Aim: The aim was to systematically evaluate whether exosomal miRNAs could be regarded as potential minimally invasive biomarkers of diagnosis for gastrointestinal cancer. Methods: A systematic review and meta analysis of exosomal miRNA expression in gastrointestinal cancer were performed. Results: A total of 370 articles were retrieved from PubMed and EMBASE. The summary receiver operating characteristic curves of three miRNAs (miR-21, miR-1246 and miR-4644) were drawn, miR-21, miR-1246 and miR-4644 exhibited sensitivities of 0.66, 0.920 and 0.750, respectively; specificities were 0.87, 0.958 and 0.769, respectively; and areas under the curve for discriminating gastrointestinal cancer patients from control subjects were 0.876, 0.969 and 0.827, respectively. Conclusion: Exosome miR-1246 had the highest level of diagnostic efficiency, which indicated that miR-1246 could be a biomarker.

BackgroundMetastasis is the major cause of high recurrence and mortality of hepatocellular carcinoma (HCC). Unfortunately, there are few reports on effective biomarkers of HCC metastasis. Previous studies have reported that SAA1 may be a predictor and prognostic biomarker for multiple malignant tumors. However, the role of SAA1 in HCC has not yet been investigated.MethodsWe applied RNA sequencing and proteomics analysis to investigate the expression landscape of HCC cell lines and patient serum, respectively. SAA1 is a common key gene and listed as a candidate biomarker of HCC metastasis. It was validated in two cell lines, 107 participants serum, and 63 matched HCC and adjacent non-tumorous liver tissues. Human Protein Atlas (HPA), Genotype-Tissue Expression (GTEx), and The Cancer Genome Atlas (TCGA) datasets were integrated to explore SAA1 expression among various cell types and organs. The diagnostic and prognostic value of SAA1 in HCC were determined through receiver operating characteristic (ROC) and Kaplan–Meier curves. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and protein-protein interaction (PPI) network were constructed for SAA1, as well as for its co−expressed genes. We further analyzed the correlation between SAA1 and co-expression genes.ResultsWe found 7 differentially expressed genes (DEGs) and 14 differentially expressed proteins (DEPs) were related to HCC metastasis. SAA1, a key candidate biomarker, was highly enriched in hepatocytes and liver organ, and it was also highly expressed in HCC cells and the serum and tissues of HCC patients. The results of ROC curve analysis indicated that SAA1 had better predictive values for distinguishing HCC metastasis from non-metastasis. Kaplan-Meier curve analysis revealed that HCC patients with higher SAA1 expression had worse overall survival.ConclusionsOur findings provide new insights into HCC metastasis by identifying candidate gene prediction biomarkers for HCC metastasis.