Histopathology-based prognostic score is independent prognostic factor of gastric carcinoma

Zhu, Zhi; Sun, Xiaodi; Wang, Jinou; Sun, Zhe; Wang, Zhenning; Zheng, Xinyu

doi:10.1186/1471-2407-14-663

Cited by 14 publications

(11 citation statements)

References 19 publications

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“…In GC, Tumor stage and tumor differentiation have been manifested to be crucial clinical prognostic markers 5 40 . Whereas the prognostic role of MSI, which results from inactivation of DNA mismatch repair systems 41 , remains uncertain in GC 42 .…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, it is crucial to identify poor prognostic indicators for GC and guide treatment for patients with dismal outcome. Many studies have displayed that some clinical parameters, such as TNM staging, histological grade, serum tumor markers and therapeutic modalities, are prognostic factors for GC 4 5 6 7 . However, biological behavior of tumor cannot be sufficiently reflected by the above clinical parameters.…”

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confidence: 99%

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Loss of ARID1A expression predicts poor survival prognosis in gastric cancer: a systematic meta-analysis from 14 studies

Yang

Wei

Zhao

et al. 2016

Sci Rep

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The chromatin remodeling gene, AT-rich interactive domain 1A gene (ARID1A), frequently mutates inactively in gastric cancer (GC). However, its prognostic value remains controversial. To address this issue, a comprehensive meta-analysis was performed. Studies published until March 2016 were systematically searched. A total of 15 cohorts from 14 literatures involving 3183 patients were subjected to this meta-analysis. The pooled data showed that ARID1A expression loss predicted poor overall survival (OS) in GC (Hazard Ratio (HR) = 1.60; 95% Confidence Interval (CI) = 1.40–1.81; P < 0.001), with low heterogeneity among these studies (I2 = 21.5%; P = 0.214). Stratification analyses revealed that ARID1A expression loss was associated with poor OS in Asians (HR = 1.65, 95% CI = 1.44–1.89), proportion of proximal disease ≤30% subgroup (HR = 1.80, 95% CI = 1.36–2.38) and Epstein-Barr virus (EBV) (+) > 5% subgroup (HR = 1.59, 95% CI = 1.18–2.15). The robust results were suggested by sensitivity analyses and no evidence of significant publication bias was detected. This study demonstrated a significant relationship between deletion of ARID1A expression and poor OS in GC. Moreover, ethnicity, tumor location and EBV infection status might be potential key factors influencing this correlation.

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Loss of ARID1A expression predicts poor survival prognosis in gastric cancer: a systematic meta-analysis from 14 studies

Yang

Wei

Zhao

et al. 2016

Sci Rep

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“…Prognostic association of histopathologic status of GCs with overall survival (OS) was demonstrated in previous studies (7,9,12,13,15). Thus, in the current study, the histopathological status was scaled by: (i) pT1-2 stage (0 score) vs pT3-4 stage (1 score); (ii) pN0 stage (0 score) vs pN+ stage (1 score); (iii) low-L&B stage (0 score, intestinal types and Borrmann types I–III) vs high-L&B stage (diffuse type or Borrmann type IV) grade; (iv) low-WHO grade (0 score, papillary carcinoma or high differentiated tubular adenocarcinoma) vs high-WHO grade (1 score, medium differentiated tubular adenocarcinoma or poorly differentiated tubular adenocarcinoma or mucinous adenocarcinoma or signet ring cell carcinoma); and (v) LVI− (0 score) vs LVI+ (1 score).…”

Section: Methodsmentioning

confidence: 77%

Machine Learning–Based Computational Models Derived From Large-Scale Radiographic-Radiomic Images Can Help Predict Adverse Histopathological Status of Gastric Cancer

Feng

et al. 2019

Clin Transl Gastroenterol

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INTRODUCTION:Adverse histopathological status (AHS) decreases outcomes of gastric cancer (GC). With the lack of a single factor with great reliability to preoperatively predict AHS, we developed a computational approach by integrating large-scale imaging factors, especially radiomic features at contrast-enhanced computed tomography, to predict AHS and clinical outcomes of patients with GC.METHODS:Five hundred fifty-four patients with GC (370 training and 184 test) undergoing gastrectomy were retrospectively included. Six radiomic scores (R-scores) related to pT stage, pN stage, Lauren & Borrmann (L&B) classification, World Health Organization grade, lymphatic vascular infiltration, and an overall histopathologic score (H-score) were, respectively, built from 7,000+ radiomic features. R-scores and radiographic factors were then integrated into prediction models to assess AHS. The developed AHS-based Cox model was compared with the American Joint Committee on Cancer (AJCC) eighth stage model for predicting survival outcomes.RESULTS:Radiomics related to tumor gray-level intensity, size, and inhomogeneity were top-ranked features for AHS. R-scores constructed from those features reflected significant difference between AHS-absent and AHS-present groups (P < 0.001). Regression analysis identified 5 independent predictors for pT and pN stages, 2 predictors for Lauren & Borrmann classification, World Health Organization grade, and lymphatic vascular infiltration, and 3 predictors for H-score, respectively. Area under the curve of models using those predictors was training/test 0.93/0.94, 0.85/0.83, 0.63/0.59, 0.66/0.63, 0.71/0.69, and 0.84/0.77, respectively. The AHS-based Cox model produced higher area under the curve than the eighth AJCC staging model for predicting survival outcomes. Furthermore, adding AHS-based scores to the eighth AJCC staging model enabled better net benefits for disease outcome stratification.DISCUSSION:The developed computational approach demonstrates good performance for successfully decoding AHS of GC and preoperatively predicting disease clinical outcomes.

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“…The alternatives of treatment necessitate accurate prediction of tumor-related risks. The TNM-stage-based classification proposed by the AJCC was the most adopted evaluation for GC 24 ; meanwhile, histopathologic grading has been reported to be an independent prognostic factor, with several related grading system under construction 25 . Clinicopathologic characteristics such as cellular dysmorphism, tumor location, comorbidity, and complications are also involved in some monograms 26 .…”

Section: Discussionmentioning

confidence: 99%

FLAD1 is up-regulated in Gastric Cancer and is a potential prediction of prognosis

Hu¹,

Pan²,

Wang³

et al. 2020

Int. J. Med. Sci.

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Background: Gastric cancer (GC) is a common malignancy throughout the world. Biomarkers for prognosis and risk evaluation of GC are rapidly discovered. We investigated the prognostic role of FLAD1, an important protein-coding gene that affects cell cycle and survival. Methods: The expression of FLAD1 at mRNA levels in GC tumor tissues and normal tissues was mined and analyzed in Oncomine database and verified in 10 pairs of GS tissues and their adjacent normal tissues in our center by RT qPCR. The FLAD1 protein expression were detected in 106 paraffin-embedded GC tissues by immunohistochemistry (IHC). Statistical analyses were applied to evaluate the clinical significance of FLAD1. The prognostic value of FLAD1 mRNA expression was also analyzed using the Kaplan-Meier plotter ( www.kmplot.com ). Results: Statistics obtained from online database suggested FLAD1 mRNA was overexpressed in GC tissues. The results were further validated in 10 pairs of GS tissues and adjacent normal tissues in our center (p=0.021). IHC and survival analysis of GC samples from 106 patients showed FLAD1 was overexpressed in 63/106 (59.4%) patients and was associated to higher TNM stage (p=0.026). Multivariate analysis revealed FLAD1 was an independent prognostic factor for GC (p < 0.001). Furthermore, FLAD1 mRNA was associated to unfavorable overall survival (OS), first progression (FP), and post-progression survival (PPS) of GC (p<0.001). Conclusion: FLAD1 in GC is overexpressed at both mRNA and protein level and could be a potential biomarker for GC prognosis.

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Histopathology-based prognostic score is independent prognostic factor of gastric carcinoma

Cited by 14 publications

References 19 publications

Loss of ARID1A expression predicts poor survival prognosis in gastric cancer: a systematic meta-analysis from 14 studies

Loss of ARID1A expression predicts poor survival prognosis in gastric cancer: a systematic meta-analysis from 14 studies

Machine Learning–Based Computational Models Derived From Large-Scale Radiographic-Radiomic Images Can Help Predict Adverse Histopathological Status of Gastric Cancer

FLAD1 is up-regulated in Gastric Cancer and is a potential prediction of prognosis

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