The high incidence of recurrence and the poor prognosis of hepatocellular carcinoma (HCC) necessitate the discovery of new predictive markers of HCC invasion and prognosis. In this study, we evaluated the expression pattern of two members of a novel oncogene family, Musashi1 (MSI1) and Musashi2 (MSI2) in 40 normal hepatic tissue specimens, 149 HCC specimens and their adjacent non‐tumourous tissues. We observed that MSI1 and MSI2 were significantly up‐regulated in HCC tissues. High expression levels of MSI1 and MSI2 were detectable in 37.6% (56/149) and 49.0% (73/149) of the HCC specimens, respectively, but were rarely detected in adjacent non‐tumourous tissues and were never detected in normal hepatic tissue specimens. Nevertheless, only high expression of MSI2 correlated with poor prognosis. In addition, MSI2 up‐regulation correlated with clinicopathological parameters representative of highly invasive HCC. Further study indicated that MSI2 might enhance invasion of HCC by inducing epithelial–mesenchymal transition (EMT). Knockdown of MSI2 significantly decreased the invasion of HCC cells and changed the expression pattern of EMT markers. Moreover, immunohistochemistry assays of 149 HCC tissue specimens further confirmed this correlation. Taken together, the results of our study demonstrated that MSI2 correlates with EMT and has the potential to be a new predictive biomarker of HCC prognosis and invasion to help guide diagnosis and treatment of post‐operative HCC patients.

Enhanced aerobic glycolysis is an essential hallmark of malignant cancer. Blocking the glycolytic pathway has been suggested as a therapeutic strategy to impair the proliferation of tumor cells. Metformin, a widely used anti-diabetes drug, exhibits anti-tumor properties. However, the underlying molecular mechanism of its action linking glucose metabolism with the suppression of proliferation has not been fully clarified. Stable isotope tracing technology and gas chromatography-mass spectrometry method were utilized to analyze the effect of metformin on glycolytic flux in HCC cells. Western blot and immunohistochemistry were utilized to analyze the expression of phosphofructokinase-1 (PFK1) and 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 (PFKFB3) in HCC cells or xenograft tumor tissues. Lactate measurement and glucose uptake assay were used to analyze the level of lactate and glucose in the presence of frucose-2,6-diphosphate (F2,6BP) in HCC cells treated with metformin. We found that metformin significantly impaired hepatoma cell proliferation by inhibiting the glycolytic flux via PFK1 blockade. Interestingly, activation of PFK1 by F2,6BP reverses the inhibitory effect of metformin on hepatoma cell proliferation and glycolysis. Mechanistically, PFKFB3，a potent allosteric activator of PFK1, was markedly suppressed through inhibiting hypoxia-induced factor 1 (HIF-1α) accumulation mediated by metformin. Taken together these data indicate that HIF-1α/PFKFB3/PFK1 regulatory axis is a vital determinant of glucose metabolic reprogramming in hepatocellular carcinoma, which gives new insights into the action of metformin in combatting liver cancer.

Currently, immunotherapy has shown great efficacy in clinical trials, and monoclonal antibodies directed against immune checkpoint PD-1/PD-L1 have shown encouraging results in first-line or second-line treatment of non-small cell lung cancer patients. Meanwhile, anti-PD-1/PD-L1 immune checkpoint drugs combined with other treatments, such as chemotherapy, targeted therapy as well as anti-CTLA-4 checkpoint therapy, are considered an attractive treatment with higher efficacy. However, toxicity associated with PD-1/PD-L1 blockade is worth attention. Understanding the adverse effects caused by anti-PD-1/PD-L1 immunosuppressive agents is vital to guide the clinical rational use of drug. In this review, we summarized the adverse effects that occurred during the clinical use of anti-PD-1/PD-L1 inhibitors in the treatment of non-small cell lung cancer and discussed how to effectively manage and respond to these adverse reactions.

AIM: To explore expressions of PIK3CA in the progression of gastric cancer from primary to metastasis and its effects on activation of phosphatidylinositol 3-kinase (PI3K)/Akt pathway.

Background: Circular RNAs (circRNAs) are a new class of noncoding RNAs that have gained increased attention in human tumor research. However, the identification and function of circRNAs are largely unknown in the context of gastric cancer (GC). This study aims to identify novel circRNAs and determine their action networks in GC.Methods: A comprehensive strategy of data mining, reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and computational biology were conducted to discover novel circRNAs and to explore their potential mechanisms in GC. Promising therapeutic drugs for GC were determined by connectivity map (CMap) analysis.Results: Six overlapped differentially expressed circRNAs (DECs) were screened from selected microarray and RNA-Seq datasets of GC, and the six DECs were then validated by sanger sequencing and RNase R treatment. Subsequent RT-qPCR analysis of GC samples confirmed decreased expressions of the six DECs (hsa_circ_0000390, hsa_circ_0000615, hsa_circ_0001438, hsa_circ_0002190, hsa_circ_0002449 and hsa_circ_0003120), all of which accumulated preferentially in the cytoplasm. MiRNA binding sites and AGO2 occupation of the six circRNAs were predicted using online databases, and circRNA–miRNA interactions including the six circRNAs and 33 miRNAs were determined. Then, 5320 target genes of the above 33 miRNAs and 1492 differently expressed genes (DEGs) from The Cancer Genome Atlas (TCGA) database were identified. After intersecting the miRNA target genes and the 889 downregulated DEGs, 320 overlapped target genes were acquired. The Kyoto Encyclopedia of Genes and Genomes enrichment analysis indicated that these target genes were related to two critical tumor-associated signaling pathways. A protein–protein interaction network with the 320 target genes was constructed using STRING, and fifteen hubgenes (ATF3, BTG2, DUSP1, EGR1, FGF2, FOSB, GNAO1, GNAI1, GNAZ, GNG7, ITPR1, ITPKB, JUND, NR4A3, PRKCB) in the network were identified. Finally, bioactive chemicals (including vorinostat, trichostatin A and astemizole) based on the fifteen hubgenes were identifed as therapeutic agents for GC through the CMap analysis.Conclusions: This study provides a novel insight for further exploration of the pathogenesis and therapy of GC from the circRNA-miRNA-mRNA network perspective.