ABSTRACT: This is a multi-center prospective case control study aiming to compare different methods of risk stratification models in predicting the risk of gastric cancer development.
Project description:The project will aim to identify and determine subgroups of patients with different risks of progression to gastric cancer and to assess appropriate follow-up intervals. Implementing risk stratification only high risk individuals will be offered and performed endoscopic surveillance.
Project description:Gastric cancer (GC) is associated with high mortality rates and an unfavorable prognosis at advanced stages. In addition, there are no effective methods for diagnosing gastric cancer at an early stage or for predicting the outcome for the purpose of selecting patient-specific treatment options. Therefore, it is important to investigate new methods for GC diagnosis. We designed a custom microarray of gastric cancer. The customized microarray contained 1042 canceration and prognosis related genes identical to the probes on the Agilent microarray. DNA microarray profilling analysis was performed on gastric cancer tissues and premalignant tissues (20 samples per group).
Project description:Background: Follicular lymphoma (FL) is an indolent malignancy of germinal center B cells with highly variable patient outcomes. Recently, a 23-gene predictor score was proposed for predicting progression-free survival. In the past, we had shown that the m7-FLIPI, a clinico-genetic risk model, allows to improve patient stratification compared to clinical risk models alone. The multitude of prognostic tools in FL raises the question whether they identify common biology. Methods: In this study, we applied a modified risk score (MRS) to an independent gene-expression dataset of FL patients treated with rituximab in combination with chemotherapy. Results: Using supervised and unsupervised approaches, we showed that the MRS identifies patient groups with diverging outcomes in our dataset. In addition, using gene set enrichment and network identification, we discovered associations between the MRS, the m7-FLIPI, EZH2 mutation status and FOXP1 expression. Conclusions: Our findings lend support to expression of dark-zone related genes as a key determinant of poor outcome following rituximab and chemotherapy.
Project description:Revised risk estimation and treatment stratification of low- and intermediate-risk neuroblastoma patients by integrating clinical and molecular prognostic markers. To optimize neuroblastoma treatment stratification, we aimed at developing a novel risk estimation system by integrating gene expression-based classification and established prognostic markers. Gene expression profiles were generated from 709 neuroblastoma specimens using customized 4x44K microarrays. Classification models were built using 75 tumors with contrasting courses of disease. Validation was performed in an independent test set (n=634) by Kaplan-Meier estimates and Cox regression analyses. Combination of gene expression-based classification and established prognostic markers improves risk estimation of LR/IR neuroblastoma patients. We propose to implement our revised treatment stratification system in a prospective clinical trial.
Project description:The risk of locoregional or distant failure in advanced HPV-negative head and neck squamous cell carcinoma (HNSCC) patients is high. However, no suitable markers for stratification are clinically available. Thus, we aimed to identify a microRNA(miRNA)-signature predicting disease recurrence. For this purpose the miRNA profiles from 162 HNSCC samples were analysed with regard to identification of a low-complex porgnostic signature. The data set consists of a discovery dataset (n=85) and a validation dataset (n=77). The study resulted in a prognostic 5-miRNA signature significantly predicting the relevant clinical endpoint freedom from recurrence.
Project description:The clinical course of Coronavirus disease 2019 (COVID-19) displays a wide variability, ranging from completely asymptomatic forms to diseases associated with severe clinical outcomes. To reduce the incidence COVID-19 severe outcomes, innovative molecular biomarkers are needed to improve the stratification of patients at the highest risk of mortality and to better customize therapeutic strategies. MicroRNAs associated with COVID-19 outcomes could allow quantifying the risk of severe outcomes and developing models for predicting outcomes, thus helping to customize the most aggressive therapeutic strategies for each patient. Here, we analyzed the circulating miRNA profiles in a set of 12 hospitalized patients with severe COVID-19, with the aim to identify miRNAs associated with in-hospital mortality.
Project description:The high incidence of differentiated thyroid carcinoma (DTC) leads to a significant increase in the number of patients with lung metastatic DTC (LMDTC), making this population a key focus and challenge in clinical practice, so there is an urgent need to find effective methods to guide risk stratification and predict the risk of metastasis in this group of patients. This study is significant in its theoretical and technical advancement as it based on primary lesion 4D label-free proteomic analysis, combined with PRM validation, to identify potential biomarkers (8 key DEPs) for predicting DTC lung metastasis.
Project description:Transcription factor Foxq1 controls mucin gene expression and granule content in mouse stomach surface mucous cells Background and Aims: The gastric mucosa provides a stringent epithelial barrier and produces acid and enzymes that initiate digestion. In this regenerating tissue, progenitors differentiate continually into 4 principal specialized cell types, yet underlying mechanisms of differentiation are poorly understood. We identified stomach-restricted expression of the forkhead transcription factor FOXQ1. Methods: We used a combination of genetic, histochemical, ultrastructural and molecular analysis to study gastric cell lineages with respect to FOXQ1. Results: Within the developing and adult gastrointestinal tract, Foxq1 mRNA is restricted to the stomach, expressed prominently in foveolar (pit) cells, the abundant mucin-producing cells that line the mucosal surface, and required for their complete differentiation. Mice carrying Foxq1 coding mutations show virtual absence of mRNA and protein for the backbone of the predominant stomach mucin, MUC5AC. These observations correspond to a paucity of foveolar-cell secretory vesicles and notable loss of stomach but not intestinal mucus. Transcriptional profiling identified a surprisingly restricted set of genes with altered expression in Foxq1 mutant stomachs. MUC5AC is a highly tissue-restricted product that similarly depends on FOXQ1 in its other major site of expression, conjunctival goblet cells. Conclusions: Taken together, these observations imply that promotion of gastric MUC5AC synthesis is a primary, cell-autonomous function of FOXQ1. This study is the first to implicate a transcription factor in terminal differentiation of foveolar cells and begins to define the requirements to assemble highly specialized organelles and cells in the gastric mucosa. Keywords: mutant mouse stomach
Project description:Background Despite improvement in diagnostic and therapeutic techniques, a significant percentage of patients with early stage laryngeal cancer still recur after treatment. Gene expression models prognostic of recurrence risk could suggest which patients with early stage laryngeal cancer would be more appropriate for testing adjuvant strategies. Patients and Methods Expression profiling using whole genome DASL arrays was performed on 56 formalin-fixed paraffin-embedded tumor samples of patients with early stage laryngeal cancer, treated with surgery or radiation therapy. We split the samples into a training set and a validation set. Using the supervised principal components survival analysis in the first cohort, we identified multiple gene expression profiles that predict the risk of recurrence. These profiles were then validated in the second independent cohort. Results Gene models comprising different number of genes (40-100) identified a subgroup of patients who were at high risk of recurrence. Of these, the best prognostic model distinguished between a high- and a low-risk group (median DFS: 92 and 123 months, log rank p<0.005, permutation p<0.05), Hazard Ratio (HR): 8.51 (95% CI, 1.01 to 71.77; p<0.05). These models performed similarly in the independent cohort of our study (median DFS: 38 vs 161 months, log rank p=0.018), HR=5.19 (95% CI, 1.14 to 23.57; p<0.05). Conclusions We have identified gene expression prognostic models which can refine the estimation of a patient’s risk of recurrence. These findings, if further validated, should aid in patient stratification for testing adjuvant treatment strategies. 56 patients with early stage laryngeal cancer were included in this study.