A robust tool for discriminative analysis and feature selection in paired samples impacts the identification of the genes essential for reprogramming lung tissue to adenocarcinoma

GRANT Motif Regulates CENP-A Incorporation and Restricts RNA Polymerase II Accessibility at Centromere

Precise chromosome segregation is essential for maintaining genomic stability, and its proper execution centers on the centromere, a chromosomal locus that mounts the kinetochore complex to mediate attachment of chromosomes to the spindle microtubules. The location of the centromere is epigenetically determined by a centromere-specific histone H3 variant, CENP-A. Many human cancers exhibit overexpression of CENP-A, which correlates with occurrence of aneuploidy in these malignancies. Centromeric targeting of CENP-A depends on its histone fold, but recent studies showed that the N-terminal tail domain (NTD) also plays essential roles. Here, we investigated implications of NTD in conferring aneuploidy formation when CENP-A is overexpressed in fission yeast. A series of mutant genes progressively lacking one amino acid of the NTD have been constructed for overexpression in wild-type cells using the intermediate strength nmt41 promoter. Constructs hosting disrupted GRANT (Genomic stability-Regulating site within CENP-A N-Terminus) motif in NTD results in growth retardation, aneuploidy, increased localization to the centromere, upregulated RNA polymerase II accessibility and transcriptional derepression of the repressive centromeric chromatin, suggesting that GRANT residues fine-tune centromeric CENP-A incorporation and restrict RNA polymerase II accessibility. This work highlighted the importance of CENP-A NTD, particularly the GRANT motif, in aneuploidy formation of overexpressed CENP-A in fission yeast.

Clomiphene citrate improved testosterone and sperm concentration in hypogonadal males

When considering empirical medical management (EMT) options for men with unexplained infertility (UI), clomiphene citrate (CC) has been shown to positively influence sperm parameters in hypogonadal men. Unfortunately, the optimal cut point for defining hypogonadism for this patient population has not been established. We hypothesized that hypogonadal men with UI having the lowest serum total testosterone (TT) (<265 ng/dL) would have a significant post-CC improvement in both TT and semen characteristics compared to those in the TT > 264 ng/dL group. We performed our study based on an IRB-approved retrospective chart review of 83 males with UI receiving more than 90 days of 50 mg daily CC. Serum TT and semen characteristics were studied in 83 patients before and in 23 patients after CC treatment. Median TT level increased from 256 ng/dL to 630 ng/dL (p < 0.001, n = 83) and SC increased from 6 ( 10 6 /ml) to 20 ( 10 6 /ml) (p < 0.016, n = 23). Overall, our results demonstrated the following: (1) CC treatment at all currently used serum TT cut-points resulted in significant improvement in both TT (p < 0.001) and sperm concentration (p = 0.03). No significant change in post-CC sperm motility or morphology was noted. (2) Correlation and linear regression analyses demonstrated that CC treatment significantly increased TT in 96% (22 of 23) of patients, and (3) when grouped as two cohorts (≤264 and >264 ng/dL), sperm concentration and TT improved 2.3 to 2.6-fold (p < 0.001) and 1.45 to 2.5-fold (p < 0.01) respectively. Thus, for hypogonadal men with UI, CC significantly improved TT and sperm concentration regardless of pre-treatment, baseline serum TT level. For this reason, CC treatment should be considered in men with UI having a TT < 400 ng/dL.

Hub genes and key pathways of non-small lung cancer identified using bioinformatics

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, accounting for ~80% of all lung cancer cases. The aim of the present study was to identify key genes and pathways in NSCLC, in order to improve understanding of the mechanism of lung cancer. The GSE33532 gene expression dataset, containing 20 normal and 80 NSCLC samples, was used. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to obtain the enrichment data of differently expressed genes (DEGs). Disease modules within NSCLC were constructed by Cytoscape, using protein-protein interaction (PPI) from the Search Tool for the Retrieval of Interacting Genes database. In addition, the Kaplan Meier plotter KMplot was used to assess the top hub genes in the PPI network. As a result, 1,795 genes were identified in NSCLC; 729 were upregulated and 1,066 were downregulated. The results of the GO analysis indicated that the upregulated DEGs were significantly enriched in 'biological processes' (BP), including 'cell cycle and nuclear division'; the downregulated DEGs were also significantly enriched in BP, including 'response to wounding', 'anatomical structure morphogenesis' and 'response to stimulus'. Upregulated DEGs were also enriched in 'cell cycle', 'DNA replication' and the 'tumor protein 53 signaling pathway', while the downregulated DEGs were also enriched in 'complement and coagulation cascades', 'malaria' and 'cell adhesion molecules'. The top 9 hub genes were cyclin-dependent kinase 9 (CDK1), polo-like kinase 1, aurora kinase B, cell division cycle 20, baculoviral initiator of apoptosis repeat containing 5, mitotic checkpoint serine/threonine kinase B, proliferating cell nuclear antigen (PCNA), centromere protein A and MAD2 mitotic arrest deficient-like 1, and the KMplot results revealed that the high expression levels of these genes resulted in significantly low survival rates, compared with low expression samples (P<0.05), with the exception of PCNA and CDK1. In the pathway crosstalk analysis, 26 nodes and 41 interactions were divided into two groups: One module of the two groups primarily included 'metabolism of amino acid' and the other primarily contained 'tumor necrosis signaling' pathways. In conclusion, the present study assisted in improving the understanding of the molecular mechanisms underlying NSCLC development, and the results may help the understanding of the biological mechanism of NSCLC.

Machine learning for biomarker identification in cancer research - developments toward its clinical application

The patterns identified from the systematically collected molecular profiles of patient tumor samples, along with clinical metadata, can assist personalized treatments for effective management of cancer patients with similar molecular subtypes. There is an unmet need to develop computational algorithms for cancer diagnosis, prognosis and therapeutics that can identify complex patterns and help in classifications based on plethora of emerging cancer research outcomes in public domain. Machine learning, a branch of artificial intelligence, holds a great potential for pattern recognition in cryptic cancer datasets, as evident from recent literature survey. In this review, we focus on the current status of machine learning applications in cancer research, highlighting trends and analyzing major achievements, roadblocks and challenges toward its implementation in clinics.

Multiple signatures of a disease in potential biomarker space: Getting the signatures consensus and identification of novel biomarkers

Background

The lack of consensus among reported gene signature subsets (GSSs) in multi-gene biomarker discovery studies is often a concern for researchers and clinicians. Subsequently, it discourages larger scale prospective studies, prevents the translation of such knowledge into a practical clinical setting and ultimately hinders the progress of the field of biomarker-based disease classification, prognosis and prediction.

Methods

We define all "gene identificators" (gIDs) as constituents of the entire potential disease biomarker space. For each gID in a GSS of interest ("tested GSS"/tGSS), our method counts the empirical frequency of gID co-occurrences/overlaps in other reference GSSs (rGSSs) and compares it with the expected frequency generated via implementation of a randomized sampling procedure. Comparison of the empirical frequency distribution (EFD) with the expected background frequency distribution (BFD) allows dichotomization of statistically novel (SN) and common (SC) gIDs within the tGSS.

Results

We identify SN or SC biomarkers for tGSSs obtained from previous studies of high-grade serous ovarian cancer (HG-SOC) and breast cancer (BC). For each tGSS, the EFD of gID co-occurrences/overlaps with other rGSSs is characterized by scale and context-dependent Pareto-like frequency distribution function. Our results indicate that while independently there is little overlap between our tGSS with individual rGSSs, comparison of the EFD with BFD suggests that beyond a confidence threshold, tested gIDs become more common in rGSSs than expected. This validates the use of our tGSS as individual or combined prognostic factors. Our method identifies SN and SC genes of a 36-gene prognostic signature that stratify HG-SOC patients into subgroups with low, intermediate or high-risk of the disease outcome. Using 70 BC rGSSs, the method also predicted SN and SC BC prognostic genes from the tested obesity and IGF1 pathway GSSs.

Conclusions

Our method provides a strategy that identify/predict within a tGSS of interest, gID subsets that are either SN or SC when compared to other rGSSs. Practically, our results suggest that there is a stronger association of the IGF1 signature genes with the 70 BC rGSSs, than for the obesity-associated signature. Furthermore, both SC and SN genes, in both signatures could be considered as perspective prognostic biomarkers of BCs that stratify the patients onto low or high risks of cancer development.

Analyzing large gene expression and methylation data profiles using StatBicRM: statistical biclustering-based rule mining

Microarray and beadchip are two most efficient techniques for measuring gene expression and methylation data in bioinformatics. Biclustering deals with the simultaneous clustering of genes and samples. In this article, we propose a computational rule mining framework, StatBicRM (i.e., statistical biclustering-based rule mining) to identify special type of rules and potential biomarkers using integrated approaches of statistical and binary inclusion-maximal biclustering techniques from the biological datasets. At first, a novel statistical strategy has been utilized to eliminate the insignificant/low-significant/redundant genes in such way that significance level must satisfy the data distribution property (viz., either normal distribution or non-normal distribution). The data is then discretized and post-discretized, consecutively. Thereafter, the biclustering technique is applied to identify maximal frequent closed homogeneous itemsets. Corresponding special type of rules are then extracted from the selected itemsets. Our proposed rule mining method performs better than the other rule mining algorithms as it generates maximal frequent closed homogeneous itemsets instead of frequent itemsets. Thus, it saves elapsed time, and can work on big dataset. Pathway and Gene Ontology analyses are conducted on the genes of the evolved rules using David database. Frequency analysis of the genes appearing in the evolved rules is performed to determine potential biomarkers. Furthermore, we also classify the data to know how much the evolved rules are able to describe accurately the remaining test (unknown) data. Subsequently, we also compare the average classification accuracy, and other related factors with other rule-based classifiers. Statistical significance tests are also performed for verifying the statistical relevance of the comparative results. Here, each of the other rule mining methods or rule-based classifiers is also starting with the same post-discretized data-matrix. Finally, we have also included the integrated analysis of gene expression and methylation for determining epigenetic effect (viz., effect of methylation) on gene expression level.

Analysis of global changes in gene expression induced by human polynucleotide phosphorylase (hPNPase(old-35))

As a strategy to identify gene expression changes affected by human polynucleotide phosphorylase (hPNPase(old-35)), we performed gene expression analysis of HeLa cells in which hPNPase(old-35) was overexpressed. The observed changes were then compared to those of HO-1 melanoma cells in which hPNPase(old-35) was stably knocked down. Through this analysis, 90 transcripts, which positively or negatively correlated with hPNPase(old-35) expression, were identified. The majority of these genes were associated with cell communication, cell cycle, and chromosomal organization gene ontology categories. For a number of these genes, the positive or negative correlations with hPNPase(old-35) expression were consistent with transcriptional data extracted from the TCGA (The Cancer Genome Atlas) expression datasets for colon adenocarcinoma (COAD), skin cutaneous melanoma (SKCM), ovarian serous cyst adenocarcinoma (OV), and prostate adenocarcinoma (PRAD). Further analysis comparing the gene expression changes between Ad.hPNPase(old-35) infected HO-1 melanoma cells and HeLa cells overexpressing hPNPase(old-35) under the control of a doxycycline-inducible promoter, revealed global changes in genes involved in cell cycle and mitosis. Overall, this study provides further evidence that hPNPase(old-35) is associated with global changes in cell cycle-associated genes and identifies potential gene targets for future investigation.

Lung cancer transcriptomes refined with laser capture microdissection

We evaluated the importance of tumor cell selection for generating gene signatures in non-small cell lung cancer. Tumor and nontumor tissue from macroscopically dissected (Macro) surgical specimens (31 pairs from 32 subjects) was homogenized, extracted, amplified, and hybridized to microarrays. Adjacent scout sections were histologically mapped; sets of approximately 1000 tumor cells and nontumor cells (alveolar or bronchial) were procured by laser capture microdissection (LCM). Within histological strata, LCM and Macro specimens exhibited approximately 67% to 80% nonoverlap in differentially expressed (DE) genes. In a representative subset, LCM uniquely identified 300 DE genes in tumor versus nontumor specimens, largely attributable to cell selection; 382 DE genes were common to Macro, Macro with preamplification, and LCM platforms. RT-qPCR validation in a 33-gene subset was confirmatory (ρ = 0.789 to 0.964, P = 0.0013 to 0.0028). Pathway analysis of LCM data suggested alterations in known cancer pathways (cell growth, death, movement, cycle, and signaling components), among others (eg, immune, inflammatory). A unique nine-gene LCM signature had higher tumor-nontumor discriminatory accuracy (100%) than the corresponding Macro signature (87%). Comparison with Cancer Genome Atlas data sets (based on homogenized Macro tissue) revealed both substantial overlap and important differences from LCM specimen results. Thus, cell selection via LCM enhances expression profiling precision, and confirms both known and under-appreciated lung cancer genes and pathways.

Secretomes are a potential source of molecular targets for cancer therapies and indicate that APOE is a candidate biomarker for lung adenocarcinoma metastasis

Identifying patients at high risk of metastasis is a major challenge in lung adenocarcinoma (ADC) therapy, therefore discovery of noninvasive biomarkers and therapeutic targets is urgent. We found significant differences between the secretomes of differentially expressed proteins in lung ADC cell lines, clinical tissue samples and serum plasma samples with high and low metastatic potential. In particular, Apolipoprotein E (APOE) levels were three-times greater in cells with lymph node metastases (LNM) than those without. Our study indicates that APOE is a potential indicator of metastatic lung ADC and that secretomes may offer a valuable resource for biomarkers of lung ADC with LNM.

An omics strategy for discovering pulmonary biomarkers potentially relevant to the evaluation of tobacco products

Smoking is known to cause serious lung diseases including chronic bronchitis, chronic obstructive lung disease, obstruction of small airways, emphysema and cancer. Tobacco smoke is a complex chemical aerosol containing at least 8000 chemical constituents, either tobacco derived or added by tobacco product manufacturers. Identification of all of the toxic agents in tobacco smoke is challenging, and efforts to understand the mechanisms by which tobacco use causes disease will be informed by new biomarkers of exposure and harm. In 2009, President Obama signed into law the Family Smoking Prevention and Tobacco Control Act granting the US FDA the authority to regulate tobacco products to protect public health. This perspective article presents the background, rationale and strategy for using omics technologies to develop new biomarkers, which may be of interest to the FDA when implementing the Family Smoking Prevention and Tobacco Control Act.

How bioinformatics influences health informatics: usage of biomolecular sequences, expression profiles and automated microscopic image analyses for clinical needs and public health

ABSTRACT

The currently hyped expectation of personalized medicine is often associated with just achieving the information technology led integration of biomolecular sequencing, expression and histopathological bioimaging data with clinical records at the individual patients' level as if the significant biomedical conclusions would be its more or less mandatory result. It remains a sad fact that many, if not most biomolecular mechanisms that translate the human genomic information into phenotypes are not known and, thus, most of the molecular and cellular data cannot be interpreted in terms of biomedically relevant conclusions. Whereas the historical trend will certainly be into the general direction of personalized diagnostics and cures, the temperate view suggests that biomedical applications that rely either on the comparison of biomolecular sequences and/or on the already known biomolecular mechanisms have much greater chances to enter clinical practice soon. In addition to considering the general trends, we exemplarily review advances in the area of cancer biomarker discovery, in the clinically relevant characterization of patient-specific viral and bacterial pathogens (with emphasis on drug selection for influenza and enterohemorrhagic E. coli) as well as progress in the automated assessment of histopathological images. As molecular and cellular data analysis will become instrumental for achieving desirable clinical outcomes, the role of bioinformatics and computational biology approaches will dramatically grow.

AUTHOR SUMMARY

With DNA sequencing and computers becoming increasingly cheap and accessible to the layman, the idea of integrating biomolecular and clinical patient data seems to become a realistic, short-term option that will lead to patient-specific diagnostics and treatment design for many diseases such as cancer, metabolic disorders, inherited conditions, etc. These hyped expectations will fail since many, if not most biomolecular mechanisms that translate the human genomic information into phenotypes are not known yet and, thus, most of the molecular and cellular data collected will not lead to biomedically relevant conclusions. At the same time, less spectacular biomedical applications based on biomolecular sequence comparison and/or known biomolecular mechanisms have the potential to unfold enormous potential for healthcare and public health. Since the analysis of heterogeneous biomolecular data in context with clinical data will be increasingly critical, the role of bioinformatics and computational biology will grow correspondingly in this process.

InCoB celebrates its tenth anniversary as first joint conference with ISCB-Asia

In 2009 the International Society for Computational Biology (ISCB) started to roll out regional bioinformatics conferences in Africa, Latin America and Asia. The open and competitive bid for the first meeting in Asia (ISCB-Asia) was awarded to Asia-Pacific Bioinformatics Network (APBioNet) which has been running the International Conference on Bioinformatics (InCoB) in the Asia-Pacific region since 2002. InCoB/ISCB-Asia 2011 is held from November 30 to December 2, 2011 in Kuala Lumpur, Malaysia. Of 104 manuscripts submitted to BMC Genomics and BMC Bioinformatics conference supplements, 49 (47.1%) were accepted. The strong showing of Asia among submissions (82.7%) and acceptances (81.6%) signals the success of this tenth InCoB anniversary meeting, and bodes well for the future of ISCB-Asia.