prevention and management. Here we reported the screening, clinical feathers, and treatment process of a family cluster involving three COVID-19 patients. The discovery of the first asymptomatic carrier in this family cluster depends on the repeated and comprehensive epidemiological investigation by disease control experts. In addition, the combination of multiple detection methods can help clinicians find asymptomatic carriers as early as possible. In conclusion, the prevention and control experience of this family cluster showed that comprehensive rigorous epidemiological investigation and combination of multiple detection methods were of great value for the detection of hidden asymptomatic carriers.

The aim of the present study is to construct a competitive endogenous RNA (ceRNA) regulatory network by using differentially expressed long noncoding RNAs (lncRNAs), microRNAs (miRNAs), and mRNAs in patients with hepatocellular carcinoma (HCC), and to construct a prognostic model for predicting overall survival (OS) of HCC patients. Differentially expressed lncRNAs, miRNAs, and mRNAs were explored between HCC tissues and normal liver tissues. A prognostic model was built for predicting OS of HCC patients and receiver operating characteristic curves were used to evaluate the performance of the prognostic model. There were 455 differentially expressed lncRNAs, 181 differentially expressed miRNAs, and 5035 differentially expressed mRNAs. A ceRNA regulatory network was constructed based on 43 lncRNAs, 37 miRNAs, and 105 mRNAs. Eight mRNA biomarkers (H2AFX, SQSTM1, ITM2A, PFKP, TPD52L1, ACSL4, STRN3, and CPEB3) were identified as independent risk factors by multivariate Cox regression and were used to develop a prognostic model for OS. The C‐indexes in the model group were 0.776 (95% confidence interval [CI], 0.730‐0.822), 0.745 (95% CI, 0.699‐0.791), and 0.789 (95% CI, 0.743‐0.835) for 1‐, 3‐, and 5‐year OS, respectively. The current study revealed potential molecular biological regulation pathways and prognostic biomarkers by the ceRNA regulatory network. A prognostic model based on prognostic mRNAs in the ceRNA network might be helpful to predict the individual mortality risk for HCC patients. The individual mortality risk calculator can be used by visiting the following URL: https://zhangzhiqiao.shinyapps.io/Smart_cancer_predictive_system_HCC/.

BackgroundThe prognostic value of Ki-67 expression in colorectal cancer patients was controversial. Therefore, this meta analysis was conducted to ascertain the prognostic value of Ki-67 expression in colorectal cancer patients.MethodsThe electronic databases, including EMBASE, PubMed, Cochrane Library and Web of Knowledge database, were searched from January 1970 to July 2017. The pooled hazard ratios and 95% confidence intervals were calculated to evaluate the prognostic value of Ki-67 expression for colorectal cancer patients.ResultsTotally 34 eligible studies and 6180 colorectal cancer patients were included in the present meta analysis. The pooled hazard ratios were 1.54(95% CI 1.17–2.02, P = 0.005) for overall survival and 1.43(1.12–1.83, P = 0.008) for disease free survival in univariate analysis. After adjustment of other prognostic factors, the pooled HR was 1.50(95% CI 1.02–2.22, P = 0.03) for overall survival in multivariate analysis.ConclusionThe present meta analysis demonstrated that high Ki-67 expression is significantly correlated with poor overall survival and disease free survival, indicating that high Ki-67 expression may serve as a valuable predictive method for poor prognosis of colorectal cancer patients.

BackgroundThe prognostic value and clinicopathologic significance of Ki-67 expression in gastric cancer patients was controversial. This meta-analysis was performed to clarify the prognostic value and clinicopathologic significance of Ki-67 expression in gastric cancer patients.Materials and MethodsSeveral electronic databases were searched for eligible studies. The pooled odds ratio (OR), hazard ratios (HR) and 95% confidence interval(CI) were calculated to explore the prognostic value and clinicopathologic significance of Ki-67 expression for disease free survival and overall survival.ResultsTotally 5600 gastric cancer patients from 29 studies were included in this study. High Ki-67 expression was significantly related with Lauren's classification (OR = 1.70; P = 0.001; 95%CI: 1.40-2.06) and tumor size(OR = 1.54; P = 0.006; 95%CI: 1.14-2.09). However, high Ki-67 expression was not significantly associated with lymph node metastasis (OR = 1.37; P = 0.138; 95% CI: 0.90-2.08), tumor stage (OR = 1.31; P = 0.296; 95% CI: 0.79-2.16) and tumor differentiation (OR = 1.03; P = 0.839; 95% CI: 0.78-1.35). The pooled HRs were 1.87(P = 0.001; 95% CI 1.30-2.69) for disease free survival and 1.23(P = 0.005; 95% CI 1.06-1.42) for overall survival.ConclusionsHigh Ki-67 expression may serve as a predictive biomarker for poor prognosis in gastric cancer patients. Stratification by Ki-67 expression may be a consideration for selection of therapeutic regimen and integrated managements.

An increasing body of evidence supports the association of immune genes with tumorigenesis and prognosis of breast cancer (BC). This research aims at exploring potential regulatory mechanisms and identifying immunogenic prognostic markers for BC, which were used to construct a prognostic signature for disease-free survival (DFS) of BC based on artificial intelligence algorithms. Differentially expressed immune genes were identified between normal tissues and tumor tissues. Univariate Cox regression identified potential prognostic immune genes. Thirty-four transcription factors and 34 immune genes were used to develop an immune regulatory network. The artificial intelligence survival prediction system was developed based on three artificial intelligence algorithms. Multivariate Cox analyses determined 17 immune genes (ADAMTS8, IFNG,

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Background Increasing evidences supported the association between long non-coding RNA (lncRNA) and disease free survival in gastric cancer (GC) patients. The purpose of the current study was to construct and verify a noninvasive preoperative predictive tool for disease free survival in GC patients. Methods There were 265 and 300 GC patients in model dataset and validation dataset respectively. The associations between the lncRNA biomarkers and disease free survival were evaluated by univariate and multivariate Cox regression. Results Thirteen lncRNA biomarkers (GAS5-AS1, AL109615.3, KDM7A-DT, AP000866.2, KCNJ2-AS1, LINC00656, LINC01777, AC046185.3, TTTY14, LINC01526, LINC02523, LINC00592, and C5orf66) were identified as prognostic biomarkers with disease free survival. These thirteen lncRNA biomarkers were combined to construct a prognostic signature for disease free survival. The C-indexes of the current predictive signature in model cohort were 0.849 (95% CI 0.803–0.895), 0.859 (95% CI 0.813–0.905) and 0.888 (95% CI 0.842–0.934) for 1-year, 3-year and 5-year disease free survival respectively. Based on thirteen-lncRNA prognostic signature, patients in model cohort could be stratified into high risk group and low risk group with significant different disease free survival rate (hazard ratio [HR] = 7.355, 95% confidence interval [CI] 4.378–12.356). Good reproducibility of thirteen-lncRNA prognostic signature was confirmed in an external validation cohort (GSE62254) with HR 3.919 and 95% CI 2.817–5.453. Further analysis demonstrated that the prognostic significance of thirteen-lncRNA prognostic signature was independent of other clinical characteristics. Conclusions In conclusion, a simple noninvasive prognostic signature was established for preoperative prediction of disease free survival in GC patients. This prognostic signature might predict the individual mortality risk of disease free survival without pathological information and facilitate individual treatment decision-making. Electronic supplementary material The online version of this article (10.1186/s12935-019-0846-6) contains supplementary material, which is available to authorized users.

Background Accumulated evidences have demonstrated that long non-coding RNAs (lncRNAs) are correlated with prognosis of patients with hepatocellular carcinoma. The current study aimed to develop and validate a prognostic lncRNA signature to improve the prediction of overall survival in hepatocellular carcinoma patients. Methods The study cohort involved 348 hepatocellular carcinoma patients with lncRNA expression information and overall survival information. Through gene mining approach, the current study established a prognostic lncRNA signature (named LncRNA risk prediction score) for predicting the overall survival of hepatocellular carcinoma patients. Results The current study built a predictive nomogram based on ten prognostic lncRNA predictors through Cox regression analysis. In model group, the Harrell’s concordance indexes of LncRNA risk prediction score were 0.811 (95% CI 0.769–0.853) for 1-year overall survival, 0.814 (95% CI 0.772–0.856) for 3-year overall survival and 0.796 (95% CI 0.754–0.838) for 5-year overall survival respectively. In validation cohort, the Harrell’s concordance indexes of LncRNA risk prediction score were 0.779 (95% CI 0.737–0.821), 0.828 (95% CI 0.786–0.870) and 0.796 (95%CI 0.754–0.838) for 1-year survival, 3-year survival and 5-year survival respectively. LncRNA risk prediction score could stratify hepatocellular carcinoma patients into low risk group and high risk group. Further survival curve analysis demonstrated that the overall survival rate of high risk patients was significantly poorer than that of low risk patients ( P < 0.001). Conclusions In conclusion, the current study developed and validated a prognostic signature to predict the individual mortality risk for hepatocellular carcinoma patients. LncRNA risk prediction score is helpful to identify the patients with high mortality risk and optimize the individualized treatment decision. The web calculator can be used by click the following URL: https://zhangzhiqiao2.shinyapps.io/Smart_cancer_predictive_system_HCC_3/ . Electronic supplementary material The online version of this article (10.1186/s12935-019-0890-2) contains supplementary material, which is available to authorized users.