Drug repurposing has become an increasingly attractive approach to drug development owing to the ever-growing cost of new drug discovery and frequent withdrawal of successful drugs caused by side effect issues. Here, we devised Functional Module Connectivity Map (FMCM) for the discovery of repurposed drug compounds for systems treatment of complex diseases, and applied it to colorectal adenocarcinoma. FMCM used multiple functional gene modules to query the Connectivity Map (CMap). The functional modules were built around hub genes identified, through a gene selection by trend-of-disease-progression (GSToP) procedure, from condition-specific gene-gene interaction networks constructed from sets of cohort gene expression microarrays. The candidate drug compounds were restricted to drugs exhibiting predicted minimal intracellular harmful side effects. We tested FMCM against the common practice of selecting drugs using a genomic signature represented by a single set of individual genes to query CMap (IGCM), and found FMCM to have higher robustness, accuracy, specificity, and reproducibility in identifying known anti-cancer agents. Among the 46 drug candidates selected by FMCM for colorectal adenocarcinoma treatment, 65% had literature support for association with anti-cancer activities, and 60% of the drugs predicted to have harmful effects on cancer had been reported to be associated with carcinogens/immune suppressors. Compounds were formed from the selected drug candidates where in each compound the component drugs collectively were beneficial to all the functional modules while no single component drug was harmful to any of the modules. In cell viability tests, we identified four candidate drugs: GW-8510, etacrynic acid, ginkgolide A, and 6-azathymine, as having high inhibitory activities against cancer cells. Through microarray experiments we confirmed the novel functional links predicted for three candidate drugs: phenoxybenzamine (broad effects), GW-8510 (cell cycle), and imipenem (immune system). We believe FMCM can be usefully applied to repurposed drug discovery for systems treatment of other types of cancer and other complex diseases.

The fact that many chemotherapeutic drugs cause chemoresistance and side effects during the course of colorectal cancer treatment necessitates development of novel cytotoxic agents aiming to attenuate new molecular targets. Here, we show that Astragalus membranaceus (Fischer) Bge. var. mongolicus (Bge.) Hsiao (AM), a traditional Chinese medicine, can inhibit tumor growth in vivo and elucidate the underlying molecular mechanisms. The antitumor effect of AM was assessed on the subcutaneous tumors of human colorectal cancer cell line HCT116 grafted into nude mice. The mice were treated with either water or 500 mg/kg AM once per day, before being sacrificed for extraction of tumors, which were then subjected to microarray expression profiling. The gene expression of the extraction was then profiled using microarray analysis. The identified genes differentially expressed between treated mice and controls reveal that administration of AM suppresses chromosome organization, histone modification, and regulation of macromolecule metabolic process. A separate analysis focused on differentially expressed microRNAs revealing involvement of macromolecule metabolism, and intracellular transport, as well as several cancer signaling pathways. For validation, the input of the identified genes to The Library of Integrated Network-based Cellular Signatures led to many chemopreventive agents of natural origin that produce similar gene expression profiles to that of AM. The demonstrated effectiveness of AM suggests a potential therapeutic drug for colorectal cancer.

Significantly expressed genes extracted from microarray gene expression data have proved very useful for identifying genetic biomarkers of diseases, including cancer. However, deriving a disease related inference from a list of differentially expressed genes has proven less than straightforward. In a systems disease such as cancer, how genes interact with each other should matter just as much as the level of gene expression. Here, in a novel approach, we used the network and disease progression properties of individual genes in state-specific gene-gene interaction networks (GGINs) to select cancer genes for human colorectal cancer (CRC) and obtain a much higher hit rate of known cancer genes when compared with methods not based on network theory. We constructed GGINs by integrating gene expression microarray data from multiple states – healthy control (Nor), adenoma (Ade), inflammatory bowel disease (IBD) and CRC – with protein-protein interaction database and Gene Ontology. We tracked changes in the network degrees and clustering coefficients of individual genes in the GGINs as the disease state changed from one to another. From these we inferred the state sequences Nor-Ade-CRC and Nor-IBD-CRC both exhibited a trend of (disease) progression (ToP) toward CRC, and devised a ToP procedure for selecting cancer genes for CRC. Of the 141 candidates selected using ToP, ∼50% had literature support as cancer genes, compared to hit rates of 20% to 30% for standard methods using only gene expression data. Among the 16 candidate cancer genes that encoded transcription factors, 13 were known to be tumorigenic and three were novel: CDK1, SNRPF, and ILF2. We identified 13 of the 141 predicted cancer genes as candidate markers for early detection of CRC, 11 and 2 at the Ade and IBD states, respectively.

Cancer stem cells (CSCs), or cancer cells with stem cell-like properties, generally exhibit drug resistance and have highly potent cancer inducing capabilities. Genome-wide expression data collected at public repositories over the last few years provide excellent material for studies that can lead to insights concerning the molecular and functional characteristics of CSCs. Here, we conducted functional genomic studies of CSC based on fourteen PCA-screened high quality public CSC whole genome gene expression datasets and, as control, four high quality non-stem-like cancer cell and non-cancerous stem cell datasets from the Gene Expression Omnibus database. A total of 6,002 molecular signatures were taken from the Molecular Signatures Database and used to characterize the datasets, which, under two-way hierarchical clustering, formed three genotypes. Type 1, consisting of mainly glia CSCs, had significantly enhanced proliferation, and significantly suppressed epithelial-mesenchymal transition (EMT), related functions. Type 2, mainly breast CSCs, had significantly enhanced EMT, but not proliferation, related functions. Type 3, composed of ovarian, prostate, and colon CSCs, had significantly suppressed proliferation related functions and mixed expressions on EMT related functions.

Gene-set-based analysis (GSA), which uses the relative importance of functional gene-sets, or molecular signatures, as units for analysis of genome-wide gene expression data, has exhibited major advantages with respect to greater accuracy, robustness, and biological relevance, over individual gene analysis (IGA), which uses log-ratios of individual genes for analysis. Yet IGA remains the dominant mode of analysis of gene expression data. The Connectivity Map (CMap), an extensive database on genomic profiles of effects of drugs and small molecules and widely used for studies related to repurposed drug discovery, has been mostly employed in IGA mode. Here, we constructed a GSA-based version of CMap, Gene-Set Connectivity Map (GSCMap), in which all the genomic profiles in CMap are converted, using gene-sets from the Molecular Signatures Database, to functional profiles. We showed that GSCMap essentially eliminated cell-type dependence, a weakness of CMap in IGA mode, and yielded significantly better performance on sample clustering and drug-target association. As a first application of GSCMap we constructed the platform Gene-Set Local Hierarchical Clustering (GSLHC) for discovering insights on coordinated actions of biological functions and facilitating classification of heterogeneous subtypes on drug-driven responses. GSLHC was shown to tightly clustered drugs of known similar properties. We used GSLHC to identify the therapeutic properties and putative targets of 18 compounds of previously unknown characteristics listed in CMap, eight of which suggest anti-cancer activities. The GSLHC website http://cloudr.ncu.edu.tw/gslhc/ contains 1,857 local hierarchical clusters accessible by querying 555 of the 1,309 drugs and small molecules listed in CMap. We expect GSCMap and GSLHC to be widely useful in providing new insights in the biological effect of bioactive compounds, in drug repurposing, and in function-based classification of complex diseases.

Objectives: Although high abundant cystatin c (CysC) in cerebrospinal fluid (CSF) is well known, its ambiguous role associated with pain still remains unclear. This study evaluated the effects of intrathecal CysC content from chronic pain caused by osteoarthritis (OA) and the novel relationship with matrix metalloproteinases 2 and 9 (MMP2 and MMP9) in CSF. Methods: Samples of CSF were obtained from 8 elderly patients (65 y and above) with OA with lower limb pain for at least 6 months (OA group) and 8 sex-matched and age-matched relatively healthy elderly individuals without any pain problems (control group). The intrathecal CysC, MMP2, and MMP9 were examined by Western blotting. The analysis of CysC cleavage under different conditions was performed through silver staining and using mass-spectroscopy (SELDI-TOF) on 2 groups. Results: Expression of full-length CysC and pro-MMP2 proteins showed statistically significant upregulation (P=0.0004 vs. 0.03), and expression of MMP9 protein showed downregulation (P=0.007) in the OA group. Both MMP9 and MMP2 initiated the mechanism for full-length CysC cleavage only in the presence of CSF. However, MMP9 showed greater ability than MMP2 for CysC cleavage in control and OA groups in sliver staining. Incubation of CSF with the MMP9 inhibitor led to the suppression of CysC cleavage in SELDI-TOF. Discussion: These findings provide the first in vivo evidence on a relationship between CysC and gelatinases (MMP2 and MMP9), and could facilitate further investigation of novel interactions among these proteins within the proteomics field, especially protein-protein interactions involved in pain.