Identifying cancer specific signaling pathways based on the dysregulation between genes

A large collection of studies has shown that the occurrence of cancer is related to the functional dysfunction of the pathways. Identification of cancer-related pathways could help researchers understand the mechanisms of complex diseases well. Whereas, most current signaling pathway analysis methods take no account of the gene interaction variations within pathways. Furthermore, considering that some pathways have connection with two or more cancer types, while some are likely to be cancer-type specific pathways. Identifying cancer-type specific pathways contributes to interpreting the different mechanisms of different cancer types. In this study, we first proposed a pathway analysis method named Pathway Analysis of Intergenic Regulation (PAIGR) to identify pathways with dysregulation between genes and compared the performance of this method with four existing methods on four colorectal cancer (CRC) datasets. The results showed that PAIGR could find cancer-related pathways more accurately. Moreover, in order to explore the relationship between the identified pathways and the cancer type, we constructed a pathway interaction network, in which nodes and edges represented pathways and interactions between pathways respectively. Highly connected pathways were considered to play a central role in an extensive range of biological processes, while sparsely connected pathways are considered to have certain specificity. Our results showed that pathways identified by PAIGR had a low nodal degree (i.e., a few numbers of interactions), which suggested that most of these pathways were cancer-type specific.     

Perturbations of pathway co-expression network identify a core network in metastatic breast cancer

Metastases are the main cause of death in advanced breast cancer (BC) patients. Although chemotherapy and hormone therapy are current treatment strategies, drug resistance is frequent and still not completely understood.In this study, a bioinformatics analysis was performed on BC patients to explore the molecular mechanisms associated with BC metastasis. Microarray gene expression profiles of metastatic and non metastatic BC patients were downloaded from Gene Expression Omnibus (GEO) dataset. Raw data were normalized and merged using the Combat tool. Pathways enriched with differently expressed genes were identified and a pathway co-expression network was generated using Pearson’s correlation. We identified from this network, which includes 17 pathways and 128 interactions, the pathways that most influence the network efficiency. Moreover, protein interaction network was investigated to identify hub genes of the pathway network. The prognostic role of the network was evaluated with a survival analysis using an independent dataset.In conclusion, the pathway co-expression network could contribute to understanding the mechanism and development of BC metastases.     

A graph-based multi-sample test for identifying pathways associated with cancer progression

Cancer is in general not a result of an abnormality of a single gene but a consequence of changes in many genes, it is therefore of great importance to understand the roles of different oncogenic and tumor suppressor pathways in tumorigenesis. In recent years, there have been many computational models developed to study the genetic alterations of different pathways in the evolutionary process of cancer. However, most of the methods are knowledge-based enrichment analyses and inflexible to analyze user-defined pathways or gene sets. In this paper, we develop a nonparametric and data-driven approach to testing for the dynamic changes of pathways over the cancer progression. Our method is based on an expansion and refinement of the pathway being studied, followed by a graph-based multivariate test, which is very easy to implement in practice. The new test is applied to the rich Cancer Genome Atlas data to study the (epi)genetic alterations of 186 KEGG pathways in the development of serous ovarian cancer. To make use of the comprehensive data, we incorporate three data types in the analysis representing gene expression level, copy number and DNA methylation level. Our analysis suggests a list of nine pathways that are closely associated with serous ovarian cancer progression, including cell cycle, ERBB, JAK-STAT signaling and p53 signaling pathways. By pairwise tests, we found that most of the identified pathways contribute only to a particular transition step. For instance, the cell cycle and ERBB pathways play key roles in the early-stage transition, while the ECM receptor and apoptosis pathways contribute to the progression from stage III to stage IV. The proposed computational pipeline is powerful in detecting important pathways and gene sets that drive cancers at certain stage(s). It offers new insights into the understanding of molecular mechanism of cancer initiation and progression.     

Identifying novel prostate cancer associated pathways based on integrative microarray data analysis

The development and diverse application of microarray and next generation sequencing technologies has made the meta-analysis widely used in expression data analysis. Although it is commonly accepted that pathway, network and systemic level approaches are more reproducible than reductionism analyses, the meta-analysis of prostate cancer associated molecular signatures at the pathway level remains unexplored. In this article, we performed a meta-analysis of 10 prostate cancer microarray expression datasets to identify the common signatures at both the gene and pathway levels. As the enrichment analysis result of GeneGo's database and KEGG database, 97.8% and 66.7% of the signatures show higher similarity at pathway level than that at gene level, respectively. Analysis by using gene set enrichment analysis (GSEA) method also supported the hypothesis. Further analysis of PubMed citations verified that 207 out of 490 (42%) pathways from GeneGo and 48 out of 74 (65%) pathways from KEGG were related to prostate cancer. An overlap of 15 enriched pathways was observed in at least eight datasets. Eight of these pathways were first described as being associated with prostate cancer. In particular, endothelin-1/EDNRA transactivation of the EGFR pathway was found to be overlapped in nine datasets. The putative novel prostate cancer related pathways identified in this paper were indirectly supported by PubMed citations and would provide essential information for further development of network biomarkers and individualized therapy strategy for prostate cancer.     

Investigating dysregulated pathways in Staphylococcus aureus (SA) exposed macrophages based on pathway interaction network

ObjectiveThis work aimed to identify dysregulated pathways for Staphylococcus aureus (SA) exposed macrophages based on pathway interaction network (PIN).MethodsThe inference of dysregulated pathways was comprised of four steps: preparing gene expression data, protein–protein interaction (PPI) data and pathway data; constructing a PIN dependent on the data and Pearson correlation coefficient (PCC); selecting seed pathway from PIN by computing activity score for each pathway according to principal component analysis (PCA) method; and investigating dysregulated pathways in a minimum set of pathways (MSP) utilizing seed pathway and the area under the receiver operating characteristics curve (AUC) index implemented in support vector machines (SVM) model.ResultsA total of 20,545 genes, 449,833 interactions and 1189 pathways were obtained in the gene expression data, PPI data and pathway data, respectively. The PIN was consisted of 8388 interactions and 1189 nodes, and Respiratory electron transport, ATP synthesis by chemiosmotic coupling, and heat production by uncoupling proteins was identified as the seed pathway. Finally, 15 dysregulated pathways in MSP (AUC = 0.999) were obtained for SA infected samples, such as Respiratory electron transport and DNA Replication.ConclusionsWe have identified 15 dysregulated pathways for SA infected macrophages based on PIN. The findings might provide potential biomarkers for early detection and therapy of SA infection, and give insights to reveal the molecular mechanism underlying SA infections. However, how these dysregulated pathways worked together still needs to be studied.     

Gene function classification using NCI-60 cell line gene expression profiles

Gene expression patterns from NCI's panel of 60 cell lines were used to train a Neural Network model for classifying genes to pathways. The model assigns probabilities to each gene for each of the 21 modeled pathways assigned by the Kyoto Encyclopedia of Genes and Genomes. Cross-validation of the model showed that 10 of the 21 pathways exhibited good performance in statistical significance and accuracy. The model was designed to output gene probabilities that could be screened for higher probabilities resulting in higher confidence in classification though yielding fewer genes per pathway. The model was deployed on 5798 genes and our approach allowed us to ascertain the most relevant genes above an estimated background. Eight pathways were identified with both good cross-validation and significant numbers above background, TCA Cycle, Oxidative Phosphorylation, Porphyrin Biosynthesis, Ribosome, Polymerases, Proteasome, Cell Cycle, and Cell Adhesion. Gene Ontology (GO) annotation was used for additional validation of gene classification results. A total of 551 GO annotated genes and 468 unannotated genes were classified to the 8 pathways. The primary and secondary classifications of genes revealed known pathway relationships and provide the potential for discovering new pathway relationships.     

A new vision of evaluating gene expression signatures

Gene expression profiles based on high-throughput technologies contribute to molecular classifications of different cell lines and consequently to clinical diagnostic tests for cancer types and other diseases. Statistical techniques and dimension reduction methods have been devised for identifying minimal gene subset with maximal discriminative power. For sets of in silico candidate genes, assuming a unique gene signature or performing a parsimonious signature evaluation seems to be too restrictive in the context of in vitro signature validation. This is mainly due to the high complexity of largely correlated expression measurements and the existence of various oncogenic pathways. Consequently, it might be more advantageous to identify and evaluate multiple gene signatures with a similar good predictive power, which are referred to as near-optimal signatures, to be made available for biological validation. For this purpose we propose the bead-chain-plot approach originating from swarm intelligence techniques, and a small scale computational experiment is conducted in order to convey our vision. We simulate the acquisition of candidate genes by using a small pool of differentially expressed genes derived from microarray-based CNS tumour data. The application of the bead-chain-plot provides experimental evidence for improved classifications by using near-optimal signatures in validation procedures.     

DERL3 functions as a tumor suppressor in gastric cancer

BackgroundGastric cancer is a common malignant tumor in the clinic with a high mortality rate, ranking the first among malignant tumors of the digestive system. Early gastric cancer exhibits no specific clinical symptoms and signs, and most of the patients were diagnosed as advanced gastric cancer. The prognosis is poor, and the 5-year overall survival rate is still lower than 30%, seriously threatening people’s life and health. However, the pathogenesis of gastric cancer is still unclear.MethodsThis study aimed to identify methylated differentially expressed genes in gastric cancer and to study the cellular functions and pathways that may be involved in its regulation, as well as the biological functions of key methylated differentially expressed genes. The gene expression data set and methylation data set of gastric cancer genes based on TCGA were analyzed to identify prognostic methylated genes.ResultsThis study showed that the methylation of the DERL3 promoter was correlated with the clinical analysis of tumors. Further studies were conducted on genes co-expressed with DERL3, whose functions and pathways to inhibit gastric cancer were adaptive immune response, T cell activation, immune response-regulating pathway, cell surface on molecules, and natural killer cell-mediated cytotoxicity. Finally, cell proliferation assay, cell scratch assay, and cell invasion assay confirmed that DERL3 as a tumor suppressor gene inhibited the malignant evolution of gastric cancer.ConclusionsThe analysis of key methylated differentially expressed genes helped elucidate the epigenetic regulation mechanism in the development of gastric cancer. DERL3, as a methylation biomarker, has a predictive and prognostic value in the accurate diagnosis and treatment of gastric cancer and provides potential targets for the precision treatment of gastric cancer.Trial RegistrationNot applicable.     

Variable selection using probability density function similarity for support vector machine classification of high-dimensional microarray data

One problem with discriminant analysis of microarray data is representation of each sample by a large number of genes that are possibly irrelevant, insignificant or redundant. Methods of variable selection are, therefore, of great significance in microarray data analysis. To circumvent the problem, a new gene mining approach is proposed based on the similarity between probability density functions on each gene for the class of interest with respect to the others. This method allows the ascertainment of significant genes that are informative for discriminating each individual class rather than maximizing the separability of all classes. Then one can select genes containing important information about the particular subtypes of diseases. Based on the mined significant genes for individual classes, a support vector machine with local kernel transform is constructed for the classification of different diseases. The combination of the gene mining approach with support vector machine is demonstrated for cancer classification using two public data sets. The results reveal that significant genes are identified for each cancer, and the classification model shows satisfactory performance in training and prediction for both data sets.     

Searching for specificity in SH domains

Elucidating protein-protein interactions has been a central feature to understanding intracellular signal transduction. Many of the binding sites of the interacting proteins in these pathways are within highly sequentially homologous and structurally conserved domains. We challenge the dogma that mutual exclusivity in signalling is derived from a high level of specificity in these domains.     

Transcriptome Profiling of HCT-116 Colorectal Cancer Cells with RNA Sequencing Reveals Novel Targets for Polyphenol Nano Curcumin

Colorectal cancer is one of the leading causes of cancer-related deaths worldwide. The gemini nanoparticle formulation of polyphenolic curcumin significantly inhibits the viability of cancer cells. However, the molecular mechanisms and pathways underlying its toxicity in colon cancer are unclear. Here, we aimed to uncover the possible novel targets of gemini curcumin (Gemini-Cur) on colorectal cancer and related cellular pathways. After confirming the cytotoxic effect of Gemini-Cur by MTT and apoptotic assays, RNA sequencing was employed to identify differentially expressed genes (DEGs) in HCT-116 cells. On a total of 3892 DEGs (padj < 0.01), 442 genes showed a log2 FC >|2| (including 244 upregulated and 198 downregulated). Gene ontology (GO) enrichment analysis was performed. Protein–protein interaction (PPI) and gene-pathway networks were constructed by using STRING and Cytoscape. The pathway analysis showed that Gemini-Cur predominantly modulates pathways related to the cell cycle. The gene network analysis revealed five central genes, namely GADD45G, ATF3, BUB1B, CCNA2 and CDK1. Real-time PCR and Western blotting analysis confirmed the significant modulation of these genes in Gemini-Cur-treated compared to non-treated cells. In conclusion, RNA sequencing revealed novel potential targets of curcumin on cancer cells. Further studies are required to elucidate the molecular mechanism of action of Gemini-Cur regarding the modulation of the expression of hub genes.     

Gene selection from microarray data for cancer classification--a machine learning approach

A DNA microarray can track the expression levels of thousands of genes simultaneously. Previous research has demonstrated that this technology can be useful in the classification of cancers. Cancer microarray data normally contains a small number of samples which have a large number of gene expression levels as features. To select relevant genes involved in different types of cancer remains a challenge. In order to extract useful gene information from cancer microarray data and reduce dimensionality, feature selection algorithms were systematically investigated in this study. Using a correlation-based feature selector combined with machine learning algorithms such as decision trees, nave Bayes and support vector machines, we show that classification performance at least as good as published results can be obtained on acute leukemia and diffuse large B-cell lymphoma microarray data sets. We also demonstrate that a combined use of different classification and feature selection approaches makes it possible to select relevant genes with high confidence. This is also the first paper which discusses both computational and biological evidence for the involvement of zyxin in leukaemogenesis.     

Methylation signature genes identification of cancers occurrence and pattern recognition

In order to identify the signature genes of tumorigenesis, the pattern-recognition method was used to analyze the gene methylation (ME) data which included only normal and cancer samples and was collected from the TCGA (The Cancer Genome Atlas) database. Here, we analyzed the DNA methylation profiles of the six types of cancer and the ME signature genes for each cancer were selected by means of a combination of correlation, student's t-test and Elastic Net. Modeling by support vector machine, the accuracy of ME signature genes can be as high as 98 % for training set and as high as 97 % for the independent test set, the recognition accuracy of stage I is more than 97 % for training set and more than 98 % for test set. Then, the common signature genes and common pathways emerging in multiple cancers were obtained. A functional analysis of these signature genes indicates that the identified signatures have direct relationship with tumorigenesis and is very important for understanding the pathogenesis of cancer and the early therapy.     

Identifying the target mRNAs of microRNAs in colorectal cancer

MicroRNAs (miRNAs) play an important role in gene regulatory networks by inhibiting the expression of target mRNAs. There is a growing interest in identifying the relationship between miRNAs and their target mRNAs. Various experimental studies have been carried out to discover miRNAs involved in cancer and to identify their target genes. At the same time, a large volume of miRNA and mRNA expression profiles have become available owing to the development of high-throughput measurement technologies. So, there is now a pressing need to develop a computational method by which we can identify the target mRNAs of given miRNAs from such massive expression data sets. In this respect, we propose an effective linear model based identification method to unravel the relationship between miRNAs and their target mRNAs in colorectal cancer by using microarray expression profiles and sequence data.     

Gene network and canonical pathway analysis in prostate cancer: a microarray study

The molecular mechanism playing a role in the development of prostate cancer (PCA) is not well defined. We decided to determine the changes in gene expression in PCA tissues and to compare them to those in non-cancerous samples. Prostate tissue samples were collected by needle biopsy from 21 PCA and 10 benign prostate hyperplasic (BPH) patients. Total RNA was isolated, cDNA was synthesized, and gene expression levels were determined by microarray method. In the progression to PCA, 738 up-regulated and 515 down-regulated genes were detected in samples. Analysis using Ingenuity Pathway Analysis (IPA) software revealed that 466 network and 423 functions-pathways eligible genes were up-regulated, and 363 network and 342 functions-pathways eligible genes were down-regulated. Up-regulated networks were identified around IL-1beta and insulin-like growth factor-1 (IGF-1) genes. The NFKB gene was centered around two up- and down-regulated networks. Up-regulated canonical pathways were assigned and four of them were evaluated in detail: acute phase response, hepatic fibrosis, actin cytoskeleton, and coagulation pathways. Axonal guidance signaling was the most significant down-regulated canonical pathway. Our data provide not only networks between the genes for understanding the biologic properties of PCA but also useful pathway maps for future understanding of disease and the construction of new therapeutic targets.     

Uncovering the anticancer mechanism of petroleum extracts of Farfarae Flos against Lewis lung cancer by metabolomics and network pharmacology analysis

Lung cancer shows the highest incidence rate in the world. Thus, it has become increasingly important to find therapeutic drugs to treat lung cancer. Farfarae Flos (FF) has been used in traditional Chinese medicine to treat pulmonary diseases such as cough, bronchitis and asthmatic disorders. In this study, the anti-proliferation effects of petroleum extracts of FF (PEFF) on Lewis lung cancer cells and the corresponding mechanisms were studied using cell metabolomics. Fifteen differential metabolites in the cell extracts and the corresponding medium related to the anti-proliferation effect of PEFF were identified, which were probably involved in pyruvate metabolism and glycine, serine and threonine metabolism. For the cellular uptake compounds in PEFF, six metabolites derived from two prototype compounds were also tentatively identified by UHPLC-Q-Orbitrap high-resolution MS. Network pharmacology analysis demonstrated that the anti-proliferation mechanism of PEFF was also probably related to the target genes, including, Aurora-A, glutathione S-transferase Mu 1 (GSTM1), glutathione S-transferase P 1 (GSTP1), progesterone receptor and heme oxygenase-1 (HO-1), and further associated with the proteoglycans and PI3K/Akt signaling pathway. Cell metabolomics and network pharmacology analysis provided a holistic method to investigate the anti-proliferation mechanisms of PEFF. However, further studies were still needed to validate the potential target genes, pathways and active compounds in PEFF.     

NoisyGOA: Noisy GO annotations prediction using taxonomic and semantic similarity

Gene Ontology (GO) provides GO annotations (GOA) that associate gene products with GO terms that summarize their cellular, molecular and functional aspects in the context of biological pathways. GO Consortium (GOC) resorts to various quality assurances to ensure the correctness of annotations. Due to resources limitations, only a small portion of annotations are manually added/checked by GO curators, and a large portion of available annotations are computationally inferred. While computationally inferred annotations provide greater coverage of known genes, they may also introduce annotation errors (noise) that could mislead the interpretation of the gene functions and their roles in cellular and biological processes. In this paper, we investigate how to identify noisy annotations, a rarely addressed problem, and propose a novel approach called NoisyGOA. NoisyGOA first measures taxonomic similarity between ontological terms using the GO hierarchy and semantic similarity between genes. Next, it leverages the taxonomic similarity and semantic similarity to predict noisy annotations. We compare NoisyGOA with other alternative methods on identifying noisy annotations under different simulated cases of noisy annotations, and on archived GO annotations. NoisyGOA achieved higher accuracy than other alternative methods in comparison. These results demonstrated both taxonomic similarity and semantic similarity contribute to the identification of noisy annotations. Our study shows that annotation errors are predictable and removing noisy annotations improves the performance of gene function prediction. This study can prompt the community to study methods for removing inaccurate annotations, a critical step for annotating gene and pathway functions.     

Screening disrupted molecular functions and pathways associated with clear cell renal cell carcinoma using Gibbs sampling

ObjectiveTo explore the disturbed molecular functions and pathways in clear cell renal cell carcinoma (ccRCC) using Gibbs sampling.MethodsGene expression data of ccRCC samples and adjacent non-tumor renal tissues were recruited from public available database. Then, molecular functions of expression changed genes in ccRCC were classed to Gene Ontology (GO) project, and these molecular functions were converted into Markov chains. Markov chain Monte Carlo (MCMC) algorithm was implemented to perform posterior inference and identify probability distributions of molecular functions in Gibbs sampling. Differentially expressed molecular functions were selected under posterior value more than 0.95, and genes with the appeared times in differentially expressed molecular functions ≥5 were defined as pivotal genes. Functional analysis was employed to explore the pathways of pivotal genes and their strongly co-regulated genes.ResultsIn this work, we obtained 396 molecular functions, and 13 of them were differentially expressed. Oxidoreductase activity showed the highest posterior value. Gene composition analysis identified 79 pivotal genes, and survival analysis indicated that these pivotal genes could be used as a strong independent predictor of poor prognosis in patients with ccRCC. Pathway analysis identified one pivotal pathway − oxidative phosphorylation.ConclusionsWe identified the differentially expressed molecular functions and pivotal pathway in ccRCC using Gibbs sampling. The results could be considered as potential signatures for early detection and therapy of ccRCC.