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Aim of the Study: To determine the significance of expression of synaptophysin, chromogranin A, and Ki-67 and their association with clinicopathological parameters, and to find out the possible prognostic factors in gastric neuroendocrine carcinoma (G-NEC)., Material and Methods: We investigated the immunohistochemical features and prognosis of 62 G-NECs, and evaluated the association among expressions of synaptophysin, chromogranin A, and Ki-67, clinicopathological variables, and outcome., Results: Chromogranin A expression was found more commonly in small-cell NECs (9/9, 100%) than in large-cell NECs (27/53, 51%) (p = 0.008). No statistical significance was found in Ki-67 (p = 0.494) or synaptophysin (p > 0.1) expression between NEC cell types. Correlation analyses revealed that Ki-67 expression was significantly associated with mid-third disease of stomach (p = 0.005) and vascular involvement (p = 0.006), and had a trend of significant correlation with tumour relapse (p = 0.078). High expression of chromogranin A was significantly associated with histology of small-cell NECs (p = 0.008) and lesser tumour greatest dimension (p = 0.038). The prognostic significance was determined by means of Kaplan-Meier survival estimates and log-rank tests, and as a result, early TNM staging and postoperative chemotherapy were found to be correlated with longer overall survival (p < 0.05). Univariate analysis revealed associations between poor prognosis in NECs and several factors, including high TNM staging (p = 0.048), vascular involvement (p = 0.023), relapse (p = 0.004), and microscopic/macroscopic residual tumour (R1/2, p < 0.001). In a multivariate analysis, relapse was identified as the sole independent prognostic factor., Conclusions: No significant correlation between survival and expression of synaptophysin, chromogranin A, or Ki-67 has been determined in G-NECs. Our study indicated that early diagnosis, no-residual-tumour resection, and postoperative chemotherapy were possible prognostic factors.

Our current understanding of cancer genetics is grounded on the principle that cancer arises from a clone that has accumulated the requisite somatically acquired genetic aberrations, leading to the malignant transformation. It also results in aberrent of gene and protein expression. Next generation sequencing (NGS) or deep sequencing platforms are being used to create large catalogues of changes in copy numbers, mutations, structural variations, gene fusions, gene expression, and other types of information for cancer patients. However, inferring different types of biological changes from raw reads generated using the sequencing experiments is algorithmically and computationally challenging. In this article, we outline common steps for the quality control and processing of NGS data. We highlight the importance of accurate and application-specific alignment of these reads and the methodological steps and challenges in obtaining different types of information. We comment on the importance of integrating these data and building infrastructure to analyse it. We also provide exhaustive lists of available software to obtain information and point the readers to articles comparing software for deeper insight in specialised areas. We hope that the article will guide readers in choosing the right tools for analysing oncogenomic datasets.