Prognosis and Biological Behavior of Gastric Signet-Ring Cell Carcinoma Better or Worse: A Meta-Analysis

Zhao, Shuai; Lv, Ling; Zheng, Kai; Tian, Yu; Zheng, Jian-Chun; Jiang, Chenggang

doi:10.3389/fonc.2021.603070

Cited by 19 publications

(21 citation statements)

References 71 publications

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“…It has been clinically reported that SRC patients have a worse prognosis than non-SRC AGC patients. 8 One cause of this may be the development of chemotherapy drug resistance in SRC patients. Understanding chemotherapy drug-resistance mechanism is important for the development of chemotherapy.…”

Section: Discussionmentioning

confidence: 99%

“…SRC gastric accounts for approximately 3.4% to 50% of gastric cancer cases. 5 , 7 , 8 SRCs have a small nucleus on one side and a cytoplasm that is full of mucus and rarely form lines or coronary structures. The prognosis of SRC gastric cancer is different in early and advanced stages.…”

Section: Introductionmentioning

confidence: 99%

“…In the early stage, the early detection and treatment of SRC are better than those of cancers of other tissue types, but in the case of progressive gastric cancer, cancer cells tend to invade, and the lymph node metastasis rate is generally high, resulting in a poor prognosis compared to that of cancers of other tissue types. 8 – 10 …”

Section: Introductionmentioning

confidence: 99%

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PHLPP1 Overexpression was Associated With a Good Prognosis With Decreased AKT Activity in Gastric Cancer

Park

Jeong

Jung

et al. 2022

Technol Cancer Res Treat

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Introduction: The aim of this study was to perform a clinicopathologic analysis of PHLPP1 expression in gastric cancer patients and analyze AKT activity with chemotherapy drug treatment in cancer subtypes. Materials and Methods: Surgically resected gastric cancer tissue specimens were obtained from 309 patients who underwent gastrectomy, and PHLPP1 expression was validated by tissue microarray analysis with immunohistochemistry. We assessed whether PHLPP1 selectively dephosphorylates Ser473 of AKT in an in-vitro study. Results: We found that the PHLPP1 overexpression (OE) group showed significantly greater proportions of differentiated subtype samples and early T stage samples, lower lymph node metastasis, and lower TNM stage than the PHLPP1 underexpression (UE) group. The overall survival of the PHLPP1-OE group was significantly higher (53.39 ± 0.96 months) than that of the PHLPP1-UE group (47.82 ± 2.57 months) ( P = .01). In vitro analysis, we found that the PHLPP1-OE group showed a significant decrease in relative AKT S-473 levels in both cell lines (MKN-74 and KATO-III). We found that treatment with chemotherapy drugs decreased the activity of Ser473 in the MKN-74 cell line with PHLPP1 OE, but it did not affect the activity of Ser473 in KATO-III cells. Conclusion: We found that patients who overexpressed PHLPP1 showed low recurrence and good prognosis. PHLPP1 was found to work by lowering the activity of AKT Ser473 in gastric cancer. Additionally, we found a clue regarding the mechanism of chemotherapeutic drug resistance in a cell line of signet ring cell origin and will uncover this mechanism in the future.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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PHLPP1 Overexpression was Associated With a Good Prognosis With Decreased AKT Activity in Gastric Cancer

Park

Jeong

Jung

et al. 2022

Technol Cancer Res Treat

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“…Besides, we found that the expression level of AADAC was high in the Kato III GC cell line with signet ring cell carcinoma (SRC). SRC, a special pathological type, has a good prognosis in EGC and a poor prognosis in AGC [ 45 , 46 ]. It may suggest that driver mutations controlling the metastatic potential of SRC can occur late in GC progression.…”

Section: Discussionmentioning

confidence: 99%

Impact of AADAC gene expression on prognosis in patients with Borrmann type III advanced gastric cancer

et al. 2022

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Background The prognosis of Borrmann type III advanced gastric cancer (AGC) is known to vary significantly among patients. This study aimed to determine which differentially expressed genes (DEGs) are directly related to the survival time of Borrmann type III AGC patients and to construct a prognostic model. Methods We selected 25 patients with Borrmann type III AGC who underwent radical gastrectomy. According to the difference in overall survival (OS), the patients were divided into group A (OS<1 year, n=11) and group B (OS>3 years, n=14). DEGs related to survival time in patients with Borrmann type III AGC were determined by mRNA sequencing. The prognosis and functional differences of DEGs in different populations were determined by The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) public databases. The expression of mRNA and protein in cell lines was detected by quantitative real-time reverse-transcription polymerase chain reaction (qRT-PCR) and Western blot (WB). Immunohistochemical (IHC) staining was used to detect protein expression in the paraffin-embedded tissues of 152 patients with Borrmann type III AGC who underwent radical gastrectomy. After survival analysis, nomograms were constructed to predict the prognosis of patients with Borrmann type III AGC. Results Arylacetamide deacetylase (AADAC) is a survival-related DEG in patients with Borrmann type III AGC. The higher the expression level of its mRNA and protein is, the better the prognosis of patients. Bioinformatics analysis found that AADAC showed significant differences in prognosis and function in European and American populations and Asian populations. In addition, the mRNA and protein expression levels of AADAC were high in differentiated gastric cancer (GC) cells. We also found that AADAC was an independent prognostic factor for patients with Borrmann type III AGC, and its high expression was significantly correlated with better OS and disease-free survival (DFS). Nomogram models of AADAC expression level combined with clinicopathological features can be used to predict the OS and DFS of Borrmann type III AGC. Conclusion AADAC can be used as a biomarker to predict the prognosis of Borrmann type III AGC and has the potential to become a new therapeutic target for GC.

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“…Among them, gastric signet ring cell carcinoma (GSRC), which is named because a large amount of mucus in cancer cells pushes the nucleus to one side like a ring, is one of the most malignant tumors, accounting for about 3.4%-39% of GC [ 5 , 6 ]. Though the incidence of GC has declined in the world since the active treatment of Helicobacter pylori , the incidence of GSRC is still increased significantly [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Predicting Overall Survival in Patients with Nonmetastatic Gastric Signet Ring Cell Carcinoma: A Machine Learning Approach

Chen

Lin

et al. 2022

Computational and Mathematical Methods in Medicine

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Background and Aims. Accurate prediction is essential for the survival of patients with nonmetastatic gastric signet ring cell carcinoma (GSRC) and medical decision-making. Current models rely on prespecified variables, limiting their performance and not being suitable for individual patients. Our study is aimed at developing a more precise model for predicting 1-, 3-, and 5-year overall survival (OS) in patients with nonmetastatic GSRC based on a machine learning approach. Methods. We selected 2127 GSRC patients diagnosed from 2004 to 2014 from the Surveillance, Epidemiology, and End Results (SEER) database and then randomly partitioned them into a training and validation cohort. We compared the performance of several machine learning-based models and finally chose the eXtreme gradient boosting (XGBoost) model as the optimal method to predict the OS in patients with nonmetastatic GSRC. The model was assessed using the receiver operating characteristic curve (ROC). Results. In the training cohort, for predicting OS rates at 1-, 3-, and 5-year, the AUCs of the XGBoost model were 0.842, 0.831, and 0.838, respectively, while in the testing cohort, the AUCs of 1-, 3-, and 5-year OS rates were 0.749, 0.823, and 0.829, respectively. Besides, the XGBoost model also performed better when compared with the American Joint Committee on Cancer (AJCC) stage. The performance for this model was stably maintained when stratified by age and ethnicity. Conclusion. The XGBoost-based model accurately predicts the 1-, 3-, and 5-year OS in patients with nonmetastatic GSRC. Machine learning is a promising way to predict the survival outcomes of tumor patients.

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Prognosis and Biological Behavior of Gastric Signet-Ring Cell Carcinoma Better or Worse: A Meta-Analysis

Cited by 19 publications

References 71 publications

PHLPP1 Overexpression was Associated With a Good Prognosis With Decreased AKT Activity in Gastric Cancer

PHLPP1 Overexpression was Associated With a Good Prognosis With Decreased AKT Activity in Gastric Cancer

Impact of AADAC gene expression on prognosis in patients with Borrmann type III advanced gastric cancer

Predicting Overall Survival in Patients with Nonmetastatic Gastric Signet Ring Cell Carcinoma: A Machine Learning Approach

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