlas datasets associated the increased expression of these proteins with poor prognosis. Tissue microarray revealed a negative association between high expression of RPL27 and TARS2 and the prognosis of hepatocellular carcinoma patients, although RPS16 was not significant.

CONCLUSIONS

These data suggest that RPL27 and TARS2 play an important role in hepatocellular carcinoma progression and may be potential prognostic biomarkers of overall survival in hepatocellular carcinoma patients.

RSL1D1 modulates cell senescence and proliferation via regulation of PPARγ mRNA stability

AIMS

In this study, we will examine if RSL1D1 influences PPARγ expression and explore the underlying mechanism that RSL1D1 regulates PPARγ expression. Moreover, the significance of RSL1D1-PPARγ pathway in cell senescence and proliferation will also be determined.

MAIN METHODS

Our main methods include western blotting, immunoprecipitation (IP), real-time PCR, RNA Immunoprecipitation (RIP), biotin-labeled RNA pull down assay, dual luciferase reporter gene assay, senescence-associated β-galactosidase staining, cell proliferation assay, colony formation assay, wound healing assay, blood biochemistry test and Oil red O staining.

KEY FINDINGS

By analyzing gene chip results we find that the expression of RSL1D1 and PPARγ might be correlated. Then we show that RSL1D1 is a posttranscriptional regulator of PPARγ. RSL1D1 overexpression elevates, while RSL1D1 knockdown inhibits, PPARγ mRNA and protein expression levels. Mechanistically, we find that RSL1D1 directly interacts with the 3'-untranslated region of PPARγ mRNA, and then promotes its stability and increases PPARγ protein expression level. We further demonstrate that RSL1D1 modulates cellular senescence and cell proliferation partially via PPARγ-regulated downstream target genes such as PTEN/p27, NF-κB, GLUT4, and ACL. Moreover, we find that RSL1D1 regulates PPARγ expression and function in a HuR-dependent manner. Last, we show that RSL1D1 knockout in mouse adipose tissue shortens mouse lifespan and leads to hepatic damage which may impair liver damage repair function.

SIGNIFICANCE

Collectively, our findings unveil a novel posttranscriptional regulation of PPARγ by RSL1D1 and uncover a critical role of RSL1D1-PPARγ-PPARγ downstream target genes in regulating cellular senescence and cell proliferation.