HLungDB: an integrated database of human lung cancer research

The diagnostic and prognostic value of pseudogene SIGLEC17P in lung adenocarcinoma and a preliminary functional study

Among malignant tumors, lung adenocarcinoma (LUAD) is the leading cause of death worldwide. This study explored the diagnostic, prognostic value, and preliminary functional verification of sialic acid binding Ig like lectin 17, pseudogene (SIGLEC17P) in LUAD. Prognostic lncRNAs for LUAD were identified by The Cancer Genome Atlas and quantitative real-time PCR (qRT-PCR) was used to detect the expression of SIGLEC17P in LUAD and paracarcinoma tissues. Subsequently, lentiviral vectors were used to overexpress SIGLEC17P in A549 and H1299 cells. The effects of SIGLEC17P overexpression on the proliferation, migration, and invasiveness of LUAD cells (A549 and H1299) were evaluated by Cell Counting Kit-8, wound healing, and transwell migration assays, respectively. Bioinformatics analyses were performed to reveal the potential pathways in which SIGLEC17P is involved in LUAD. qRT-PCR results revealed low SIGLEC17P expression in LUAD tissues and a significant association with the N stage, T stage, and tumor node metastasis stage. Furthermore, the receiver operating characteristic curve demonstrated a reliable diagnostic value. The proliferation, migration, and invasion of LUAD cells were inhibited by overexpression of SIGLEC17P. Bioinformatics analyses suggested that SIGLEC17P might exert antioncogenic effects in LUAD through the mir-20-3p/ADH1B or mir-4476-5p/DPYSL axis. In summary, our results revealed that SIGLEC17P acts as a prognostic biomarker, independent prognostic factor, and potential therapeutic target for patients with LUAD.

Silencing lung cancer genes using miRNAs identified by 7mer-seed matching

Lung cancer (LC) is the main cause of cancer-associated deaths in both men and women globally with a very high mortality rate. The microRNAs (miRNAs) are a class of noncoding RNAs consisting of 18-25 nucleotides. They inhibit translation of protein through binding to complementary target mRNAs. The non-coding miRNAs are recognized as potent biomarkers for detection, development and treatment of malignancy. In this study, we screened a set of 12 genes over expressed in small cell lung cancer, non small cell lung cancer and the genes involved in both categories and their binding sites for human miRNAs as no work was reported yet. Screening of human miRNAs revealed that a few genes showed numerous miRNA binding sites. Free energy values of mRNA sequences revealed that they might acquire compact folded structure causing complexity for miRNAs to interact. GC content in the target site was relatively higher than that of their flanks. It was observed through analysis of cosine similarity metric and compAI parameters that the genes related to lung cancer were encoded with non optimal codons and thus might be translationally less efficient for producing polypeptides. Gene ontology analysis was carried out to understand the diverse functions of these 12 genes.

Pain Chemogenomics Knowledgebase (Pain-CKB) for Systems Pharmacology Target Mapping and Physiologically Based Pharmacokinetic Modeling Investigation of Opioid Drug-Drug Interactions

More than 50 million adults in America suffer from chronic pain. Opioids are commonly prescribed for their effectiveness in relieving many types of pain. However, excessive prescribing of opioids can lead to abuse, addiction, and death. Non-steroidal anti-inflammatory drugs (NSAIDs), another major class of analgesic, also have many problematic side effects including headache, dizziness, vomiting, diarrhea, nausea, constipation, reduced appetite, and drowsiness. There is an urgent need for the understanding of molecular mechanisms that underlie drug abuse and addiction to aid in the design of new preventive or therapeutic agents for pain management. To facilitate pain related small-molecule signaling pathway studies and the prediction of potential therapeutic target(s) for the treatment of pain, we have constructed a comprehensive platform of a pain domain-specific chemogenomics knowledgebase (Pain-CKB) with integrated data mining computing tools. Our new computing platform describes the chemical molecules, genes, proteins, and signaling pathways involved in pain regulation. Pain-CKB is implemented with a friendly user interface for the prediction of the relevant protein targets and analysis and visualization of the outputs, including HTDocking, TargetHunter, BBB predictor, and Spider Plot. Combining these with other novel tools, we performed three case studies to systematically demonstrate how further studies can be conducted based on the data generated from Pain-CKB and its algorithms and tools. First, systems pharmacology target mapping was carried out for four FDA approved analgesics in order to identify the known target and predict off-target interactions. Subsequently, the target mapping outcomes were applied to build physiologically based pharmacokinetic (PBPK) models for acetaminophen and fentanyl to explore the drug-drug interaction (DDI) between this pair of drugs. Finally, pharmaco-analytics was conducted to explore the detailed interaction pattern of acetaminophen reactive metabolite and its hepatotoxicity target, thioredoxin reductase.

A database of integrated molecular and phytochemical interactions of the foxm1 pathway for lung cancer

The FoxM1 pathway is an oncogenic signaling pathway involved in essential mechanisms including control cell-cycle progression, apoptosis and cell growth which are the common hallmarks of various cancers. Although its biological functions in the tumor development and progression are known, the mechanism by which it participates in those processes is not understood. The present work reveals images of the oncogenic FoxM1 pathway controlling the cell cycle process with alternative treatment options via phytochemical substances in the lung cancer study. The downstream significant protein modules of the FoxM1 pathway were extracted by the Molecular Complex Detection (MCODE) and the maximal clique (Mclique) algorithms. Furthermore, the effects of post-transcriptional modification by microRNA, transcription factor binding and the phytochemical compounds are observed through their interactions with the lung cancer protein modules. We provided two case studies to demonstrate the usefulness of our database. Our results suggested that the combination of various phytochemicals is effective in the treatment of lung cancer. The ultimate goal of the present work is to partly support the discovery of plant-derived compounds in combination treatment of classical chemotherapeutic agents to increase the efficacy of lung cancer method probably with minor side effects. Furthermore, a web-based system displaying results of the present work is set up for investigators posing queries at http://sit.mfu.ac.th/lcgdb/index_FoxM1.php.Communicated by Ramaswamy H. Sarma.

Minichromosome maintenance 6 complex component identified by bioinformatics analysis and experimental validation in esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC), the main subtype of esophageal cancer (EC), is a common lethal type of cancer with a high mortality rate. The aim of the present study was to select key relevant genes and identify potential mechanisms involved in the development of ESCC based on bioinformatics analysis. Minichromosome maintenance 6 complex component (MCM6) has been identified to be upregulated in multiple malignancies; however, its contributions to ESCC remain unclear. For the purposes of the present study, four datasets were downloaded from the Gene Expression Omnibus (GSE63941, GSE26886, GSE17351 and GSE77861), and the intersection of the differentially expressed genes was obtained using a Venn diagram. The protein‑protein interaction was then constructed, and the modules were verified by Cytoscape, in which the key genes have a high connectivity degree with other genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway were subsequently filtered out to analyze the development of ESCC. MCM6, an upregulated gene, was selected and connected with most of the other genes, for further research validation. The expression levels of MCM6 were then assessed using the Oncomine, GEPIA and UALCAN databases and validated in both ESCC tissues samples and cell lines by immunohistochemistry and RT‑qPCR. Cell counting kit‑8 (CCK‑8), flow cytometry, wound healing and Transwell assays were used to determine the proliferation, apoptosis, cell cycle, migration and invasion of ESCC cells. A total of 24 genes were identified by a series of bioinformatics analyses and the results revealed that the genes were associated with DNA replication and cell cycle. Experimental validation revealed that MCM6 expression was significantly elevated in both ESCC tissues and cell lines. The results were consistent with those of bioinformatics analysis. Furthermore, the knockdown of MCM6 inhibited cell proliferation, migration and invasion and promoted cell apoptosis, and made cells arrested in S stage. In summary, the findings of bioinformatics analysis provided a novel hypothesis for ESCC progression. In particular, the aberrantly elevated expression of MCM6 is a potential biomarker for ESCC diagnosis and treatment.

Global Genetics Research in Prostate Cancer: A Text Mining and Computational Network Theory Approach

Prostate cancer is the most common cancer type in men in Finland and second worldwide. In this paper, we analyze almost 150, 000 published papers about prostate cancer, authored by ten thousands of scientists worldwide, with an integrated text mining and computational network theory approach. We demonstrate how to integrate text mining with network analysis investigating research contributions of countries and collaborations within and between countries. Furthermore, we study the time evolution of individually and collectively studied genes. Finally, we investigate a collaboration network of Finland and compare studied genes with globally studied genes in prostate cancer genetics. Overall, our results provide a global overview of prostate cancer research in genetics. In addition, we present a specific discussion for Finland. Our results shed light on trends within the last 30 years and are useful for translational researchers within the full range from genetics to public health management and health policy.

HColonDB: A Database for Human Colon Cancer Research

HColonDB (Human Colon cancer Database) is an important database which integrates genes, pathways, networks, drugs, and other information related to colon cancer. The purpose of the database is to provide a platform for the systematic research of colon cancer. The relationships between genes and pathways, genes and networks, and networks and pathways are obtained from the database KEGG. Furthermore, the information of the drugs used to treat colon cancer is available in HColonDB, which is collected and organized from DrugBank and PubChem database. In brief, we have summarized 81 genes, 112 pathways, 108 networks, and 15 drugs associated with colon cancer. The current version of HColonDB contains 322 associations between genes and pathways, 242 associations between genes and networks, and 68 associations between networks and pathways. In addition, HColonDB provides a friendly interface for users to browse and search. We hope that the database can make it more convenient for researchers to get the data they need and help in the treatment of colon cancer.

ESCC ATLAS: A population wide compendium of biomarkers for Esophageal Squamous Cell Carcinoma

Esophageal cancer (EC) is the eighth most aggressive malignancy and its treatment remains a challenge due to the lack of biomarkers that can facilitate early detection. EC is identified in two major histological forms namely - Adenocarcinoma (EAC) and Squamous cell carcinoma (ESCC), each showing differences in the incidence among populations that are geographically separated. Hence the detection of potential drug target and biomarkers demands a population-centric understanding of the molecular and cellular mechanisms of EC. To provide an adequate impetus to the biomarker discovery for ESCC, which is the most prevalent esophageal cancer worldwide, here we have developed ESCC ATLAS, a manually curated database that integrates genetic, epigenetic, transcriptomic, and proteomic ESCC-related genes from the published literature. It consists of 3475 genes associated to molecular signatures such as, altered transcription (2600), altered translation (560), contain copy number variation/structural variations (233), SNPs (102), altered DNA methylation (82), Histone modifications (16) and miRNA based regulation (261). We provide a user-friendly web interface ( http://www.esccatlas.org , freely accessible for academic, non-profit users) that facilitates the exploration and the analysis of genes among different populations. We anticipate it to be a valuable resource for the population specific investigation and biomarker discovery for ESCC.

ESCC ATLAS: A population wide compendium of biomarkers for Esophageal Squamous Cell Carcinoma

Esophageal cancer (EC) is the eighth most aggressive malignancy and its treatment remains a challenge due to the lack of biomarkers that can facilitate early detection. EC is identified in two major histological forms namely - Adenocarcinoma (EAC) and Squamous cell carcinoma (ESCC), each showing differences in the incidence among populations that are geographically separated. Hence the detection of potential drug target and biomarkers demands a population-centric understanding of the molecular and cellular mechanisms of EC. To provide an adequate impetus to the biomarker discovery for ESCC, which is the most prevalent esophageal cancer worldwide, here we have developed ESCC ATLAS, a manually curated database that integrates genetic, epigenetic, transcriptomic, and proteomic ESCC-related genes from the published literature. It consists of 3475 genes associated to molecular signatures such as, altered transcription (2600), altered translation (560), contain copy number variation/structural variations (233), SNPs (102), altered DNA methylation (82), Histone modifications (16) and miRNA based regulation (261). We provide a user-friendly web interface ( http://www.esccatlas.org , freely accessible for academic, non-profit users) that facilitates the exploration and the analysis of genes among different populations. We anticipate it to be a valuable resource for the population specific investigation and biomarker discovery for ESCC.

Long and short noncoding RNAs in lung cancer precision medicine: Opportunities and challenges

The long and short noncoding RNAs have been involved in the molecular diagnosis, targeted therapy, and predicting prognosis of lung cancer. Utilizing noncoding RNAs as biomarkers and systemic RNA interference as an innovative therapeutic strategy has an immense likelihood to generate novel concepts in precision oncology. Targeting of RNA interference payloads such as small interfering RNAs, microRNA mimetic, or anti-microRNA (antagomirs) into specific cell types has achieved initial success. The clinical trials of noncoding RNA-based therapies are on the way with some positive results. Many attempts are done for developing novel noncoding RNA delivery strategies that could overcome systemic or local barriers. Furthermore, it precipitates concerted efforts to define the molecular subtypes of lung cancer, characterize the genomic landscape of lung cancer subtypes, identify novel therapeutic targets, and reveal mechanisms of sensitivity and resistance to targeted therapies. These efforts contribute a visible effect now in lung cancer precision medicine: patients receive molecular testing to determine whether their tumor harbors an actionable come resistance to the first-generation drugs are in clinical trials, and drugs targeting the immune system are showing activity in patients. This extraordinary promise is tempered by the sobering fact that even the newest treatments for metastatic disease are rarely curative and are effective only in a small fraction of all patients. Thus, ongoing and future efforts to find new vulnerabilities of lung cancers unravel the complexity of drug resistance, increase the efficacy of immunotherapies, and perform biomarker-driven clinical trials are necessary to improve the outcome of lung cancer patients.

BcCluster: A Bladder Cancer Database at the Molecular Level

BACKGROUND

Bladder Cancer (BC) has two clearly distinct phenotypes. Non-muscle invasive BC has good prognosis and is treated with tumor resection and intravesical therapy whereas muscle invasive BC has poor prognosis and requires usually systemic cisplatin based chemotherapy either prior to or after radical cystectomy. Neoadjuvant chemotherapy is not often used for patients undergoing cystectomy. High-throughput analytical omics techniques are now available that allow the identification of individual molecular signatures to characterize the invasive phenotype. However, a large amount of data produced by omics experiments is not easily accessible since it is often scattered over many publications or stored in supplementary files.

OBJECTIVE

To develop a novel open-source database, BcCluster (http://www.bccluster.org/), dedicated to the comprehensive molecular characterization of muscle invasive bladder carcinoma.

MATERIALS

A database was created containing all reported molecular features significant in invasive BC. The query interface was developed in Ruby programming language (version 1.9.3) using the web-framework Rails (version 4.1.5) (http://rubyonrails.org/).

RESULTS

BcCluster contains the data from 112 published references, providing 1,559 statistically significant features relative to BC invasion. The database also holds 435 protein-protein interaction data and 92 molecular pathways significant in BC invasion. The database can be used to retrieve binding partners and pathways for any protein of interest. We illustrate this possibility using survivin, a known BC biomarker.

CONCLUSIONS

BcCluster is an online database for retrieving molecular signatures relative to BC invasion. This application offers a comprehensive view of BC invasiveness at the molecular level and allows formulation of research hypotheses relevant to this phenotype.

MGDB: a comprehensive database of genes involved in melanoma

The Melanoma Gene Database (MGDB) is a manually curated catalog of molecular genetic data relating to genes involved in melanoma. The main purpose of this database is to establish a network of melanoma related genes and to facilitate the mechanistic study of melanoma tumorigenesis. The entries describing the relationships between melanoma and genes in the current release were manually extracted from PubMed abstracts, which contains cumulative to date 527 human melanoma genes (422 protein-coding and 105 non-coding genes). Each melanoma gene was annotated in seven different aspects (General Information, Expression, Methylation, Mutation, Interaction, Pathway and Drug). In addition, manually curated literature references have also been provided to support the inclusion of the gene in MGDB and establish its association with melanoma. MGDB has a user-friendly web interface with multiple browse and search functions. We hoped MGDB will enrich our knowledge about melanoma genetics and serve as a useful complement to the existing public resources.

Database URL:http://bioinfo.ahu.edu.cn:8080/Melanoma/index.jsp

Pachymic acid induces apoptosis via activating ROS-dependent JNK and ER stress pathways in lung cancer cells

BACKGROUND

Pachymic acid (PA), a lanostane-type triterpenoid from Poria cocos, has been reported to possess anti-emetic, anti-inflammatory, and anti-cancer properties. Nonetheless, the anti-tumor effect of PA in lung cancer cells remains unclear. Herein, we report the chemotherapeutic effects and underlying mechanisms of PA against human lung cancer.

METHODS

The anti-proliferative ability of PA on lung cancer cells was assessed by MTT, colony formation and EdU proliferation assays. Flow cytometric analysis was used to detect cell cycle changes. Apoptosis was determined by annexin V/PI double-staining and the DNA ladder formation assays. The expressions of the apoptosis-related proteins were analysed by western blot. The in vivo efficacy of PA was measured using a NCI-H23 xenograft model in nude mice.

RESULTS

PA exhibited anti-tumor effects in vitro accompanied by induction of G2/M phase arrest and apoptosis in NCI-H23 and NCI-H460 lung cancer cells. Mechanistically, our data showed that PA induced reactive oxygen species (ROS) production, resulting in the activation of both c-Jun N-terminal kinase (JNK) and endoplasmic reticulum (ER) stress apoptotic pathways in lung cancer cells. Moreover, blockage of ROS production reversed PA-induced JNK and ER stress activation. Finally, PA inhibited the growth of NCI-H23 xenograft tumors without causing any host toxicity, and inhibited cell proliferation and induction of apoptosis of tumor cells in tumor xenograft tissues.

CONCLUSIONS

In summary, our study demonstrates that PA induces apoptosis through activation of the JNK and ER stress pathways in human lung cancer cells. Our findings provide a rationale for the potential application of PA in lung cancer therapy.

Prioritization of cancer-related genomic variants by SNP association network

We have developed a general framework to construct an association network of single nucleotide polymorphisms (SNPs) (SNP association network, SAN) based on the functional interactions of genes located in the flanking regions of SNPs. SAN, which was constructed based on protein-protein interactions in the Human Protein Reference Database (HPRD), showed significantly enriched signals in both linkage disequilibrium (LD) and long-range chromatin interaction (Hi-C). We used this network to further develop two methods for predicting and prioritizing disease-associated genes from genome-wide association studies (GWASs). We found that random walk with restart (RWR) using SAN (RWR-SAN) can greatly improve the prediction of lung-cancer-associated genes by comparing RWR with the use of network in HPRD (AUC 0.81 vs 0.66). In a reanalysis of the GWAS dataset of age-related macular degeneration (AMD), SAN could identify more potential AMD-associated genes that were previously ranked lower in the GWAS study. The interactions in SAN could facilitate the study of complex diseases.

Prediction of cancer proteins by integrating protein interaction, domain frequency, and domain interaction data using machine learning algorithms

Many proteins are known to be associated with cancer diseases. It is quite often that their precise functional role in disease pathogenesis remains unclear. A strategy to gain a better understanding of the function of these proteins is to make use of a combination of different aspects of proteomics data types. In this study, we extended Aragues's method by employing the protein-protein interaction (PPI) data, domain-domain interaction (DDI) data, weighted domain frequency score (DFS), and cancer linker degree (CLD) data to predict cancer proteins. Performances were benchmarked based on three kinds of experiments as follows: (I) using individual algorithm, (II) combining algorithms, and (III) combining the same classification types of algorithms. When compared with Aragues's method, our proposed methods, that is, machine learning algorithm and voting with the majority, are significantly superior in all seven performance measures. We demonstrated the accuracy of the proposed method on two independent datasets. The best algorithm can achieve a hit ratio of 89.4% and 72.8% for lung cancer dataset and lung cancer microarray study, respectively. It is anticipated that the current research could help understand disease mechanisms and diagnosis.

Verification of the biomarker candidates for non-small-cell lung cancer using a targeted proteomics approach

Lung cancer, with its high metastatic potential and high mortality rate, is the worldwide leading cause of cancer-related deaths. High-throughput "omics"-based platforms have accelerated the discovery of biomarkers for lung cancer, and the resulting candidates are to be evaluated for their diagnostic potential as noninvasive biomarkers. The evaluation of the biomarker candidates involves the quantitative measurement of large numbers of proteins in bodily fluids using advanced mass spectrometric techniques. In this study, a robust pipeline based on targeted proteomics was developed for biomarker verification in plasma samples and applied to verifying lung cancer biomarker candidates. Highly multiplexed liquid chromatrography-selected reaction monitoring (LC-SRM) assays for 95 potential tumor markers for non-small-cell lung cancer (NSCLC) were generated to screen plasma samples obtained from 72, early to late stage, patients. A total of 17 proteins were verified as potent tumor markers detectable in plasma and, where available, verified by enzyme-linked immunosorbent assays (ELISAs). A novel plasma-based biomarker, zyxin, fulfilled the criteria for a potential early diagnostic marker for NSCLC.

Beta-Lapachone Suppresses Non-small Cell Lung Cancer Proliferation through the Regulation of Specificity Protein 1

Lung cancer is the leading cause of cancer-related death worldwide, and non-small cell lung cancer (NSCLC) is the most common pathological type with a reported frequency of about 85% of all cases. Despite recent advances in therapeutic agents and targeted therapies, the prognosis for NSCLC remains poor, and therefore it is important to identify the biological targets of this complex disease since a blockade of such targets would affect multiple downstream signaling cascades. β-Lapachone (β-Lap) is an antiproliferative agent that selectively induces apoptosis-related cell death in a variety of human cancer cells. However, the mechanisms of its action require further investigation. In this study, we show that treatment with β-lap triggers apoptosis and cell-cycle arrest in two NSCLC cell lines: H1299 and NCI-H358. The transcription factor specificity protein 1 (Sp1) was markedly inhibited by β-lap in a dose- and time-dependent manner. Furthermore, β-lap modulated the protein expression levels of the Sp1 regulatory genes, including cell-cycle regulatory proteins and antiapoptotic proteins, resulting in apoptosis. Taken together, our results indicate that β-lap may be a potential antiproliferative agent candidate by inducing apoptotic cell death in NSCLC tissue through downregulation of Sp1.

Lentiviral vector-mediated RBM5 overexpression downregulates EGFR expression in human non-small cell lung cancer cells

Background

RNA binding motif 5 (RBM5) is a tumor suppressor gene that modulates apoptosis through the regulation of alternative splicing of apoptosis-related genes. Our previous studies suggested that RBM5 expression was negatively correlated with the expression of epidermal growth factor receptor (EGFR) in non-small cell lung cancer (NSCLC) tissues. This study was aimed at determining whether RBM5 is able to regulate EGFR expression.

Methods

Both in vitro and in vivo studies were carried out to determine the effect of RBM5 on the expression of EGFR. Lentiviral vector-mediated RBM5 overexpression was employed in lung adenocarcinoma cell line A549. A549 xenograft mice were treated with recombinant RBM5 plasmid carried by attenuated Salmonella typhi Ty21a. Real-time quantitative polymerase chain reaction and Western blot were carried out to detect RBM5 and EGFR expression.

Results

Both in vivo and in vitro studies indicated that the expression of EGFR mRNA and protein was decreased significantly in the RBM5 overexpression group compared to control groups as shown by real-time PCR and Western blot analysis. We identified that RBM5 overexpression inhibited EGFR expression both in A549 cells and in A549 xenograft mice model.

Conclusions

Our study demonstrated that EGFR expression is regulated by RBM5 in lung adenocarcinomas cells either in a direct or indirect way, which might be meaningful with regards to target therapy in lung cancer.

Human cancer databases (review)

Cancer is one of the four major non‑communicable diseases (NCD), responsible for ~14.6% of all human deaths. Currently, there are >100 different known types of cancer and >500 genes involved in cancer. Ongoing research efforts have been focused on cancer etiology and therapy. As a result, there is an exponential growth of cancer‑associated data from diverse resources, such as scientific publications, genome‑wide association studies, gene expression experiments, gene‑gene or protein‑protein interaction data, enzymatic assays, epigenomics, immunomics and cytogenetics, stored in relevant repositories. These data are complex and heterogeneous, ranging from unprocessed, unstructured data in the form of raw sequences and polymorphisms to well‑annotated, structured data. Consequently, the storage, mining, retrieval and analysis of these data in an efficient and meaningful manner pose a major challenge to biomedical investigators. In the current review, we present the central, publicly accessible databases that contain data pertinent to cancer, the resources available for delivering and analyzing information from these databases, as well as databases dedicated to specific types of cancer. Examples for this wealth of cancer‑related information and bioinformatic tools have also been provided.

Growth arrest and apoptosis via caspase activation of dioscoreanone in human non-small-cell lung cancer A549 cells

Background

Dioscoreanone (DN) isolated from Dioscorea membranacea Pierre has been reported to exert potent cytotoxic effects against particular types of cancer. The present study was carried out to investigate the cytotoxicity of DN against a panel of different human lung cancer cell lines. The study further examined the underlying mechanisms of its anticancer activity in the human lung adenocarcinoma cell line A549.

Methods

Antiproliferative effects of DN were determined by SRB and CFSE assays. The effect of DN on cell cycle distribution was assessed by flow cytometric analysis. Apoptotic effects of DN were determined by sub-G₁ quantitation and Annexin V-FITC/PI flow cytometric analyses, as well as by changes in caspase-3 activity and relative levels of Bax and Bcl-2 mRNA.

Results

DN exerted antiproliferative and cytotoxic effects on all three subtypes of non-small cell lung cancer (NSCLC) cells, but not on small cell lung cancer (SCLC) cells and normal lung fibroblasts. DN slowed down the cell division and arrested the cell cycle at the G2/M phase in treated A549 cells, leading to a dose- and time- dependent increase of the sub-G1 population (apoptotic cells). Consistently, early apoptotic cells (AnnexinV ⁺/PI^-) were detected in those cells that were treated for 24 h and increased progressively over time. Moreover, the highest activity of caspase-3 in DN-treated A549 cells was detected within the first 24 h, and pretreatment with the general caspase inhibitor z-VAD-fmk completely abolished such activity and also DN-induced apoptosis in a dose-dependent manner. Additionally, DN increased the Bax/Bcl-2 ratio in treated A549 cells with time, indicating its induction of apoptosis via the mitochondrial pathway.

Conclusions

This study reveals for the first time that the anticancer activity of DN was induced through regulation of the Bcl-2 family protein-mediated mitochondrial pathway and the subsequent caspase-3 activation in A549 cancer cells, thus supporting its potential role as a natural apoptosis-inducing agent for NSCLC.

Association between DNA methylation and multidrug resistance in human glioma SHG-44 cells

The aim of the present study was to evaluate the association between DNA methylation and multidrug resistance (MDR) in glioma and identify novel effectors responsible for MDR in human gliomas. An MDR glioma cell line, SGH-44/ADM, was developed using adriamycin (ADM) impulse treatment. Cryopreservation, recovery and withdrawal were performed to evaluate the stability of SGH-44/ADM cells. The adherence rate and cellular morphology were observed by microscopy, and the cell growth curve and doubling time were determined. DNA methylation was analyzed using a methylated DNA immunoprecipitation microarray chip (MeDIP-Chip). The cell cycle, Rh123 ingestion and exudation, and SGH-44/ADM apoptosis were analyzed by flow cytometry. SGH-44/ADM cells showed little difference as compared with parental cells, except that SGH-44/ADM cells were bigger in size with a wizened nucleus. Compared to SGH-44 cells, a larger proportion of SGH-44/ADM cells remained in G1 and S phase, as measured by flow cytometry. The MDR of SGH-44/ADM was associated with the upregulation of multi-drug resistance 1, prostaglandin-endoperoxide synthase 2 (COX-2); protein kinase C α (PKCα); however, the expression of these genes was not associated with DNA methylation. In the MeDIP-Chip analysis, 74 functions were markedly enhanced, and seven significant pathways were observed. Genes including SNAP47, ARRB2, PARD6B, TGFB1, VPS4B and CBLB were identified by gene ontology analysis. The predominant molecular mechanism of MDR in SGH-44/ADM cells was identified as exocytosis and efflux. The expression of COX-2, PKCα and P-glycoprotein (Pgp) was not found to be associated with DNA methylation. Genes including SNAP47, VAMP4 and VAMP3 may serve as the downstream effectors of Pgp, COX-2 or PKCα; however, further experiments are required to verify these observations.

Identification of featured biomarkers in different types of lung cancer with DNA microarray

Lung cancer is a worldwide leading cause of cancer-related death. The aim of this study was to identify target genes and specific biomarkers for identification and treatment of different types of lung cancer with DNA microarray. Gene expression profile GSE6044 and miRNA microarray profile GSE17681 were downloaded from Gene Expression Omnibus database. The differentially expressed genes (DEGs) and miRNAs were screened with multtest package in R language. Then, functional enrichment analysis of identified DEGs was performed. Furthermore, the verified target genes based on screened miRNAs were selected from miRTarBase and miRecords databases. Then miRNA-target gene regulation network was constructed. APOE, CDC6 and ATP2B1were involved in most of the functions obtained for adenocarcinomas, small cell lung cancer and squamous cell carcinomas, respectively. The target DEGs of differentially expressed hsa-miR-29a included FGG in adenocarcinoma, RAN and COL4A1 in small cell lung cancer, GLUL in squamous cell carcinoma. The target DEGs of has-miR-7 were SNCA and SLC7A5 in adenocarcinoma and small cell lung cancer, respectively. ICAM1 and KIT were the target DEGs of hsa-miR-222 in adenocarcinoma and squamous cell carcinoma. The miRNAs and their differentially expressed target genes have the potential to be used in clinic for diagnosis and treatment of different kinds of lung cancer in the future.

AlzPlatform: an Alzheimer's disease domain-specific chemogenomics knowledgebase for polypharmacology and target identification research

Alzheimer’s disease (AD) is one of the most complicated progressive neurodegeneration diseases that involve many genes, proteins, and their complex interactions. No effective medicines or treatments are available yet to stop or reverse the progression of the disease due to its polygenic nature. To facilitate discovery of new AD drugs and better understand the AD neurosignaling pathways involved, we have constructed an Alzheimer’s disease domain-specific chemogenomics knowledgebase, AlzPlatform (www.cbligand.org/AD/) with cloud computing and sourcing functions. AlzPlatform is implemented with powerful computational algorithms, including our established TargetHunter, HTDocking, and BBB Predictor for target identification and polypharmacology analysis for AD research. The platform has assembled various AD-related chemogenomics data records, including 928 genes and 320 proteins related to AD, 194 AD drugs approved or in clinical trials, and 405 188 chemicals associated with 1 023 137 records of reported bioactivities from 38 284 corresponding bioassays and 10 050 references. Furthermore, we have demonstrated the application of the AlzPlatform in three case studies for identification of multitargets and polypharmacology analysis of FDA-approved drugs and also for screening and prediction of new AD active small chemical molecules and potential novel AD drug targets by our established TargetHunter and/or HTDocking programs. The predictions were confirmed by reported bioactivity data and our in vitro experimental validation. Overall, AlzPlatform will enrich our knowledge for AD target identification, drug discovery, and polypharmacology analyses and, also, facilitate the chemogenomics data sharing and information exchange/communications in aid of new anti-AD drug discovery and development.

Genome-wide transcriptional analysis of apoptosis-related genes and pathways regulated by H2AX in lung cancer A549 cells

Histone H2AX is a novel tumor suppressor protein and plays an important role in apoptosis of cancer cells. However, the role of H2AX in lung cancer cells is unclear. The detailed mechanism and epigenetic regulation by H2AX remain elusive in cancer cells. We showed that H2AX was involved in apoptosis of lung cancer A549 cells as in other tumor cells. Knockdown of H2AX strongly suppressed apoptosis of A549 cells. We clarified the molecular mechanisms of apoptosis regulated by H2AX based on genome-wide transcriptional analysis. Microarray data analysis demonstrated that H2AX knockdown in A549 cells affected expression of 3,461 genes, including upregulation of 1,435 and downregulation of 2,026. These differentially expressed genes were subjected to bioinformatic analysis for exploring biological processes regulated by H2AX in lung cancer cells. Gene ontology analysis showed that H2AX affected expression of many genes, through which, many important functions including response to stimuli, gene expression, and apoptosis were involved in apoptotic regulation of lung cancer cells. Pathway analysis identified the mitogen-activated protein kinase signaling pathway and apoptosis as the most important pathways targeted by H2AX. Signal transduction pathway networks analysis and chromatin immunoprecipitation assay showed that two core genes, NFKB1 and JUN, were involved in apoptosis regulated by H2AX in lung cancer cells. Taken together, these data provide compelling clues for further exploration of H2AX function in cancer cells.

Functional and pathway enrichment analysis for integrated regulatory network of high- and low-metastatic lung cancer

Metastasis is a common feature of lung cancer, involving relationships between genes, proteins and miRNAs. However, lack of early detection and limited options for targeted therapies are weaknesses that cantribute to the dismal statistics observed in lung cancer metastasis. In this paper, gene expression profiling analysis for genes differentially expressed between high- (95D) and low-metastatic lung cancer cell lines (95C) was performed using gene annotation, pathway analysis, literature mining, and the integrated regulatory network as well as motif analysis of miRNA-DEG and TF-DEG. In addition, the expression of EGR-1 (early growth reponse-1) in surgically resected lung squamous carcinomas, adenocarcinomas and normal lung tissue was detected by immunohistochemistry to reveal the relationships between EGR-1 and lung cancer metastasis. A total of 570 different expressed genes (DEGs) were screened, the vast majority of up-regulated DEGs were connected to cell adhesion and focal adhesion. EGR-1 was observed in the center node of the regulatory network, which seems to play a role in the process of cancer metastasis, and further immunohistochemistry detection confirmed this reasoning. Besides EGR-1, several significant module-related DEGs were enriched in the pathway within cancer and focal adhesion according to KEGG pathway enrichment analysis of network modules. The construction of an integrated regulatory network and the functional prediction of EGR-1 provided us with the cytological basis of lung cancer metastasis research and an understanding of the mechanism of metastasis in lung cancer. EGR-1 should be considered as a potential target gene in therapeutic agent for lung cancer metastasis.

Genome-wide identification of cancer-related polyadenylated and non-polyadenylated RNAs in human breast and lung cell lines

Eukaryotic mRNAs consist of two forms of transcripts: poly(A)+ and poly(A)-, based on the presence or absence of poly(A) tails at the 3' end. Poly(A)+ mRNAs are mainly protein coding mRNAs, whereas the functions of poly(A)- mRNA are largely unknown. Previous studies have shown that a significant proportion of gene transcripts are poly(A)- or bimorphic (containing both poly(A)+ and poly(A)- transcripts). We compared the expression levels of poly(A)- and poly(A)+ RNA mRNAs in normal and cancer cell lines. We also investigated the potential functions of these RNA transcripts using an integrative workflow to explore poly(A)+ and poly(A)- transcriptome sequences between a normal human mammary gland cell line (HMEC) and a breast cancer cell line (MCF-7), as well as between a normal human lung cell line (NHLF) and a lung cancer cell line (A549). The data showed that normal and cancer cell lines differentially express these two forms of mRNA. Gene ontology (GO) annotation analyses hinted at the functions of these two groups of transcripts and grouped the differentially expressed genes according to the form of their transcript. The data showed that cell cycle-, apoptosis-, and cell death-related functions corresponded to most of the differentially expressed genes in these two forms of transcripts, which were also associated with the cancers. Furthermore, translational elongation and translation functions were also found for the poly(A)- protein-coding genes in cancer cell lines. We demonstrate that poly(A)- transcripts play an important role in cancer development.

Inferring pathway crosstalk networks using gene set co-expression signatures

Constructing molecular interaction networks in cells is important for understanding the underlying mechanisms of biological processes. Except for single gene analysis, several gene set-based methods have been proposed to infer pathway crosstalk by analyzing large-scale gene expression data. But most of them take all pathway genes as a whole to infer the crosstalk. Biological evidence suggests that the pathway crosstalk usually occurs between some subsets rather than the whole sets of pathway genes. In this study, we propose a novel method, sGSCA (signature-based gene set co-expression analysis) which can use the co-expression correlations between subsets of pathway genes to infer the pathway crosstalk networks. The method applies sparse canonical correlation analysis (sCCA) to measure the pathway level co-expression and simultaneously obtain the subsets or signature genes that contribute to the co-expression of pathways. On simulated datasets, sGSCA can efficiently detect pathway crosstalk and the corresponding highly correlated signature genes. We applied sGSCA to two cancer gene expression datasets (one for hepatocellular cancer and the other for lung cancer). In the inferred networks, we found several important pathway crosstalks related to the cancers. The identified signature genes also show high enrichment for the cancer related genes. sGSCA can infer pathway crosstalk networks using large-scale gene expression data, and should be a useful tool for systematically studying the molecular mechanisms of complex diseases on both pathway and gene levels at the same time.

ADH IB expression, but not ADH III, is decreased in human lung cancer

Endogenous S-nitrosothiols, including S-nitrosoglutathione (GSNO), mediate nitric oxide (NO)-based signaling, inflammatory responses, and smooth muscle function. Reduced GSNO levels have been implicated in several respiratory diseases, and inhibition of GSNO reductase, (GSNOR) the primary enzyme that metabolizes GSNO, represents a novel approach to treating inflammatory lung diseases. Recently, an association between decreased GSNOR expression and human lung cancer risk was proposed in part based on immunohistochemical staining using a polyclonal GSNOR antibody. GSNOR is an isozyme of the alcohol dehydrogenase (ADH) family, and we demonstrate that the antibody used in those studies cross reacts substantially with other ADH proteins and may not be an appropriate reagent. We evaluated human lung cancer tissue arrays using monoclonal antibodies highly specific for human GSNOR with minimal cross reactivity to other ADH proteins. We verified the presence of GSNOR in ≥85% of specimens examined, and extensive analysis of these samples demonstrated no difference in GSNOR protein expression between cancerous and normal lung tissues. Additionally, GSNOR and other ADH mRNA levels were evaluated quantitatively in lung cancer cDNA arrays by qPCR. Consistent with our immunohistochemical findings, GSNOR mRNA levels were not changed in lung cancer tissues, however the expression levels of other ADH genes were decreased. ADH IB mRNA levels were reduced (>10-fold) in 65% of the lung cancer cDNA specimens. We conclude that the previously reported results showed an incorrect association of GSNOR and human lung cancer risk, and a decrease in ADH IB, rather than GSNOR, correlates with human lung cancer.

Dynamic network of transcription and pathway crosstalk to reveal molecular mechanism of MGd-treated human lung cancer cells

Recent research has revealed various molecular markers in lung cancer. However, the organizational principles underlying their genetic regulatory networks still await investigation. Here we performed Network Component Analysis (NCA) and Pathway Crosstalk Analysis (PCA) to construct a regulatory network in human lung cancer (A549) cells which were treated with 50 uM motexafin gadolinium (MGd), a metal cation-containing chemotherapeutic drug for 4, 12, and 24 hours. We identified a set of key TFs, known target genes for these TFs, and signaling pathways involved in regulatory networks. Our work showed that putative interactions between these TFs (such as ESR1/Sp1, E2F1/Sp1, c-MYC-ESR, Smad3/c-Myc, and NFKB1/RELA), between TFs and their target genes (such as BMP41/Est1, TSC2/Myc, APE1/Sp1/p53, RARA/HOXA1, and SP1/USF2), and between signaling pathways (such as PPAR signaling pathway and Adipocytokines signaling pathway). These results will provide insights into the regulatory mechanism of MGd-treated human lung cancer cells.

RCDB: Renal Cancer Gene Database

BACKGROUND

Renal cell carcinoma or RCC is one of the common and most lethal urological cancers, with 40% of the patients succumbing to death because of metastatic progression of the disease. Treatment of metastatic RCC remains highly challenging because of its resistance to chemotherapy as well as radiotherapy, besides surgical resection. Whereas RCC comprises tumors with differing histological types, clear cell RCC remains the most common. A major problem in the clinical management of patients presenting with localized ccRCC is the inability to determine tumor aggressiveness and accurately predict the risk of metastasis following surgery. As a measure to improve the diagnosis and prognosis of RCC, researchers have identified several molecular markers through a number of techniques. However the wealth of information available is scattered in literature and not easily amenable to data-mining. To reduce this gap, this work describes a comprehensive repository called Renal Cancer Gene Database, as an integrated gateway to study renal cancer related data.

FINDINGS

Renal Cancer Gene Database is a manually curated compendium of 240 protein-coding and 269 miRNA genes contributing to the etiology and pathogenesis of various forms of renal cell carcinomas. The protein coding genes have been classified according to the kind of gene alteration observed in RCC. RCDB also includes the miRNAsdysregulated in RCC, along with the corresponding information regarding the type of RCC and/or metastatic or prognostic significance. While some of the miRNA genes showed an association with other types of cancers few were unique to RCC. Users can query the database using keywords, category and chromosomal location of the genes. The knowledgebase can be freely accessed via a user-friendly web interface at http://www.juit.ac.in/attachments/jsr/rcdb/homenew.html.

CONCLUSIONS

It is hoped that this database would serve as a useful complement to the existing public resources and as a good starting point for researchers and physicians interested in RCC genetics.

Differential expression of RBM5, EGFR and KRAS mRNA and protein in non-small cell lung cancer tissues

Background

RNA binding motif 5 (RBM5) is a tumor suppressor gene that modulates apoptosis through the regulation of alternative splicing of apoptosis-related genes. This study aimed to detect RBM5 expression in non-small cell lung cancer (NSCLC) and to associate RBM5 expression with clinicopathological data from NSCLC patients and EGFR and KRAS expression to better understand the potential role of RBM5 in NSCLC.

Method

Semi-quantitative reverse transcription-polymerase chain reaction (RT-PCR) and Western blotting were performed to detect expression of mRNA and protein, respectively, of RBM5, EGFR and KRAS in 120 paired non-tumor and tumor samples of NSCLC.

Results

The data showed that expression of RBM5 mRNA and protein was significantly reduced in NSCLC compared to normal tissues, whereas expression of both EGFR and KRAS genes was increased in NSCLC compared to normal tissues. Furthermore, the reduced RBM5 protein expression correlated with smoking status, tumor stage and lymph node metastasis of NSCLC, while overexpression of EGFR and KRAS proteins correlated with tumor stage and lymph node metastasis of NSCLC. Overexpression of KRAS protein was more frequent in smokers with NSCLC. In addition, expression of RBM5 mRNA and protein was negatively correlated with expression of EGFR and KRAS mRNA and protein in NSCLC tissues.

Conclusion

This study suggests further evaluation of RBM5 expression is warranted for use of RBM5 as a biomarker for NSCLC patients.

IGDB.NSCLC: integrated genomic database of non-small cell lung cancer

Lung cancer is the most common cause of cancer-related mortality with more than 1.4 million deaths per year worldwide. To search for significant somatic alterations in lung cancer, we analyzed, integrated and manually curated various data sets and literatures to present an integrated genomic database of non-small cell lung cancer (IGDB.NSCLC, http://igdb.nsclc.ibms.sinica.edu.tw). We collected data sets derived from hundreds of human NSCLC (lung adenocarcinomas and/or squamous cell carcinomas) to illustrate genomic alterations [chromosomal regions with copy number alterations (CNAs), gain/loss and loss of heterozygosity], aberrant expressed genes and microRNAs, somatic mutations and experimental evidence and clinical information of alterations retrieved from literatures. IGDB.NSCLC provides user friendly interfaces and searching functions to display multiple layers of evidence especially emphasizing on concordant alterations of CNAs with co-localized altered gene expression, aberrant microRNAs expression, somatic mutations or genes with associated clinicopathological features. These significant concordant alterations in NSCLC are graphically or tabularly presented to facilitate and prioritize as the putative cancer targets for pathological and mechanistic studies of lung tumorigenesis and for developing new strategies in clinical interventions.

Human protein reference database and human proteinpedia as discovery resources for molecular biotechnology

In the recent years, research in molecular biotechnology has transformed from being small scale studies targeted at a single or a small set of molecule(s) into a combination of high throughput discovery platforms and extensive validations. Such a discovery platform provided an unbiased approach which resulted in the identification of several novel genetic and protein biomarkers. High throughput nature of these investigations coupled with higher sensitivity and specificity of Next Generation technologies provided qualitatively and quantitatively richer biological data. These developments have also revolutionized biological research and speed of data generation. However, it is becoming difficult for individual investigators to directly benefit from this data because they are not easily accessible. Data resources became necessary to assimilate, store and disseminate information that could allow future discoveries. We have developed two resources--Human Protein Reference Database (HPRD) and Human Proteinpedia, which integrate knowledge relevant to human proteins. A number of protein features including protein-protein interactions, post-translational modifications, subcellular localization, and tissue expression, which have been studied using different strategies were incorporated in these databases. Human Proteinpedia also provides a portal for community participation to annotate and share proteomic data and uses HPRD as the scaffold for data processing. Proteomic investigators can even share unpublished data in Human Proteinpedia, which provides a meaningful platform for data sharing. As proteomic information reflects a direct view of cellular systems, proteomics is expected to complement other areas of biology such as genomics, transcriptomics, molecular biology, cloning, and classical genetics in understanding the relationships among multiple facets of biological systems.

Lung Cancer: Are we up to the Challenge?

Lung cancer is the leading cause of cancer deaths worldwide among both men and women, with more than 1 million deaths annually. Non–small cell lung cancer (NSCLC) accounts for about 80% of all lung cancers.

Although recent advances have been made in diagnosis and treatment strategies, the prognosis of NSCLC patients is poor and it is basically due to a lack of early diagnostic tools.

However, in the last years genetic and biochemical studies have provided more information about the protein and gene’s mutations involved in lung tumors. Additionally, recent proteomic and microRNA’s approaches have been introduced to help biomarker discovery.

Here we would like to discuss the most recent discoveries in lung cancer pathways, focusing on the genetic and epigenetic factors that play a crucial role in malignant cell proliferation, and how they could be helpful in diagnosis and targeted therapy.

CCDB: a curated database of genes involved in cervix cancer

The Cervical Cancer gene DataBase (CCDB, http://crdd.osdd.net/raghava/ccdb) is a manually curated catalog of experimentally validated genes that are thought, or are known to be involved in the different stages of cervical carcinogenesis. In spite of the large women population that is presently affected from this malignancy still at present, no database exists that catalogs information on genes associated with cervical cancer. Therefore, we have compiled 537 genes in CCDB that are linked with cervical cancer causation processes such as methylation, gene amplification, mutation, polymorphism and change in expression level, as evident from published literature. Each record contains details related to gene like architecture (exon–intron structure), location, function, sequences (mRNA/CDS/protein), ontology, interacting partners, homology to other eukaryotic genomes, structure and links to other public databases, thus augmenting CCDB with external data. Also, manually curated literature references have been provided to support the inclusion of the gene in the database and establish its association with cervix cancer. In addition, CCDB provides information on microRNA altered in cervical cancer as well as search facility for querying, several browse options and an online tool for sequence similarity search, thereby providing researchers with easy access to the latest information on genes involved in cervix cancer.