Abstract
Evaluating the prognostic value of genes of interest in different populations of gastric cancer (GC) is difficult and time-consuming for basic and translational researchers even though many datasets are available in public dataset depositories. In the current study, we developed a robust web-based portal called OSgc (Online consensus Survival analysis of gastric cancer) that enables easy and swift verification of known and novel biomarker candidates in GC. OSgc is composed of gene expression profiling data and clinical follow-up information of 1,824 clinical GC cases, which are collected from 7 public independent datasets derived from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). By OSgc, users input the official gene symbol and will promptly retrieve the Kaplan–Meier survival plot with hazard ratio (HR) and log rank p value on the output webpage, by which users could assess the prognostic value of interesting genes for GC patients. Five survival end points containing overall survival, progression-free survival, progression-free interval, relapse-free survival, and disease-free survival could be measured in OSgc. OSgc can greatly help cancer biologists and clinicians to explore the effect of gene expression on patient survival. OSgc is freely available without restrictions at http://bioinfo.henu.edu.cn/GC/GCList.jsp.
Highlights
Gastric cancer (GC) is the fourth leading factor of cancer mortality in the world
Clinical Information of gastric cancer (GC) Datasets Used in OSgc
OSgc provides the largest compilation of expression profiling datasets related to clinical outcomes, FIGURE 1 | The flowchart of OSgc establishment
Summary
Gastric cancer (GC) is the fourth leading factor of cancer mortality in the world. In 2020, GC occurred in 1,089,103 people and resulted in 768,793 deaths. There are many advances in treatment of GC, patients have poor prognosis and the 5-year survival rate is just 5%–20%. Prognostic biomarkers can assist clinicians in assessing the risk of clinical outcomes including cancer recurrence or disease progression in the future [1, 2]. Molecular characteristics such as gene expression and somatic mutations have been reported to represent the primary source of prognostic biomarker [3, 4]. A recent study showed that high SETD2 (SET domain-containing protein 2, known as HYPB) expression
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