RNA sequencing and metabolic analysis of imiquimod-induced psoriasis-like mice with chronic restrain stress.

AIM: Psoriasis is one of the most common dermatological disorders, characterized by increased epidermal hyperplasia and immune cell infiltration. Psychological stress has been reported to contribute to the severity, aggravation, and relapse of psoriasis. However, the exact mechanism involved in psychological stress's impact on psoriasis is still unclear. We aim to investigate the role of psychological stress in psoriasis from a transcriptomic and metabolomic perspective. MAIN METHOD: We developed a chronic restrain stress (CRS)-imiquimod (IMQ)-induced psoriasis-like mouse model and performed a comprehensive comparative transcriptomic and metabolic analysis with control mice, CRS-treated mice, and IMQ-treated mice to investigate how psychological stress affects psoriasis. KEY FINDING: We found that CRS-IMQ-induced psoriasis-like mice showed significant exacerbation of psoriasis-like skin inflammation compared with mice treated with IMQ only. Mice of the CRS + IMQ group showed increased expression of keratinocyte proliferation and differentiation genes, differential regulation of cytokines, and promotion of linoleic acid metabolism. Correlation analysis of differentially expressed genes in the CRS-IMQ-induced psoriasis-like mice and human psoriasis datasets compared with respective controls revealed 96 overlapping genes of which 30 genes showed consistent induced or repressed expression in all human and mouse datasets. SIGNIFICANCE: Our study provides new insights into the effects of psychological stress on psoriasis pathogenesis and the mechanisms involved, which provides clues for development of therapeutics or biomarkers.

Tumor-Infiltrating Immune Cell Landscapes in the Lymph Node Metastasis of Papillary Thyroid Cancer.

Regional lymph node metastasis (LNM) increases the risk of distant metastasis in papillary thyroid cancer (PTC) patients. However, it remains unclear how tumor cells in PTC patients with LNM evade immune system surveillance and proceed to colonize distant organs. Here, we comprehensively characterize the tumor-infiltrating immune cell landscape in PTC with LNM. LNM-related genes include multiple important soluble mediators such as CXCL6, IL37, MMP10, and COL11A1, along with genes involved in areas such as extracellular matrix organization and TLR regulation by endogenous ligands. In PTC without LNM, the tumor infiltration of activated dendritic cells and M0 macrophages showed increases from normal cells, but with yet greater increases and correspondingly worse prognosis in PTC with LNM. Conversely, the tumor infiltration of activated NK cells and eosinophils was decreased in PTC without LNM, as compared to normal cells, and yet further decreased in PTC with LNM, with such decreases associated with poor prognosis. We further demonstrate that mutations of driver genes in tumor cells influence the infiltration of surrounding immune cells in the tumor microenvironment (TME). Particularly, patients carrying TG mutations tend to show increased filtration of M2 macrophages and activated NK cells in the TME, whereas patients carrying HRAS mutations tend to show reduced filtration of M0 macrophages and show enhanced filtration of activated dendritic cells in the TME. These findings increase our understanding of the mechanisms of regional lymph node metastasis in PTC and its associated tumor microenvironment, potentially facilitating the development of personalized treatment regimens to combat immunotherapy failure.

WMDS.net: a network control framework for identifying key players in transcriptome programs.

MOTIVATION: Mammalian cells can be transcriptionally reprogramed to other cellular phenotypes. Controllability of such complex transitions in transcriptional networks underlying cellular phenotypes is an inherent biological characteristic. This network controllability can be interpreted by operating a few key regulators to guide the transcriptional program from one state to another. Finding the key regulators in the transcriptional program can provide key insights into the network state transition underlying cellular phenotypes. RESULTS: To address this challenge, here, we proposed to identify the key regulators in the transcriptional co-expression network as a minimum dominating set (MDS) of driver nodes that can fully control the network state transition. Based on the theory of structural controllability, we developed a weighted MDS network model (WMDS.net) to find the driver nodes of differential gene co-expression networks. The weight of WMDS.net integrates the degree of nodes in the network and the significance of gene co-expression difference between two physiological states into the measurement of node controllability of the transcriptional network. To confirm its validity, we applied WMDS.net to the discovery of cancer driver genes in RNA-seq datasets from The Cancer Genome Atlas. WMDS.net is powerful among various cancer datasets and outperformed the other top-tier tools with a better balance between precision and recall. AVAILABILITY AND IMPLEMENTATION: https://github.com/chaofen123/WMDS.net. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Integrated bioinformatics and statistical approaches to explore molecular biomarkers for breast cancer diagnosis, prognosis and therapies.

Integrated bioinformatics and statistical approaches are now playing the vital role in identifying potential molecular biomarkers more accurately in presence of huge number of alternatives for disease diagnosis, prognosis and therapies by reducing time and cost compared to the wet-lab based experimental procedures. Breast cancer (BC) is one of the leading causes of cancer related deaths for women worldwide. Several dry-lab and wet-lab based studies have identified different sets of molecular biomarkers for BC. But they did not compare their results to each other so much either computationally or experimentally. In this study, an attempt was made to propose a set of molecular biomarkers that might be more effective for BC diagnosis, prognosis and therapies, by using the integrated bioinformatics and statistical approaches. At first, we identified 190 differentially expressed genes (DEGs) between BC and control samples by using the statistical LIMMA approach. Then we identified 13 DEGs (AKR1C1, IRF9, OAS1, OAS3, SLCO2A1, NT5E, NQO1, ANGPT1, FN1, ATF6B, HPGD, BCL11A, and TP53INP1) as the key genes (KGs) by protein-protein interaction (PPI) network analysis. Then we investigated the pathogenetic processes of DEGs highlighting KGs by GO terms and KEGG pathway enrichment analysis. Moreover, we disclosed the transcriptional and post-transcriptional regulatory factors of KGs by their interaction network analysis with the transcription factors (TFs) and micro-RNAs. Both supervised and unsupervised learning's including multivariate survival analysis results confirmed the strong prognostic power of the proposed KGs. Finally, we suggested KGs-guided computationally more effective seven candidate drugs (NVP-BHG712, Nilotinib, GSK2126458, YM201636, TG-02, CX-5461, AP-24534) compared to other published drugs by cross-validation with the state-of-the-art alternatives top-ranked independent receptor proteins. Thus, our findings might be played a vital role in breast cancer diagnosis, prognosis and therapies.

Integrated analysis of virus and host transcriptomes in cervical cancer in Asian and Western populations.

Race may influence vulnerability to HPV variants in viral infection and perisistence. Integrated analysis of the virus and host transcriptomes from different populations provides an unprecedented opportunity to understand these racial disparities in the prevalence of HPV and cervical cancers. We performed RNA-Seq analysis of 90 tumors and 39 adjacent normal tissues from cervical cancer patients at Zhejiang University (ZJU) in China, and conducted a comparative analysis with RNA-Seq data of 286 cervical cancers from TCGA. We found a modestly higher rate of HPV positives and HPV integrations in TCGA than in ZJU. In addition to LINC00393 and HSPB3 as new common integration hotspots in both cohorts, we found new hotspots such as SH2D3C and CASC8 in TCGA, and SCGB1A1 and ABCA1 in ZJU. We described the first, to our knowledge, virus-transcriptome-based classification of cervical cancer associated with clinical outcome. Particularly, patients with expressed E5 performed better than those without E5 expression. However, the constituents of these virus-transcriptome-based tumor subtypes differ dramatically between the two cohorts. We further characterized the immune infiltration landscapes between different HPV statuses and revealed significantly elevated levels of regulatory T cells and M0 macrophages in HPV positive tumors, which were associated with poor prognosis. These findings increase our understanding of the racial disparities in the prevalence of HPV and its associated cervical cancers between the two cohorts, and also have important implications in the classification of tumor subtypes, prognosis, and anti-cancer immunotherapy in cervical cancer.

OncotRF: an online resource for exploration of tRNA-derived fragments in human cancers.

Transfer RNA-derived fragments (tRFs) are a new class of small non-coding RNAs whose biological roles in cancers are not well understood. Emerging evidence suggests that tRFs are involved in gene regulation at multiple levels. In this study, we constructed an integrative database, OncotRF (http://bioinformatics.zju.edu.cn/OncotRF), for in silico exploration of tRF functions, and identification of diagnostic and prognostic biomarkers in cancers. The database contains an analysis pipeline for tRF identification and characterization, analysis results of 11,211 small RNA sequencing samples and 8,776 RNA sequencing samples, and clinicopathologic annotation data from The Cancer Genome Atlas (TCGA). The results include: tRF identification and quantification across 33 cancers, abnormally expressed tRFs and genes, tRF-gene correlations, tRF-gene networks, survival analyses, and tRF-related functional enrichment analyses. Users are also able to identify differentially expressed tRFs, predict their functions, and assess the relevance of the tRF expression levels to the clinical outcome according to user-defined groups. Additionally, an online Kaplan-Meier plotter is available in OncotRF for plotting survival curves according to user-defined groups. OncotRF will be a valuable online database and functional annotation tool for researchers studying the roles, functions, and mechanisms of tRFs in human cancers.

MDEHT: a multivariate approach for detecting differential expression of microRNA isoform data in RNA-sequencing studies.

MOTIVATION: miRNA isoforms (isomiRs) are produced from the same arm as the archetype miRNA with a few nucleotides different at 5 and/or 3 termini. These well-conserved isomiRs are functionally important and have contributed to the evolution of miRNA genes. Accurate detection of differential expression of miRNAs can bring new insights into the cellular function of miRNA and a further improvement in miRNA-based diagnostic and prognostic applications. However, very few methods take isomiR variations into account in the analysis of miRNA differential expression. RESULTS: To overcome this challenge, we developed a novel approach to take advantage of the multidimensional structure of isomiR data from the same miRNAs, termed as a multivariate differential expression by Hotelling's T2 test (MDEHT). The utilization of the information hidden in isomiRs enables MDEHT to increase the power of identifying differentially expressed miRNAs that are not marginally detectable in univariate testing methods. We conducted rigorous and unbiased comparisons of MDEHT with seven commonly used tools in simulated and real datasets from The Cancer Genome Atlas. Our comprehensive evaluations demonstrated that the MDEHT method was robust among various datasets and outperformed other commonly used tools in terms of Type I error rate, true positive rate and reproducibility. AVAILABILITY AND IMPLEMENTATION: The source code for identifying and quantifying isomiRs and performing miRNA differential expression analysis is available at https://github.com/amanzju/MDEHT. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Pott's Spine: Diagnostic Imaging Modalities and Technology Advancements.

Spinal tuberculosis (TB) or Pott's spine is the commonest extrapulmonary manifestation of TB. It spreads through hematogenous route. Clinically, it presents with constitutional symptoms, back pain, tenderness, paraplegia or paraparesis, and kyphotic or scoliotic deformities. Pott's spine accounts for 2% of all cases of TB, 15% of extrapulmonary, and 50% of skeletal TB. The paradiscal, central, anterior subligamentous, and neural arch are the common vertebral lesions. Thoracic vertebrae are commonly affected followed by lumbar and cervical vertebrae. Plain radiographs are usually the initial investigation in spinal TB. For a radiolucent lesion to be apparent on a plain radiograph there should be 30% of bone mineral loss. Computed tomographic scanning provides much better bony detail of irregular lytic lesions, sclerosis, disc collapse, and disruption of bone circumference than plain radiograph. Magnetic resonance imaging (MRI) is the best diagnostic modality for Pott's spine and is more sensitive than other modalities. MRI frequently demonstrates disc collapse/destruction, cold abscess, vertebral wedging/collapse, marrow edema, and spinal deformities. Ultrasound and computed tomographic guided needle aspiration or biopsy is the technique for early histopathological diagnosis. Recently, the coexistence of human immunodeficiency virus infections and TB has been increased globally. In recent years, diffusion-weighted MRI (DW-MRI) and apparent diffusion coefficient values in combination with MRI are used to some extent in the diagnosis of spinal TB. We have reviewed related literature through internet. The terms searched on Google scholar and PubMed are TB, extrapulmonary TB, skeletal TB, spinal TB, Pott's spine, Pott's paraplegia, MRI, and computed tomography (CT).

Esthesioneuroblastoma: one of the causes of proptosis.

Esthesioneuroblatoma (Olfactory neuroblastoma) is a rare malignant neoplasm arising from the olfactory epithelium with bimodal age distribution between with first peak in second decades and second peak in sixth decade. Proptosis due to esthesioneuroblastoma is one of the rare causes. They have a long natural history characterized by frequent local or regional recurrence. Computed tomography and magnetic resonance imaging are the imaging modalities for diagnosing these tumors. A multidisciplinary approach with surgery and radiation therapy is an excellent treatment options for these tumors with chemotherapy being used to treat advanced or recurrent disease.