The role of circulating microRNAs for the diagnosis of hepatitis B virus-associated hepatocellular carcinoma with low alpha-fetoprotein level: a systematic review and meta-analysis.

BACKGROUND: Alpha-fetoprotein (AFP) has been widely used for many years as a serum marker for hepatocellular carcinoma (HCC). However, AFP has been recognized as having poor sensitivity. More and more studies have concluded that circulating microRNAs (miRNAs) might be a promising biomarker that could complement AFP. However, the diagnostic ability of circulating miRNAs has varied among the studies. Therefore, we performed the present meta-analysis to appraise the diagnostic performance of circulating miRNAs as a biomarker for hepatitis B virus-associated HCC (HBV-HCC) patients with low AFP levels. METHODS: We performed a systematic review and meta-analysis of the published literature to assess the diagnostic accuracy of circulating miRNAs in differentiating HBV-HCC patients with low AFP levels from non-HCC controls. RESULTS: Circulating miRNAs showed promising potential in the diagnosis of HBV-HCC patients with low AFP levels. In the low-AFP HBV-HCC patients, the area under the curve (AUC) was 0.88 (95% confidence interval [CI]: 0.84-0.90). The pooled sensitivity and specificity were 0.84 (95% CI: 0.78-0.88) and 0.76 (95% CI: 0.69-0.83), respectively. CONCLUSIONS: The detection of circulating miRNAs provides a valuable method for the diagnosis of HBV-HCC in patients with low AFP levels.

An endoplasmic reticulum stress-related signature could robustly predict prognosis and closely associate with response to immunotherapy in pancreatic ductal adenocarcinoma.

PURPOSE: Pancreatic ductal adenocarcinoma (PDAC) is one of the most malignant tumors. Endoplasmic reticulum stress (ERS) plays an essential role in PDAC progression. Here, we aim to identify the ERS-related genes in PDAC and build reliable risk models for diagnosis, prognosis and immunotherapy response of PDAC patients as well as investigate the potential mechanism. METHODS: We obtained PDAC cohorts with transcriptional profiles and clinical data from the ArrayExpress, The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. Univariate Cox regression, LASSO regression and multivariate Cox regression analyses were used to construct an ERS-related prognostic signature. The CIBERSORT and ssGSEA algorithms were applied to explore the correlation between the prognostic signature and immune cell infiltration and immune-related pathways. The GDSC database and TIDE algorithm were used to predict responses to chemotherapy and immunotherapy, identifying potential drugs for treating patients with PDAC. RESULTS: We established and validated an ERS-related prognostic signature comprising eight genes (HMOX1, TGFB1, JSRP1, GAPDH, CAV1, CHRNE, CD74 and ERN2). Patients with higher risk scores displayed worse outcomes than those with lower risk scores. PDAC patients in low-risk groups might benefit from immunotherapy. Dasatinib and lapatinib might have potential therapeutic implications in high-risk PDAC patients. CONCLUSION: We established and validated an ERS-related prognostic signature comprising eight genes to predict the overall survival outcome of PDAC patients, which closely correlating with the response to immunotherapy and sensitivity to anti-tumor drugs, as well as could be beneficial for formulating clinical strategies and administering individualized treatments.