Utility of GDF‐15 as a diagnostic biomarker in gastric cancer: an investigation combining GEO, TCGA and meta‐analysis

Abstract

It was recently suggested that growth differentiation factor‐15 ( GDF ‐15) is associated with gastric cancer ( GC ) carcinogenesis. However, the diagnostic potential of GDF ‐15 for GC remains unclear. To address this issue, we obtained RNA sequencing and microarray data from the Gene Expression Omnibus ( GEO ) and The Cancer Genome Atlas ( TCGA ) databases, and searched PubMed, Google Scholar and Web of Science for relevant literature. We then used STATA to perform a meta‐analysis. In total, reports of 253 GC patients and 112 healthy controls who contributed peripheral blood samples were taken from the four literature sources, while information on 754 GC tumor and 263 gastric normal tissues was drawn from TCGA and seven GEO datasets. The expression level of GDF ‐15 mRNA was significantly higher in tumor tissues than in normal tissues, with a standard mean difference ( SMD ) of 0.79% and a 95% confidence interval (95% CI ) of 0.63–0.95. Consistently, the GDF ‐15 protein in blood was significantly increased in GC patients as compared to controls ( SMD = 3.74, 95% CI = 1.81–5.68). In addition, based on information from TCGA and GEO datasets, the expression level of GDF ‐15 mRNA may be of use for the diagnosis of GC , with a combined sensitivity, specificity and odds ratio of 0.69 (95% CI = 0.58–0.79), 0.90 (95% CI = 0.84–0.93) and 6.32 (95% CI = 4.22–9.49), respectively. The summary receiver operating characteristic curve demonstrated that the area under the curve was 0.90 (95% CI = 0.87–0.93). The results suggest higher levels of GDF ‐15 may be associated with GC tumorigenesis and may have the potential to be a diagnostic biomarker of GC.

<p>Pseudogene DUXAP10 acts as a diagnostic and prognostic marker and promotes cell proliferation by activating PI3K/AKT pathway in hepatocellular carcinoma</p>

“…By benefiting from a substantial number of expressed genes and clinical data regarding cancer patients in a public online database, many novel genes have been identified that are aberrantly expressed and have good clinical benefits in human cancers. For instance, a meta-analysis of data accessed from the GEO database showed that low TUBA4B levels were closely associated with short OS in various human cancers, which indicated that TUBA4B could be a novel prognostic biomarker for cancer patients.19 Also, using SROC curve analyses based on information downloaded from TCGA and GEO databases, CTD-2547G23.4 was found to have good diagnostic value for HCC patients,20 and GDF-15 was shown to have good diagnostic value for gastric cancer patients.21…”

Association between MIC-1 and Type 2 Diabetes: A Combined Analysis

“…The upregulated production of MIC-1 is triggered in response to inflammation such as tissue injury, biomechanical stress, and anoxia [8, 9]. MIC-1 has been considered to play a pivotal role in the development and progression of many diseases such as cardiovascular diseases and malignant cancer [10–12]. Recently, MIC-1 is considered as a long-term metabolism regulator with a function of increasing lipolysis as an adipokine in a paracrine fashion [13–15].…”

Transcriptional Biomarkers in Oral Cancer: An Integrative Analysis and the Cancer Genome Atlas Validation

“…Adopting a similar method, reports on microarray datasets have led the discovery of novel biomarkers in thyroid cancer, gastric cancer and head and neck cancer (Liu et al, 2018;Reddy et al, 2016;Zhao and Li, 2018). This is also translated in clinics to predict recurrence and treatment outcome in breast cancer (Ademuyiwa et al, 2011;Madden et al, 2013).…”

“…*For Correspondence: prabhudas.patel@gcriindia.org Kinjal D Patel, Hemangini H Vora, Prabhudas S Patel* expression omnibus (GEO) and array express along with advanced computational data analysis tools such as GeneSpring GX, INMEX and R/Bioconductor allow us to congregate and re-examine gene expression data in a single experiment. Ergo, various groups have effectively extracted significant insights using meticulous analytical strategies (Liu et al, 2018;Reddy et al, 2016;Zhao and Li, 2018). Here, to overcome the aforementioned limitations, we integrated single chip microarray data (HG-U133_Plus_2) and stratified normal samples into two classes: healthy normal tissues and adjacent normal tissues.…”