Modeling Human Liver Cancer Heterogeneity: Virally Induced Transgenic Models and Mouse Genetic Models of Chronic Liver Inflammation

Ringelhan, Marc; Reisinger, Florian; Yuan, Detian; Weber, Achim; Heikenwälder, Mathias

doi:10.1002/0471141755.ph1431s67

Cited by 4 publications

(2 citation statements)

References 86 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To develop a detailed understanding of the oncological functions of SAMSN1, further functional studies are required. For example, studies that aim to identify the binding partners of SAMSN1 or those that can take advantage of mouse models of GC to evaluate the effects of the presence or absence of SAMSN1 on premalignant and malignant phenotypes would be of great value in advancing our understanding of the role of this tumor suppressor in GC (40).…”

mentioning

confidence: 99%

Prognostic relevance of SAMSN1 expression in gastric cancer

et al. 2016

View full text Add to dashboard Cite

Abstract.The prognosis for patients with advanced gastric cancer (GC) remains poor. The identification of biomarkers relevant to the recurrence and metastasis of GC is advantageous for stratifying patients and proposing novel molecular targets. In the present study the oncological roles of SAM domain, SH3 domain and nuclear localization signals 1 (SAMSN1), a mediator of B-cell function, were elucidated in GC. The expression and methylation status of SAMSN1 were investigated in a panel of 11 GC cell lines. Immunohistochemical staining was performed to determine the pattern of SAMSN1 protein expression in gastric tissues. The prognostic impact of SAMSN1 expression was determined by analyzing 175 pairs of surgically resected gastric tissues. A marked decrease in the level of SAMSN1 mRNA was detected in 8/11 GC cell lines as compared with that in a non-transformed intestinal epithelium cell line (FHs 74) without promoter methylation. The mean expression level of SAMSN1 mRNA was reduced in GC tissues compared with normal adjacent tissues, an observation that was independent of tumor differentiation. The pattern of SAMSN1 protein expression was significantly correlated with that of SAMSN1 mRNA. Low SAMSN1 mRNA expression was significantly associated with tumor size (>60 mm; P=0.026) and shorter overall survival times (P=0.004). Multivariate analysis identified low SAMSN1 mRNA expression as an independent prognostic factor for poor overall survival (hazard ratio, 1.80; 95% confidence interval, 1.07-3.05; P= 0.025). The difference in survival between the low and high SAMSN1 expression groups was more marked in patients with stage II/III GC compared to those with stage IV GC. In patients with stage II/III GC who underwent curative surgery, low SAMSN1 expression was associated with reduced disease free survival times. The results of the present study indicate that downregulation of SAMSN1 transcription may affect the progression and recurrence of GC, and therefore may represent a novel biomarker of GC.

show abstract

mentioning

confidence: 99%

Prognostic relevance of SAMSN1 expression in gastric cancer

et al. 2016

View full text Add to dashboard Cite

show abstract

“…Hepatocellular carcinoma (HCC) is the second most common cause of cancer-related death [ 1 ]. To date, there is no clinical evidence that most of the adjuvant agents studied can improve survival in any stage of HCC [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of programmed cell death protein 1 in hepatocellular carcinoma patients using radiomics analysis with radiofrequency-based ultrasound multifeature maps

Wang

Xiao

et al. 2022

BioMed Eng OnLine

View full text Add to dashboard Cite

Background This study explored the feasibility of radiofrequency (RF)-based radiomics analysis techniques for the preoperative prediction of programmed cell death protein 1 (PD-1) in patients with hepatocellular carcinoma (HCC). Methods The RF-based radiomics analysis method used ultrasound multifeature maps calculated from the RF signals of HCC patients, including direct energy attenuation (DEA) feature map, skewness of spectrum difference (SSD) feature map, and noncentrality parameter S of the Rician distribution (NRD) feature map. From each of the above ultrasound maps, 345 high-throughput radiomics features were extracted. Then, the useful radiomics features were selected by the sparse representation method and input into support vector machine (SVM) classifier for PD-1 prediction. Results and conclusion Among all the RF-based prediction models and the ultrasound grayscale comparative model, the RF-based model using all of the three ultrasound feature maps had the highest prediction accuracy (ACC) and area under the curve (AUC), which were 92.5% and 94.23%, respectively. The method proposed in this paper is effective for the meaningful feature extraction of RF signals and can effectively predict PD-1 in patients with HCC.

show abstract