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Head and neck cancer is the sixth most common malignancy around the world, and 90% of cases are squamous cell carcinomas. In this study, we performed a systematic investigation of the immunogenomic landscape to identify prognostic biomarkers for head and neck squamous cell carcinoma (HNSCC). We analyzed the expression profiles of immune‐related genes (IRGs) and clinical characteristics by interrogating RNA-seq data from 527 HNSCC patients in the cancer genome atlas (TCGA) dataset, including 41 HPV+ and 486 HPV− samples. We found that differentially expressed immune genes were closely associated with patient prognosis in HNSCC by comparing the differences in gene expression between cancer and normal samples and performing survival analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to annotate the biological functions of the differentially expressed immunogenomic prognosis-related genes. Two additional cohorts from the Oncomine database were used for validation. 65, 56 differentially expressed IRGs was associated with clinical prognosis in total and HPV- samples, respectively. Furthermore, we extracted 10, 11 prognosis-related IRGs from 65, 56 differentially expressed IRGs, respectively. They were significantly correlated with clinical prognosis and used to construct the prognosis prediction models. The multivariable ROC curves (specifically, the AUC) were used to measure the accuracy of the prognostic models. These genes were mainly enriched in several gene ontology (GO) terms related to immunocyte migration and receptor and ligand activity. KEGG pathway analysis revealed enrichment of pathways related to cytokine−cytokine receptor interactions, which are primarily involved in biological processes. In addition, we identified 63 differentially expressed transcription factors (TFs) from 4784 differentially expressed genes, and 16 edges involving 18 nodes were formed in the regulatory network between differentially expressed TFs and the high-risk survival-associated IRGs. B cell and CD4 T cell infiltration levels were significantly negatively correlated with the expression of prognosis-related immune genes regardless of HPV status. In conclusion, this comprehensive analysis identified the prognostic IRGs as potential biomarkers, and the model generated in this study may enable an accurate prediction of survival. [ABSTRACT FROM AUTHOR]

While high-throughput technologies are expected to play a critical role in clinical translational research for complex disease diagnosis, the ability to accurately and consistently discriminate disease phenotypes by determining the gene and protein expression patterns as signatures of different clinical conditions remains a challenge in translational bioinformatics. In this study, we propose a novel feature selection algorithm: Multi-Resolution-Test (MRT-test) that can produce significantly accurate and consistent phenotype discrimination across a series of omics data. Our algorithm can capture those features contributing to subtle data behaviors instead of selecting the features contributing to global data behaviors, which seems to be essential in achieving clinical level diagnosis for different expression data. Furthermore, as an effective biomarker discovery algorithm, it can achieve linear separation for high-dimensional omics data with few biomarkers. We apply our MRT-test to complex disease phenotype diagnosis by combining it with state-of-the-art classifiers and attain exceptional diagnostic results, which suggests that our method's advantage in molecular diagnostics. Experimental evaluation showed that MRT-test based diagnosis is able to generate consistent and robust clinical-level phenotype separation for various diseases. In addition, based on the seed biomarkers detected by the MRT-test, we design a novel network marker synthesis (NMS) algorithm to decipher the underlying molecular mechanisms of tumorigenesis from a systems viewpoint. Unlike existing top-down gene network building approaches, our network marker synthesis method has a less dependence on the global network and enables it to capture the gene regulators for different subnetwork markers, which will provide biologically meaningful insights for understanding the genetic basis of complex diseases. [ABSTRACT FROM AUTHOR]

Background: Although high-throughput microarray based molecular diagnostic technologies show a great promise in cancer diagnosis, it is still far from a clinical application due to its low and instable sensitivities and specificities in cancer molecular pattern recognition. In fact, high-dimensional and heterogeneous tumor profiles challenge current machine learning methodologies for its small number of samples and large or even huge number of variables (genes). This naturally calls for the use of an effective feature selection in microarray data classification. Methods: We propose a novel feature selection method: multi-resolution independent component analysis (MICA) for large-scale gene expression data. This method overcomes the weak points of the widely used transform-based feature selection methods such as principal component analysis (PCA), independent component analysis (ICA), and nonnegative matrix factorization (NMF) by avoiding their global feature-selection mechanism. In addition to demonstrating the effectiveness of the multi-resolution independent component analysis in meaningful biomarker discovery, we present a multi-resolution independent component analysis based support vector machines (MICASVM) and linear discriminant analysis (MICA-LDA) to attain high-performance classifications in low-dimensional spaces. Results: We have demonstrated the superiority and stability of our algorithms by performing comprehensive experimental comparisons with nine state-of-the-art algorithms on six high-dimensional heterogeneous profiles under cross validations. Our classification algorithms, especially, MICA-SVM, not only accomplish clinical or near-clinical level sensitivities and specificities, but also show strong performance stability over its peers in classification. Software that implements the major algorithm and data sets on which this paper focuses are freely available at https://sites.google.com/site/heyaumapbc2011/. Conclusions: This work suggests a new direction to accelerate microarray technologies into a clinical routine through building a high-performance classifier to attain clinical-level sensitivities and specificities by treating an input profile as a 'profile-biomarker'. The multi-resolution data analysis based redundant global feature suppressing and effective local feature extraction also have a positive impact on large scale 'omics' data mining. [ABSTRACT FROM AUTHOR]

The identification of ante- and post-mortem burns is challenging in forensic pathology. In this study, microarray analysis was used to detect the mRNA expression profiles in the skin of an experimental burn mouse model; the results were validated using RT-qPCR. Differentially expressed mRNAs (DE-mRNAs) were assessed using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Our results revealed that mRNA expression of 501 genes was significantly different, of which 273 were upregulated and 228 were downregulated in ante-mortem burned mice skin. The expression levels of eight random mRNAs were consistent when measured using the microarray assay-based method and RT-qPCR. Genes from different functional categories and signalling pathways were enriched, including interleukin-20 binding, type IV hypersensitivity, negative regulation of acute inflammatory response, sensory organ development, endocytosis, neuroactive ligand-receptor interaction, and Jak-STAT signalling pathway. Only five of the eight mRNAs exhibited consistent changes in expression between burned skin samples of mice and human autopsy specimens. Our findings showed that DE-mRNAs revealed using microarray are potential biomarkers of ante-mortem burns. However, DE-mRNAs identified from experimental animal models cannot be directly extended to autopsy specimens without careful validation. [ABSTRACT FROM AUTHOR]

Non-small-cell lung cancer (NSCLC) is one of the most devastating diseases worldwide. The study is aimed at identifying reliable prognostic biomarkers and to improve understanding of cancer initiation and progression mechanisms. RNA-Seq data were downloaded from The Cancer Genome Atlas (TCGA) database. Subsequently, comprehensive bioinformatics analysis incorporating gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and the protein-protein interaction (PPI) network was conducted to identify differentially expressed genes (DEGs) closely associated with NSCLC. Eight hub genes were screened out using Molecular Complex Detection (MCODE) and cytoHubba. The prognostic and diagnostic values of the hub genes were further confirmed by survival analysis and receiver operating characteristic (ROC) curve analysis. Hub genes were validated by other datasets, such as the Oncomine, Human Protein Atlas, and cBioPortal databases. Ultimately, logistic regression analysis was conducted to evaluate the diagnostic potential of the two identified biomarkers. Screening removed 1,411 DEGs, including 1,362 upregulated and 49 downregulated genes. Pathway enrichment analysis of the DEGs examined the Ras signaling pathway, alcoholism, and other factors. Ultimately, eight prioritized genes (GNGT1, GNG4, NMU, GCG, TAC1, GAST, GCGR1, and NPSR1) were identified as hub genes. High hub gene expression was significantly associated with worse overall survival in patients with NSCLC. The ROC curves showed that these hub genes had diagnostic value. The mRNA expressions of GNGT1 and NMU were low in the Oncomine database. Their protein expressions and genetic alterations were also revealed. Finally, logistic regression analysis indicated that combining the two biomarkers substantially improved the ability to discriminate NSCLC. GNGT1 and NMU identified in the current study may empower further discovery of the molecular mechanisms underlying NSCLC's initiation and progression. [ABSTRACT FROM AUTHOR]

Adipose-derived stem cells (ADSCs) have been documented as possible candidates for skin rejuvenation. However, the effects of ADSC-derived exosomes on photoaged skin remain to be fully elucidated. This study was aimed at determining the antiaging effects of ADSC-derived exosomes on photoaged skin. Human ADSCs were isolated from the adipose tissue of healthy women and cultured in vitro. Then, exosomes were extracted from the cultured ADSCs, purified by ultracentrifugation, and verified by examination of cell morphology using transmission electron microscopy and the identification of specific biomarkers. Meanwhile, the optimal exosome concentration and treatment time were selected. The photoaged skin model was created by subjecting Sprague-Dawley rats to ultraviolet B radiation. Exosomes were injected into the photoaged skin in a single therapeutic dose. The thickness of the epidermis and dermis was observed by HE staining. The relative mRNA expression of type I collagen, type III collagen, and matrix metalloproteinases (MMP-1 and MMP-3) was determined by real-time PCR. In the rat model of photoaged skin, the injected exosomes markedly decreased the epidermal thickness and increased the dermal thickness of the photoaged skin 7 days after treatment. Moreover, the proportion of the stratum corneum of the epidermis was decreased. Furthermore, real-time RT-PCR showed that the mRNA expression of type I collagen was increased and that of type III collagen, MMP-1, and MMP-3 was decreased. Our results demonstrate that ADSC-derived exosome treatment could significantly improve skin photodamage and that ADSC-derived exosomes may be a potential agent for photoaged skin treatment. [ABSTRACT FROM AUTHOR]

Abstract Correlating aberrant miRNA levels with imported malaria during the blood stage is required to better understand the in vivo biological and molecular processes that are involved in the response to Plasmodium falciparum infection and to find new biomarkers and diagnosis tools. We used a parallel microarray-based approach to investigate an integrated view of how the host miRNA expression profile changes in response to P. falciparum infection in whole blood obtained from six subjects with adult imported falciparum malaria (AIFM) and six normal subjects. We first investigated the functions of 1007 significantly differentially expressed genes from microarrays and predicted 56 putative pathways participating in malaria pathogenesis, which reflected systemic changes in the host under falciparum infection. Then, we validated the in silico -predicted targets of 50 differentially modulated miRNAs (7 upregulated and 43 downregulated) by examining the actual mRNA expression of these particular genes in our gene expression profile database; putative target gene sets were grouped into functional categories to investigate the roles of differentially expressed miRNAs. We considered 36 intersecting putative pathways, most of which were involved in multiple immune response processes, such as cell defense response, immune response, TNF signaling pathway, and T cell receptor signaling pathway, which may actually be regulated by differentially expressed miRNAs. Additionally, we identified five enriched upregulated miRNA sets (miR-3135b, miR-6780b-5p, miR-1246, miR-6126, and miR-3613-5p) as potential blood biomarkers of immunopathological status and prediction of early host responses, disease prognosis, and severe outcomes in AIFM. Highlights • A parallel microarray-based method studies host miRNA expression profile changes under malaria. • Most of putative pathways are involved in immune response processes during falciparum malaria. • Five miRNAs were identified as potential blood biomarkers for falciparum malaria. [ABSTRACT FROM AUTHOR]