Sun N, Tan BB, Li Y. Nomogram model including LATS2 expression was constructed to predict the prognosis of advanced gastric cancer after surgery. World J Gastrointest Surg 2024; 16(2): 518-528 [PMID: 38463354 DOI: 10.4240/wjgs.v16.i2.518]
Corresponding Author of This Article
Yong Li, PhD, Chief Doctor, Professor, Third Department of General Surgery, The Fourth Hospital of Hebei Medical University, No. 12 Jiankang Road, Chang'an District, Shijiazhuang 050011, Hebei Province, China. liyong780728@163.com
Research Domain of This Article
Oncology
Article-Type of This Article
Retrospective Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Gastrointest Surg. Feb 27, 2024; 16(2): 518-528 Published online Feb 27, 2024. doi: 10.4240/wjgs.v16.i2.518
Nomogram model including LATS2 expression was constructed to predict the prognosis of advanced gastric cancer after surgery
Nan Sun, Bi-Bo Tan, Yong Li
Nan Sun, Department of Blood Transfusion, The Fourth Hospital of Hebei Medical University, Shijiazhuang 050011, Hubei Province, China
Bi-Bo Tan, Yong Li, Third Department of General Surgery, the Fourth Hospital of Hebei Medical University, Shijiazhuang 050011, Hebei Province, China
Author contributions: Sun N designed the research study; Sun N, Tan BB and Li Y performed the research; Sun N and Tan BB contributed analytic tools; Sun N and Li Y analyzed the data and wrote the manuscript; all authors have read and approve the final manuscript.
Institutional review board statement: This was a retrospective study approved by the Ethics Committee of the Fourth Hospital of Hebei Medical University (Approval No. 2019ME0039).
Informed consent statement: Patients were not required to give informed consent to the study because the analysis used anonymous clinical data that were obtained after each patient agreed to treatment by written consent.
Conflict-of-interest statement: All authors claim no conflicts of interest.
Data sharing statement: Raw data, statistical codes and datasets are available from the corresponding author.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Yong Li, PhD, Chief Doctor, Professor, Third Department of General Surgery, The Fourth Hospital of Hebei Medical University, No. 12 Jiankang Road, Chang'an District, Shijiazhuang 050011, Hebei Province, China. liyong780728@163.com
Received: December 12, 2023 Peer-review started: December 12, 2023 First decision: January 2, 2024 Revised: January 13, 2024 Accepted: January 24, 2024 Article in press: January 24, 2024 Published online: February 27, 2024 Processing time: 75 Days and 6.7 Hours
Abstract
BACKGROUND
Gastric cancer is a leading cause of cancer-related deaths worldwide. Prognostic assessments are typically based on the tumor-node-metastasis (TNM) staging system, which does not account for the molecular heterogeneity of this disease. LATS2, a tumor suppressor gene involved in the Hippo signaling pathway, has been identified as a potential prognostic biomarker in gastric cancer.
AIM
To construct and validate a nomogram model that includes LATS2 expression to predict the survival prognosis of advanced gastric cancer patients following radical surgery, and compare its predictive performance with traditional TNM staging.
METHODS
A retrospective analysis of 245 advanced gastric cancer patients from the Fourth Hospital of Hebei Medical University was conducted. The patients were divided into a training group (171 patients) and a validation group (74 patients) to develop and test our prognostic model. The performance of the model was determined using C-indices, receiver operating characteristic curves, calibration plots, and decision curves.
RESULTS
The model demonstrated a high predictive accuracy with C-indices of 0.829 in the training set and 0.862 in the validation set. Area under the curve values for three-year and five-year survival prediction were significantly robust, suggesting an excellent discrimination ability. Calibration plots confirmed the high concordance between the predictions and actual survival outcomes.
CONCLUSION
We developed a nomogram model incorporating LATS2 expression, which significantly outperformed conventional TNM staging in predicting the prognosis of advanced gastric cancer patients postsurgery. This model may serve as a valuable tool for individualized patient management, allowing for more accurate stratification and improved clinical outcomes. Further validation in larger patient cohorts will be necessary to establish its generalizability and clinical utility.
Core Tip: This study focuses on developing a prognostic model for patients with advanced gastric cancer postsurgery by integrating LATS2 expression and clinicopathological features into a nomogram. It highlights the significance of the LATS2 gene in improving prognostic predictions beyond traditional tumor-node-metastasis staging. Our model demonstrates excellent predictive accuracy and clinical utility, which indicates its potential for enhancing individualized patient care through better risk stratification. Future studies should focus on external validation to confirm the model’s applicability across diverse patient populations.
