Prognostic value of differentiation status in gastric cancer

Feng, Fan; Liu, Jinqiang; Wang, Fei; Zheng, Gaozan; Wang, Qiao; Liu, Shushang; Xu, Guanghui; Guo, Man; Lian, Xiao; Zhang, Hongwei

doi:10.1186/s12885-018-4780-0

Cited by 44 publications

(37 citation statements)

References 26 publications

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“…We found that MICA-129 single nucleotide polymorphism (SNP) (rs1051792) showed a strong linkage disequilibrium with other SNPs related to residues 36, 173, 206, We studied the relevance of the MICA alleles, both MICA-sequence and MICA-STR, on the clinicopathological characteristics of the tumor. We observed that the majority of patients with the MICA * A9/A6 heterozygote allele showed mainly a poorly differentiated tumor (p = 0.031) (Supplementary Table 2), indicating that the presence of this genotype is related to an advanced stage of the tumor, as also observed previously (37). However, we did not find an association between the presence of the most frequent MICAsequence alleles ( * 002, * 008 and * 009/049) and tumor size or differentiation grade (Supplementary Table 2).…”

Section: Mica Polymorphisms and Development Of Gcsupporting

confidence: 86%

Major Histocompatibility Complex Class I-Related Chain A (MICA) Allelic Variants Associate With Susceptibility and Prognosis of Gastric Cancer

et al. 2021

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Gastric cancer (GC) is the fifth most prevalent type of cancer worldwide. Gastric tumor cells express MICA protein, a ligand to NKG2D receptor that triggers natural killer (NK) cells effector functions for early tumor elimination. MICA gene is highly polymorphic, thus originating alleles that encode protein variants with a controversial role in cancer. The main goal of this work was to study MICA gene polymorphisms and their relationship with the susceptibility and prognosis of GC. Fifty patients with GC and 50 healthy volunteers were included in this study. MICA alleles were identified using Sanger sequencing methods. The analysis of MICA gene sequence revealed 13 MICA sequences and 5 MICA-short tandem repeats (STR) alleles in the studied cohorts We identified MICA*002 (*A9) as the most frequent allele in both, patients and controls, followed by MICA*008 allele (*A5.1). MICA*009/049 allele was significantly associated with increased risk of GC (OR: 5.11 [95% CI: 1.39–18.74], p = 0.014). The analysis of MICA-STR alleles revealed a higher frequency of MICA*A5 in healthy individuals than GC patients (OR = 0.34 [95% CI: 0.12–0.98], p = 0.046). Survival analysis after gastrectomy showed that patients with MICA*002/002 or MICA*002/004 alleles had significantly higher survival rates than those patients bearing MICA*002/008 (p = 0.014) or MICA*002/009 (MICA*002/049) alleles (p = 0.040). The presence of threonine in the position MICA-181 (MICA*009/049 allele) was more frequent in GC patients than controls (p = 0.023). Molecular analysis of MICA-181 showed that the presence of threonine provides greater mobility to the protein than arginine in the same position (MICA*004), which could explain, at least in part, some immune evasion mechanisms developed by the tumor. In conclusion, our findings suggest that the study of MICA alleles is crucial to search for new therapeutic approaches and may be useful for the evaluation of risk and prognosis of GC and personalized therapy.

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Section: Mica Polymorphisms and Development Of Gcsupporting

confidence: 86%

Major Histocompatibility Complex Class I-Related Chain A (MICA) Allelic Variants Associate With Susceptibility and Prognosis of Gastric Cancer

et al. 2021

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“… 3 The 5-year survival rate of patients with positive peritoneal lavage cytology is approximately 12%, and the median survival time of patients with peritoneal metastasis is approximately 6–7 months. 4 , 5 However, the sensitivity of gastric cancer peritoneal metastasis imaging and tumour marker detection is low. Therefore, the need to find sensitive diagnostic markers of gastric cancer with peritoneal metastasis is urgent.…”

Section: Introductionmentioning

confidence: 99%

<p>Discovering Biomarkers in Peritoneal Metastasis of Gastric Cancer by Metabolomics</p>

Pan¹,

Ma²,

Suo³

et al. 2020

OTT

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Background and Objective: Metabolomics has recently been applied in the field of oncology. In this study, we aimed to use metabolomics to explore biomarkers in peritoneal metastasis of gastric cancer. Methods: Peritoneal lavage fluid (PLF) of 65 gastric cancer patients and related clinical data were collected from the First Hospital of Jilin University. The metabolic components were identified by liquid chromatography-mass spectrometry (LC-MS). Total ion current (TIC) spectra, principal component analysis (PCA), and the Student's t-test were used to identify differential metabolites in PLF. A support vector machine (SVM) was used to screen the differential metabolites in PLF with a weight of 100%. Cluster analysis was used to evaluate the similarity between samples. Receiver operating characteristic (ROC) curve analysis was used to assess the diagnostic ability of the metabolites. Univariate and multivariate logistic regression analyses were used to identify potential risk factors for peritoneal metastasis of gastric cancer. Results: We found the differential levels of PLF metabolites by LC-MS, TIC spectra, PCA and the t-test. Cluster analysis showed the co-occurrence of metabolites in the peritoneal metastasis group (p<0.05). ROC analysis showed the diagnostic ability of metabolites (p<0.05). Univariate and multivariate logistic regression analyses showed the potential independent risk factors for peritoneal metastasis in gastric cancer patients (p<0.05). Conclusion: Through the statistical analysis of metabolomics, we found that TG (54:2

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“…In recent years, ME-NBI, a technology enabling detailed visualization of the microvascular and microsurface patterns of the gastric mucosa, has been widely used for diagnosing and classifying gastric lesions [35,36]. However, ME-NBI patterns are difficult to interpret, requiring endoscopists to have a thorough knowledge and be well trained [35,37], which greatly weakens the potential of this technology in the clinic.…”

Section: Discussionmentioning

confidence: 99%

A deep learning-based system for identifying differentiation status and delineating the margins of early gastric cancer in magnifying narrow-band imaging endoscopy

Ling

et al. 2020

Endoscopy

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INTRODUCTION:Accurate identification of early gastric cancer (EGC) differentiation status and margins is critical for determining the surgical strategy and achieving curative resection in EGC patients. The aim of this study was to develop a real-time system for accurately identifying differentiation status and delineating margins of EGC in Magnifying Narrow-band Imaging (ME-NBI) endoscopy. METHODS:2217 images from 145 EGC patients and 1870 images from 139 EGC patients were retrospectively collected to train and test the convolutional neural network (CNN) 1 for identifying EGC differentiation status. 882 images from 58 EGC patients were used to compare the performance of CNN1 with that of experts. 928 images from 132 EGC patients and 742 images from 87 EGC patients were used to train and test CNN2 for delineating EGC margins. RESULTS:The system correctly predicted differentiation status of EGCs with an accuracy of 83.3% (95%CI: [81.5%-84.9%]) in testing dataset. In the man-machine contest, CNN1 performed significantly better compared to the five experts [86.2% (95%CI: [75.1%-92.8%]) vs. 69.66 (95%CI: [64.1%-74.7%])). For delineating EGC margins, the system achieved an accuracy of 82.7% (95%CI: [78.6%-86.1%]) in differentiated EGC and 88.1% (95%CI: [84.2%-91.1%]) in undifferentiated EGC under an overlap ratio of 0.80. In unprocessed EGD videos, the system achieved real-time EGC differentiation status diagnosis and EGC margin delineation in ME-NBI endoscopy. CONCLUSION:We developed a deep learning-based system for accurately identifying differentiation status and delineating margins of EGC in ME-NBI endoscopy. This system achieved a superior performance when compared with experts, and was successfully tested in real EGC videos.

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Prognostic value of differentiation status in gastric cancer

Cited by 44 publications

References 26 publications

Major Histocompatibility Complex Class I-Related Chain A (MICA) Allelic Variants Associate With Susceptibility and Prognosis of Gastric Cancer

Major Histocompatibility Complex Class I-Related Chain A (MICA) Allelic Variants Associate With Susceptibility and Prognosis of Gastric Cancer

<p>Discovering Biomarkers in Peritoneal Metastasis of Gastric Cancer by Metabolomics</p>

A deep learning-based system for identifying differentiation status and delineating the margins of early gastric cancer in magnifying narrow-band imaging endoscopy

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