Breast cancer is one of the most common malignancies. The molecular mechanisms of its pathogenesis are still to be investigated. The aim of this study was to identify the potential genes associated with the progression of breast cancer. Weighted gene co-expression network analysis (WGCNA) was used to construct free-scale gene co-expression networks to explore the associations between gene sets and clinical features, and to identify candidate biomarkers. The gene expression profiles of GSE1561 were selected from the Gene Expression Omnibus (GEO) database. RNA-seq data and clinical information of breast cancer from TCGA were used for validation. A total of 18 modules were identified via the average linkage hierarchical clustering. In the significant module (R2 = 0.48), 42 network hub genes were identified. Based on the Cancer Genome Atlas (TCGA) data, 5 hub genes (CCNB2, FBXO5, KIF4A, MCM10, and TPX2) were correlated with poor prognosis. Receiver operating characteristic (ROC) curve validated that the mRNA levels of these 5 genes exhibited excellent diagnostic efficiency for normal and tumor tissues. In addition, the protein levels of these 5 genes were also significantly higher in tumor tissues compared with normal tissues. Among them, CCNB2, KIF4A, and TPX2 were further upregulated in advanced tumor stage. In conclusion, 5 candidate biomarkers were identified for further basic and clinical research on breast cancer with co-expression network analysis.
The simultaneous expression of human papillomavirus type 16 (HPV16) E6 and E7 oncogenes is pivotal for malignant transformation and maintenance of malignant phenotypes. Silencing these oncogenes is considered to be applicable in molecular therapies of human cervical cancer. However, it remains to be determined whether HPV16 E6 and E7 could be both silenced to obtain most efficient antitumor activity by using RNA interference (RNAi) technology. Herein, we designed a small interfering RNA (siRNA) targeting HPV16-E7 region to degrade either E6, or truncated E6 (E6*) and E7 mRNAs and to simultaneously knockdown both E6 and E7 expression. Firstly, the sequence targeting HPV16-E7 region was inserted into the shRNA packing vector pSIREN-DNR, yielding pSIREN-16E7 to stably express corresponding shRNA. HPV16-transformed SiHa and CaSki cells were used as a model system; RT-PCR, Western Blotting, MTT assay, TUNEL staining, Annexin V apoptosis assay and flow cytometry were applied to examine the effects of pSIREN-16E7. Our results indicated that HPV16-E7 specific shRNA (16E7-shRNA) induced selective degradation of E6 and E7 mRNAs and proteins. E6 silencing induced accumulation of cellular p53 and p21. In contrast, E7 silencing induced hypophosphorylation of retinoblastoma (Rb) protein. The loss of E6 and E7 reduced cell growth and ultimately resulted in massive apoptotic cell death selectively in HPV-positive cancer cells, compared with the HPV-negative ones. We demonstrated that 16E7-shRNA can induce simultaneous E6 and E7 suppression and lead to striking apoptosis in HPV16-related cancer cells by activating cellular p53, p21 and Rb. Therefore, RNAi using E7 shRNA may have the gene-specific therapy potential for HPV16-related cancers.
Breast cancer is one of the most common malignancies among females, and its prognosis is affected by a complex network of gene interactions. In this study, we constructed free-scale gene co-expression networks using weighted gene co-expression network analysis (WGCNA). The gene expression profiles of GSE25055 were downloaded from the Gene Expression Omnibus (GEO) database to identify potential biomarkers associated with breast cancer progression. GSE42568 was downloaded for validation. A total of 9 modules were established via the average linkage hierarchical clustering. We identified 3 hub genes (ASPM, CDC20, and TTK) in the significant module ( R 2 = 0.52), which were significantly correlated with poor prognosis both in test and validation datasets. In the datasets GSE25055 and GSE42568, higher expression levels of ASPM, CDC20, and TTK correlated with advanced tumor grades. Immunohistochemistry data from the Human Protein Atlas also demonstrated that their protein levels were higher in tumor samples. According to gene set enrichment analysis, 4 commonly enriched pathways were identified: cell cycle pathway, DNA replication pathway, homologous recombination pathway, and P53 signaling pathway. In addition, strong correlations were found among their expression levels. In conclusion, our WGCNA analysis identified candidate prognostic biomarkers for further basic and clinical researches.
Thyroid cancer is one of the most common endocrine malignancies. Multiple evidences revealed that a large number of microRNAs and mRNAs were abnormally expressed in thyroid cancer tissues. These microRNAs and mRNAs play important roles in tumorigenesis. In the present study, we identified 72 microRNAs and 1,766 mRNAs differentially expressed between thyroid cancer tissues and normal thyroid tissues and evaluated their prognostic values using Kaplan-Meier survival curves by log-rank test. Seven microRNAs (miR-146b, miR-184, miR-767, miR-6730, miR-6860, miR-196a-2 and miR-509-3) were associated with the overall survival. Among them, three microRNAs were linked with six differentially expressed mRNAs (miR-767 was predicted to target COL10A1, PLAG1 and PPP1R1C; miR-146b was predicted to target MMP16; miR-196a-2 was predicted to target SYT9). To identify the key genes in the protein-protein interaction network , we screened out the top 10 hub genes (NPY, NMU, KNG1, LPAR5, CCR3, SST, PPY, GABBR2, ADCY8 and SAA1) with higher degrees. Only LPAR5 was associated with the overall survival. Multivariate analysis demonstrated that miR-184, miR-146b, miR-509-3 and LPAR5 were an independent risk factors for prognosis. Our results of the present study identified a series of prognostic microRNAs and mRNAs that have the potential to be the targets for treatment of thyroid cancer.
With the exception of non-melanoma skin cancer, breast cancer is the most frequently diagnosed malignant disease among women, with the majority of mortality being attributable to metastatic disease. Thus, even with improved early screening and more targeted treatments which may enable better detection and control of early disease progression, metastatic disease remains a significant problem. While targeted therapies exist for breast cancer patients with particular subtypes of the disease (Her2+ and ER/PR+), even in these subtypes the therapies are often not efficacious once the patient's tumor metastasizes. Increases in stemness or epithelial-to-mesenchymal transition (EMT) in primary breast cancer cells lead to enhanced plasticity, enabling tumor progression, therapeutic resistance, and distant metastatic spread. Numerous signaling pathways, including MAPK, PI3K, STAT3, Wnt, Hedgehog, and Notch, amongst others, play a critical role in maintaining cell plasticity in breast cancer. Understanding the cellular and molecular mechanisms that regulate breast cancer cell plasticity is essential for understanding the biology of breast cancer progression and for developing novel and more effective therapeutic strategies for targeting metastatic disease. In this review we summarize relevant literature on mechanisms associated with breast cancer plasticity, tumor progression, and drug resistance.
Typhoid and paratyphoid fevers (TPF), systemic emerging infectious diseases, is a serious health problem for society. If the incidence trend of TPF can be predicted, prevention and control measures can be taken in advance to reduce the harm to the people's health.Grey Model First Order One Variable [GM (1, 1)] was applied to predict the incidence trend of TPF with the incidence data of TPF in Wuhan City of China from 2004 to 2015. The original data were acquired from the national surveillance system.The GM (1, 1) model was established as ŷ (t + 1) = 0.88 e−0.21t + 0.15. The goodness-of-fit test indicated that the precision (degree 2) was qualified (C = 0.40, P = .91). We further compared actual values with predicted values in 2016 and found that GM (1, 1) model we built has excellent performance in incidence trend prediction.Our prediction shows that the TPF incidences in Wuhan City will be slowly decreasing in the next 3 years. It is, however, still necessary to strengthen the comprehensive prevention and control to reduce the incidence level of TPF.
It is well established that a subset of cells within primary breast cancers can undergo an epithelial to mesenchymal transition (EMT), although the role of EMT in metastasis remains controversial. We previously demonstrated that breast cancer cells that had undergone an oncogenic EMT could increase metastasis of neighboring cancer cells via non-canonical paracrine-mediated activation of GLI activity that is dependent on SIX1 expression in the EMT cancer cells. However, the mechanism by which these SIX1-expressing EMT cells activate GLI signaling remained unclear. In this study, we demonstrate a novel mechanism for activation of GLI-mediated signaling in epithelial breast tumor cells via EMT cell-induced production and secretion of VEGF-C. We show that VEGF-C, secreted by breast cancer cells that have undergone an EMT, promotes paracrine-mediated increases in proliferation, migration and invasion of epithelial breast cancer cells, via non-canonical activation of GLI-signaling. We further show that the aggressive phenotypes, including metastasis, imparted by EMT cells on adjacent epithelial cancer cells can be disrupted by either inhibiting VEGF-C in EMT cells or by knocking down NRP2, a receptor which interacts with VEGF-C, in neighboring epithelial cancer cells. Interrogation of TCGA and GEO public datasets supports the relevance of this pathway in human breast cancer, demonstrating that VEGF-C strongly correlates with activation of Hedgehog signaling and EMT in the human disease. Our study suggests that the VEGF-C/NRP2/GLI axis is a novel and conserved paracrine means by which EMT cells enhance metastasis, and provides potential targets for therapeutic intervention in this heterogeneous disease.
Focal adhesion kinase (FAK) is a 125-kDa, cytosolic, non-receptor, protein tyrosine kinase localized at focal adhesions that can be activated by multiple inputs and in different manners. FAK is implicated in signaling pathways regulating cell movement, invasion, survival, gene expression and cancer stem cell self-renewal. The aim of the present study was to investigate whether FAK plays a role in the apoptosis of bladder cancer cells. The study employed in situ deoxynucleotidyl transferase-mediated dUTP-biotin nick end labeling and Annexin V labeling flow cytometry. It was found that both the knockdown of FAK and the suppression of FAK phosphorylation were able to induce apoptosis in bladder cancer cells. Caspase-3 was activated during the apoptosis induced by the suppression of FAK phosphorylation. Src was involved in FAK-regulated apoptosis in bladder cancer cells, while the suppression of Src phosphorylation was able to inhibit FAK tyrosine phosphorylation and induce apoptosis. Furthermore, phosphatidylinositol 3-kinase (PI3K)/Akt signaling was inhibited via the suppression of FAK tyrosine phosphorylation. Conversely, the expression of neither the general nor the tyrosine-phosphorylated FAK was regulated by inhibiting PI3K/Akt, which suggested that PI3K/Akt acted downstream of FAK to regulate apoptosis in bladder cancer cells. These findings indicate the presence of a mechanism of apoptosis involving FAK-mediated oncogenic signaling. FAK may function as an important regulator of extracellular signaling-mediated apoptosis in bladder cancer and be used as a novel therapeutic target in the treatment of the condition.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.