To determine the expression of components in Toll-like receptors (TLRs)/Nod-like receptors (NLRs)/ infiammasome/caspase-llinterleukin (IL-l)-P pathway, we examined the expression profiles of those genes by analyzing the data from expression sequence tag cDNA cloning and sequencing. We made several important findings: firstly, among 11 tissues examined, vascular tissues and heart express fewer types of TLRs and NLRs than immune and defense tissues including blood, lymph nodes, thymus and trachea; secondly, brain, lymph nodes and thymus do not express proinflammatory cytokines IL-IP and IL-18 constitutively, suggesting that these two cytokines need to be up regulated in the tissues; and thirdly, based on the expression data of three characterized inflammasomes (NALPl, NALP3 and IPAF inflammasome), the examined tissues can be classified into three tiers: the first tier tissues including brain, placenta, blood and thymus express inflammasome(s) in constitutive status; the second tier tissues have inflammasome(s) in nearly-ready expression status (with the requirement of upregulation of one component); the third tier tissues, like heart and bone marrow, require upregulation of at least two components in order to assemble functional inflammasomes. Our original model of three-tier expression of inflammasomes would suggest a new concept oftissue inflammation privilege, and provides an insight to the differences among tissues in initiating acute inflammation in response to stimuli.

Serum and plasma contain abundant biological information that reflect the body's physiological and pathological conditions and are therefore a valuable sample type for disease biomarkers. However, comprehensive profiling of the serological proteome is challenging due to the wide range of protein concentrations in serum. Methods : To address this challenge, we developed a novel in-depth serum proteomics platform capable of analyzing the serum proteome across ~10 orders or magnitude by combining data obtained from Data Independent Acquisition Mass Spectrometry (DIA-MS) and customizable antibody microarrays. Results : Using psoriasis as a proof-of-concept disease model, we screened 50 serum proteomes from healthy controls and psoriasis patients before and after treatment with traditional Chinese medicine (YinXieLing) on our in-depth serum proteomics platform. We identified 106 differentially-expressed proteins in psoriasis patients involved in psoriasis-relevant biological processes, such as blood coagulation, inflammation, apoptosis and angiogenesis signaling pathways. In addition, unbiased clustering and principle component analysis revealed 58 proteins discriminating healthy volunteers from psoriasis patients and 12 proteins distinguishing responders from non-responders to YinXieLing. To further demonstrate the clinical utility of our platform, we performed correlation analyses between serum proteomes and psoriasis activity and found a positive association between the psoriasis area and severity index (PASI) score with three serum proteins (PI3, CCL22, IL-12B). Conclusion : Taken together, these results demonstrate the clinical utility of our in-depth serum proteomics platform to identify specific diagnostic and predictive biomarkers of psoriasis and other immune-mediated diseases.

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A model adjusted for reference arm response rates was found to fit clinical trial data significantly better than unadjusted models.

PurposeColorectal cancer (CRC) is one of the most common malignant tumors worldwide. This study aimed to explore the prognostic value of lncRNAs in CRC.Material and methodsWe performed gene expression profiling to identify differentially expressed lncRNAs between 51 normal and 646 tumor tissues from The Cancer Genome Atlas database. Cox regression and robust likelihood-based survival models were used to find prognosis-related lncRNAs. A lncRNA signature was developed to predict the overall survival of patients with CRC. In addition, a receiver operating characteristic curve analysis was performed to identify the optimal cutoff with the best Youden index to divide patients into different groups based on risk level.ResultsEighty survival-related lncRNAs were identified and a 15-lncRNA signature was developed on the basis of a risk score to comprehensively predict the overall survival of patients with CRC. The prognostic value of the 15-lncRNA risk score was validated using the internal testing set and total set. The risk indicator was shown to be an independent prognostic factor (hazard ratio =2.92; 95% CI: 1.73–4.94; P<0.001). Notably, all 15 lncRNAs (AC024581.1, FOXD3-AS1, AC012531.1, AC003101.2, LINC01219, AC083967.1, AL590483.1, AC105118.1, AC010789.1, AC067930.5, AC105219.2, LINC01354, LINC02474, LINC02257, and AC079612.1) were newly found to correlate with the prognosis of patients with CRC. Furthermore, the function of 15 lncRNAs was explored through the ceRNA network. These lncRNAs regulated coding genes that were involved in many key cancer pathways.ConclusionA 15-lncRNA expression signature was discovered as a prognostic indicator for patients with CRC, which may act as competing endogenous RNA (ceRNAs) to play a crucial role in the modulation of cancer-related pathways. These findings may allow a better understanding of the prognostic value of lncRNAs.