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Bintintan V, Burz C, Pintea I, Muntean A, Deleanu D, Lupan I, Samasca G. Predictive Factors of Immunotherapy in Gastric Cancer: A 2024 Update. Diagnostics (Basel) 2024; 14:1247. [PMID: 38928662 PMCID: PMC11202567 DOI: 10.3390/diagnostics14121247] [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: 05/24/2024] [Revised: 06/11/2024] [Accepted: 06/11/2024] [Indexed: 06/28/2024] Open
Abstract
Many studies on gastric cancer treatment have identified predictors of immunotherapy benefits. This article provides an update on the major developments in research related to predictive factors of immunotherapy for gastric cancer. We used the search term "predictive factors, immunotherapy, gastric cancer" to find the most current publications in the PubMed database related to predictive factors of immunotherapy in gastric cancer. Programmed cell death, genetic, and immunological factors are the main study topics of immunotherapy's predictive factors in gastric cancer. Other preventive factors for immunotherapy in gastric cancer were also found, including clinical factors, tumor microenvironment factors, imaging factors, and extracellular factors. Since there is currently no effective treatment for gastric cancer, we strongly propose that these studies be prioritized.
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Affiliation(s)
- Vasile Bintintan
- Department of Surgery 1, Iuliu Hatieganu University of Medicine and Pharmacy, 400006 Cluj-Napoca, Romania;
| | - Claudia Burz
- Institute of Oncology, “Prof. Ion Chiricuta”, 400015 Cluj-Napoca, Romania;
- Department of Immunology, Iuliu Hatieganu University of Medicine and Pharmacy, 400006 Cluj-Napoca, Romania; (I.P.); (A.M.); (D.D.)
| | - Irena Pintea
- Department of Immunology, Iuliu Hatieganu University of Medicine and Pharmacy, 400006 Cluj-Napoca, Romania; (I.P.); (A.M.); (D.D.)
| | - Adriana Muntean
- Department of Immunology, Iuliu Hatieganu University of Medicine and Pharmacy, 400006 Cluj-Napoca, Romania; (I.P.); (A.M.); (D.D.)
| | - Diana Deleanu
- Department of Immunology, Iuliu Hatieganu University of Medicine and Pharmacy, 400006 Cluj-Napoca, Romania; (I.P.); (A.M.); (D.D.)
| | - Iulia Lupan
- Department of Molecular Biology, Babes-Bolyai University, 400084 Cluj-Napoca, Romania;
| | - Gabriel Samasca
- Department of Immunology, Iuliu Hatieganu University of Medicine and Pharmacy, 400006 Cluj-Napoca, Romania; (I.P.); (A.M.); (D.D.)
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Yang W, Lv Y, Ma T, Wang N, Chen P, Liu Q, Yan H. Exploring the association between inflammatory biomarkers and gastric cancer development: A two-sample mendelian randomization analysis. Medicine (Baltimore) 2024; 103:e36458. [PMID: 38306562 PMCID: PMC10843383 DOI: 10.1097/md.0000000000036458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 11/13/2023] [Indexed: 02/04/2024] Open
Abstract
This study aimed to elucidate the potential causative links between inflammatory biomarkers and gastric cancer risk via a two-sample Mendelian randomization approach. Leveraging genome-wide association study (GWAS) data, we conducted a two-sample Mendelian randomization analysis. Instrumental variable selection for inflammatory markers - namely, tissue factor, monocyte chemotactic protein-1, E-selectin, interleukin 6 receptor, and fatty acid-binding protein 4 - was informed by SNP data from the IEU database. Strongly associated SNPs served as instrumental variables. We applied a suite of statistical methods, including Inverse Variance Weighted (IVW), Weighted Median Estimator (WME), MR-Egger, and mode-based estimates, to compute the odds ratios (ORs) that articulate the impact of these markers on gastric cancer susceptibility. The IVW method revealed that the interleukin 6 receptor was inversely correlated with gastric cancer progression (OR = 0.86, 95% CI = 0.74-0.99, P = .03), whereas fatty acid-binding protein 4 was found to elevate the risk (OR = 1.21, 95% CI = 1.05-1.39, P = .03). Instrumental variables comprised 5, 4, 7, 2, and 3 SNPs respectively. Convergent findings from WME, MR-Egger, and mode-based analyses corroborated these associations. Sensitivity checks, including heterogeneity, horizontal pleiotropy assessments, and leave-one-out diagnostics, affirmed the robustness and reliability of our instruments across diverse gastric malignancy tissues without substantial bias. Our research suggests that the interleukin 6 receptor potentially mitigates, while fatty acid-binding protein 4 may contribute to the pathogenesis of gastric cancer (GC). Unraveling the intricate biological interplay between inflammation and oncogenesis offers valuable insights for preemptive strategies and therapeutic interventions in gastric malignancy management.
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Affiliation(s)
- Wenjing Yang
- General Hospital of Ningxia Medical University, Yinchuan, China
| | - Ye Lv
- General Hospital of Ningxia Medical University, Yinchuan, China
| | - Tao Ma
- General Hospital of Ningxia Medical University, Yinchuan, China
| | - Ningju Wang
- General Hospital of Ningxia Medical University, Yinchuan, China
| | - Ping Chen
- General Hospital of Ningxia Medical University, Yinchuan, China
| | - Quanxia Liu
- General Hospital of Ningxia Medical University, Yinchuan, China
| | - Hui Yan
- General Hospital of Ningxia Medical University, Yinchuan, China
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Cao K, Lyu Y, Chen J, He C, Lyu X, Zhang Y, Chen L, Jiang Y, Xiang J, Liu B, Wu C. Prognostic Implication of Plasma Metabolites in Gastric Cancer. Int J Mol Sci 2023; 24:12774. [PMID: 37628957 PMCID: PMC10454100 DOI: 10.3390/ijms241612774] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 08/02/2023] [Accepted: 08/07/2023] [Indexed: 08/27/2023] Open
Abstract
Gastric cancer (GC) typically carries a poor prognosis as it is often diagnosed at a late stage. Altered metabolism has been found to impact cancer outcomes and affect patients' quality of life, and the role of metabolites in gastric cancer prognosis has not been sufficiently understood. We aimed to establish a prognostic prediction model for GC patients based on a metabolism-associated signature and identify the unique role of metabolites in the prognosis of GC. Thus, we conducted untargeted metabolomics to detect the plasma metabolites of 218 patients with gastric adenocarcinoma and explored the metabolites related to the survival of patients with gastric cancer. Firstly, we divided patients into two groups based on the cutoff value of the abundance of each of the 60 metabolites and compared the differences using Kaplan-Meier (K-M) survival analysis. As a result, 23 metabolites associated with gastric cancer survival were identified. To establish a risk score model, we performed LASSO regression and Cox regression analysis on the 60 metabolites and identified 8 metabolites as an independent prognostic factor. Furthermore, a nomogram incorporating clinical parameters and the metabolic signature was constructed to help individualize outcome predictions. The results of the ROC curve and nomogram plot showed good predictive performance of metabolic risk features. Finally, we performed pathway analysis on the 24 metabolites identified in the two parts, and the results indicated that purine metabolism and arachidonic acid metabolism play important roles in gastric cancer prognosis. Our study highlights the important role of metabolites in the progression of gastric cancer and newly identified metabolites could be potential biomarkers or therapeutic targets for gastric cancer patients.
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Affiliation(s)
- Kang Cao
- Department of Preventive Medicine, School of Public Health, Fujian Medical University, Fuzhou 350122, China; (K.C.)
- The Key Laboratory of Environment and Health, School of Public Health, Fujian Medical University, Fuzhou 350122, China
| | - Yanping Lyu
- Department of Preventive Medicine, School of Public Health, Fujian Medical University, Fuzhou 350122, China; (K.C.)
- The Key Laboratory of Environment and Health, School of Public Health, Fujian Medical University, Fuzhou 350122, China
| | - Jingwen Chen
- Department of Preventive Medicine, School of Public Health, Fujian Medical University, Fuzhou 350122, China; (K.C.)
- The Key Laboratory of Environment and Health, School of Public Health, Fujian Medical University, Fuzhou 350122, China
| | - Chenzhou He
- Department of Preventive Medicine, School of Public Health, Fujian Medical University, Fuzhou 350122, China; (K.C.)
- The Key Laboratory of Environment and Health, School of Public Health, Fujian Medical University, Fuzhou 350122, China
| | - Xuejie Lyu
- Department of Preventive Medicine, School of Public Health, Fujian Medical University, Fuzhou 350122, China; (K.C.)
- The Key Laboratory of Environment and Health, School of Public Health, Fujian Medical University, Fuzhou 350122, China
| | - Yuling Zhang
- Department of Preventive Medicine, School of Public Health, Fujian Medical University, Fuzhou 350122, China; (K.C.)
- The Key Laboratory of Environment and Health, School of Public Health, Fujian Medical University, Fuzhou 350122, China
| | - Liangping Chen
- Department of Preventive Medicine, School of Public Health, Fujian Medical University, Fuzhou 350122, China; (K.C.)
- The Key Laboratory of Environment and Health, School of Public Health, Fujian Medical University, Fuzhou 350122, China
| | - Yu Jiang
- Department of Preventive Medicine, School of Public Health, Fujian Medical University, Fuzhou 350122, China; (K.C.)
- The Key Laboratory of Environment and Health, School of Public Health, Fujian Medical University, Fuzhou 350122, China
| | - Jianjun Xiang
- Department of Preventive Medicine, School of Public Health, Fujian Medical University, Fuzhou 350122, China; (K.C.)
- The Key Laboratory of Environment and Health, School of Public Health, Fujian Medical University, Fuzhou 350122, China
| | - Baoying Liu
- Department of Preventive Medicine, School of Public Health, Fujian Medical University, Fuzhou 350122, China; (K.C.)
- The Key Laboratory of Environment and Health, School of Public Health, Fujian Medical University, Fuzhou 350122, China
| | - Chuancheng Wu
- Department of Preventive Medicine, School of Public Health, Fujian Medical University, Fuzhou 350122, China; (K.C.)
- The Key Laboratory of Environment and Health, School of Public Health, Fujian Medical University, Fuzhou 350122, China
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