A unified apparent porosity/permeability model of organic porous media: Coupling complex pore structure and multi-migration mechanism

 Shale gas resources are widely distributed and abundant in China, which is an important field for strategic replacement and development of oil and gas resources. Shale gas reservoirs has adsorption gas, free gas. The structure of different scale media, such as organic pores, are difficult to describe. Therefore, flow behavior cannot be simulated by conventional method. In this paper, the micro-scale fluid migration in shale gas reservoirs was established in a single pore, which coupled surface diffusion, slip flow, and viscous flow. On this basis, the fractal scale relationship was applied to describe the distribution of pore radius, tortuosity, and surface roughness. Based on the comprehensive characterization of static structure haracteristics of porous media, such as pore size distribution, pore shapes, tortuosity and surface roughness, and the dynamic pore size influenced by various stresses, the apparent porosity/permeability model of organic matter considering single-phase multi-migration mechanism was established. The gas migration in organic porous media was analyzed with the apparent porosity/permeability model. The results show that the small pores in organic matter are the main storage space of gas (more than 95% of the gas is stored in pores less than 10 nm), and the large pores are gas flow channel. At the same time, the apparent porosity/permeability model combined with conventional Darcy equation can be used to describe the single-phase gas flow in shale gas reservoirs.Cited as: Sheng, G., Su, Y., Zhao, H., Liu, J. A unified apparent porosity/permeability model of organic porous media: Coupling complex pore structure and multi-migration mechanism. Advances in Geo-Energy Research, 2020, 4(2): 115-125, doi: 10.26804/ager.2020.02.0

A new fault diagnosis method using deep belief network and compressive sensing

Compressive sensing provides a new idea for machinery monitoring, which greatly reduces the burden on data transmission. After that, the compressed signal will be used for fault diagnosis by feature extraction and fault classification. However, traditional fault diagnosis heavily depends on the prior knowledge and requires a signal reconstruction which will cost great time consumption. For this problem, a deep belief network (DBN) is used here for fault detection directly on compressed signal. This is the first time DBN is combined with the compressive sensing. The PCA analysis shows that DBN has successfully separated different features. The DBN method which is tested on compressed gearbox signal, achieves 92.5 % accuracy for 25 % compressed signal. We compare the DBN on both compressed and reconstructed signal, and find that the DBN using compressed signal not only achieves better accuracies, but also costs less time when compression ratio is less than 0.35. Moreover, the results have been compared with other classification methods

Highly robust model of transcription regulator activity predicts breast cancer overall survival

Background: While several multigene signatures are available for predicting breast cancer prognosis, particularly in early stage disease, effective molecular indicators are needed, especially for triple-negative carcinomas, to improve treatments and predict diagnostic outcomes. The objective of this study was to identify transcriptional regulatory networks to better understand mechanisms giving rise to breast cancer development and to incorporate this information into a model for predicting clinical outcomes. Methods: Gene expression profiles from 1097 breast cancer patients were retrieved from The Cancer Genome Atlas (TCGA). Breast cancer-specific transcription regulatory information was identified by considering the binding site information from ENCODE and the top co-expressed targets in TCGA using a nonlinear approach. We then used this information to predict breast cancer patient survival outcome. Result: We built a multiple regulator-based prediction model for breast cancer. This model was validated in more than 5000 breast cancer patients from the Gene Expression Omnibus (GEO) databases. We demonstrated our regulator model was significantly associated with clinical stage and that cell cycle and DNA replication related pathways were significantly enriched in high regulator risk patients. Conclusion: Our findings demonstrate that transcriptional regulator activities can predict patient survival. This finding provides additional biological insights into the mechanisms of breast cancer progression

Methylation of NF-κB and its Role in Gene Regulation

The nuclear factor κB (NF-κB) is one of the most pivotal transcription factors in mammalian cells. In many pathologies NF-κB is activated abnormally. This contributes to the development of various disorders such as cancer, acute kidney injury, lung disease, chronic inflammatory diseases, cardiovascular disease, and diabetes. This book chapter focuses on how methylation of NF-κB regulates its target genes differentially. The knowledge from this chapter will provide scientific strategies for innovative therapeutic intervention of NF-κB in a wide range of diseases

Transcription factor expression as a predictor of colon cancer prognosis: a machine learning practice

Background Colon cancer is one of the leading causes of cancer deaths in the USA and around the world. Molecular level characters, such as gene expression levels and mutations, may provide profound information for precision treatment apart from pathological indicators. Transcription factors function as critical regulators in all aspects of cell life, but transcription factors-based biomarkers for colon cancer prognosis were still rare and necessary. Methods We implemented an innovative process to select the transcription factors variables and evaluate the prognostic prediction power by combining the Cox PH model with the random forest algorithm. We picked five top-ranked transcription factors and built a prediction model by using Cox PH regression. Using Kaplan-Meier analysis, we validated our predictive model on four independent publicly available datasets (GSE39582, GSE17536, GSE37892, and GSE17537) from the GEO database, consisting of 925 colon cancer patients. Results A five-transcription-factors based predictive model for colon cancer prognosis has been developed by using TCGA colon cancer patient data. Five transcription factors identified for the predictive model is HOXC9, ZNF556, HEYL, HOXC4 and HOXC6. The prediction power of the model is validated with four GEO datasets consisting of 1584 patient samples. Kaplan-Meier curve and log-rank tests were conducted on both training and validation datasets, the difference of overall survival time between predicted low and high-risk groups can be clearly observed. Gene set enrichment analysis was performed to further investigate the difference between low and high-risk groups in the gene pathway level. The biological meaning was interpreted. Overall, our results prove our prediction model has a strong prediction power on colon cancer prognosis. Conclusions Transcription factors can be used to construct colon cancer prognostic signatures with strong prediction power. The variable selection process used in this study has the potential to be implemented in the prognostic signature discovery of other cancer types. Our five TF-based predictive model would help with understanding the hidden relationship between colon cancer patient survival and transcription factor activities. It will also provide more insights into the precision treatment of colon cancer patients from a genomic information perspective

Comprehensive comparison of molecular portraits between cell lines and tumors in breast cancer

Background: Proper cell models for breast cancer primary tumors have long been the focal point in the cancer’s research. The genomic comparison between cell lines and tumors can investigate the similarity and dissimilarity and help to select right cell model to mimic tumor tissues to properly evaluate the drug reaction in vitro. In this paper, a comprehensive comparison in copy number variation (CNV), mutation, mRNA expression and protein expression between 68 breast cancer cell lines and 1375 primary breast tumors is conducted and presented. Results: Using whole genome expression arrays, strong correlations were observed between cells and tumors. PAM50 gene expression differentiated them into four major breast cancer subtypes: Luminal A and B, HER2amp, and Basal-like in both cells and tumors partially. Genomic CNVs patterns were observed between tumors and cells across chromosomes in general. High C > T and C > G trans-version rates were observed in both cells and tumors, while the cells had slightly higher somatic mutation rates than tumors. Clustering analysis on protein expression data can reasonably recover the breast cancer subtypes in cell lines and tumors. Although the drug-targeted proteins ER/PR and interesting mTOR/GSK3/TS2/PDK1/ER_P118 cluster had shown the consistent patterns between cells and tumor, low protein-based correlations were observed between cells and tumors. The expression consistency of mRNA verse protein between cell line and tumors reaches 0.7076. These important drug targets in breast cancer, ESR1, PGR, HER2, EGFR and AR have a high similarity in mRNA and protein variation in both tumors and cell lines. GATA3 and RP56KB1 are two promising drug targets for breast cancer. A total score developed from the four correlations among four molecular profiles suggests that cell lines, BT483, T47D and MDAMB453 have the highest similarity with tumors. Conclusions: The integrated data from across these multiple platforms demonstrates the existence of the similarity and dissimilarity of molecular features between breast cancer tumors and cell lines. The cell lines only mirror some but not all of the molecular properties of primary tumors. The study results add more evidence in selecting cell line models for breast cancer research

Novel Serine 176 Phosphorylation of YBX1 Activates NF-κB in Colon Cancer

Y box protein 1 (YBX1) is a well known oncoprotein that has tumor-promoting functions. YBX1 is widely considered to be an attractive therapeutic target in cancer. To develop novel therapeutics to target YBX1, it is of great importance to understand how YBX1 is finely regulated in cancer. Previously, we have shown that YBX1 could function as a tumor promoter through phosphorylation of its Ser-165 residue, leading to the activation of the NF-κB signaling pathway (1). In this study, using mass spectrometry analysis, we discovered a distinct phosphorylation site, Ser-176, on YBX1. Overexpression of the YBX1-S176A (serine-to-alanine) mutant in either HEK293 cells or colon cancer HT29 cells showed dramatically reduced NF-κB-activating ability compared with that of WT-YBX1, confirming that Ser-176 phosphorylation is critical for the activation of NF-κB by YBX1. Importantly, the mutant of Ser-176 and the previously reported Ser-165 sites regulate distinct groups of NF-κB target genes, suggesting the unique and irreplaceable function of each of these two phosphorylated serine residues. Our important findings could provide a novel cancer therapy strategy by blocking either Ser-176 or Ser-165 phosphorylation or both of YBX1 in colon cancer

The Role and Therapeutic Potential of miRNAs in Colorectal Liver Metastasis

Colorectal cancer (CRC) is the fourth leading cause of cancer-related deaths worldwide. Liver metastasis is the major cause of CRC patient mortality, occurring in 60% patients with no effective therapies. Although studies have indicated the role of miRNAs in CRC, an in-depth miRNA expression analysis is essential to identify clinically relevant miRNAs and understand their potential in targeting liver metastasis. Here we analyzed miRNA expressions in 405 patient tumors from publicly available colorectal cancer genome sequencing project database. Our analyses showed miR-132, miR-378f, miR-605 and miR-1976 to be the most significantly downregulated miRNAs in primary and CRC liver metastatic tissues, and CRC cell lines. Observations in CRC cell lines indicated that ectopic expressions of miR-378f, -605 and -1976 suppress CRC cell proliferation, anchorage independent growth, metastatic potential, and enhance apoptosis. Consistently, CRC patients with higher miR-378f and miR-1976 levels exhibited better survival. Together, our data suggests an anti-tumorigenic role of these miRNAs in CRC and warrant future in vivo evaluation of the molecules for developing biomarkers or novel therapeutic strategies