Marker Clustering Research Articles

Highly-Multiplexed Immunofluorescence PhenoCycler Panel for Murine FFPE Yields Insight into Tumor Microenvironment Immunoengineering

Spatial proteomics profiling is an emerging set of technologies that has the potential to elucidate the cell types, interactions, and molecular signatures that make up complex tissue microenvironments, with applications in the study of cancer, immunity, and much more. An emerging technique in the field is Co-Detection-by-indEXing (CODEX), recently renamed as the PhenoCycler system. This is a highly-multiplexed immunofluorescence imaging technology that relies on oligonucleotide-barcoded antibodies and cyclic immunofluorescence to visualize many antibody markers in a single specimen while preserving tissue architecture. Existing PhenoCycler panels are primarily designed for fresh-frozen tissues. Formalin-fixed paraffin-embedded (FFPE) blocks offer several advantages in preclinical research, but few antibody clones have been identified in this setting for PhenoCycler imaging. Here, we present a novel PhenoCycler panel of 28 validated antibodies for murine FFPE tissues. We describe our workflow for selecting and validating clones, barcoding antibodies, designing our panel, and performing multiplex imaging. We further detail our analysis pipeline for comparing marker expressions, clustering and phenotyping single-cell proteomics data, and quantifying spatial relationships. We then apply our panel and analysis protocol to profile the effects of three gene-delivery nanoparticle formulations, in combination with systemic anti-PD1, on the murine melanoma tumor immune microenvironment. Intralesional delivery of genes expressing the costimulatory molecule 4-1BBL and the cytokine IL-12 led to a shift towards intratumoral M1 macrophage polarization and promoted closer associations between intratumoral CD8 T cells and macrophages. Delivery of IFNγ, in addition to 4-1BBL and IL-12, further increased markers of antigen presentation on tumor cells and intratumoral antigen-presenting cells but also promoted greater expression of checkpoint marker PD-L1 and closer associations between intratumoral CD8 T cells and PD-L1-expressing tumor cells. These findings help to explain the benefits of 4-1BBL and IL-12 delivery while offering additional mechanistic insights into the limitations of IFNγ therapeutic efficacy.

A Fusion Learning Model Based on Deep Learning for Single-Cell RNA Sequencing Data Clustering.

Single-cell RNA sequencing (scRNA-seq) technology provides a means for studying biology from a cellular perspective. The fundamental goal of scRNA-seq data analysis is to discriminate single-cell types using unsupervised clustering. Few single-cell clustering algorithms have taken into account both deep and surface information, despite the recent slew of suggestions. Consequently, this article constructs a fusion learning framework based on deep learning, namely scGASI. For learning a clustering similarity matrix, scGASI integrates data affinity recovery and deep feature embedding in a unified scheme based on various top feature sets. Next, scGASI learns the low-dimensional latent representation underlying the data using a graph autoencoder to mine the hidden information residing in the data. To efficiently merge the surface information from raw area and the deeper potential information from underlying area, we then construct a fusion learning model based on self-expression. scGASI uses this fusion learning model to learn the similarity matrix of an individual feature set as well as the clustering similarity matrix of all feature sets. Lastly, gene marker identification, visualization, and clustering are accomplished using the clustering similarity matrix. Extensive verification on actual data sets demonstrates that scGASI outperforms many widely used clustering techniques in terms of clustering accuracy.

Comprehensive analysis of human monocyte subsets using full-spectrum flow cytometry and hierarchical marker clustering.

Exploring monocytes' roles within the tumor microenvironment is crucial for crafting targeted cancer treatments. This study unveils a novel methodology utilizing four 20-color flow cytometry panels for comprehensive peripheral immune system phenotyping, specifically targeting classical, intermediate, and non-classical monocyte subsets. By applying advanced dimensionality reduction techniques like t-distributed stochastic neighbor embedding (tSNE) and FlowSom analysis, we performed an extensive profiling of monocytes, assessing 50 unique cell surface markers related to a wide range of immunological functions, including activation, differentiation, and immune checkpoint regulation. This in-depth approach significantly refines the identification of monocyte subsets, directly supporting the development of personalized immunotherapies and enhancing diagnostic precision. Our pioneering panel for monocyte phenotyping marks a substantial leap in understanding monocyte biology, with profound implications for the accuracy of disease diagnostics and the success of checkpoint-inhibitor therapies. Key findings include revealing distinct marker expression patterns linked to tumor progression and providing new avenues for targeted therapeutic interventions.

Spatial analysis of the tumor immune microenvironment in papillary renal cell carcinoma.

471 Background: Spatial analysis of the tumor immune microenvironment (TIME) has yet to be explored in papillary renal cell carcinoma (pRCC). We utilized multiplex immunofluorescence (mIF) and spatial transcriptomics using spatial molecular imaging (SMI) to evaluate TIME properties in pRCC and contrasted these results with clear cell RCC (ccRCC). Methods: Tumor specimens were obtained from localized RCC tumors. mIF was performed on regions of interest (ROIs) selected from matched compartments from tumor, stroma, and tumor/stromal subsets of the interface. Two antibody panels were used for markers against T cells and B cells/macrophages. Marker abundance and clustering differences between pRCC and ccRCC were evaluated across ROIs. Select markers were also explored across pathologic tumor staging in pRCC. The SMI platform used for validation utilized probes against 959 transcripts. Cells were phenotyped using InSituType using the Kidney Cell Atlas as a reference. Cell clustering was quantified by univariate and bivariate Ripley’s K using the spatialTIME package in R. Results: mIF was performed on 1178 ROIs from 16 pRCC tumors and 70 ccRCC tumors. Compared to ccRCC, pRCC immune cell abundance was statistically lower amongst many T cell types and M2-like macrophages (Figure). M1-like macrophages were the only cell line seen at higher levels in interface compartments only. Increased macrophage clustering was observed in pRCC, including doubly positive M2-like macrophages in interface compartments (p=0.001 and 0.007). Higher abundance of CD8+ and FOXP3+ T cells in pRCC was associated with worse clinical stage, but no trend was seen with marker clustering. Four ROIs from 2 pRCC patients underwent SMI validation. On SMI of the tumor compartment, T cells were clustered with other T cells, B cells, and M1 macrophages. Conclusions: Compared to ccRCC, pRCC has fewer T cells and macrophages but more macrophage clustering. Using spatial transcriptomics, we found significant clustering between T cells, macrophages, and B cells in pRCC.

Endometrial Stem/Progenitor cell (ES/PC) Marker Expression Profile in Adenosarcoma and Endometrial Stromal Sarcoma

BackgroundThe uterus is one of the most dynamic organs in the human body, and this dynamic homeostasis is supported by endometrial stem/progenitor cells (ES/PCs), which are heterogeneous in their phenotype and degree of differentiation. ES/PCs are generally localized in the endometrial stroma, the site of origin for adenosarcoma and endometrial stromal sarcoma (ESS). Subsets of ESSs and adenosarcomas harbor SUZ12 or DICER1 gene alterations, two genes with roles in embryonic stem cell biology. However, the possible contribution of ES/PCs to tumorigenesis is unexplored. MethodWe examined the expression of eleven ES/PC markers, along with three proteins expressed in the mature endometrial stroma (ER, PR and CD10) in 60 uterine tumors (24 low-, 11 high-grade ESS, 25 adenosarcomas). Protein expression profiles were assessed by unsupervised hierarchical clustering. miRNA expression profiles were examined in a subset of adenosarcoma with/without DICER1 mutations, using the NanoString platform. ResultsES/PC markers were variably expressed, and the tumors exhibited limited immunophenotypic resemblance to different ES/PCs. Within the ESSs, the ES/PC marker clustering pattern was prognostic for both overall and disease-free survival. Comparing adenosarcomas and ESSs, most high-grade ESSs clustered with one another, while low-grade ESSs and adenosarcomas tended to cluster with one another. Among the adenosarcomas, the miRNA expression profiles were varied with respect to the DICER1 mutation status, with pathway analysis pointing to dysregulated signal transduction and stem cell biology. ConclusionsESSs and adenosarcomas exhibit varying immunophenotypic resemblance to ES/PCs. These expression profiles have prognostic implications and may be genetically driven.

High Dimensional Tissue-Based Spatial Analysis of the Tumor Microenvironment of Follicular Lymphoma Reveals Unique Immune Niches inside Malignant Follicles

High Dimensional Tissue-Based Spatial Analysis of the Tumor Microenvironment of Follicular Lymphoma Reveals Unique Immune Niches inside Malignant Follicles

Genetic diversity analysis of Hippeastrum rutilum cultivars based on SCoT markers

Objective The objective of this study is to further analyze the genetic diversity, genetic relationship and parental identification of Hippeastrum rutilum cultivars at molecular level. Method The SCoT marker system of H. rutilum was screened by orthogonal design method, and genetic diversity and genetic relationship of 41 cultivars were analyzed. Result (1) The optimum reaction system of SCoT markers for H. rutilum (20 μL) included DNA 40 ng, primer 0.1 μmol·L−1, MgCl2 2.0 mmol·L−1, dNTPs 0.4 mmol·L−1 and rTaq DNA polymerase 0.75 U (1 U=16.67 nkat). (2) 77 polymorphic bands were obtained from 41 H. rutilum cultivars by 12 SCoT primers, and the average polymorphic band ratio was up to 86.52%. The genetic similarity coefficient between H. rutilum cultivars was 0.292 3−0.834 3, indicating that the 41 cultivars had high genetic diversity and wide genetic range. (3)UPGMA(unweighted pair-group method with arithmetic means) cluster analysis showed that the SCoT marker clustering of H. rutilum had significant correlation with flower color, but not with the petal type. At genetic similarity coefficient of 0.420 0, the 41 cultivars were divided into two groups. The first group had both double and single petal cultivars. The first group was divided into four subgroups, among which those with similar flower colors were clustered into one group. In subgroupⅠd, the single-petal, white ‘Hydrangea’ (No. 20) and red ‘Miracle’ (No. 22) were the possible parents of double-petal, orange-red and white multicolor flowers (No. 21 ‘Yingchun’). The second group was mostly multicolor flowers. Conclusion SCoT marker technique can be effectively applied to genetic diversity analysis and identification of possible parents of H. rutilum cultivars. [Ch, 6 fig. 4 tab. 21 ref.]

Artificial-cell-type aware cell-type classification in CITE-seq.

MotivationCellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq), couples the measurement of surface marker proteins with simultaneous sequencing of mRNA at single cell level, which brings accurate cell surface phenotyping to single-cell transcriptomics. Unfortunately, multiplets in CITE-seq datasets create artificial cell types (ACT) and complicate the automation of cell surface phenotyping.ResultsWe propose CITE-sort, an artificial-cell-type aware surface marker clustering method for CITE-seq. CITE-sort is aware of and is robust to multiplet-induced ACT. We benchmarked CITE-sort with real and simulated CITE-seq datasets and compared CITE-sort against canonical clustering methods. We show that CITE-sort produces the best clustering performance across the board. CITE-sort not only accurately identifies real biological cell types (BCT) but also consistently and reliably separates multiplet-induced artificial-cell-type droplet clusters from real BCT droplet clusters. In addition, CITE-sort organizes its clustering process with a binary tree, which facilitates easy interpretation and verification of its clustering result and simplifies cell-type annotation with domain knowledge in CITE-seq.Availability and implementation http://github.com/QiuyuLian/CITE-sort.Supplementary information Supplementary data is available at Bioinformatics online.

Genetic diversity analysis of phenotypic character and SRAP molecular markers in 45 tree peony cultivars

Tree peony (Paeonia suffruticosa Andrews) is a famous ornamental flowering species and favorite of people all over the world. In the present study, the genetic diversity of 12 phenotypic characters and 11 sequence-related amplified polymorphism (SRAP) markers was analyzed using the SPSS software in 45 tree peony cultivars. (1) Morphological-clustering results showed that 45 cultivars were classified into three groups: Group 1 includes 21 Central Plains cultivars, group 2 includes four Japanese cultivars and six Central Plains cultivars, and group 3 includes five Paeonia rockii T. Hong et J. J. Li cultivars, 8 Central Plains cultivars and one Japanese cultivar. (2) SRAP molecular markers analysis results showed that the 45 tree peony cultivars were divided into three groups, and five P. rockii cultivars clustered together as a group. ‘Dou Lv’ was independent group. Thirty-four Central Plains cultivars and five Japanese introduced cultivars clustered into a class were divided into four subgroups. The origin of the initial species of Japanese cultivars was Central Plains in China. (3) The analysis results by two classification methods evidently indicates that the SPAR molecular marker clustering presented the more exact and reliable genetic relationship, and both the external morphological characteristics and molecular marker clustering analysis combined was benefit for clear the germplasm resources diversity of tree peony cultivars and breeding improved varieties. SRAP molecular markers analysis results provided more details about tree peony genetic diversity and phyletic evolution than morphological-clustering results.

Segregation distortion: Utilizing simulated genotyping data to evaluate statistical methods

Segregation distortion is the phenomenon in which genotypes deviate from expected Mendelian ratios in the progeny of a cross between two varieties or species. There is not currently a widely used consensus for the appropriate statistical test, or more specifically the multiple testing correction procedure, used to detect segregation distortion for high-density single-nucleotide polymorphism (SNP) data. Here we examine the efficacy of various multiple testing procedures, including chi-square test with no correction for multiple testing, false-discovery rate correction and Bonferroni correction using an in-silico simulation of a biparental mapping population. We find that the false discovery rate correction best approximates the traditional p-value threshold of 0.05 for high-density marker data. We also utilize this simulation to test the effect of segregation distortion on the genetic mapping process, specifically on the formation of linkage groups during marker clustering. Only extreme segregation distortion was found to effect genetic mapping. In addition, we utilize replicate empirical mapping populations of wheat varieties Avalon and Cadenza to assess how often segregation distortion conforms to the same pattern between closely related wheat varieties.

Performance Testing on Marker Clustering and Heatmap Visualization Techniques: A Comparative Study on JavaScript Mapping Libraries

We are now generating exponentially more data from more sources than a few years ago. Big data, an already familiar term, has been generally defined as a massive volume of structured, semi-structured, and/or unstructured data, which may not be effectively managed and processed using traditional databases and software techniques. It could be problematic to visualize easily and quickly a large amount of data via an Internet platform. From this perspective, the main aim of the paper is to test point data visualization possibilities of selected JavaScript Mapping Libraries to measure their performance and ability to cope with a big amount of data. Nine datasets containing 10,000 to 3,000,000 points were generated from the Nature Conservation Database. Five libraries for marker clustering and two libraries for heatmap visualization were analyzed. Loading time and the ability to visualize large data sets were compared for each dataset and each library. The best-evaluated library was a Mapbox GL JS (Graphics Library JavaScript) with the highest overall performance. Some of the tested libraries were not able to handle the desired amount of data. In general, an amount of less than 100,000 points was indicated as the threshold for implementation without a noticeable slowdown in performance. Their usage can be a limiting factor for point data visualization in such a dynamic environment as we live nowadays.

Microbial Translocation Does Not Drive Immune Activation in Ugandan Children Infected With HIV.

ObjectiveImmune activation is associated with morbidity and mortality during human immunodeficiency virus (HIV) infection, despite receipt of antiretroviral therapy (ART). We investigated whether microbial translocation drives immune activation in HIV-infected Ugandan children.MethodsNineteen markers of immune activation and inflammation were measured over 96 weeks in HIV-infected Ugandan children in the CHAPAS-3 Trial and HIV-uninfected age-matched controls. Microbial translocation was assessed using molecular techniques, including next-generation sequencing.ResultsOf 249 children included, 142 were infected with HIV; of these, 120 were ART naive, with a median age of 2.8 years (interquartile range [IQR], 1.7–4.0 years) and a median baseline CD4+ T-cell percentage of 20% (IQR, 14%–24%), and 22 were ART experienced, with a median age of 6.5 years (IQR, 5.9–9.2 years) and a median baseline CD4+ T-cell percentage of 35% (IQR, 31%–39%). The control group comprised 107 children without HIV infection. The median increase in the CD4+ T-cell percentage was 17 percentage points (IQR, 12–22 percentage points) at week 96 among ART-naive children, and the viral load was <100 copies/mL in 76% of ART-naive children and 91% of ART-experienced children. Immune activation decreased with ART use. Children could be divided on the basis of immune activation markers into the following 3 clusters: in cluster 1, the majority of children were HIV uninfected; cluster 2 comprised a mix of HIV-uninfected children and HIV-infected ART-naive or ART-experienced children; and in cluster 3, the majority were ART naive. Immune activation was low in cluster 1, decreased in cluster 3, and persisted in cluster 2. Blood microbial DNA levels were negative or very low across groups, with no difference between clusters except for Enterobacteriaceae organisms (the level was higher in cluster 1; P < .0001).ConclusionImmune activation decreased with ART use, with marker clustering indicating different activation patterns according to HIV and ART status. Levels of bacterial DNA in blood were low regardless of HIV status, ART status, and immune activation status. Microbial translocation did not drive immune activation in this setting.Clinical Trials RegistrationISRCTN69078957.

Crime analysis in India using data mining techniques

An approach for crime detection in India using Data mining techniques is proposed in this paper. The approach consists of the following steps - Data pre-processing, clustering, classification and visualization. Data mining techniques are often applied to Criminology as it provides good results. Criminology is a field which studies about various crime characteristics. Analyzing crime data means exploring crime data. Crime is identified using k-means clustering and the clusters are formed based on the similarity of the crime attributes. The Random Forest algorithm and Neural networks are applied on the data for classification. Visualization is achieved using the Google marker clustering and the crime spots are marked on the India map. The accuracy is verified using WEKA tool. This approach will benefit the Crime department of India in analyzing crime with better prediction. The paper focuses on the crime analysis of various Indian states and union territories during 2001 to 2012.

THE APPLICATION OF MOLECULAR MARKER TECHNOLOGY IN THE CLASSIFICATION AND BREEDING OF CHRYSANTHEMUM

Chrysanthemum (Chrysanthemum ×morifolium Ramat.) is a very important commodity flower, and is also widely used in the landscape. But the classification of chrysanthemum cultivars has been controversial. Chrysanthemum classification research has continued from the traditional morphological classification to application of using molecular markers, as auxiliary tool. The results of molecular marker clustering analysis of wild and cultivated chrysanthemum and relatives were not fully congruent with the morphological classification. In terms of origin, the study of RAPD showed that Chrysanthemum vestitum was the most important species in the origin process of cultivated chrysanthemum. Research into the genetic relationships of chrysanthemum taxa using molecular marker methods such as AFLP, SSR, ISSR, and RAPD and the application of these to the classification and breeding of chrysanthemum were discussed. Among these methods, ISSR was seen as the best, and it was combined with the high polymorphism of AFLP, SSR and the commonality of RAPD. Sometimes, the same molecular marker method yielded different results in the different studies. The differences and similarities between classification methods in China and elsewhere were discussed in terms of molecular marker technology and morphology. With the combination of molecular marker technology and traditional morphological approaches, breeding success will be improved. It will clarify the cross-compatibility among chrysanthemum cultivars and expand the range of breeding resources for genetic improvement. The breeding period will be shortened with the directional transfer of favorable genes and the accumulation of target genes. At the same time, this research will provide an accurate reference library for the protection of chrysanthemum germplasm resources and the innovation of taxonomy.

The Wnt/Planar Cell Polarity Pathway Component Vangl2 Induces Synapse Formation through Direct Control of N-Cadherin

Although regulators of the Wnt/planar cell polarity (PCP) pathway are widely expressed in vertebrate nervous systems, their roles at synapses are unknown. Here, we show that Vangl2 is a postsynaptic factor crucial for synaptogenesis and that it coprecipitates with N-cadherin and PSD-95 from synapse-rich brain extracts. Vangl2 directly binds N-cadherin and enhances its internalization in a Rab5-dependent manner. This physical and functional interaction is suppressed by β-catenin, which binds the same intracellular region of N-cadherin as Vangl2. In hippocampal neurons expressing reduced Vangl2 levels, dendritic spine formation as well as synaptic marker clustering is significantly impaired. Furthermore, Prickle2, another postsynaptic PCP component, inhibits the N-cadherin-Vangl2 interaction and is required for normal spine formation. These results demonstrate direct control of classic cadherin by PCP factors; this control may play a central role in the precise formation and maturation of cell-cell adhesions at the synapse.

Social and Behavioral Risk Marker Clustering Associated with Biological Risk Factors for Coronary Heart Disease: NHANES 2001–2004

Background. Social and behavioral risk markers (e.g., physical activity, diet, smoking, and socioeconomic position) cluster; however, little is known whether clustering is associated with coronary heart disease (CHD) risk. Objectives were to determine if sociobehavioral clustering is associated with biological CHD risk factors (total cholesterol, HDL cholesterol, systolic blood pressure, body mass index, waist circumference, and diabetes) and whether associations are independent of individual clustering components. Methods. Participants included 4,305 males and 4,673 females aged ≥20 years from NHANES 2001–2004. Sociobehavioral Risk Marker Index (SRI) included a summary score of physical activity, fruit/vegetable consumption, smoking, and educational attainment. Regression analyses evaluated associations of SRI with aforementioned biological CHD risk factors. Receiver operator curve analyses assessed independent predictive ability of SRI. Results. Healthful clustering (SRI = 0) was associated with improved biological CHD risk factor levels in 5 of 6 risk factors in females and 2 of 6 risk factors in males. Adding SRI to models containing age, race, and individual SRI components did not improve C-statistics. Conclusions. Findings suggest that healthful sociobehavioral risk marker clustering is associated with favorable CHD risk factor levels, particularly in females. These findings should inform social ecological interventions that consider health impacts of addressing social and behavioral risk factors.

Malt Genotypic Screening of Polymorphism Information Content (PIC) of PCR-based Marker in Barley, Based on Physiological Traits

Brewing genotypes were described by polymorphism information content (PIC) of 22 SSR markers in barley (Hordeum vulgare L.), associated with malt quality. Notably, the values of diastatic power set ranged from 5.53 to 24.13 U/Kg in these samples, suitable to assess the relative importance of biochemical components of malt variations and genetic characteristics. Cluster analysis revealed a significant correlation between eco-geographic origin and SSR marker clustering, resolving the accessions into four subgroups. Moreover, SSRs with higher PIC values and higher average number of alleles per marker in the population with higher values of diastatic power were found to be an efficient utility for distinguishing brewing genotypes in barley. These results possibly indicated that SSRs linked to brewing traits could be very useful for application in monitoring malt traits, evaluating genetic diversity, and determining the sampled eco-geographic origins in barley.

SRAP Markers and Morphological Traits Could Be Used in Test of Distinctiveness, Uniformity, and Stability (DUS) of Lettuce (Lactuca sativa) Varieties

The test of distinctiveness, uniformity, and stability (DUS) is a necessary step for variety identification and new variety application. The objective of this study is to provide molecular marker-assisted approach combined with morphological trait-based testing for more convenient and fast DUS test and identification of varieties. Eighteen pairs of SRAP markers and 40 morphological traits for DUS test were applied for genetic diversity analysis of 50 lettuce (Lactuca sativa) varieties. Average polymorphism information content (PIC) of the SRAP markers was 0.80, ranging from 0.39 to 0.97. Cluster analysis using UPGMA of the band patterns amplified by SRAP marker and morphological trait-based clustering separated the varieties into three groups. The correlation coefficient of SRAP marker and morphological traits was 0.5455 reflecting that the two clustering results shared some similarity and consistence. It revealed that the combination of both SRAP marker and morphological trait analysis is more conducive to proper identification and classification of plant varieties, which will undoubtedly bring an alternative choice to DUS testing of plant new varieties and conservation of plant germplasm.

A bioinformatic implementation of mutual information as a distance measure for identification of clusters of variables

The size of data sets produced in genetic experiments is steadily increasing. Very often there are many more variables than observations, leading to the so-called ``large $p$, small $n$" problem. For such data, clustering and distance based procedures are useful tools for identifying groups of variables associated with outcomes of interest. We develop a novel approach using mutual information as a measure of distance (here dependency) between probability distributions that is valid for comparisons between pairs of variables that are both continuous, both discrete, or one of each. This gives an overall information matrix to be used as a distance matrix in clustering procedures and to define a so-called weighted network of associations between variables. We present computational aspects of implementing our procedures in R. References T. M. Cover and J. A. Thomas. Elements of information theory. Wiley, 2006. doi:10.1002/0471200611. Z. Dawy, B. Goebel, J. Hagenauer, C. Andreoli, T. Meitinger, and J. C. Mueller. Gene mapping and marker clustering using Shannon's mutual information. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 3(1):47--56, 2006. doi:10.1109/TCBB.2006.9. J. J. Faith, B. Hayete, J. T. Thaden, I. Mogno, J. Wierzbowski, G. Cottarel, S. Kasif, J. J. Collins, and T. S. Gardner. Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biol, 5(1):e8, 2007. doi:10.1371/journal.pbio.0050008. T. F. Fuller, A. Ghazalpour, J. E. Aten, T. A. Drake, A. J. Lusis, and S. Horvath. Weighted gene coexpression network analysis strategies applied to mouse weight. Mammalian Genome, 18(6):463--472, 2007. doi:10.1007/s00335-007-9043-3. A. Ghazalpour, S. Doss, B. Zhang, S. Wang, C. Plaisier, R. Castellanos, A. Brozell, E. E. Schadt, T. A. Drake, A. J. Lusis, et al. Integrating genetic and network analysis to characterize genes related to mouse weight. PLoS Genet, 2(8):e130, 2006. doi:10.1371/journal.pgen.0020130. Hung T. Nguyen. On modeling of linguistic information using random sets. Information Sciences, 34(3):265 -- 274, 1984. doi:10.1016/0020-0255(84)90052-5. P. Qiu, A. J. Gentles, and S. K. Plevritis. Fast calculation of pairwise mutual information for gene regulatory network reconstruction. Computer Methods and Programs in Biomedicine, 94(2):177--180, 2009. doi:10.1016/0020-0255(84)90052-5. S. J. Sheather and M. C. Jones. A reliable data-based bandwidth selection method for kernel density estimation. Journal of the Royal Statistical Society. Series B (Methodological), pages 683--690, 1991. M. P. Wand and M. C. Jones. Kernel smoothing. Chapman and Hall/CRC, 1995. B. Zhang and S. Horvath. A general framework for weighted gene co-expression network analysis. Statistical Applications in Genetics and Molecular Biology, 4(1):1128, 2005. doi:10.2202/1544-6115.1128.

Simple indices of inflammation as predictors of death from cancer or cardiovascular disease in a prospective cohort after two decades of follow-up

Previous studies have identified sub-clinical inflammation as a potential factor in the pathogenesis of cancer and cardiovascular disease (CVD) but the possibility that simple, readily measured indices of sub-clinical inflammation might predict both CVD and cancer has not been tested in the context of a single, prospective analysis. To evaluate simply measured indices of inflammation as long-term predictors of death from either cancer or CVD. Prospective open cohort study. A total of 1192 white males received measurements of a range of risk markers including the inflammation indices white blood cell count (WBC), erythrocyte sedimentation rate (ESR) and serum globulin concentrations. Inflammation marker clustering was quantified as a factor-analysis-derived inflammation score and survival time to death from any cancer or CVD was modeled on baseline measures using the Cox proportional hazards model. A total of 1010 participants met inclusion criteria, of whom 94 died of cancer and 67 of CVD. Mean follow-up times among cases and survivors ranged from 18.2-21.9 years. Independently of established risk factors [age, body mass index (BMI), smoking, alcohol and exercise], WBC, ESR and globulin levels were all individually predictive of both cancer (hazard ratio 1.43, P = 0.002; 1.27, P = 0.02; 1.26, P = 0.02, respectively) and CVD mortality (1.29, P = 0.06; 1.43, P = 0.007; 1.50, P = 0.001). The inflammation score predicted both cancer mortality (1.35, P = 0.003) and CVD mortality (1.46, P = 0.002). Risks associated with high inflammation score were equivalent to and independent of smoking cigarettes for cancer or, for CVD, having a serum cholesterol concentration ≥6.2 mmol/l. Simple indices of inflammation predict death from cancer or CVD two decades later as strongly as smoking predicts cancer or cholesterol predicts CVD. Their measurement could contribute to evaluation of both cancer and CVD risk.