Background: This study was to explore the infiltration pattern of immune cells in the prostate cancer (PCa) microenvironment and evaluate the possibility of specific infiltrating immune cells as potential prognostic biomarkers in PCa.Methods: Infiltrating percentage of 22 immune cells were extracted from 27 normalized datasets by CIBERSORT algorithm. Samples with CIBERSORT p-value < 0.05 were subsequently merged and divided into normal or tumor groups. The differences of 22 immune cells between normal and tumor tissues were analyzed along with potential infiltrating correlations among 22 immune cells and Gleason grades. SNV data from TCGA was used to calculate the TMB score. A univariate and multivariate regression were used to evaluate the prognostic effects of immune cells in PCa.Results: Ten immune cells with significant differences were identified, including seven increased and three decreased infiltrating immune cells from 190 normal prostate tissues and 537 PCa tissues. Among them, the percentage of infiltration of resting NK cells increased the most, whereas the percentage of infiltration of resting mast cells decreased the most. In normal tissues, CD8+ T cells had the strongest infiltrating correlation with monocytes, while activated NK cells and naive B cells were the highest in PCa tissues. Moreover, the infiltration of five immune cells was significantly associated with TMB score and mutations of immune gene change the infiltration of immune cells. The Area Under Curve (AUC) of the multivariate regression model for the fiveand 10-year survival prediction of PCa reached 0.796 and 0.862. The validation cohort proved that the model was reproducible.Conclusions: This study demonstrated that different infiltrating immune cells in prostate cancer, especially higher infiltrating M1 macrophages and neutrophils in PCa tissue, are associated with patients' prognosis, suggesting that these two immune cells might be potential targets for PCa diagnosis and prognosis of treatment.

Skeletal muscle differentiation can be regulated by various transcription factors and non-coding RNAs. In our previous work, miR-223 is differentially expressed in the skeletal muscle of chicken with different growth rates, but its role, expression and action mechanism in muscle development still remains unknown. Here, we found that MYOD transcription factor can upregulate miR-223 expression by binding to an E-box region of the gga-miR-223 gene promoter during avian myoblast differentiation. IGF2 and ZEB1 are two target genes of miR-223. The target inhibition of miR-223 on IGF2 and ZEB1 are dynamic from proliferation to differentiation of myoblast. miR-223 inhibits IGF2 expression only in the proliferating myoblast, whereas it inhibits ZEB1 mainly in the differentiating myoblast. The inhibition of IGF2 by miR-223 resulted in the repression of myoblast proliferation. During myoblast differentiation, miR-223 would be upregulated owing to the promoting effect of MYOD, and the upregulation of miR-223 would inhibit ZEB1 to promote myoblast differentiation. These results not only demonstrated that the well-known muscle determination factor MYOD can promote myoblast differentiation by upregulate miR-223 transcription, but also identified that miR-223 can influence myoblast proliferation and differentiation by a dynamic manner regulates the expression of its target genes.

miR-17 family microRNAs (miRNAs) are crucial for embryo development, however, their role in muscle development is still unclear. miR-20a-5p and miR-20b-5p belong to the miR-17 family and are transcribed from the miR-17~92 and miR-106a~363 clusters respectively. In this study, we found that miR-20a-5p and miR-20b-5p promoted myoblast differentiation and repressed myoblast proliferation by directly binding the 3′ UTR of E2F transcription factor 1 (E2F1) mRNA. E2F1 is an important transcriptional factor for organism’s normal development. Overexpression of E2F1 in myoblasts promoted myoblast proliferation and inhibited myoblast differentiation. Conversely, E2F1 inhibition induced myoblast differentiation and repressed myoblast proliferation. Moreover, E2F1 can bind directly to promoters of the miR-17~92 and miR-106a~363 clusters and activate their transcription, and E2F1 protein expression is correlated with the expression of pri-miR-17~92 and pri-miR-106a~363 during myoblast differentiation. These results suggested an auto-regulatory feedback loop between E2F1 and miR-20a-5p/20b-5p, and indicated that miR-20a-5p, miR-20b-5p and E2F1 are involved in myoblast proliferation and differentiation through the auto-regulation between E2F1 and miR-20a-5p/20b-5p. These findings provide new insight into the mechanism of muscle differentiation, and further shed light on the understanding of muscle development and muscle diseases.

Growth performance is an important economic trait in chicken. MicroRNAs (miRNAs) have been shown to play important roles in various biological processes, but their functions in chicken growth are not yet clear. To investigate the function of miRNAs in chicken growth, breast muscle tissues of the two-tail samples (highest and lowest body weight) from Recessive White Rock (WRR) and Xinghua Chickens (XH) were performed on high throughput small RNA deep sequencing. In this study, a total of 921 miRNAs were identified, including 733 known mature miRNAs and 188 novel miRNAs. There were 200, 279, 257 and 297 differentially expressed miRNAs in the comparisons of WRRh vs. WRRl, WRRh vs. XHh, WRRl vs. XHl, and XHh vs. XHl group, respectively. A total of 22 highly differentially expressed miRNAs (fold change > 2 or < 0.5; p-value < 0.05; q-value < 0.01), which also have abundant expression (read counts > 1000) were found in our comparisons. As far as two analyses (WRRh vs. WRRl, and XHh vs. XHl) are concerned, we found 80 common differentially expressed miRNAs, while 110 miRNAs were found in WRRh vs. XHh and WRRl vs. XHl. Furthermore, 26 common miRNAs were identified among all four comparisons. Four differentially expressed miRNAs (miR-223, miR-16, miR-205a and miR-222b-5p) were validated by quantitative real-time RT-PCR (qRT-PCR). Regulatory networks of interactions among miRNAs and their targets were constructed using integrative miRNA target-prediction and network-analysis. Growth hormone receptor (GHR) was confirmed as a target of miR-146b-3p by dual-luciferase assay and qPCR, indicating that miR-34c, miR-223, miR-146b-3p, miR-21 and miR-205a are key growth-related target genes in the network. These miRNAs are proposed as candidate miRNAs for future studies concerning miRNA-target function on regulation of chicken growth.