Genetic Association Analysis of Cell Cycle Regulators Reveals YWHAZ Has Prognostic Significance in Prostate Cancer

Single Nucleotide Polymorphisms Associated with Prostate Cancer Progression: A Systematic Review

BACKGROUND

New biomarkers of progression in patients with prostate cancer (PCa) are needed to improve their classification and clinical management. This systematic review investigated the relationship between single nucleotide polymorphisms (SNPs) and PCa progression.

METHODS

A keyword search was performed in Pubmed, EMBASE, Scopus, Web of Science, and Cochrane for publications between 2007 and 2022. We included articles with adjusted and significant associations, a median follow-up greater than or equal to 24 months, patients taken to radical prostatectomy (RP) as a first therapeutic option, and results presented based on biochemical recurrence (BCR).

RESULTS

In the 27 articles selected, 73 SNPs were identified in 39 genes, organized in seven functional groups. Of these, 50 and 23 SNPs were significantly associated with a higher and lower risk of PCa progression, respectively. Likewise, four haplotypes were found to have a significant association with PCa progression.

CONCLUSION

This article highlights the importance of SNPs as potential markers of PCa progression and their possible functional relationship with some genes relevant to its development and progression. However, most variants were identified only in cohorts from two countries; no additional studies reproduce these findings.

Design, synthesis and biological activity evaluation of novel covalent S-acylation inhibitors

In order to obtain diverse S-acylation inhibitors and address the defects of existing S-acylation inhibitors, a series of novel covalent S-acylation inhibitors are designed through synthesis. According to the results of MTT assay, most compounds produce a better anti-proliferation effect on MCF-7, MGC-803 and U937 cell lines than 2-BP. Among them, 8d, 8i, 8j and 10e exert a significant inhibitory effect on MCF-7 cell, with the IC₅₀ values falling below 20 μM. Besides, the toxic effects of some compounds on 3T3 cell line are less significant than 2-BP. According to the results of acyl-biotin exchange (ABE) experiment, most of them could inhibit S-acylation, and 8i performs best in this respect, with the inhibitory rate reaching 89.3% at the concentration of 20 μM. The results of molecular docking show the conjugation of 8i with surrounding amino acids. Additionally, 8i could not only suppress the migration of MCF-7 cell line, but also cause it to stagnate in G0/G1 phase, thus promoting cell apoptosis.

Circ_0035381 Regulates Acute Myeloid Leukemia Development by Modulating YWHAZ Expression via Adsorbing miR-582-3p

Acute myeloid leukemia (AML) is a common hematopoietic disorder. Many circular RNAs (circRNAs) are abnormally expressed in AML, including hsa_circ_0035381 (circ_0035381). Nevertheless, the function and mechanism of circ_0035381 in AML remain mostly unclear. Expression of circ_0035381 was determined by qRT-PCR. The impacts of circ_0035381 on AML cell proliferation, apoptosis, and mitochondrial damage were validated via performing loss-of-function experiments. Targeting relationship was predicted by bioinformatics analysis and verified via dual-luciferase reporter and RNA immunoprecipitation assays. Circ_0035381 was upregulated in AML bone marrow samples and cells. Circ_0035381 downregulation decreased AML cell growth in nude mice and restrained AML cell proliferation and contributed to AML apoptosis and mitochondrial damage in vitro. Circ_0035381 acted as a miR-582-3p sponge, and miR-582-3p downregulation mitigated the impacts of circ_0035381 interference on AML cell proliferation, apoptosis, and mitochondrial damage. MiR-582-3p targeted Tyrosine3-monooxygenase/tryptophan5-monooxygenase activation protein zeta (YWHAZ), and it restrained AML cell proliferation and facilitated AML cell apoptosis and mitochondrial damage by decreasing YWHAZ expression. Notably, circ_0035381 regulated YWHAZ expression via miR-582-3p. Circ_0035381 knockdown repressed cell proliferation and promoted cell apoptosis and mitochondrial damage via regulating the miR-582-3p/YWHAZ axis in AML.

Gene Expression Analysis Reveals Prognostic Biomarkers of the Tyrosine Metabolism Reprogramming Pathway for Prostate Cancer

Background

Tyrosine metabolism pathway-related genes were related to prostate cancer progression, which may be used as potential prognostic markers.

Aims

To dissect the dysregulation of tyrosine metabolism in prostate cancer and build a prognostic signature based on tyrosine metabolism-related genes for prostate cancer. Materials and Method. Cross-platform gene expression data of prostate cancer cohorts were collected from both The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Based on the expression of tyrosine metabolism-related enzymes (TMREs), an unsupervised consensus clustering method was used to classify prostate cancer patients into different molecular subtypes. We employed the least absolute shrinkage and selection operator (LASSO) Cox regression analysis to evaluate prognostic characteristics based on TMREs to obtain a prognostic effect. The nomogram model was established and used to synthesize molecular subtypes, prognostic characteristics, and clinicopathological features. Kaplan–Meier plots and logrank analysis were used to clarify survival differences between subtypes.

Results

Based on the hierarchical clustering method and the expression profiles of TMREs, prostate cancer samples were assigned into two subgroups (S1, subgroup 1; S2, subgroup 2), and the Kaplan–Meier plot and logrank analysis showed distinct survival outcomes between S1 and S2 subgroups. We further established a four-gene-based prognostic signature, and both in-group testing dataset and out-group testing dataset indicated the robustness of this model. By combining the four gene-based signatures and clinicopathological features, the nomogram model achieved better survival outcomes than any single classifier. Interestingly, we found that immune-related pathways were significantly concentrated on S1-upregulated genes, and the abundance of memory B cells, CD4+ resting memory T cells, M0 macrophages, resting dendritic cells, and resting mast cells were significantly different between S1 and S2 subgroups.

Conclusions

Our results indicate the prognostic value of genes related to tyrosine metabolism in prostate cancer and provide inspiration for treatment and prevention strategies.

A network-based integration for understanding racial disparity in prostate cancer

Compared to Caucasians (CAs), African Americans (AAs) have a higher rate of incidence and mortality in prostate cancer and are prone to be diagnosed at later stages. To understand this racial disparity, molecular features of different types, including gene expression, DNA methylation and other genomic alterations, have been compared between tumor samples from the two races, but led to different disparity associated genes (DAGs). In this study, we applied a network-based algorithm to integrate a comprehensive set of genomic datasets and identified 130 core DAGs. Out of these genes, 78 were not identified by any individual dataset but prioritized and selected through network propagation. We found DAGs were highly enriched in several critical prostate cancer-related signaling transduction and cell cycle pathways and were more likely to be associated with patient prognosis in prostate cancer. Furthermore, DAGs were over-represented in prostate cancer risk genes identified from previous genome wide association studies. We also found DAGs were enriched in kinase and transcription factor encoding genes. Interestingly, for many of these prioritized kinases their association with racial disparity did not manifest from the original genomic/transcriptomic data but was reflected by their differential phosphorylation levels between AA and CA prostate tumor samples. Similarly, the disparity relevance of some transcription factors was not reflected at the mRNA or protein expression level, but at the activity level as demonstrated by their differential ability in regulating target gene expression. Our integrative analysis provided new candidate targets for improving prostate cancer treatment and addressing the racial disparity problem.

Construction of Circular RNA-MicroRNA-Messenger RNA Regulatory Network of Recurrent Implantation Failure to Explore Its Potential Pathogenesis

Background: Many studies on circular RNAs (circRNAs) have recently been published. However, the function of circRNAs in recurrent implantation failure (RIF) is unknown and remains to be explored. This study aims to determine the regulatory mechanisms of circRNAs in RIF.

Methods: Microarray data of RIF circRNA (GSE147442), microRNA (miRNA; GSE71332), and messenger RNA (mRNA; GSE103465) were downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed circRNA, miRNA, and mRNA. The circRNA–miRNA–mRNA network was constructed by Cytoscape 3.8.0 software, then the protein–protein interaction (PPI) network was constructed by STRING database, and the hub genes were identified by cytoHubba plug-in. The circRNA–miRNA–hub gene regulatory subnetwork was formed to understand the regulatory axis of hub genes in RIF. Finally, the Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the hub genes were performed by clusterProfiler package of Rstudio software, and Reactome Functional Interaction (FI) plug-in was used for reactome analysis to comprehensively analyze the mechanism of hub genes in RIF.

Results: A total of eight upregulated differentially expressed circRNAs (DECs), five downregulated DECs, 56 downregulated differentially expressed miRNAs (DEmiRs), 104 upregulated DEmiRs, 429 upregulated differentially expressed genes (DEGs), and 1,067 downregulated DEGs were identified regarding RIF. The miRNA response elements of 13 DECs were then predicted. Seven overlapping miRNAs were obtained by intersecting the predicted miRNA and DEmiRs. Then, 56 overlapping mRNAs were obtained by intersecting the predicted target mRNAs of seven miRNAs with 1,496 DEGs. The circRNA–miRNA–mRNA network and PPI network were constructed through six circRNAs, seven miRNAs, and 56 mRNAs; and four hub genes (YWHAZ, JAK2, MYH9, and RAP2C) were identified. The circRNA–miRNA–hub gene regulatory subnetwork with nine regulatory axes was formed in RIF. Functional enrichment analysis and reactome analysis showed that these four hub genes were closely related to the biological functions and pathways of RIF.

Conclusion: The results of this study provide further understanding of the potential pathogenesis from the perspective of circRNA-related competitive endogenous RNA network in RIF.

Tyrosinase-mediated melanogenesis in melanoma cells: Array comparative genome hybridization integrating proteomics and bioinformatics studies

We investigated the tyrosinase-associated melanogenesis in melanoma cells by using OMICS techniques. We characterized the chromosome copy numbers, including Chr 11q21 where the tyrosinase gene is located, from several melanoma cell lines (TXM13, G361, and SK-MEL-28) by using array CGH. We revealed that 11q21 is stable in TXM13 cells, which is directly related to a spontaneous high melanin pigment production. Meanwhile, significant loss of copy number of 11q21 was found in G361 and SK-MEL-28. We further profiled the proteome of TXM13 cells by LC-ESI-MSMS and detected more than 900 proteins, then predicted 11 hub proteins (YWHAZ; HSP90AA1; HSPA5; HSPA1L; HSPA9; HSP90B1; HSPA1A; HSPA8; FKSG30; ACTB; DKFZp686DQ972) by using an interactomic algorithm. YWHAZ (25% interaction in the network) is thought to be a most important protein as a linking factor between tyrosinase-triggered melanogenesis and melanoma growth. Bioinformatic tools were further applied for revealing various physiologic mechanisms and functional classification. The results revealed clues for the spontaneous pigmentation capability of TXM13 cells, contrary to G361 and SK-MEL-28 cells, which commonly have depigmentation properties during subculture. Our study comparatively conducted the genome-wide screening and proteomic profiling integrated interactomics prediction for TXM13 cells and suggests new insights for studying both melanogenesis and melanoma.