Learning the cellular activity representation based on gene regulatory networks for prediction of tumor response to drugs.

Predicting the response of tumor cells to anti-tumor drugs is critical to realizing cancer precision medicine. Currently, most existing methods ignore the regulatory relationships between genes and thus have unsatisfactory predictive performance. In this paper, we propose to predict anti-tumor drug efficacy via learning the activity representation of tumor cells based on a priori knowledge of gene regulation networks (GRNs). Specifically, the method simulates the cellular biosystem by synthesizing a cell-gene activity network and then infers a new low-dimensional activity representation for tumor cells from the raw high-dimensional expression profile. The simulated cell-gene network mainly comprises known gene regulatory networks collected from multiple resources and fuses tumor cells by linking them to hotspot genes that are over- or under-expressed in them. The resulting activity representation could not only reflect the shallow expression profile (hotspot genes) but also mines in-depth information of gene regulation activity in tumor cells before treatment. Finally, we build deep learning models on the activity representation for predicting drug efficacy in tumor cells. Experimental results on the benchmark GDSC dataset demonstrate the superior performance of the proposed method over SOTA methods with the highest AUC of 0.954 in the efficacy label prediction and the best R2 of 0.834 in the regression of half maximal inhibitory concentration (IC50) values, suggesting the potential value of the proposed method in practice.

Personalized anti-tumor drug efficacy prediction based on clinical data.

Anti-tumor drug efficacy prediction poses an unprecedented challenge to realizing personalized medicine. This paper proposes to predict personalized anti-tumor drug efficacy based on clinical data. Specifically, we encode the clinical text as numeric vectors featured with hidden topics for patients using Latent Dirichlet Allocation model. Then, to classify patients into two classes, responsive or non-responsive to a drug, drug efficacy predictors are established by machine learning based on the Latent Dirichlet Allocation topic representation. To evaluate the proposed method, we collected and collated clinical records of lung and bowel cancer patients treated with platinum. Experimental results on the data sets show the efficacy and effectiveness of the proposed method, suggesting the potential value of clinical data in cancer precision medicine. We hope that it will promote the research of drug efficacy prediction based on clinical data.

Effect of Torrefaction on the Physiochemical Characteristics and Pyrolysis of the Corn Stalk.

Torrefaction of biomass is one of the most promising pretreatment methods for deriving biofuels from biomass via thermochemical conversion processes. In this work, the changes in physicochemical properties and morphology features of the torrefied corn stalk, the changes in physicochemical properties and morphology features of the torrefied corn stalk were investigated. The results of this study showed that the elemental content and proximate analysis of the torrefied corn stalk significantly changed compared with those of the raw corn stalk. In particular, at 300 °C, the volatile content decreased to 41.79%, while the fixed carbon content and higher heating value increased to 42.22% and 21.31 MJ/kg, respectively. The H/C and O/C molar ratios of torrefied corn stalk at the 300 °C were drastically reduced to 0.99 and 0.27, respectively, which are similar to those of conventional coals in China. Numerous cracks and pores were observed in the sample surface of torrefied corn stalk at the torrefaction temperature range of 275 °C-300 °C, which could facilitate the potential application of the sample in the adsorption process and promote the release of gas products in pyrolysis. In the pyrolysis phase, the liquid products of the torrefied corn stalk decreased, but the H2/CO ratio and the lower heating value of the torrefied corn stalk increased compared with those of the raw corn stalk. This work paves a new strategy for the investigation of the effect of torrefaction on the physiochemical characteristics and pyrolysis of the corn stalk, highlighting the application potential in the conversion of biomass.

Proteomics Analysis of Serum Proteins in Gestational Diabetes.

OBJECTIVE: The purpose of this study was to screen serum proteins for biomarkers of gestational diabetes mellitus (GDM) and to investigate its pathogenesis by analyzing the differences in serum proteomics between pregnant women with GDM and healthy pregnant women. METHODS: Patients who were admitted to the First Affiliated Hospital of Fujian Medical University from June 2019 to January 2020 were included. According to the medical history and the results of the 75 g oral glucose tolerance test (OGTT), they were divided into the normal pregnant women group and GDM pregnant women group. The serum of two groups of patients was collected. High performance liquid chromatography-mass spectrometry was used to identify differentially expressed serum proteins between pregnant women with GDM and healthy pregnant women, and bioinformatics analysis was then performed on the identified proteins. RESULTS: A total of 1152 quantifiable proteins were detected; among them, 15 were upregulated in serum of GDM pregnant women, while 26 were downregulated. The subsequent parallel reaction monitoring (PRM) assay validated the expression levels of 12 out of 41 differentially expressed proteins. Moreover, bioinformatics analysis revealed that the differentially expressed proteins are involved in multiple biological processes and signaling pathways related to the lipid metabolism, glycan degradation, immune response, and platelet aggregation. CONCLUSIONS: This study identified 41 serum proteins with differential expression between pregnant women with GDM and healthy pregnant women, providing new candidate molecules for elucidating GDM pathogenesis and screening therapeutic targets.

Globally learning gene regulatory networks based on hidden atomic regulators from transcriptomic big data.

BACKGROUND: Genes are regulated by various types of regulators and most of them are still unknown or unobserved. Current gene regulatory networks (GRNs) reverse engineering methods often neglect the unknown regulators and infer regulatory relationships in a local and sub-optimal manner. RESULTS: This paper proposes a global GRNs inference framework based on dictionary learning, named dlGRN. The method intends to learn atomic regulators (ARs) from gene expression data using a modified dictionary learning (DL) algorithm, which reflects the whole gene regulatory system, and predicts the regulation between a known regulator and a target gene in a global regression way. The modified DL algorithm fits the scale-free property of biological network, rendering dlGRN intrinsically discern direct and indirect regulations. CONCLUSIONS: Extensive experimental results on simulation and real-world data demonstrate the effectiveness and efficiency of dlGRN in reverse engineering GRNs. A novel predicted transcription regulation between a TF TFAP2C and an oncogene EGFR was experimentally verified in lung cancer cells. Furthermore, the real application reveals the prevalence of DNA methylation regulation in gene regulatory system. dlGRN can be a standalone tool for GRN inference for its globalization and robustness.

miR-221 regulates proliferation and apoptosis of ovarian cancer cells by targeting BMF.

To observe the expression of microRNA-221 (miR-221) in ovarian cancer tissues and its effect and associated mechanism on proliferation and apoptosis in the ovarian cancer SKOV3 cell line. The expression of miR-221 and B-cell lymphoma 2 modifying factor (BMF) mRNA in ovarian cancer and para-carcinoma tissues was detected by reverse transcription-quantitative polymerase chain reaction, the expression of BMF was detected by western blot. MicroRNA.org online predicted that BMF was the possible target gene of miR-221, and the regulatory association was validated by a dual-luciferase reporter gene assay. SKOV3 cells were divided into 8 transfection groups: Anti-miR-negative control (NC); anti-miR-221; phosphorylated internal ribosome entry site 2 (pIRES2)-blank, pIRES2-BMF, small interfering (si)-NC, si-BMF, anti-miR-221+si-BMF and anti-miR-221+pIRES2-BMF groups. Cell proliferation was detected by EdU staining flow cytometry. The effect of transfection on cell apoptosis was detected by Annexin V/PI double staining, and the activity of caspase-3 was detected by spectrophotometry. The effect of anti-miR-221 or pIRES2-BMF transfection on SKOV3 cell proliferation was detected by MTT assay and flow cytometry, and the effect on apoptosis was detected by the Annexin V/PI double staining. Compared with para-cancer tissues, the miR-221 expression was significantly upregulated (P<0.001), the BMF mRNA expression was significantly downregulated (P<0.001), and the expression of BMF proteins was significantly downregulated in the ovarian cancer tissues. Dual-luciferase reporter gene assay confirmed a targeted regulatory association between miR-221 and BMF. The anti-miR-221 or pIRES2-BMF transfection significantly upregulated BMF expression in SKOV3 cells, significantly decreased cell proliferation and significantly increased cell apoptosis. The overexpression of BMF may enhance the proapoptotic and proliferation-inhibition effect of anti-miR-221 on SKOV3 cells. The transfection of si-BMF significantly promoted cell proliferation, reduced cell apoptosis and attenuated the proapoptotic and proliferation-inhibition effect of anti-miR-221 on cells. The expression of miR-221 was significantly upregulated and the expression of BMF was significantly down-regulated in ovarian cancer tissues. The overexpression of miR-221 antagonized the apoptosis of ovarian cancer SKOV3 cell and promoted the cell proliferation by targeted inhibition of the expression of BMF, which may serve a role in the pathogenesis of ovarian cancer.

Adaptively capturing the heterogeneity of expression for cancer biomarker identification.

BACKGROUND: Identifying cancer biomarkers from transcriptomics data is of importance to cancer research. However, transcriptomics data are often complex and heterogeneous, which complicates the identification of cancer biomarkers in practice. Currently, the heterogeneity still remains a challenge for detecting subtle but consistent changes of gene expression in cancer cells. RESULTS: In this paper, we propose to adaptively capture the heterogeneity of expression across samples in a gene regulation space instead of in a gene expression space. Specifically, we transform gene expression profiles into gene regulation profiles and mathematically formulate gene regulation probabilities (GRPs)-based statistics for characterizing differential expression of genes between tumor and normal tissues. Finally, an unbiased estimator (aGRP) of GRPs is devised that can interrogate and adaptively capture the heterogeneity of gene expression. We also derived an asymptotical significance analysis procedure for the new statistic. Since no parameter needs to be preset, aGRP is easy and friendly to use for researchers without computer programming background. We evaluated the proposed method on both simulated data and real-world data and compared with previous methods. Experimental results demonstrated the superior performance of the proposed method in exploring the heterogeneity of expression for capturing subtle but consistent alterations of gene expression in cancer. CONCLUSIONS: Expression heterogeneity largely influences the performance of cancer biomarker identification from transcriptomics data. Models are needed that efficiently deal with the expression heterogeneity. The proposed method can be a standalone tool due to its capacity of adaptively capturing the sample heterogeneity and the simplicity in use. SOFTWARE AVAILABILITY: The source code of aGRP can be downloaded from https://github.com/hqwang126/aGRP .

DynSig: Modelling Dynamic Signaling Alterations along Gene Pathways for Identifying Differential Pathways.

Although a number of methods have been proposed for identifying differentially expressed pathways (DEPs), few efforts consider the dynamic components of pathway networks, i.e., gene links. We here propose a signaling dynamics detection method for identification of DEPs, DynSig, which detects the molecular signaling changes in cancerous cells along pathway topology. Specifically, DynSig relies on gene links, instead of gene nodes, in pathways, and models the dynamic behavior of pathways based on Markov chain model (MCM). By incorporating the dynamics of molecular signaling, DynSig allows for an in-depth characterization of pathway activity. To identify DEPs, a novel statistic of activity alteration of pathways was formulated as an overall signaling perturbation score between sample classes. Experimental results on both simulation and real-world datasets demonstrate the effectiveness and efficiency of the proposed method in identifying differential pathways.

Clinical significance of Twist, E-cadherin, and N-cadherin protein expression in endometrioid adenocarcinoma.

OBJECTIVE: The aim of the study was to investigate the expression of Twist, E-cadherin, and N-cadherin both in normal endometrium and in endometrioid adenocarcinoma tissues (NET and EAT), and further discuss the relationship between the proteins expression and the clinical parameters. METHODS: Seventy-six EAT and 50 NET were collected from endometrioid adenocarcinoma patients and patients who received hysterectomy. We used immunohistochemistry (two steps methods) to detect the expression of Twist, E-cadherin, and N-cadherin proteins in EAT and NET. The Twist, E-cadherin, and N-cadherin protein positive expression rate in EAT and NET were compared by Chi-square test. Moreover, the correlation between patients' clincial characteristics and Twist, E-cadherin, and N-cadherin protein expression was evaluated. RESULTS: The positive expression of Twist and N-cadherin proteins in EAT was significantly higher than those in NET (u = 14.8, 9.04, P < 0.05), the positive expression of E-cadherin protein in ENT was significantly lower than those in NET (u = 4.14, P < 0.05). The Twist, E-cadherin, and N-cadherin expressions were related with endometrioid adenocarcinoma under different International Federation of Gynecology and Obstetrics (FIGO) clinical stages (P < 0.05), depths of tumor invasion (P < 0.05), and tumor differentiation degrees (P < 0.05). However, these proteins exerted no influence on vessel and lymph metastases (P > 0.05). The Spearman rank correlation analysis showed that the expression of the Twist protein and that of the E-cadherin (r = -0.584, P < 0.05), N-cadherin protein (r = 0.460, P < 0.05) in endometrioid adenocarcinoma was significant correlated with statistical difference. CONCLUSION: Twist, E-cadherin, and N-cadherin protein were different expressed in EAT and NET which indicating their potential function for endometrioid adenocarcinoma development. Twist may participate in the occurrence of epithelial-mesenchymal transition, affect the expression of E-cadherin and N-cadherin and may be related to metastasis and progression of endometrioid adenocarcinoma.

A regulation probability model-based meta-analysis of multiple transcriptomics data sets for cancer biomarker identification.

BACKGROUND: Large-scale accumulation of omics data poses a pressing challenge of integrative analysis of multiple data sets in bioinformatics. An open question of such integrative analysis is how to pinpoint consistent but subtle gene activity patterns across studies. Study heterogeneity needs to be addressed carefully for this goal. RESULTS: This paper proposes a regulation probability model-based meta-analysis, jGRP, for identifying differentially expressed genes (DEGs). The method integrates multiple transcriptomics data sets in a gene regulatory space instead of in a gene expression space, which makes it easy to capture and manage data heterogeneity across studies from different laboratories or platforms. Specifically, we transform gene expression profiles into a united gene regulation profile across studies by mathematically defining two gene regulation events between two conditions and estimating their occurring probabilities in a sample. Finally, a novel differential expression statistic is established based on the gene regulation profiles, realizing accurate and flexible identification of DEGs in gene regulation space. We evaluated the proposed method on simulation data and real-world cancer datasets and showed the effectiveness and efficiency of jGRP in identifying DEGs identification in the context of meta-analysis. CONCLUSIONS: Data heterogeneity largely influences the performance of meta-analysis of DEGs identification. Existing different meta-analysis methods were revealed to exhibit very different degrees of sensitivity to study heterogeneity. The proposed method, jGRP, can be a standalone tool due to its united framework and controllable way to deal with study heterogeneity.

ctPath: Demixing pathway crosstalk effect from transcriptomics data for differential pathway identification.

Identifying differentially expressed pathways (DEPs) plays important roles in understanding tumor etiology and promoting clinical treatment of cancer or other diseases. By assuming gene expression to be a sparse non-negative linear combination of hidden pathway signals, we propose a pathway crosstalk-based transcriptomics data analysis method (ctPath) for identifying differentially expressed pathways. Biologically, pathways of different functions work in concert at the systematic level. The proposed method interrogates the crosstalks between pathways and discovers hidden pathway signals by mapping high-dimensional transcriptomics data into a low-dimensional pathway space. The resulted pathway signals reflect the activity level of pathways after removing pathway crosstalk effect and allow a robust identification of DEPs from inherently complex and noisy transcriptomics data. CtPath can also correct incomplete and inaccurate pathway annotations which frequently occur in public repositories. Experimental results on both simulation data and real-world cancer data demonstrate the superior performance of ctPath over other popular approaches. R code for ctPath is available for non-commercial use at the URL http://micblab.iim.ac.cn/Download/.