Gene expression patterns can be used as prognostic biomarkers in various types of cancers. We aimed to identify a gene expression pattern for individual distant metastatic risk assessment in patients ...with locoregionally advanced nasopharyngeal carcinoma.
In this multicentre, retrospective, cohort analysis, we included 937 patients with locoregionally advanced nasopharyngeal carcinoma from three Chinese hospitals: the Sun Yat-sen University Cancer Center (Guangzhou, China), the Affiliated Hospital of Guilin Medical University (Guilin, China), and the First People's Hospital of Foshan (Foshan, China). Using microarray analysis, we profiled mRNA gene expression between 24 paired locoregionally advanced nasopharyngeal carcinoma tumours from patients at Sun Yat-sen University Cancer Center with or without distant metastasis after radical treatment. Differentially expressed genes were examined using digital expression profiling in a training cohort (Guangzhou training cohort; n=410) to build a gene classifier using a penalised regression model. We validated the prognostic accuracy of this gene classifier in an internal validation cohort (Guangzhou internal validation cohort, n=204) and two external independent cohorts (Guilin cohort, n=165; Foshan cohort, n=158). The primary endpoint was distant metastasis-free survival. Secondary endpoints were disease-free survival and overall survival.
We identified 137 differentially expressed genes between metastatic and non-metastatic locoregionally advanced nasopharyngeal carcinoma tissues. A distant metastasis gene signature for locoregionally advanced nasopharyngeal carcinoma (DMGN) that consisted of 13 genes was generated to classify patients into high-risk and low-risk groups in the training cohort. Patients with high-risk scores in the training cohort had shorter distant metastasis-free survival (hazard ratio HR 4·93, 95% CI 2·99–8·16; p<0·0001), disease-free survival (HR 3·51, 2·43–5·07; p<0·0001), and overall survival (HR 3·22, 2·18–4·76; p<0·0001) than patients with low-risk scores. The prognostic accuracy of DMGN was validated in the internal and external cohorts. Furthermore, among patients with low-risk scores in the combined training and internal cohorts, concurrent chemotherapy improved distant metastasis-free survival compared with those patients who did not receive concurrent chemotherapy (HR 0·40, 95% CI 0·19–0·83; p=0·011), whereas patients with high-risk scores did not benefit from concurrent chemotherapy (HR 1·03, 0·71–1·50; p=0·876). This was also validated in the two external cohorts combined. We developed a nomogram based on the DMGN and other variables that predicted an individual's risk of distant metastasis, which was strengthened by adding Epstein–Barr virus DNA status.
The DMGN is a reliable prognostic tool for distant metastasis in patients with locoregionally advanced nasopharyngeal carcinoma and might be able to predict which patients benefit from concurrent chemotherapy. It has the potential to guide treatment decisions for patients at different risk of distant metastasis.
The National Natural Science Foundation of China, the National Science & Technology Pillar Program during the Twelfth Five-year Plan Period, the Natural Science Foundation of Guang Dong Province, the National Key Research and Development Program of China, the Innovation Team Development Plan of the Ministry of Education, the Health & Medical Collaborative Innovation Project of Guangzhou City, China, and the Program of Introducing Talents of Discipline to Universities.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Abstract
Happiness studies generally investigate average levels of happiness rather than happiness inequality between regions, and studies of social inequality usually measure it based on the ...distribution of life opportunities (e.g., income) rather than life results (e.g., happiness). Inspired by the Kuznets curve, which illustrates the inverted U-shaped correlation between income inequality and economic growth, this study investigates whether there is a subjective wellbeing Kuznets curve. It uses data from ten waves of the Chinese General Social Survey to construct a panel data set and runs panel data models to investigate the hypothesized curvilinear relationship between happiness inequality and economic growth. The results show that happiness inequality, measured as the standard deviations of respondents’ self-reported happiness, first increases and then decreases as per-capita GDP increases in Chinese provinces. These findings strongly support the subjective wellbeing Kuznets curve hypothesis and suggest that strategies for reducing happiness inequality must consider stages of economic development.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Nasopharyngeal carcinoma (NPC) is an aggressive malignancy with extremely skewed ethnic and geographic distributions. Increasing evidence indicates that targeting the tumor microenvironment (TME) ...represents a promising therapeutic approach in NPC, highlighting an urgent need to deepen the understanding of the complex NPC TME. Here, we generated single-cell transcriptome profiles for 7581 malignant cells and 40,285 immune cells from fifteen primary NPC tumors and one normal sample. We revealed malignant signatures capturing intratumoral transcriptional heterogeneity and predicting aggressiveness of malignant cells. Diverse immune cell subtypes were identified, including novel subtypes such as CLEC9A
dendritic cells (DCs). We further revealed transcriptional regulators underlying immune cell diversity, and cell-cell interaction analyses highlighted promising immunotherapeutic targets in NPC. Moreover, we established the immune subtype-specific signatures, and demonstrated that the signatures of macrophages, plasmacytoid dendritic cells (pDCs), CLEC9A
DCs, natural killer (NK) cells, and plasma cells were significantly associated with improved survival outcomes in NPC. Taken together, our findings represent a unique resource providing in-depth insights into the cellular heterogeneity of NPC TME and highlight potential biomarkers for anticancer treatment and risk stratification, laying a new foundation for precision therapies in NPC.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
. The normalized mutual information (NMI) has been widely used to evaluate the accuracy of community detection algorithms. However in this article we show that the NMI is seriously affected by ...systematic errors due to finite size of networks, and may give a wrong estimate of performance of algorithms in some cases. We give a simple theory to the finite-size effect of NMI and test our theory numerically. Then we propose a new metric for the accuracy of community detection, namely the relative normalized mutual information (rNMI), which considers statistical significance of the NMI by comparing it with the expected NMI of random partitions. Our numerical experiments show that the rNMI overcomes the finite-size effect of the NMI.
We propose a general framework for solving statistical mechanics of systems with finite size. The approach extends the celebrated variational mean-field approaches using autoregressive neural ...networks, which support direct sampling and exact calculation of normalized probability of configurations. It computes variational free energy, estimates physical quantities such as entropy, magnetizations and correlations, and generates uncorrelated samples all at once. Training of the network employs the policy gradient approach in reinforcement learning, which unbiasedly estimates the gradient of variational parameters. We apply our approach to several classic systems, including 2D Ising models, the Hopfield model, the Sherrington-Kirkpatrick model, and the inverse Ising model, for demonstrating its advantages over existing variational mean-field methods. Our approach sheds light on solving statistical physics problems using modern deep generative neural networks.
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CMK, CTK, FMFMET, IJS, NUK, PNG, UL, UM
The American Joint Committee on Cancer (AJCC) staging system is inadequate for an accurate prognosis in nasopharyngeal carcinoma (NPC). Thus, new biomarkers are under intense investigation. Here, we ...investigated whether the density of TILs could predict prognosis in NPC. First, we used 1490 cases of nasopharyngeal carcinoma samples from two independent cohorts to evaluate the density and distribution of tumor‐infiltrating lymphocytes (TILs). Second, in one cohort, we assessed associations between TILs and clinical outcomes in 593 randomly selected samples (defined as the training set) and validated findings in the remaining 593 samples (defined as the validation set). Furthermore, we confirmed the prognostic value of TILs in a second independent cohort of 304 cases (defined as the independent set). Based on multivariable Cox regression analysis, we also established an effective prognostic nomogram including TILs to improve accuracy in predicting disease‐free survival (DFS) for patients with nondisseminated NPC. We found that high TILs in the training set were significantly associated with favorable DFS hazard ratio (HR) 0.41, 95% confidence interval (CI) 0.28–0.58, p < 0.001, overall survival (OS, HR 0.42, 95% CI 0.27–0.64, p < 0.001), distant metastasis‐free survival (DMFS, HR 0.37, 95% CI 0.23–0.58, p < 0.001) and local‐regional recurrent free survival (LRRFS, HR 0.43, 95% CI 0.25–0.73, p = 0.002). Multivariate analysis showed that TILs are an independent prognostic indicator for DFS in all cohorts. In summary, this study indicated that TILs may reflect the immunological heterogeneity of NPC and could represent a new prognostic biomarker.
What's new?
Doctors typically use tumor stage to help determine cancer prognosis, but for nasopharyngeal cancer, it is not precise enough. These authors turned to the immune system for prognostic clues. They looked at the density and distribution of tumor‐infiltrating lymphocytes (TILs) in NPC patients from China. TILs turned out to be a strong independent predictor of disease‐free survival: greater numbers of TILs, they found, meant better outcomes. Once a standardized method for evaluating TILs can be developed, this metric could be extremely valuable for predicting disease progression in NPC patients.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
This review summarizes the progress in the fluorination and fluoroalkylation of electron‐rich systems with diverse fluorine (F) and fluoroalkyl (Rfn) reagents employing hypervalent iodine compounds ...as initiators in the last few decades. Because of the strong electrophilicity, high oxidizing properties, low toxicity, air and moisture stability, ready availability, ease of handling, and mild reaction conditions, the hypervalent iodine reagents have been widely utilized in modern organic chemistry. In particular, the use of hypervalent iodine reagents to initiate the C−F and C−Rfn (Rfn=CF2H, CF3, perfluoroalkyl, OCH2CF3, SCF3, SeCF3 and etc) bond formation has been increasingly developed. In these reactions, hypervalent iodine compounds behave as powerful oxidants or electrophiles and activate the fluorination/fluoroalkylation reagents, the transition‐metal catalysts, or the substrates to in situ form electrophilic or radical intermediates, which subsequently participate in fluorination, difluoromethylation, trifluoromethylation, perfluoroalkylation, trifluoroethoxylation, fluoroalkylthiolation, trifluoromethylselenolation and others under mild conditions. Although great achievements have been made in this area, they are just the initial phase and still require a wide scope for improvement. It is anticipated that this review will draw much attention from the organic chemistry community and inspire more contributions in the development of new hypervalent‐iodine‐mediated fluorination and fluoroalkylation reactions.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Modularity is a popular measure of community structure. However, maximizing the modularity can lead to many competing partitions, with almost the same modularity, that are poorly correlated with each ...other. It can also produce illusory ‘‘communities’’ in random graphs where none exist. We address this problem by using the modularity as a Hamiltonian at finite temperature and using an efficient belief propagation algorithm to obtain the consensus of many partitions with high modularity, rather than looking for a single partition that maximizes it. We show analytically and numerically that the proposed algorithm works all of the way down to the detectability transition in networks generated by the stochastic block model. It also performs well on real-world networks, revealing large communities in some networks where previous work has claimed no communities exist. Finally we show that by applying our algorithm recursively, subdividing communities until no statistically significant subcommunities can be found, we can detect hierarchical structure in real-world networks more efficiently than previous methods.
Significance Most work on community detection does not address the issue of statistical significance, and many algorithms are prone to overfitting. We address this using tools from statistical physics. Rather than trying to find the partition of a network that maximizes the modularity, our approach seeks the consensus of many high-modularity partitions. We do this with a scalable message-passing algorithm, derived by treating the modularity as a Hamiltonian and applying the cavity method. We show analytically that our algorithm succeeds all the way down to the detectability transition in the stochastic block model; it also performs well on real-world networks. It also provides a principled method for determining the number of groups or hierarchies of communities and subcommunities.
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BFBNIB, NMLJ, NUK, PNG, SAZU, UL, UM, UPUK
COVID-19 has remained an uncontained, worldwide pandemic. While battling for the disease in China, six Traditional Chinese Medicine (TCM) recipes have been shown to be remarkably effective for ...treating patients with COVID-19. The present review discusses principles of TCM in curing infectious disease, and clinical evidence and mechanisms of the 6 most effective TCM recipes used in treating COVID-19 in 92% of all of the confirmed cases in China. Applications of TCM and specific recipes in the treatment of other viral infections, such as those caused by SARS-CoV, MERS-CoV, hepatitis B virus, hepatitis C virus, influenza A virus (including H1N1 and H7N9), influenza B, dengue virus as well as Ebola virus, are also discussed. Among the 6 TCM recipes, Jinhua Qinggan (JHQG) granules and Lianhua Qingwen (LHQW) capsules are recommended during medical observation; Lung Cleansing and Detoxifying Decoction (LCDD) is recommended for the treatment of both severe and non-severe patients; Xuanfeibaidu (XFBD) granules are recommended for treating moderate cases; while Huashibaidu (HSBD) and Xuebijing (XBJ) have been used in managing severe cases effectively. The common components and the active ingredients of the six TCM recipes have been summarized to reveal most promising drug candidates. The potential molecular mechanisms of the active ingredients in the six TCM recipes that target ACE2, 3CLpro and IL-6, revealed by molecular biological studies and/or network pharmacology prediction/molecular docking analysis/visualization analysis, are fully discussed. Therefore, further investigation of these TCM recipes may be of high translational value in enabling novel targeted therapies for COVID-19, potentially via purification and characterization of the active ingredients in the effective TCM recipes.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
We present a general method for approximately contracting tensor networks with an arbitrary connectivity. This enables us to release the computational power of tensor networks to wide use in ...inference and learning problems defined on general graphs. We show applications of our algorithm in graphical models, specifically on estimating free energy of spin glasses defined on various of graphs, where our method largely outperforms existing algorithms, including the mean-field methods and the recently proposed neural-network-based methods. We further apply our method to the simulation of random quantum circuits and demonstrate that, with a trade-off of negligible truncation errors, our method is able to simulate large quantum circuits that are out of reach of the state-of-the-art simulation methods.
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