Analysis on regulatory network linked to Hpa gene in invasion and metastasis of colon cancer

MNNMDA: Predicting human microbe-disease association via a method to minimize matrix nuclear norm

Identifying the potential associations between microbes and diseases is the first step for revealing the pathological mechanisms of microbe-associated diseases. However, traditional culture-based microbial experiments are expensive and time-consuming. Thus, it is critical to prioritize disease-associated microbes by computational methods for further experimental validation. In this study, we proposed a novel method called MNNMDA, to predict microbe-disease associations (MDAs) by applying a Matrix Nuclear Norm method into known microbe and disease data. Specifically, we first calculated Gaussian interaction profile kernel similarity and functional similarity for diseases and microbes. Then we constructed a heterogeneous information network by combining the integrated disease similarity network, the integrated microbe similarity network and the known microbe-disease bipartite network. Finally, we formulated the microbe-disease association prediction problem as a low-rank matrix completion problem, which was solved by minimizing the nuclear norm of a matrix with a few regularization terms. We tested the performances of MNNMDA in three datasets including HMDAD, Disbiome, and Combined Data with small, medium and large sizes respectively. We also compared MNNMDA with 5 state-of-the-art methods including KATZHMDA, LRLSHMDA, NTSHMDA, GATMDA, and KGNMDA, respectively. MNNMDA achieved area under the ROC curves (AUROC) of 0.9536 and 0.9364 respectively on HDMAD and Disbiome, better than the AUCs of compared methods under the 5-fold cross-validation for all microbe-disease associations. It also obtained a relatively good performance with AUROC 0.8858 in the combined data. In addition, MNNMDA was also better than other methods in area under precision and recall curve (AUPR) under the 5-fold cross-validation for all associations, and in both AUROC and AUPR under the 5-fold cross-validation for diseases and the 5-fold cross-validation for microbes. Finally, the case studies on colon cancer and inflammatory bowel disease (IBD) also validated the effectiveness of MNNMDA. In conclusion, MNNMDA is an effective method in predicting microbe-disease associations.

Availability

The codes and data for this paper are freely available at Github https://github.com/Haiyan-Liu666/MNNMDA.

Genistein inhibits invasion and migration of colon cancer cells by recovering WIF1 expression

Colon cancer is characterized by invasion and migration. DNA methylation of CpG islands in tumor suppressor genes is considered to be an epigenetic mechanism underlying cancer development. Epigenetic silencing of a gene may be reversed by drugs, including genistein. The present study aimed to determine the effect of genistein on Wnt inhibitory factor 1 (WIF1) and invasion, and migration of colon cancer cells. The viability of HT29 colon cancer cells was suppressed by genistein in a dose dependent manner. Following 72 h of treatment with 10, 20 and 60 µmol/l genistein, increased demethylation of WIF1 was induced in a dose‑dependent manner. Additionally, the invasive/migratory abilities of cells treated with genistein decreased in a dose‑dependent manner. Reverse transcription‑quantitative polymerase chain reaction and western blot analyses were performed to identify the mRNA and protein expression levels of invasion/migration‑associated factors. Following treatment with genistein, matrix metalloproteinase (MMP) 2 and MMP9 expression levels decreased, whereas the expression of metalloproteinase inhibitor 1 and E‑cadherin increased significantly. In addition, the expression levels of proto‑oncogene Wnt‑1 (Wnt‑1)/β‑catenin pathway‑associated factors, β‑catenin, c‑Myc proto‑oncogene protein and cyclin D1 decreased in a dose‑dependent manner following treatment with genistein. The invasive/migratory abilities of cells transfected with WIF1‑small interfering (si) RNA, and those transfected with WIF1‑siRNA and treated with genistein, increased notably compared with the control group. The present study demonstrated that genistein was able to inhibit the cell invasion and migration of colon cancer cells by inducing demethylation, and recovering the activity of WIF1 by altering the expression of invasion‑associated factors, and components of the Wnt signaling pathway.