Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 204

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Prognostic value of pre-operative inflammatory response biomarkers in gastric cancer patients and the construction of a predictive model

Open access
Deng, Qiwen, He, Bangshun, Liu, Xian, Yue, Jin, Ying, Houqun, Pan, Yuqin, Sun, Huiling, Chen, Jie, Wang, Feng, Gao, Tianyi, Zhang, Lei, Wang, Shukui·2015·Journal of Translational Medicine

Abstract

BACKGROUND: Inflammation plays an integral role in carcinogenesis and tumor progression. Inflammatory response biomarkers have shown to be promising prognostic factors for improving the predictive accuracy in various cancers. The aim of this study is to investigate the prognostic significance of pre-operative neutrophil to lymphocyte ratio (NLR), derived neutrophil to lymphocyte ratio (dNLR), platelet to lymphocyte ratio (PLR) and lymphocyte to monocyte ratio (LMR) in gastric cancer (GC). METHODS: 389 patients who had undergone gastrectomy were enrolled from 2007 to 2009 in this study. NLR, dNLR, PLR and LMR were calculated from peripheral blood cell count taken at pre-operation. Receiver operating curve (ROC) was used to determine the optimal cut-off levels for these biomarkers. A predictive model or nomogram was established to predict prognosis for cancer-specific survival (CSS) and disease-free survival (DFS), and the predictive accuracy of the nomogram was determined by concordance index (c-index). RESULTS: The median follow-up period was 24 months ranging from 3 months to 60 months. The optimal cut-off levels were 2.36 for NLR, 1.85 for dNLR, 132 for PLR and 4.95 for LMR by ROC curves analysis. Elevated NLR, dNLR and PLR were significantly associated with worse overall survival (OS), CSS and DFS, however, elevated LMR showed an adverse effect on worse OS, CSS and DFS. Multivariate analysis revealed that elevated dNLR was an independent factor for worse OS, and NLR was superior to dNLR, PLR and LMR in terms of hazard ratio (HR = 1.53, 95% CI = 1.11-2.11, P = 0.010), which was shown to be independent prognostic indicators for both CSS and DFS. Moreover, the nomogram could more accurately predict CSS (c-index: 0.89) and DFS (c-index: 0.84) in surgical GC patients. CONCLUSIONS: Pre-operative NLR and dNLR may serve as potential prognostic biomarkers in patients with GC who underwent surgical resection. The proposed nomograms can be used for the prediction of CSS and DFS in patients with GC who have undergone gastrectomy.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1186/s12967-015-0409-0

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 2 references

Cited by

See all 204

Metadata

Authors
Deng, Q., He, B., Liu, X., Yue, J., Ying, H., Pan, Y., Sun, H., Chen, J., Wang, F., Gao, T., Zhang, L., & Wang, S.
Venue
Journal of Translational Medicine
Published
2015-02-18
Volume
13
Issue
1
Pages
66-66
ISSN
1479-5876 (ISSN-L)
OpenAlex
W2121692731
Cited by
204
References
2
FWCI
10.92
Type
journal article
Language
en
Open access
gold

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Prognostic value of pre-operative inflammatory response biomarkers in gastric cancer patients and the construction of a predictive model. https://app.cochat.ai/papers/10.1186/s12967-015-0409-0
The paper
Deng, Q., He, B., Liu, X., Yue, J., Ying, H., Pan, Y., Sun, H., Chen, J., Wang, F., Gao, T., Zhang, L., & Wang, S. (2015). Prognostic value of pre-operative inflammatory response biomarkers in gastric cancer patients and the construction of a predictive model. Journal of Translational Medicine. https://doi.org/10.1186/s12967-015-0409-0

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.