Cancer diagnosis marker extraction for soft tissue sarcomas based on gene expression profiling data by using projective adaptive resonance theory (PART) filtering method

Cyfip2 controls the acoustic startle threshold through FMRP, actin polymerization, and GABAB receptor function

Animals process a constant stream of sensory input, and to survive they must detect and respond to dangerous stimuli while ignoring innocuous or irrelevant ones. Behavioral responses are elicited when certain properties of a stimulus such as its intensity or size reach a critical value, and such behavioral thresholds can be a simple and effective mechanism to filter sensory information. For example, the acoustic startle response is a conserved and stereotyped defensive behavior induced by sudden loud sounds, but dysregulation of the threshold to initiate this behavior can result in startle hypersensitivity that is associated with sensory processing disorders including schizophrenia and autism. Through a previous forward genetic screen for regulators of the startle threshold a nonsense mutation in Cytoplasmic Fragile X Messenger Ribonucleoprotein (FMRP)-interacting protein 2 (cyfip2) was found that causes startle hypersensitivity in zebrafish larvae, but the molecular mechanisms by which Cyfip2 establishes the acoustic startle threshold are unknown. Here we used conditional transgenic rescue and CRISPR/Cas9 to determine that Cyfip2 acts though both Rac1 and FMRP pathways, but not the closely related FXR1 or FXR2, to establish the acoustic startle threshold during early neurodevelopment. To identify proteins and pathways that may be downstream effectors of Rac1 and FMRP, we performed a candidate-based drug screen that indicated that Cyfip2 can also act acutely to maintain the startle threshold branched actin polymerization and N-methyl D-aspartate receptors (NMDARs). To complement this approach, we used unbiased discovery proteomics to determine that loss of Cyfip2 alters cytoskeletal and extracellular matrix components while also disrupting oxidative phosphorylation and GABA receptor signaling. Finally, we functionally validated our proteomics findings by showing that activating GABAB receptors, which like NMDARs are also FMRP targets, restores normal startle sensitivity in cyfip2 mutants. Together, these data reveal multiple mechanisms by which Cyfip2 regulates excitatory/inhibitory balance in the startle circuit to control the processing of acoustic information.

Clustering of genes from microarray data using hierarchical projective adaptive resonance theory: a case study of tuberculosis

We propose the hierarchical Projective Adaptive Resonance Theory (PART) algorithm for classification of gene expression data. This algorithm is realized by combing transposed quasi-supervised PART and unsupervised PART. We develop the corresponding validation statistics for each process and compare it with other clustering algorithms in a case study of tuberculosis (TB). First, we use sample-based transposed quasi-supervised PART to obtain optimal clustering results of samples distinguished by time post-infection and the representative genes for each cluster including up-regulated, down-regulated and stable genes. The up- and down-regulated genes show more than 90% similarity to the result derived from Linear Models for Microarray Data and are verified by weighted k-nearest neighbor model on TB projection. Second, we use gene-based unsupervised PART algorithm to cluster these representative genes where functional enrichment analysis is conducted in each cluster. We further confirm the main immune response of human macrophage-like THP-1 cells against TB within 2 days is type I interferon-mediated innate immunity. This study demonstrates how hierarchical PART algorithm analyzes microarray data. The sample-based quasi-supervised PART extracts representative genes and narrows down the shortlist of disease-relevant genes and gene-based unsupervised PART classifies representative genes that help to interpret immune response against TB.

WeCoMXP: Weighted Connectivity Measure Integrating Co-Methylation, Co-Expression and Protein-Protein Interactions for Gene-Module Detection

The identification of modules (groups of several tightly interconnected genes) in gene interaction network is an essential task for better understanding of the architecture of the whole network. In this article, we develop a novel weighted connectivity measure integrating co-methylation, co-expression, and protein-protein interactions (called WeCoMXP) to detect gene-modules for multi-omics dataset. The proposed measure goes beyond the fundamental degree centrality measure through considering some formulation of higher-order connections. Thereafter, we apply the average linkage clustering method using the corresponding dissimilarity (distance) values of WeCoMXP scores, and utilize a dynamic tree cut method for identifying some gene-modules. We validate the modules through literature search, KEGG pathway, and gene-ontology analyses on the genes representing the modules. Furthermore, the top 10 TFs/miRNAs that are connected with the maximum number of gene-modules and that regulate/target the maximum number of genes from these connected gene-modules, are identified. Moreover, our proposed method provides a better performance than the existing methods in terms of several cluster-validity indices in maximum times.

High-grade sarcoma diagnosis and prognosis: Biomarker discovery by mass spectrometry imaging

The combination of high heterogeneity, both intratumoral and intertumoral, with their rarity has made diagnosis, prognosis of high-grade sarcomas difficult. There is an urgent need for more objective molecular biomarkers, to differentiate between the many different subtypes, and to also provide new treatment targets. Mass spectrometry imaging (MSI) has amply demonstrated its ability to identify potential new markers for patient diagnosis, survival, metastasis and response to therapy in cancer research. In this study, we investigated the ability of MALDI-MSI of proteins to distinguish between high-grade osteosarcoma (OS), leiomyosarcoma (LMS), myxofibrosarcoma (MFS) and undifferentiated pleomorphic sarcoma (UPS) (Ntotal = 53). We also investigated if there are individual proteins or protein signatures that are statistically associated with patient survival. Twenty diagnostic protein signals were found characteristic for specific tumors (p ≤ 0.05), amongst them acyl-CoA-binding protein (m/z 11 162), macrophage migration inhibitory factor (m/z 12 350), thioredoxin (m/z 11 608) and galectin-1 (m/z 14 633) were assigned. Another nine protein signals were found to be associated with overall survival (p ≤ 0.05), including proteasome activator complex subunit 1 (m/z 9753), indicative for non-OS patients with poor survival; and two histone H4 variants (m/z 11 314 and 11 355), indicative of poor survival for LMS patients.

Construction of possible integrated predictive index based on EGFR and ANXA3 polymorphisms for chemotherapy response in fluoropyrimidine-treated Japanese gastric cancer patients using a bioinformatic method

Background

Variability in drug response between individual patients is a serious concern in medicine. To identify single-nucleotide polymorphisms (SNPs) related to drug response variability, many genome-wide association studies have been conducted.

Methods

We previously applied a knowledge-based bioinformatic approach to a pharmacogenomics study in which 119 fluoropyrimidine-treated gastric cancer patients were genotyped at 109,365 SNPs using the Illumina Human-1 BeadChip. We identified the SNP rs2293347 in the human epidermal growth factor receptor (EGFR) gene as a novel genetic factor related to chemotherapeutic response. In the present study, we reanalyzed these hypothesis-free genomic data using extended knowledge.

Results

We identified rs2867461 in annexin A3 (ANXA3) gene as another candidate. Using logistic regression, we confirmed that the performance of the rs2867461 + rs2293347 model was superior to those of the single factor models. Furthermore, we propose a novel integrated predictive index (iEA) based on these two polymorphisms in EGFR and ANXA3. The p value for iEA was 1.47 × 10⁻⁸ by Fisher’s exact test. Recent studies showed that the mutations in EGFR is associated with high expression of dihydropyrimidine dehydrogenase, which is an inactivating and rate-limiting enzyme for fluoropyrimidine, and suggested that the combination of chemotherapy with fluoropyrimidine and EGFR-targeting agents is effective against EGFR-overexpressing gastric tumors, while ANXA3 overexpression confers resistance to tyrosine kinase inhibitors targeting the EGFR pathway.

Conclusions

These results suggest that the iEA index or a combination of polymorphisms in EGFR and ANXA3 may serve as predictive factors of drug response, and therefore could be useful for optimal selection of chemotherapy regimens.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-015-1721-z) contains supplementary material, which is available to authorized users.

Regulators of G-protein-signaling proteins: negative modulators of G-protein-coupled receptor signaling

Regulators of G-protein-signaling (RGS) proteins are a category of intracellular proteins that have an inhibitory effect on the intracellular signaling produced by G-protein-coupled receptors (GPCRs). RGS along with RGS-like proteins switch on through direct contact G-alpha subunits providing a variety of intracellular functions through intracellular signaling. RGS proteins have a common RGS domain that binds to G alpha. RGS proteins accelerate GTPase and thus enhance guanosine triphosphate hydrolysis through the alpha subunit of heterotrimeric G proteins. As a result, they inactivate the G protein and quickly turn off GPCR signaling thus terminating the resulting downstream signals. Activity and subcellular localization of RGS proteins can be changed through covalent molecular changes to the enzyme, differential gene splicing, and processing of the protein. Other roles of RGS proteins have shown them to not be solely committed to being inhibitors but behave more as modulators and integrators of signaling. RGS proteins modulate the duration and kinetics of slow calcium oscillations and rapid phototransduction and ion signaling events. In other cases, RGS proteins integrate G proteins with signaling pathways linked to such diverse cellular responses as cell growth and differentiation, cell motility, and intracellular trafficking. Human and animal studies have revealed that RGS proteins play a vital role in physiology and can be ideal targets for diseases such as those related to addiction where receptor signaling seems continuously switched on.

Analysis of gene expression profiles of soft tissue sarcoma using a combination of knowledge-based filtering with integration of multiple statistics

The diagnosis and treatment of soft tissue sarcomas (STS) have been difficult. Of the diverse histological subtypes, undifferentiated pleomorphic sarcoma (UPS) is particularly difficult to diagnose accurately, and its classification per se is still controversial. Recent advances in genomic technologies provide an excellent way to address such problems. However, it is often difficult, if not impossible, to identify definitive disease-associated genes using genome-wide analysis alone, primarily because of multiple testing problems. In the present study, we analyzed microarray data from 88 STS patients using a combination method that used knowledge-based filtering and a simulation based on the integration of multiple statistics to reduce multiple testing problems. We identified 25 genes, including hypoxia-related genes (e.g., MIF, SCD1, P4HA1, ENO1, and STAT1) and cell cycle- and DNA repair-related genes (e.g., TACC3, PRDX1, PRKDC, and H2AFY). These genes showed significant differential expression among histological subtypes, including UPS, and showed associations with overall survival. STAT1 showed a strong association with overall survival in UPS patients (logrank p = 1.84 × 10(-6) and adjusted p value 2.99 × 10(-3) after the permutation test). According to the literature, the 25 genes selected are useful not only as markers of differential diagnosis but also as prognostic/predictive markers and/or therapeutic targets for STS. Our combination method can identify genes that are potential prognostic/predictive factors and/or therapeutic targets in STS and possibly in other cancers. These disease-associated genes deserve further preclinical and clinical validation.

Application of a combination of a knowledge-based algorithm and 2-stage screening to hypothesis-free genomic data on irinotecan-treated patients for identification of a candidate single nucleotide polymorphism related to an adverse effect

Interindividual variation in a drug response among patients is known to cause serious problems in medicine. Genomic information has been proposed as the basis for “personalized” health care. The genome-wide association study (GWAS) is a powerful technique for examining single nucleotide polymorphisms (SNPs) and their relationship with drug response variation; however, when using only GWAS, it often happens that no useful SNPs are identified due to multiple testing problems. Therefore, in a previous study, we proposed a combined method consisting of a knowledge-based algorithm, 2 stages of screening, and a permutation test for identifying SNPs. In the present study, we applied this method to a pharmacogenomics study where 109,365 SNPs were genotyped using Illumina Human-1 BeadChip in 168 cancer patients treated with irinotecan chemotherapy. We identified the SNP rs9351963 in potassium voltage-gated channel subfamily KQT member 5 (KCNQ5) as a candidate factor related to incidence of irinotecan-induced diarrhea. The p value for rs9351963 was 3.31×10⁻⁵ in Fisher's exact test and 0.0289 in the permutation test (when multiple testing problems were corrected). Additionally, rs9351963 was clearly superior to the clinical parameters and the model involving rs9351963 showed sensitivity of 77.8% and specificity of 57.6% in the evaluation by means of logistic regression. Recent studies showed that KCNQ4 and KCNQ5 genes encode members of the M channel expressed in gastrointestinal smooth muscle and suggested that these genes are associated with irritable bowel syndrome and similar peristalsis diseases. These results suggest that rs9351963 in KCNQ5 is a possible predictive factor of incidence of diarrhea in cancer patients treated with irinotecan chemotherapy and for selecting chemotherapy regimens, such as irinotecan alone or a combination of irinotecan with a KCNQ5 opener. Nonetheless, clinical importance of rs9351963 should be further elucidated.

Macrophage migration inhibitory factor and stearoyl-CoA desaturase 1: potential prognostic markers for soft tissue sarcomas based on bioinformatics analyses

The diagnosis and treatment of soft tissue sarcomas (STSs) has been particularly difficult, because STSs are a group of highly heterogeneous tumors in terms of histopathology, histological grade, and primary site. Recent advances in genome technologies have provided an excellent opportunity to determine the complete biological characteristics of neoplastic tissues, resulting in improved diagnosis, treatment selection, and investigation of therapeutic targets. We had previously developed a novel bioinformatics method for marker gene selection and applied this method to gene expression data from STS patients. This previous analysis revealed that the extracted gene combination of macrophage migration inhibitory factor (MIF) and stearoyl-CoA desaturase 1 (SCD1) is an effective diagnostic marker to discriminate between subtypes of STSs with highly different outcomes. In the present study, we hypothesize that the combination of MIF and SCD1 is also a prognostic marker for the overall outcome of STSs. To prove this hypothesis, we first analyzed microarray data from 88 STS patients and their outcomes. Our results show that the survival rates for MIF- and SCD1-positive groups were lower than those for negative groups, and the p values of the log-rank test are 0.0146 and 0.00606, respectively. In addition, survival rates are more significantly different (p = 0.000116) between groups that are double-positive and double-negative for MIF and SCD1. Furthermore, in vitro cell growth inhibition experiments by MIF and SCD1 inhibitors support the hypothesis. These results suggest that the gene set is useful as a prognostic marker associated with tumor progression.

Gene cloning, expression, and localization of antigen 5 in the life cycle of Echinococcus granulosus

Antigen 5 (Ag5) has been identified as a dominant component of cyst fluid of Echinococcus granulosus and is considered as a member of serine proteases family, which in other helminth, plays an important role in the egg hatch and larva invasion. However, whether Ag5 is expressed and secreted in all life stages is unknown. In this study, according to the sequence in GenBank, we cloned and sequenced the open reading frame (ORF) of Ag5 gene from the protoscolices of E. granulosus isolated from the sheep in Qinhai Province of China, and found several substitutions and a base insert and deletion in a short region near the stop code, leading to a frameshift mutation which is conserved with the homologue of other cestode. The ORF is 1,455 bp in length, encoding 484 amino acids with a secretory signal peptide. Bioinformatics analysis predicted several phosphorylation and myristoylation sites and a N-glycosylation site and a species-specific linear B epitope in the protein. The ORF was cloned into the plasmid pET28a(+) vector and expressed in Escherichia coli . The recombinant protein was purified by affinity chromatography. Anti-rEgAg5 antiserum was prepared in rats and used to analyze the localization of Ag5 in protoscolex and adult worm by immunofluorescence technique. Results demonstrated that the Ag5 is strongly expressed in the tegument of protoscolex and the embryonic membrane of egg and surface of oncosphere; meanwhile, it is also weakly expressed in tegument of the adult. This study showed that Ag5 is expressed in all stages of life cycle, secreted from the surface of the worm and may be anchored in membrane by its myristoylation sites; these characteristics make it a candidate antigen for diagnosis and vaccine for both intermediate and definitive hosts.

Genetic variations in the regulator of G-protein signaling genes are associated with survival in late-stage non-small cell lung cancer

The regulator of G-protein signaling (RGS) pathway plays an important role in signaling transduction, cellular activities, and carcinogenesis. We hypothesized that genetic variations in RGS gene family may be associated with the response of late-stage non-small cell lung cancer (NSCLC) patients to chemotherapy or chemoradiotherapy. We selected 95 tagging single nucleotide polymorphisms (SNPs) in 17 RGS genes and genotyped them in 598 late-stage NSCLC patients. Thirteen SNPs were significantly associated with overall survival. Among them, rs2749786 of RGS12 was most significant. Stratified analysis by chemotherapy or chemoradiation further identified SNPs that were associated with overall survival in subgroups. Rs2816312 of RGS1 and rs6689169 of RGS7 were most significant in chemotherapy group and chemoradiotherapy group, respectively. A significant cumulative effect was observed when these SNPs were combined. Survival tree analyses identified potential interactions between rs944343, rs2816312, and rs1122794 in affecting survival time in patients treated with chemotherapy, while the genotype of rs6429264 affected survival in chemoradiation-treated patients. To our knowledge, this is the first study to reveal the importance of RGS gene family in the survival of late-stage NSCLC patients.

Genetic variations in regulator of G-protein signaling genes as susceptibility loci for second primary tumor/recurrence in head and neck squamous cell carcinoma

Curatively treated patients with early-stage head and neck squamous cell carcinoma (HNSCC) are at high risks for second primary tumor (SPT) and recurrence. The regulator of G-protein signaling (RGS) is important in essential signaling transduction and cellular activities. We hypothesize that genetic variations of RGS may modulate the risk of SPT/recurrence in patients with early-stage HNSCC. In a nested case-control study, we evaluated 98 single-nucleotide polymorphisms (SNPs) in 17 RGS genes for the risk of SPT/recurrence among 450 HNSCC patients. Eight SNPs showed significant associations with the risk of SPT/recurrence, with the most significant one of rs2179653, which is located in the 5'-flanking region of RGS2 gene. Under a recessive genetic model, the homozygous variant genotype of this SNP was associated with 2.95-fold [95% confidence interval (CI): 1.52-5.74] increased risk of SPT/recurrence. This association remained significant after the adjustment for multiple comparisons. Cumulative effects analysis revealed that the risk increased significantly with the increasing numbers of unfavorable genotypes. Compared with subjects carrying 0-2 unfavorable genotypes, the hazard ratios (95% CIs) for those carrying 3 or 4+ were 1.73 (1.10-2.70) and 3.05 (1.92-4.83), respectively. Furthermore, survival tree analysis revealed potential higher order gene-gene interactions and indicated different outcomes based on distinct genotype profiles. Genetic variations of RGS genes may modulate the susceptibility to SPT/recurrence in early-stage HNSCC patients individually and cumulatively. Our results stressed the importance of taking a polygenic approach to evaluate the cumulative and interaction effects of genetic variations in the prediction of cancer risk and prognosis.

Regulator of G-protein signaling (RGS) proteins in cancer biology

The regulator of G-protein signaling (RGS) family is a diverse group of multifunctional proteins that regulate cellular signaling events downstream of G-protein coupled receptors (GPCRs). In recent years, GPCRs have been linked to the initiation and progression of multiple cancers; thus, regulators of GPCR signaling are also likely to be important to the pathophysiology of cancer. This review highlights recent studies detailing changes in RGS transcript expression during oncogenesis, single nucleotide polymorphisms in RGS proteins linked to lung and bladder cancers, and specific roles for RGS proteins in multiple cancer types.

Accurate molecular characterization of formalin-fixed, paraffin-embedded tissues by microRNA expression profiling

Formalin-fixed, paraffin-embedded tissues are an invaluable tool for biomarker discovery and validation. As these archived specimens are not always compatible with modern genomic techniques such as gene expression arrays, we assessed the use of microRNA (miRNA) as an alternative means for the reliable molecular characterization of formalin-fixed, paraffin-embedded tissues. Expression profiling using two different microarray platforms and multiple mouse and human formalin-fixed, paraffin-embedded tissue types resulted in the correlation ratios of miRNA expression levels between frozen and fixed tissue pairs ranging from 0.82 to 0.99, depending on the cellular heterogeneity of the tissue type. The same miRNAs were identified as differentially expressed between tissues using both fixed and frozen specimens. While formalin fixation time had only marginal effects on microarray performance, extended storage times for tissue blocks (up to 11 years) resulted in a gradual loss of detection of miRNAs expressed at low levels. Method reproducibility and accuracy were also evaluated in two different tissues stored for different lengths of time. The technical variation between full process replicates, including independent RNA isolation methods, was approximately 5%, and the correlation of expression levels between microarray and real-time quantitative reverse transcriptase polymerase chain reaction was 0.98. Together, these data demonstrate that miRNA expression profiling is an accurate and robust method for the molecular analysis of archived clinical specimens, potentially extending the use of miRNAs as new diagnostic, prognostic, and treatment response biomarkers.

Arsenic-induced changes in the gene expression of lung epithelial L2 cells: implications in carcinogenesis

Background

Arsenic is a carcinogen that is known to induce cell transformation and tumor formation. Although studies have been performed to examine the modulation of signaling molecules caused by arsenic exposure, the molecular mechanisms by which arsenic causes cancer are still unclear. We hypothesized that arsenic alters gene expression leading to carcinogenesis in the lung.

Results

In this study, we examined global gene expression in response to 0.75 μM arsenic treatment for 1–7 days in a rat lung epithelial cell line (L2) using an in-house 10 k rat DNA microarray. One hundred thirty one genes were identified using the one-class statistical analysis of microarray (SAM) test. Of them, 33 genes had a fold change of ≥ 2 between at least two time points. These genes were then clustered into 5 groups using K-means cluster analysis based on their expression patterns. Seven selected genes, all associated with cancer, were confirmed by real-time PCR. These genes have functions directly or indirectly related to metabolism, glycolysis, cell proliferation and differentiation, and regulation of transcription.

Conclusion

Our findings provide important insight for the future studies of arsenic-mediated lung cancer.

Gene expression analysis of soft tissue sarcomas: characterization and reclassification of malignant fibrous histiocytoma

In soft tissue sarcomas, the diagnosis of malignant fibrous histiocytoma (MFH) has been a very controversial issue, and MFH is now considered to be reclassified into pleomorphic subtypes of other sarcomas. To characterize MFH genetically, we used an oligonucleotide microarray to analyze gene expression in 105 samples from 10 types of soft tissue tumors. Spindle cell and pleomorphic sarcomas, such as dedifferentiated liposarcoma, myxofibrosarcoma, leiomyosarcoma, malignant peripheral nerve sheath tumor (MPNST), fibrosarcoma and MFH, showed similar gene expression patterns compared to other tumors. Samples from those five sarcoma types could be classified into respective clusters based on gene expression by excluding MFH samples. We calculated distances between MFH samples and other five sarcoma types (dedifferentiated liposarcoma, myxofibrosarcoma, leiomyosarcoma, MPNST and fibrosarcoma) based on differentially expressed genes and evaluated similarities. Three of the 21 MFH samples showed marked similarities to one of the five sarcoma types, which were supported by histological findings. Although most of the remaining 18 MFH samples showed little or no histological resemblance to one of the five sarcoma types, 12 of them showed moderate similarities in terms of gene expression. These results explain the heterogeneity of MFH and show that the majority of MFHs could be reclassified into pleomorphic subtypes of other sarcomas. Taken together, gene expression profiling could be a useful tool to unveil the difference in the underlying molecular backgrounds, which leads to a rational taxonomy and diagnosis of a diverse group of soft tissue sarcomas.