Identification of Potential miRNAs Biomarkers for High-Grade Prostate Cancer by Integrated Bioinformatics Analysis

Serum miRNA-1 may serve as a promising noninvasive biomarker for predicting treatment response in breast cancer patients receiving neoadjuvant chemotherapy

BACKGROUND

MicroRNA-1 (miR-1) is a tumour suppressor that can inhibit cell proliferation and invasion in several cancer types. In addition, miR-1 was found to be associated with drug sensitivity. Circulating miRNAs have been proven to be potential biomarkers with predictive and prognostic value. However, studies of miR-1 expression in the serum of breast cancer (BC) patients are relatively scarce, especially in patients receiving neoadjuvant chemotherapy (NAC).

METHODS

Serum samples from 80 patients were collected before chemotherapy, and RT-PCR was performed to detect the serum expression of miR-1. The correlation between miR-1 expression in serum and clinicopathological factors, including pathological complete response (pCR), was analyzed by the chi-squared test and logistic regression. KEGG and GSEA analysis were also performed to determine the biological processes and signalling pathways involved.

RESULTS

The miR-1 high group included more patients who achieved a pCR than did the miR-1 low group (p < 0.001). Higher serum miR-1 levels showed a strong correlation with decreased ER (R = 0.368, p < 0.001) and PR (R = 0.238, p = 0.033) levels. The univariate model of miR-1 for predicting pCR achieved an AUC of 0.705 according to the ROC curve. According to the interaction analysis, miR-1 interacted with Ki67 to predict the NAC response. According to the Kaplan-Meier plot, a high serum miR-1 level was related to better disease-free survival (DFS) in the NAC cohort. KEGG analysis and GSEA results indicated that miR-1 may be related to the PPAR signalling pathway and glycolysis.

CONCLUSIONS

In summary, our data suggested that miR-1 could be a potential biomarker for pCR and survival outcomes in patients with BC treated with NAC.

AP003352.1/miR-141-3p axis enhances the proliferation of osteosarcoma by LPAR3

Osteosarcoma (OS) is a highly malignant tumor with a poor prognosis and a growing incidence. LncRNAs and microRNAs control the occurrence and development process of osteosarcoma through ceRNA patterns. The LPAR3 gene is important in cancer cell proliferation, apoptosis and disease development. However, the regulatory mechanism of the ceRNA network through which LPAR3 participates in osteosarcoma has not been clarified. Herein, our study demonstrated that the AP003352.1/miR-141-3p axis drives LPAR3 expression to induce the malignant progression of osteosarcoma. First, the expression of LPAR3 is regulated by the changes in AP003352.1 and miR-141-3p. Similar to the ceRNA of miR-141-3p, AP003352.1 regulates the expression of LPAR3 through this mechanism. In addition, the regulation of AP003352.1 in malignant osteosarcoma progression depends to a certain degree on miR-141-3p. Importantly, the AP003352.1/miR-141-3p/LPAR3 axis can better serve as a multi-gene diagnostic marker for osteosarcoma. In conclusion, our research reveals a new ceRNA regulatory network, which provides a novel potential target for the diagnosis and treatment of osteosarcoma.

ARRDC3 inhibits liver fibrosis and epithelial-to-mesenchymal transition via the ITGB4/PI3K/Akt signaling pathway

Objective The effect of ARRDC3 has not been reported in liver fibrosis. Our study aimed to explore the molecular mechanisms by which ARRDC3 attenuates liver fibrosis.Methods The vectors pcDNA-ARRDC3 (which promotes ARRDC3 expression) and si-ITGB4 (which blocks IGTB4 expression) and their negative controls were constructed. The rat liver fibrosis model was established by intraperitoneal injection of CCl4 with or without intraperitoneal injection of pcDNA-ARRDC3. ELISA was used to detect the concentrations of γ-GGT, ALT, AST, and ALP in serum. HE, Masson's trichome, and Sirius red staining were used to observe the pathological changes in liver tissue. LX-2 cells were treated with TGF-β, and pcDNA-ARRDC3 or si-ITGB4RNA was transfected to promote ARRDC3 expression or knock down ITGB4 expression. Western blotting was used to detect the expression levels of proteins.Results ARRDC3 effectively reduced liver injury, improved liver function, and decreased collagen production and deposition in the CCl4-induced rat fibrosis model. The studies showed that overexpressed ARRDC3 remarkably reduced the expression of E-cadherin and collagen-related protein and increased the expression of mesenchymal markers and EMT-related transcription factors, consequently inhibiting the activity of the ITGB4/PI3K/Akt signaling pathway.Conclusion Our study shows that ARRDC3 could ameliorate CCl4-induced liver fibrosis and EMT progression via the ITGB4/PI3K/Akt signaling pathway, which provides a meaningful reference for the clinical targeted treatment of liver fibrosis.

Bioinformatics analysis identified hub genes in prostate cancer tumorigenesis and metastasis

OBJECTIVE

Prostate cancer (PCa) is the most frequent cancer found in males worldwide, and its mortality rate is increasing every year. To discover key molecular changes in PCa development and metastasis, we analyzed microarray data of localized PCa, metastatic PCa and normal prostate tissue samples from clinical specimens.

METHODS

Gene expression profiling datasets of PCa were analyzed online. The DAVID was used to perform GO functional and KEGG pathway enrichment analyses. CytoHubba in Cytoscape software was applied to identify hub genes. Survival data were downloaded from GEPIA. Gene expression data were obtained from ONCOMINE and UALCAN.

RESULTS

We obtained 4 sets of differentially expressed genes (DEGs), DEGs 1: a comparison of the gene expression between 4 normal prostate and 5 localized PCa samples in GSE27616; DEGs 2: a comparison of the gene expression between 6 normal prostate and 7 localized PCa samples in GSE3325; DEGs 3: a comparison of the gene expression between 5 localized PCa and 4 metastatic PCa samples in GSE27616; DEGs 4: a comparison of the gene expression between 7 localized PCa and 6 metastatic PCa samples in GSE3325. A comparison of these 4 sets of genes revealed 51 overlapped genes. GO function analysis revealed enrichment of the 51 DEGs in functions related to the proteinaceous extracellular matrix and centrosome, protein homodimerization activity and chromatin binding were the main functions of these genes, which participated in regulating cell division, mitotic nuclear division, proteinaceous extracellular matrix, cell adhesion and apoptotic process. KEGG pathway analysis indicated that these identified DEGs were mainly enriched in progesterone-mediated oocyte maturation, oocyte meiosis and cell cycle. We defined the 16 genes with the highest degree of connectivity as the hub genes in the 51 overlapped DEGs. Cox regression revealed TOP2A, CCNB2, BUB1, CDK1 and EZH2 were related to Disease-free survival (DFS). The expression levels of the 5 genes were 2.232-, 1.786-, 2.303-, 1.699-, and 1.986-fold higher in PCa than the levels in normal tissues, respectively (P < 0.05). We obtained 20 hub genes from DEGs by the comparison of normal prostate tissue vs. localized cancer tissue. Among them, KIF20A, CDKN3, PBK and CDCA2, were expressed higher in PCa than in normal tissues, and were associated with the DFS of PCa patients. Meanwhile, we obtained 20 hub genes from DEGs by the comparison of localized cancer tissue vs. metastatic cancer tissue. Cox regression revealed PLK1, CCNA2 and CDC20, were associated with both the DFS and overall survival of PCa patients.

CONCLUSIONS

The results suggested that the functions of KIF20A, CDKN3, PBK and CDCA2 may contribute to PCa development and the functions of PLK1, CCNA2 and CDC20 may contribute to PCa metastasis. Meanwhile, the functions of TOP2A, CCNB2, BUB1, CDK1 and EZH2 may contribute to both PCa development and metastasis.

Identification of UBE2C as hub gene in driving prostate cancer by integrated bioinformatics analysis

BACKGROUND

The aim of this study was to identify novel genes in promoting primary prostate cancer (PCa) progression and to explore its role in the prognosis of prostate cancer.

METHODS

Four microarray datasets containing primary prostate cancer samples and benign prostate samples were downloaded from Gene Expression Omnibus (GEO), then differentially expressed genes (DEGs) were identified by R software (version 3.6.2). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed to identify the function of DEGs. Using STRING and Cytoscape (version 3.7.1), we constructed a protein-protein interaction (PPI) network and identified the hub gene of prostate cancer. Clinical data on GSE70770 and TCGA was collected to show the role of hub gene in prostate cancer progression. The correlations between hub gene and clinical parameters were also indicated by cox regression analysis. Gene Set Enrichment Analysis (GSEA) was performed to highlight the function of Ubiquitin-conjugating enzyme complex (UBE2C) in prostate cancer.

RESULTS

243 upregulated genes and 298 downregulated genes that changed in at least two microarrays have been identified. GO and KEGG analysis indicated significant changes in the oxidation-reduction process, angiogenesis, TGF-beta signaling pathway. UBE2C, PDZ-binding kinase (PBK), cyclin B1 (CCNB1), Cyclin-dependent kinase inhibitor 3 (CDKN3), topoisomerase II alpha (TOP2A), Aurora kinase A (AURKA) and MKI67 were identified as the candidate hub genes, which were all correlated with prostate cancer patient' disease-free survival in TCGA. In fact, only UBE2C was highly expressed in prostate cancer when compared with benign prostate tissue in TCGA and the expression of UBE2C was also in parallel with the Gleason score of prostate cancer. Cox regression analysis has indicated UBE2C could function as the independent prognostic factor of prostate cancer. GSEA showed UBE2C had played an important role in the pathway of prostate cancer, such as NOTCH signaling pathway, WNT-β-catenin signaling pathway.

CONCLUSIONS

UBE2C was pivotal for the progression of prostate cancer and the level of UBE2C was important to predict the prognosis of patients.

Prognostic value of miR-339-5p in patients with prostate cancer and its effects on tumor progression

Prostate cancer places a serious health burden on males. The present study aimed to explore the potential prognostic significance and biological function of microRNA (miR)-339-5p in patients with prostate cancer. The expression of miR-339-5p was detected in prostate cancer tissues and cell lines by using reverse transcription-quantitative PCR. Kaplan-Meier survival curves and Cox regression analyses were used to investigate the prognostic significance of miR-339-5p in prostate cancer. The Cell Counting Kit-8 assay was used to determine the effect of miR-339-5p on prostate cancer cell proliferation. Transwell assays were used to assess the effect of miR-339-5p on cell migration and invasion. The results indicated that the expression of miR-339-5p was downregulated in prostate cancer tissues and cell lines. Downregulation of miR-339-5p was significantly associated with the Gleason score, lymph node metastasis and TNM stage. Patients with high miR-339-5p expression levels had a longer survival time than those with low expression levels. Multivariate Cox regression analysis indicated that miR-339-5p may be an independent prognostic factor for the overall survival of patients with prostate cancer. Overexpression of miR-339-5p inhibited the proliferation, migration and invasion of prostate cancer cells. Taken together, these results indicated that miR-339-5p functions as a suppressor gene in prostate cancer and acts by inhibiting cell proliferation, migration and invasion of prostate cancer cells. miR-339-5p may serve as an independent prognostic biomarker and therapeutic target for the treatment of prostate cancer.

Bioinformatic analysis of dysregulated proteins in prostate cancer patients reveals putative urinary biomarkers and key biological pathways

Prostate cancer (PCa) is one of the most common cancer types among men. The quantification of prostate-specific antigen used for PCa detection has revealed limited applicability. Thus, it is crucial to identify new minimally invasive biomarkers for PCa. It is believed that the integration of proteomics data from different studies is vital for identifying new biomarkers for PCa, but studies carried out in this regard have few converging results. Using a different approach, this study aimed to unveil molecular features consistently dysregulated in PCa and potential urinary biomarkers for PCa. The novelty of this analysis relies on the comparison of urinary and tissue proteomes from PCa patients and consequent exclusion of kidney and bladder cancer interference. The conducted bioinformatic analysis revealed molecular processes dysregulated in urine from PCa patients that mirror the alterations in prostate tumor tissue. To identify putative urinary biomarkers, proteins previously detected in kidney and bladder tissues were eliminated from the final list of potential urinary biomarkers for PCa. After a detailed analysis, MSMB, KLK3, ITIH4, ITIH2, HPX, GP2, APOA2 and AZU1 proteins stood out as candidate urinary biomarkers for PCa.

Emerging roles of MiR-133a in human cancers

MicroRNAs (miRNAs) can post-transcriptionally regulate the expression of cancer-relevant genes via binding to the 3'-untranslated region (3'-UTR) of the target mRNAs. MiR-133a, as a miRNA, participate in tumorigenesis, progression, autophagy and drug-resistance in various malignancies. Based on the recent insights, we discuss the functions of miR-133a in physiological and pathological processes and its potential effects on cancer diagnosis, prognosis and therapy.

Silybin Prevents Prostate Cancer by Inhibited the ALDH1A1 Expression in the Retinol Metabolism Pathway

Background

Silybin was known to exert inhibition in prostate cancer, but the underlying mechanism remained largely unknown. This study was designed to find out the potential target of Silybin on prostate cancer and explore the relative mechanisms.

Methods

Firstly, we screened the possible targets of Silybin through the PubChem database and Subpathway – GM. Then DU145 cells were transferred to investigate the correction about related targets, magnetic bead sorting and flow cytometry were used to sort and identify the cells. Proliferation, migration and invasion ability of DU145 cells were detected by MTT assay, Transwell assay, plate clonality and sphere formation assay. BALB/c nude mice were constructed models with implanted sarcoma and measured the tumor volume every 5 days as wells tumor weight. The levels of proteins were detected by Western blot and immunocytochemistry. RT-PCR was selected to test the expression of protein’s mRNA.

Results

It was screened out the ALDH1A1 was highly correlated with subpathways of the Silybin risk metabolic pathway. And ALDH1A1 expression was positively correlated RARα with Ets1 by interfering with the ALDH1A1 gene. Importantly, ALDH1A1(+) cells showed proliferation, migration and invasion ability. In addition, it showed that Silybin exerted the inhibition on prostate cells by suppressed the proliferation, migration and invasion ability of cells in vitro experiment. Silybin also reduced the tumor volume and weight. And Silybin displayed obviously reduced the proteins and mRNA of ALDH1A1, RARα, Ets1 and MMP9 expressions.

Conclusion

Our results indicated that Silybin showed inhibition of prostate cancer and the mechanism was involving with downregulating ALDH1A1 expression, thereby inhibiting the activation of RARα and preventing the activation of Ets1 to inhibit the growth and invasion of prostate cancer.

Annexin A1 promotes the nuclear localization of the epidermal growth factor receptor in castration-resistant prostate cancer

Epidermal growth factor receptor is a cancer driver whose nuclear localization has been associated with the progression of prostate cancer to the castration-resistant phenotype. Previous reports indicated a functional interaction between this receptor and the protein Annexin A1, which has also been associated with aggressive tumors. The molecular pathogenesis of castration-resistant prostate cancer remains largely unresolved, and herein we have demonstrated the correlation between the expression levels and localization of the epidermal growth factor receptor and Annexin A1 in prostate cancer samples and cell lines. Interestingly, a higher expression of both proteins was detected in castration-resistant prostate cancer cell lines and the strongest correlation was seen at the nuclear level. We verified that Annexin A1 interacts with the epidermal growth factor receptor, and by using prostate cancer cell lines knocked down for Annexin A1, we succeeded in demonstrating that Annexin A1 promotes the nuclear localization of epidermal growth factor receptor. Finally, we showed that Annexin A1 activates an autocrine signaling in castration-resistant prostate cells through the formyl peptide receptor 1. The inhibition of such signaling by Cyclosporin H inhibits the nuclear localization of epidermal growth factor receptor and its downstream signaling. The present work sheds light on the functional interaction between nuclear epidermal growth factor receptor and nuclear Annexin A1 in castration-resistant prostate cancer. Therefore, strategies to inhibit the nuclear localization of epidermal growth factor receptor through the suppression of the Annexin A1 autocrine loop could represent an important intervention strategy for castration-resistant prostate cancer.

LPAR1, Correlated With Immune Infiltrates, Is a Potential Prognostic Biomarker in Prostate Cancer

Prostate cancer is a common malignancy in men worldwide. Lysophosphatidic acid receptor 1 (LPAR1) is a critical gene and it mediates diverse biologic functions in tumor. However, the correlation between LPAR1 and prognosis in prostate cancer, as well as the potential mechanism, remains unclear. In the present study, LPAR1 expression analysis was based on The Cancer Genome Atlas (TCGA) and the Oncomine database. The correlation of LPAR1 on prognosis was also analyzed based on R studio. The association between LPAR1 and tumor-infiltrating immune cells were evaluated in the Tumor Immune Estimation Resource site, ssGSEA, and MCPcounter packages in R studio. Gene Set Enrichment Analysis and Gene Ontology analysis were used to analyze the function of LPAR1. TCGA datasets and the Oncomine database revealed that LPAR1 was significantly downregulated in prostate cancer. High LPAR1 expression was correlated with favorable overall survival. LPAR1 was involved in the activation, proliferation, differentiation, and migration of immune cells, and its expression was positively correlated with immune infiltrates, including CD4+ T cells, B cells, CD8+ T cells, neutrophils, macrophages, dendritic cells, and natural killer cells. Moreover, LPAR1 expression was positively correlated with those chemokine/chemokine receptors, indicating that LPAR1 may regulate the migration of immune cells. In summary, LPAR1 is a potential prognostic biomarker and plays an important part in immune infiltrates in prostate cancer.

Data-driven translational prostate cancer research: from biomarker discovery to clinical decision

Prostate cancer (PCa) is a common malignant tumor with increasing incidence and high heterogeneity among males worldwide. In the era of big data and artificial intelligence, the paradigm of biomarker discovery is shifting from traditional experimental and small data-based identification toward big data-driven and systems-level screening. Complex interactions between genetic factors and environmental effects provide opportunities for systems modeling of PCa genesis and evolution. We hereby review the current research frontiers in informatics for PCa clinical translation. First, the heterogeneity and complexity in PCa development and clinical theranostics are introduced to raise the concern for PCa systems biology studies. Then biomarkers and risk factors ranging from molecular alternations to clinical phenotype and lifestyle changes are explicated for PCa personalized management. Methodologies and applications for multi-dimensional data integration and computational modeling are discussed. The future perspectives and challenges for PCa systems medicine and holistic healthcare are finally provided.