Interaction between smoking history and gene expression levels impacts survival of breast cancer patients

Differential expression of NAT1 pharmacogene in hormone receptor positive vs. negative female breast tumors may affect drug treatment

Studies have reported overexpression of NAT1 gene for xenobiotic metabolizing arylamine N-acetyltransferase type 1 in estrogen receptor positive breast tumors, and this association has been linked to patient chemoresistance and response to tamoxifen. We probed the expression of NAT1, using quantitative reverse transcription PCR to screen clinically characterized breast cancer tissue cDNA arrays. Primers detecting all NAT1 alternative transcripts were used, and the protocol and results are reported according to consensus guidelines. The clinical information about 166 tumor samples screened is provided, including tumor stage, estrogen and progesterone receptor status and HER2 expression. NAT1 was found to be significantly (P < 0.001) upregulated in hormone receptor positive vs. negative tumors. No correlation was apparent between NAT1 and tumor stage or HER2 expression. Our findings demonstrate a strong correlation between the expression of NAT1 and steroid hormone receptors in breast tumors, supporting its possible utility as a pharmacogenetic biomarker or drug target. Of the two polymorphic NAT genes, NAT1 is the one primarily expressed in breast tissue, and is subjected to regulation by two differential promoters and more than one polyadenylation signal. Hormonal factors may enhance NAT1 gene expression at the transcriptional or epigenetic level, and tamoxifen has additionally been shown to inhibit NAT1 enzymatic activity. The outcome of tamoxifen treatment is also more favorable in patients with NAT1 overexpressing tumors. The study adds to the growing body of evidence implicating NAT1 in breast cancer and its pharmacological treatment.

Global research trends and hotspots for leukocyte cell-derived chemotaxin-2 from the past to 2023: a combined bibliometric review

Leukocyte cell-derived chemotaxin-2 (LECT2) is an important cytokine synthesized by liver. Significant research interest is stimulated by its crucial involvement in inflammatory response, immune regulation, disease occurrence and development. However, bibliometric study on LECT2 is lacking. In order to comprehend the function and operation of LECT2 in human illnesses, we examined pertinent studies on LECT2 investigation in the Web of Science database, followed by utilizing CiteSpace, VOSview, and Scimago Graphica for assessing the yearly quantity of papers, countries/regions involved, establishments, authors, publications, citations, and key terms. Then we summarized the current research hotspots in this field. Our study found that the literature related to LECT2 has a fluctuating upward trend. "Angiogenesis", "ALECT2", "diagnosis", and "biliary atresia" are the current investigative frontiers. Our findings indicated that liver diseases (e.g. liver fibrosis and hepatic cell carcinoma), systemic inflammatory disease, and amyloidosis are the current research focus of LECT2. The current LECT2 research outcomes are not exceptional. We hope to promote the scientific research of LECT2 and exploit its potential for clinical diagnosis and treatment of related diseases through a comprehensive bibliometric review.

CENPN suppresses autophagy and increases paclitaxel resistance in nasopharyngeal carcinoma cells by inhibiting the CREB-VAMP8 signaling axis

Chemotherapeutic resistance is one of the most common reasons for poor prognosis of patients with nasopharyngeal carcinoma (NPC). We found that CENPN can promote the growth, proliferation and apoptosis resistance of NPC cells, but its relationship with chemotherapeutic resistance in NPC is unclear. Here we verified that the CENPN expression level in NPC patients was positively correlated with the degree of paclitaxel (PTX) resistance and a poor prognosis through analysis of clinical cases. VAMP8 expression was significantly increased after knockdown of CENPN by transcriptome sequencing. We found in cell experiments that CENPN inhibited macroautophagy/autophagy and VAMP8 expression and significantly increased PTX resistance. Overexpression of CENPN reduced the inhibitory effects of PTX on survival, cell proliferation, cell cycle progression and apoptosis resistance in NPC cells by inhibiting autophagy. In turn, knockdown of CENPN can affect the phenotype of NPC cells by increasing autophagy to achieve PTX sensitization. Sequential knockdown of CENPN and VAMP8 reversed the PTX-sensitizing effect of CENPN knockdown alone. Experiments in nude mice confirmed that knockdown of CENPN can increase VAMP8 expression, enhance autophagy and increase the sensitivity of NPC cells to PTX. Mechanistic studies showed that CENPN inhibited the translocation of p-CREB into the nucleus of NPC cells, resulting in the decreased binding of p-CREB to the VAMP8 promoter, thereby inhibiting the transcription of VAMP8. These results demonstrate that CENPN may be a marker for predicting chemotherapeutic efficacy and a potential target for inducing chemosensitization to agents such as PTX.Abbreviations: 3-MA: 3-methyladenine; ATG5: autophagy related 5; CENPN: centromere protein N; CQ: chloroquine; CREB: cAMP responsive element binding protein; ChIP: chromatin immunoprecipitation assay; IC50: half-maximal inhibitory concentration; LAMP2A: lysosomal associated membrane protein 2A; MAP1LC3/LC3: microtubule associated protein 1 light chain 3; NPC: nasopharyngeal carcinoma; NPG: nasopharyngitis; oeCENPN: overexpressed CENPN; PTX: paclitaxel; RAPA: rapamycin; RNA-seq: transcriptome sequencing; shCENPN: small hairpin RNA expression vector targeting the human CENPN gene; shCENPN-shVAMP8: sequential knockdown targeting the human CENPN gene and VAMP8 gene; shVAMP8: small hairpin RNA expression vector targeting the human VAMP8 gene; TEM: transmission electron microscopy; TIR: tumor inhibitory rate; VAMP8: vesicle associated membrane protein 8.

Associations of breast cancer related exposures and gene expression profiles in normal breast tissue-The Norwegian Women and Cancer normal breast tissue study

BACKGROUND

Normal breast tissue is utilized in tissue-based studies of breast carcinogenesis. While gene expression in breast tumor tissue is well explored, our knowledge of transcriptomic signatures in normal breast tissue is still incomplete. The aim of this study was to investigate variability of gene expression in a large sample of normal breast tissue biopsies, according to breast cancer related exposures (obesity, smoking, alcohol, hormone therapy, and parity).

METHODS

We analyzed gene expression profiles from 311 normal breast tissue biopsies from cancer-free, post-menopausal women, using Illumina bead chip arrays. Principal component analysis and K-means clustering was used for initial analysis of the dataset. The association of exposures and covariates with gene expression was determined using linear models for microarrays.

RESULTS

Heterogeneity of the breast tissue and cell composition had the strongest influence on gene expression profiles. After adjusting for cell composition, obesity, smoking, and alcohol showed the highest numbers of associated genes and pathways, whereas hormone therapy and parity were associated with negligible gene expression differences.

CONCLUSION

Our results provide insight into associations between major exposures and gene expression profiles and provide an informative baseline for improved understanding of exposure-related molecular events in normal breast tissue of cancer-free, post-menopausal women.

High miR203a-3p and miR-375 expression in the airways of smokers with and without COPD

Smoking is a leading cause of chronic obstructive pulmonary disease (COPD). It is known to have a significant impact on gene expression and (inflammatory) cell populations in the airways involved in COPD pathogenesis. In this study, we investigated the impact of smoking on the expression of miRNAs in healthy and COPD individuals. We aimed to elucidate the overall smoking-induced miRNA changes and those specific to COPD. In addition, we investigated the downstream effects on regulatory gene expression and the correlation to cellular composition. We performed a genome-wide miRNA expression analysis on a dataset of 40 current- and 22 ex-smoking COPD patients and a dataset of 35 current- and 38 non-smoking respiratory healthy controls and validated the results in an independent dataset. miRNA expression was then correlated with mRNA expression in the same patients to assess potential regulatory effects of the miRNAs. Finally, cellular deconvolution analysis was used to relate miRNAs changes to specific cell populations. Current smoking was associated with increased expression of three miRNAs in the COPD patients and 18 miRNAs in the asymptomatic smokers compared to respiratory healthy controls. In comparison, four miRNAs were lower expressed with current smoking in asymptomatic controls. Two of the three smoking-related miRNAs in COPD, miR-203a-3p and miR-375, were also higher expressed with current smoking in COPD patients and the asymptomatic controls. The other smoking-related miRNA in COPD patients, i.e. miR-31-3p, was not present in the respiratory healthy control dataset. miRNA-mRNA correlations demonstrated that miR-203a-3p, miR-375 and also miR-31-3p expression were negatively associated with genes involved in pro-inflammatory pathways and positively associated with genes involved in the xenobiotic pathway. Cellular deconvolution showed that higher levels of miR-203a-3p were associated with higher proportions of proliferating-basal cells and secretory (club and goblet) cells and lower levels of fibroblasts, luminal macrophages, endothelial cells, B-cells, amongst other cell types. MiR-375 expression was associated with lower levels of secretory cells, ionocytes and submucosal cells, but higher levels of endothelial cells, smooth muscle cells, and mast cells, amongst other cell types. In conclusion, we identified two smoking-induced miRNAs (miR-375 and miR-203a-3p) that play a role in regulating inflammation and detoxification pathways, regardless of the presence or absence of COPD. Additionally, in patients with COPD, we identified miR-31-3p as a miRNA induced by smoking. Our identified miRNAs should be studied further to unravel which smoking-induced inflammatory mechanisms are reactive and which are involved in COPD pathogenesis.

Comprehensive Analysis of the Expression and Prognostic Significance of the CENP Family in Breast Cancer

Background

Centromere proteins (CENPs) are a set of protein-coding genes involved in the transient assembly of the kinetochore which occurs during mitosis. This study intended to clarify the expression patterns, prognosis and potential mechanisms of CENPs in breast cancer (BC).

Methods

Coexpedia was used to screen GEO datasets and PubMed articles related to CENPs and BC. CENPs expressions, prognosis and alteration were analyzed by Oncomine, Ualcan and Kaplan Meier plotter and cBioPortal. The correlation and interaction of CENPs was performed by Breast Cancer Gene-Expression Miner, GeneMANIA and STRING portal. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were conducted to clarify the functional roles of CENPs. CENPF, E, U, A, N, I, K, W, M, L were selected for further analysis.

Results

All CENPs were highly expressed in BC compared to normal tissue. High expression of CENPF, E, U, A, N, I, W, M, L and CENPF, E, U, A, N, I, M correlated with worse relapse free survival (RFS) and worse overall survival (OS), respectively. All of 10 CENPs indicated positive correlations and complex interactions between each other at mRNA expression and protein level. CENPs were enriched GO terms mainly in centromere complex assembly and KEGG terms in progesterone-mediated oocyte maturation, cell cycle and oocyte meiosis.

Conclusion

The 10 CENPs could be diagnostic biomarkers and all of them except CENPK can be used as prognosis biomarkers in BC. CENPs play an oncogenic role and may be the potential therapy targets of treatment for BC patients.

In silico recognition of a prognostic signature in basal-like breast cancer patients

Background

Triple-negative breast cancers (TNBCs) display poor prognosis, have a high risk of tumour recurrence, and exhibit high resistance to drug treatments. Based on their gene expression profiles, the majority of TNBCs are classified as basal-like breast cancers. Currently, there are not available widely-accepted prognostic markers to predict outcomes in basal-like subtype, so the selection of new prognostic indicators for this BC phenotype represents an unmet clinical challenge.

Results

Here, we attempted to address this challenging issue by exploiting a bioinformatics pipeline able to integrate transcriptomic, genomic, epigenomic, and clinical data freely accessible from public repositories. This pipeline starts from the application of the well-established network-based SWIM methodology on the transcriptomic data to unveil important (switch) genes in relation with a complex disease of interest. Then, survival and linear regression analyses are performed to associate the gene expression profiles of the switch genes with both the patients’ clinical outcome and the disease aggressiveness. This allows us to identify a prognostic gene signature that in turn is fed to the last step of the pipeline consisting of an analysis at DNA level, to investigate whether variations in the expression of identified prognostic switch genes could be related to genetic (copy number variations) or epigenetic (DNA methylation differences) alterations in their gene loci, or to the activities of transcription factors binding to their promoter regions. Finally, changes in the protein expression levels corresponding to the so far identified prognostic switch genes are evaluated by immunohistochemical staining results taking advantage of the Human Protein Atlas.

Conclusion

The application of the proposed pipeline on the dataset of The Cancer Genome Atlas (TCGA)-Breast Invasive Carcinoma (BRCA) patients affected by basal-like subtype led to an in silico recognition of a basal-like specific gene signature composed of 11 potential prognostic biomarkers to be further investigated.

CENPN Acts as a Novel Biomarker that Correlates With the Malignant Phenotypes of Glioma Cells

Background: Gliomas are the most common intracranial malignant neoplasms and have high recurrence and mortality rates. Recent literatures have reported that centromere protein N (CENPN) participates in tumor development. However, the clinicopathologic significance and biological functions of CENPN in glioma are still unclear.

Methods: Clinicopathologic data and gene expression profiles of glioma cases downloaded from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases were utilized to determine the associations between the expression of CENPN and clinical features of glioma. Kaplan-Meier and ROC curves were plotted for prognostic analysis. Gene set enrichment analysis (GSEA) and single sample gene set enrichment analysis (ssGSEA) were applied to identify immune-related functions and pathways associated with CENPN’ differential expression. In vitro experiments were conducted to investigate the impacts of CENPN on human glioma cells.

Results: Elevated CENPN expression was associated with unfavorable clinical variables of glioma patients, which was validated in clinical specimens obtained from our institution by immunohistochemical staining (IHC). The GSEA and ssGSEA results revealed that CENPN expression was strongly correlated with inflammatory activities, immune-related signaling pathways and the infiltration of immune cells. Cell experiments showed that CENPN deficiency impaired cell proliferation, migration and invasion ability and increased glioma apoptosis.

Conclusion: CENPN could be a promising therapeutic target for glioma.

Centromere protein N may be a novel malignant prognostic biomarker for hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is one of the deadliest tumors. The majority of HCC is detected in the late stage, and the clinical results for HCC patients are poor. There is an urgent need to discover early diagnostic biomarkers and potential therapeutic targets for HCC.

Methods

The GSE87630 and GSE112790 datasets from the Gene Expression Omnibus (GEO) database were downloaded to analyze the differentially expressed genes (DEGs) between HCC and normal tissues. R packages were used for Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses of the DEGs. A Search Tool for Retrieval of Interacting Genes (STRING) database was used to develop a protein-protein interaction (PPI) network, and also cytoHubba, Molecular Complex Detection (MCODE), EMBL-EBI, CCLE, Gene Expression Profiling Interactive Analysis (GEPIA), and Oncomine analyses were performed to identify hub genes. Gene expression was verified with a third GEO dataset, GSE25097. The Cancer Genome Atlas (TCGA) database was used to explore the correlations between the hub genes and clinical indexes of HCC patients. The functions of the hub genes were enriched by gene set enrichment analysis (GSEA), and the biological significance of the hub genes was explored by real-time polymerase chain reaction (qRT-PCR), western blot, immunofluorescence, CCK-8, colony formation, Transwell and flow cytometry assays with loss-of-function experiments in vitro.

Results

Centromere protein N (CENPN) was screened as a hub gene affecting HCC tumorigenesis. Evaluation by Cox regression showed that a high level of CENPN expression was an independent danger variable for poor prognosis of HCC. GSEA showed that high CENPN expression was linked to the following pathways: liver cancer subclass proliferation, cell cycle, p53 signaling pathway, Rb1 pathway, positive regulation of cell cycle G1/S phase transition, and DNA damage response signal transduction by p53 class moderators. Further cell experiments showed that knocking down CENPN expression decreased the proliferation and colony-forming abilities of HepG2 and Huh7 cells as well as Ki67 expression in these cell lines. The cell cycle was arrested in G1 phase, which is consistent with previous experiments on CENPN downregulation., but neither migration nor invasion were significantly affected. Western blot results revealed that the expression of p53, p27, p21, CDK4, cyclin D1, CDK2, cyclin E, pRb, E2F1 and c-myc decreased after CENPN knockdown, but there was no significant change in total Rb levels. In addition, CENPN-knockdown cells subjected to irradiation showed significantly enhanced of γ-H2AX expression and reduced colony formation.

Conclusion

CENPN functions as an oncogene in HCC and may be a therapeutic target and promising prognostic marker for HCC.

LECT 2 Antagonizes FOXM1 Signaling via Inhibiting MET to Retard PDAC Progression

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers with minimally effective treatments, highlighting the importance of developing novel biomarkers and therapeutic targets. Here, we disclosed the mechanisms that leukocyte cell-derived chemotaxin-2 (LECT2) modulates PDAC development using in vitro and in vivo models. LECT2 is downregulated in metastatic PDACs compared with the primary tumor, and its expression is correlated with multiple clinical pathologic features and prognosis. The absence promotes multiple malignant behaviors, including cell proliferation, epithelial-mesenchymal transition, migration, and invasion. In vivo studies showed that LECT2 overexpression inhibits tumor growth and lung metastasis. Mechanistically, LECT2 inhibits FOXM1 signaling by targeting HGF/MET to retard PDAC progression, revealing LECT2 as a promising biomarker and therapeutic target for PDAC in the future.

Prediction and interpretation of cancer survival using graph convolution neural networks

The survival rate of cancer has increased significantly during the past two decades for breast, prostate, testicular, and colon cancer, while the brain and pancreatic cancers have a much lower median survival rate that has not improved much over the last forty years. This has imposed the challenge of finding gene markers for early cancer detection and treatment strategies. Different methods including regression-based Cox-PH, artificial neural networks, and recently deep learning algorithms have been proposed to predict the survival rate for cancers. We established in this work a novel graph convolution neural network (GCNN) approach called Surv_GCNN to predict the survival rate for 13 different cancer types using the TCGA dataset. For each cancer type, 6 Surv_GCNN models with graphs generated by correlation analysis, GeneMania database, and correlation + GeneMania were trained with and without clinical data to predict the risk score (RS). The performance of the 6 Surv_GCNN models was compared with two other existing models, Cox-PH and Cox-nnet. The results showed that Cox-PH has the worst performance among 8 tested models across the 13 cancer types while Surv_GCNN models with clinical data reported the best overall performance, outperforming other competing models in 7 out of 13 cancer types including BLCA, BRCA, COAD, LUSC, SARC, STAD, and UCEC. A novel network-based interpretation of Surv_GCNN was also proposed to identify potential gene markers for breast cancer. The signatures learned by the nodes in the hidden layer of Surv_GCNN were identified and were linked to potential gene markers by network modularization. The identified gene markers for breast cancer have been compared to a total of 213 gene markers from three widely cited lists for breast cancer survival analysis. About 57% of gene markers obtained by Surv_GCNN with correlation + GeneMania graph either overlap or directly interact with the 213 genes, confirming the effectiveness of the identified markers by Surv_GCNN.

Identifying breast cancer subtypes associated modules and biomarkers by integrated bioinformatics analysis

Breast cancer is the most common form of cancer afflicting women worldwide. Patients with breast cancer of different molecular classifications need varied treatments. Since it is known that the development of breast cancer involves multiple genes and functions, identification of functional gene modules (clusters of the functionally related genes) is indispensable as opposed to isolated genes, in order to investigate their relationship derived from the gene co-expression analysis. In total, 6315 differentially expressed genes (DEGs) were recognized and subjected to the co-expression analysis. Seven modules were screened out. The blue and turquoise modules have been selected from the module trait association analysis since the genes in these two modules are significantly correlated with the breast cancer subtypes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment show that the blue module genes engaged in cell cycle, DNA replication, p53 signaling pathway, and pathway in cancer. According to the connectivity analysis and survival analysis, 8 out of 96 hub genes were filtered and have shown the highest expression in basal-like breast cancer. Furthermore, the hub genes were validated by the external datasets and quantitative real-time PCR (qRT-PCR). In summary, hub genes of Cyclin E1 (CCNE1), Centromere Protein N (CENPN), Checkpoint kinase 1 (CHEK1), Polo-like kinase 1 (PLK1), DNA replication and sister chromatid cohesion 1 (DSCC1), Family with sequence similarity 64, member A (FAM64A), Ubiquitin Conjugating Enzyme E2 C (UBE2C) and Ubiquitin Conjugating Enzyme E2 T (UBE2T) may serve as the prognostic markers for different subtypes of breast cancer.

A Novel XGBoost Method to Identify Cancer Tissue-of-Origin Based on Copy Number Variations

The discovery of cancer of unknown primary (CUP) is of great significance in designing more effective treatments and improving the diagnostic efficiency in cancer patients. In the study, we develop an appropriate machine learning model for tracing the tissue of origin of CUP with high accuracy after feature engineering and model evaluation. Based on a copy number variation data consisting of 4,566 training cases and 1,262 independent validation cases, an XGBoost classifier is applied to 10 types of cancer. Extremely randomized tree (Extra tree) is used for dimension reduction so that fewer variables replace the original high-dimensional variables. Features with top 300 weights are selected and principal component analysis is applied to eliminate noise. We find that XGBoost classifier achieves the highest overall accuracy of 0.8913 in the 10-fold cross-validation for training samples and 0.7421 on independent validation datasets for predicting tumor tissue of origin. Furthermore, by contrasting various performance indices, such as precision and recall rate, the experimental results show that XGBoost classifier significantly improves the classification performance of various tumors with less prediction error, as compared to other classifiers, such as K-nearest neighbors (KNN), Bayes, support vector machine (SVM), and Adaboost. Our method can infer tissue of origin for the 10 cancer types with acceptable accuracy in both cross-validation and independent validation data. It may be used as an auxiliary diagnostic method to determine the actual clinicopathological status of specific cancer.

Detecting Prognosis Risk Biomarkers for Colon Cancer Through Multi-Omics-Based Prognostic Analysis and Target Regulation Simulation Modeling

Background

Colon cancer is one of the most common health threats for humans since its high morbidity and mortality. Detecting potential prognosis risk biomarkers (PRBs) is essential for the improvement of therapeutic strategies and drug development. Currently, although an integrated prognostic analysis of multi-omics for colon cancer is insufficient, it has been reported to be valuable for improving PRBs’ detection in other cancer types.

Aim

This study aims to detect potential PRBs for colon adenocarcinoma (COAD) samples through the cancer genome atlas (TCGA) by integrating muti-omics.

Materials and Methods

The multi-omics-based prognostic analysis (MPA) model was first constructed to systemically analyze the prognosis of colon cancer based on four-omics data of gene expression, exon expression, DNA methylation and somatic mutations on COAD samples. Then, the essential features related to prognosis were functionally annotated through protein–protein interaction (PPI) network and cancer-related pathways. Moreover, the significance of those essential prognostic features were further confirmed by the target regulation simulation (TRS) model. Finally, an independent testing dataset, as well as the single cell-based expression dataset were utilized to validate the generality and repeatability of PRBs detected in this study.

Results

By integrating the result of MPA modeling, as well the PPI network, integrated pathway and TRS modeling, essential features with gene symbols such as EPB41, PSMA1, FGFR3, MRAS, LEP, C7orf46, LOC285000, LBP, ZNF35, SLC30A3, LECT2, RNF7, and DYNC1I1 were identified as PRBs which provide high potential as drug targets for COAD treatment. Validation on the independent testing dataset demonstrated that these PRBs could be applied to distinguish the prognosis of COAD patients. Moreover, the prognosis of patients with different clinical conditions could also be distinguished by the above PRBs.

Conclusions

The MPA and TRS models constructed in this paper, as well as the PPI network and integrated pathway analysis, could not only help detect PRBs as potential therapeutic targets for COAD patients but also make it a paradigm for the prognostic analysis of other cancers.

Identification of Long Noncoding RNAs as Predictors of Survival in Triple-Negative Breast Cancer Based on Network Analysis

Breast cancer is the most common cancer observed in adult females, worldwide. Due to the heterogeneity and varied molecular subtypes of breast cancer, the molecular mechanisms underlying carcinogenesis in different subtypes of breast cancer are distinct. Recently, long noncoding RNAs (lncRNAs) have been shown to be oncogenic or play important roles in cancer suppression and are used as biomarkers for diagnosis and therapy. In this study, we identified 134 lncRNAs and 6,414 coding genes were differentially expressed in triple-negative (TN), human epidermal growth factor receptor 2- (HER2-) positive, luminal A-positive, and luminal B-positive breast cancer. Of these, 37 lncRNAs were found to be dysregulated in all four subtypes of breast cancers. Subtypes of breast cancer special modules and lncRNA-mRNA interaction networks were constructed through weighted gene coexpression network analysis (WGCNA). Survival analysis of another public datasets was used to verify the identified lncRNAs exhibiting potential indicative roles in TN prognosis. Results from heat map analysis of the identified lncRNAs revealed that five blocks were significantly displayed. High expressions of lncRNAs, including LINC00911, CSMD2-AS1, LINC01192, SNHG19, DSCAM-AS1, PCAT4, ACVR28-AS1, and CNTFR-AS1, and low expressions of THAP9-AS1, MALAT1, TUG1, CAHM, FAM2011, NNT-AS1, COX10-AS1, and RPARP-AS1 were associated with low survival possibility in TN breast cancers. This study provides novel lncRNAs as potential biomarkers for the therapeutic and prognostic classification of different breast cancer subtypes.

Relationships of protein biomarkers of the urokinase plasminogen activator system with expression of their cognate genes in primary breast carcinomas

Background

uPA, its receptor uPAR, and inhibitors PAI‐1 and PAI‐2 play key roles in membrane remodeling/invasion and in predicting response to chemotherapy. We identified novel relationships of these biomarkers with ER/PR that indicate clinical utility for assessing breast carcinoma outcomes.

Methods

Retrospective studies were performed with de‐identified results of (a) uPA, uPAR, and PAI‐1; (b) estrogen (ER) and progestin receptor (PR); and (c) clinical outcomes. Relative expression of 22 000 genes from microarray of RNA from LCM‐procured breast cancer cells was used with R Studio version 3.4.1.

Results

Primary ER/PR status was related to uPA, uPAR, or PAI‐1 levels. ER− or PR− cancers expressed elevated uPA, uPAR, and PAI2 mRNA compared to ER+ or PR+ cells. Inverse relationships between ER/PR protein and expression of uPA, uPAR, and PAI‐2 were observed, whereas HER2 status was unrelated. qPCR analyses showed RERG and NQO‐1 expressions were elevated in uPA− lesions, while CD34 and EDG‐1 were elevated in uPAR− cancers. ERBB4 was overexpressed in PAI‐1+ carcinomas. Cox regression analyses revealed relationships of ER/PR status and uPA system members with regard to clinical outcomes of breast cancer.

Conclusions

uPA, uPAR, PAI1, or PAI2 expression was increased in either ER− or PR− cancers similar to that of protein content in ER−/PR− carcinomas, suggesting sex hormones regulate the uPA system in breast cancer. Results revealed protein content of uPA system members was related to ER/PR status of primary lesions. Use of LCM‐procured carcinoma cells uncovered relationships between expression of known cancer−associated genes and protein content of uPA system members. Collectively, results indicate evaluation of ER and PR protein of primary breast cancers combined with analyses of uPA, uPAR, and PAI‐1 protein content improves assessment of clinical outcomes.

Assessment of CEP55, PLK1 and FOXM1 expression in patients with bladder cancer in comparison with healthy individuals

This case/control study is aimed at investigating the expression of CEP55, PLK1 and FOXM1 in bladder cancer tissues and comparing it with healthy tissue and their relationship with clinicopathological features of BC. Total RNA was extracted; then, gene expression was performed using real-time PCR relative to 18 s rRNA. 2-ΔΔCT method was used to calculate the relative expression of genes. A significant over expression of FOXM1, PLK1 and CEP55 was observed in tumor samples compared to adjacent and normal bladder tissues (all p = 0.001). Therefore, they may be supposed as potential candidate's biomarkers for early diagnosis and targets for cancer therapy.

Impact of smoking history on the outcomes of women with early-stage breast cancer: a secondary analysis of a randomized study

To assess the impact of smoking history on the outcomes of early-stage breast cancer patients treated with sequential anthracyclines-taxanes in a randomized study. This is a secondary analysis of patient-level data of 1242 breast cancer patients referred for adjuvant chemotherapy in the BCIRG005 clinical trial. Overall survival was assessed according to smoking history through Kaplan-Meier analysis. Univariate and multivariate Cox regression analyses of factors affecting overall and relapse-free survival were subsequently conducted. Factors that were evaluated included: age, performance status, number of chemotherapy cycles, T stage, lymph node ratio, estrogen receptor status, adjuvant radiotherapy and smoking history. Kaplan-Meier analysis of overall survival according to smoking status (ever smoker vs. never smoker) was conducted. There was a trend toward a better overall survival among never smokers compared to ever smokers; however, it was not statistically significant (P = 0.098). The following factors were associated with better overall survival in multivariate analysis: older age (P = 0.011), complete chemotherapy course (P = 0.002), lower T stage (P < 0.0001), lower lymph node ratio (P < 0.0001) and positive estrogen receptor status (P = 0.006). Otherwise, the following factors were associated with better relapse-free survival in multivariate analysis: older age (P = 0.001), never smoking status (P = 0.021), lower T stage (P = 0.028), lower lymph node ratio (P < 0.0001) and positive estrogen receptor status (P < 0.0001). Early-stage breast cancer patients with a positive smoking history experienced worse relapse-free survival compared to never smokers. Physicians managing breast cancer patients should prioritize discussion about the benefits of smoking cessation when counseling their patients.

High N-Acetyltransferase 1 Expression Is Associated with Estrogen Receptor Expression in Breast Tumors, but Is not Under Direct Regulation by Estradiol, 5α-androstane-3β,17β-Diol, or Dihydrotestosterone in Breast Cancer Cells

N-acetyltransferase 1 (NAT1) is an enzyme that metabolizes carcinogens, which suggests a potential role in breast carcinogenesis. High NAT1 expression in breast tumors is associated with estrogen receptor α (ERα+) and the luminal subtype. We report that NAT1 mRNA transcript, protein, and enzyme activity were higher in human breast tumors with high expression of ERα/ESR1 compared with normal breast tissue. There was a strong correlation between NATb promoter and NAT1 protein expression/enzyme activity. High NAT1 expression in tumors was not the result of adipocytes, as evidenced by low perilipin (PLIN) expression. ESR1, NAT1, and XBP1 expression were associated in tumor biopsies. Direct regulation of NAT1 transcription by estradiol (E₂) was investigated in ERα (+) MCF-7 and T47D breast cancer cells. E₂ did not increase NAT1 transcript expression but increased progesterone receptor expression in a dose-dependent manner. Likewise, NAT1 transcript levels were not increased by dihydrotestosterone (DHT) or 5α-androstane-3β, (3β-adiol) 17β-diol. Dithiothreitol increased levels of the activated, spliced XBP1 in ERα (+) MCF-7 and T47D breast cancer cells but did not affect NAT1 or ESR1 expression. We conclude that NAT1 expression is not directly regulated by E₂, DHT, 3β-adiol, or dithiothreitol despite high NAT1 and ESR1 expression in luminal A breast cancer cells, suggesting that ESR1, XBP1, and NAT1 expression may share a common transcriptional network arising from the luminal epithelium associated with better survival in breast cancer. Clusters of high-expression genes, including NAT1, in breast tumors might serve as potential targets for novel therapeutic drug development.

Expression of Genes for Methylxanthine Pathway-Associated Enzymes Accompanied by Sex Steroid Receptor Status Impacts Breast Carcinoma Progression

Consumption of methylxanthine alkaloids appears to induce activities by antagonizing adenosine receptors, implicated in breast cancer behavior in vitro. Our goal was to evaluate expression of genes for methylxanthine receptors and metabolizing enzymes to assess risk of breast carcinoma recurrence. Clinical outcomes, estrogen/progestin receptor results, and gene expression assays guided selection. RNA was isolated from laser capture microdissection-procured carcinoma cells for microarray using established protocols. Gene expression levels of eight methylxanthine receptors, eight metabolizing enzymes, and various phosphodiesterases were retrieved from microarray results. Univariable Cox regressions and Kaplan-Meier plots were determined for each gene with R software. Individually, lower expressions of PDE4A, CYP2A6, or CYP2E were related to decreased progression-free survival (PFS) and overall survival (OS). PDE1A over-expression predicted decreased PFS and OS. ADORA2B and RYR1 over-expressions predicted diminished OS. ER+ cancers exhibited lower ADORA1, ADORA2B, and RYR1 and elevated PDE4A, CYP2A6, and CYP2E expressions. Of PR+ carcinomas, diminished ADORA2B and RYR1 and elevated expressions of ADORA3, PDE4A, CYP2C8, and CYP2E were noted. Least absolute shrinkage and selection operator (LASSO) revealed that CYP2E, PDE1A, and PDE4A expressions collectively predicted PFS whereas ADORA1, CYP2E, PDE1A, PDE1B, and PDE4A expressions jointly predicted OS. Models were clinically significant when validated externally. LASSO also derived a six-gene model and five-gene model that predicted PFS of ER- or PR- carcinomas, respectively. Similarly, five-gene and four-gene models predicted OS in ER- or PR- carcinomas, respectively. Collectively, expression of genes involved in methylxanthine action and metabolism in single-cell types predicted clinical outcomes of breast carcinoma indicating promise for developing diagnostics and design of new therapeutics.

State of the evidence 2017: an update on the connection between breast cancer and the environment

BACKGROUND

In this review, we examine the continually expanding and increasingly compelling data linking radiation and various chemicals in our environment to the current high incidence of breast cancer. Singly and in combination, these toxicants may have contributed significantly to the increasing rates of breast cancer observed over the past several decades. Exposures early in development from gestation through adolescence and early adulthood are particularly of concern as they re-shape the program of genetic, epigenetic and physiological processes in the developing mammary system, leading to an increased risk for developing breast cancer. In the 8 years since we last published a comprehensive review of the relevant literature, hundreds of new papers have appeared supporting this link, and in this update, the evidence on this topic is more extensive and of better quality than that previously available.

CONCLUSION

Increasing evidence from epidemiological studies, as well as a better understanding of mechanisms linking toxicants with development of breast cancer, all reinforce the conclusion that exposures to these substances - many of which are found in common, everyday products and byproducts - may lead to increased risk of developing breast cancer. Moving forward, attention to methodological limitations, especially in relevant epidemiological and animal models, will need to be addressed to allow clearer and more direct connections to be evaluated.

Clinical outcomes linked to expression of gene subsets for protein hormones and their cognate receptors from LCM-procured breast carcinoma cells

PURPOSE

Certain peptide hormones and/or their cognate receptors influencing normal cellular pathways also have been detected in breast cancers. The hypothesis is that gene subsets of these regulatory molecules predict risk of breast carcinoma recurrence in patients with primary disease.

METHODS

Gene expression levels of 61 hormones and 81 receptors were determined by microarray with LCM-procured carcinoma cells of 247 de-identified biopsies. Univariable and multivariable Cox regressions were determined using expression levels of each hormone/receptor gene, individually or as a pair.

RESULTS

Molecular signatures for ER+/PR+, ER-/PR-, and ER- carcinoma cells deciphered by LASSO were externally validated at HRs (CI) of 2.8 (1.84-4.4), 1.53 (1.01-2.3), and 1.72 (1.15-2.56), respectively. Using LCM-procured breast carcinoma cells, a 16-gene molecular signature was derived for ER+/PR+ biopsies, whereas a 10-gene signature was deciphered for ER-/PR- cancers. Four genes, POMC, CALCR, AVPR1A, and GH1, of this 10-gene signature were identified in a 6-gene molecular signature for ER- specimens.

CONCLUSIONS

Applying these signatures, Kaplan-Meier plots definitively identified a cohort of patients with either ER-/PR- or ER- carcinomas that exhibited low risk of recurrence. In contrast, the ER+/PR+ signature identified a cohort of patients with high risk of breast cancer recurrence. Each of the three molecular signatures predicted clinical outcomes of breast cancer patients with greater accuracy than observed with either single-gene analysis or by ER/PR protein content alone. Collectively, our results suggest that gene expression profiles of breast carcinomas with suspected poor prognosis (ER-/PR-) have identified a subset of patients with decreased risk of recurrence.

Effect of Genetic Polymorphisms and Long-Term Tobacco Exposure on the Risk of Breast Cancer

INTRODUCTION

Tobacco smoke contains many potentially harmful compounds that may act differently and at different stages in breast cancer development. The focus of this work was to assess the possible role of cigarette smoking (status, dose, duration or age at initiation) and polymorphisms in genes coding for enzymes involved in tobacco carcinogen metabolism (CYP1A1, CYP2A6) or in DNA repair (XRCC1, APEX1, XRCC3 and XPD) in breast cancer development.

METHODS

We designed a case control study with 297 patients, 217 histologically verified breast cancers (141 smokers and 76 non-smokers) and 80 healthy smokers in a cohort of Spanish women.

RESULTS

We found an association between smoking status and early age at diagnosis of breast cancer. Among smokers, invasive carcinoma subtype incidence increased with intensity and duration of smoking (all P_trend < 0.05). When smokers were stratified by smoking duration, we only observed differences in long-term smokers, and the CYP1A1 Ile462Ile genotype was associated with increased risk of breast cancer (OR = 7.12 (1.98-25.59)).

CONCLUSIONS

Our results support the main effect of CYP1A1 in estrogenic metabolism rather than in tobacco carcinogen activation in breast cancer patients and also confirmed the hypothesis that CYP1A1 Ile462Val, in association with long periods of active smoking, could be a breast cancer risk factor.