Analysis of tumor suppressor genes based on gene ontology and the KEGG pathway

Key chromatin regulator-related genes associated with the risk of coronary artery disease regulate the expression of HCFC1, RNF8, TNP1 and SET

Chromatin regulators are indispensable upstream epigenetic regulators.The emergence and progression of atherosclerosis has been demonstrated to be influenced by smooth muscle-related chromatin regulators, such as ZEB2 and MAFF. However, specific chromatin regulators and their possible roles have not been clarified. Information was gathered from 51 patients diagnosed with coronary artery disease (CAD) and 50 individuals in good health from the GEO database. 440 genes were identified as having differential expression across the two datasets, and these genes were linked to cellular reactions. Enrichment of pathways related to histone modification and transcriptional regulatory factors was observed in GO and KEGG analyses. Four machine learning models (RF, SVM, GLM, and XGB) were developed using the expression profiles of 440 chromatin-associated genes in the CAD cohort to pinpoint genes with significant diagnostic potential. After evaluating residuals, root mean square errors, receiver operating characteristic curves, and immune-infiltration, four key genes (HCFC1, RNF8, TNP1, and SET) were identified. Gene expression in different blood vessel levels in atherosclerotic plaques in a mouse model of coronary artery disease showed significant variations. The gene expression levels in macrophages aligned with clinical data from the GEO database as expected. This discovery is crucial for future analysis and the prediction of drug and miRNA targets. In conclusion, we found that the four hub genes are important in the mechanism of CAD. These findings provide new ideas for the study of potential epigenetic predictive markers and therapeutic targets to be used in determining a treatment strategy for CAD.

Bioinformatic identification of differentially expressed genes regulated by DNA-methylation in glioblastoma

DNA methylation-driven differentially expressed genes (DEGs) play potentially important roles in glioblastoma (GBM). In the present study, we applied bioinformatic analyses to identify key methylation-regulated DEGs (MeDEGs) in glioblastoma and elucidate their functions. Gene expression and methylation profile data from glioblastoma samples along with clinical information were obtained from GEO and TCGA databases. A total of 65 genes were identified as MeDEGs from the aforementioned data. Subsequently, gene ontology and kyoto encyclopedia of genes and genomes enrichment analyses of these MeDEGs exhibited that MeDEGs were mostly enriched in several tumor-related terms such as "activation of cysteine-type endopeptidase activity involved in apoptotic process" and "phospholipid scrambling". Kaplan-Meier survival analysis demonstrated significant correlation of CASP1, CFH, and TTLL7 hyper-methylation with patient prognosis. Finally, CASP1 protein could indirectly interact with CFH protein, but interaction of TTLL7 protein with CASP1 or CFH protein was not evident. Based on gene set enrichment analysis, hyper-methylation of CASP1, CFH, and TTLL7 were found enriched in tumor-related KEGG terms, such as "RNA degradation", "apyruvate metabolism", and "nitrogen metabolism". Methylation levels of CASP1, CFH, and TTLL7 were addressed to negatively correlate with their mRNA levels in GBM cell lines. In sum, the present identification of MeDEGs associated with overall survival put forth potential molecular targets for translation towards improved diagnosis and treatment of GBM and specifically, methylation levels of CASP1, CFH, and TTLL7 genes could serve as key prognostic biomarkers in GBM.

Identification of Novel Choroidal Neovascularization-Related Genes Using Laplacian Heat Diffusion Algorithm

Choroidal neovascularization (CNV) is a type of eye disease that can cause vision loss. In recent years, many studies have attempted to investigate the major pathological processes and molecular pathogenic mechanisms of CNV. Because many diseases are related to genes, the genes associated with CNV need to be identified. In this study, we proposed a network-based approach for identifying novel CNV-associated genes. To execute such method, we first employed a protein-protein interaction network reported in STRING. Then, we applied a network diffusion algorithm, Laplacian heat diffusion, on this network by selecting validated CNV-related genes as the seed nodes. As a result, some novel genes that had unknown but strong relationships with validated genes were identified. Furthermore, we used a screening procedure to extract the most essential genes. Eleven latent CNV-related genes were finally obtained. Extensive analyses were performed to confirm that these genes are novel CNV-related genes.

Understanding Cervical Cancer through Proteomics

Cancer is one of the leading public health issues worldwide, and the number of cancer patients increases every day. Particularly, cervical cancer (CC) is still the second leading cause of cancer death in women from developing countries. Thus, it is essential to deepen our knowledge about the molecular pathogenesis of CC and propose new therapeutic targets and new methods to diagnose this disease in its early stages. Differential expression analysis using high-throughput techniques applied to biological samples allows determining the physiological state of normal cells and the changes produced by cancer development. The cluster of differential molecular profiles in the genome, the transcriptome, or the proteome is analyzed in the disease, and it is called the molecular signature of cancer. Proteomic analysis of biological samples of patients with different grades of cervical intraepithelial neoplasia (CIN) and CC has served to elucidate the pathways involved in the development and progression of cancer and identify cervical proteins associated with CC. However, several cervical carcinogenesis mechanisms are still unclear. Detecting pathologies in their earliest stages can significantly improve a patient's survival rate, prognosis, and recurrence. The present review is an update on the proteomic study of CC.

Identifying Key MicroRNAs Targeted by Narenmandula in a Rodent Nephropathy Model

Background

Untreated nephropathy can progress to renal failure. The traditional Mongolian remedy Narenmandula regulates the kidney “yang.” This study aimed to identify key microRNAs (miRNAs) targeted by Narenmandula in a rat model of nephropathy.

Methods

Fifteen rats exhibiting normal renal function were randomized to three study arms. Nephropathy was induced in n = 10 rats using doxorubicin hydrochloride, followed by either Narenmandula treatment (treatment group) or no treatment (control group). In n = 5 rats, no doxorubicin was given and renal function remained unchanged (healthy group). Microarray analysis identified miRNAs which were differentially expressed (DE-miRNAs) between groups. Target genes of DE-miRNAs were predicted using miRWalk version 2.0, followed by enrichment analysis using DAVID, and construction of the miRNA coregulatory network using Cytoscape.

Results

Nephropathy was successfully induced, with doxorubicin resulting in differential expression of 3645 miRNAs (1324 upregulated and 2321 downregulated). Narenmandula treatment induced differential expression of a total of 159 miRNAs (102 upregulated and 57 downregulated). Upregulated DE-miRNAs (e.g., miR-497-5p, miR-195-5p, miR-181a-5p, miR-181c-5p, and miR-30e-5p) and downregulated DE-miRNAs (e.g., miR-330-3p and miR-214-3p) regulated a high number of target genes. Moreover, the miRNA pairs (e.g., miR-195-5p—miR-497-5p, miR-181a-5p—miR-181c-5p, and miR-30e-5p—miR-30a-5p) coregulated a high number of genes. Enrichment analysis indicated functional synergy between miR-30e-5p—miR-30a-3p, miR-34a-5p—miR-30e-5p, miR-30e-5p—miR-195-3p, and miR-30a-3p—miR-195-3p pairs.

Conclusion

Narenmandula may modulate doxorubicin-induced nephropathy via targeting miR-497-5p, miR-195-5p, miR-181a-5p, miR-181c-5p, miR-30e-5p, miR-330-3p, miR-214-3p, miR-34a-5p, miR-30a-3p, and miR-30a-5p.

Comparative proteomic analysis uncovers potential biomarkers involved in the anticancer effect of Scutellarein in human gastric cancer cells

Scutellarein (SCU), a flavone that belongs to the flavonoid family and abundantly present in Scutellaria baicalensis a flowering plant in the family Lamiaceae, has been reported to exhibit anticancer effects in several cancer cell lines including gastric cancer (GC). Although our previous study documented the mechanisms of Scutellarein‑induced cytotoxic effects, the literature shows that the proteomic changes that are associated with the cellular response to SCU have been poorly understood. To avoid adverse side‑effects and significant toxicity of chemotherapy in patients who react poorly, biomarkers anticipating therapeutic responses are imperative. In the present study, we utilized a comparative proteomic analysis to identify proteins associated with Scutellarein (SCU)‑induced cell death in GC cells (AGS and SNU484), by integrating two‑dimensional gel electrophoresis (2‑DE), mass spectrometry (MS), and bioinformatics to analyze the proteins. Proteomic analysis between SCU‑treated and DMSO (control) samples successfully identified 41 (AGS) and 31 (SNU484) proteins by MALDI‑TOF/MS analysis and protein database search. Comparative proteomics analysis between AGS and SNU484 cells treated with SCU revealed a total of 7 protein identities commonly expressed and western blot analysis validated a subset of identified critical proteins, which were consistent with those of the 2‑DE outcome. Molecular docking studies also confirmed the binding affinity of SCU towards these critical proteins. Phosphatidylinositol 4,5‑bisphosphate 3‑kinase catalytic subunit β isoform (PIK3CB) protein expression was accompanied by a distinct group of cellular functions, including cell growth, and proliferation. Cancerous inhibitor of protein phosphatase 2A (CIP2A), is one of the oncogenic molecules that have been shown to promote tumor growth and resistance to apoptosis and senescence‑inducing therapies. In the present study, both PIK3CB and CIP2A proteins were downregulated in SCU‑treated cells, which boosts our previous results of SCU to induce apoptosis and inhibits GC cell growth by regulating these critical proteins. The comparative proteomic analysis has yielded candidate biomarkers of response to SCU treatment in GC cell models and further validation of these biomarkers will help the future clinical development of SCU as a novel therapeutic drug.

Identification and Analysis of Glioblastoma Biomarkers Based on Single Cell Sequencing

Glioblastoma (GBM) is one of the most common and aggressive primary adult brain tumors. Tumor heterogeneity poses a great challenge to the treatment of GBM, which is determined by both heterogeneous GBM cells and a complex tumor microenvironment. Single-cell RNA sequencing (scRNA-seq) enables the transcriptomes of great deal of individual cells to be assayed in an unbiased manner and has been applied in head and neck cancer, breast cancer, blood disease, and so on. In this study, based on the scRNA-seq results of infiltrating neoplastic cells in GBM, computational methods were applied to screen core biomarkers that can distinguish the discrepancy between GBM tumor and pericarcinomatous environment. The gene expression profiles of GBM from 2343 tumor cells and 1246 periphery cells were analyzed by maximum relevance minimum redundancy (mRMR). Upon further analysis of the feature lists yielded by the mRMR method, 31 important genes were extracted that may be essential biomarkers for GBM tumor cells. Besides, an optimal classification model using a support vector machine (SVM) algorithm as the classifier was also built. Our results provided insights of GBM mechanisms and may be useful for GBM diagnosis and therapy.

The Functional Effects of Key Driver KRAS Mutations on Gene Expression in Lung Cancer

Lung cancer is a common malignant cancer. Kirsten rat sarcoma oncogene (KRAS) mutations have been considered as a key driver for lung cancers. KRAS p.G12C mutations were most predominant in NSCLC which was comprised about 11–16% of lung adenocarcinomas (p.G12C accounts for 45–50% of mutant KRAS). But it is still not clear how the KRAS mutation triggers lung cancers. To study the molecular mechanisms of KRAS mutation in lung cancer. We analyzed the gene expression profiles of 156 KRAS mutation samples and other negative samples with two stage feature selection approach: (1) minimal Redundancy Maximal Relevance (mRMR) and (2) Incremental Feature Selection (IFS). At last, 41 predictive genes for KRAS mutation were identified and a KRAS mutation predictor was constructed. Its leave one out cross validation MCC was 0.879. Our results were helpful for understanding the roles of KRAS mutation in lung cancer.

The Gene Expression Biomarkers for Chronic Obstructive Pulmonary Disease and Interstitial Lung Disease

COPD (chronic obstructive pulmonary disease) and ILD (interstitial lung disease) are two common respiratory diseases. They share similar clinical traits but require different therapeutic treatments. Identifying the biomarkers that are differentially expressed between them will not only help the diagnosis of COPD and ILD, but also provide candidate drug targets that may facilitate the development of new treatment for COPD and ILD. Due to the irreversible complex pathological changes of COPD, there are very limited therapeutic options for COPD patients. In this study, we analyzed the gene expression profiles of two datasets: one training dataset that includes 144 COPD patients and 194 ILD patients, and one test dataset that includes 75 COPD patients and 61 ILD patients. Advanced feature selection methods, mRMR (minimal Redundancy Maximal Relevance) and incremental feature selection (IFS), were applied to identify the 38-gene biomarker. An SVM (support vector machine) classifier was built based on the 38-gene biomarker. Its accuracy, sensitivity, and specificity on training dataset evaluated by leave one out cross-validation were 0.905, 0.896, and 0.912, respectively. And on independent test dataset, the accuracy, sensitivity, and specificity on were as great as and were 0.904, 0.933, and 0.869, respectively. The biological function analysis of the 38 genes indicated that many of them can be potential treatment targets that may benefit COPD and ILD patients.

The transcriptome difference between colorectal tumor and normal tissues revealed by single-cell sequencing

The previous cancer studies were difficult to reproduce since the tumor tissues were analyzed directly. But the tumor tissues were actually a mixture of different cancer cells. The transcriptome of single-cell was much robust than the transcriptome of a mixed tissue. The single-cell transcriptome had much smaller variance. In this study, we analyzed the single-cell transcriptome of 272 colorectal cancer (CRC) epithelial cells and 160 normal epithelial cells and identified 342 discriminative transcripts using advanced machine learning methods. The most discriminative transcripts were LGALS4, PHGR1, C15orf48, HEPACAM2, PERP, FABP1, FCGBP, MT1G, TSPAN1 and CKB. We further clustered the 342 transcripts into two categories. The upregulated transcripts in CRC epithelial cells were significantly enriched in Ribosome, Protein processing in endoplasmic reticulum, Antigen processing and presentation and p53 signaling pathway. The downregulated transcripts in CRC epithelial cells were significantly enriched in Mineral absorption, Aldosterone-regulated sodium reabsorption and Oxidative phosphorylation pathways. The biological analysis of the discriminative transcripts revealed the possible mechanism of colorectal cancer.

MicroRNA regulation in colorectal cancer tissue and serum

Colorectal cancer is recognized as the fourth leading cause of cancer-related deaths worldwide. Thus, there is ongoing search for potential new biomarkers allowing quicker and less invasive detection of the disease and prediction of the treatment outcome. Therefore, the aim of our study was to identify colorectal cancer specific miRNAs expressed in cancerous and healthy tissue from the same patient and to further correlate the presence of the same miRNAs in the circulation as potential biomarkers for diagnosis. In the current study we detected a set of 40 miRNAs differentially regulated in tumor tissue when comparing with healthy tissue. Additionally, we found 8 miRNAs differentially regulated in serum of colorectal cancer patients. Interestingly, there was no overlap in miRNAs regulated in tissue and serum, suggesting that serum regulated miRNAs may be not actively secreted from colorectal tumor cells. However, four of differentially expressed miRNAs, including miR-21, miR-17, miR-20a and miR-32 represent the miRNAs characteristic for different tumor types, including breast, colon, lung, pancreas, prostate and stomach cancer. This finding suggests important groups of miRNAs which can be further validated as markers for diagnosis of tumor tissue and regulation of carcinogenesis.

Inferring novel genes related to colorectal cancer via random walk with restart algorithm

Colorectal cancer (CRC) is the third most common type of cancer. In recent decades, genomic analysis has played an increasingly important role in understanding the molecular mechanisms of CRC. However, its pathogenesis has not been fully uncovered. Identification of genes related to CRC as complete as possible is an important way to investigate its pathogenesis. Therefore, we proposed a new computational method for the identification of novel CRC-associated genes. The proposed method is based on existing proven CRC-associated genes, human protein-protein interaction networks, and random walk with restart algorithm. The utility of the method is indicated by comparing it to the methods based on Guilt-by-association or shortest path algorithm. Using the proposed method, we successfully identified 298 novel CRC-associated genes. Previous studies have validated the involvement of the majority of these 298 novel genes in CRC-associated biological processes, thus suggesting the efficacy and accuracy of our method.

Transcriptomic analysis reveals the molecular mechanism of Alzheimer-related neuropathology induced by sevoflurane in mice

Anesthetics could induce cognitive dysfunctions, such as Alzheimer's disease in humans or mice. However, the precise molecular mechanism is unclear. Sevoflurane is a common anesthetic widely used in clinical practice. Here, we demonstrated the induction of cognitive dysfunction induced by Sev in mice to corroborate the signaling pathway and the differentially expressed genes (DEGs) followed by analyzing their functions. The cognitive function of mice was measured by the Morris water maze test. Transcriptomic data were annotated with Illumina HiSeq. 2000. Further, the changes in related proteins or genes were analyzed by western blotting and real-time quantitative polymerase chain reaction. Our results showed that Sev could cause a decline in cognitive competence in mice. The transcriptomic data indicated that adding up to 566 genes were upregulated and 1073 genes were downregulated. The genes of Plin4, Lcn2, Lrg1, Foxf1, and Ctla2a were significantly upregulated, while the genes of Arc, Npas4, Egr2, Hes5, and Cdh9 were downregulated dramatically. The Gene Ontology term with the highest enrichment of DEGs are involved in the regulation of cellular and macromolecule metabolism and cation and nucleic acid binding, respectively. The Kyoto encyclopedia of genes and genomes analysis indicated that the mitogen-activated protein kinases (MAPK) pathway was one of the most important metabolic pathways. In addition, the metabolic pathways related to cognitive function, such as the nervous system and neurodegenerative disease showed significant changes. Furthermore, we found that p38, c-Jun N-terminal kinase, and extracellular signal-regulated kinase of the MAPK signaling pathway played important roles in this process. In conclusion, these results provide the first important clues for identifying the DEGs and signaling pathways in the hippocampus due to a Sev-induced cognitive deficiency in mice.

A feature-based analysis identifies COL1A2 as a regulator in pancreatic cancer

This study aimed to identify genetic biomarkers in pancreatic cancer (PC) and explore its function in PC via a feature-base analysis of bioinformatics. OMIM and DisGeNET databases discovered 209 PC connected genes and then 516 connected genes were identified. We selected 29 genes according to optimal features and chose COL1A2, which had the highest expression, for the following experiment. The expression of COL1A2 was determined by qRT-PCR; cell proliferation was determined by MTT assay; migration and invasion after COL1A2 and miR-25-3p transfection was evaluated by Transwell assay. COL1A2 presented the highest expression in PC tissues, which was validated in functional experiments. MiR-25-3p suppressed the expression of COL1A2 in cell lines and inhibited migration, invasion and proliferation of PC cells. MiR-25-3p could suppress the expression of COL1A2 and inhibit the proliferation, migration and invasion of PC cells which provided a new idea for the detection and treatment of PC.

Identification of tumor-educated platelet biomarkers of non-small-cell lung cancer

BACKGROUND

Lung cancer is a severe cancer with a high death rate. The 5-year survival rate for stage III lung cancer is much lower than stage I. Early detection and intervention of lung cancer patients can significantly increase their survival time. However, conventional lung cancer-screening methods, such as chest X-rays, sputum cytology, positron-emission tomography (PET), low-dose computed tomography (CT), magnetic resonance imaging, and gene-mutation, -methylation, and -expression biomarkers of lung tissue, are invasive, radiational, or expensive. Liquid biopsy is non-invasive and does little harm to the body. It can reflect early-stage dysfunctions of tumorigenesis and enable early detection and intervention.

METHODS

In this study, we analyzed RNA-sequencing data of tumor-educated platelets (TEPs) in 402 non-small-cell lung cancer (NSCLC) patients and 231 healthy controls. A total of 48 biomarker genes were selected with advanced minimal-redundancy, maximal-relevance, and incremental feature-selection (IFS) methods.

RESULTS

A support vector-machine (SVM) classifier based on the 48 biomarker genes accurately predicted NSCLC with leave-one-out cross-validation (LOOCV) sensitivity, specificity, accuracy, and Matthews correlation coefficients of 0.925, 0.827, 0.889, and 0.760, respectively. Network analysis of the 48 genes revealed that the WASF1 actin cytoskeleton module, PRKAB2 kinase module, RSRC1 ribosomal protein module, PDHB carbohydrate-metabolism module, and three intermodule hubs (TPM2, MYL9, and PPP1R12C) may play important roles in NSCLC tumorigenesis and progression.

CONCLUSION

The 48-gene TEP liquid-biopsy biomarkers will facilitate early screening of NSCLC and prolong the survival of cancer patients.

Identification of synthetic lethality based on a functional network by using machine learning algorithms

Synthetic lethality is the synthesis of mutations leading to cell death. Tumor-specific synthetic lethality has been targeted in research to improve cancer therapy. With the advances of techniques in molecular biology, such as RNAi and CRISPR/Cas9 gene editing, efforts have been made to systematically identify synthetic lethal interactions, especially for frequently mutated genes in cancers. However, elucidating the mechanism of synthetic lethality remains a challenge because of the complexity of its influencing conditions. In this study, we proposed a new computational method to identify critical functional features that can accurately predict synthetic lethal interactions. This method incorporates several machine learning algorithms and encodes protein-coding genes by an enrichment system derived from gene ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways to represent their functional features. We built a random forest-based prediction engine by using 2120 selected features and obtained a Matthews correlation coefficient of 0.532. We examined the top 15 features and found that most of them have potential roles in synthetic lethality according to previous studies. These results demonstrate the ability of our proposed method to predict synthetic lethal interactions and provide a basis for further characterization of these particular genetic combinations.

Cyclin D1 overexpression induces global transcriptional downregulation in lymphoid neoplasms

Cyclin D1 is an oncogene frequently overexpressed in human cancers that has a dual function as cell cycle and transcriptional regulator, although the latter is widely unexplored. Here, we investigated the transcriptional role of cyclin D1 in lymphoid tumor cells with cyclin D1 oncogenic overexpression. Cyclin D1 showed widespread binding to the promoters of most actively transcribed genes, and the promoter occupancy positively correlated with the transcriptional output of targeted genes. Despite this association, the overexpression of cyclin D1 in lymphoid cells led to a global transcriptional downmodulation that was proportional to cyclin D1 levels. This cyclin D1-dependent global transcriptional downregulation was associated with a reduced nascent transcription and an accumulation of promoter-proximal paused RNA polymerase II (Pol II) that colocalized with cyclin D1. Concordantly, cyclin D1 overexpression promoted an increase in the Poll II pausing index. This transcriptional impairment seems to be mediated by the interaction of cyclin D1 with the transcription machinery. In addition, cyclin D1 overexpression sensitized cells to transcription inhibitors, revealing a synthetic lethality interaction that was also observed in primary mantle cell lymphoma cases. This finding of global transcriptional dysregulation expands the known functions of oncogenic cyclin D1 and suggests the therapeutic potential of targeting the transcriptional machinery in cyclin D1-overexpressing tumors.

Computational Approach to Investigating Key GO Terms and KEGG Pathways Associated with CNV

Choroidal neovascularization (CNV) is a severe eye disease that leads to blindness, especially in the elderly population. Various endogenous and exogenous regulatory factors promote its pathogenesis. However, the detailed molecular biological mechanisms of CNV have not been fully revealed. In this study, by using advanced computational tools, a number of key gene ontology (GO) terms and KEGG pathways were selected for CNV. A total of 29 validated genes associated with CNV and 17,639 nonvalidated genes were encoded based on the features derived from the GO terms and KEGG pathways by using the enrichment theory. The widely accepted feature selection method-maximum relevance and minimum redundancy (mRMR)-was applied to analyze and rank the features. An extensive literature review for the top 45 ranking features was conducted to confirm their close associations with CNV. Identifying the molecular biological mechanisms of CNV as described by the GO terms and KEGG pathways may contribute to improving the understanding of the pathogenesis of CNV.

Target delivery of doxorubicin tethered with PVP stabilized gold nanoparticles for effective treatment of lung cancer

Development of drug delivery system conjugated with doxorubicin (dox) on the surface of AuNPs with polyvinylpyrrolidone (Dox@PVP-AuNPs), we have demonstrated that human lung cancer cells can significantly overcome by the combination of highly effective cellular entry and responsive intracellular release of doxorubicin from Dox@PVP-AuNPs complex. Previously drug release from doxorubicin-conjugated AuNPs was confirmed by the recovered fluorescence of doxorubicin from quenching due to the nanosurface energy transfer between doxorubicinyl groups and AuNPs. Dox@PVP-AuNPs achieved enhanced inhibition of lung cancer cells growth than free Doxorubicin and PVP-AuNPs. The in vitro cytotoxic effect of PVP-AuNPs, free Dox and Dox@PVP-AuNPs inhibited the proliferation of human lung cancer cells with IC50 concentration. Compared with control cells, PVP-AuNPs and free Dox, Dox@PVP-AuNPs can increases ROS generation, sensitize mitochondrial membrane potential and induces both early and late apoptosis in lung cancer cells. Moreover, Dox@PVP-AuNPs highly upregulates the expression of tumor suppressor genes than free Dox and PVP-AuNPs and induces intrinsic apoptosis in lung cancer cells. From the results, Dox@PVP-AuNPs can be considered as an potential drug delivery system for effective treatment of human lung cancer.

A computational method using the random walk with restart algorithm for identifying novel epigenetic factors

Epigenetic regulation has long been recognized as a significant factor in various biological processes, such as development, transcriptional regulation, spermatogenesis, and chromosome stabilization. Epigenetic alterations lead to many human diseases, including cancer, depression, autism, and immune system defects. Although efforts have been made to identify epigenetic regulators, it remains a challenge to systematically uncover all the components of the epigenetic regulation in the genome level using experimental approaches. The advances of constructing protein-protein interaction (PPI) networks provide an excellent opportunity to identify novel epigenetic factors computationally in the genome level. In this study, we identified potential epigenetic factors by using a computational method that applied the random walk with restart (RWR) algorithm on a protein-protein interaction (PPI) network using reported epigenetic factors as seed nodes. False positives were identified by their specific roles in the PPI network or by a low-confidence interaction and a weak functional relationship with epigenetic regulators. After filtering out the false positives, 26 candidate epigenetic factors were finally accessed. According to previous studies, 22 of these are thought to be involved in epigenetic regulation, suggesting the robustness of our method. Our study provides a novel computational approach which successfully identified 26 potential epigenetic factors, paving the way on deepening our understandings on the epigenetic mechanism.

Prediction and analysis of essential genes using the enrichments of gene ontology and KEGG pathways

Identifying essential genes in a given organism is important for research on their fundamental roles in organism survival. Furthermore, if possible, uncovering the links between core functions or pathways with these essential genes will further help us obtain deep insight into the key roles of these genes. In this study, we investigated the essential and non-essential genes reported in a previous study and extracted gene ontology (GO) terms and biological pathways that are important for the determination of essential genes. Through the enrichment theory of GO and KEGG pathways, we encoded each essential/non-essential gene into a vector in which each component represented the relationship between the gene and one GO term or KEGG pathway. To analyze these relationships, the maximum relevance minimum redundancy (mRMR) was adopted. Then, the incremental feature selection (IFS) and support vector machine (SVM) were employed to extract important GO terms and KEGG pathways. A prediction model was built simultaneously using the extracted GO terms and KEGG pathways, which yielded nearly perfect performance, with a Matthews correlation coefficient of 0.951, for distinguishing essential and non-essential genes. To fully investigate the key factors influencing the fundamental roles of essential genes, the 21 most important GO terms and three KEGG pathways were analyzed in detail. In addition, several genes was provided in this study, which were predicted to be essential genes by our prediction model. We suggest that this study provides more functional and pathway information on the essential genes and provides a new way to investigate related problems.

A network-based method using a random walk with restart algorithm and screening tests to identify novel genes associated with Menière's disease

As a chronic illness derived from hair cells of the inner ear, Menière’s disease (MD) negatively influences the quality of life of individuals and leads to a number of symptoms, such as dizziness, temporary hearing loss, and tinnitus. The complete identification of novel genes related to MD would help elucidate its underlying pathological mechanisms and improve its diagnosis and treatment. In this study, a network-based method was developed to identify novel MD-related genes based on known MD-related genes. A human protein-protein interaction (PPI) network was constructed using the PPI information reported in the STRING database. A classic ranking algorithm, the random walk with restart (RWR) algorithm, was employed to search for novel genes using known genes as seed nodes. To make the identified genes more reliable, a series of screening tests, including a permutation test, an interaction test and an enrichment test, were designed to select essential genes from those obtained by the RWR algorithm. As a result, several inferred genes, such as CD4, NOTCH2 and IL6, were discovered. Finally, a detailed biological analysis was performed on fifteen of the important inferred genes, which indicated their strong associations with MD.

Microarray analysis reveals differentially expressed lncRNAs in benign epithelial ovarian cysts and normal ovaries

Recent studies suggest that long non-coding RNAs (lncRNAs) play crucial roles in many types of human malignant cancers. However, the function of lncRNAs in benign tumors remains poorly understood. In the present study, to explored the potential roles of lncRNAs in benign epithelial ovarian cysts (BEOCs) which commonly occur in young women and possess malignant potential, we described the expression profile of the lncRNAs between BEOC and normal ovarian tissues using lncRNA microarray techniques. The results showed that 1,325 transcripts of lncRNAs (1,014 upregulated and 311 downregulated) were differentially expressed in the BEOCs compared with the normal controls [absolute fold-change ≥2, false discovery rate (FDR) <0.05]. We also conducted quantitative real-time PCR (qPCR) to confirm the microarray data. The results of qPCR revealed that the expression trend of 6 randomly selected lncRNAs was consistent with the microarray data. Furthermore, candidate lncRNAs were characterized by pathway analysis and Gene Ontology (GO). The present study is the first to demonstrate different expression profiles of lncRNAs between BEOCs and normal ovarian tissues. These lncRNAs may play a crucial role in the pathological process of BEOCs.

Determination of Genes Related to Uveitis by Utilization of the Random Walk with Restart Algorithm on a Protein-Protein Interaction Network

Uveitis, defined as inflammation of the uveal tract, may cause blindness in both young and middle-aged people. Approximately 10–15% of blindness in the West is caused by uveitis. Therefore, a comprehensive investigation to determine the disease pathogenesis is urgent, as it will thus be possible to design effective treatments. Identification of the disease genes that cause uveitis is an important requirement to achieve this goal. To begin to answer this question, in this study, a computational method was proposed to identify novel uveitis-related genes. This method was executed on a large protein–protein interaction network and employed a popular ranking algorithm, the Random Walk with Restart (RWR) algorithm. To improve the utility of the method, a permutation test and a procedure for selecting core genes were added, which helped to exclude false discoveries and select the most important candidate genes. The five-fold cross-validation was adopted to evaluate the method, yielding the average F1-measure of 0.189. In addition, we compared our method with a classic GBA-based method to further indicate its utility. Based on our method, 56 putative genes were chosen for further assessment. We have determined that several of these genes (e.g., CCL4, Jun, and MMP9) are likely to be important for the pathogenesis of uveitis.

Identifying novel fruit-related genes in Arabidopsis thaliana based on the random walk with restart algorithm

Fruit is essential for plant reproduction and is responsible for protection and dispersal of seeds. The development and maturation of fruit is tightly regulated by numerous genetic factors that respond to environmental and internal stimulation. In this study, we attempted to identify novel fruit-related genes in a model organism, Arabidopsis thaliana, using a computational method. Based on validated fruit-related genes, the random walk with restart (RWR) algorithm was applied on a protein-protein interaction (PPI) network using these genes as seeds. The identified genes with high probabilities were filtered by the permutation test and linkage tests. In the permutation test, the genes that were selected due to the structure of the PPI network were discarded. In the linkage tests, the importance of each candidate gene was measured from two aspects: (1) its functional associations with validated genes and (2) its similarity with validated genes on gene ontology (GO) terms and KEGG pathways. Finally, 255 inferred genes were obtained, subsequent extensive analysis of important genes revealed that they mainly contribute to ubiquitination (UBQ9, UBQ8, UBQ11, UBQ10), serine hydroxymethyl transfer (SHM7, SHM5, SHM6) or glycol-metabolism (HXKL2_ARATH, CSY5, GAPCP1), suggesting essential roles during the development and maturation of fruit in Arabidopsis thaliana.

Analysis of cancer-related lncRNAs using gene ontology and KEGG pathways

BACKGROUND

Cancer is a disease that involves abnormal cell growth and can invade or metastasize to other tissues. It is known that several factors are related to its initiation, proliferation, and invasiveness. Recently, it has been reported that long non-coding RNAs (lncRNAs) can participate in specific functional pathways and further regulate the biological function of cancer cells. Studies on lncRNAs are therefore helpful for uncovering the underlying mechanisms of cancer biological processes.

METHODS

We investigated cancer-related lncRNAs using gene ontology (GO) terms and KEGG pathway enrichment scores of neighboring genes that are co-expressed with the lncRNAs by extracting important GO terms and KEGG pathways that can help us identify cancer-related lncRNAs. The enrichment theory of GO terms and KEGG pathways was adopted to encode each lncRNA. Then, feature selection methods were employed to analyze these features and obtain the key GO terms and KEGG pathways.

RESULTS

The analysis indicated that the extracted GO terms and KEGG pathways are closely related to several cancer associated processes, such as hormone associated pathways, energy associated pathways, and ribosome associated pathways. And they can accurately predict cancer-related lncRNAs.

CONCLUSIONS

This study provided novel insight of how lncRNAs may affect tumorigenesis and which pathways may play important roles during it. These results could help understanding the biological mechanisms of lncRNAs and treating cancer.

Identifying and Analyzing Novel Epilepsy-Related Genes Using Random Walk with Restart Algorithm

As a pathological condition, epilepsy is caused by abnormal neuronal discharge in brain which will temporarily disrupt the cerebral functions. Epilepsy is a chronic disease which occurs in all ages and would seriously affect patients' personal lives. Thus, it is highly required to develop effective medicines or instruments to treat the disease. Identifying epilepsy-related genes is essential in order to understand and treat the disease because the corresponding proteins encoded by the epilepsy-related genes are candidates of the potential drug targets. In this study, a pioneering computational workflow was proposed to predict novel epilepsy-related genes using the random walk with restart (RWR) algorithm. As reported in the literature RWR algorithm often produces a number of false positive genes, and in this study a permutation test and functional association tests were implemented to filter the genes identified by RWR algorithm, which greatly reduce the number of suspected genes and result in only thirty-three novel epilepsy genes. Finally, these novel genes were analyzed based upon some recently published literatures. Our findings implicate that all novel genes were closely related to epilepsy. It is believed that the proposed workflow can also be applied to identify genes related to other diseases and deepen our understanding of the mechanisms of these diseases.

SNHG8 is identified as a key regulator of epstein-barr virus(EBV)-associated gastric cancer by an integrative analysis of lncRNA and mRNA expression

The Epstein-Barr virus (EBV) is associated with a variety of cancers, including gastric cancer, which has one of the highest mortality rates of all human cancers. Long non-coding RNAs (lncRNAs) have been suggested to have important causal roles in gastric cancer. However, the interaction between lncRNAs and EBV has not yet been studied. To this end, we sequenced 11,311 lncRNAs and 144,826 protein-coding transcripts from four types of tissue: one non-EBV-infected gastric carcinoma (EBVnGC) and its adjacent normal tissue, and one EBV-associated gastric carcinoma (EBVaGC) and its adjacent normal tissue. Five lncRNAs showed EBVaGC-specific expression; of those, one (SNHG8) was validated using real-time PCR in an independent cohort with 88 paired gastric cancer and adjacent tissue samples. To explore the functions of SNHG8, we identified its mRNA targets on the lncRNA-mRNA co-expression network of the Illumina Body Map, which contains the RNA sequencing data of mRNAs and lncRNAs from 16 normal human tissues. SNHG8 lncRNA was found to affect several gastric cancer-specific pathways and target genes of EBV. Our results reveal the intertwined tumorigenesis mechanisms of lncRNA and EBV and identify SNHG8 as a highly possible candidate biomarker and drug target of gastric cancer.

Investigating genetic characteristics of hepatitis B virus-associated and -non-associated hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is a primary liver malignancy that mainly occurs in patients with chronic liver disease and cirrhosis. Risk factors for HCC include hepatitis B virus (HBV) infection. However, the specific role of HBV infection in HCC development is not yet completely understood. In order to reveal the effects of HBV on HCC, we compare the genes of HCC patients infected with HBV with those who are not infected.

METHODS

We encoded the genes of these two types of HCC in databases using enrichment scores of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway terms. A random forest algorithm was employed in order to distinguish these two types in the classifier, and a series of feature selection approaches was used in order to select their optimal features. Novel HBV-associated and -non-associated HCC genes were predicted, respectively, based on their optimal features in the classifier. A shortest-path algorithm was also employed in order to find all of the shortest-paths genes connecting the known related genes.

RESULTS

A total of 54 different features between HBV-associated and -non-associated HCC genes were identified. In total, 1236 and 881 novel related genes were predicted for HBV-associated and -non-associated HCC, respectively. By integrating the predicted genes and shortest path genes in their gene interaction network, we identified 679 common genes involved in the two types of HCC.

CONCLUSION

We identified the significantly different genetic features between two types of HCC. We also predicted related genes for the two types based on their specific features. Finally, we determined the common genes and features that were involved in both of these two types of HCC.

Analysis of Important Gene Ontology Terms and Biological Pathways Related to Pancreatic Cancer

Pancreatic cancer is a serious disease that results in more than thirty thousand deaths around the world per year. To design effective treatments, many investigators have devoted themselves to the study of biological processes and mechanisms underlying this disease. However, it is far from complete. In this study, we tried to extract important gene ontology (GO) terms and KEGG pathways for pancreatic cancer by adopting some existing computational methods. Genes that have been validated to be related to pancreatic cancer and have not been validated were represented by features derived from GO terms and KEGG pathways using the enrichment theory. A popular feature selection method, minimum redundancy maximum relevance, was employed to analyze these features and extract important GO terms and KEGG pathways. An extensive analysis of the obtained GO terms and KEGG pathways was provided to confirm the correlations between them and pancreatic cancer.

A genetic features and gene interaction study for identifying the genes that cause hereditary spherocytosis

OBJECTIVE

Hereditary spherocytosis (HS) is a hemolytic disorder characterized by the presence of spherical-shaped red blood cells on the peripheral blood smear. Non-dominant HS cases are due to de novo mutations of the type associated with dominant inheritance or recessive genes. This study is aimed to identify HS-related biological mechanisms and predicting HS candidate genes.

METHODS

We searched the known HS-related genes from the public databases. By analyzing the gene ontology (GO) and biological pathway of these genes, we extracted the optimal features to encode HS genes. Based on them, we predicted the HS-related genes from genes of whole genomes using the Random Forest classification. We used the gene interaction networks analysis to further identify the core regulatory genes that were related to HS.

RESULTS

Forty-one known HS-related genes were found out and encoded. Three hundred and sixty-seven GO terms and ten biological pathway terms were identified as the optimal features for prediction. We subsequently predicted 150 novel HS-related genes and identified the core regulatory genes in the interaction network of predicted and known genes. These features and genes that we identified could complement the genetic features of HS.

Identification of compound-protein interactions through the analysis of gene ontology, KEGG enrichment for proteins and molecular fragments of compounds

Compound-protein interactions play important roles in every cell via the recognition and regulation of specific functional proteins. The correct identification of compound-protein interactions can lead to a good comprehension of this complicated system and provide useful input for the investigation of various attributes of compounds and proteins. In this study, we attempted to understand this system by extracting properties from both proteins and compounds, in which proteins were represented by gene ontology and KEGG pathway enrichment scores and compounds were represented by molecular fragments. Advanced feature selection methods, including minimum redundancy maximum relevance, incremental feature selection, and the basic machine learning algorithm random forest, were used to analyze these properties and extract core factors for the determination of actual compound-protein interactions. Compound-protein interactions reported in The Binding Databases were used as positive samples. To improve the reliability of the results, the analytic procedure was executed five times using different negative samples. Simultaneously, five optimal prediction methods based on a random forest and yielding maximum MCCs of approximately 77.55 % were constructed and may be useful tools for the prediction of compound-protein interactions. This work provides new clues to understanding the system of compound-protein interactions by analyzing extracted core features. Our results indicate that compound-protein interactions are related to biological processes involving immune, developmental and hormone-associated pathways.

Analysis of the chemical toxicity effects using the enrichment of Gene Ontology terms and KEGG pathways

BACKGROUND

Chemical toxicity is one of the major barriers for designing and detecting new chemical entities during drug discovery. Unexpected toxicity of an approved drug may lead to withdrawal from the market and significant loss of the associated costs. Better understanding of the mechanisms underlying various toxicity effects can help eliminate unqualified candidate drugs in early stages, allowing researchers to focus their attention on other more viable candidates.

METHODS

In this study, we aimed to understand the mechanisms underlying several toxicity effects using Gene Ontology (GO) terms and KEGG pathways. GO term and KEGG pathway enrichment theories were adopted to encode each chemical, and the minimum redundancy maximum relevance (mRMR) was used to analyze the GO terms and the KEGG pathways. Based on the feature list obtained by the mRMR method, the most related GO terms and KEGG pathways were extracted.

RESULTS

Some important GO terms and KEGG pathways were uncovered, which were concluded to be significant for determining chemical toxicity effects.

CONCLUSIONS

Several GO terms and KEGG pathways are highly related to all investigated toxicity effects, while some are specific to a certain toxicity effect.

GENERAL SIGNIFICANCE

The findings in this study have the potential to further our understanding of different chemical toxicity mechanisms and to assist scientists in developing new chemical toxicity prediction algorithms. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.

Genetic differences among ethnic groups

BACKGROUND

Many differences between different ethnic groups have been observed, such as skin color, eye color, height, susceptibility to some diseases, and response to certain drugs. However, the genetic bases of such differences have been under-investigated. Since the HapMap project, large-scale genotype data from Caucasian, African and Asian population samples have been available. The project found that these populations were located in different areas of the PCA (Principal Component Analysis) plot. However, as an unsupervised method, PCA does not measure the differences in each single nucleotide polymorphism (SNP) among populations.

RESULTS

We applied an advanced mutual information-based feature selection method to detect associations between SNP status and ethnic groups using the latest HapMap Phase 3 release version 3, which included more sub-populations. A total of 299 SNPs were identified, and they can accurately predicted the ethnicity of all HapMap populations. The 10-fold cross validation accuracy of the SMO (sequential minimal optimization) model on training dataset was 0.901, and the accuracy on independent test dataset was 0.895.

CONCLUSIONS

In-depth functional analysis of these SNPs and their nearby genes revealed the genetic bases of skin and eye color differences among populations.

Finding gastric cancer related genes and clinical biomarkers for detection based on gene-gene interaction network

BACKGROUND/OBJECTIVE

Gastric cancer (GC) is the second leading cause of death resulted from cancer globally. The most common cause of GC is the infection of Helicobacter pylori, approximately 11% of cases are caused by genetic factors. The objective of this study was to develop an effective computational method to meaningfully interpret these GC-related genes and to predict potential prognostic genes for clinical detection.

METHODS

We employed the shortest path algorithm and permutation test to probe the genes that have relationship with known GC genes in gene-gene interaction network. We calculated the enrichment scores of gene ontology and pathways of gastric cancer related genes to characterize these genes in terms of molecular features. The optimal features that primly representing the gastric cancer related genes were selected using Random Forest classification and incremental feature selection. Random Forest classification was also used for the prediction of the novel gastric cancer related genes based on the selected features and the identification of novel prognostic genes based on the expression of genes.

RESULTS

Based on the shortest path analysis of 36 known GC genes, 39 genes occurring in shortest path were identified as GC-related genes. In subsequent classification, 4153 gene ontology terms and 157 pathway terms were identified as the optimal features to depict these gastric cancer related genes. Based on them, a total of 886 genes were predicted as related genes. These 886 genes could serve as expression biomarkers for clinical detection and they achieved a 100% accuracy for distinguishing gastric cancer from a case-control dataset, better than any of 886 random selected genes did.

CONCLUSION

By analyzing the features of known GC-related genes, we employed a systematic method to predict gastric cancer related genes and novel prognostic genes for accurate clinical detection.

Genomic Copy Number Variation Affecting Genes Involved in the Cell Cycle Pathway: Implications for Somatic Mosaicism

Somatic genome variations (mosaicism) seem to represent a common mechanism for human intercellular/interindividual diversity in health and disease. However, origins and mechanisms of somatic mosaicism remain a matter of conjecture. Recently, it has been hypothesized that zygotic genomic variation naturally occurring in humans is likely to predispose to nonheritable genetic changes (aneuploidy) acquired during the lifetime through affecting cell cycle regulation, genome stability maintenance, and related pathways. Here, we have evaluated genomic copy number variation (CNV) in genes implicated in the cell cycle pathway (according to Kyoto Encyclopedia of Genes and Genomes/KEGG) within a cohort of patients with intellectual disability, autism, and/or epilepsy, in which the phenotype was not associated with genomic rearrangements altering this pathway. Benign CNVs affecting 20 genes of the cell cycle pathway were detected in 161 out of 255 patients (71.6%). Among them, 62 individuals exhibited >2 CNVs affecting the cell cycle pathway. Taking into account the number of individuals demonstrating CNV of these genes, a support for this hypothesis appears to be presented. Accordingly, we speculate that further studies of CNV burden across the genes implicated in related pathways might clarify whether zygotic genomic variation generates somatic mosaicism in health and disease.