Monocarboxylate transporters in breast cancer and adipose tissue are novel biomarkers and potential therapeutic targets

Abstract BackgroundMitochondria-nuclear cross talk and mitochondrial retrograde regulation are involved in the genesis and development of breast cancer (BC). Therefore, mitochondria can be regarded as a promising target for BC therapeutic strategies. In the present study, we aimed to construct regulating network and seek the potential biomarkers of BC diagnosis, prognosis and also the molecular therapeutic targets from the perspective of mitochondrial dysfunction. MethodsThe microarray data of mitochondria-related encoding genes of BC were downloaded from GEO including GSE128610 and GSE72319. GSE128610 was treated as test set and validation sets consisted of GSE72319 and TCGA, which were used for identifying mitochondria-related differential expressed genes (mrDEGs). We performed enrichment analysis, PPI network, hub mrDEGs, and overall survival analysis and constructed transcription factor (TF)-miRNA-hub mrDEGs network. ResultsA total of 23 up-regulated and 71 down-regulated mrDEGs were identified and validated. Enrichment analyses indicated that mrDEGs were associated with several cancer-related biological processes, Moreover, 9 hub mrDEGs were identified and validated in tissues. Finally, 5 hub coregulated mrDEGs, 21 miRNA and 117 TF were used to construct TF-miRNA-hub mrDEGs network. MAZ, HDGF and SP2 could regulate 3 hub mrDEGs. hsa-mir-21-5p, hsa-mir-1-3p, hsa-mir-218-5p, hsa-mir-26a-5p, and hsa-mir-335-5p regulated 2 hub mrDEGs. Overall survival analysis suggested that the up-regulated FN1 and down-regulated DDR2 conferred to poor BC prognosis. ConclusionTF-miRNA-hub mrDEGs has instruction significance for the etiology exploration of BC. The identified hub mrDEGs, such as FN1 and DDR2, were likely to regulate mitochondrial function and might be novel biomarkers of BC diagnosis and prognosis as well as the therapeutic targets.

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Unbiased Lipidomic Profiling of Triple-Negative Breast Cancer Tissues Reveals the Association of Sphingomyelin Levels with Patient Disease-Free Survival

Metabolites ◽

10.3390/metabo8030041 ◽

2018 ◽

Vol 8 (3) ◽

pp. 41 ◽

Cited By ~ 8

Author(s):

Preeti Purwaha ◽

Franklin Gu ◽

Danthasinghe Piyarathna ◽

Theckelnaycke Rajendiran ◽

Anindita Ravindran ◽

...

Keyword(s):

Breast Cancer ◽

Cancer Progression ◽

Triple Negative ◽

Therapeutic Targets ◽

Disease Free Survival ◽

Sphingolipid Metabolism ◽

Free Survival ◽

Cancer Aggressiveness ◽

Novel Biomarkers ◽

Disease Free

The reprogramming of lipid metabolism is a hallmark of many cancers that has been shown to promote breast cancer progression. While several lipid signatures associated with breast cancer aggressiveness have been identified, a comprehensive lipidomic analysis specifically targeting the triple-negative subtype of breast cancer (TNBC) may be required to identify novel biomarkers and therapeutic targets for this most aggressive subtype of breast cancer that still lacks effective therapies. In this current study, our global LC-MS-based lipidomics platform was able to measure 684 named lipids across 15 lipid classes in 70 TNBC tumors. Multivariate survival analysis found that higher levels of sphingomyelins were significantly associated with better disease-free survival in TNBC patients. Furthermore, analysis of publicly available gene expression datasets identified that decreased production of ceramides and increased accumulation of sphingoid base intermediates by metabolic enzymes were associated with better survival outcomes in TNBC patients. Our LC-MS lipidomics profiling of TNBC tumors has, for the first time, identified sphingomyelins as a potential prognostic marker and implicated enzymes involved in sphingolipid metabolism as candidate therapeutic targets that warrant further investigation.

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A Novel Mouse Model for Genetic Validation of Therapeutic Targets in Breast Cancer

10.21236/ada462839 ◽

2006 ◽

Author(s):

Gerardi Evans

Keyword(s):

Breast Cancer ◽

Mouse Model ◽

Therapeutic Targets

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MicroRNAs as therapeutic targets in breast cancer metastasis

Drug Delivery and Translational Research ◽

10.1007/s13346-021-00999-2 ◽

2021 ◽

Author(s):

Fahima Danesh Pouya ◽

Yousef Rasmi ◽

Maria Gazouli ◽

Eleni Zografos ◽

Mohadeseh Nemati

Keyword(s):

Breast Cancer ◽

Cancer Metastasis ◽

Therapeutic Targets ◽

Breast Cancer Metastasis

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Genetic co-expression networks contribute to creating predictive model and exploring novel biomarkers for the prognosis of breast cancer

Scientific Reports ◽

10.1038/s41598-021-84995-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Chi-Ming Chu ◽

Huan-Ming Hsu ◽

Chi-Wen Chang ◽

Yuan-Kuei Li ◽

Yu-Jia Chang ◽

...

Keyword(s):

Breast Cancer ◽

Prediction Models ◽

Reference Model ◽

External Validation ◽

Risk Scores ◽

Consistent Finding ◽

Hazard Ratios ◽

Genetic Predictors ◽

Novel Biomarkers ◽

Cox Proportional Hazard Regression

AbstractGenetic co-expression network (GCN) analysis augments the understanding of breast cancer (BC). We aimed to propose GCN-based modeling for BC relapse-free survival (RFS) prediction and to discover novel biomarkers. We used GCN and Cox proportional hazard regression to create various prediction models using mRNA microarray of 920 tumors and conduct external validation using independent data of 1056 tumors. GCNs of 34 identified candidate genes were plotted in various sizes. Compared to the reference model, the genetic predictors selected from bigger GCNs composed better prediction models. The prediction accuracy and AUC of 3 ~ 15-year RFS are 71.0–81.4% and 74.6–78% respectively (rfm, ACC 63.2–65.5%, AUC 61.9–74.9%). The hazard ratios of risk scores of developing relapse ranged from 1.89 ~ 3.32 (p < 10–8) over all models under the control of the node status. External validation showed the consistent finding. We found top 12 co-expressed genes are relative new or novel biomarkers that have not been explored in BC prognosis or other cancers until this decade. GCN-based modeling creates better prediction models and facilitates novel genes exploration on BC prognosis.

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DNA Methylation Based Molecular Subtypes Predict Prognosis in Breast Cancer Patients

Cancer Control ◽

10.1177/1073274820988519 ◽

2021 ◽

Vol 28 ◽

pp. 107327482098851

Author(s):

Zeng-Hong Wu ◽

Yun Tang ◽

Yan Zhou

Keyword(s):

Breast Cancer ◽

Dna Methylation ◽

Molecular Subtypes ◽

Methylation Status ◽

The Cancer Genome Atlas ◽

Training Dataset ◽

Consensus Clustering ◽

Breast Cancer Patients ◽

Cancer Subtypes ◽

Novel Biomarkers

Background: Epigenetic changes are tightly linked to tumorigenesis development and malignant transformation’ However, DNA methylation occurs earlier and is constant during tumorigenesis. It plays an important role in controlling gene expression in cancer cells. Methods: In this study, we determining the prognostic value of molecular subtypes based on DNA methylation status in breast cancer samples obtained from The Cancer Genome Atlas database (TCGA). Results: Seven clusters and 204 corresponding promoter genes were identified based on consensus clustering using 166 CpG sites that significantly influenced survival outcomes. The overall survival (OS) analysis showed a significant prognostic difference among the 7 groups (p<0.05). Finally, a prognostic model was used to estimate the results of patients on the testing set based on the classification findings of a training dataset DNA methylation subgroups. Conclusions: The model was found to be important in the identification of novel biomarkers and could be of help to patients with different breast cancer subtypes when predicting prognosis, clinical diagnosis and management.

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Recent Discoveries of Macromolecule- and Cell-Based Biomarkers and Therapeutic Implications in Breast Cancer

International Journal of Molecular Sciences ◽

10.3390/ijms22020636 ◽

2021 ◽

Vol 22 (2) ◽

pp. 636

Author(s):

Hsing-Ju Wu ◽

Pei-Yi Chu

Keyword(s):

Breast Cancer ◽

Normal Breast ◽

New Drugs ◽

Cancer Type ◽

Luminal A ◽

Luminal B ◽

Therapeutic Implications ◽

Whole Cells ◽

Diagnosis And Prognosis ◽

Novel Biomarkers

Breast cancer is the most commonly diagnosed cancer type and the leading cause of cancer-related mortality in women worldwide. Breast cancer is fairly heterogeneous and reveals six molecular subtypes: luminal A, luminal B, HER2+, basal-like subtype (ER−, PR−, and HER2−), normal breast-like, and claudin-low. Breast cancer screening and early diagnosis play critical roles in improving therapeutic outcomes and prognosis. Mammography is currently the main commercially available detection method for breast cancer; however, it has numerous limitations. Therefore, reliable noninvasive diagnostic and prognostic biomarkers are required. Biomarkers used in cancer range from macromolecules, such as DNA, RNA, and proteins, to whole cells. Biomarkers for cancer risk, diagnosis, proliferation, metastasis, drug resistance, and prognosis have been identified in breast cancer. In addition, there is currently a greater demand for personalized or precise treatments; moreover, the identification of novel biomarkers to further the development of new drugs is urgently needed. In this review, we summarize and focus on the recent discoveries of promising macromolecules and cell-based biomarkers for the diagnosis and prognosis of breast cancer and provide implications for therapeutic strategies.

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Cellular and Molecular Players in the Interplay between Adipose Tissue and Breast Cancer

International Journal of Molecular Sciences ◽

10.3390/ijms22031359 ◽

2021 ◽

Vol 22 (3) ◽

pp. 1359

Author(s):

Francesca Reggiani ◽

Paolo Falvo ◽

Francesco Bertolini

Keyword(s):

Breast Cancer ◽

Adipose Tissue ◽

Metabolic Disorders ◽

Current Knowledge ◽

Therapeutic Interventions ◽

Preclinical Research ◽

The World ◽

Adipose Cells

The incidence and severity of obesity are rising in most of the world. In addition to metabolic disorders, obesity is associated with an increase in the incidence and severity of a variety of types of cancer, including breast cancer (BC). The bidirectional interaction between BC and adipose cells has been deeply investigated, although the molecular and cellular players involved in these mechanisms are far from being fully elucidated. Here, we review the current knowledge on these interactions and describe how preclinical research might be used to clarify the effects of obesity over BC progression and morbidity, with particular attention paid to promising therapeutic interventions.

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