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Qu W, Li L, Ma J, Li Y. Screening high-risk individuals for primary gastric carcinoma: evaluating overall survival probability score in the presence and absence of lymphatic metastasis post-gastrectomy. World J Surg Oncol 2024; 22:196. [PMID: 39054533 PMCID: PMC11271195 DOI: 10.1186/s12957-024-03481-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Accepted: 07/17/2024] [Indexed: 07/27/2024] Open
Abstract
OBJECTIVE The aim of this study was to develop and validate prognostic models for predicting overall survival in individuals with gastric carcinoma, specifically focusing on both negative and positive lymphatic metastasis. METHODS A total of 1650 patients who underwent radical gastric surgery at Shanxi Cancer Hospital between May 2002 and December 2020 were included in the analysis. Multiple Cox Proportional Hazards analysis was performed to identify key variables associated with overall survival in both negative and positive lymphatic metastasis cases. Internal validation was conducted using bootstrapping to assess the prediction accuracy of the models. Calibration curves were used to demonstrate the accuracy and consistency of the predictions. The discriminative abilities of the prognostic models were evaluated and compared with the 8th edition of AJCC-TNM staging using Harrell's Concordance index, decision curve analysis, and time-dependent receiver operating characteristic curves. RESULTS The nomogram for node-negative lymphatic metastasis included variables such as age, pT stage, and maximum tumor diameter. The C-index for this model in internal validation was 0.719, indicating better performance compared to the AJCC 8th edition TNM staging. The nomogram for node-positive lymphatic metastasis included variables such as gender, age, maximum tumor diameter, neural invasion, Lauren classification, and expression of Her-2, CK7, and CD56. The C-index for this model was 0.674, also outperforming the AJCC 8th edition TNM staging. Calibration curves, time-dependent receiver operating characteristic curves, and decision curve analysis for both nomograms demonstrated excellent prediction ability. Furthermore, significant differences in prognosis between low- and high-risk groups supported the models' strong risk stratification performance. CONCLUSION This study provides valuable risk stratification models for lymphatic metastasis in gastric carcinoma, encompassing both node-positive and negative cases. These models can help identify low-risk individuals who may not require further intervention, while high-risk individuals can benefit from targeted therapies aimed at addressing lymphatic metastasis.
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Affiliation(s)
- Wenqing Qu
- Hepatobiliary, Pancreatic and Gastrointestinal Surgery, Shanxi Hospital Affiliated to Carcinoma Hospital, Chinese Academy of Medical Sciences, Shanxi Province Carcinoma Hospital, Carcinoma Hospital Affiliated to Shanxi Medical University, Taiyuan, 030013, Shanxi, P.R. China
| | - Ling Li
- Shanxi Medical University, 030013, Taiyuan, Shanxi, P.R. China
| | - Jinfeng Ma
- Hepatobiliary, Pancreatic and Gastrointestinal Surgery, Shanxi Hospital Affiliated to Carcinoma Hospital, Chinese Academy of Medical Sciences, Shanxi Province Carcinoma Hospital, Carcinoma Hospital Affiliated to Shanxi Medical University, Taiyuan, 030013, Shanxi, P.R. China.
| | - Yifan Li
- Hepatobiliary, Pancreatic and Gastrointestinal Surgery, Shanxi Hospital Affiliated to Carcinoma Hospital, Chinese Academy of Medical Sciences, Shanxi Province Carcinoma Hospital, Carcinoma Hospital Affiliated to Shanxi Medical University, Taiyuan, 030013, Shanxi, P.R. China.
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Wang X, Niu X, Zhang F, Wu J, Wu H, Li T, Yang J, Ding P, Guo H, Tian Y, Yang P, Zhang Z, Wang D, Zhao Q. Nomogram models for predicting overall and cancer-specific survival in early-onset gastric cancer patients: a population-based cohort study. Am J Cancer Res 2024; 14:1747-1767. [PMID: 38726268 PMCID: PMC11076259 DOI: 10.62347/fprm7701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Accepted: 04/03/2024] [Indexed: 05/12/2024] Open
Abstract
To develop nomogram models for predicting the overall survival (OS) and cancer-specific survival (CSS) of early-onset gastric cancer (EOGC) patients. A total of 1077 EOGC patients from the Surveillance, Epidemiology, and End Results (SEER) database were included, and an additional 512 EOGC patients were recruited from the Fourth Hospital of Hebei Medical University, serving as an external test set. Univariate and multivariate Cox regression analyses were performed to identify independent prognostic factors. Based on these factors, two nomogram models were established, and web-based calculators were developed. These models were validated using receiver operating characteristics (ROC) curve analysis, calibration curves, and decision curve analysis (DCA). Multivariate analysis identified gender, histological type, stage, N stage, tumor size, surgery, primary site, and lung metastasis as independent prognostic factors for OS and CSS in EOGC patients. Calibration curves and DCA curves demonstrated that the two constructed nomogram models exhibited good performance. These nomogram models demonstrated superior performance compared to the 7th edition of the AJCC tumor-node-metastasis (TNM) classification (internal validation set: 1-year OS: 0.831 vs 0.793, P = 0.072; 1-year CSS: 0.842 vs 0.816, P = 0.190; 3-year OS: 0.892 vs 0.857, P = 0.039; 3-year CSS: 0.887 vs 0.848, P = 0.018; 5-year OS: 0.906 vs 0.880, P = 0.133; 5-year CSS: 0.900 vs 0.876, P = 0.109). In conclusion, this study developed two nomogram models: one for predicting OS and the other for CSS of EOGC patients, offering valuable assistance to clinicians.
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Affiliation(s)
- Xiaoyan Wang
- Third Department of Surgery, The Fourth Hospital of Hebei Medical UniversityShijiazhuang 050011, Hebei, China
- Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric CancerShijiazhuang 050011, Hebei, China
- Big Data Analysis and Mining Application for Precise Diagnosis and Treatment of Gastric Cancer Hebei Provincial Engineering Research CenterShijiazhuang 050011, Hebei, China
- Medical Oncology, Shijiazhuang People’s HospitalShijiazhuang 050050, Hebei, China
| | - Xiaoman Niu
- Third Department of Surgery, The Fourth Hospital of Hebei Medical UniversityShijiazhuang 050011, Hebei, China
- Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric CancerShijiazhuang 050011, Hebei, China
- Big Data Analysis and Mining Application for Precise Diagnosis and Treatment of Gastric Cancer Hebei Provincial Engineering Research CenterShijiazhuang 050011, Hebei, China
| | - Fengbin Zhang
- Third Department of Surgery, The Fourth Hospital of Hebei Medical UniversityShijiazhuang 050011, Hebei, China
- Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric CancerShijiazhuang 050011, Hebei, China
- Department of Gastroenterology, The Fourth Hospital of Hebei Medical UniversityShijiazhuang 050011, Hebei, China
| | - Jiaxiang Wu
- Third Department of Surgery, The Fourth Hospital of Hebei Medical UniversityShijiazhuang 050011, Hebei, China
- Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric CancerShijiazhuang 050011, Hebei, China
- Big Data Analysis and Mining Application for Precise Diagnosis and Treatment of Gastric Cancer Hebei Provincial Engineering Research CenterShijiazhuang 050011, Hebei, China
| | - Haotian Wu
- Third Department of Surgery, The Fourth Hospital of Hebei Medical UniversityShijiazhuang 050011, Hebei, China
- Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric CancerShijiazhuang 050011, Hebei, China
- Big Data Analysis and Mining Application for Precise Diagnosis and Treatment of Gastric Cancer Hebei Provincial Engineering Research CenterShijiazhuang 050011, Hebei, China
| | - Tongkun Li
- Third Department of Surgery, The Fourth Hospital of Hebei Medical UniversityShijiazhuang 050011, Hebei, China
- Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric CancerShijiazhuang 050011, Hebei, China
- Big Data Analysis and Mining Application for Precise Diagnosis and Treatment of Gastric Cancer Hebei Provincial Engineering Research CenterShijiazhuang 050011, Hebei, China
| | - Jiaxuan Yang
- Third Department of Surgery, The Fourth Hospital of Hebei Medical UniversityShijiazhuang 050011, Hebei, China
- Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric CancerShijiazhuang 050011, Hebei, China
- Big Data Analysis and Mining Application for Precise Diagnosis and Treatment of Gastric Cancer Hebei Provincial Engineering Research CenterShijiazhuang 050011, Hebei, China
| | - Ping’an Ding
- Third Department of Surgery, The Fourth Hospital of Hebei Medical UniversityShijiazhuang 050011, Hebei, China
- Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric CancerShijiazhuang 050011, Hebei, China
- Big Data Analysis and Mining Application for Precise Diagnosis and Treatment of Gastric Cancer Hebei Provincial Engineering Research CenterShijiazhuang 050011, Hebei, China
| | - Honghai Guo
- Third Department of Surgery, The Fourth Hospital of Hebei Medical UniversityShijiazhuang 050011, Hebei, China
- Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric CancerShijiazhuang 050011, Hebei, China
- Big Data Analysis and Mining Application for Precise Diagnosis and Treatment of Gastric Cancer Hebei Provincial Engineering Research CenterShijiazhuang 050011, Hebei, China
| | - Yuan Tian
- Third Department of Surgery, The Fourth Hospital of Hebei Medical UniversityShijiazhuang 050011, Hebei, China
- Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric CancerShijiazhuang 050011, Hebei, China
- Big Data Analysis and Mining Application for Precise Diagnosis and Treatment of Gastric Cancer Hebei Provincial Engineering Research CenterShijiazhuang 050011, Hebei, China
| | - Peigang Yang
- Third Department of Surgery, The Fourth Hospital of Hebei Medical UniversityShijiazhuang 050011, Hebei, China
- Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric CancerShijiazhuang 050011, Hebei, China
- Big Data Analysis and Mining Application for Precise Diagnosis and Treatment of Gastric Cancer Hebei Provincial Engineering Research CenterShijiazhuang 050011, Hebei, China
| | - Zhidong Zhang
- Third Department of Surgery, The Fourth Hospital of Hebei Medical UniversityShijiazhuang 050011, Hebei, China
- Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric CancerShijiazhuang 050011, Hebei, China
- Big Data Analysis and Mining Application for Precise Diagnosis and Treatment of Gastric Cancer Hebei Provincial Engineering Research CenterShijiazhuang 050011, Hebei, China
| | - Dong Wang
- Third Department of Surgery, The Fourth Hospital of Hebei Medical UniversityShijiazhuang 050011, Hebei, China
- Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric CancerShijiazhuang 050011, Hebei, China
- Big Data Analysis and Mining Application for Precise Diagnosis and Treatment of Gastric Cancer Hebei Provincial Engineering Research CenterShijiazhuang 050011, Hebei, China
| | - Qun Zhao
- Third Department of Surgery, The Fourth Hospital of Hebei Medical UniversityShijiazhuang 050011, Hebei, China
- Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric CancerShijiazhuang 050011, Hebei, China
- Big Data Analysis and Mining Application for Precise Diagnosis and Treatment of Gastric Cancer Hebei Provincial Engineering Research CenterShijiazhuang 050011, Hebei, China
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Lu T, Lu M, Liu H, Song D, Wang Z, Guo Y, Fang Y, Chen Q, Li T. Establishment of a prognostic model for gastric cancer patients who underwent radical gastrectomy using machine learning: a two-center study. Front Oncol 2024; 13:1282042. [PMID: 38665864 PMCID: PMC11043579 DOI: 10.3389/fonc.2023.1282042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 12/21/2023] [Indexed: 04/28/2024] Open
Abstract
Objective Gastric cancer is a prevalent gastrointestinal malignancy worldwide. In this study, a prognostic model was developed for gastric cancer patients who underwent radical gastrectomy using machine learning, employing advanced computational techniques to investigate postoperative mortality risk factors in such patients. Methods Data of 295 patients with gastric cancer who underwent radical gastrectomy at the Department of General Surgery of Affiliated Hospital of Xuzhou Medical University (Xuzhou, China) between March 2016 and November 2019 were retrospectively analyzed as the training group. Additionally, 109 patients who underwent radical gastrectomy at the Department of General Surgery Affiliated to Jining First People's Hospital (Jining, China) were included for external validation. Four machine learning models, including logistic regression (LR), decision tree (DT), random forest (RF), and gradient boosting machine (GBM), were utilized. Model performance was assessed by comparing the area under the curve (AUC) for each model. An LR-based nomogram model was constructed to assess patients' clinical prognosis. Results Lasso regression identified eight associated factors: age, sex, maximum tumor diameter, nerve or vascular invasion, TNM stage, gastrectomy type, lymphocyte count, and carcinoembryonic antigen (CEA) level. The performance of these models was evaluated using the AUC. In the training group, the AUC values were 0.795, 0.759, 0.873, and 0.853 for LR, DT, RF, and GBM, respectively. In the validation group, the AUC values were 0.734, 0.708, 0.746, and 0.707 for LR, DT, RF, and GBM, respectively. The nomogram model, constructed based on LR, demonstrated excellent clinical prognostic evaluation capabilities. Conclusion Machine learning algorithms are robust performance assessment tools for evaluating the prognosis of gastric cancer patients who have undergone radical gastrectomy. The LR-based nomogram model can aid clinicians in making more reliable clinical decisions.
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Affiliation(s)
- Tong Lu
- Department of Emergency Medicine, Jining No.1 People’s Hospital, Jining, China
| | - Miao Lu
- Wuxi Mental Health Center, Wuxi, China
| | - Haonan Liu
- Department of Oncology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Daqing Song
- Department of Emergency Medicine, Jining No.1 People’s Hospital, Jining, China
| | - Zhengzheng Wang
- Department of Gastroenterology, The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Yahui Guo
- Department of Gastroenterology, Xuzhou First People’s Hospital, Xuzhou, China
| | - Yu Fang
- Jiangsu Normal University, Xuzhou, China
| | - Qi Chen
- Department of Gastroenterology, Jining First People’s Hospital, Jining, China
| | - Tao Li
- Department of Emergency Medicine, Jining No.1 People’s Hospital, Jining, China
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Ju M, Gao Z, Gu G, Huang H, Sun A, Zheng C, Li H, Zhang Y, Li K. Prognostic value of circulating tumor cells associated with white blood cells in solid cancer: a systematic review and meta-analysis of 1471 patients with solid tumors. BMC Cancer 2023; 23:1224. [PMID: 38087278 PMCID: PMC10717563 DOI: 10.1186/s12885-023-11711-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Accepted: 12/04/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND The clinical relevance of circulating tumor cell-white blood cell (CTC-WBC) clusters in cancer prognosis is a subject of ongoing debate. This study aims to unravel their contentious predictive value for patient outcomes. METHODS We conducted a comprehensive literature search of PubMed, Embase, and Cochrane Library up to December 2022. Eligible studies that reported survival outcomes and examined the presence of CTC-WBC clusters in solid tumor patients were included. Hazard ratios (HR) were pooled to assess the association between CTC-WBC clusters and overall survival (OS), as well as progression-free survival (PFS)/disease-free survival (DFS)/metastasis-free survival (MFS)/recurrence-free survival (RFS). Subgroup analyses were performed based on sampling time, treatment method, detection method, detection system, and cancer type. RESULTS A total of 1471 patients from 10 studies were included in this meta-analysis. The presence of CTC-WBCs was assessed as a prognostic factor for overall survival and PFS/DFS/MFS/RFS. The pooled analysis demonstrated that the presence of CTC-WBC clusters was significantly associated with worse OS (HR = 2.44, 95% CI: 1.74-3.40, P < 0.001) and PFS/DFS/MFS/RFS (HR = 1.83, 95% CI: 1.49-2.24, P < 0.001). Subgroup analyses based on sampling time, treatment method, detection method, detection system, cancer type, and study type consistently supported these findings. Further analyses indicated that CTC-WBC clusters were associated with larger tumor size (OR = 2.65, 95% CI: 1.58-4.44, P < 0.001) and higher alpha-fetoprotein levels (OR = 2.52, 95% CI: 1.50-4.22, P < 0.001) in hepatocellular carcinoma. However, no significant association was found between CTC-WBC clusters and TNM stage, depth of tumor invasion, or lymph node metastasis in the overall analysis. CONCLUSIONS CTC-WBC clusters are negative predictors for OS and PFS/DFS/MFS/RFS in patients with solid tumors. Monitoring CTC-WBC levels may provide valuable information for predicting disease progression and guiding treatment decisions.
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Affiliation(s)
- Mingguang Ju
- Department of Surgical Oncology and General Surgery, Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors, Ministry of Education, Heping District, The First Affiliated Hospital of China Medical University, 155 North Nanjing Street, Shenyang City, 110001, China
| | - Ziming Gao
- Department of Surgical Oncology and General Surgery, Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors, Ministry of Education, Heping District, The First Affiliated Hospital of China Medical University, 155 North Nanjing Street, Shenyang City, 110001, China
| | - Gaoxiang Gu
- Department of Surgical Oncology and General Surgery, Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors, Ministry of Education, Heping District, The First Affiliated Hospital of China Medical University, 155 North Nanjing Street, Shenyang City, 110001, China
| | - Haibo Huang
- VIP International Department, Heping District, The First Affiliated Hospital of China Medical University, 155 North Nanjing Street, Shenyang City, 110001, China
| | - Anqi Sun
- VIP International Department, Heping District, The First Affiliated Hospital of China Medical University, 155 North Nanjing Street, Shenyang City, 110001, China
| | - Chen Zheng
- Department of Anesthesiology, Heping District, The First Affiliated Hospital of China Medical University, 155 North Nanjing Street, Shenyang City, 110001, China
| | - He Li
- Department of Ultrasound, Heping District, The First Affiliated Hospital of China Medical University, 155 North Nanjing Street, Shenyang City, 110001, China
| | - Yixiao Zhang
- Department of Surgical Oncology and General Surgery, Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors, Ministry of Education, Heping District, The First Affiliated Hospital of China Medical University, 155 North Nanjing Street, Shenyang City, 110001, China
| | - Kai Li
- Department of Surgical Oncology and General Surgery, Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors, Ministry of Education, Heping District, The First Affiliated Hospital of China Medical University, 155 North Nanjing Street, Shenyang City, 110001, China.
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Sanchez A, Lhuillier J, Grosjean G, Ayadi L, Maenner S. The Long Non-Coding RNA ANRIL in Cancers. Cancers (Basel) 2023; 15:4160. [PMID: 37627188 PMCID: PMC10453084 DOI: 10.3390/cancers15164160] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 08/14/2023] [Accepted: 08/15/2023] [Indexed: 08/27/2023] Open
Abstract
ANRIL (Antisense Noncoding RNA in the INK4 Locus), a long non-coding RNA encoded in the human chromosome 9p21 region, is a critical factor for regulating gene expression by interacting with multiple proteins and miRNAs. It has been found to play important roles in various cellular processes, including cell cycle control and proliferation. Dysregulation of ANRIL has been associated with several diseases like cancers and cardiovascular diseases, for instance. Understanding the oncogenic role of ANRIL and its potential as a diagnostic and prognostic biomarker in cancer is crucial. This review provides insights into the regulatory mechanisms and oncogenic significance of the 9p21 locus and ANRIL in cancer.
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Affiliation(s)
| | | | | | - Lilia Ayadi
- CNRS, Université de Lorraine, IMoPA, F-54000 Nancy, France
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