Prognostic Value of Differentially Expressed LncRNAs in Triple-Negative Breast Cancer: A Systematic Review and Meta-Analysis

2020 ◽  
Vol 30 (5) ◽  
pp. 447-456
Author(s):  
Dilihumaer Tuluhong ◽  
Wangmu Dunzhu ◽  
Jingjie Wang ◽  
Tao Chen ◽  
Hanjun Li ◽  
...  
2021 ◽  
Vol 32 ◽  
pp. S43-S44
Author(s):  
K.S. Harborg ◽  
R. Zachariae ◽  
J. Olsen ◽  
M. Johannsen ◽  
D. Cronin-Fenton ◽  
...  

Diagnostics ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 704 ◽  
Author(s):  
Parisa Lotfinejad ◽  
Mohammad Asghari Jafarabadi ◽  
Mahdi Abdoli Shadbad ◽  
Tohid Kazemi ◽  
Fariba Pashazadeh ◽  
...  

This meta-analysis aimed to evaluate the prognostic value of tumor-infiltrating lymphocytes (TILs) and programmed death-ligand 1 (PD-L1), their associations with the clinicopathological characteristics, and the association between their levels in patients with triple-negative breast cancer (TNBC). PubMed, EMBASE, Scopus, ProQuest, Web of Science, and Cochrane Library databases were searched to obtain the relevant papers. Seven studies with 1152 patients were included in this study. Like the level of TILs, there were no significant associations between PD-L1 expression and tumor size, tumor stage, lymph node metastasis, histological grade, and Ki67 (All p-values ≥ 0.05). Furthermore, there was no significant association between PD-L1 expression with overall survival (OS) and disease-free survival (DFS). In assessment of TILs and survival relationship, the results showed that a high level of TILs was associated with long-term OS (hazard ratios (HR) = 0.48, 95% CI: 0.30 to 0.77, p-value < 0.001) and DFS (HR = 0.53, 95% CI: 0.35 to 0.78, p-value < 0.001). The results displayed that tumoral PD-L1 expression was strongly associated with high levels of TILs in TNBC patients (OR = 8.34, 95% CI: 2.68 to 25.95, p-value < 0.001). In conclusion, the study has shown the prognostic value of TILs and a strong association between tumoral PD-L1 overexpression with TILs in TNBC patients.


2016 ◽  
Vol 2 (6) ◽  
pp. 412-421 ◽  
Author(s):  
Gurprataap S. Sandhu ◽  
Sebhat Erqou ◽  
Heidi Patterson ◽  
Aju Mathew

Purpose There is considerable variation in prevalence rates of triple-negative breast cancer (TNBC) reported by various studies from India. We performed a systematic review and literature-based meta-analysis of these studies. Methods We searched databases of Medline, Scopus, EMBASE, and Web of Science for studies that reported on the prevalence of TNBC in India that were published between January 1, 1999, and December 31, 2015. We extracted relevant information from each study by using a standardized form. We pooled study-specific estimates by using random-effects meta-analysis to provide summary estimates. We explored sources of heterogeneity by using subgroup analyses and metaregression. Results Data were obtained from 17 studies that involved 7,237 patients with breast cancer. Overall combined prevalence of TNBC was 31% (95% CI, 27% to 35%). There was substantial heterogeneity across the studies (I2 of 91% [95% CI, 88% to 94%]; P < .001) that was not explained by available study level characteristics, including study location, definition of human epidermal growth factor receptor 2 or estrogen receptor, mean age of participants, proportion of patients with premenopausal cancer, grade 3 disease, or tumor size > 5 cm. Overall combined prevalence of hormone receptor–positive and human epidermal growth factor receptor 2–positive breast cancer was 48% (95% CI, 42% to 54%) and 27% (95% CI, 24% to 31%), respectively. There was no evidence of publication bias. Conclusion Prevalence of TNBC in India is considerably higher compared with that seen in Western populations. As many as as one in three women with breast cancer could have triple-negative disease. This finding has significant clinical relevance as it may contribute to poor outcomes in patients with breast cancer in India. Additional research is needed to understand the determinants of TNBC in India.


Oncotarget ◽  
2016 ◽  
Vol 7 (29) ◽  
pp. 46482-46491 ◽  
Author(s):  
Changjun Wang ◽  
Bo Pan ◽  
Hanjiang Zhu ◽  
Yidong Zhou ◽  
Feng Mao ◽  
...  

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