Recent development in immunotherapy for cancer treatment has substantiated to be more effective than most of the other treatments. Immunity is the first line of defence of the body; nevertheless, cancerous cells can manipulate immunity compartments to play several roles in tumour progression. Tumour-associated macrophages (TAMs), one of the most dominant components in the tumour microenvironment, are recognized as anti-tumour suppressors. Unfortunately, the complete behaviour of TAMs is still unclear and understudied. TAM density is directly correlated with the progression and poor prognosis of hepatocellular carcinoma (HCC), therefore studying TAMs from different points of view passing by all the factors that may affect its existence, polarization, functions and repolarization are of great importance. Different epigenetic regulations were reported to have a direct relation with both HCC and TAMs. Here, this review discusses different epigenetic regulations that can affect TAMs in HCC whether positively or negatively.

The iron-regulatory hormone hepcidin is transcriptionally up-regulated by gluconeogenic signals. Recent evidence suggeststhat increases in circulating hepcidin may decrease dietary iron absorption following prolonged exercise, however evidence is limited on whether gluconeogenic signals contribute to post-exercise increases in hepcidin. Mice with genetic knockout of regulated in development and DNA response-1 (REDD1) display greater glycogen depletion following exercise, possibly indicating greater gluconeogenesis. The objective of the present study was to determine liver hepcidin, markers of gluconeogenesis and iron metabolism in REDD1 knockout and wild-type mice following prolonged exercise. Twelve-week-old male REDD1 knockout and wild-type mice were randomised to rest or 60 min treadmill running with 1, 3 or 6 h recovery (n = 5–8/genotype/group). Liver gene expression of hepcidin (Hamp) and gluconeogenic enzymes (Ppargc1a, Creb3l3, Pck1, Pygl) were determined by qRT-PCR. Effects of genotype, exercise and their interaction were assessed by two-way ANOVAs with Tukey's post-hoc tests, and Pearson correlations were used to assess the relationships between Hamp and study outcomes. Liver Hamp increased 1- and 4-fold at 3 and 6 h post-exercise, compared to rest (P-adjusted < 0⋅009 for all), and was 50% greater in REDD1 knockout compared to wild-type mice (P = 0⋅0015). Liver Ppargc1a, Creb3l3 and Pck1 increased with treadmill running (P < 0⋅0001 for all), and liver Ppargc1a, Pck1 and Pygl were greater with REDD1 deletion (P < 0⋅02 for all). Liver Hamp was positively correlated with liver Creb3l3 (R = 0⋅62, P < 0⋅0001) and Pck1 (R = 0⋅44, P = 0⋅0014). In conclusion, REDD1 deletion and prolonged treadmill running increased liver Hamp and gluconeogenic regulators of Hamp, suggesting gluconeogenic signalling of hepcidin with prolonged exercise.

Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) is an important technology to analyse gene expression levels during plant development or in response to different treatments. An important requirement to measure gene expression levels accurately is a properly validated set of reference genes. In this context, we analysed the potential use of 17 candidate reference genes across a diverse set of samples, including several tissues, different stages and environmental conditions, encompassing seed germination and seedling growth in Ricinus communis L. These genes were tested by RT-qPCR and ranked according to the stability of their expression using two different approaches: GeNorm and NormFinder. GeNorm and Normfinder indicated that ACT, POB and PP2AA1 comprise the optimal combination for normalization of gene expression data in inter-tissue (heterogeneous sample panel) studies. We also describe the optimal combination of reference genes for a subset of root, endosperm and cotyledon samples. In general, the most stable genes suggested by GeNorm are very consistent with those indicated by NormFinder, which highlights the strength of the selection of reference genes in our study. We also validated the selected reference genes by normalizing the expression levels of three target genes involved in energy metabolism with the reference genes suggested by GeNorm and NormFinder. The approach used in this study to identify stably expressed genes, and thus potential reference genes, was applied successfully for R. communis and it provides important guidelines for RT-qPCR studies in seeds and seedlings for other species (especially in those cases where extensive microarray data are not available).