In Silico Analysis of Ion Channels and Their Correlation with Epithelial to Mesenchymal Transition in Breast Cancer

TRPV3 regulates Breast Cancer Cell Proliferation and Apoptosis by EGFR/AKT pathway

Breast cancer (BC) is one of the most common cancer types worldwide and the first cause of cancer-related deaths in women. Transient receptor potential vanillin 3 (TRPV3) has been preliminarily discovered to play an important role in various cancers, including BC. Here, we explored the effect of TRPV3 on breast cancer cells and its potential mechanism. TRPV3 level was measured in BC tissue and adjacent noncancerous breast tissue using real-time RT-PCR and Western blot. Wound healing was used to detect cell migration. MTT and EDU were detected cell proliferation. TUNEL and Caspase-3 activity were used to detect cell apoptosis. We found that TRPV3 expression significantly increased in both human BC tissues and breast cells line. TRPV3 siRNA (TRPV3 inhibition) dramatically suppressed cell migration and proliferation, promoted the apoptosis, and decreased [Ca2+]i; whereas Carvacrol (TRPV3 agonist) has opposite effect in MCF-7 cells. We validated EGFR (Epidermal growth factor receptor) is a direct target protein of TRPV3. Mechanism studies have shown that Carvacrol increased phosphorylation levels of EGFR and AKT, and were decreased by suppression of TRPV3. Moreover, Erlotinib (EGFR inhibitor) and LY294002 (PI3K inhibitor) diminished Carvacrol induced cell migration and proliferation, promoted cell apoptosis, and increased [Ca2+]i in Carvacrol group. Our results collectively suggest that TRPV3 siRNA inhibits migration and proliferation, and promoted apoptosis in breast cancer cells by EGFR/AKT pathway. These findings indicate that TRPV3 may represent a novel therapeutic strategy for breast cancer.

Increased heterogeneity in expression of genes associated with cancer progression and drug resistance

Fluctuations in the number of regulatory molecules and differences in timings of molecular events can generate variation in gene expression among genetically identical cells in the same environmental condition. This variation, termed as expression noise, can create differences in metabolic state and cellular functions, leading to phenotypic heterogeneity. Expression noise and phenotypic heterogeneity have been recognized as important contributors to intra-tumor heterogeneity, and have been associated with cancer growth, progression, and therapy resistance. However, how expression noise changes with cancer progression in actual cancer patients has remained poorly explored. Such an analysis, through identification of genes with increasing expression noise, can provide valuable insights into generation of intra-tumor heterogeneity, and could have important implications for understanding immune-suppression, drug tolerance and therapy resistance. In this work, we performed a genome-wide identification of changes in gene expression noise with cancer progression using single-cell RNA-seq data of lung adenocarcinoma patients at different stages of cancer. We identified 37 genes in epithelial cells that showed an increasing noise trend with cancer progression, many of which were also associated with cancer growth, EMT and therapy resistance. We found that expression of several of these genes was positively associated with expression of mitochondrial genes, suggesting an important role of mitochondria in generation of heterogeneity. In addition, we uncovered substantial differences in sample-specific noise profiles which could have implications for personalized prognosis and treatment.

Aberrations in ion channels interacting with lipid metabolism and epithelial-mesenchymal transition in esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is the most prevalent malignant gastrointestinal tumor. Ion channels contribute to tumor growth and progression through interactions with their neighboring molecules including lipids. The dysregulation of membrane ion channels and lipid metabolism may contribute to the epithelial-mesenchymal transition (EMT), leading to metastatic progression. Herein, transcriptome profiles of patients with ESCC were analyzed by performing differential gene expression and weighted gene co-expression network analysis to identify the altered ion channels, lipid metabolism- and EMT-related genes in ESCC. A total of 1,081 differentially expressed genes, including 113 ion channels, 487 lipid metabolism-related, and 537 EMT-related genes, were identified in patients with ESCC. Thereafter, EMT scores were correlated with altered co-expressed genes. The altered co-expressed genes indicated a correlation with EMT signatures. Interactions among 22 ion channels with 3 hub lipid metabolism- and 13 hub EMT-related proteins were determined using protein-protein interaction networks. A pathway map was generated to depict deregulated signaling pathways including insulin resistance and the estrogen receptor-Ca²⁺ signaling pathway in ESCC. The relationship between potential ion channels and 5-year survival rates in ESCC was determined using Kaplan-Meier plots and Cox proportional hazard regression analysis. Inositol 1,4,5-trisphosphate receptor type 3 (ITPR3) was found to be associated with poor prognosis of patients with ESCC. Additionally, drugs interacting with potential ion channels, including GJA1 and ITPR3, were identified. Understanding alterations in ion channels with lipid metabolism and EMT in ESCC pathophysiology would most likely provide potential targets for the better treatment of patients with ESCC.

Chloride Intracellular Channel Proteins (CLICs) and Malignant Tumor Progression: A Focus on the Preventive Role of CLIC2 in Invasion and Metastasis

Simple Summary

Although chloride intracellular channel proteins (CLICs) have been identified as ion channel proteins, their true functions are still elusive. Recent in silico analyses show that CLICs may be prognostic markers in cancer. This review focuses on CLIC2 that plays preventive roles in malignant cell invasion and metastasis. CLIC2 is secreted extracellularly and binds to matrix metalloproteinase 14 (MMP14), while inhibiting its activity. As a result, CLIC2 may contribute to the development/maintenance of junctions between blood vessel endothelial cells and the inhibition of invasion and metastasis of tumor cells. CLIC2 may be a novel therapeutic target for malignancies.

Abstract

CLICs are the dimorphic protein present in both soluble and membrane fractions. As an integral membrane protein, CLICs potentially possess ion channel activity. However, it is not fully clarified what kinds of roles CLICs play in physiological and pathological conditions. In vertebrates, CLICs are classified into six classes: CLIC1, 2, 3, 4, 5, and 6. Recently, in silico analyses have revealed that the expression level of CLICs may have prognostic significance in cancer. In this review, we focus on CLIC2, which has received less attention than other CLICs, and discuss its role in the metastasis and invasion of malignant tumor cells. CLIC2 is expressed at higher levels in benign tumors than in malignant ones, most likely preventing tumor cell invasion into surrounding tissues. CLIC2 is also expressed in the vascular endothelial cells of normal tissues and maintains their intercellular adhesive junctions, presumably suppressing the hematogenous metastasis of malignant tumor cells. Surprisingly, CLIC2 is localized in secretory granules and secreted into the extracellular milieu. Secreted CLIC2 binds to MMP14 and inhibits its activity, leading to suppressed MMP2 activity. CLIC4, on the other hand, promotes MMP14 activity. These findings challenge the assumption that CLICs are ion channels, implying that they could be potential new targets for the treatment of malignant tumors.

A bioinformatics analysis: ZFHX4 is associated with metastasis and poor survival in ovarian cancer

Background

Metastasis was the major cause of the high mortality in ovarian cancer. Although some mechanisms of metastasis in ovarian cancer were proposed, few have been targeted in the clinical practice. In the study, we aimed to identify novel genes contributing to metastasis and poor clinical outcome in ovarian cancer from bioinformatics databases.

Methods

Studies collecting matched primary tumors and metastases from ovarian cancer patients were searched in the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were screened by software R language. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis for the DEGs were implemented by Metascape. Venn diagram was plotted to present overlapping DEGs. The associations between the overlapping DEGs and prognosis were tested by Cox proportional hazard regression model using a cohort of ovarian cancer patients from the TCGA database. Genes affecting patients’ outcomes significantly were served as hub genes. The mechanisms of the hub genes in promoting ovarian cancer metastasis were then predicted by R software.

Results

Two gene expression profiles (GSE30587 and GSE73168) met the inclusion criteria and were finally analyzed. A total of 259 genes were significantly differentially expressed in GSE30587, whereas 712 genes were in GSE73168. In GSE30587, DEGs were mainly involved in extracellular matrix (ECM) organization; For GSE73168, most of DEGs showed ion trans-membrane transport activity. There were 9 overlapping genes between the two datasets (RUNX2, FABP4, CLDN20, SVEP1, FAM169A, PGM5, ZFHX4, DCN and TAS2R50). ZFHX4 was proved to be an independent adverse prognostic factor for ovarian cancer patients (HR = 1.44, 95%CI: 1.13–1.83, p = 0.003). Mechanistically, ZFHX4 was positively significantly correlated with epithelial-mesenchymal transition (EMT) markers (r = 0.54, p = 2.59 × 10⁻²⁹) and ECM-related genes (r = 0.52, p = 2.86 × 10⁻²⁷).

Conclusions

ZFHX4 might promote metastasis in ovarian cancer by regulating EMT and reprogramming ECM. For clinical applications, ZFHX4 was expected to be a prognostic biomarker for ovarian cancer metastasis.

Establishment and Analysis of an Individualized EMT-Related Gene Signature for the Prognosis of Breast Cancer in Female Patients

Background. The current high mortality rate of female breast cancer (BC) patients emphasizes the necessity of identifying powerful and reliable prognostic signatures in BC patients. Epithelial-mesenchymal transition (EMT) was reported to be associated with the development of BC. The purpose of this study was to identify prognostic biomarkers that predict overall survival (OS) in female BC patients by integrating data from TCGA database. Method. We first downloaded the dataset in TCGA and identified gene signatures by overlapping candidate genes. Differential analysis was performed to find differential EMT-related genes. Univariate regression analysis was then performed to identify candidate prognostic variables. We then developed a prognostic model by multivariate analysis to predict OS. Calibration curves, receiver operating characteristics (ROC) curves, $C$ -index, and decision curve analysis (DCA) were used to test the veracity of the prognostic model. Result. In this study, we identified and validated a prognostic model integrating age and six genes (CD44, P3H1, SDC1, COL4A1, TGFβ1, and SERPINE1). $C$ -index values for BC patients were 0.672 (95% CI 0.611–0.732) and 0.692 (95% CI 0.586–0.798) in the training cohort and test set, respectively. The calibration curve and the DCA curve show the good predictive performance of the model. Conclusion. This study offered a robust predictive model for OS prediction in female BC patients and may provide a more accurate treatment strategy and personalized therapy in the future.