scholarly journals Long noncoding RNA UCA1 as a novel biomarker of lymph node metastasis and prognosis in human cancer: a meta-analysis

2019 ◽  
Vol 39 (4) ◽  
Author(s):  
Congmin Liu ◽  
Jing Jin ◽  
Jin Shi ◽  
Liqun Wang ◽  
Zhaoyu Gao ◽  
...  
Keyword(s):  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Cancer Patients ◽  
Long Noncoding Rna ◽  
Noncoding Rna ◽  
Hot Spot ◽  
Meta Analysis ◽  
Cochrane Library ◽  
Node Metastasis ◽  
Increased Risk

AbstractBackground: Urothelial carcinoma associated 1 (UCA1), a novel long noncoding RNA (lncRNA) which is first discovered in 2006 in human bladder cancer and has become a hot spot in recent years. UCA1 has been demonstrated correlated with clinical outcomes in various cancers. However, the results from each study are insufficient and not completely consistent. Therefore, we perform a systematic meta-analysis to evaluate the value for a feasible biomarker for metastasis and prognosis of cancer. Methods: Relevant English literatures were searched in PubMed, Cochrane Library, Web of science, Embase databases and Chinese literatures were searched in Chinese National Knowledge Infrastructure Wanfang from inception up to 17 April 2018. The pooled odds ratio (OR) and hazard ratio (HR) with 95% confidence interval (CI) using random/fixed-effect were used to identify the relationship between UCA1 and lymph node metastasis (LNM) or overall survival (OS) of cancer patients. Subgroup analysis and sensitivity analysis were performed. The current meta-analysis was performed using Review Manager 5.3 and Stata 12.0 software. Results: A total of 3411 patients from 38 studies were finally included. Patients who with high UCA1 expression suffered from an increased risk of LNM (OR = 2.50; 95% CI: 1.93–3.25). UCA1 was also significantly associated with OS (HR = 2.05; 95% CI: 1.77–2.38). Subgroup analyses across several different variables also showed the similar results in LNM and OS of cancer patients. Conclusion: High expression of UCA1 was linked with poor clinical outcome. UCA1 can serve as a potential molecular marker for metastasis and prognosis in different types of cancers.

scholarly journals Long noncoding RNA ANRIL as a novel biomarker of lymph node metastasis and prognosis in human cancer: a meta-analysis

Oncotarget ◽  
2017 ◽  
Vol 9 (18) ◽  
pp. 14608-14618 ◽  
Author(s):  
Han Wang ◽  
Yang Liu ◽  
Jianhua Zhong ◽  
Chenglong Wu ◽  
Yuantang Zhong ◽  
...  
Keyword(s):  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Long Noncoding Rna ◽  
Noncoding Rna ◽  
Human Cancer ◽  
Meta Analysis ◽  
Node Metastasis

scholarly journals Role of long noncoding RNA UCA1 as a common molecular marker for lymph node metastasis and prognosis in various cancers: a meta-analysis

Oncotarget ◽  
2016 ◽  
Vol 8 (1) ◽  
pp. 1937-1943 ◽  
Author(s):  
Anbang He ◽  
Rong Hu ◽  
Zhicong Chen ◽  
Xinhui Liao ◽  
Jianfa Li ◽  
...  
Keyword(s):  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Molecular Marker ◽  
Long Noncoding Rna ◽  
Noncoding Rna ◽  
Meta Analysis ◽  
Node Metastasis

scholarly journals The Association between Abnormal Long Noncoding RNA MALAT-1 Expression and Cancer Lymph Node Metastasis: A Meta-Analysis

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Jun Wang ◽  
Yongsheng Pan ◽  
Jie Wu ◽  
Cheng Zhang ◽  
Yuan Huang ◽  
...  
Keyword(s):  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Noncoding Rna ◽  
Meta Analysis ◽  
Chinese Patients ◽  
Cochrane Library ◽  
Multiple Cancer ◽  
Expression Levels ◽  
Node Metastasis ◽  
Significant Difference

Previous studies have investigated that the expression levels of MALAT-1 were higher in cancerous tissues than matched histologically normal tissues. And, to some extent, overexpression of MALAT-1 was inclined to lymph node metastasis. This meta-analysis collected all relevant articles and explored the association between MALAT-1 expression levels and lymph node metastasis. We searched PubMed, EmBase, Web of Science, Cochrane Library, and OVID to address the level of MALAT-1 expression in cancer cases and noncancerous controls (accessed February 2015). And 8 studies comprising 696 multiple cancer patients were included to assess this association. The odds ratio (OR) and its corresponding 95% confidence interval (CI) were calculated to assess the strength of the association using Stata 12.0 version software. The results revealed there was a significant difference in the incidence of lymph node metastasis between high MALAT-1 expression group and low MALAT-1 expression group (OR = 1.94, 95% CI 1.15–3.28, P=0.013 random-effects model). Subgroup analysis indicated that MALAT-1 high expression had an unfavorable impact on lymph node metastasis in Chinese patients (OR = 1.87, 95% CI 1.01–2.46). This study demonstrated that the incidence of lymph node metastasis in patients detected with high MALAT-1 expression was higher than that in patients with low MALAT-1 expression in China.

scholarly journals Artificial intelligence for pre-operative lymph node staging in colorectal cancer: a systematic review and meta-analysis

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sergei Bedrikovetski ◽  
Nagendra N. Dudi-Venkata ◽  
Hidde M. Kroon ◽  
Warren Seow ◽  
Ryash Vather ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Colorectal Cancer ◽  
Systematic Review ◽  
Deep Learning ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Meta Analysis ◽  
Cochrane Library ◽  
Learning Models ◽  
Node Metastasis

Abstract Background Artificial intelligence (AI) is increasingly being used in medical imaging analysis. We aimed to evaluate the diagnostic accuracy of AI models used for detection of lymph node metastasis on pre-operative staging imaging for colorectal cancer. Methods A systematic review was conducted according to PRISMA guidelines using a literature search of PubMed (MEDLINE), EMBASE, IEEE Xplore and the Cochrane Library for studies published from January 2010 to October 2020. Studies reporting on the accuracy of radiomics models and/or deep learning for the detection of lymph node metastasis in colorectal cancer by CT/MRI were included. Conference abstracts and studies reporting accuracy of image segmentation rather than nodal classification were excluded. The quality of the studies was assessed using a modified questionnaire of the QUADAS-2 criteria. Characteristics and diagnostic measures from each study were extracted. Pooling of area under the receiver operating characteristic curve (AUROC) was calculated in a meta-analysis. Results Seventeen eligible studies were identified for inclusion in the systematic review, of which 12 used radiomics models and five used deep learning models. High risk of bias was found in two studies and there was significant heterogeneity among radiomics papers (73.0%). In rectal cancer, there was a per-patient AUROC of 0.808 (0.739–0.876) and 0.917 (0.882–0.952) for radiomics and deep learning models, respectively. Both models performed better than the radiologists who had an AUROC of 0.688 (0.603 to 0.772). Similarly in colorectal cancer, radiomics models with a per-patient AUROC of 0.727 (0.633–0.821) outperformed the radiologist who had an AUROC of 0.676 (0.627–0.725). Conclusion AI models have the potential to predict lymph node metastasis more accurately in rectal and colorectal cancer, however, radiomics studies are heterogeneous and deep learning studies are scarce. Trial registration PROSPERO CRD42020218004.

scholarly journals Prognosis assessment of CD44+/CD24- in Breast Cancer Patients: a systematic review and meta-analysis

2021 ◽  
Author(s):  
Jingjing Gu ◽  
Dandan Chen ◽  
Zhiqiang li ◽  
Yongliang Yang ◽  
Zhaoming Ma ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Cancer Patients ◽  
Distant Metastasis ◽  
Tumor Size ◽  
Meta Analysis ◽  
Breast Cancer Patients ◽  
Clinical Prognosis ◽  
Node Metastasis

Abstract Purpose: This meta-analysis investigated the relationships between the CD44+/CD24- phenotype and tumor size, lymph node metastasis, distant metastasis, disease-free survival (DFS), and overall survival (OS) in 8036 postoperative breast cancer patients enrolled in 23 studies.Methods: A literature search of PubMed, Medline, Cochrane, Embase, and PMC was conducted to identify eligible studies. The combined odds ratios (ORs) and 95% confidence intervals (95%CIs) were analyzed to evaluate the relationships between the CD44+/CD24- phenotype and the pathological and biological characteristics of breast cancer patients, and the combined hazard ratios (HRs) and 95% CIs were calculated to evaluate the relationships between CD44+/CD24- and DFS and OS of breast cancer petients using Stata12.0 software.Results: The CD44+/CD24- phenotype were not related to the tumor size (tumor size > 2.0 cm vs ≤ 2.0 cm, combined OR = 0.98, 95%CI: 0.68–1.34, p = 0.792) and didn’t promote lymph node metastasis (lymph node metastasis vs. no lymph node metastasis, combined OR = 0.94, 95% CI: 0.71–1.26, p = 0.692) and distant metastasis (distant metastasis vs no distant metastasis, combined OR = 3.88, 95% CI: 0.93–16.24, p = 0.064). The CD44+/CD24- phenotype was negatively correlated with postoperative DFS (HR = 1.67, 95% CI: 1.35–2.07, p <0.00001) and OS (combined HR = 1.52, 95%CI: 1.21–1.91, p = 0.0004).Conclusion: These results suggested expression of the CD44+/CD24- phenotype can be used as a reliable indicator of clinical prognosis and a potential therapeutic targets in breastcancer patients.

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