Stage-specific differential expression of zinc transporter SLC30A and SLC39A family proteins during prostate tumorigenesis

Emerging Perspectives in Zinc Transporter Research in Prostate Cancer: An Updated Review

Dysregulation of zinc and zinc transporters families has been associated with the genesis and progression of prostate cancer. The prostate epithelium utilizes two types of zinc transporters, the ZIP (Zrt-, Irt-related Protein) and the ZnTs (Zinc Transporter), to transport zinc from the blood plasma to the gland lumen. ZIP transporters uptake zinc from extracellular space and organelle lumen, while ZnT transporters release zinc outside the cells or to organelle lumen. In prostate cancer, a commonly observed low zinc concentration in prostate tissue has been correlated with downregulations of certain ZIPs (e.g., ZIP1, ZIP2, ZIP3, ZIP14) and upregulations of specific ZnTs (e.g., ZnT1, ZnT9, ZnT10). These alterations may enable cancer cells to adapt to toxic high zinc levels. While zinc supplementation has been suggested as a potential therapy for this type of cancer, studies have yielded inconsistent results because some trials have indicated that zinc supplementation could exacerbate cancer risk. The reason for this discrepancy remains unclear, but given the high molecular and genetic variability present in prostate tumors, it is plausible that some zinc transporters-comprising 14 ZIP and 10 ZnT members-could be dysregulated in others patterns that promote cancer. From this perspective, this review highlights novel dysregulation, such as ZIP-Up/ZnT-Down, observed in prostate cancer cell lines for ZIP4, ZIP8, ZnT2, ZnT4, ZnT5, etc. Additionally, an in silico analysis of an available microarray from mouse models of prostate cancer (Nkx3.1;Pten) predicts similar dysregulation pattern for ZIP4, ZIP8, and ZnT2, which appear in early stages of prostate cancer progression. Furthermore, similar dysregulation patterns are supported by an in silico analysis of RNA-seq data from human cancer tumors available in cBioPortal. We discuss how these dysregulations of zinc transporters could impact zinc supplementation trials, particularly focusing on how the ZIP-Up/ZnT-Down dysregulation through various mechanisms might promote prostate cancer progression.

Cellular zinc metabolism and zinc signaling: from biological functions to diseases and therapeutic targets

Zinc metabolism at the cellular level is critical for many biological processes in the body. A key observation is the disruption of cellular homeostasis, often coinciding with disease progression. As an essential factor in maintaining cellular equilibrium, cellular zinc has been increasingly spotlighted in the context of disease development. Extensive research suggests zinc's involvement in promoting malignancy and invasion in cancer cells, despite its low tissue concentration. This has led to a growing body of literature investigating zinc's cellular metabolism, particularly the functions of zinc transporters and storage mechanisms during cancer progression. Zinc transportation is under the control of two major transporter families: SLC30 (ZnT) for the excretion of zinc and SLC39 (ZIP) for the zinc intake. Additionally, the storage of this essential element is predominantly mediated by metallothioneins (MTs). This review consolidates knowledge on the critical functions of cellular zinc signaling and underscores potential molecular pathways linking zinc metabolism to disease progression, with a special focus on cancer. We also compile a summary of clinical trials involving zinc ions. Given the main localization of zinc transporters at the cell membrane, the potential for targeted therapies, including small molecules and monoclonal antibodies, offers promising avenues for future exploration.

Roles of zinc in cancers: From altered metabolism to therapeutic applications

Zinc (Zn) is a crucial trace element involved in various cellular processes, including oxidative stress, apoptosis and immune response, contributing to cellular homeostasis. Dysregulation of Zn homeostasis occurs in certain cancers. This review discusses the role of Zn in cancer and its associated components, such as Zn-related proteins, their potential as biomarkers and the use of Zn-based strategies for tumor treatment. ZIP and ZnT proteins regulate Zn metabolism under normal conditions, but their expression is aberrant in cancer. These Zn proteins can serve as prognostic or diagnostic biomarkers, aiding in early cancer detection and disease monitoring. Moreover, targeting Zn and its pathways offers potential therapeutic approaches for cancer treatment. Modulating Zn biodistribution within cells using metal-binding agents allows for the control of downstream signaling pathways. Direct utilization of zinc as a therapeutic agent, including Zn supplementation or Zn oxide nanoparticle administration, holds promise for improving the prognosis of cancer patients.

scTIGER: A Deep-Learning Method for Inferring Gene Regulatory Networks from Case versus Control scRNA-seq Datasets

Inferring gene regulatory networks (GRNs) from single-cell RNA-seq (scRNA-seq) data is an important computational question to find regulatory mechanisms involved in fundamental cellular processes. Although many computational methods have been designed to predict GRNs from scRNA-seq data, they usually have high false positive rates and none infer GRNs by directly using the paired datasets of case-versus-control experiments. Here we present a novel deep-learning-based method, named scTIGER, for GRN detection by using the co-differential relationships of gene expression profiles in paired scRNA-seq datasets. scTIGER employs cell-type-based pseudotiming, an attention-based convolutional neural network method and permutation-based significance testing for inferring GRNs among gene modules. As state-of-the-art applications, we first applied scTIGER to scRNA-seq datasets of prostate cancer cells, and successfully identified the dynamic regulatory networks of AR, ERG, PTEN and ATF3 for same-cell type between prostatic cancerous and normal conditions, and two-cell types within the prostatic cancerous environment. We then applied scTIGER to scRNA-seq data from neurons with and without fear memory and detected specific regulatory networks for BDNF, CREB1 and MAPK4. Additionally, scTIGER demonstrates robustness against high levels of dropout noise in scRNA-seq data.