Identification of differentially expressed genes between triple and non-triple-negative breast cancer using bioinformatics analysis

PTGER3 knockdown inhibits the vulnerability of triple-negative breast cancer to ferroptosis

Prostaglandin E receptor 3 (PTGER3) is involved in a variety of biological processes in the human body and is closely associated with the development and progression of a variety of cancer types. However, the role of PTGER3 in triple-negative breast cancer (TNBC) remains unclear. In the present study, low PTGER3 expression was found to be associated with poor prognosis in TNBC patients. PTGER3 plays a crucial role in regulating TNBC cell invasion, migration, and proliferation. Upregulation of PTGER3 weakens the epithelial-mesenchymal phenotype in TNBC and promotes ferroptosis both in vitro and in vivo by repressing glutathione peroxidase 4 (GPX4) expression. On the other hand, downregulation of PTGER3 inhibits ferroptosis by increasing GPX4 expression and activating the PI3K-AKT pathway. Upregulation of PTGER3 also enhances the sensitivity of TNBC cells to paclitaxel. Overall, this study has elucidated critical pathways in which low PTGER3 expression protects TNBC cells from undergoing ferroptosis, thereby promoting its progression. PTGER3 may thus serve as a novel and promising biomarker and therapeutic target for TNBC.

Hsa_circ_0000851 promotes PDK1/p-AKT-mediated cell proliferation and migration by regulating miR-1183 in triple-negative breast cancer

Breast cancer (BC) is the most common cause of cancer-related mortality in women worldwide. Circular RNAs (circRNAs), a type of non-coding RNA, have garnered interest because of their unique looped structure. In recent years, circRNAs have been shown to be involved in various diseases, including carcinogenesis, and to serve as biomarkers for early risk assessment and survival prediction of different tumour types. This study aimed to identify a novel circRNA, hsa_circ_0000851, generated from the sixth intron of the oncogene TCF4, reported to be involved in BC pathogenesis. Our study showed that hsa_circ_0000851 was mainly located in the cytoplasm of BC cells and upregulated in BC cell lines and tissue samples. Higher hsa_circ_0000851 expression levels resulted in increased proliferation of BC cells both in vitro and in vivo, while treatment of BC cells with hsa_circ_0000851 siRNA decreased their proliferation. We found that hsa_circ_0000851 bound directly to miR-1183, accelerating the expression of its target gene PDK1, which facilities BC cell proliferation and migration through PDK1/p-AKT.

Machine learning assisted analysis of breast cancer gene expression profiles reveals novel potential prognostic biomarkers for triple-negative breast cancer

Tumor heterogeneity and the unclear metastasis mechanisms are the leading cause for the unavailability of effective targeted therapy for Triple-negative breast cancer (TNBC), a breast cancer (BrCa) subtype characterized by high mortality and high frequency of distant metastasis cases. The identification of prognostic biomarker can improve prognosis and personalized treatment regimes. Herein, we collected gene expression datasets representing TNBC and Non-TNBC BrCa. From the complete dataset, a subset reflecting solely known cancer driver genes was also constructed. Recursive Feature Elimination (RFE) was employed to identify top 20, 25, 30, 35, 40, 45, and 50 gene signatures that differentiate TNBC from the other BrCa subtypes. Five machine learning algorithms were employed on these selected features and on the basis of model performance evaluation, it was found that for the complete and driver dataset, XGBoost performs the best for a subset of 25 and 20 genes, respectively. Out of these 45 genes from the two datasets, 34 genes were found to be differentially regulated. The Kaplan-Meier (KM) analysis for Distant Metastasis Free Survival (DMFS) of these 34 differentially regulated genes revealed four genes, out of which two are novel that could be potential prognostic genes (POU2AF1 and S100B). Finally, interactome and pathway enrichment analyses were carried out to investigate the functional role of the identified potential prognostic genes in TNBC. These genes are associated with MAPK, PI3-AkT, Wnt, TGF-β, and other signal transduction pathways, pivotal in metastasis cascade. These gene signatures can provide novel molecular-level insights into metastasis.

ROSIE: RObust Sparse ensemble for outlIEr detection and gene selection in cancer omics data

The extraction of novel information from omics data is a challenging task, in particular, since the number of features (e.g. genes) often far exceeds the number of samples. In such a setting, conventional parameter estimation leads to ill-posed optimization problems, and regularization may be required. In addition, outliers can largely impact classification accuracy.

Here we introduce ROSIE, an ensemble classification approach, which combines three sparse and robust classification methods for outlier detection and feature selection and further performs a bootstrap-based validity check. Outliers of ROSIE are determined by the rank product test using outlier rankings of all three methods, and important features are selected as features commonly selected by all methods.

We apply ROSIE to RNA-Seq data from The Cancer Genome Atlas (TCGA) to classify observations into Triple-Negative Breast Cancer (TNBC) and non-TNBC tissue samples. The pre-processed dataset consists of 16,600 genes and more than 1,000 samples. We demonstrate that ROSIE selects important features and outliers in a robust way. Identified outliers are concordant with the distribution of the commonly selected genes by the three methods, and results are in line with other independent studies. Furthermore, we discuss the association of some of the selected genes with the TNBC subtype in other investigations. In summary, ROSIE constitutes a robust and sparse procedure to identify outliers and important genes through binary classification. Our approach is ad hoc applicable to other datasets, fulfilling the overall goal of simultaneously identifying outliers and candidate disease biomarkers to the targeted in therapy research and personalized medicine frameworks.

MiR-26b-5p inhibits cell proliferation and EMT by targeting MYCBP in triple-negative breast cancer

Background

The study was designed to elucidate the association and functional roles of miR-26b-5p and c-MYC binding protein (MYCBP) in triple-negative breast cancer (TNBC).

Method

Luciferase reporter assay was used to confirm the relationship between miR-26b-5p and MYCBP in TNBC cells. The expression levels of miR-26b-5p and MYCBP in tissue specimens and cell lines were determined using reverse transcription-quantitative PCR. Cell proliferation, migration and invasion were assessed using CCK-8 assay, colony formation and transwell assay.

Results

We first observed that miR-26b-5p directly targets the 3′-UTR of MYCBP to inhibit MYCBP expression in MDA-MB-468 and BT-549 cells. The expression of miR-26b-5p was inversely correlated with MYCBP expression in TNBC tissues. We further demonstrated that MYCBP knockdown suppressed the proliferation, migration and invasion of TNBC cells. Furthermore, MYCBP overexpression counteracted the suppressive effect of miR-26b-5p on TNBC cell behaviors. Western blot analysis demonstrated that the E-cadherin protein level was increased, while protein levels of N-cadherin and vimentin were decreased in cells transfected with miR-26b-5p, which were all reversed by ectopic expression of MYCBP.

Conclusions

In summary, our findings revealed the tumor suppressive role of miR-26b-5p in regulating TNBC cell proliferation and mobility, possibly by targeting MYCBP.

MYBL2-induced PITPNA-AS1 upregulates SIK2 to exert oncogenic function in triple-negative breast cancer through miR-520d-5p and DDX54

Background

In recent years, long non-coding RNAs (lncRNAs) have attracted much attention because of its regulatory role in occurrence and progression of tumors, including triple-negative breast cancer (TNBC). LncRNA PITPNA antisense RNA 1 (PITPNA-AS1) has been explored in some cancers, whereas its function and molecular mechanism in TNBC remain unclear.

Methods

PITPNA-AS1 expression in TNBC tissues and cells was determined by RT-qPCR. TNBC cell viability, proliferation, migration, invasion were assessed with CCK-8, colony formation, wound healing, transwell assays. Cell apoptosis was evaluated by flow cytometry. Expression of EMT-related markers was detected by western blot analyses. The molecular mechanism of PITPNA-AS1 was explored by RNA pull down, luciferase reporter, RIP and ChIP assays.

Results

PITPNA-AS1 showed high expression levels in TNBC tissues and cells. PITPNA-AS1 knockdown suppressed TNBC cell viability, proliferation, migration, invasion in vitro and inhibited xenograft tumor growth in mice. Mechanistically, PITPNA-AS1 upregulated SIK2 expression by sponging miR-520d-5p and recruiting DDX54 protein. Results of rescue assays suggested that the inhibitive effects of silenced PITPNA-AS1 on TNBC cellular processes were partially rescued by overexpressing SIK2 or combination of miR-520d-5p inhibition and DDX54 overexpression. More importantly, we found that the upregulation of PITPNA-AS1 in TNBC cells was attributed to transcription factor MYBL2.

Conclusion

PITPNA-AS1 activated by MYBL2 plays an oncogenic role in TNBC through upregulating SIK2.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-02956-6.

miR-497 inhibits proliferation and invasion in triple-negative breast cancer cells via YAP1

MicroRNA (miR)-497 has been reported as a tumor suppressor in various cancer types. Nonetheless, the regulation of triple-negative breast cancer (TNBC) by miR-497 remains poorly understood. The present study aimed to investigate the potential function and mechanism of miR-497 in TNBC. A total of 36 TNBC and matched non-cancerous tissue samples were collected for analysis. Reverse transcription-quantitative PCR was performed to detect the miR-497 levels in TNBC tissue. The association between miR-497 expression, clinical characteristics and survival was then analyzed. To investigate the role of miR-497 in TNBC, MTT, colony formation, Transwell invasion, cell cycle and cell apoptosis assays were conducted following transfection of miR-497 mimics into the MDA-MB-231 and MDA-MB-468 cell lines. Luciferase reporter assays and western blot analysis were used to confirm the regulation of a putative target of miR-497. The results indicated that the expression of miR-497 was downregulated in the TNBC specimens. Further analysis demonstrated that the expression of miR-497 was downregulated in patients with advanced TNBC stages and that low miR-497 was associated with poor prognosis in patients with TNBC. Transfection of miR-497 mimics inhibited TNBC cell proliferation and increased cell apoptosis in MDA-MB-231 and MDA-MB-468 cells. Moreover, cell migration was inhibited following overexpression of miR-497, which also led to the arrest of the breast cancer cells in the G₀/G₁ phase of the cell cycle. Yes-associated protein 1 (YAP1), a critical molecule in the Hippo pathway, was identified as a target of miR-497. Notably, the protein and mRNA expression levels of YAP1 in MDA-MB-231 and MDA-MB-468 cells were downregulated following overexpression of miR-497. Overall, the findings of the present study indicated that miR-497 inhibited TNBC cell proliferation and migration and induced cell apoptosis by negatively regulating YAP1 expression. Thus, targeting miR-497 may represent a potential strategy for the treatment of TNBC.

STAT3-induced HLA-F-AS1 promotes cell proliferation and stemness characteristics in triple negative breast cancer cells by upregulating TRABD

Breast cancer (BC) is one of the most common malignances and is a leading cause of cancer-related deaths in women globally. Triple negative breast cancer (TNBC) is a common subtype of BC. Emerging evidence has indicated the crucial roles of long noncoding RNAs (lncRNAs) in the tumorigenesis of TNBC. Our aim was to explore the role and regulatory mechanism of lncRNA HLA-F antisense RNA 1 (HLA-F-AS1) in TNBC cells. Cell counting kit-8 (CCK-8) assay, colony formation assay, flow cytometry analysis and western blot analysis were used to measure HLA-F-AS1-mediated cellular behaviors in TNBC. Xenograft tumor assay was applied to assess biological function of HLA-F-AS1 in vivo. Luciferase reporter assay and RNA pull down assay were used to verify the binding ability between molecules. Our findings demonstrated that HLA-F-AS1 expression was significantly upregulated in TNBC tissues and cells, and high level of HLA-F-AS1 indicated the poor prognosis of patients with TNBC. HLA-F-AS1 promoted TNBC progression by facilitating cell proliferation and stemness maintenance and inhibiting cell cycle arrest at G0/G1 stage and apoptosis in vitro as well as inducing tumor growth in vivo. HLA-F-AS1. In addition, signal transducer and activator of transcription 3 (STAT3) transcriptionally induced HLA-F-AS1 upregulation in TNBC cells via interacting with HLA-F-AS1 promoter. Moreover, HLA-F-AS1 acted as the molecular sponge of microRNA 541-3p (miR-541-3p) to elevate TRABD (TraB domain containing) expression in TNBC cells. Rescue experiments confirmed that the decrease of cell proliferation and stemness characteristics under silenced HLA-F-AS1 was rescued by TRABD overexpression in TNBC cells. In conclusion, STAT3-induced HLA-F-AS1 facilitates cell proliferation and stemness characteristics in TNBC by miR-541-3p-dependent upregulation of TRABD, which might provide a potential novel direction for the treatment of TNBC.

Dual relationship between long non-coding RNAs and STAT3 signaling in different cancers: New insight to proliferation and metastasis

Uncontrolled growth and metastasis of cancer cells is an increasing challenge for overcoming cancer, and improving survival of patients. Complicated signaling networks account for proliferation and invasion of cancer cells that need to be elucidated for providing effective cancer therapy, and minimizing their malignancy. Long non-coding RNAs (lncRNAs) are RNA molecules with a length of more than 200 nucleotides. They participate in cellular events, and their dysregulation in a common phenomenon in different cancers. Noteworthy, lncRNAs can regulate different molecular pathways, and signal transducer and activator of transcription 3 (STAT3) is one of them. STAT3 is a tumor-promoting factors in cancers due to its role in cancer proliferation (cell cycle progression and apoptosis inhibition) and metastasis (EMT induction). LncRNAs can function as upstream mediators of STAT3 pathway, reducing/enhancing its expression. This dual relationship is of importance in affecting proliferation and metastasis of cancer cells. The response of cancer cells to therapy such as chemotherapy and radiotherapy is regulated by lncRNA/STAT3 axis. Tumor-promoting lncRNAs including NEAT1, SNHG3 and H19 induces STAT3 expression, while tumor-suppressing lncRNAs such as MEG3, PTCSC3 and NKILA down-regulate STAT3 expression. Noteworthy, upstream mediators of STAT3 such as microRNAs can be regulated by lncRNAs. These complicated signaling networks are mechanistically described in the current review.

Bioinformatics analysis of key genes in triple negative breast cancer and validation of oncogene PLK1

Background

Breast cancer is the most common malignancy in women. Triple-negative breast cancer (TNBC) refers to a special subtype that is deficient in the expression of estrogen (ER), progesterone (PR), and human epidermal growth factor receptor 2 (HER-2). In this study, a variety of bioinformatics analysis tools were used to screen Hub genes related to the occurrence and development of triple negative breast cancer, and their biological functions were analyzed.

Methods

Gene Expression Omnibus (GEO) breast cancer microarray data GSE62931 was selected as the research object. The differentially expressed genes (DEGs) were screened and the protein-protein interaction (PPI) network of DEGs was constructed using bioinformatics tools. The Hub genes were also screened. The Gene Ontology (GO) knowledgebase and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were used for biological enrichment analysis. The Gene Expression Profiling Interactive Analysis (GEPIA) online tool was used to verify the expression of the screened genes and patient survival. The effects of polo-like kinase 1 (PLK1) on the proliferation, invasion, migration, and dryness of breast cancer cells were verified using cell counting kit 8 (CCK-8), transwell migration assays, scratch tests, and clone formation tests. An animal model of subcutaneous xenotransplantation of breast cancer was established to evaluate the effect of PLK1 on the proliferation of breast cancer.

Results

A total of 824 DEGs were screened by GSE62931 microarray data; 405 of which were up-regulated and 419 of which were down-regulated. Functional enrichment analysis showed that these DEGs were mainly enriched in cancer-related pathways and were primarily involved in biological processes (BP) such as cell and mitotic division. From the Hub gene screening, PLK1 was further identified as the Hub gene associated with TNBC. Cell and animal experiments indicated that PLK1 promotes the proliferation, invasion, migration, and clone formation of breast cancer cells.

Conclusions

Gene chip combined with bioinformatics methods can effectively analyze the DEGs related to the occurrence and development of breast cancer, and the screening of PLK1 can provide theoretical guidance for further research on the molecular mechanism of breast cancer and the screening of molecular markers.

Unraveling the Genomic-Epigenomic Interaction Landscape in Triple Negative and Non-Triple Negative Breast Cancer

Background: The recent surge of next generation sequencing of breast cancer genomes has enabled development of comprehensive catalogues of somatic mutations and expanded the molecular classification of subtypes of breast cancer. However, somatic mutations and gene expression data have not been leveraged and integrated with epigenomic data to unravel the genomic-epigenomic interaction landscape of triple negative breast cancer (TNBC) and non-triple negative breast cancer (non-TNBC). Methods: We performed integrative data analysis combining somatic mutation, epigenomic and gene expression data from The Cancer Genome Atlas (TCGA) to unravel the possible oncogenic interactions between genomic and epigenomic variation in TNBC and non-TNBC. We hypothesized that within breast cancers, there are differences in somatic mutation, DNA methylation and gene expression signatures between TNBC and non-TNBC. We further hypothesized that genomic and epigenomic alterations affect gene regulatory networks and signaling pathways driving the two types of breast cancer. Results: The investigation revealed somatic mutated, epigenomic and gene expression signatures unique to TNBC and non-TNBC and signatures distinguishing the two types of breast cancer. In addition, the investigation revealed molecular networks and signaling pathways enriched for somatic mutations and epigenomic changes unique to each type of breast cancer. The most significant pathways for TNBC were: retinal biosynthesis, BAG2, LXR/RXR, EIF2 and P2Y purigenic receptor signaling pathways. The most significant pathways for non-TNBC were: UVB-induced MAPK, PCP, Apelin endothelial, Endoplasmatic reticulum stress and mechanisms of viral exit from host signaling Pathways. Conclusion: The investigation revealed integrated genomic, epigenomic and gene expression signatures and signing pathways unique to TNBC and non-TNBC, and a gene signature distinguishing the two types of breast cancer. The study demonstrates that integrative analysis of multi-omics data is a powerful approach for unravelling the genomic-epigenomic interaction landscape in TNBC and non-TNBC.

KEGG-expressed genes and pathways in triple negative breast cancer: Protocol for a systematic review and data mining

BACKGROUND

The incidence of triple negative breast cancer (TNBC) is at a relatively high level, and our study aimed to identify differentially expressed genes (DEGs) in TNBC and explore the key pathways and genes of TNBC.

METHODS

The gene expression profiling (GSE86945, GSE86946 and GSE102088) data were obtained from Gene Expression Omnibus Datasets, DEGs were identified by using R software, Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of DEGs were performed by the Database for Annotation, Visualization and Integrated Discovery (DAVID) tools, and the protein-protein interaction (PPI) network of the DEGs was constructed by the STRING database and visualized by Cytoscape software. Finally, the survival value of hub DEGs in breast cancer patients were performed by the Kaplan-Meier plotter online tool.

RESULTS

A total of 2998 DEGs were identified between TNBC and health breast tissue, including 411 up-regulated DEGs and 2587 down-regulated DEGs. GO analysis results showed that down-regulated DEGs were enriched in gene expression (BP), extracellular exosome (CC), and nucleic acid binding, and up-regulated were enriched in chromatin assembly (BP), nucleosome (CC), and DNA binding (MF). KEGG pathway results showed that DEGs were mainly enriched in Pathways in cancer and Systemic lupus erythematosus and so on. Top 10 hub genes were picked out from PPI network by connective degree, and 7 of top 10 hub genes were significantly related with adverse overall survival in breast cancer patients (P < .05). Further analysis found that only EGFR had a significant association with the prognosis of triple-negative breast cancer (P < .05).

CONCLUSIONS

Our study showed that DEGs were enriched in pathways in cancer, top 10 DEGs belong to up-regulated DEGs, and 7 gene connected with poor prognosis in breast cancer, including HSP90AA1, SRC, HSPA8, ESR1, ACTB, PPP2CA, and RPL4. These can provide some guidance for our research on the diagnosis and prognosis of TNBC, and further research is needed to evaluate their value in the targeted therapy of TNBC.

Long noncoding RNA HOST2, working as a competitive endogenous RNA, promotes STAT3-mediated cell proliferation and migration via decoying of let-7b in triple-negative breast cancer

Background

Human ovarian cancer specific transcript 2 (HOST2) is a long non-coding RNA (lncRNA) reported to be specifically high expressed in human ovarian cancer. However, the mechanism that how HOST2 regulates triple negative breast cancer (TNBC) need to be explored.

Methods

In this study, expression of HOST2 was determined in 40 TNBC patients and matched non-cancerous tissues by qRT-PCR and in situ hybridization (ISH) assay. The biological functions of HOST2 was measured by losing features. The effect of HOST2 on viability, proliferation and migration was evaluated by MTT, colony formation assay, EDU analysis, transwell invasion assay and nude mouse xenograft model. Fluorescence in situ hybridization (FISH), Luciferase report assay, RNA immunoprecipitation (RIP) assay and Western blot were fulfilled to measure molecular mechanisms.

Results

The results showed that HOST2 was up-regulated in BC tissues and cell lines. Clinical outcome analysis demonstrated that high expression of HOST2 was associated with poor prognosis of TNBC patients. Functional experiments illustrated that knockdown of HOST2 significantly suppressed TNBC cell proliferation and migration. Western blot assays, qRT-PCR assays, RIP assays and luciferase reporter assays revealed that HOST2 regulated STAT3 via crosstalk with let-7b. Depression of HOST2 suppressed STAT3-mediated proliferation and migration in TNBC cells. HOST2 could function as a decoy of let-7b to depress expression of STAT3.

Conclusions

HOST2 could function as a oncogene and promoted STAT3-mediated proliferation and migration through acting as a competing endogenous RNA, which might act as a potential biomarker for TNBC patients.

Differentially expressed genes and key molecules of BRCA1/2-mutant breast cancer: evidence from bioinformatics analyses

Background

BRCA1 and BRCA2 genes are currently proven to be closely related to high lifetime risks of breast cancer. To date, the closely related genes to BRCA1/2 mutations in breast cancer remains to be fully elucidated. This study aims to identify the gene expression profiles and interaction networks influenced by BRCA1/2 mutations, so as to reflect underlying disease mechanisms and provide new biomarkers for breast cancer diagnosis or prognosis.

Methods

Gene expression profiles from The Cancer Genome Atlas (TCGA) database were downloaded and combined with cBioPortal website to identify exact breast cancer patients with BRCA1/2 mutations. Gene set enrichment analysis (GSEA) was used to analyze some enriched pathways and biological processes associated BRCA mutations. For BRCA1/2-mutant breast cancer, wild-type breast cancer and corresponding normal tissues, three independent differentially expressed genes (DEGs) analysis were performed to validate potential hub genes with each other. Protein-protein interaction (PPI) networks, survival analysis and diagnostic value assessment helped identify key genes associated with BRCA1/2 mutations.

Results

The regulation process of cell cycle was significantly enriched in mutant group compared with wild-type group. A total of 294 genes were identified after analysis of DEGs between mutant patients and wild-type patients. Interestingly, by the other two comparisons, we identified 43 overlapping genes that not only significantly expressed in wild-type breast cancer patients relative to normal tissues, but more significantly expressed in BRCA1/2-mutant breast patients. Based on the STRING database and cytoscape software, we constructed a PPI network using 294 DEGs. Through topological analysis scores of the PPI network and 43 overlapping genes, we sought to select some genes, thereby using survival analysis and diagnostic value assessment to identify key genes pertaining to BRCA1/2-mutant breast cancer. CCNE1, NPBWR1, A2ML1, EXO1 and TTK displayed good prognostic/diagnostic value for breast cancer and BRCA1/2-mutant breast cancer.

Conclusion

Our research provides comprehensive and new insights for the identification of biomarkers connected with BRCA mutations, availing diagnosis and treatment of breast cancer and BRCA1/2-mutant breast cancer patients.