Background: Hepatocellular carcinoma (HCC) recurrence appears commonly after liver transplantation (LT), and it severely affected the long-term survival of patients. Previous studies have proved that Rap1A is involved in hepatocarcinogenesis and metastasis, and demonstrated the significant association between Rap1A gene rs494453 polymorphism and HCC. However, the relationship between Rap1A rs494453 polymorphism and HCC recurrence after LT remained unclear. Methods: A total of 74 HCC patients who underwent LT from July 2005 to June 2015 was analyzed. The genotypes of both donors and recipients had been confirmed as Rap1A rs494453. The independent risk factors that associated with HCC recurrence were investigated with univariate and multivariate logistic regression analysis. The recurrence-free (RFS) and overall survival (OS) were calculated with Cox regression analysis. The Rap1A rs494453 genotype frequencies were determined using the Χ² test and the minor allele frequencies (MAFs) of Rap1A rs494453 genotypes were calculated by Hardy-Weinberg equilibrium. Results: We found that the donor Rap1A rs494453 polymorphism was profoundly associated with HCC recurrence after LT. Moreover, the Milan criteria, microvascular invasion and donor Rap1A rs494453 genotype were proved to be independent risk factors for HCC recurrence. Patients with donor AG/GG genotypes had a distinct lower RFS and OS than AA genotype. The TNM stage, Milan criteria, microvascular invasion, and donor Rap1A rs494453 genotype were independent factors for the RFS of LT patients. Conclusions: Donor Rap1A rs494453 is a potential predictive marker for HCC recurrence risk after LT.
Background Conventional blood and stool tests are normally used for early screening of colorectal cancer (CRC) but the accuracy and efficiency remain to be improved. Recent findings suggest Fusobacterium nucleatum to be a biomarker for CRC. This study evaluated the role of F. nucleatum and developed CRC diagnostic models by combining F. nucleatum with fecal occult blood (FOB), transferrin (TRF), carcinoembryonic antigen (CEA), carbohydrate antigen 19‐9 (CA19‐9), gender, and age. Materials and Methods Candidates including 71 healthy individuals and 59 CRC patients were recruited. Abundance of F. nucleatum in stool or tissue samples was measured by quantitative real‐time PCR. CEA, CA19‐9, TRF, and FOB were measured in parallel. These biomarkers together with genders and ages were the seven parameters used to develop CRC diagnostic models. Ten different machine learning algorithms were tested to achieve the best performance. Results Fecal F. nucleatum abundance was found significantly higher in CRC group compared to healthy group (p = 0.0005). Among the CRC patients, F. nucleatum abundance in tumor tissue was significantly higher than that in paracancerous tissue (p = 0.0087). CRC diagnostic models using different parameters were generated based on Logistic Regression algorithm, which showed good performance. The area under the curve (AUC) score of fecal F. nucleatum as the single diagnostic biomarker was 0.68 while the accuracy was 0.56. The diagnostic performance was obviously improved with the highest AUC (0.93) and accuracy (0.87) achieved when using all the 7 clinical parameters. The combination F. nucleatum + FOB + gender + age had the second highest AUC (0.92) and accuracy (0.85). A more utilitarian model using F. nucleatum + FOB showed relatively high AUC at 0.86 and accuracy at 0.81. Conclusions F. nucleatum is valuable for CRC diagnosis. Combination of different clinical parameters could significantly improve CRC diagnostic performance. The combination F. nucleatum + FOB + gender + age may be an effective and noninvasive method for clinical application.
Due to the poor prognosis for hepatocellular carcinoma (HCC) presently, a systemic analysis supported by the multi-omics data is extremely necessary to search for gene markers for the clinical prognostic prediction of HCC. The data on RNA-seq, single nucleotide polymorphism (SNP), and copy number variation (CNV), etc. were downloaded from TCGA, leading to a final of 367 samples, which were divided into training set and testing set randomly. In the training set, both prognosis-related genes and those with SNP or CNV were screened, which were incorporated for feature selection using the random forest method. The testing and GEO verification sets (N = 265) were used to verify the constructed gene-related prognosis model. qPCR was used to detect the expression of 5 genes in clinical specimens. After including genomic variant and prognosis-related genes, we got 78 candidate genes and 5 feature genes (CISH, LHPP, MGMT, PDRG1, and LCAT) eventually through random forest feature selection. The 5-gene signature is an independent prognostic risk factor for HCC patients. In addition, the signature shows good predicting performance and clinical practicality in train- ing set, testing set and external verification set. The results of qPCR based on clinical samples showed that the expression of PDRG1 was increased in colon cancer tissues and the expression of CISH, LHPP, MGMT and LCAT were decreased in colon cancer tissues. We identify the ability of 5-gene signature to serve as an innovative marker of survival prediction for patients with HCC.
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