Identification of MAGEA12 as a prognostic outlier gene in gastric cancers

Wu, Jing; Wang, J; Shen, Wilma Grace

doi:10.4149/neo_2017_210

Cited by 12 publications

(8 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A previous report showed that overexpression of MAGEA6 is related to overall recurrence, but not each pattern of recurrence [35]. Furthermore, many studies have revealed that MAGEA protein expression, such as MAGEA1, A3, A6, A10, and A12, correlated with lymph node metastasis, advanced stage of cancer, and worse survival in gastric cancer [26,[35][36][37][38][39]. The present study showed a high incidence of hepatic recurrence and poor RFS in patients with positive MAGEA10 staining.…”

Section: Discussionmentioning

confidence: 43%

MAGEA10 expression is a predictive marker of early hepatic recurrence after curative gastrectomy for gastric and gastroesophageal junction cancer

et al. 2020

View full text Add to dashboard Cite

Background Resection for hepatic recurrence after gastrectomy in patients with gastric cancer may be curative; however, the prediction of hepatic recurrence remains intractable. Therefore, we aimed to explore predictive markers for hepatic recurrence in gastric and gastroesophageal junction cancer based on genetic information. Methods This study recruited 154 patients who underwent curative gastrectomy for pathological stage II or III primary gastric and gastroesophageal junction adenocarcinoma. Genes associated with hepatic recurrence were comprehensively analyzed using whole-exome sequencing and gene expression profiling (GEP), followed by immunohistochemistry analysis for MAGEA10. The cumulative incidences of hepatic recurrence, relapse-free survival, and overall survival were evaluated. Results A total of 12 patients with early hepatic recurrences were found within 2 years of surgery. Although there were no distinct gene mutations in recurrent patients, upregulation of MAGEA10 was identified in patients with early hepatic recurrence using GEP analysis. Immunostaining for MAGEA10 stained the cell nuclei in 29 (18.8%) of 154 samples. Furthermore, protein expression of MAGEA10 on immunohistochemistry was significantly related to a high MAGEA10 mRNA expression, high cumulative incidences of hepatic recurrence, and poor relapse-free survival. Overall survival did not differ significantly between positive and negative immunohistochemical staining for MAGEA10. The sensitivity and specificity of MAGEA10 staining for early hepatic recurrence were 58.3% and 84.5%, respectively. Conclusions MAGEA10 represents a promising predictive marker for early hepatic recurrence after curative gastrectomy for gastric and gastroesophageal junction cancer.

show abstract

Section: Discussionmentioning

confidence: 43%

MAGEA10 expression is a predictive marker of early hepatic recurrence after curative gastrectomy for gastric and gastroesophageal junction cancer

et al. 2020

View full text Add to dashboard Cite

show abstract

“…This problem is very relevant in cancer studies due to highly intra- and inter-tumour heterogeneity (Gyanchandani et al , 2016; Rubio-Perez et al , 2015). Interestingly, COPA method, after more than a decade from its publication, is still used to find outlier profiles due to its simplicity (Gaykalova et al , 2017; Teng et al , 2016; Wu et al , 2017), despite the fact that it is not the best method.…”

Section: Discussionmentioning

confidence: 99%

DECO: decompose heterogeneous population cohorts for patient stratification and discovery of sample biomarkers using omic data profiling

Campos‐Laborie

Risueño²,

Ortiz-Estévez³

et al. 2019

Bioinformatics

View full text Add to dashboard Cite

Motivation Patient and sample diversity is one of the main challenges when dealing with clinical cohorts in biomedical genomics studies. During last decade, several methods have been developed to identify biomarkers assigned to specific individuals or subtypes of samples. However, current methods still fail to discover markers in complex scenarios where heterogeneity or hidden phenotypical factors are present. Here, we propose a method to analyze and understand heterogeneous data avoiding classical normalization approaches of reducing or removing variation. Results DEcomposing heterogeneous Cohorts using Omic data profiling (DECO) is a method to find significant association among biological features (biomarkers) and samples (individuals) analyzing large-scale omic data. The method identifies and categorizes biomarkers of specific phenotypic conditions based on a recurrent differential analysis integrated with a non-symmetrical correspondence analysis. DECO integrates both omic data dispersion and predictor–response relationship from non-symmetrical correspondence analysis in a unique statistic (called h-statistic), allowing the identification of closely related sample categories within complex cohorts. The performance is demonstrated using simulated data and five experimental transcriptomic datasets, and comparing to seven other methods. We show DECO greatly enhances the discovery and subtle identification of biomarkers, making it especially suited for deep and accurate patient stratification. Availability and implementation DECO is freely available as an R package (including a practical vignette) at Bioconductor repository (http://bioconductor.org/packages/deco/). Supplementary information Supplementary data are available at Bioinformatics online.

show abstract

“…Overexpression of MAGE family proteins has been associated with the poor prognosis of many cancers in several studies. For example, MAGE-A12 was related to the high TNM staging of gastric cancer, and was demonstrated to be an independent indicator of poor prognosis in patients with gastric cancer (26). The expression of MAGE-A1-6 in the peritoneal lavage after gastric cancer surgery is correlated with the patient's reduced disease-free survival rate, indicating its association with tumor recurrence (27).…”

Section: Mage-c1mentioning

confidence: 99%

High expression of MAGE-C1 gene in colorectal cancer is associated with its poor prognosis

Tian¹,

Liang²,

Zhang³

et al. 2021

J Gastrointest Oncol

View full text Add to dashboard Cite

Background: The aim of this study was to explore the relationship between melanoma antigen gene C1 (MAGE-C1) expression and the prognosis for colorectal cancer (CRC), and to establish a mathematical model to comprehensively evaluate the prognosis of patients with CRC.Methods: MAGE-C1 was selected by bioinformatics for its greater expression differences in CRC patients.Immunohistochemistry (IHC) was used to detect the expression level of MAGE-C1 in tissue samples of 156 patients with CRC. Kaplan-Meier analysis was employed to assess the relationship between MAGE-C1 and the prognosis of patients with CRC. Univariate and multivariate Cox regression models analyzed the factors affecting the prognosis of CRC patients. Also, the clinicopathological characteristics of patients and genes with clinical concern were integrated to establish a model to comprehensively predict the prognosis of patients with CRC.Results: MAGE-C1 was found to be highly expressed in 28.8% of CRC patients. MAGE-C1 expression was associated with tumor size, number, and metastasis. Survival analysis showed that CRC patients with high expression of MAGE-C1 had a poor prognosis. Regression analysis demonstrated that MAGE-C1 protein status, T stage, differentiation, Kirsten rat sarcoma (KRAS) status, and v-RAF murine sarcoma viral oncogene homolog B1 (BRAF) status were the independent factors influencing the overall survival of patients with CRC. Meanwhile, MAGE-C1 combined with clinicopathological characteristics and hotspot gene mutations could be used to evaluate the prognosis of CRC. Conclusions:Our study shows that MAGE-C1 is differentially expressed in patients with CRC and affects the prognosis of patients. The combination of MAGE-C1, clinicopathological characteristics, and genes with clinical concern can be used to assess the prognosis of CRC.

show abstract

Identification of MAGEA12 as a prognostic outlier gene in gastric cancers

Cited by 12 publications

References 15 publications

MAGEA10 expression is a predictive marker of early hepatic recurrence after curative gastrectomy for gastric and gastroesophageal junction cancer

MAGEA10 expression is a predictive marker of early hepatic recurrence after curative gastrectomy for gastric and gastroesophageal junction cancer

DECO: decompose heterogeneous population cohorts for patient stratification and discovery of sample biomarkers using omic data profiling

High expression of MAGE-C1 gene in colorectal cancer is associated with its poor prognosis

Contact Info

Product

Resources

About