Presently, no effective markers are available to facilitate gallbladder cancer （GBC） diagnosis. This study aims to explore available markers for GBC diagnosis. Clinical data of 144 GBC and 116 cholelithiasis patient...Presently, no effective markers are available to facilitate gallbladder cancer （GBC） diagnosis. This study aims to explore available markers for GBC diagnosis. Clinical data of 144 GBC and 116 cholelithiasis patients were retrospectively reviewed. Logistic regression analysis was performed to evaluate GBC risk factors. A receiver operating characteristic （ROC） curve was used to assess the diagnosis value of the risk factors. By comparing the characteristic of GBC and cholelithiasis patients, the following factors exhibited statistical difference： age, gender, gallstones, total bilirubin （TB）, alkaline phosphatase （ALP）, aspartate aminotransferase （AST）, alanine aminotransferase （ALT）, platelet count （PLT）, CA125 （carcinoembryonic antigen 125）, and CA199 （carbohydrate antigen 199）. Logistic regression analysis indicated that age [odds ratio （OR）, 1.032; 95% confidence interval （95~o CI）, 1.004 to 1.061; P = 0.024], gender （OR, 0.346; 95% CI, 0.167 to 0.716; P = 0.004）, gallstones （OR, 0.027; 95~ CI, 0.007 to 0.095; P 〈 0.001）, ALP （OR, 1.003; 95~ CI, 1.000 to 1.006; P = 0.032）, TB （OR, 1.004; 95% CI, 1.000 to 1.009; P = 0.042）, and CA125 （OR, 1.007; 95% CI, 1.002 to 1.013; P = 0.011） were independent risk factors for GBC. According to the ROC curve, CA125 [area under curve （AUC）, 0.720], ALP （AUC, 0.713）, TB （AUC, 0.636）, and age （AUC, 0.573） were valuable diagnosis markers. Additionally, based on the independent risk factors, the GBC diagnosis model was established. Age, TB, ALP, and CA125 can be used as auxiliary diagnosis factors of GBC. The diagnosis model provides a quantitative tool for GBC diagnosis when comprehensively considering various risk factors.展开更多
We performed weighted gene coexpression network analysis （WGCNA） to gain insights into the molecular aspects of hepatocellular carcinoma （HCC）. Raw microarray datasets （including 488 samples） were downloaded fro...We performed weighted gene coexpression network analysis （WGCNA） to gain insights into the molecular aspects of hepatocellular carcinoma （HCC）. Raw microarray datasets （including 488 samples） were downloaded from the Gene Expression Omnibus （GEO） website. Data were normalized using the RMA algorithm. We utilized the WGCNA to identify the coexpressed genes （modules） after non-specific filtering. Correlation and survival analyses were conducted using the modules, and gene ontology （GO） enrichment was applied to explore the possible mechanisms. Eight distinct modules were identified by the WGCNA. Pink and red modules were associated with liver function, whereas turquoise and black modules were inversely correlated with tumor staging. Poor outcomes were found in the low expression group in the turquoise module and in the high expression group in the red module. In addition, GO enrichment analysis suggested that inflammation, immune, virus-related, and interferon-mediated pathways were enriched in the turquoise module. Several potential biomarkers, such as cyclin-dependent kinase 1 （CDK1）, topoisomerase 2α （TOP2A）, and serpin peptidase inhibitor clade C （antithrombin） member 1 （SERPINC1）, were also identified. In conclusion, gene signatures identified from the genome-based assays could contribute to HCC stratification. WGCNA was able to identify significant groups of genes associated with cancer prognosis.展开更多
文摘Presently, no effective markers are available to facilitate gallbladder cancer （GBC） diagnosis. This study aims to explore available markers for GBC diagnosis. Clinical data of 144 GBC and 116 cholelithiasis patients were retrospectively reviewed. Logistic regression analysis was performed to evaluate GBC risk factors. A receiver operating characteristic （ROC） curve was used to assess the diagnosis value of the risk factors. By comparing the characteristic of GBC and cholelithiasis patients, the following factors exhibited statistical difference： age, gender, gallstones, total bilirubin （TB）, alkaline phosphatase （ALP）, aspartate aminotransferase （AST）, alanine aminotransferase （ALT）, platelet count （PLT）, CA125 （carcinoembryonic antigen 125）, and CA199 （carbohydrate antigen 199）. Logistic regression analysis indicated that age [odds ratio （OR）, 1.032; 95% confidence interval （95~o CI）, 1.004 to 1.061; P = 0.024], gender （OR, 0.346; 95% CI, 0.167 to 0.716; P = 0.004）, gallstones （OR, 0.027; 95~ CI, 0.007 to 0.095; P 〈 0.001）, ALP （OR, 1.003; 95~ CI, 1.000 to 1.006; P = 0.032）, TB （OR, 1.004; 95% CI, 1.000 to 1.009; P = 0.042）, and CA125 （OR, 1.007; 95% CI, 1.002 to 1.013; P = 0.011） were independent risk factors for GBC. According to the ROC curve, CA125 [area under curve （AUC）, 0.720], ALP （AUC, 0.713）, TB （AUC, 0.636）, and age （AUC, 0.573） were valuable diagnosis markers. Additionally, based on the independent risk factors, the GBC diagnosis model was established. Age, TB, ALP, and CA125 can be used as auxiliary diagnosis factors of GBC. The diagnosis model provides a quantitative tool for GBC diagnosis when comprehensively considering various risk factors.
基金This manuscript was supported by the National Natural Science Foundation of China （No. 81402022） and the Fund of China Scholarship Council （No. 201406280106）.
文摘We performed weighted gene coexpression network analysis （WGCNA） to gain insights into the molecular aspects of hepatocellular carcinoma （HCC）. Raw microarray datasets （including 488 samples） were downloaded from the Gene Expression Omnibus （GEO） website. Data were normalized using the RMA algorithm. We utilized the WGCNA to identify the coexpressed genes （modules） after non-specific filtering. Correlation and survival analyses were conducted using the modules, and gene ontology （GO） enrichment was applied to explore the possible mechanisms. Eight distinct modules were identified by the WGCNA. Pink and red modules were associated with liver function, whereas turquoise and black modules were inversely correlated with tumor staging. Poor outcomes were found in the low expression group in the turquoise module and in the high expression group in the red module. In addition, GO enrichment analysis suggested that inflammation, immune, virus-related, and interferon-mediated pathways were enriched in the turquoise module. Several potential biomarkers, such as cyclin-dependent kinase 1 （CDK1）, topoisomerase 2α （TOP2A）, and serpin peptidase inhibitor clade C （antithrombin） member 1 （SERPINC1）, were also identified. In conclusion, gene signatures identified from the genome-based assays could contribute to HCC stratification. WGCNA was able to identify significant groups of genes associated with cancer prognosis.
