scholarly journals Neoadjuvant Chemotherapy Alters Neuropilin-1, PlGF, and SNAI1 Expression Levels and Predicts Breast Cancer Patients Response

2019 ◽  
Vol 9 ◽  
Author(s):  
Noura Al-Zeheimi ◽  
Adviti Naik ◽  
Charles Saki Bakheit ◽  
Marwa Al Riyami ◽  
Adil Al Ajarrah ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neoadjuvant Chemotherapy ◽  
Cancer Patients ◽  
Breast Cancer Patients ◽  
Expression Levels ◽  
Neuropilin 1

scholarly journals Differential Gene -Based Predictors of Neoadjuvant Chemotherapy Efficacy in Breast Cancer

2021 ◽  
Author(s):  
Mei Lu ◽  
JieYa Zou ◽  
Rong Guo ◽  
XiaoJuan Yang ◽  
Ji Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neoadjuvant Chemotherapy ◽  
Prediction Model ◽  
Cancer Patients ◽  
Residual Disease ◽  
Area Under The Curve ◽  
Complete Response ◽  
Rna Seq ◽  
Breast Cancer Patients ◽  
Expression Levels

Abstract Background and objectiveChemotherapy is the most common treatment in breast cancer , and neoadjuvant chemotherapy (NAC) is wildly used because of it’s efficiency and safety. To identify significantly differentially expressed genes and select the most suitable breast cancer patients for neoadjuvant chemotherapy (NAC) before treatment. MethodsWe collected a total of 60 breast cancer patient samples before and after NAC. All the samples were subjected to high-throughput RNA sequencing (RNA-seq). Then , we identified AHNAK, CIDEA, ADIPOQ, and AKAP12 as candidate genes related to tumour chemotherapeutic resistance. Next, we analysed the expression levels of AHNAK, CIDEA, ADIPOQ, and AKAP12 by logistic regression and based on the result, we constructed a predictive model visualized by a nomogram. ResultsThe RNA-seq results show that AHNAK, CIDEA, ADIPOQ and AKAP12 are upregulated in residual disease after NAC (P<0.05), and compared with the pathological complete response (pCR) group, the non-pCR group presented high AHNAK, CIDEA, ADIPOQ and AKAP12 expression levels (P<0.05). Logistic analysis showed that high AHNAK, CIDEA, ADIPOQ and AKAP12 expression levels significantly reduced the pCR rate of NAC for breast cancer (P<0.05). In addition, our prediction model , which included AHNAK, CIDEA, ADIPOQ and AKAP12 , showed a good fitting effect with the H1 test (χ2=6.3967, P=0.4945) and the receiver operating characteristic (ROC) curve (area under the curve (AUC) 0.8249, 95% CI 0.722–0.9271). ConclusionHigh expression of AHNAK, CIDEA, ADIPOQ and AKAP12 indicates poor treatment response in breast cancer patients treated with NAC . The efficacy prediction model based on these results is expected to be a new method to select the optimal population of breast cancer patients for NAC.

Targeting PKM2 promotes chemosensitivity of breast cancer cells in vitro and in vivo

2021 ◽  
pp. 1-10
Author(s):  
Yu Wang ◽  
Han Zhao ◽  
Ping Zhao ◽  
Xingang Wang
Keyword(s):  
Breast Cancer ◽  
Neoadjuvant Chemotherapy ◽  
Cancer Cells ◽  
Cancer Patients ◽  
Poor Prognosis ◽  
Breast Cancer Cells ◽  
Breast Cancer Patients ◽  
Cancer Tissues

BACKGROUND: Pyruvate kinase M2 (PKM2) was overexpressed in many cancers, and high PKM2 expression was related with poor prognosis and chemoresistance. OBJECTIVE: We investigated the expression of PKM2 in breast cancer and analyzed the relation of PKM2 expression with chemotherapy resistance to the neoadjuvant chemotherapy (NAC). We also investigated whether PKM2 could reverse chemoresistance in breast cancer cells in vitro and in vivo. METHODS: Immunohistochemistry (IHC) was performed in 130 surgical resected breast cancer tissues. 78 core needle biopsies were collected from breast cancer patients before neoadjuvant chemotherapy. The relation of PKM2 expression and multi-drug resistance to NAC was compared. The effect of PKM2 silencing or overexpression on Doxorubicin (DOX) sensitivity in the MCF-7 cells in vitro and in vivo was compared. RESULTS: PKM2 was intensively expressed in breast cancer tissues compared to adjacent normal tissues. In addition, high expression of PKM2 was associated with poor prognosis in breast cancer patients. The NAC patients with high PKM2 expression had short survival. PKM2 was an independent prognostic predictor for surgical resected breast cancer and NAC patients. High PKM2 expression was correlated with neoadjuvant treatment resistance. High PKM2 expression significantly distinguished chemoresistant patients from chemosensitive patients. In vitro and in vivo knockdown of PKM2 expression decreases the resistance to DOX in breast cancer cells in vitro and tumors in vivo. CONCLUSION: PKM2 expression was associated with chemoresistance of breast cancers, and could be used to predict the chemosensitivity. Furthermore, targeting PKM2 could reverse chemoresistance, which provides an effective treatment methods for patients with breast cancer.

scholarly journals Association between the nucleosome footprint of plasma DNA and neoadjuvant chemotherapy response for breast cancer

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Xu Yang ◽  
Geng-Xi Cai ◽  
Bo-Wei Han ◽  
Zhi-Wei Guo ◽  
Ying-Song Wu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Neoadjuvant Chemotherapy ◽  
Cancer Patients ◽  
Cell Receptor ◽  
Chemotherapy Response ◽  
Breast Cancer Patients ◽  
Plasma Dna ◽  
Transcription Start Sites ◽  
Response To Chemotherapy

AbstractGene expression signatures have been used to predict the outcome of chemotherapy for breast cancer. The nucleosome footprint of cell-free DNA (cfDNA) carries gene expression information of the original tissues and thus may be used to predict the response to chemotherapy. Here we carried out the nucleosome positioning on cfDNA from 85 breast cancer patients and 85 healthy individuals and two cancer cell lines T-47D and MDA-MB-231 using low-coverage whole-genome sequencing (LCWGS) method. The patients showed distinct nucleosome footprints at Transcription Start Sites (TSSs) compared with normal donors. In order to identify the footprints of cfDNA corresponding with the responses to neoadjuvant chemotherapy in patients, we mapped on nucleosome positions on cfDNA of patients with different responses: responders (pretreatment, n = 28; post-1 cycle, post-3/4 cycles, and post-8 cycles of treatment, n = 12) and nonresponders (pretreatment, n = 10; post-1 cycle, post-3/4 cycles, and post-8 cycles of treatment, n = 10). The coverage depth near TSSs in plasma cfDNA differed significantly between responders and nonresponders at pretreatment, and also after neoadjuvant chemotherapy treatment cycles. We identified 232 TSSs with differential footprints at pretreatment and 321 after treatment and found enrichment in Gene Ontology terms such as cell growth inhibition, tumor suppressor, necrotic cell death, acute inflammatory response, T cell receptor signaling pathway, and positive regulation of vascular endothelial growth factor production. These results suggest that cfDNA nucleosome footprints may be used to predict the efficacy of neoadjuvant chemotherapy for breast cancer patients and thus may provide help in decision making for individual patients.

scholarly journals Serum Long Non-Coding RNAs PVT1, HOTAIR, and NEAT1 as Potential Biomarkers in Egyptian Women with Breast Cancer

Biomolecules ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 301
Author(s):  
Amal Ahmed Abd El-Fattah ◽  
Nermin Abdel Hamid Sadik ◽  
Olfat Gamil Shaker ◽  
Amal Mohamed Kamal ◽  
Nancy Nabil Shahin
Keyword(s):  
Breast Cancer ◽  
Logistic Regression ◽  
Cancer Patients ◽  
Breast Cancer Diagnosis ◽  
Serum Levels ◽  
Breast Cancer Patients ◽  
Multivariate Logistic Regression ◽  
Expression Levels ◽  
Non Coding Rnas ◽  
Pai 1

Long non-coding RNAs play an important role in tumor growth, angiogenesis, and metastasis in several types of cancer. However, the clinical significance of using lncRNAs as biomarkers for breast cancer diagnosis and prognosis is still poorly investigated. In this study, we analyzed the serum expression levels of lncRNAs PVT1, HOTAIR, NEAT1, and MALAT1, and their associated proteins, PAI-1, and OPN, in breast cancer patients compared to fibroadenoma patients and healthy subjects. Using quantitative real-time PCR (qRT-PCR), we compared the serum expression levels of the four circulating lncRNAs in patients with breast cancer (n = 50), fibroadenoma (n = 25), and healthy controls (n = 25). The serum levels of PAI-1 and OPN were measured using ELISA. Receiveroperating-characteristic (ROC) analysis and multivariate logistic regression were used to evaluate the diagnostic value of the selected parameters. The serum levels of HOTAIR, PAI-1, and OPN were significantly higher in breast cancer patients compared to controls and fibroadenoma patients. The serum level of PVT1 was significantly higher in breast cancer patients than in the controls, while that of NEAT1 was significantly lower in breast cancer patients compared to controls and fibroadenoma patients. Both ROC and multivariate logistic regression analyses revealed that PAI-1 has the greatest power in discriminating breast cancer from the control, whereas HOTAIR, PAI-1, and OPN have the greatest power in discriminating breast cancer from fibroadenoma patients. In conclusion, our data suggest that the serum levels of PVT1, HOTAIR, NEAT1, PAI-1, and OPN could serve as promising diagnostic biomarkers for breast cancer.

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