Clinicopathological Significance and Prognostic Implications of REG4 Immunohistochemical Expression in Colorectal Cancer.

Background and objectives: The present study aimed to evaluate the clinicopathological significance and prognostic implications of REG4 immunohistochemical expression in colorectal cancer (CRC). Materials and Methods: We performed immunohistochemical analysis for REG4 cytoplasmic expression in 266 human CRC tissues. Correlations between REG4 expression, clinicopathological characteristics, and survival were investigated in CRC. Results: REG4 was expressed in 84 of 266 CRC tissues (31.6%). REG4 expression was significantly more frequent in the right colon than that in the left colon and rectum (p = 0.002). However, we observed no significant correlation between REG4 expression and other clinicopathological parameters. REG4 expression was significantly higher in CRCs with low stroma than in those with high stroma (p = 0.006). In addition, REG4 was more frequently expressed in CRCs with the mucinous component than in those without it (p < 0.001). There was no significant correlation between REG4 expression and overall recurrence-free survival (p = 0.132 and p = 0.480, respectively). Patients with REG4 expression showed worse overall and recurrence-free survival in the high-stroma subgroup (p = 0.001 and p = 0.017, respectively), but no such correlation was seen in the low stroma subgroup (p = 0.232 and p = 0.575, respectively). Conclusions: REG4 expression was significantly correlated with tumor location, amount of stroma, and mucinous component in CRCs. In patients with high stroma, REG4 expression was significantly correlated with poor overall and recurrence-free survival.

An Improved Method for Prediction of Cancer Prognosis by Network Learning.

Accurate identification of prognostic biomarkers is an important yet challenging goal in bioinformatics. Many bioinformatics approaches have been proposed for this purpose, but there is still room for improvement. In this paper, we propose a novel machine learning-based method for more accurate identification of prognostic biomarker genes and use them for prediction of cancer prognosis. The proposed method specifies the candidate prognostic gene module by graph learning using the generative adversarial networks (GANs) model, and scores genes using a PageRank algorithm. We applied the proposed method to multiple-omics data that included copy number, gene expression, DNA methylation, and somatic mutation data for five cancer types. The proposed method showed better prediction accuracy than did existing methods. We identified many prognostic genes and their roles in their biological pathways. We also showed that the genes identified from different omics data were complementary, which led to improved accuracy in prediction using multi-omics data.

G2Vec: Distributed gene representations for identification of cancer prognostic genes.

Identification of cancer prognostic genes is important in that it can lead to accurate outcome prediction and better therapeutic trials for cancer patients. Many computational approaches have been proposed to achieve this goal; however, there is room for improvement. Recent developments in deep learning techniques can aid in the identification of better prognostic genes and more accurate outcome prediction, but one of the main problems in the adoption of deep learning for this purpose is that data from cancer patients have too many dimensions, while the number of samples is relatively small. In this study, we propose a novel network-based deep learning method to identify prognostic gene signatures via distributed gene representations generated by G2Vec, which is a modified Word2Vec model originally used for natural language processing. We applied the proposed method to five cancer types including liver cancer and showed that G2Vec outperformed extant feature selection methods, especially for small number of samples. Moreover, biomarkers identified by G2Vec was useful to find significant prognostic gene modules associated with hepatocellular carcinoma.

Association between a change in donor kidney function and long-term allograft outcomes in kidney transplant recipients.

Reserve capacity of donated kidney may be an important determinant of allograft survival in kidney transplantation (KT). Here, we investigate change in estimated glomerular filtration rate of donor kidney (ΔeGFR(Donor)) over 30 days after KT as a predictor of the allograft function. A total of 222 recipients were divided into two groups according to ΔeGFR(Donor) as follows: Group I (n = 110), ΔeGFR(Donor) ≥ -25%; Group II (n = 112), ΔeGFR(Donor) < -25%. Three years after KT, Group I had a higher eGFR(Recipient) than Group II (55 ± 21 vs. 47 ± 22 mL/min/1.73 m2, P < 0.05). However, no differences in eGFR(Recipient) were detected between the two groups after 10 years. Linear regression analysis showed that ΔeGFR(Donor) was significantly associated with the eGFR(Recipient) at 3 years post-transplantation, but not at 10 years post-transplantation. In Kaplan-Meier analysis, Group I had a greater dialysis-free survival rate than Group II at the 10-year follow-up (84% vs. 76%, P < 0.05). However, no difference in overall survival rate between groups was detected. In the multivariate-adjusted Cox proportional-hazard model, ΔeGFR(Donor) was independently associated with future allograft loss (hazard ratio 0.973; 95% confidence interval 0.949-0.999). These results suggest that larger recovery of donor kidney function after KT donation is associated with better short/intermediate-term allograft outcomes. Follow-up assessment of donor kidney function may be useful to monitor KT recipients at risk for allograft loss.

Inhibition of erythropoiesis by Smad6 in human cord blood hematopoietic stem cells.

Bone morphogenetic proteins (BMPs) that belong to the transforming growth factor-ß (TGF-ß) superfamily cytokines, play crucial roles in hematopoiesis. However, roles of Smad6 in hematopoiesis remained unknown in contrast to the other inhibitory Smad (I-Smad), Smad7. Here we show that Smad6 inhibits erythropoiesis in human CD34(+) cord blood hematopoietic stem cells (HSCs). Smad6 was specifically expressed in CD34(+) cord blood HSCs, which was correlated with the expression of BMP2/4/6/7 and BMP type I receptor (BMPRI). BMP-specific receptor-regulated Smads (R-Smads), Smad1 and Smad5 in cooperation with Smad4 induced transcription of the Smad6 gene. Instead of affecting cell cycle, apoptosis, self-renewal, and stemness of CD34(+) cells, Smad6 knockdown enhanced, whereas Smad6 overexpression suppressed erythropoiesis in stem cell culture and colony formation assay. Consistently, Smad6 suppressed the expression of the genes essential for erythropoiesis, such as Kruppel-like factor 1 (erythroid) (KLF1/EKLF) and GATA binding protein 2 (GATA-2). Promoter analyses showed that Smad6 repressed Smad5/4-induced transcription of the Klf1 gene. Thus, our data suggest that Smad6 indirectly maintains stemness by preventing spontaneous erythropoiesis in HSCs.