Exosomal miR-1246 in serum as a potential biomarker for early diagnosis of gastric cancer

2019 ◽  
Vol 25 (1) ◽  
pp. 89-99 ◽  
Author(s):  
Yuntao Shi ◽  
Zhonghong Wang ◽  
Xiaojuan Zhu ◽  
Ling Chen ◽  
Yilan Ma ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Early Diagnosis ◽  
Potential Biomarker

scholarly journals Inverse correlation between Interleukin-34 and gastric cancer, a potential biomarker for prognosis

2020 ◽  
Author(s):  
Qinghua Liu ◽  
Ying Zhang ◽  
Jiwei Zhang ◽  
Kun Tao ◽  
Brett D Hambly ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Prognostic Factor ◽  
Early Diagnosis ◽  
Precision Medicine ◽  
Cellular Therapy ◽  
Inverse Correlation ◽  
Independent Prognostic Factor ◽  
Cancer Tissue ◽  
High Morbidity ◽  
Potential Biomarker

Abstract Background Gastric cancer (GC) is a malignancy with high morbidity/mortality, partly due to a lack of reliable biomarkers for early diagnosis. It is important to develop reliable biomarker(s) with specificity, sensitivity and convenience for early diagnosis. The role of tumour-associated macrophages (TAMs) and survival of GC patients are controversial. Macrophage colony stimulating factor (MCSF) regulates monocytes/macrophages. Elevated MCSF is correlated with invasion, metastasis and poor survival of tumour patients. IL-34, a ligand of the MCSF receptor, acts as a “twin” to MCSF, demonstrating overlapping and complimentary actions. IL-34 involvement in tumours is controversial, possibly due to the levels of MCSF receptors. While the IL34/MCSF/MCSFR axis is very important for regulating macrophage differentiation, the specific interplay between these cytokines, macrophages and tumour development is unclear.Methods A multi-factorial evaluation could provide more objective utility, particularly for either prediction and/or prognosis of gastric cancer. Precision medicine requires molecular diagnosis to determine the specifically mutant function of tumours, and is becoming popular in the treatment of malignancy. Therefore, elucidating specific molecular signalling pathways in specific cancers facilitates the success of a precision medicine approach. Gastric cancer tissue arrays were generated from stomach samples with TNM stage, invasion depth and the demography of these patients (n = 185). Using immunohistochemistry/histopathology, MCSF, IL-34 and macrophages were determined.Results We found that IL-34 may serve as a predictive biomarker, but not as an independent, prognostic factor in GC; MCSF inversely correlated with survival of GC in TNM III‑IV subtypes. Increased CD68+TAMs were a good prognostic factor in some cases and could be used as an independent prognostic factor in male T3 stage GC.Conclusion Our data support the potency of IL-34, MCSF, TAMs and the combination of IL34/TAMs as novel biological markers for GC, and may provide new insight for both diagnosis and cellular therapy of GC.

scholarly journals Inverse correlation between Interleukin-34 and gastric cancer, a potential biomarker for prognosis

2020 ◽  
Author(s):  
Qinghua Liu ◽  
Ying Zhang ◽  
Jiwei Zhang ◽  
Kun Tao ◽  
Brett D Hambly ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Prognostic Factor ◽  
Early Diagnosis ◽  
Precision Medicine ◽  
Cellular Therapy ◽  
Inverse Correlation ◽  
Independent Prognostic Factor ◽  
Cancer Tissue ◽  
High Morbidity ◽  
Potential Biomarker

Abstract Background : Gastric cancer (GC) is a malignancy with high morbidity/mortality, partly due to a lack of reliable biomarkers for early diagnosis. It is important to develop reliable biomarker(s) with specificity, sensitivity and convenience for early diagnosis. The role of tumour-associated macrophages (TAMs) and survival of GC patients are controversial. Macrophage colony stimulating factor (MCSF) regulates monocytes/macrophages. Elevated MCSF is correlated with invasion, metastasis and poor survival of tumour patients. IL-34, a ligand of the MCSF receptor, acts as a “twin” to MCSF, demonstrating overlapping and complimentary actions. IL-34 involvement in tumours is controversial, possibly due to the levels of MCSF receptors. While the IL‑34/MCSF/MCSFR axis is very important for regulating macrophage differentiation, the specific interplay between these cytokines, macrophages and tumour development is unclear. Methods : A multi-factorial evaluation could provide more objective utility, particularly for either prediction and/or prognosis of gastric cancer. Precision medicine requires molecular diagnosis to determine the specifically mutant function of tumours, and is becoming popular in the treatment of malignancy. Therefore, elucidating specific molecular signalling pathways in specific cancers facilitates the success of a precision medicine approach. Gastric cancer tissue arrays were generated from stomach samples with TNM stage, invasion depth and the demography of these patients (n=185). Using immunohistochemistry/histopathology, MCSF, IL-34 and macrophages were determined. Results : We found that IL-34 may serve as a predictive biomarker, but not as an independent, prognostic factor in GC; MCSF inversely correlated with survival of GC in TNM III‑IV subtypes. Increased CD68 + TAMs were a good prognostic factor in some cases and could be used as an independent prognostic factor in male T3 stage GC. Conclusion : Our data support the potency of IL-34, MCSF, TAMs and the combination of IL‑34/TAMs as novel biological markers for GC, and may provide new insight for both diagnosis and cellular therapy of GC.

scholarly journals The miR-17-92 cluster as a potential biomarker for the early diagnosis of gastric cancer: evidence and literature review

Oncotarget ◽  
2017 ◽  
Vol 8 (28) ◽  
pp. 45060-45071 ◽  
Author(s):  
Hong Li ◽  
Qiong Wu ◽  
Ting Li ◽  
Changhao Liu ◽  
Lin Xue ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Early Diagnosis ◽  
Literature Review ◽  
Potential Biomarker

scholarly journals Prediction of Gastric Cancer-Related Proteins Based on Graph Fusion Method

2021 ◽  
Vol 9 ◽  
Author(s):  
Hao Zhang ◽  
Ruisi Xu ◽  
Meng Ding ◽  
Ying Zhang
Keyword(s):  
Gastric Cancer ◽  
Early Diagnosis ◽  
High Speed ◽  
Large Scale ◽  
Interaction Network ◽  
Proteomics Data ◽  
Speed Increase ◽  
Topological Features ◽  
Potential Biomarker ◽  
Related Proteins

Gastric cancer is a common malignant tumor of the digestive system with no specific symptoms. Due to the limited knowledge of pathogenesis, patients are usually diagnosed in advanced stage and do not have effective treatment methods. Proteome has unique tissue and time specificity and can reflect the influence of external factors that has become a potential biomarker for early diagnosis. Therefore, discovering gastric cancer-related proteins could greatly help researchers design drugs and develop an early diagnosis kit. However, identifying gastric cancer-related proteins by biological experiments is time- and money-consuming. With the high speed increase of data, it has become a hot issue to mine the knowledge of proteomics data on a large scale through computational methods. Based on the hypothesis that the stronger the association between the two proteins, the more likely they are to be associated with the same disease, in this paper, we constructed both disease similarity network and protein interaction network. Then, Graph Convolutional Networks (GCN) was applied to extract topological features of these networks. Finally, Xgboost was used to identify the relationship between proteins and gastric cancer. Results of 10-cross validation experiments show high area under the curve (AUC) (0.85) and area under the precision recall (AUPR) curve (0.76) of our method, which proves the effectiveness of our method.

scholarly journals Metabolomic profiling on plasma reveals potential biomarkers for screening and early diagnosis of gastric cancer and precancerous stages

2020 ◽  
Author(s):  
Lijing Du ◽  
Shasha Li ◽  
Xue Xiao ◽  
Jin Li ◽  
Huizi Jin ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Early Diagnosis ◽  
High Performance ◽  
Diagnostic Performance ◽  
Roc Analysis ◽  
Characteristic Curve ◽  
Disease Process ◽  
Potential Biomarker ◽  
Disease Stratification ◽  
Potential Biomarkers

Abstract Background: Gastric cancer (GC) remains one of the most common cancers all over the world. The greatest challenge for GC is that it is often detected at advanced stages, leading to the loss of optimum time for treatment and giving rise to poor prognosis. Thus, there is a critical need to develop effective and noninvasive strategies for early diagnosis of the disease process. Methods: In total, 82 participants were enrolled in the study, including 50 chronic superficial gastritis (CSG) patients, 7 early gastric cancer (EGC) and 25 advanced gastric cancer (AGC) ones. Metabolites profiling on patient plasma was performed using ultra-high performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry ( UPLC-Q-TOF/MS ). Principal components analysis as well as orthogonal partial least squares-discriminant analysis was utilized to evaluate the variation on endogenous metabolites for GC patients and to screen potential biomarkers. Furthermore, the biomarker panels detected above were used to create logistic regression models, which discrimination efficiency and accuracy was ascertained by receiver operating characteristic curve (ROC) analysis. Metabolic pathways were carried out on MetaboAnalyst. Results: Totally 50 metabolites were detected differentially expressed among CSG, EGC and AGC patients. L-carnitine, L-proline, pyruvaldehyde, phosphatidylcholines (PC) (14:0/18:0), lysophosphatidylcholine (14:0) (LysoPC 14:0), lysinoalanine were defined as the potential biomarker panel for the diagnosis among CSG and EGC patients. Compared with EGC patients, 6 significantly changed metabolites, PC(O-18:0/0:0) and LysoPC(20:4(5Z,8Z,11Z,14Z)) were found to be up-regulated, whereas L-proline, L-valine, adrenic acid and pyruvaldehyde to be down-regulated in AGC patients. ROC analysis demonstrated a high diagnostic performance for metabolite panels with area under the curve (AUC) of 0.931 to 1. Moreover, the metabolomic pathway analysis revealed several metabolism pathway disruptions, including amino acid and lipid metabolisms, in GC patients. Conclusions: In this study, a total of six differential metabolites that contributed to GC and precancerous stages were identified, respectively. The biomarker panels further improve diagnostic performance for detecting GC, with AUC values of more than 93.1%. It indicated that the biomarker panels may be sensitive to the early diagnosis of GC disease, which can be used as a promising diagnostic and prognostic tool for disease stratification studies.

scholarly journals Metabolomic profiling on plasma reveals potential biomarkers for screening and early diagnosis of intestinal-type gastric cancer and precancerous stages

2020 ◽  
Author(s):  
Lijing Du ◽  
Shasha Li ◽  
Xue Xiao ◽  
Jin Li ◽  
Huizi Jin ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Early Diagnosis ◽  
Pathway Analysis ◽  
Roc Analysis ◽  
Characteristic Curve ◽  
Disease Process ◽  
Intestinal Type ◽  
Metabolic Pathway Analysis ◽  
Potential Biomarker ◽  
Potential Biomarkers

Abstract Background: Gastric cancer (GC) with majority of intestinal-type adenocaricinoma remains one of the most common cancers all over the world. GC faces a great challenge in the clinical diagnosis, that it often can be detected at advanced stages, and leads to the loss of optimum time for treatment and poor prognosis. Thus, there is a critical need to develop effective and noninvasive strategies for early diagnosis of the disease process. Methods: Totally, 82 participants were enrolled in the study, including 50 chronic superficial gastritis (CSG) patients, 7 intestinal-type early gastric cancer (EGC) and 25 intestinal-type advanced gastric cancer (AGC) ones. Metabolites profiling on patient plasma was performed using ultra-high performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry (UPLC-Q-TOF/MS). Principal components analysis, orthogonal partial least squares-discriminant analysis as well as Random forest were utilized to evaluate the variation on endogenous metabolites for intestinal-type GC patients and to screen potential biomarkers. Furthermore, the proposed biomarkers were used to create logistic regression models, which discrimination efficiency and accuracy was ascertained by receiver operating characteristic curve (ROC) analysis. Metabolic pathway analysis were carried out on MetaboAnalyst.Results: Totally 50 metabolites were detected with differentially expression among CSG, intestinal-type EGC and AGC patients. L-carnitine, L-proline, pyruvaldehyde, phosphatidylcholines (PC) (14:0/18:0), lysophosphatidylcholine (14:0) (LysoPC 14:0), lysinoalanine were defined as the potential biomarker panel for the diagnosis among CSG and EGC patients. Compared with EGC patients, 6 significantly changed metabolites, PC(O-18:0/0:0) and LysoPC(20:4(5Z,8Z,11Z,14Z)) were found to be up-regulated in AGC patients, whereas L-proline, L-valine, adrenic acid and pyruvaldehyde down-regulated. ROC analysis demonstrated a high diagnostic performance for metabolite panels with area under the curve (AUC) of 0.931 to 1. Moreover, the metabolomic pathway analysis revealed several metabolism pathway disorder, including amino acid and lipid metabolisms, in intestinal-type GC patients.Conclusions: In this study, a total of six metabolites were identified to contribute significantly to the diagnosis of intestinal-type GC and precancerous stages, respectively, and over 93.1% AUC value was achieved in AUC test on biomarker panels, It indicated that the biomarker panels are· sensitive to the early diagnosis of intestinal-type GC disease, which is expected to be developed as a promising diagnostic and prognostic tool for disease stratification studies.

scholarly journals Metabolomic profiling on plasma reveals potential biomarkers for screening and early diagnosis of intestinal-type gastric cancer and precancerous stages

2020 ◽  
Author(s):  
Lijing Du ◽  
Shasha Li ◽  
Xue Xiao ◽  
Jin Li ◽  
Huizi Jin ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Early Diagnosis ◽  
Pathway Analysis ◽  
Roc Analysis ◽  
Characteristic Curve ◽  
Disease Process ◽  
Intestinal Type ◽  
Metabolic Pathway Analysis ◽  
Potential Biomarker ◽  
Potential Biomarkers

Abstract Background: Gastric cancer (GC) with majority of intestinal-type adenocaricinoma remains one of the most common cancers all over the world. GC faces a great challenge in the clinical diagnosis, that it often can be detected at advanced stages, and leads to the loss of optimum time for treatment and poor prognosis. Thus, there is a critical need to develop effective and noninvasive strategies for early diagnosis of the disease process. Methods: Totally, 82 participants were enrolled in the study, including 50 chronic superficial gastritis (CSG) patients, 7 intestinal-type early gastric cancer (EGC) and 25 intestinal-type advanced gastric cancer (AGC) ones. Metabolites profiling on patient plasma was performed using ultra-high performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry (UPLC-Q-TOF/MS). Principal components analysis, orthogonal partial least squares-discriminant analysis as well as Random Forest were utilized to evaluate the variation on endogenous metabolites for intestinal-type GC patients and to screen potential biomarkers. Furthermore, the proposed biomarkers were used to create logistic regression models, which discrimination efficiency and accuracy was ascertained by receiver operating characteristic curve (ROC) analysis. Metabolic pathway analysis was carried out on MetaboAnalyst.Results: Totally 50 metabolites were detected with differentially expression among CSG, intestinal-type EGC and AGC patients. L-carnitine, L-proline, pyruvaldehyde, phosphatidylcholines (PC) (14:0/18:0), lysophosphatidylcholine (14:0) (LysoPC 14:0), lysinoalanine were defined as the potential biomarker panel for the diagnosis among CSG and EGC patients. Compared with EGC patients, 6 significantly changed metabolites, PC(O-18:0/0:0) and LysoPC(20:4(5Z,8Z,11Z,14Z)) were found to be up-regulated in AGC patients, whereas L-proline, L-valine, adrenic acid and pyruvaldehyde down-regulated. ROC analysis demonstrated a high diagnostic performance for metabolite panels with area under the curve (AUC) of 0.931 to 1. Moreover, the metabolomic pathway analysis revealed several metabolism pathway disorders, including amino acid and lipid metabolisms, in intestinal-type GC patients.Conclusions: In this study, a total of six metabolites were identified to contribute significantly to the diagnosis of intestinal-type GC and precancerous stages, respectively, and over 93.1% AUC value was achieved in AUC test on biomarker panels. It indicated that the biomarker panels are sensitive to the early diagnosis of intestinal-type GC disease, which is expected to be developed as a promising diagnostic and prognostic tool for disease stratification studies.

UPDATE ON THE DIAGNOSIS AND MANAGEMENT OF EARLY GASTRIC CANCER

2015 ◽  
pp. 5-14
Author(s):  
Van Huy Tran ◽  
Quang Trung Tran
Keyword(s):  
Gastric Cancer ◽  
Early Gastric Cancer ◽  
Early Diagnosis ◽  
Cancer Diagnosis ◽  
Good Prognosis ◽  
Accurate Diagnosis ◽  
White Light Endoscopy ◽  
Key Factor ◽  
Magnified Endoscopy ◽  
Endoscopic Characteristics

The prognosis of gastric cancer depends principally upon an early diagnosis. An early and accurate diagnosis of gastric cancer needs some basic knowledges about the endoscopic characteristics of white light endoscopy, chromoendoscopy, magnified endoscopy, FICE and NBI…A strategy of screening is also a key factor for early diagnosis. The treatment of early gastric cancer by endoscopy techniques have showed more and more advantages. Beside of EMR, the technique of ESD is now applied more widely and lead to a very good prognosis and nearly a curative treatment for the patients with early gastric cancer. Key words: gastric cancer, early gastric cancer, diagnosis, endoscopy

Sign in / Sign up

Export Citation Format

Share Document