Table2_Comprehensive analysis of the cancer driver genes constructs a seven-gene signature for prediction of survival and tumor immunity in hepatocellular carcinoma.DOC

Hepatocellular carcinoma (HCC) is a highly malignant and heterogeneous tumor with poor prognosis. Cancer driver genes (CDGs) play an important role in the carcinogenesis and progression of HCC. In this study, we comprehensively investigated the expression, mutation, and prognostic significance of 568 CDGs in HCC. A prognostic risk model was constructed based on seven CDGs (CDKN2C, HRAS, IRAK1, LOX, MYCN, NRAS, and PABPC1) and verified to be an independent prognostic factor in both TCGA and ICGC cohorts. The low-score group, which showed better prognosis, had a high proportion of CD8+ T cells and elevated expression of interferon-related signaling pathways. Additionally, we constructed a nomogram to extend the clinical applicability of the prognostic model, which exhibits excellent predictive accuracy for survival. Our study showed the important role of CDGs in HCC and provides a novel prognostic indicator for HCC.</p

Table1_Comprehensive analysis of the cancer driver genes constructs a seven-gene signature for prediction of survival and tumor immunity in hepatocellular carcinoma.XLS

Hepatocellular carcinoma (HCC) is a highly malignant and heterogeneous tumor with poor prognosis. Cancer driver genes (CDGs) play an important role in the carcinogenesis and progression of HCC. In this study, we comprehensively investigated the expression, mutation, and prognostic significance of 568 CDGs in HCC. A prognostic risk model was constructed based on seven CDGs (CDKN2C, HRAS, IRAK1, LOX, MYCN, NRAS, and PABPC1) and verified to be an independent prognostic factor in both TCGA and ICGC cohorts. The low-score group, which showed better prognosis, had a high proportion of CD8+ T cells and elevated expression of interferon-related signaling pathways. Additionally, we constructed a nomogram to extend the clinical applicability of the prognostic model, which exhibits excellent predictive accuracy for survival. Our study showed the important role of CDGs in HCC and provides a novel prognostic indicator for HCC.</p

Visualization 1: Cerebral capillary velocimetry based on temporal OCT speckle contrast

Multimedia for Fig 4b Originally published in Biomedical Optics Express on 01 December 2016 (boe-7-12-4859

Media 1: 4D optical coherence tomography-based micro-angiography achieved by 1.6-MHz FDML swept source

Originally published in Optics Letters on 15 April 2015 (ol-40-8-1779

Uric acid distributed in blank Uox^-/- rat’s organs (mean + SD, n = 9 (WT) or 8 (Uox-/-)).

A, Uric acid content (μg/g) in the organs. B, Uric acid index of the organ, which was calculated by dividing tissue uric acid content by its serum uric acid concentration. C, The part of uric acid content of the organ excessing their SUA, which was calculated by subtracting serum uric acid concentration from its tissue uric acid content. D, Total uric acid in the organ, which was calculated by multiplying tissue uric acid content by its organ weight. SUA, serum uric acid; WT, normal wild-type rats; Uox-/-, blank Uox-/- rats. Skeleton muscle was the gluteus maximus. Serum uric acid in Fig 6A (the last column) was cited from Fig 2A. *, P<0.05 vs WT, Student’s t-test.</p

This the raw data for Fig 6.

(XLSX)</p

Table1_Integrative Analysis Constructs an Extracellular Matrix-Associated Gene Signature for the Prediction of Survival and Tumor Immunity in Lung Adenocarcinoma.XLSX

Background: Lung adenocarcinoma (LUAD) accounts for the majority of lung cancers, and the survival of patients with advanced LUAD is poor. The extracellular matrix (ECM) is a fundamental component of the tumor microenvironment (TME) that determines the oncogenesis and antitumor immunity of solid tumors. However, the prognostic value of extracellular matrix-related genes (ERGs) in LUAD remains unexplored. Therefore, this study is aimed to explore the prognostic value of ERGs in LUAD and establish a classification system to predict the survival of patients with LUAD.Methods: LUAD samples from The Cancer Genome Atlas (TCGA) and GSE37745 were used as discovery and validation cohorts, respectively. Prognostic ERGs were identified by univariate Cox analysis and used to construct a prognostic signature by Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. The extracellular matrix-related score (ECMRS) of each patient was calculated according to the prognostic signature and used to classify patients into high- and low-risk groups. The prognostic performance of the signature was evaluated using Kaplan–Meier curves, Cox regression analyses, and ROC curves. The relationship between ECMRS and tumor immunity was determined using stepwise analyses. A nomogram based on the signature was established for the convenience of use in the clinical practice. The prognostic genes were validated in multiple databases and clinical specimens by qRT-PCR.Results: A prognostic signature based on eight ERGs (FERMT1, CTSV, CPS1, ENTPD2, SERPINB5, ITGA8, ADAMTS8, and LYPD3) was constructed. Patients with higher ECMRS had poorer survival, lower immune scores, and higher tumor purity in both the discovery and validation cohorts. The predictive power of the signature was independent of the clinicopathological parameters, and the nomogram could also predict survival precisely.Conclusions: We constructed an ECM-related gene signature which can be used to predict survival and tumor immunity in patients with LUAD. This signature can serve as a novel prognostic indicator and therapeutic target in LUAD.</p

Genes associated with uric acid transportation, synthesis and degradation expressed at RNA level in Uox^-/- rats’ organs (FPKM, mean + SD, n = 3).

Genes associated with uric acid transportation, synthesis and degradation expressed at RNA level in Uox-/- rats’ organs (FPKM, mean + SD, n = 3).</p

Inosine adnosine.xlsx.

This the raw data for Fig 3. (XLSX)</p

Uox^-/- rats treated with adenosine (2.2 g/L) or inosine (2.2 g/L) by free drinking for two weeks (mean + SD, n = 6).

SUA (serum uric acid, A), Creatinine (Cr, B), and urea (BUN, C) affected by adenosine or inosine. Blk group, Uox-/- rats without treatment. *, P < 0.05 vs Blk, ANOVA.</p