A Novel Gene List Identifies Tumors with a Stromal-Mesenchymal Phenotype and Worse Prognosis in Gastric Cancer.

BACKGROUND: Molecular biomarkers that predict disease progression can help identify tumor subtypes and shape treatment plans. In this study, we aimed to identify robust biomarkers of prognosis in gastric cancer based on transcriptomic data obtained from primary gastric tumors. METHODS: Microarray, RNA sequencing, and single-cell RNA sequencing-based gene expression data from gastric tumors were obtained from public databases. Freshly frozen gastric tumors (n = 42) and matched FFPE (formalin-fixed, paraffin-embedded) (n = 40) tissues from a Turkish gastric cancer cohort were used for quantitative real-time PCR and immunohistochemistry-based assessments of gene expression, respectively. RESULTS: A novel list of 20 prognostic genes was identified and used for the classification of gastric tumors into two major tumor subgroups with differential stromal gene expression ("Stromal-UP" (SU) and "Stromal-DOWN" (SD)). The SU group had a more mesenchymal profile with an enrichment of extracellular matrix-related gene sets and a poor prognosis compared to the SD group. Expression of the genes within the signature correlated with the expression of mesenchymal markers ex vivo. A higher stromal content in FFPE tissues was associated with shorter overall survival. CONCLUSIONS: A stroma-rich, mesenchymal subgroup among gastric tumors identifies an unfavorable clinical outcome in all cohorts tested.

Transcriptomic Analysis of Hepatitis B Infected Liver for Prediction of Hepatocellular Carcinoma.

Hepatocellular cancer (HCC) is a leading cause of cancer-related mortality worldwide, and chronic hepatitis B virus infection (CHB) has been a major risk factor for HCC development. The pathogenesis of HBV-related HCC has been a major focus revealing the interplay of a multitude of intracellular signaling pathways, yet the precise mechanisms and their implementations to clinical practice remain to be elucidated. This study utilizes publicly available transcriptomic data from the livers of CHB patients in order to identify a population with a higher risk of malignant transformation. We report the identification of a novel list of genes (PCM1) which can generate clear transcriptomic sub-groups among HBV-infected livers. PCM1 includes genes related to cell cycle activity and liver cancer development. In addition, markers of inflammation, M1 macrophages and gamma delta T cell infiltration are present within the signature. Genes within PCM1 are also able to differentiate HCC from normal liver, and some genes within the signature are associated with poor prognosis of HCC at the mRNA level. The analysis of the immunohistochemical stainings validated that proteins coded by a group of PCM1 genes were overexpressed in liver cancer, while minimal or no expression was detected in normal liver. Altogether, our findings suggest that PCM1 can be developed into a clinically applicable method to identify CHB patients with a higher risk of HCC development.