Discovery of novel inhibitors of histone deacetylase 6: Structure-based virtual screening, molecular dynamics simulation, enzyme inhibition and cell viability assays.

Histone deacetylase 6 (HDAC6) contributes to cancer metastasis in several cancers, including triple-negative breast cancer (TNBC)-the most lethal form that lacks effective therapy. Although several efforts have been invested to develop selective HDAC6 inhibitors, none have been approved by the FDA. Toward this goal, existing computational studies used smaller compound libraries and shorter MD simulations. Here, we conducted a structure-based virtual screening of ZINC "Druglike" library containing 17,900,742 compounds using a Glide virtual screening protocol comprising various filters with increasing accuracy. The top 20 hits were subjected to molecular dynamics simulation, MM-GBSA binding energy calculations, and further ADMET prediction. Furthermore, enzyme inhibition assay and cell viability assay were performed on six available compounds from the identified hits. C4 (ZINC000077541942) with a good profile of predicted drug properties was found to inhibit HDAC6 (IC50: 4.7 ± 11.6 µM) with comparative affinity to that of the known HDAC6 selective inhibitor Tubacin (TA) in our experiments. C4 also demonstrated cytotoxic effects against triple-negative breast cancer (TNBC) cell line MDA-MB-231 with EC50 of 40.6 ± 12.7 µM comparable to that of TA (2-20 µM). Therefore, this compound, with pharmacophore features comprising a non-hydroxamic acid zinc-binding group, heteroaromatic linker, and cap group, is proposed as a novel HDAC6 inhibitor.

Air-jet spinning corn zein protein nanofibers for drug delivery: Effect of biomaterial structure and shape on release properties.

Nanofiber materials are commonly used as delivery vehicles for dermatological drugs due to their high surface-area-to-volume ratio, porosity, flexibility, and reproducibility. In this study air-jet spinning was used as a novel and economic method to fabricate corn zein nanofiber meshes with model drugs of varying solubility, molecular weight and charge. The release profiles of these drugs were compared to their release from corn zein films to elucidate the effect of geometry and structure on drug delivery kinetics. In film samples, over 50% of drug was released after only 2 h. However, fiber samples exhibited more sustained release, releasing less than 50% after one day. FTIR, SEM, and DSC were performed on nanofibers and films before and after release of the drugs. Structural analysis revealed that the incorporation of model drugs into the fibers would transform the zein proteins from a random coil network to a more alpha helical structure. Upon release, the protein fiber reverted to its original random coil network. In addition, thermal analysis indicated that fibers can protect the drug molecules in high temperature above 160 °C, while drugs within films will degrade below 130 °C. These findings can likely be attributed to the mechanical infiltration of the drug molecules into the ordered structure of the zein fibers during their solution fabrication. The slow release from fiber samples can be attributed to this biophysical interaction, illustrating that release is dictated by more than diffusion in protein-based carriers. The controlled release of a wide variety of drugs from the air-jet spun corn zein nanofiber meshes demonstrates their success as drug delivery vehicles that can potentially be incorporated into different biological materials in the future.

The Study of Sarcoma Microenvironment Heterogeneity Associated With Prognosis Based on an Immunogenomic Landscape Analysis.

Microenvironment-driven tumor heterogeneity causes the limitation of immunotherapy of sarcomas. Nonetheless, systematical studies of various molecular levels can enhance the understanding of tumor microenvironment (TME) related to prognosis and provide novel insights of precision immunotherapy. Three prognostic-related TME phenotypes were identified by consensus clustering of the relative infiltration of 22 immune cells from 869 samples of sarcomas. Additionally, integrative immunogenomic analysis is applied to explore the characteristics of different TME groups. The results revealed that most of the immune cell infiltration is higher in the better prognostic group, which are more affected by lower DNA methylation levels and fewer copy number variations in the worse prognostic group. The signaling pathway crosstalk analysis suggested that the changes in the TME will cause considerable variation in the flow of information between pathways, especially when the degree of relative infiltration of immune cells is low, patient's endocrine system may also be significantly affected. Also, the endogenous competitive network analysis indicated that several differentially expressed long non-coding RNAs (lncRNAs) associated with the prognosis or tumor recurrence of sarcoma patients affected the regulatory relationship between miRNAs and different genes when the sarcoma microenvironment changes. In summary, the significant relationship between genetic alterations and prognostic-related TME characteristics in sarcomas were determined in this study. These findings may provide new clues for the treatment of sarcomas.

Investigating the correlation between DNA methylation and immune­associated genes of lung adenocarcinoma based on a competing endogenous RNA network.

In recent years, there have been major breakthroughs in immunotherapies for the treatment of cancer. However, different patients have different responses to immunotherapy. Numerous studies have shown that the accumulation of epigenetic abnormalities, such as DNA methylation, serve an important role in the immune response of lung adenocarcinoma (LUAD). To investigate the effects of DNA methylation on tumor immunity with survival and prognosis, relevant studies can be performed based on the regulatory mechanisms of RNA molecules. For example, long non­coding RNAs (lncRNAs), which regulate gene expression through epigenetic levels. By constructing an immune-associated competitive endogenous RNA (ceRNA) network, the present study identified the regulatory associations among 3 key immune­associations mRNAs, 2 microRNAs (miRs) and 29 lncRNAs that were closely associated with the prognosis of patients with LUAD. The molecular biology analysis indicated that hypomethylation of the 1101320­1104290 regions of chromosome 1 resulted in the low expression levels of LINC00337 and that LINC00337 may affect the expression levels of CHEK1 by competitively binding with human (has)­miR­373 and hsa­miR­195. Therefore, abnormal DNA methylation in lncRNA­associated regions caused their abnormal expression levels, which further affected the interactions between RNA molecules. The interactions between these RNA molecules may have regulatory effects on tumor immunity and the prognosis of patients with LUAD.

Association Analysis of Peripheral and CSF Biomarkers in Late Mild Cognitive Impairment.

Research shows that late mild cognitive impairment (LMCI) has a high risk of turning into Alzheimer's disease (AD). Due to the invasion of detection methods and physical damage to the patients, it is not a convenient way to diagnose and detect early AD and LMCI by cerebrospinal fluid (CSF) data. So there is an urgent need to find the correlation between peripheral biological data and CSF data in the brain, and to find new diagnostic methods through changes in the peripheral biological data. Studies have shown that during the pathogenesis of LMCI and AD, peripheral immune cells specifically infiltrate into the brain through the blood-brain barrier, causing an imbalance in the brain's immune response and dysregulating the clearance of Aß in CSF. Therefore, in this paper, canonical correlation analysis (CCA) algorithm is presented to derive the correlation between peripheral and CSF biomarkers based on LMCI peripheral gene expression data and plasma marker information. Firstly, to explore the influence of the infiltration of peripheral blood immune cells on the brain, the abundance of 28 immune cells were calculated by using the gene set enrichment analysis algorithm of LMCI samples. Then, to identify the correlation between biomarkers inside and outside of the brain, we performed CCA to calculate the relationship between CSF and peripheral biomarkers. Results of CCA showed significant correlations between the variable sets of 8 peripheral biomarkers and the variable sets of CSF biomarkers (at 0.794). Finally, according to Kyoto Encyclopedia of Genes and Genomes and Gene Ontology analysis, it was found that the obtained peripheral biomarkers are involved in many immune-related pathways and functions which can be activated in peripheral blood of LMCI patients. Most related genes enriched in immune-related pathways and functions were up-regulated. Through receiver operating characteristic curve (ROC) analysis, it was also found that FP40/FP42 and type 1 T helper can accurately predict the pathological changes of LMCI (at 0.747).

Air-Jet Spun Corn Zein Nanofibers and Thin Films with Topical Drug for Medical Applications.

Diabetic patients are especially susceptible to chronic wounds of the skin, which can lead to serious complications. Sodium citrate is one potential therapeutic molecule for the topical treatment of diabetic ulcers, but its viability requires the assistance of a biomaterial matrix. In this study, nanofibers and thin films fabricated from natural corn zein protein are explored as a drug delivery vehicle for the topical drug delivery of sodium citrate. Corn zein is cheap and abundant in nature, and easily extracted with high purity, while nanofibers are frequently cited as ideal drug carriers due to their high surface area and high porosity. To further reduce costs, the 1-D nanofibers in this study were fabricated through an air jet-spinning method rather than the conventional electrospinning method. Thin films were also created as a comparative 2-D material. Corn zein composite nanofibers and thin films with different concentration of sodium citrate (1-30%) were analyzed through FTIR, DSC, TGA, and SEM. Results reveal that nanofibers are a much more effective vehicle than films, with the ability to interact with sodium citrate. Thermal analysis results show a stable material with low degradation, while FTIR reveals strong control over the protein secondary structures and hold of citrate. These tunable properties and morphologies allow the fibers to provide a sustained release of citrate and then revert to their structure prior to citrate loading. A statistical analysis via t-test confirmed a significant difference between fiber and film drug release. A biocompatibility study also confirms that cells are much more tolerant of the porous nanofiber structure than the nonporous protein films, and lower percentages of sodium citrate (1-5%) were outperformed to higher percentages (15-30%). This study demonstrated that protein-based nanofiber materials have high potential as vehicles for the delivery of topical diabetic drugs.

Exposure to CuO Nanoparticles Mediates NFκB Activation and Enhances Amyloid Precursor Protein Expression.

Amyloid precursor protein (APP) is directly related to Aß amyloidosis-a hallmark of Alzheimer's disease (AD). However, the impact of environmental factors upon APP biology and Aß amyloid pathology have not been well studied. The increased use of nanoparticles (NPs) or engineered nanomaterials (ENMs) has led to a growing body of evidence suggesting that exposure to metal/metal oxide NPs, such as Fe2O3, CuO, and ZnO, may contribute to the pathophysiology of neurodegenerative diseases such as AD through neuroinflammation. Our previous studies indicated that exposure to CuO nanoparticles (CuONPs) induce potent in vitro neurotoxicity. Herein, we investigated the effects on APP expression in neuronal cells exposed to different metal oxide NPs. We found a low dose of CuONPs effectively activated the NFκB signaling pathway and increased APP expression. Moreover, the inhibition of p65 expression using siRNA abolished CuONP-mediated APP expression, suggesting that NFκB-regulated APP expression in response to CuONP exposure may be associated with AD pathology.

Multi-Constrained Joint Non-Negative Matrix Factorization With Application to Imaging Genomic Study of Lung Metastasis in Soft Tissue Sarcomas.

OBJECTIVE: The study of pathogenic mechanism at the genetic level by imaging genetics methods enables to effectively reveal the association of histopathology and genetics. However, there is a lack of effective and accurate tools to establish association models from macroscopic to microscopic. METHODS: The multi-constrained joint non-negative matrix factorization (MCJNMF) was developed for simultaneous integration of genomic data and image data to identify common modules related to disease. Two types of data matrices were projected onto a common feature space, in which heterogeneous variables with large coefficients in the same projected direction form a common module. Meanwhile, the correlation between original data features was integrated by using regularization constraints to improve the biological relevance. Sparsity constraints and orthogonal constraints were performed on decomposition factors to minimize the redundancy between different bases and to reduce algorithm complexity. RESULTS: This algorithm was successfully performed on the module identification of lung metastasis in soft tissue sarcomas (STSs) by integrating FDG-PET image and DNA methylation data features. Multilevel analysis on the top extracted modules revealed that these modules were closely related to the lung metastasis. Particularly, several genes with diagnostic potential for lung metastasis can be discovered from high score modules. CONCLUSION: This method not only can be applied for the accurate identification of patterns related to pathogenic mechanism of diseases, but also has a significant implication for discovering protein biomarkers. SIGNIFICANCE: This method provides avenues for further studies of identifying complex association patterns of diseases according to different types of biological data.

Construction of Transcriptional Regulatory Network of Alzheimer's Disease Based on PANDA Algorithm.

The focus of modern biomedical research concentrates on molecular level regulatory mechanisms and how the normal and abnormal phenotypes of tissue functional are affected by regulatory mechanisms. Most of the research on regulatory mechanism starts from the reconstruction of gene regulation network. At present, a large number of reconstruction methods construct the network using a single data set. These methods of inferring and predicting the relationship between the target gene and the transcription factor (TF) can be used to identify individual interactions between genes, while there is not much research on the interaction of many functional-related genes. In this paper, an integrated approach based on multi-data fusion is used to reconstruct the network on Alzheimer's disease (AD) which is the most common form of dementia. It not only considers the interaction between many functional-related genes and the TFs that have important implications for regulatory mechanisms, but also detects new genes associated with specific gene function expression. Protein interaction data, motif data and gene expression data of AD were integrated to gain insight into the underlying biological processes of AD. This method takes into account the TF on the target gene regulation, at the same time also considers co-expression mechanism of the TF and co-regulatory mechanism of the target gene. Eventually, not only a number of genes such as E2F4 and ATF1 related to the pathogenesis of AD have been identified, but also several significant biological processes, such as immunoregulation and neurogenesis, have been found to be associated with AD.

Comprehensive analysis of tumor immune infiltration associated with endogenous competitive RNA networks in lung adenocarcinoma.

Cancer immunotherapy has achieved unprecedented success in the treatment of cancer. However, different patients have different responses to immunotherapy. More and more studies have shown that tumor immune heterogeneity has an important influence on the prognosis of cancer. Therefore, understanding the clinical impact of tumor immune infiltration and the regulatory mechanism of RNA molecules is crucial for exploring the pathogenesis of lung adenocarcinoma (LUAD) and the development of immunotherapy protocols.The endogenous competitive RNA hypothesis provides new ideas for studying immune heterogeneity. Therefore, by using the method of immune genomics, this article explores the relationship between immune infiltration and prognosis of patients with lung adenocarcinoma, and found that B-cell immune infiltration highly affects the survival of patients. Through differential analysis, differential mRNAs, lncRNAs and miRNAs were extracted, and 318 differentially expressed mRNAs related to B cell immunity were screened by correlation analysis, and prognosis of patients with COX risk regression model was predicted and analyzed. Through multiple database searches, an immune-related ceRNA regulatory network was constructed, containing 3 key mRNAs, 4 miRNAs, and 50 lncRNAs. Three mRNAs and most miRNAs, lncRNAs, are significantly associated with LUAD prognosis. Bioinformatics analysis of the network showed that LINC00337 may up-regulate the expression of PBK and KIF23 through competitive binding of has-mir-373 and has-mir-519d. The competitive binding of has-mir-373 and has-mir-372 can up-regulate the expression of SLC7A11. The interaction between these RNAs may have an important regulatory role in the immune infiltration in lung adenocarcinoma, thereby affecting the patient's prognosis and immunotherapy efficacy.

A new method for excavating feature lncRNA in lung adenocarcinoma based on pathway crosstalk analysis.

Recent theoretical and experimental studies indicate that long-chain noncoding RNAs (lncRNAs) are essential for the growth and differentiation of cells and the occurrence and development of tumors in epigenetics, but the regulation of lncRNA on gene expression, transcriptional activation, and transcriptional interference in diseases is still unclear. There is an urgent need for effective methods to discover significant lncRNAs with their functions on gene regulatory mechanisms. For this purpose, a new method of extracting significant lncRNA based on pathway crosstalk and dysfunction caused by the differentially expressed genes in lung adenocarcinoma (LUAD) was proposed. The pathway analysis method based on global influence (PAGI) was first applied to find the feature genes that play an important role in the crosstalks of disease-related pathways. Then to explore the hub lncRNAs, the weighted gene coexpression network analysis (WGCNA) was used to construct coexpression models of the feature genes and lncRNAs. The experiment results showed that 64 out of the 322 hub lncRNAs were closely related to the clinical features of patients with LUAD. Among them, nine lncRNAs (UCA1, LINC00857, PVT1, PCAT6, LINC00460, LINC00319, AP000553.1, AP000439.2, and AP005233.2) were identified to be tightly correlated with non-small-cell lung cancer (NSCLC) pathways. In summary, we offer an effective way to extract significant lncRNA by dysfunctional pathway crosstalk in LUAD which allows the selected lncRNAs with more biologically interpreted and reproducible results. This method can be applied to other diseases and provide useful information for understanding the pathogenesis of human cancer.

Prior Knowledge Driven Joint NMF Algorithm for ceRNA Co-Module Identification.

MRNA and lncRNA serve as a type of endogenous RNA in cell, which can competitively bind to the same miRNA through miRNA response elements (MREs), thereby regulating their respective expression levels, playing an important role in post-transcriptional regulation, and regulating the progress of tumors. The proposed competing endogenous RNA (ceRNA) hypothesis provides novel clues for the occurrence and development of tumors, but the integrative analysis methods of diverse RNA data are significantly limited. In order to find out the relationship among miRNA, mRNA and lncRNA, the previous studies only used individual dataset as seeds to search two other related data in the database to construct ceRNA network, but it was difficult to identify the synchronized effects from multiple regulatory levels. Here, we developed the joint matrix factorization method integrating prior knowledge to map the three types of RNA data of lung cancer to the common coordinate system and construct the ceRNA network corresponding to the common module. The results show that more than 90% of the modules are closely related to cancer, including lung cancer. Furthermore, the resulting ceRNA network not only accurately excavates the known correlation of the three types of RNA molecular, but also further discovers the potential biological associations of them. Our work provides support and foundation for future biological validation how competitive relationships of multiple RNAs affects the development of tumors.

Identifying novel candidate biomarkers of RCC based on WGCNA analysis.

AIM: Extracting differential expression genes (DEGs) is an effective approach to improve the accuracy of determining the candidate biomarker genes. However, the previous DEGs analysis methods ignore that the expression levels of genes in different pathology stages of cancers are complex and various. METHODS: In our study, staging DEGs analysis and weighted gene co-expression network analysis were applied to gene expression data of renal cell carcinoma (RCC). RESULTS: According to construct gene topology network for exploring hub genes, 12 genes were identified as hub genes. CONCLUSION: Combining with the effect of hub gene expression level on RCC patient survival and different biological data analysis, three hub genes were found that they might be three novel candidate biomarkers of RCC.

Dysregulated Pathway Identification of Alzheimer's Disease Based on Internal Correlation Analysis of Genes and Pathways.

Dysregulated pathway identification is an important task which can gain insight into the underlying biological processes of disease. Current pathway-identification methods focus on a set of co-expression genes and single pathways and ignore the correlation between genes and pathways. The method proposed in this study, takes into account the internal correlations not only between genes but also pathways to identifying dysregulated pathways related to Alzheimer's disease (AD), the most common form of dementia. In order to find the significantly differential genes for AD, mutual information (MI) is used to measure interdependencies between genes other than expression valves. Then, by integrating the topology information from KEGG, the significant pathways involved in the feature genes are identified. Next, the distance correlation (DC) is applied to measure the pairwise pathway crosstalks since DC has the advantage of detecting nonlinear correlations when compared to Pearson correlation. Finally, the pathway pairs with significantly different correlations between normal and AD samples are known as dysregulated pathways. The molecular biology analysis demonstrated that many dysregulated pathways related to AD pathogenesis have been discovered successfully by the internal correlation detection. Furthermore, the insights of the dysregulated pathways in the development and deterioration of AD will help to find new effective target genes and provide important theoretical guidance for drug design.

Differences of immune disorders between Alzheimer's disease and breast cancer based on transcriptional regulation.

Although chronic inflammation and immune disorders are of great importance to the pathogenesis of both dementia and cancer, the pathophysiological mechanisms are not clearly understood. In recent years, growing epidemiological evidence and meta-analysis data suggest an inverse association between Alzheimer's disease (AD), which is the most common form of dementia, and cancer. It has been revealed that some common genes and biological processes play opposite roles in AD and cancer; however, the biological immune mechanism for the inverse association is not clearly defined. An unsupervised matrix decomposition two-stage bioinformatics procedure was adopted to investigate the opposite behaviors of the immune response in AD and breast cancer (BC) and to discover the underlying transcriptional regulatory mechanisms. Fast independent component analysis (FastICA) was applied to extract significant genes from AD and BC microarray gene expression data. Based on the extracted data, the shared transcription factors (TFs) from AD and BC were captured. Second, the network component analysis (NCA) algorithm in this study was presented to quantitatively deduce the TF activities and regulatory influences because quantitative dynamic regulatory information for TFs is not available via microarray techniques. Based on the NCA results and reconstructed transcriptional regulatory networks, inverse regulatory processes and some known innate immune responses were described in detail. Many of the shared TFs and their regulatory processes were found to be closely related to the adaptive immune response from dramatically different directions and to play crucial roles in both AD and BC pathogenesis. From the above findings, the opposing cellular behaviors demonstrate an invaluable opportunity to gain insights into the pathogenesis of these two types of diseases and to aid in developing new treatments.

Tissue Regeneration: A Silk Road.

Silk proteins are natural biopolymers that have extensive structural possibilities for chemical and mechanical modifications to facilitate novel properties, functions, and applications in the biomedical field. The versatile processability of silk fibroins (SF) into different forms such as gels, films, foams, membranes, scaffolds, and nanofibers makes it appealing in a variety of applications that require mechanically superior, biocompatible, biodegradable, and functionalizable biomaterials. There is no doubt that nature is the world's best biological engineer, with simple, exquisite but powerful designs that have inspired novel technologies. By understanding the surface interaction of silk materials with living cells, unique characteristics can be implemented through structural modifications, such as controllable wettability, high-strength adhesiveness, and reflectivity properties, suggesting its potential suitability for surgical, optical, and other biomedical applications. All of the interesting features of SF, such as tunable biodegradation, anti-bacterial properties, and mechanical properties combined with potential self-healing modifications, make it ideal for future tissue engineering applications. In this review, we first demonstrate the current understanding of the structures and mechanical properties of SF and the various functionalizations of SF matrices through chemical and physical manipulations. Then the diverse applications of SF architectures and scaffolds for different regenerative medicine will be discussed in detail, including their current applications in bone, eye, nerve, skin, tendon, ligament, and cartilage regeneration.

The construction of common and specific significance subnetworks of Alzheimer's disease from multiple brain regions.

Alzheimer's disease (AD) is a progressively and fatally neurodegenerative disorder and leads to irreversibly cognitive and memorial damage in different brain regions. The identification and analysis of the dysregulated pathways and subnetworks among affected brain regions will provide deep insights for the pathogenetic mechanism of AD. In this paper, commonly and specifically significant subnetworks were identified from six AD brain regions. Protein-protein interaction (PPI) data were integrated to add molecular biological information to construct the functional modules of six AD brain regions by Heinz algorithm. Then, the simulated annealing algorithm based on edge weight is applied to predicting and optimizing the maximal scoring networks for common and specific genes, respectively, which can remove the weak interactions and add the prediction of strong interactions to increase the accuracy of the networks. The identified common subnetworks showed that inflammation of the brain nerves is one of the critical factors of AD and calcium imbalance may be a link among several causative factors in AD pathogenesis. In addition, the extracted specific subnetworks for each brain region revealed many biologically functional mechanisms to understand AD pathogenesis.

[Significant genes extraction and analysis of gene expression data based on matrix factorization techniques].

It is generally considered that various regulatory activities between genes are contained in the gene expression datasets. Therefore, the underlying gene regulatory relationship and the biologically useful information can be found by modeling the gene regulatory network from the gene expression data. In our study, two unsupervised matrix factorization methods, independent component analysis (ICA) and nonnegative matrix factorization (NMF), were proposed to identify significant genes and model the regulatory network using the microarray gene expression data of Alzheimer's disease (AD). By bio-molecular analyzing of the pathways, the differences between ICA and NMF have been explored and the fact, which the inflammatory reaction is one of the main pathological mechanisms of AD, is also emphasized. It was demonstrated that our study gave a novel and valuable method for the research of early detection and pathological mechanism, biomarkers' findings of AD.

Dynamic regulatory network reconstruction for Alzheimer's disease based on matrix decomposition techniques.

Alzheimer's disease (AD) is the most common form of dementia and leads to irreversible neurodegenerative damage of the brain. Finding the dynamic responses of genes, signaling proteins, transcription factor (TF) activities, and regulatory networks of the progressively deteriorative progress of AD would represent a significant advance in discovering the pathogenesis of AD. However, the high throughput technologies of measuring TF activities are not yet available on a genome-wide scale. In this study, based on DNA microarray gene expression data and a priori information of TFs, network component analysis (NCA) algorithm is applied to determining the TF activities and regulatory influences on TGs of incipient, moderate, and severe AD. Based on that, the dynamical gene regulatory networks of the deteriorative courses of AD were reconstructed. To select significant genes which are differentially expressed in different courses of AD, independent component analysis (ICA), which is better than the traditional clustering methods and can successfully group one gene in different meaningful biological processes, was used. The molecular biological analysis showed that the changes of TF activities and interactions of signaling proteins in mitosis, cell cycle, immune response, and inflammation play an important role in the deterioration of AD.

Integrating gene expression and protein interaction data for signaling pathway prediction of Alzheimer's disease.

Discovering the signaling pathway and regulatory network would provide significant advance in genome-wide understanding of pathogenesis of human diseases. Despite the rich transcriptome data, the limitation for microarray data is unable to detect changes beyond transcriptional level and insufficient in reconstructing pathways and regulatory networks. In our study, protein-protein interaction (PPI) data is introduced to add molecular biological information for predicting signaling pathway of Alzheimer's disease (AD). Combining PPI with gene expression data, significant genes are selected by modified linear regression model firstly. Then, according to the biological researches that inflammation reaction plays an important role in the generation and deterioration of AD, NF- κ B (nuclear factor-kappa B), as a significant inflammatory factor, has been selected as the beginning gene of the predicting signaling pathway. Based on that, integer linear programming (ILP) model is proposed to reconstruct the signaling pathway between NF- κ B and AD virulence gene APP (amyloid precursor protein). The results identify 6 AD virulence genes included in the predicted inflammatory signaling pathway, and a large amount of molecular biological analysis shows the great understanding of the underlying biological process of AD.