CXCL11 negatively regulated by MED19 favours antitumour immune infiltration in breast cancer.

BACKGROUND: Through microarray results, we found that the C-X-C motif chemokine ligand 11 (CXCL11) was negatively regulated by mediator complex subunit 19 (MED19), a protumour factor. However, the biological role and potential mechanism of CXCL11 need to be explored in breast cancer (BRCA). METHODS: The BRCA dataset was obtained from the Cancer Genome Atlas (TCGA) dataset. Our microarray data and the BRCA dataset of TCGA were analysed and visualized using the R software package. The mRNA and protein levels were measured by qRT-PCR and western blotting. RESULTS: Inhibition of MED19 in MDA-MB-231 cells caused CXCL11 upregulation. The relative positive regulation of cytokine pathways was enriched after MED19 knockdown. High CXCL11 was determined to be positively correlated with immune response activation, increased antitumour immune cell infiltration, immune checkpoint molecule expression, and enhanced sensitivity to immunotherapy and chemotherapy. Collectively, CXCL11 promoted antitumour immunity and was regulated by MED19 in BRCA. Clarifying the prognostic value and underlying mechanism of CXCL11 in BRCA could provide a theoretical basis to find new diagnostic and therapeutic targets.

Identification of a Risk Predictive Signature Based on Genes Associated with Tumor Size and Lymph Node Involvement in Breast Cancer.

Background: Breast cancer is a heterogeneous disease. Small tumors with extensive lymph node involvement (STEL) in breast cancer often reflect a biologically aggressive phenotype and poor prognosis. The aim of this study was to identify key genes associated with STEL and investigate their prognostic values in breast cancer. Methods: RNA sequence data from breast cancer specimens were acquired from The Cancer Genome Atlas (TCGA) database for differential analysis. Weighted gene correlation network analyses (WGCNA) were performed to identify coexpressed gene modules associated with tumor size and lymph node metastases. Gene set enrichment analysis (GSEA) was employed to investigate the biological functions of the identified genes. A combination of LASSO and Cox regression analyses was conducted to establish a risk predictive signature, and time-dependent receiver operating characteristic (tdROC) and Kaplan-Meier analyses were used to evaluate its prediction precision. Quantitative RT-PCR was employed to validate the expression levels of the key genes from the signature set. Results: A total of 2777 genes from three coexpressed gene modules were identified by WGCNA, and 880 differentially expressed genes were identified by transcriptome analyses. The 63 overlapping genes identified by both methods were considered STEL-associated genes, and a 9-gene risk-predictive signature was established based on them, with AUCs at 3, 5, and 7 years reaching 0.810, 0.811, and 0.753, respectively. Conclusion: This study demonstrated the transcriptomic profile of STEL breast cancer and successfully established a risk predictive signature with satisfactory accuracy. These findings may provide insights in to the genetic etiology of breast cancer.

FGFR1 Is Associated With Tamoxifen Resistance and Poor Prognosis of ER-Positive Breast Cancers by Suppressing ER Protein Expression.

Fibroblast growth factor receptor 1 (FGFR1) is widely recognized as a key player in mammary carcinogenesis and associated with the prognosis and therapeutic response of breast cancers. With the aim of investigating the correlation between FGFR1 expression and estrogen receptor (ER) and exploring the effect of FGFR1 on endocrine therapy response and ER+ breast cancer prognosis, we examined the FGFR1 protein expression among 184 ER-positive breast cancers by the immunohistochemistry (IHC) method, analyzed the association between FGFR1 expression and disease characters using the Pearson's chi-square test, and assessed the prognostic role of FGFR1 among breast cancers using Cox regression and Kaplan-Meier analyses. Moreover, in vitro assays were conducted to confirm the correlation between FGFR1 and ER expression and investigate the effect of FGFR1 on tamoxifen (TAM) sensitivity in ER+ breast cancer. The results showed that ER expression was negatively correlated with FGFR1 expression (P = 0.011, r = -0.221). Moreover, FGFR1 expression was one of the prognostic factors of ER-positive breast cancer (OR = 1.974, 95% CI = 1.043-3.633), and high FGFR1 expression was correlated with decreased breast cancer overall survival. In addition, knocking down FGFR1 inhibited cell proliferation and enhanced TAM sensitivity in TAM-resistant cells. In conclusion, we found that there was a significant negative correlation between FGFR1 and ER levels in ER+ breast cancers, high FGFR1 protein expression was associated with poor breast cancer prognosis, down-regulating FGFR1 could elevate ER expression and is associated with enhanced TAM sensitivity in ER+ breast cancers.

Identification of Hub Genes and Pathways of Triple Negative Breast Cancer by Expression Profiles Analysis.

PURPOSE: Triple negative breast cancer (TNBC) is an intrinsic subtype of breast cancer with a poor prognosis, characterized by a lack of ER and PR expression and the absence of HER2 amplification. The aim of this study is to characterize hub genes (key genes in the molecular interaction network) expression in TNBC, which may serve as prognostic predictors for TNBC treatment. METHODS: Four transcriptome microarray datasets GSE27447, GSE39004, GSE43358 and GSE45827 were obtained from the Gene Expression Omnibus (GEO) database, and R package limma and RobustRankAggreg were employed to identify common differentially expressed genes (DEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted by DAVID and KOBAS database. Thereafter, protein-protein interaction (PPI) network was constructed according to STRING online database. Functional modules and hub genes were screened by MCODE and cytohubba plug-ins, and the Cancer Genome Atlas (TCGA) survival analysis and qRT-PCR were utilized to validate the expression of these hub genes on TNBC. RESULTS: A total of 134 DEGs were identified by differential expression analysis, consisting of 88 up- and 46 down-regulated genes. GO and KEGG analyses showed that the terms and pathways enriched were mainly associated with cell adhesion, tumorigenesis and cellular immunity. From the PPI network, we identified six hub genes, including CD3D, CD3E, CD3G, FYN, GRAP2 and ITK. Survival analysis and the qRT-PCR results confirmed the robustness of the identified hub genes. CONCLUSION: This study provides a new insight into the understanding of the molecular mechanisms associated with TNBC and suggested that the hub genes may serve as prognostic predictors.

Identification of Hub Genes in Anaplastic Thyroid Carcinoma: Evidence From Bioinformatics Analysis.

Anaplastic thyroid carcinoma (ATC) is a rare type of thyroid cancer that results in fatal clinical outcomes; the pathogenesis of this life-threatening disease has yet to be fully elucidated. This study aims to identify the hub genes of ATC that may play key roles in ATC development and could serve as prognostic biomarkers or therapeutic targets. Two microarray datasets (GSE33630 and GSE53072) were obtained from the Gene Expression Omnibus database; these sets included 16 ATC and 49 normal thyroid samples. Differential expression analyses were performed for each dataset, and 420 genes were screened as common differentially expressed genes using the robust rank aggregation method. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were conducted to explore the potential bio-functions of these differentially expressed genes (DEGs). The terms and enriched pathways were primarily associated with cell cycle, cell adhesion, and cancer-related signaling pathways. Furthermore, a protein-protein interaction network of DEG expression products was constructed using Cytoscape. Based on the whole network, we identified 7 hub genes that included CDK1, TOP2A, CDC20, KIF11, CCNA2, NUSAP1, and KIF2C. The expression levels of these hub genes were validated using quantitative polymerase chain reaction analyses of clinical specimens. In conclusion, the present study identified several key genes that are involved in ATC development and provides novel insights into the understanding of the molecular mechanisms of ATC development.

Identification of Potential Key Genes and Pathways for Inflammatory Breast Cancer Based on GEO and TCGA Databases.

INTRODUCTION: Inflammatory breast cancer (IBC) is a rare type of breast cancer with poor prognosis, and the pathogenesis of this life-threatening disease is yet to be fully elucidated. This study aims to identify key genes of IBC, which could be potential diagnostic or therapeutic targets. METHODS: Four datasets GSE5847, GSE22597, GSE23720, and GSE45581 were downloaded from the Gene Expression Omnibus (GEO) and differential expression analysis was performed. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to understand the potential bio-functions of the differentially expressed genes (DEGs). Protein-protein interaction (PPI) network was constructed for functional modules analysis and hub genes identification, and TCGA survival analysis and qRT-PCR of clinical samples were used to further explore and validate the effect of hub genes on IBC. RESULTS: A total of 114 DEGs were identified from the GEO datasets. GO and KEGG analyses showed that the DEGs were mainly enriched in oncogenesis and cell adhesion. From the PPI network, we screened out five hub genes, including PTPRC, IL6, SELL, CD40, and SPN. Survival analysis and expression validation verified the robustness of the hub genes. DISCUSSION: The present study provides new insight into the understanding of IBC pathogenesis and the identified hub genes may serve as potential targets for diagnosis and treatment.

Screening and Identification of Key Biomarkers in Inflammatory Breast Cancer Through Integrated Bioinformatic Analyses.

Background: Inflammatory breast cancer (IBC) is a rare type of breast cancer with poor prognoses, moreover its pathogenesis is not entirely clear. The aim of this study was to identify key genes of IBC, which might serve as diagnostic biomarkers and/or therapeutic targets. Methods: Two microarray datasets, GSE23720 and GSE45581, were obtained from the Gene Expression Omnibus database, and a differential expression analysis was performed. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to understand the potential biological functions of the differentially expressed genes (DEGs). Next, a protein-protein interaction (PPI) network was constructed and visualized by Cytoscape. Functional modules and hub genes were screened using MCODE and cytohubba plug-ins, and the Cancer Genome Atlas survival analysis along with quantitative reverse transcriptional polymerase chain reactions of clinical samples was used to validate the effect that the hub genes have on IBC. Results: A total of 215 DEGs were identified, consisting of 105 upregulated and 110 downregulated genes. GO and KEGG analyses showed that the enriched terms and pathways were mainly associated with cell cycle, proliferation, drug metabolism, and oncogenesis. From the PPI network, we identified six hub genes, including Cell Division Cycle 45 (CDC45), Polo Like Kinase 1 (PLK1), BUB1 Mitotic Checkpoint Serine/Threonine Kinase B (BUB1B), Cell Division Cycle 20 (CDC20), Aurora Kinase A (AURKA), and Mitotic Arrest Deficient 2 Like 1 (MAD2L1). The survival analyses and expression validation studies verified the robustness of these hub genes. Conclusion: This study provides new insights into the understanding of the molecular mechanisms of IBC; in addition, the identified hub genes may serve as potential targets for diagnosis and treatment.

Identification of a four-long non-coding RNA signature in predicting breast cancer survival.

Long non-coding RNAs (lncRNAs) serve key roles in tumorigenesis and are differentially expressed in cancer. Using bioinformatics and statistical methods, the present study aimed to identify an lncRNA signature to predict breast cancer survival. The gene expression data of 768 patients with breast cancer were downloaded from The Cancer Genome Atlas database, and Cox regression, Kaplan-Meier and receiver operating characteristic (ROC) analyses were performed to construct and validate a predictive model. Gene Ontology term enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analysis were employed to predict the functions of the indicated lncRNAs. A signature consisting of four lncRNAs, including PVT1, MAPT-AS1, LINC00667 and LINC00938, was identified, and patients were subsequently divided into high- and low-risk groups according to the median risk score. Kaplan-Meier analysis confirmed that patients in the high-risk group exhibited significantly poorer overall survival rate in both the training (P=0.0151) and the validation set (P=0.0016); furthermore, ROC analysis confirmed that the model could predict patient survival with a certain sensitivity and specificity. In conclusion, the four-lncRNA signature presents a potential prognostic biomarker for breast cancer that may be relevant for clinical application.

Landscape of tumor suppressor long noncoding RNAs in breast cancer.

BACKGROUND: The landscape and biological functions of tumor suppressor long noncoding RNAs in breast cancer are still unknown. METHODS: Data from whole transcriptome sequencing of 33 breast specimens in the Harbin Medical University Cancer Center cohort and The Cancer Genome Atlas was applied to identify and validate the landscape of tumor suppressor long noncoding RNAs, which was further validated by The Cancer Genome Atlas pancancer data including 33 cancer types and 12,839 patients. Next, the expression model, prognostic roles, potential biological functions and epigenetic regulation of tumor suppressor long noncoding RNAs were investigated and validated in the breast cancer and pancancer cohorts. Finally, EPB41L4A-AS2 was selected to validate our novel finding, and the tumor suppressive roles of EPB41L4A-AS2 in breast cancer were examined. RESULTS: We identified and validated the landscape of tumor suppressor long noncoding RNAs in breast cancer. The expression of the identified long noncoding RNAs was downregulated in cancer tissue samples compared with normal tissue samples, and these long noncoding RNAs correlated with a favorable prognosis in breast cancer patients and the patients in the pancancer cohort. Multiple carcinogenesis-associated biological functions were predicted to be regulated negatively by these long noncoding RNAs. Moreover, these long noncoding RNAs were transcriptionally regulated by epigenetic modification, including DNA methylation and histone methylation modification. Finally, EPB41L4A-AS2 inhibited breast cancer cell proliferation, migration and invasion and induced cell apoptosis in vitro. Mechanistically, EPB41L4A-AS2, acting at least in part as a tumor suppressor, upregulated tumor suppressor gene expression. Moreover, ZNF217 recruited EZH2 to the EPB41L4A-AS2 locus and suppressed the expression of EPB41L4A-AS2 by epigenetically increasing H3K27me3 enrichment. CONCLUSIONS: This work enlarges the functional landscape of known long noncoding RNAs in human cancer and provides novel insights into the suppressive roles of these long noncoding RNAs.

Upregulation of DLEU1 expression by epigenetic modification promotes tumorigenesis in human cancer.

The function of DLEU1 in human cancer is largely unknown. The Cancer Genome Atlas data were applied to identify the landscape of differential genes between tumor tissues and normal tissues, which was further validated by our cohort data and pan-cancer data including 33 cancer types with 11,060 patients. Next, DLEU1 was selected to validate the novel finding and result showed that it promoted tumorigenesis in vitro and in vivo. Mechanistically, DLEU1 promotes SRP4 expression via increasing H3K27ac enrichment to SRP4 locus epigenetically. Moreover, epigenetic modification leads to upregulation of DLEU1 expression via decreased DNA methylation and increased H3K27ac and H3K4me3 histone modification in its locus. Finally, high expression of DLEU1 correlates with worse prognosis not only in specific cancer type patients but also in patients in the pan-cancer cohort. In summary, the work broadens the function landscape of known long noncoding RNAs in human cancer and provides novel insights into their roles in tumorigenesis.

Effects of FGFR1 Gene Polymorphisms on the Risk of Breast Cancer and FGFR1 Protein Expression.

BACKGROUND/AIMS: Fibroblast growth factor receptor 1 (FGFR1) is widely considered to play an important role in mammary carcinogenesis. Some common variants in FGFR1 might be associated with its expression, and further affect breast cancer risk. The aim of this study was to investigate effects of single-nucleotide polymorphisms (SNPs) in FGFR1 on breast cancer susceptibility and FGFR1 protein expression. METHODS: SNPs rs17182023, rs17175624 and rs10958704 in FGFR1 were genotyped in 747 breast cancer cases and 716 healthy controls by SNaPshot method. The associations between SNPs and breast cancer were examined by logistic regression. Immunohistochemistry(IHC) was used to detect FGFR1 protein expression, and the association of FGFR1 polymorphisms with its protein expression was analyzed by Pearson's chi-square test. Additionally, Cox regression and Kaplan-Meier analysis was used to evaluate the association between FGFR1 protein expression and breast cancer prognosis. RESULTS: The minor allele of rs17182023 in FGFR1 was significantly associated with reduced breast cancer risk, with an odds ratio of 0.800 (95%CI = 0.684-0.935). No significant associations were detected between other SNPs and breast cancer. Moreover, rs17182023 was correlated to FGFR1 protein expression (P = 0.006), and patients with high FGFR1 protein expression tended to have poor outcomes. CONCLUSIONS: SNP rs17182023 was correlated to reduced breast cancer risk, and was associated with FGFR1 protein expression. High FGFR1 protein expression was an independent risk factor of breast cancer, and resulted in poor prognosis.

Ethylene is Involved in Brassinosteroids Induced Alternative Respiratory Pathway in Cucumber (Cucumis sativus L.) Seedlings Response to Abiotic Stress.

Effects of brassinosteroids (BRs) on cucumber (Cucumis sativus L.) abiotic stresses resistance to salt, polyethylene glycol (PEG), cold and the potential mechanisms were investigated in this work. Previous reports have indicated that BRs can induce ethylene production and enhance alternative oxidase (AOX) pathway. The mechanisms whether ethylene is involved as a signal molecule which connected BR with AOX in regulating stress tolerance are still unknown. Here, we found that pretreatment with 1 µM brassinolide (BL, the most active BRs) relieved stress-caused oxidative damage in cucumber seedlings and clearly enhanced the capacity of AOX and the ethylene biosynthesis. Furthermore, transcription level of ethylene signaling biosynthesis genes including ripening-related ACC synthase1 (C S ACS1), ripening-related ACC synthase2 (C S ACS2), ripening-related ACC synthase3 (C S ACS3), 1-aminocyclopropane-1-carboxylate oxidase1 (C S ACO1), 1-aminocyclopropane-1-carboxylate oxidase2 (C S ACO2), and C S AOX were increased after BL treatment. Importantly, the application of the salicylhydroxamic acid (SHAM, AOX inhibitor) and ethylene biosynthesis inhibitor aminooxyacetic acid (AOA) decreased plant resistance to environmental stress by blocking BRs-induced alternative respiration. Taken together, our results demonstrated that ethylene was involved in BRs-induced AOX activity which played important roles in abiotic stresses tolerance in cucumber seedlings.