Anterior gradient 2-derived peptide upregulates major histocompatibility complex class I-related chains A/B in hepatocellular carcinoma cells.

AIMS: Hepatocellular carcinoma (HCC) is a leading cause of cancer mortality worldwide. Decrease in NKG2D ligand levels and exhaustion of NK cells in HCC patients are major causes of immune escape, high recurrence, poor prognosis, and low overall survival. Enhancing the susceptibility of HCC to NK cells by upregulating NKG2DLs on tumor cells is an effective treatment strategy. This study aimed to identify the effect of the Anterior gradient 2 (AGR2)-derived peptide P1, which was reported to bind to HLA-A*0201 as an epitope, on both the expression of major histocompatibility complex class I-related chains A/B (MICA/B) on HCC cells and the cytotoxicity of NK cells. MAIN METHODS: The effect of P1 on MICA/B expression on HCC cells was determined by qRT-PCR, western blotting, and flow cytometry analysis. HCC cells were pre-treated with various pathway inhibitors to identify the molecular pathways associated with P1 treatment. The cytotoxicity of NK cells toward HCC was investigated by LDH cytotoxicity assay. The tumor-suppression effect of P1 was determined in vivo using a NOD/SCID mice HCC model. KEY FINDINGS: P1 significantly increased MICA/B expression on HCC cells, thereby enhancing their susceptibility to the cytotoxicity of NK cells in vitro and in vivo. Further, p38 MAPK cell signaling pathway inhibitor SB203580 significantly attenuated the effects of P1 in vivo and in vitro. SIGNIFICANCE: P1 upregulates MICA and MICB expression on HCC cells, thereby promoting their recognition and elimination by NK cells, which makes P1 an attractive novel immunotherapy agent.

Bioinformatic profiling identifies a platinum-resistant-related risk signature for ovarian cancer.

Most high-grade serous ovarian cancer (HGSOC) patients develop resistance to platinum-based chemotherapy and recur. Many biomarkers related to the survival and prognosis of drug-resistant patients have been delved by mining databases; however, the prediction effect of single-gene biomarker is not specific and sensitive enough. The present study aimed to develop a novel prognostic gene signature of platinum-based resistance for patients with HGSOC. The gene expression profiles were obtained from Gene Expression Omnibus and The Cancer Genome Atlas database. A total of 269 differentially expressed genes (DEGs) associated with platinum resistance were identified (P < .05, fold change >1.5). Functional analysis revealed that these DEGs were mainly involved in apoptosis process, PI3K-Akt pathway. Furthermore, we established a set of seven-gene signature that was significantly associated with overall survival (OS) in the test series. Compared with the low-risk score group, patients with a high-risk score suffered poorer OS (P < .001). The area under the curve (AUC) was found to be 0.710, which means the risk score had a certain accuracy on predicting OS in HGSOC (AUC > 0.7). Surprisingly, the risk score was identified as an independent prognostic indicator for HGSOC (P < .001). Subgroup analyses suggested that the risk score had a greater prognostic value for patients with grade 3-4, stage III-IV, venous invasion and objective response. In conclusion, we developed a seven-gene signature relating to platinum resistance, which can predict survival for HGSOC and provide novel insights into understanding of platinum resistance mechanisms and identification of HGSOC patients with poor prognosis.

Identification of a novel cell cycle-related gene signature predicting survival in patients with gastric cancer.

Gastric cancer (GC) is one of the most fatal cancers in the world. Thousands of biomarkers have been explored that might be related to survival and prognosis via database mining. However, the prediction effect of single gene biomarkers is not specific enough. Increasing evidence suggests that gene signatures are emerging as a possible better alternative. We aimed to develop a novel gene signature to improve the prognosis prediction of GC. Using the messenger RNA (mRNA)-mining approach, we performed mRNA expression profiling in a large GC cohort (n = 375) from The Cancer Genome Atlas (TCGA) database. Gene Set Enrichment Analysis (GSEA) was performed, and we recovered genes related to the G2/M checkpoint, which we identified with a Cox proportional regression model. We identified a set of five genes (MARCKS, CCNF, MAPK14, INCENP, and CHAF1A), which were significantly associated with overall survival (OS) in the test series. Based on this five-gene signature, the test series patients could be classified into high-risk or low-risk subgroups. Multivariate Cox regression analysis indicated that the prognostic power of this five-gene signature was independent of clinical features. In conclusion, we developed a five-gene signature related to the cell cycle that can predict survival for GC. Our findings provide novel insight that is useful for understanding cell cycle mechanisms and for identifying patients with GC with poor prognoses.