Classification Of Gastric Cancer Research Articles

A Proposed New Clinical Classification of Metastatic Gastric Cancer: Pyloric and Antral Gastric Cancer

We aimed to classify metastatic pyloric/antral gastric cancer in terms of macroscopic morphology and metastatic form.Thirty-eight patients with pyloric/antral gastric cancer were included in the study. Patients were classified according to a combination of Borrmann classification type and metastatic type, and the clinicopathological characteristics of each group were compared.Of the 38 patients, 33 (type II: 9 and type III: 24) (87%) had ulcerative gastric cancer. Ulcerative gastric cancer was classified into four groups: lymphatic only group (L+H-P-), lymphatic + hematogenous group (L+H+P-), disseminated ± lymphatic group (L±H-P+), and lymphatic + hematogenous + disseminated group (L+H+P+). In the L+H-P- group, all patients had bulky lymph nodes and serum levels of both carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19-9) were high; the condition of patients was good, and the therapeutic response was good. In the L+H+P- group, metastases other than liver metastases were rare, and serum CEA levels were high. In the L±H-P+ group, the predominant histological type was signet ring cell carcinoma; both serum CEA and CA19-9 levels were low. Patients in the L+H+P+ group had higher serum CA19-9 levels and were more prone to hematogenous metastasis to various organs; these patients had worse patient status and lower treatment response. Gastric cancer other than ulcerative type was only detected in five patients (type V: 3, type IV: 1, type I: 1).Classification by a combination of macroscopic and metastatic form in pyloric/antral metastatic gastric cancer might be useful for diagnosis and treatment.

Spatial Metabolomics Identifies Distinct Tumor-Specific Subtypes in Gastric Cancer Patients.

Current systems of gastric cancer molecular classification include genomic, molecular, and morphological features. Gastric cancer classification based on tissue metabolomics remains lacking. This study aimed to define metabolically distinct gastric cancer subtypes and identify their clinicopathological and molecular characteristics. Spatial metabolomics by high mass resolution imaging mass spectrometry was performed in 362 patients with gastric cancer. K-means clustering was used to define tumor and stroma-related subtypes based on tissue metabolites. The identified subtypes were linked with clinicopathological characteristics, molecular features, and metabolic signatures. Responses to trastuzumab treatment were investigated across the subtypes by introducing an independent patient cohort with HER2-positive gastric cancer from a multicenter observational study. Three tumor- and three stroma-specific subtypes with distinct tissue metabolite patterns were identified. Tumor-specific subtype T1(HER2+MIB+CD3+) positively correlated with HER2, MIB1, DEFA-1, CD3, CD8, FOXP3, but negatively correlated with MMR. Tumor-specific subtype T2(HER2-MIB-CD3-) negatively correlated with HER2, MIB1, CD3, FOXP3, but positively correlated with MMR. Tumor-specific subtype T3(pEGFR+) positively correlated with pEGFR. Patients with tumor subtype T1(HER2+MIB+CD3+) had elevated nucleotide levels, enhanced DNA metabolism, and a better prognosis than T2(HER2-MIB-CD3-) and T3(pEGFR+). An independent validation cohort confirmed that the T1 subtype benefited from trastuzumab therapy. Stroma-specific subtypes had no association with clinicopathological characteristics, however, linked to distinct metabolic pathways and molecular features. Patient subtypes derived by tissue-based spatial metabolomics are a valuable addition to existing gastric cancer molecular classification systems. Metabolic differences between the subtypes and their associations with molecular features could provide a valuable tool to aid in selecting specific treatment approaches.

Standardizing the classification of gastric cancer patients with limited and adequate number of retrieved lymph nodes: an externally validated approach using real-world data

BackgroundCurrently, there is no formal consensus regarding a standard classification for gastric cancer (GC) patients with < 16 retrieved lymph nodes (rLNs). Here, this study aimed to validate a practical lymph node (LN) staging strategy to homogenize the nodal classification of GC cohorts comprising of both < 16 (Limited set) and ≥ 16 (Adequate set) rLNs.MethodsAll patients in this study underwent R0 gastrectomy. The overall survival (OS) difference between the Limited and Adequate set from a large Chinese multicenter dataset was analyzed. Using the 8th American Joint Committee on Cancer (AJCC) pathological nodal classification (pN) for GC as base, a modified nodal classification (N’) resembling similar analogy as the 8th AJCC pN classification was developed. The performance of the proposed and 8th AJCC GC subgroups was compared and validated using the Surveillance, Epidemiology, and End Results (SEER) dataset comprising of 10,208 multi-ethnic GC cases.ResultsSignificant difference in OS between the Limited and Adequate set (corresponding N0–N3a) using the 8th AJCC system was observed but the OS of N0limited vs. N1adequate, N1limited vs. N2adequate, N2limited vs. N3aadequate, and N3alimited vs. N3badequate subgroups was almost similar in the Chinese dataset. Therefore, we formulated an N’ classification whereby only the nodal subgroups of the Limited set, except for pT1N0M0 cases as they underwent less extensive surgeries (D1 or D1 + gastrectomy), were re-classified to one higher nodal subgroup, while those of the Adequate set remained unchanged (N’0 = N0adequate + pT1N0M0limited, N’1 = N1adequate + N0limited (excluding pT1N0M0limited), N’2 = N2adequate + N1limited, N’3a = N3aadequate + N2limited, and N’3b = N3badequate + N3alimited). This N’ classification demonstrated less heterogeneity in OS between the Limited and Adequate subgroups. Further analyses demonstrated superior statistical performance of the pTN’M system over the 8th AJCC edition and was successfully validated using the SEER dataset.ConclusionThe proposed nodal staging strategy was successfully validated in large multi-ethnic GC datasets and represents a practical approach for homogenizing the classification of GC cohorts comprising of patients with < 16 and ≥ 16 rLNs.

Identification of gastric cancer with convolutional neural networks: a systematic review.

The identification of diseases is inseparable from artificial intelligence. As an important branch of artificial intelligence, convolutional neural networks play an important role in the identification of gastric cancer. We conducted a systematic review to summarize the current applications of convolutional neural networks in the gastric cancer identification. The original articles published in Embase, Cochrane Library, PubMed and Web of Science database were systematically retrieved according to relevant keywords. Data were extracted from published papers. A total of 27 articles were retrieved for the identification of gastric cancer using medical images. Among them, 19 articles were applied in endoscopic images and 8 articles were applied in pathological images. 16 studies explored the performance of gastric cancer detection, 7 studies explored the performance of gastric cancer classification, 2 studies reported the performance of gastric cancer segmentation and 2 studies analyzed the performance of gastric cancer delineating margins. The convolutional neural network structures involved in the research included AlexNet, ResNet, VGG, Inception, DenseNet and Deeplab, etc. The accuracy of studies was 77.3 – 98.7%. Good performances of the systems based on convolutional neural networks have been showed in the identification of gastric cancer. Artificial intelligence is expected to provide more accurate information and efficient judgments for doctors to diagnose diseases in clinical work.

Clinicopathological characteristics and prognosis of poorly cohesive cell subtype of gastric cancer.

The new World Health Organization (WHO) classification of gastric cancer includes a histological subtype of poorly cohesive carcinoma (PCC), which includes signet-ring cell (SRC) phenotype. We aimed to examine the concordance between preoperative clinical and postoperative histological diagnoses according to the 2010 WHO histological subtypes and to compare the prognoses of these subtypes. The study cohort comprised 665 patients who underwent gastrectomy from 2005 to 2019. Histological subtypes were classified into PCC-NOS (non-signet ring cell subtype), SRC, and non-PCC, which were defined by the predominant component in accordance with the 2010 WHO classification of gastric cancer. The concordance of clinical and pathological diagnosis was examined and clinicopathological characteristics and survival outcome of the three subtypes compared. The cancers of 443 patients (66.7%) were classified as non-PCC, of 112 patients (16.8%) as PCC-NOS, and of 110 patients (16.5%) as SRC predominant subtypes. Significant differences in sex, age, tumor location, size, macroscopic type, and pathological TNM category (all P<0.05) were found. The concordance rate of preoperative and postoperative histological subtypes was significantly lower for poorly cohesive than other subtypes (P<0.0001). Preoperative stage tended to be underestimated for PCC-NOS subtype and these patients had poorer overall survival than those with the other two subtypes (P=0.005). Multivariate logistic regression analysis of overall survival showed that WHO histological subtype (PCC-NOS vs. non-PCC/SRC, HR: 1.64, 95% CI: 1.18-2.29, P=0.0034) was a significant independent prognostic factor. Our results suggest that poorly cohesive carcinoma subtypes have different biological characteristics and prognoses.

Toward Automatic Detection of Gastric Lesion for Upper Gastrointestinal Endoscopy with Neural Network

In this paper, we compared the performance of several neural networks in the classification of early gastric cancer (EGC) images and proposed a method of converting the output value of the network into a calorific value to locate the lesion. The algorithm was improved using transfer learning and fine-tuning principles. The test set accuracy rate reached 0.72, sensitivity reached 0.67, specificity reached 0.77, and precision rate reached 0.78. The experimental results show the potential to meet clinical demands for automatic detection of gastric lesion.

Molecular Pathology of Gastric Cancer.

Gastric cancer (GC) is one of the most common lethal malignant neoplasms worldwide, with limited treatment options for both locally advanced and/or metastatic conditions, resulting in a dismal prognosis. Although the widely used morphological classifications may be helpful for endoscopic or surgical treatment choices, they are still insufficient to guide precise and/or personalized therapy for individual patients. Recent advances in genomic technology and high-throughput analysis may improve the understanding of molecular pathways associated with GC pathogenesis and aid in the classification of GC at the molecular level. Advances in next-generation sequencing have enabled the identification of several genetic alterations through single experiments. Thus, understanding the driver alterations involved in gastric carcinogenesis has become increasingly important because it can aid in the discovery of potential biomarkers and therapeutic targets. In this article, we review the molecular classifications of GC, focusing on The Cancer Genome Atlas (TCGA) classification. We further describe the currently available biomarker-targeted therapies and potential biomarker-guided therapies. This review will help clinicians by providing an inclusive understanding of the molecular pathology of GC and may assist in selecting the best treatment approaches for patients with GC.

Application of graph-based features in computer-aided diagnosis for histopathological image classification of gastric cancer

Background: The gold standard for gastric cancer detection is gastric histopathological image analysis, but there are certain drawbacks in the existing histopathological detection and diagnosis. Method: In this paper, based on the study of computer-aided diagnosis (CAD) system, graph-based features are applied to gastric cancer histopathology microscopic image analysis, and a classifier is used to classify gastric cancer cells from benign cells. Firstly, image segmentation is performed. After finding the region, cell nuclei are extracted using the k-means method, the minimum spanning tree (MST) is drawn, and graph-based features of the MST are extracted. The graph-based features are then put into the classifier for classification. Result: Different segmentation methods are compared in the tissue segmentation stage, among which are Level-Set, Otsu thresholding, watershed, SegNet, U-Net and Trans-U-Net segmentation; Graph-based features, Red, Green, Blue features, Grey-Level Co-occurrence Matrix features, Histograms of Oriented Gradient features and Local Binary Patterns features are compared in the feature extraction stage; Radial Basis Function (RBF) Support Vector Machine (SVM), Linear SVM, Artificial Neural Network, Random Forests, k-NearestNeighbor, VGG16, and Inception-V3 are compared in the classifier stage. It is found that using U-Net to segment tissue areas, then extracting graph-based features, and finally using RBF SVM classifier gives the optimal results with 94.29%. Conclusion: This paper focus on a graph-based features microscopic image analysis method for gastric cancer histopathology. The final experimental data shows that our analysis method is better than other methods in classifying histopathological images of gastric cancer.

Molecular Classification of Gastric Cancer With Emphasis on PDL-1 Expression: The First Report From Iran.

Background:Gastric cancer is one of the lethal cancers and there is no effective treatment for these patients and still, 5-year survival rate is about 25% to 30%. Finding reliable biomarkers for early-stage diagnosis, targeted therapy, and survival prediction is a priority in this cancer.Objectives:In this study we were trying to know about the molecular classification of gastric cancers in a group of patients from the South of Iran.Patients and Methods:In a cross sectional study, 50 specimens of gastric cancer were selected that have enough tissue to be stained by immunohistochemistry (IHC). IHC was performed for Her-2, mismatch repair genes (MLH-1, MSH-2, MSH-6, and PMS-2), and PDL-1. Frequency of positive makers was compared with survival and outcome.Results and Conclusion:In our study, deficient MMR (dMMR) was detected in 4 patients (8.0%). PD-L1 expression in tumor cells (TC) was observed in 1 of 4 cases (25%) with PMS2 loss. However, PD-L1 in TCs and TILs (tumor infiltrating lymphocytes) was negative in 1 case with MLH1 loss and in 3 of 4 cases with PMS2 loss, which was not statistically significant. All of our 50 cases were positive for MSH2 and MSH6, 24% of which showed TCs with PDL-1 expression and 32% of them in TIL. HER2 was positive in 2 (2/50, 4.0%) cases, among which all of the cases were positive for PD-L1 expression in TCs and TILs, respectively. However, in HER2-negative group, 26.2% (11/42) and 28.6% (12/42) of tumors were positive for PD-L1 in TCs and TILs, respectively. The expression rate of PD-L1 in HER2 negative TCs was significantly higher than that in HER2 positive TCs (P = .033). Immunohistochemistry for Her-2 was equivocal in 6 cases (12.0%) none of which expressed PD-L1 in tumor cells. In our study minimum and maximum survival times from detection of gastric cancer were 1 and 87 months, respectively. The mean ± SD and median ± SD of overall survival time were 30.69 ± 4.88 and 18 ± 1.45 months, respectively. One and 3-year survival rates of 40% and 24%, respectively. PD-L1 expression was not associated with survival, but its expression was associated with intestinal type Lauren classification and negative HER-2. PD-L1 positivity in tumor cells or tumor infiltrating lymphocytes was not an independent prognostic factor in gastric cancer.

The dawn of precision medicine in diffuse-type gastric cancer.

Gastric cancer (GC) is one of the most common malignancies worldwide. The histology- and morphology-based Lauren classification of GC has been widely used for over 50 years in clinical practice. The Lauren classification divides GC into intestinal and diffuse types, which have distinct etiology, molecular profiles, and clinicopathological features. Diffuse-type GC (DGC) accounts for approximately 30% of GCs. Tumor cells lack adhesion and infiltrate the stroma as single cells or small subgroups, leading to easy dissemination in the abdominal cavity. Clinically, DGC has aggressive traits with a high risk of recurrence and metastasis, which results in unfavorable prognosis. Although systemic chemotherapy is the main therapeutic approach for recurrent or metastatic GC patients, clinical benefits are limited for patients with DGC. Therefore, it is urgent to develop effective therapeutic strategies for DGC patients. Considerable research studies have characterized the molecular and genomic landscape of DGC, of which tight junction protein claudin-18 isoform 2 (CLDN18.2) and fibroblast growing factors receptor-2 isoform IIIb (FGFR2-IIIb) are the most attractive targets because of their close association with DGC. Recently, the impressive results of two phase II FAST and FIGHT trials demonstrate proof-of-concept, suggesting that anti-CLDN18.2 antibody (zolbetuximab) and FGFR2-IIIb antibody (bemarituzumab) are promising approaches for patients with CLDN18.2-positive and FGFR2-IIIb-positive GC, respectively. In this review, we summarize the clinicopathological features and molecular profiles of DGC and highlight a potential therapeutic target based on the findings of pivotal clinical trials.

BiT-Based Early Gastric Cancer Classification Using Endoscopic Images

BiT-Based Early Gastric Cancer Classification Using Endoscopic Images

Endoscopic Classifications of Early Gastric Cancer: A Literature Review.

Simple SummaryThroughout the years, endoscopic technologies have advanced to facilitate better assessment of gastric lesions and early detection of gastric cancer. With improvements in conventional white light endoscopy, we have also witnessed the development of newer endoscopic diagnostic modalities, giving rise to several classifications for early gastric cancer. Different endoscopic classifications of early gastric based on several endoscopic diagnostic modalities were included in this review. In addition to this, newer and novel endoscopic classifications that were specifically developed for the stomach for assessing and diagnosing gastric lesions have also been included. Illustrative representations of each classification have also been provided to aid readers in better understanding of these endoscopic classifications of early gastric cancer.Endoscopic technologies have been continuously advancing throughout the years to facilitate improvement in the detection and diagnosis of gastric lesions. With the development of different endoscopic diagnostic modalities for EGC, several classifications have been advocated for the evaluation of gastric lesions, aiming for an early detection and diagnosis. Sufficient knowledge on the appearance of EGC on white light endoscopy is fundamental for early detection and management. On the other hand, those superficial EGC with subtle morphological changes that are challenging to be detected with white light endoscopy may now be clearly defined by means of image-enhanced endoscopy (IEE). By combining magnifying endoscopy and IEE, irregularities in the surface structures can be evaluated and highlighted, leading to improvements in EGC diagnostic accuracy. The main scope of this review article is to offer a closer look at the different classifications of EGC based on several endoscopic diagnostic modalities, as well as to introduce readers to newer and novel classifications, specifically developed for the stomach, for the assessment and diagnosis of gastric lesions.

Increased tensin 4 expression is related to the histological type of gastric cancer.

BACKGROUNDGastric cancer (GC) is one of the most common malignant tumors worldwide. Tensin 4 (TNS4) is an adhesive protein belonging to the tensin family. This protein is located in focal adhesion sites. The TNS4 gene is considered an oncogene in numerous cancers. This protein plays an important role in adhesion, migration and proliferation of cells.AIMTo evaluate expression of TNS4 protein in GC tissues and analysis of the clinical and histopathological parameters as well as the overall survival rate of patients.METHODSThe expression of TNS4 was assessed in 89 patients using immunohistochemistry.RESULTSPositive expression of TNS4 was observed in 49 of 89 patients (55.06%). Higher TNS4 expression was more common in GC tumors with a diameter ≥ 5 cm (P = 0.040). We demonstrated that an increase in TNS4 expression was more frequent in tumors of the histological type without mucinous components than in tumors from mucosal cancers (P = 0.023). Furthermore, TNS4 expression was higher in moderately differentiated tumors than in poorly differentiated and non-differentiated tumors (P = 0.002). Increased TNS4 expression was also noted in the intestinal type of GC according to Lauren’s classification (P = 0.020). No statistically significant correlation was found between the expression of TNS4 and the overall survival rate of patients.CONCLUSIONTNS4 expression was significantly higher in tumors with a diameter ≥ 5 cm of the moderately differentiated intestinal type (according to Lauren’s classification) of GC without a mucinous component. Therefore, increased TNS4 expression is related to the histological type of GC with a better prognosis.

Computerized Tomography Imaging Omics under Iterative Reconstruction Algorithm in Diagnosis of Gastric Cancer

This study aimed to explore the application value of computed tomography (CT) imaging radiomics based on a sinogram-affirmed iterative reconstruction algorithm (SAFIRE) in the diagnosis of gastric cancer. 59 patients who were clinically diagnosed with gastric cancer were selected as research objects and arranged CT examinations. The images obtained were optimized by the SAFIRE for the staging of gastric cancer. The pathological biopsy results were used as the gold standard to evaluate its diagnostic effect and compared with the filtered back-projection (FBP) method. The results showed that the carrier-to-noise ratio (CNR) (0.979) and signal-to-noise ratio (SNR) (0.967) of the CT image after the algorithm processing were significantly higher than those (0.781, 0.744) before ( P &lt; 0.05 ). There was no significant difference in CT values between the FBP algorithm and S1, S2, and S3 ( P &gt; 0.05 ); the area under the curve (AUC) (0.999) and sensitivity (0.98) of the CT training group under the SAFIRE algorithm for gastric cancer classification were higher than those of the verification group (0.958, 0.92). The preoperative CT staging kappa value was consistent with the postoperative pathological diagnosis of 0.882. CT images guided by SAFIRE can objectively and noninvasively assess the tumor asymmetry, discover additional information from subjective evaluation beyond the naked eye, and perform reasonable staging diagnosis of gastric cancer, which was useful for clinicians to develop high-quality individualized treatment plans.

Comprehensive Analysis of Alternative Splicing in Gastric Cancer Identifies Epithelial-Mesenchymal Transition Subtypes Associated with Survival.

This study presents a comprehensive analysis of alternative splicing in the context of patient classification, molecular mechanisms, and prognosis in gastric cancer.

Gastric Cancer: Applicability and Feasibility of Molecular and Histological Classification in Clinical Practice

Gastric cancer (GC) is the fifth most common type of cancer and the third leading cause of cancer-related deaths in the world. GC is a heterogeneous disease with diverse molecular and histological subtypes, which, may have different therapeutic implications. Using sophisticated molecular technologies and analyses, 3 separate groups recently provided genetic and epigenetic molecular classifications of GC: Singapore-Duke, The Cancer Genome Atlas project (TCGA) and the Asian Cancer Research Group (ACRG). These molecular classifications are time-consuming, complex, and costly and require sophisticated molecular technologies, which, prevent their widespread availability and use in clinical practice. Therefore, several practical pathological classifications were developed using immunohistochemical stains, fluorescent in situ hybridization and/or polymerase chain reaction (PCR), which, approximate, albeit not perfectly, the molecular classifications of GC. These are simple algorithms, less expensive and easy to reproduce in any pathology laboratory. Both molecular and histological classifications should be used for choosing adequate therapy and stratification purposes in clinical trials. This is a review of current molecular and pathological classification of GC.

Identification and Development of Subtypes with Poor Prognosis in Gastric Cancer Based on Both Hypoxia and Immune Cell Infiltration.

PurposeHypoxia and immune cell infiltration play an important role in the progression and metastasis of gastric cancer. However, the molecular classification of gastric cancer combined with hypoxia and immune cell infiltration remains unknown.Materials and MethodsssGSEA was used to evaluate the hypoxic state and immune cell infiltration of 1059 gastric cancer samples collected from the GEO and TCGA database. Based on the results, unsupervised clustering was performed to obtain different gastric cancer subtypes. The differentially expressed genes related to OS between these subtypes were utilized for LASSO analysis to construct a prognostic signature (HIscore). Subsequently, small-molecule drugs were predicted using the Connectivity Map (CMAP) database.ResultsWe obtained three hypoxic-immune infiltration patterns (HIcluster A-C) with different prognoses and classified them as low hypoxic/low immune, high hypoxic/high immune, and low hypoxic/high immune subtypes. Based on the differential genes between HIclusters, we have also obtained other three gastric cancer subtypes (genecluster A-C) and a 13-gene signature (HIscore). At the same time, we extensively explored the clinical and transcriptome traits in different clusters and groups with high or low HIscore. We proved that HIscore is an independent prognostic biomarker and an indicator of genome stability and EMT. Using the CMAP database, we found 96 small-molecule drugs that could reverse the poor prognosis and could serve as therapeutic drugs, especially for gastric cancer patients with high HIscore.ConclusionOur study evaluated the hypoxic state and immune cell infiltration in gastric tumors, and identified different gastric cancer subtypes. In addition, we established a hypoxia-immune signature to predict prognosis which is tightly linked to tumor EMT and genomic stability. Based on HIscore, we used the CMAP database to explore small-molecule drugs that may have the potential in serving as therapeutic drugs.

Immune landscape of advanced gastric cancer tumor microenvironment identifies immunotherapeutic relevant gene signature

BackgroundAdvanced gastric cancer (AGC) is a disease with poor prognosis due to the current lack of effective therapeutic strategies. Immune checkpoint blockade treatments have shown effective responses in patient subgroups but biomarkers remain challenging. Traditional classification of gastric cancer (GC) is based on genomic profiling and molecular features. Therefore, it is critical to identify the immune-related subtypes and predictive markers by immuno-genomic profiling.MethodsSingle-sample gene-set enrichment analysis (ssGSEA) and ESTIMATE algorithm were used to identify the immue-related subtypes of AGC in two independent GEO datasets. Weighted gene co-expression network analysis (WGCNA) and Molecular Complex Detection (MCODE) algorithm were applied to identify hub-network of immune-related subtypes. Hub genes were confirmed by prognostic data of KMplotter and GEO datasets. The value of hub-gene in predicting immunotherapeutic response was analyzed by IMvigor210 datasets. MTT assay, Transwell migration assay and Western blotting were performed to confirm the cellular function of hub gene in vitro.ResultsThree immune-related subtypes (Immunity_H, Immunity_M and Immunity_L) of AGC were identified in two independent GEO datasets. Compared to Immunity_L, the Immuntiy_H subtype showed higher immune cell infiltration and immune activities with favorable prognosis. A weighted gene co-expression network was constructed based on GSE62254 dataset and identified one gene module which was significantly correlated with the Immunity_H subtype. A Hub-network which represented high immune activities was extracted based on topological features and Molecular Complex Detection (MCODE) algorithm. Furthermore, ADAM like decysin 1 (ADAMDEC1) was identified as a seed gene among hub-network genes which is highly associated with favorable prognosis in both GSE62254 and external validation datasets. In addition, high expression of ADAMDEC1 correlated with immunotherapeutic response in IMvigor210 datasets. In vitro, ADAMDEC1 was confirmed as a potential protein in regulating proliferation and migration of gastric cancer cell. Deficiency of ADAMDEC1 of gastric cancer cell also associated with high expression of PD-L1 and Jurkat T cell apoptosis.ConclusionsWe identified immune-related subtypes and key tumor microenvironment marker in AGC which might facilitate the development of novel immune therapeutic targets.

Establishment of a Prognostic Signature of Stromal/Immune-Related Genes for Gastric Adenocarcinoma Based on ESTIMATE Algorithm.

Different subtypes of gastric cancer differentially respond to immune checkpoint inhibitors (ICI). This study aimed to investigate whether the Estimation of STromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithm is related to the classification and prognosis of gastric cancer and to establish an ESTIMATE-based gene signature to predict the prognosis for patients. The immune/stromal scores of 388 gastric cancer patients from TCGA were used in this analysis. The upregulated differentially expressed genes (DEGs) in patients with high stromal/immune scores were identified. The immune-related hub DEGs were selected based on protein-protein interaction (PPI) analysis. The prognostic values of the hub DEGs were evaluated in the TCGA dataset and validated in the GSE15460 dataset using the Kaplan-Meier curves. A prognostic signature was built using the hub DEGs by Cox proportional hazards model, and the accuracy was assessed using receiver operating characteristic (ROC) analysis. Different subtypes of gastric cancer had significantly different immune/stromal scores. High stromal scores but not immune scores were significantly associated with short overall survivals of TCGA patients. Nine hub DEGs were identified in PPI analysisThe expression of these hub DEG negatively correlated with the overall survival in the TCGA cohort, which was validated in the GSE15460 cohort. A 9-gene prognostic signature was constructed. The risk factor of patients was calculated by this signature. High-risk patients had significantly shorter overall survival than low-risk patients. ROC analysis showed that the prognostic model accurately identified high-risk individuals within different time frames. We established an effective 9-gene-based risk signature to predict the prognosis of gastric cancer patients, providing guidance for prognostic stratification.

The updated WHO classification of digestive system tumours-gastric adenocarcinoma and dysplasia.

The fifth edition of the World Health Organization (WHO) classification of digestive system tumours was published in 2019. The classification of invasive carcinoma is basically the same as in the fourth edition, but the description of each histological type has been updated, and some rare subtypes such as micropapillary carcinoma, gastric adenocarcinoma of the fundic gland type and undifferentiated carcinoma have been added and explained. Although this classification did not provide specific numerical criteria for the diagnosis of signet-ring cell carcinoma in poorly cohesive carcinoma, an additional study defined signet-ring cell carcinoma as having more than 90% signet-ring cells. The molecular classification of gastric cancer (Epstein-Barr virus-positive type, microsatellite instability type, genomically stable type, chromosomally unstable type) was additionally introduced. Many pages in the present classification have been devoted to precancerous lesions, and this article focuses on foveolar-type adenoma/dysplasia.