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Sasajima N, Sumazaki M, Oshima Y, Ito M, Yajima S, Takizawa H, Wang H, Li SY, Zhang BS, Yoshida Y, Hiwasa T, Shimada H. Stage-Specific Alteration and Prognostic Relationship of Serum Fumarate Hydratase Autoantibodies in Gastric Cancer. Int J Mol Sci 2024; 25:5470. [PMID: 38791507 PMCID: PMC11121488 DOI: 10.3390/ijms25105470] [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: 04/10/2024] [Revised: 05/09/2024] [Accepted: 05/13/2024] [Indexed: 05/26/2024] Open
Abstract
The relationship between energy production and cancer is attracting attention. This study aimed to investigate the clinicopathological significance of fumarate hydratase (FH), a tricarboxylic acid cycle enzyme, in gastric cancer using autoantibodies as biomarkers. The study analyzed 116 patients who underwent gastric cancer surgery and 96 healthy controls. Preoperative serum FH autoantibody (s-FH-Ab) titers were analyzed using an immunosorbent assay with an amplified luminescent proximity homogeneous assay. Receiver operating characteristic analysis was used to determine the cutoff s-FH-Ab titer. Clinicopathological factors and prognosis were compared between the high and low s-FH-Ab groups. The s-FH-Ab levels were significantly higher in the gastric cancer group than in the control group (p = 0.01). Levels were elevated even in patients with stage I gastric cancer compared with healthy controls (p = 0.02). A low s-FH-Ab level was significantly associated with distant metastasis (p = 0.01), peritoneal dissemination (p < 0.05), and poor overall survival (p < 0.01). Multivariate analysis revealed that low s-FH-Ab levels were an independent risk factor for poor prognosis (p < 0.01). Therefore, s-FH-Ab levels may be a useful biomarker for early diagnosis and the prediction of prognosis in patients with gastric cancer.
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Affiliation(s)
- Natsuko Sasajima
- Department of Gastroenterological Surgery, Toho University School of Medicine, Tokyo 143-8541, Japan; (N.S.); (Y.O.); (S.Y.)
| | - Makoto Sumazaki
- Department of Clinical Oncology, Toho University Graduate School of Medicine, Tokyo 143-8541, Japan; (M.S.); (M.I.); (T.H.)
| | - Yoko Oshima
- Department of Gastroenterological Surgery, Toho University School of Medicine, Tokyo 143-8541, Japan; (N.S.); (Y.O.); (S.Y.)
| | - Masaaki Ito
- Department of Clinical Oncology, Toho University Graduate School of Medicine, Tokyo 143-8541, Japan; (M.S.); (M.I.); (T.H.)
| | - Satoshi Yajima
- Department of Gastroenterological Surgery, Toho University School of Medicine, Tokyo 143-8541, Japan; (N.S.); (Y.O.); (S.Y.)
| | - Hirotaka Takizawa
- Port Square Kashiwado Clinic, Kashiwado Memorial Foundation, Chiba 260-0025, Japan;
| | - Hao Wang
- Department of Neurological Surgery, Chiba University Graduate School of Medicine, Chiba 260-8670, Japan; (H.W.); (S.-Y.L.); (B.-S.Z.); (Y.Y.)
| | - Shu-Yang Li
- Department of Neurological Surgery, Chiba University Graduate School of Medicine, Chiba 260-8670, Japan; (H.W.); (S.-Y.L.); (B.-S.Z.); (Y.Y.)
| | - Bo-Shi Zhang
- Department of Neurological Surgery, Chiba University Graduate School of Medicine, Chiba 260-8670, Japan; (H.W.); (S.-Y.L.); (B.-S.Z.); (Y.Y.)
| | - Yoichi Yoshida
- Department of Neurological Surgery, Chiba University Graduate School of Medicine, Chiba 260-8670, Japan; (H.W.); (S.-Y.L.); (B.-S.Z.); (Y.Y.)
| | - Takaki Hiwasa
- Department of Clinical Oncology, Toho University Graduate School of Medicine, Tokyo 143-8541, Japan; (M.S.); (M.I.); (T.H.)
- Department of Neurological Surgery, Chiba University Graduate School of Medicine, Chiba 260-8670, Japan; (H.W.); (S.-Y.L.); (B.-S.Z.); (Y.Y.)
| | - Hideaki Shimada
- Department of Gastroenterological Surgery, Toho University School of Medicine, Tokyo 143-8541, Japan; (N.S.); (Y.O.); (S.Y.)
- Department of Clinical Oncology, Toho University Graduate School of Medicine, Tokyo 143-8541, Japan; (M.S.); (M.I.); (T.H.)
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Burz C, Pop V, Silaghi C, Lupan I, Samasca G. Prognosis and Treatment of Gastric Cancer: A 2024 Update. Cancers (Basel) 2024; 16:1708. [PMID: 38730659 PMCID: PMC11083929 DOI: 10.3390/cancers16091708] [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: 04/03/2024] [Revised: 04/23/2024] [Accepted: 04/26/2024] [Indexed: 05/13/2024] Open
Abstract
Due to the high death rate associated with gastric cancer, a great deal of research has been conducted on this disease. The goal of this paper was to start a trimestral review of 2024 for the year that had just started. The scientific literature from 1 January 2024 was chosen with consideration of the the guidelines of the European Society of Medical Oncology (ESMO), which are updated with new findings but not systematically reviewed annually. We used the search term "gastric cancer" to find the most current publications in the PubMed database related to the prognosis and treatment of gastric cancer. As previously said, the only articles that satisfied the inclusion criteria were those from 2024. Articles with case reports were eliminated since they had nothing to do with our research. The treatment of gastric cancer is the focus of the majority of articles from 2024. The primary research axes include surgery and immunonutrition, immunotherapy and Helicobacter pylori, and therapeutic targets. Patients with GC may experience less psychological, social, and financial hardship if the recently identified markers discovered in circulation are better assessed and validated. This could be achieved by either including the markers in an artificial intelligence-based diagnostic score or by using them in conjunction with traditional diagnostic methods. Due to the rising death rate associated with GC, funding for research into diagnosis, prognosis, therapy, and therapeutic targets is essential.
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Affiliation(s)
- Claudia Burz
- Institute of Oncology “Prof. Dr. Ion Chiricuta”, 400015 Cluj-Napoca, Romania; (C.B.); (V.P.)
- Department of Immunology, Iuliu Hatieganu University of Medicine and Pharmacy, 400162 Cluj-Napoca, Romania
| | - Vlad Pop
- Institute of Oncology “Prof. Dr. Ion Chiricuta”, 400015 Cluj-Napoca, Romania; (C.B.); (V.P.)
| | - Ciprian Silaghi
- Department of Biochemistry, Iuliu Hatieganu University of Medicine and Pharmacy, 400338 Cluj-Napoca, Romania;
| | - Iulia Lupan
- Institute for Interdisciplinary Research in Bio-Nano-Sciences, 400271 Cluj-Napoca, Romania;
- Department of Molecular Biology, Babes-Bolyai University, 400084 Cluj-Napoca, Romania
| | - Gabriel Samasca
- Department of Immunology, Iuliu Hatieganu University of Medicine and Pharmacy, 400162 Cluj-Napoca, Romania
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Fasano R, Serratì S, Rafaschieri T, Longo V, Di Fonte R, Porcelli L, Azzariti A. Small-Cell Lung Cancer: Is Liquid Biopsy a New Tool Able to Predict the Efficacy of Immunotherapy? Biomolecules 2024; 14:396. [PMID: 38672414 PMCID: PMC11048475 DOI: 10.3390/biom14040396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 03/18/2024] [Accepted: 03/22/2024] [Indexed: 04/28/2024] Open
Abstract
Small-cell lung cancer (SCLC) cases represent approximately 15% of all lung cancer cases, remaining a recalcitrant malignancy with poor survival and few treatment options. In the last few years, the addition of immunotherapy to chemotherapy improved clinical outcomes compared to chemotherapy alone, resulting in the current standard of care for SCLC. However, the advantage of immunotherapy only applies to a few SCLC patients, and predictive biomarkers selection are lacking for SCLC. In particular, due to some features of SCLC, such as high heterogeneity, elevated cell plasticity, and low-quality tissue samples, SCLC biopsies cannot be used as biomarkers. Therefore, the characterization of the tumor and, subsequently, the selection of an appropriate therapeutic combination may benefit greatly from liquid biopsy. Soluble factors, circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), and extracellular vesicles (EVs) are now useful tools in the characterization of SCLC. This review summarizes the most recent data on biomarkers detectable with liquid biopsy, emphasizing their role in supporting tumor detection and their potential role in SCLC treatment choice.
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Affiliation(s)
- Rossella Fasano
- Laboratory of Experimental Pharmacology, IRCCS Istituto Tumori Giovanni Paolo II, V.Le O. Flacco, 65, 70124 Bari, Italy
| | - Simona Serratì
- Laboratory of Experimental Pharmacology, IRCCS Istituto Tumori Giovanni Paolo II, V.Le O. Flacco, 65, 70124 Bari, Italy
| | - Tania Rafaschieri
- Laboratory of Experimental Pharmacology, IRCCS Istituto Tumori Giovanni Paolo II, V.Le O. Flacco, 65, 70124 Bari, Italy
| | - Vito Longo
- Medical Thoracic Oncology Unit, IRCCS Istituto Tumori Giovanni Paolo II, 70124 Bari, Italy
| | - Roberta Di Fonte
- Laboratory of Experimental Pharmacology, IRCCS Istituto Tumori Giovanni Paolo II, V.Le O. Flacco, 65, 70124 Bari, Italy
| | - Letizia Porcelli
- Laboratory of Experimental Pharmacology, IRCCS Istituto Tumori Giovanni Paolo II, V.Le O. Flacco, 65, 70124 Bari, Italy
| | - Amalia Azzariti
- Laboratory of Experimental Pharmacology, IRCCS Istituto Tumori Giovanni Paolo II, V.Le O. Flacco, 65, 70124 Bari, Italy
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Nakajima D, Konno R, Miyashita Y, Ishikawa M, Ohara O, Kawashima Y. Proteome Analysis of Serum Purified Using Solanum tuberosum and Lycopersicon esculentum Lectins. Int J Mol Sci 2024; 25:1315. [PMID: 38279312 PMCID: PMC10816257 DOI: 10.3390/ijms25021315] [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: 12/27/2023] [Revised: 01/17/2024] [Accepted: 01/19/2024] [Indexed: 01/28/2024] Open
Abstract
Serum and plasma exhibit a broad dynamic range of protein concentrations, posing challenges for proteome analysis. Various technologies have been developed to reduce this complexity, including high-abundance depletion methods utilizing antibody columns, extracellular vesicle enrichment techniques, and trace protein enrichment using nanobead cocktails. Here, we employed lectins to address this, thereby extending the scope of biomarker discovery in serum or plasma using a novel approach. We enriched serum proteins using 37 different lectins and subjected them to LC-MS/MS analysis with data-independent acquisition. Solanum tuberosum lectin (STL) and Lycopersicon esculentum lectin (LEL) enabled the detection of more serum proteins than the other lectins. STL and LEL bind to N-acetylglucosamine oligomers, emphasizing the significance of capturing these oligomer-binding proteins when analyzing serum trace proteins. Combining STL and LEL proved more effective than using them separately, allowing us to identify over 3000 proteins from serum through single-shot proteome analysis. We applied the STL/LEL trace-protein enrichment method to the sera of systemic lupus erythematosus model mice. This revealed differences in >1300 proteins between the systemic lupus erythematosus model and control mouse sera, underscoring the utility of this method for biomarker discovery.
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Affiliation(s)
- Daisuke Nakajima
- Department of Applied Genomics, Kazusa DNA Research Institute, 2-5-23 Kazusa Kamatari, Kisarazu 292-0818, Chiba, Japan; (D.N.); (R.K.); (Y.M.); (M.I.); (O.O.)
| | - Ryo Konno
- Department of Applied Genomics, Kazusa DNA Research Institute, 2-5-23 Kazusa Kamatari, Kisarazu 292-0818, Chiba, Japan; (D.N.); (R.K.); (Y.M.); (M.I.); (O.O.)
| | - Yasuomi Miyashita
- Department of Applied Genomics, Kazusa DNA Research Institute, 2-5-23 Kazusa Kamatari, Kisarazu 292-0818, Chiba, Japan; (D.N.); (R.K.); (Y.M.); (M.I.); (O.O.)
- Department of Developmental Biology, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba 260-8670, Chiba, Japan
| | - Masaki Ishikawa
- Department of Applied Genomics, Kazusa DNA Research Institute, 2-5-23 Kazusa Kamatari, Kisarazu 292-0818, Chiba, Japan; (D.N.); (R.K.); (Y.M.); (M.I.); (O.O.)
| | - Osamu Ohara
- Department of Applied Genomics, Kazusa DNA Research Institute, 2-5-23 Kazusa Kamatari, Kisarazu 292-0818, Chiba, Japan; (D.N.); (R.K.); (Y.M.); (M.I.); (O.O.)
| | - Yusuke Kawashima
- Department of Applied Genomics, Kazusa DNA Research Institute, 2-5-23 Kazusa Kamatari, Kisarazu 292-0818, Chiba, Japan; (D.N.); (R.K.); (Y.M.); (M.I.); (O.O.)
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Ke X, Cai X, Bian B, Shen Y, Zhou Y, Liu W, Wang X, Shen L, Yang J. Predicting early gastric cancer risk using machine learning: A population-based retrospective study. Digit Health 2024; 10:20552076241240905. [PMID: 38559579 PMCID: PMC10979538 DOI: 10.1177/20552076241240905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 03/05/2024] [Indexed: 04/04/2024] Open
Abstract
Background Early detection and treatment are crucial for reducing gastrointestinal tumour-related mortality. The diagnostic efficiency of the most commonly used diagnostic markers for gastric cancer (GC) is not very high. A single laboratory test cannot meet the requirements of early screening, and machine learning methods are needed to aid the early diagnosis of GC by combining multiple indicators. Methods Based on the XGBoost algorithm, a new model was developed to distinguish between GC and precancerous lesions in newly admitted patients between 2018 and 2023 using multiple laboratory tests. We evaluated the ability of the prediction score derived from this model to predict early GC. In addition, we investigated the efficacy of the model in correctly screening for GC given negative protein tumour marker results. Results The XHGC20 model constructed using the XGBoost algorithm could distinguish GC from precancerous disease well (area under the receiver operating characteristic curve [AUC] = 0.901), with a sensitivity, specificity and cut-off value of 0.830, 0.806 and 0.265, respectively. The prediction score was very effective in the diagnosis of early GC. When the cut-off value was 0.27, and the AUC was 0.888, the sensitivity and specificity were 0.797 and 0.807, respectively. The model was also effective at evaluating GC given negative conventional markers (AUC = 0.970), with the sensitivity and specificity of 0.941 and 0.906, respectively, which helped to reduce the rate of missed diagnoses. Conclusions The XHGC20 model established by the XGBoost algorithm integrates information from 20 clinical laboratory tests and can aid in the early screening of GC, providing a useful new method for auxiliary laboratory diagnosis.
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Affiliation(s)
- Xing Ke
- Department of Clinical Laboratory, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Faculty of Medical Laboratory Science, College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Institute of Artificial Intelligence Medicine, Shanghai Academy of Experimental Medicine, Shanghai, China
- Department of Pathology, Ruijin Hospital and College of Basic Medical Sciences, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xinyu Cai
- Department of Clinical Laboratory, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Bingxian Bian
- Department of Clinical Laboratory, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yuanheng Shen
- Department of Clinical Laboratory, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yunlan Zhou
- Department of Clinical Laboratory, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wei Liu
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing, China
| | - Xu Wang
- Department of Pathology, Ruijin Hospital and College of Basic Medical Sciences, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lisong Shen
- Department of Clinical Laboratory, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Faculty of Medical Laboratory Science, College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Institute of Artificial Intelligence Medicine, Shanghai Academy of Experimental Medicine, Shanghai, China
| | - Junyao Yang
- Department of Clinical Laboratory, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Faculty of Medical Laboratory Science, College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Institute of Artificial Intelligence Medicine, Shanghai Academy of Experimental Medicine, Shanghai, China
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Ji C, Cai H, Jin X, Yin K, Zhao D, Feng Z, Liu L. Identification of Immune Infiltrating Cell-Related Biomarkers in Early Gastric Cancer Progression. Technol Cancer Res Treat 2024; 23:15330338241262724. [PMID: 38860335 PMCID: PMC11168250 DOI: 10.1177/15330338241262724] [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: 12/24/2023] [Revised: 04/25/2024] [Accepted: 05/28/2024] [Indexed: 06/12/2024] Open
Abstract
OBJECTIVES Gastric cancer (GC) is one of the most prevalent malignancies worldwide, and early detection is crucial for improving patient survival rates. We aimed to identify immune infiltrating cell-related biomarkers in early gastric cancer (EGC) progression. METHODS The GSE55696 and GSE130823 datasets with low-grade intraepithelial neoplasia (LGIN), high-grade intraepithelial neoplasia (HGIN), and EGC samples were downloaded from the Gene Expression Omnibus database to perform an observational study. Immune infiltration analysis was performed by single sample gene set enrichment analysis and Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data. Weighted gene co-expression network analysis was used to explore the co-expression modules and genes, and further enrichment analysis was performed on these genes. A protein-protein interaction (PPI) network of these genes was constructed to identify biomarkers associated with EGC progression. Screened hub genes were validated by the rank sum test and reverse transcription quantitative polymerase chain reaction. RESULTS Immune scores were significantly elevated in EGC samples compared to LGIN and HGIN samples. The green-yellow module exhibited the strongest correlation with both immune score and disease progression. The 87 genes within this module were associated with the chemokine signaling pathways, the PI3K-Akt signaling pathways, leukocyte transendothelial migration, and Ras signaling pathways. Through PPI network analysis, the hub genes identified were protein tyrosine phosphatase receptor-type C (PTPRC), pleckstrin, CD53, CD48, lymphocyte cytosolic protein 1 (LCP1), hematopoietic cell-specific Lyn substrate 1, IKAROS Family Zinc Finger 1, Bruton tyrosine kinase, and Vav guanine nucleotide exchange factor 1. Notably, CD48, LCP1, and PTPRC showed high expression levels in EGC samples, with the remaining hub genes demonstrating a similar expression trend. CONCLUSION This study identified 9 immune cell-related biomarkers that may be actively involved in the progression of EGC and serve as potential targets for GC diagnosis and treatment.
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Affiliation(s)
- Chenguang Ji
- Department of Gastroenterology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, P.R. China
| | - Hongmei Cai
- Deparment of Oncology, Hebei Chest Hospital, Shijiazhuang, Hebei, P.R. China
| | - Xiaoxu Jin
- Department of Gastroenterology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, P.R. China
| | - Kaige Yin
- Department of Gastroenterology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, P.R. China
| | - Dongqiang Zhao
- Department of Gastroenterology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, P.R. China
| | - Zhijie Feng
- Department of Gastroenterology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, P.R. China
| | - Li Liu
- Department of Gastroenterology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, P.R. China
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