In:
Annals of Surgery, Ovid Technologies (Wolters Kluwer Health), Vol. 273, No. 3 ( 2021-03), p. 523-531
Abstract:
This study was intended to identify prognostic biomarkers for lymph node (LN)-positive locoregional esophageal squamous cell carcinoma (ESCC) patients. Summary of Background Data: Surgery is a major treatment for LN-positive locoregional ESCC patients in China. However, patient outcomes are poor and heterogeneous. Methods: ESCC-associated miRNAs were identified by microarray and validated by quantitative real-time polymerase chain reaction analyses in ESCC and normal esophageal epithelial samples. A multi-miRNA based classifier was established using a least absolute shrinkage and selection operator model in a training set of 145 LN-positive locoregional ESCCs, and further assessed in internal testing and independent validation sets of 145 and 243 patients, respectively. Results: Twenty ESCC-associated miRNAs were identified and validated. A 4-miRNA based classifier (miR-135b-5p, miR-139-5p, miR-29c-5p, and miR-338-3p) was generated to classify LN-positive locoregional ESCC patients into high and low-risk groups. Patients with high-risk scores in the training set had a lower 5-year overall survival rate [8.7%, 95% confidence interval (CI): 0–20.3] than those with low-risk scores (50.3%, 95% CI: 40.0–60.7; P 〈 0.0001). The prognostic accuracy of the classifier was validated in the internal testing ( P 〈 0.0001) and independent validation sets ( P = 0.00073). Multivariate survival analyses showed that the 4-miRNA based classifier was an independent prognostic factor, and the combination of the 4-miRNA based classifier and clinicopathological prognostic factors significantly improved the prognostic accuracy of clinicopathological prognostic factors alone. Conclusion: Our 4-miRNA based classifier is a reliable prognostic prediction tool for overall survival in LN-positive locoregional ESCC patients and might offer a novel probability of ESCC treatment individualization.
Type of Medium:
Online Resource
ISSN:
0003-4932
,
1528-1140
DOI:
10.1097/SLA.0000000000003369
Language:
English
Publisher:
Ovid Technologies (Wolters Kluwer Health)
Publication Date:
2021
detail.hit.zdb_id:
2641023-0
detail.hit.zdb_id:
2002200-1
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