Cancer Hallmarks, Biomarkers and Breast Cancer Molecular Subtypes

Dai, Xiaofeng; Xiang, Liangjian; Li, Ting; Bai, Zhonghu

doi:10.7150/jca.13141

Cited by 350 publications

(348 citation statements)

References 175 publications

Supporting

Mentioning

308

Contrasting

Unclassified

Order By: Relevance

“…1,2 Breast cancers are classified into distinct subtypes according to the expression patterns of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) as follows: luminal A (ER-positive, PR-positive, or both and HER2-negative), luminal B (ER-positive, PR-positive, or both and HER2-positive), HER2-overexpressing (ER-and PR-negative, HER2-positive), and triple-negative (TNBC, ER/PR/HER2-negative). [3][4][5][6] TNBCs comprise approximately 15% of all breast cancers and are associated with poorer prognosis, lower overall survival, and shorter relapse-free intervals compared with other subtypes. 7-10 Further, targeted therapy is unavailable for treating patients with TNBC.…”

Section: Introductionmentioning

confidence: 99%

Picrasidine G decreases viability of MDA-MB 468 EGFR-overexpressing triple-negative breast cancer cells through inhibition of EGFR/STAT3 signaling pathway

Yamashita

Kondo

Zhao

et al. 2017

Bioorganic & Medicinal Chemistry Letters

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Picrasidine G decreases viability of MDA-MB 468 EGFR-overexpressing triple-negative breast cancer cells through inhibition of EGFR/STAT3 signaling pathway

Yamashita

Kondo

Zhao

et al. 2017

Bioorganic & Medicinal Chemistry Letters

View full text Add to dashboard Cite

“…A number of factors may be responsible for breast pain in breast cancer patients. Prior to surgery, breast pain may result from the release of pain mediators by the tumour or inflammatory changes in the breast tissue (following tissue biopsy) which is mainly mediated by pro-inflammatory cytokines [108][109][110][111]. On the other hand, post-operative pain in breast cancer patients may arise from or be associated with nerve injury that may occur during surgery or following radiotherapy [104,112,113].…”

Section: Painmentioning

confidence: 99%

Genetic polymorphisms associated with common symptoms experienced by breast cancer patients

Abubakar

Gan²

2018

biomedicalresearch

View full text Add to dashboard Cite

The recent advances have led to a considerable increase in the proportion of breast cancer survivors, however, majority of them experience symptoms such as fatigue, pain, depression and sleep disturbances that impair their Quality of Life (QOL) significantly. In addition, a significant inter-individual variability in QOL of breast cancer patients exists which has been partly attributed to genetic variations. However, details of how these variations are influenced by genetic factors remain largely unknown. Therefore, detecting patients with greater susceptibility to reduced QOL using genetic markers may help in designing intervention strategies for personalized medicine. Although limited number of studies have reported on the associations between genetic polymorphisms and QOL, cytokine gene polymorphisms has consistently been linked to increased susceptibility to the development of common symptoms (fatigue, pain, depression and sleep disturbances) in breast cancer patients. More interestingly, these symptoms share some common molecular pathways making it possible to consider them as symptom cluster. In this review, the relationship between genetic variation and common symptoms among breast cancer patients is discussed. In order for genetic factors to be integrated into clinical practice and nursing care of breast cancer patients with impaired QOL, additional studies are encouraged to understand the underlying molecular mechanisms involved in the development of these symptoms and their impact on QOL for identification of patients based on the presence of a symptom cluster using genetic biomarkers.

show abstract

“…Human complex disease like cancers and cardiovascular diseases are known to be associated with more than one genetic factor 2,3 and the classic single-factor correlation analysis tends to detect statistically significant factors 4 . So the existing complex disease diagnosis panels usually use the genetic information of multiple genes 5,6 .…”

Section: Introductionmentioning

confidence: 99%

RIFS: a randomly restarted incremental feature selection algorithm

Zhang

Zheng

et al. 2017

Sci Rep

View full text Add to dashboard Cite

The advent of big data era has imposed both running time and learning efficiency challenges for the machine learning researchers. Biomedical OMIC research is one of these big data areas and has changed the biomedical research drastically. But the high cost of data production and difficulty in participant recruitment introduce the paradigm of “large p small n” into the biomedical research. Feature selection is usually employed to reduce the high number of biomedical features, so that a stable data-independent classification or regression model may be achieved. This study randomly changes the first element of the widely-used incremental feature selection (IFS) strategy and selects the best feature subset that may be ranked low by the statistical association evaluation algorithms, e.g. t-test. The hypothesis is that two low-ranked features may be orchestrated to achieve a good classification performance. The proposed Randomly re-started Incremental Feature Selection (RIFS) algorithm demonstrates both higher classification accuracy and smaller feature number than the existing algorithms. RIFS also outperforms the existing methylomic diagnosis model for the prostate malignancy with a larger accuracy and a lower number of transcriptomic features.

show abstract

Cancer Hallmarks, Biomarkers and Breast Cancer Molecular Subtypes

Cited by 350 publications

References 175 publications

Picrasidine G decreases viability of MDA-MB 468 EGFR-overexpressing triple-negative breast cancer cells through inhibition of EGFR/STAT3 signaling pathway

Picrasidine G decreases viability of MDA-MB 468 EGFR-overexpressing triple-negative breast cancer cells through inhibition of EGFR/STAT3 signaling pathway

Genetic polymorphisms associated with common symptoms experienced by breast cancer patients

RIFS: a randomly restarted incremental feature selection algorithm

Contact Info

Product

Resources

About