Abstract
BackgroundPrognosis remains one of most crucial determinants of gastric cancer (GC) treatment, but current methods do not predict prognosis accurately. Identification of additional biomarkers is urgently required to identify patients at risk of poor prognoses.MethodsTissue microarrays were used to measure expression of nine GC-associated proteins in GC tissue and normal gastric tissue samples. Hierarchical cluster analysis of microarray data and feature selection for factors associated with survival were performed. Based on these data, prognostic scoring models were established to predict clinical outcomes. Finally, ingenuity pathway analysis (IPA) was used to identify a biological GC network.ResultsEight proteins were upregulated in GC tissues versus normal gastric tissues. Hierarchical cluster analysis and feature selection showed that overall survival was worse in cyclin dependent kinase (CDK)2, Akt1, X-linked inhibitor of apoptosis protein (XIAP), Notch4, and phosphorylated (p)-protein kinase C (PKC) α/β2 immunopositive patients than in patients that were immunonegative for these proteins. Risk score models based on these five proteins and clinicopathological characteristics were established to determine prognoses of GC patients. These proteins were found to be involved in cancer related-signaling pathways and upstream regulators were identified.ConclusionThis study identified proteins that can be used as clinical biomarkers and established a risk score model based on these proteins and clinicopathological characteristics to assess GC prognosis.
Highlights
Prognosis remains one of most crucial determinants of gastric cancer (GC) treatment, but current methods do not predict prognosis accurately
P-PKCα was expressed in 66.7% (20/30) of the normal tissues, with weakly positive, moderately positive, and strongly positive expression in 10, 19, 1 and 0 cases, respectively. p-PKCα/β2 was expressed in 82.6% (100/121) of the GC cases; weakly positive, moderately positive, and strongly positive expression was seen in 59, 37, and 4 cases, respectively, whereas weakly positive p-PKCα/ β2 expression was observed in 32.5% (13/30) of the normal tissues
The most significant upstream regulators included embelin (CDK2, CDK6, X-linked inhibitor of apoptosis protein (XIAP); p = 4.73 × 10−9), butyric acid (CDK2, CDK6, proliferating cell nuclear antigen (PCNA), PRKCB, XIAP; p = 1.06 × 10−7), silibinin (CDK2, CDK6, XIAP; p = 5.19 × 10−7), silicon phthalocyanine (CDK2, CDK6; p = 1.34 × 10−6), ingenol mebutate (PRKCA, PRKCB; p = 1.34 × 10−6), rottlerin (CDK6, PCNA, XIAP; p = 1.41 × 10−6), methylselenic acid (AKT1, CDK2, PCNA, PRKCA; p = 1.41 × 10−6), Discussion In this study, to reduce the limitations to clinical application, we used the tissue microarray method on formalin-fixed specimens to evaluate the expression of nine proteins that we identified in a previous protein pathway array (PPA) study [1] in 121 primary GC tissues and 30 normal gastric tissues
Summary
Prognosis remains one of most crucial determinants of gastric cancer (GC) treatment, but current methods do not predict prognosis accurately. Identification of additional biomarkers is urgently required to identify patients at risk of poor prognoses. It is difficult to cure unless it is detected at an early stage. Because early GC patients present with few symptoms, the cancer is usually at an advanced stage when diagnosed [3]. GC treatments include surgical resection, chemotherapy, and/or radiation therapy [4, 5]. Surgery together with chemotherapy or other therapy methods has been shown to be much more effective than surgery alone [6]. The 5-year survival rate for advanced GC is less than 10% [7, 8]; early diagnosis is vital for successful treatment
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