scholarly journals Construction and validation of a 6-gene nomogram discriminating lung metastasis risk of breast cancer patients

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0244693
Author(s):  
Lingchen Wang ◽  
Wenhua Wang ◽  
Shaopeng Zeng ◽  
Huilie Zheng ◽  
Quqin Lu
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Lung Metastasis ◽  
Cancer Metastasis ◽  
External Validation ◽  
Breast Cancer Patients ◽  
Training Set ◽  
Validation Set ◽  
Robust Likelihood ◽  
Six Genes

Breast cancer is the most common malignant disease in women. Metastasis is the foremost cause of death. Breast tumor cells have a proclivity to metastasize to specific organs. The lung is one of the most common sites of breast cancer metastasis. Therefore, we aimed to build a useful and convenient prediction tool based on several genes that may affect lung metastasis-free survival (LMFS). We preliminarily identified 319 genes associated with lung metastasis in the training set GSE5327 (n = 58). Enrichment analysis of GO functions and KEGG pathways was conducted based on these genes. The best genes for modeling were selected using a robust likelihood-based survival modeling approach: GOLGB1, TMEM158, CXCL8, MCM5, HIF1AN, and TSPAN31. A prognostic nomogram for predicting lung metastasis in breast cancer was developed based on these six genes. The effectiveness of the nomogram was evaluated in the training set GSE5327 and the validation set GSE2603. Both the internal validation and the external validation manifested the effectiveness of our 6-gene prognostic nomogram in predicting the lung metastasis risk of breast cancer patients. On the other hand, in the validation set GSE2603, we found that neither the six genes in the nomogram nor the risk predicted by the nomogram were associated with bone metastasis of breast cancer, preliminarily suggesting that these genes and nomogram were specifically associated with lung metastasis of breast cancer. What’s more, five genes in the nomogram were significantly differentially expressed between breast cancer and normal breast tissues in the TIMER database. In conclusion, we constructed a new and convenient prediction model based on 6 genes that showed practical value in predicting the lung metastasis risk for clinical breast cancer patients. In addition, some of these genes could be treated as potential metastasis biomarkers for antimetastatic therapy in breast cancer. The evolution of this nomogram will provide a good reference for the prediction of tumor metastasis to other specific organs.

scholarly journals A prediction model using 2-propanol and 2-butanone in urine distinguishes breast cancer

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shoko Kure ◽  
Sera Satoi ◽  
Toshihiko Kitayama ◽  
Yuta Nagase ◽  
Nobuo Nakano ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Volatile Organic Compounds ◽  
Cancer Patients ◽  
Organic Compounds ◽  
External Validation ◽  
Healthy Controls ◽  
Breast Cancer Patients ◽  
Training Set ◽  
Volatile Organic ◽  
Validation Set

AbstractSafe and noninvasive methods for breast cancer screening with improved accuracy are urgently needed. Volatile organic compounds (VOCs) in biological samples such as breath and blood have been investigated as noninvasive novel markers of cancer. We investigated volatile organic compounds in urine to assess their potential for the detection of breast cancer. One hundred and ten women with biopsy-proven breast cancer and 177 healthy volunteers were enrolled. The subjects were divided into two groups: a training set and an external validation set. Urine samples were collected and analyzed by gas chromatography and mass spectrometry. A predictive model was constructed by multivariate analysis, and the sensitivity and specificity of the model were confirmed using both a training set and an external set with reproducibility tests. The training set included 60 breast cancer patients (age 34–88 years, mean 60.3) and 60 healthy controls (age 34–81 years, mean 58.7). The external validation set included 50 breast cancer patients (age 35–85 years, mean 58.8) and 117 healthy controls (age 18–84 years, mean 51.2). One hundred and ninety-one compounds detected in at least 80% of the samples from the training set were used for further analysis. The predictive model that best-detected breast cancer at various clinical stages was constructed using a combination of two of the compounds, 2-propanol and 2-butanone. The sensitivity and specificity in the training set were 93.3% and 83.3%, respectively. Triplicated reproducibility tests were performed by randomly choosing ten samples from each group, and the results showed a matching rate of 100% for the breast cancer patient group and 90% for the healthy control group. Our prediction model using two VOCs is a useful complement to the current diagnostic tools. Further studies inclusive of benign tumors and non-breast malignancies are warranted.

scholarly journals A Novel Systematic Oxidative Stress Score Predicts the Prognosis of Patients with Operable Breast Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Kaiming Zhang ◽  
Liqin Ping ◽  
Tian Du ◽  
Yan Wang ◽  
Ya Sun ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Oxidative Stress ◽  
Cancer Patients ◽  
Operable Breast Cancer ◽  
Breast Cancer Patients ◽  
Training Set ◽  
Oxidative Stress Biomarkers ◽  
Stress Biomarkers ◽  
Validation Set ◽  
The Relationship

Background. Breast cancer was associated with imbalance between oxidation and antioxidation. Local oxidative stress in tumors is closely related to the occurrence and development of breast cancer. However, the relationship between systematic oxidative stress and breast cancer remains unclear. This study is aimed at exploring the prognostic value of systematic oxidative stress in patients with operable breast cancer. Methods. A total of 1583 operable female breast cancer patients were randomly assigned into the training set and validation set. The relationship between systematic oxidative stress biomarkers and prognosis were analyzed in the training and validation sets. Results. The systematic oxidative stress score (SOS) was established based on five systematic oxidative stress biomarkers including serum creatinine (CRE), serum albumin (ALB), total bilirubin (TBIL), lactate dehydrogenase (LDH), and blood urea nitrogen (BUN). SOS was an independent prognostic factor for operable breast cancer patients. A nomogram based on SOS and clinical characteristics could accurately predict the prognosis of operable breast cancer patients, and the area under the curve (AUC) of the nomogram was 0.823 in the training set and 0.872 in the validation set, which was much higher than the traditional prognostic indicators. Conclusions. SOS is an independent prognostic indicator for operable breast cancer patients. A prediction model based on SOS could accurately predict the outcome of operable breast cancer patients.

scholarly journals Identification of a 6-gene signature for the survival prediction of breast cancer patients based on integrated multi-omics data analysis

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0241924
Author(s):  
Wenju Mo ◽  
Yuqin Ding ◽  
Shuai Zhao ◽  
Dehong Zou ◽  
Xiaowen Ding
Keyword(s):  
Breast Cancer ◽  
Feature Selection ◽  
Cancer Patients ◽  
Copy Number ◽  
Gene Signature ◽  
Breast Cancer Patients ◽  
Training Set ◽  
Test Set ◽  
Genomic Variants ◽  
Validation Set

Purpose To identify a gene signature for the prognosis of breast cancer using high-throughput analysis. Methods RNASeq, single nucleotide polymorphism (SNP), copy number variation (CNV) data and clinical follow-up information were downloaded from The Cancer Genome Atlas (TCGA), and randomly divided into training set or verification set. Genes related to breast cancer prognosis and differentially expressed genes (DEGs) with CNV or SNP were screened from training set, then integrated together for feature selection of identify robust biomarkers using RandomForest. Finally, a gene-related prognostic model was established and its performance was verified in TCGA test set, Gene Expression Omnibus (GEO) validation set and breast cancer subtypes. Results A total of 2287 prognosis-related genes, 131 genes with amplified copy numbers, 724 gens with copy number deletions, and 280 genes with significant mutations screened from Genomic Variants were closely correlated with the development of breast cancer. A total of 120 candidate genes were obtained by integrating genes from Genomic Variants and those related to prognosis, then 6 characteristic genes (CD24, PRRG1, IQSEC3, MRGPRX, RCC2, and CASP8) were top-ranked by RandomForest for feature selection, noticeably, several of these have been previously reported to be associated with the progression of breast cancer. Cox regression analysis was performed to establish a 6-gene signature, which can stratify the risk of samples from training set, test set and external validation set, moreover, the five-year survival AUC of the model in the training set and validation set was both higher than 0.65. Thus, the 6-gene signature developed in the current study could serve as an independent prognostic factor for breast cancer patients. Conclusion This study constructed a 6-gene signature as a novel prognostic marker for predicting the survival of breast cancer patients, providing new diagnostic/prognostic biomarkers and therapeutic targets for breast cancer patients.

scholarly journals Mitochondrial Malic Enzyme 2 Promotes Breast Cancer Metastasis via Stabilizing HIF-1α Under Hypoxia

2021 ◽  
Author(s):  
Duo You ◽  
Danfeng Du ◽  
Xueke Zhao ◽  
Xinmin Li ◽  
Minfeng Ying ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Cells ◽  
Cancer Patients ◽  
Breast Cancer Cells ◽  
Cancer Metastasis ◽  
Breast Cancer Metastasis ◽  
High Expression ◽  
Breast Cancer Patients ◽  
Cancer Model ◽  
Breast Cancer Model

Abstract Background: α-ketoglutarate (α-KG) is the substrate to hydoxylate collagen and hypoxia-inducible factor-1α (HIF-1α), which are important for cancer metastasis. Previous studies showed that upregulation of collagen prolyl 4-hydroxylase in breast cancer cells stabilizes HIF-1α via depleting α-KG in breast cancer cells. We propose that mitochondrial malate enzyme 2 (ME2) may also affect HIF-1α via modulating α-KG level in breast cancer cells. Methods: ME2 protein expression was evaluated by immunohistochemistry on 100 breast cancer patients and correlated with clinicopathological indicators. The effect of ME2 knockout on cancer metastasis was evaluated by an orthotopic breast cancer model. The effect of ME2 knockout or knockdown on the levels of α-KG and HIF-1α protein in breast cancer cell lines (4T1 and MDA-MB-231) was determined in vitro and in vivo.Results: The high expression of ME2 was observed in the human breast cancerous tissues compared to the matched precancerous tissues (P=0.000). The breast cancer patients with a high expression of ME2 had an inferior survival than the patients with low expression of ME2 (P=0.019). ME2 high expression in breast cancer tissues was also related with lymph node metastasis (P=0.016), pathological staging (P=0.033) and vascular cancer embolus (P=0.014). In a 4T1 orthotopic breast cancer model, ME2 knockout significantly inhibited lung metastasis. In the tumors formed by ME2 knockout 4T1 cells, α-KG level significantly increased, collagen hydroxylation level did not change significantly, but HIF-1α protein level significantly decreased, in comparison to control. In cell culture, ME2 knockout or knockdown cells demonstrated a significantly higher α-KG level but significantly lower HIF-1α protein level than control cells under hypoxia. Exogenous malate and α-KG exerted similar effect on HIF-1α in breast cancer cells to ME2 knockout or knockdown. Treatment with malate significantly decreased 4T1 breast cancer lung metastasis. ME2 expression was associated with HIF-1α level in human breast cancer samples (P=0.027).Conclusion: We provide evidence that upregulation of ME2 is associated with a poor prognosis of breast cancer patients and propose a mechanistic understanding of a link between ME2 and breast cancer metastasis.

scholarly journals Relationship between platelet-lymphocyte ratio and tumour infiltrating lymphocyte on metastatic in breast cancer patient

2019 ◽  
Vol 7 (6) ◽  
pp. 2045
Author(s):  
Indro Wibowo Sejati ◽  
Ida Bagus Tjakra Wibawa Manuaba ◽  
Putu Anda Tusta ◽  
Gede Budhi Setiawan
Keyword(s):  
Breast Cancer ◽  
Metastatic Breast Cancer ◽  
Cancer Patients ◽  
Cancer Metastasis ◽  
Breast Cancer Subtype ◽  
Metastatic Breast ◽  
Breast Cancer Patients ◽  
Sample Group ◽  
Lymphocyte Ratio ◽  
Platelet Lymphocyte Ratio

Background: Platelet-lymphocyte ratio (PLR) is known associated with the prognosis of distant metastatic breast cancer. Tumor-infiltrating lymphocyte (TIL) in breast cancer also associated with the prognosis of distant metastatic breast cancer. In this study, we will examine the relationship between PLR and TIL, in association with the metastatic incidence in breast cancer.Methods: This research is a retrospective, analytic, cross-sectional study. Data was taken from medical records of breast cancer patients at Sanglah general hospital. Samples were taken by nested sampling by selecting all breast cancer patients from the period of January 1st, 2017, to December 31st, 2018, which had complete medical record data, with total sample 211. The PLR and TIL were calculated and analyzed in relation to metastasis incidence of breast cancer.Results: The sample characteristics were sorted by age, education, occupation, the area of origin, menstrual status, breast cancer staging, breast cancer subtype, TIL levels, lymphovascular invasion (LVI) status, metastatic status, and breast cancer grading. The data were analyzed to know the association of PLR, TIL, confounding factors in relation to metastatic incidences. In the sample group with PLR ≥ 156 10µ /µL, there were 22.9% cases of metastases (p = 0.002). The sample group at low TIL had metastatic event 12.5% with (p=0.442).Conclusions: PLR was associated with higher metastasis in breast cancer patients and low TIL had no association with breast cancer metastasis.

scholarly journals Clinical Impact of Sphingosine-1-Phosphate in Breast Cancer

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Junko Tsuchida ◽  
Masayuki Nagahashi ◽  
Kazuaki Takabe ◽  
Toshifumi Wakai
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Cancer Metastasis ◽  
Clinical Importance ◽  
Breast Cancer Metastasis ◽  
Lipid Mediator ◽  
Breast Cancer Patients ◽  
Cellular Functions ◽  
Sphingosine 1 Phosphate ◽  
Underlying Mechanisms

Breast cancer metastasizes to lymph nodes or other organs, which determine the prognosis of patients. It is difficult to cure the breast cancer patients with distant metastasis due to resistance to drug therapies. Elucidating the underlying mechanisms of breast cancer metastasis and drug resistance is expected to provide new therapeutic targets. Sphingosine-1-phosphate (S1P) is a pleiotropic, bioactive lipid mediator that regulates many cellular functions, including proliferation, migration, survival, angiogenesis/lymphangiogenesis, and immune responses. S1P is formed in cells by sphingosine kinases and released from them, which acts in an autocrine, paracrine, and/or endocrine manner. S1P in extracellular space, such as interstitial fluid, interacts with components in the tumor microenvironment, which may be important for metastasis. Importantly, recent translational research has demonstrated an association between S1P levels in breast cancer patients and clinical outcomes, highlighting the clinical importance of S1P in breast cancer. We suggest that S1P is one of the key molecules to overcome the resistance to the drug therapies, such as hormonal therapy, anti-HER2 therapy, or chemotherapy, all of which are crucial aspects of a breast cancer treatment.

Serum ANGPTL2 Levels Reflect Clinical Features of Breast Cancer Patients: Implications for the Pathogenesis of Breast Cancer Metastasis

2014 ◽  
Vol 29 (3) ◽  
pp. 239-245 ◽  
Author(s):  
Motoyoshi Endo ◽  
Yutaka Yamamoto ◽  
Masahiro Nakano ◽  
Tetsuro Masuda ◽  
Haruki Odagiri ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Clinical Features ◽  
Cancer Progression ◽  
Breast Cancer Cells ◽  
Cancer Metastasis ◽  
Ductal Carcinoma ◽  
Breast Cancer Metastasis ◽  
Breast Cancer Patients

Introduction Breast cancer is a leading cause of cancer-related death in women worldwide, and its metastasis is a major cause of disease mortality. Therefore, identification of the mechanisms underlying breast cancer metastasis is crucial for the development of therapeutic and diagnostic strategies. Our recent study of immunodeficient female mice transplanted with MDA-MB231 breast cancer cells demonstrated that tumor cell-derived angiopoietin-like protein 2 (ANGPTL2) accelerates metastasis through both increasing tumor cell migration in an autocrine/paracrine manner, and enhancing tumor angiogenesis. To determine whether ANGPTL2 contributes to its clinical pathogenesis, we asked whether serum ANGPTL2 levels reflect the clinical features of breast cancer progression. Methods We monitored the levels of secreted ANGPTL2 in supernatants of cultured proliferating MDA-MB231 cells. We also determined whether the circulating ANGPTL2 levels were positively correlated with cancer progression in an in vivo breast cancer xenograft model using MDA-MB231 cells. Finally, we investigated whether serum ANGPTL2 levels were associated with clinical features in breast cancer patients. Results Both in vitro and in vivo experiments showed that the levels of ANGPTL2 secreted from breast cancer cells increased with cell proliferation and cancer progression. Serum ANGPTL2 levels in patients with metastatic breast cancer were significantly higher than those in healthy subjects or in patients with ductal carcinoma in situ or non-metastatic invasive ductal carcinoma. Serum ANGPTL2 levels in patients negative for estrogen receptors and progesterone receptors, particularly triple-negative cases, reflected histological grades. Conclusions These findings suggest that serum ANGPTL2 levels in breast cancer patients could represent a potential marker of breast cancer metastasis.

Blood-Borne Cancer Cells – Quo Vadis?

2000 ◽  
Vol 15 (1) ◽  
pp. 111-113 ◽  
Author(s):  
B. Brandt ◽  
H. Schmitt ◽  
J.C. Feldner ◽  
R.J. Lellé ◽  
A. Semjonow ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Cells ◽  
Cancer Patients ◽  
Peripheral Blood ◽  
Cancer Metastasis ◽  
Specific Antigen ◽  
Clinical Staging ◽  
Breast Cancer Patients ◽  
Cell Clusters ◽  
The Impact

The detection of blood-borne cancer cells may help in clinical staging and further understanding of cancer metastasis. We developed a cytokeratin-based immunomagnetic method to isolate epithelium-derived cells from the circulating blood of patients. The number of cell clusters positive for cytokeratin/prostate-specific antigen (PSA) from the peripheral blood of prostate cancer patients and cytokeratin/p185c-erbB-2 from the peripheral blood of breast cancer patients has been related to stage of the disease. Breast cancer patients who presented cytokeratin/p185c-erbB-2-positive cell clusters showed a decrease in such cells under adriamycin adjuvant therapy with Further molecular characterization by a highly sensitive microsatellite multiplex-PCR enabled reproducible detection of microsatellite alterations. The impact of these individually targeted results may contribute to an individual diagnostic and therapeutic strategy.

The predictive potential of the spatial signature of lymphocytes in breast cancer patients.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 530-530
Author(s):  
Nora Balint-Lahat ◽  
Chen Mayer ◽  
Noa Ben-Baruch ◽  
Ady Yosepovich ◽  
Kira Sacks ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Early Stage ◽  
Principal Component ◽  
Treatment Decision ◽  
High Ratio ◽  
Luminal Breast Cancer ◽  
Breast Cancers ◽  
Breast Cancer Patients ◽  
Validation Set

530 Background: Tumor-infiltrating lymphocytes in breast cancer have emerged as both a prognostic and a potentially predictive immunotherapy biomarker. Advancements in artificial intelligence can extract pathology-based spatial immune fingerprints for use as treatment decision support tools. Methods: We examined 908 primary breast cancer patients with whole slide images (WSI) available from TCGA database. Digital structuring of WSIs included automated detection of lymphocytes, tumor and tumor adjacent stroma, using deep learning-based semantic segmentation. Prognosis was defined as progression free interval (PFI). A Cox Survival analysis was used to detect prognostic spatial features. We used principal component analysis (PCA) to reduce and decorrelate significant features. The resulting PCA features were used to fit the final model. The model was then validated on an independent database of WSI of breast lumpectomies, from two tertiary hospitals in Israel. Results: The analysis included 908 WSI. The average age was 58.4 years old, with a majority of early stage breast cancer (76.7%, stage I and II). The detection performance for tumor area and lymphocytes reached F1 scores of 99% and 97% respectively, in comparison to human annotation. In the Kaplan Meier (KM) analysis of 414 early stage luminal breast cancers, a high number of lymphocyte clusters (LC) and a high ratio between stromal lymphocyte density and tumor lymphocyte density (LD-S/LD-T) were significantly associated with longer PFI (p = 0.005 and p = 0.038, respectively). Based on these features, two continuous PCA features were added to the multivariate model, and remained significantly associated with PFI after adjusting for age (HR = 1.19, 95% CI 1.05-1.35; HR = 1.26 95% CI 1.03-1.55). The validation set was underpowered (n = 79) and data is still being collected. In a preliminary KM analysis of 37 early stage luminal breast cancer cases from the validation set, LD-S/LD-T was significantly associated with longer PFI (p = 0.046). Conclusions: In our study, LC and LD-S/LD-T, presumably surrogate measures of peritumoral lymphocytes, were found significantly associated with longer PFI.

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