Impact of TGF-b on breast cancer from a quantitative proteomic analysis

Factors influencing the diagnostic and prognostic values of circulating tumor cells in breast cancer: a meta-analysis of 8,935 patients

Background

Circulating tumor cells (CTCs) could serve as a predictive biomarker in breast cancer (BC). Due to its high heterogeneity, the diagnostic and prognostic values of CTC are challenging.

Methods

We searched published studies from the databases of PubMed, Cochrane Library, Embase, and MEDLINE. The detection capability and hazard ratios (HRs) of CTCs were extracted as the clinical diagnosis and prognosis evaluation. Subgroup analyses were divided according to the detection methods, continents, treatment periods, therapeutic plans, and cancer stages.

Results

In this study, 35 publications had been retrieved with 8,935 patients enrolled. The diagnostic efficacy of CTC detection has 74% sensitivity and 98% specificity. The positive CTC detection (CTC+ ) would predict worse OS and PFS/DFS in both mid-therapy and post-therapy (HROS, 3.09; 95% CI, 2.17–4.39; HRPFS/DFS, 2.06; 95% CI, 1.72–2.47). Moreover, CTC+ indicated poor survival irrespective of the treatment phases and sampling times (HROS, 2.43; 95% CI, 1.85–3.19; HRPFS/DFS, 1.82; 95% CI, 1.66–1.99). The CTC+ was associated with poor survival regardless of the continents of patients (HROS = 2.43; 95% CI, 1.85–3.19).

Conclusion

Our study suggested that CTC+ was associated with a worse OS and PFS/DFS in the Asian population. The detection method, the threshold level of CTC+ , therapeutic approaches, and sampling times would not affect its diagnostic and prognostic values.

Biological Networks for Cancer Candidate Biomarkers Discovery

Due to its extraordinary heterogeneity and complexity, cancer is often proposed as a model case of a systems biology disease or network disease. There is a critical need of effective biomarkers for cancer diagnosis and/or outcome prediction from system level analyses. Methods based on integrating omics data into networks have the potential to revolutionize the identification of cancer biomarkers. Deciphering the biological networks underlying cancer is undoubtedly important for understanding the molecular mechanisms of the disease and identifying effective biomarkers. In this review, the networks constructed for cancer biomarker discovery based on different omics level data are described and illustrated from recent advances in the field.