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Differentially expressed genes between early and advanced hepatocellular carcinoma (HCC) as a potential tool for selecting liver transplant recipients.

ABSTRACT: Hepatocellular carcinoma (HCC) is the fifth most common cancer in the world. Liver transplantation (LT) represents a curative treatment for "small" HCC. Preoperative staging is critical in selecting optimal candidates for LT to optimize the use of this scarce resource. From December 1997 to February 2004, 148 patients diagnosed with cirrhosis and HCC were evaluated at our center. After staging, the patients were listed for LT according to United Network for Organ Sharing (UNOS) criteria. When pretransplant liver MRIs were compared with the findings of the explanted livers, 8 of 35 patients (22.8%) were understaged. Three of the 8 patients (37.5%) had recurrence post-LT. A retrospective gene expression profiling study was done using microarray technology for tumor samples in the pretransplant hepatitis C virus (HCV)-HCC understaged patients and in a contemporaneous group of HCV-HCC patients that were accurately staged. Two sample t tests comparing the early versus advanced HCV-HCCs with respect to gene expression showed an important set of genes differentially expressed among the samples. Hierarchical clustering analysis of the gene expression profiling classified 93.8% of the total tumor samples and 85.7% of the understaged samples in concordance with the explanted pathological staging. We found a distinctive pattern of gene expression between early and advanced HCV-HCCs. These results suggest that gene expression profiling could improve the pre-LT HCC staging to more closely mimic the explant pathology. Whether gene expression profiling of HCC will be refined to the point of predicting potential metastatic biologic behavior to predict post-LT recurrence will require longitudinal prospective study of this gene array technology.

SUBMITTER: Mas VR

PROVIDER: S-EPMC1578766 | biostudies-literature | 2006 Apr-Jun

REPOSITORIES: biostudies-literature

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Differentially expressed genes between early and advanced hepatocellular carcinoma (HCC) as a potential tool for selecting liver transplant recipients.

Mas Valeria R VR Maluf Daniel G DG Archer Kellie J KJ Yanek Kenneth K Williams Bridgette B Fisher Robert A RA

Molecular medicine (Cambridge, Mass.) 20060401 4-6

Hepatocellular carcinoma (HCC) is the fifth most common cancer in the world. Liver transplantation (LT) represents a curative treatment for "small" HCC. Preoperative staging is critical in selecting optimal candidates for LT to optimize the use of this scarce resource. From December 1997 to February 2004, 148 patients diagnosed with cirrhosis and HCC were evaluated at our center. After staging, the patients were listed for LT according to United Network for Organ Sharing (UNOS) criteria. When pr ...[more]

PMID: 16953559

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Project description:Variations in candidate and donor acceptance criteria may influence access and mortality for liver transplantation. We sought to understand how recipient and donor characteristics vary across centers and how patients interpret this information, and we used these data to develop a tool to provide tailored information to candidates seeking a center (www.transplantcentersearch.org). We analyzed liver recipient data from the Scientific Registry of Transplant Recipients to determine how recipient and donor characteristics (eg, age, Medicaid use, and human immunodeficiency virus status) varied across programs. Data included recipients and donors at each US program between January 1, 2015, and December 31, 2017. The variation in characteristics was plotted with centers stratified by total transplant volume and by volume of each characteristic. A subset of characteristics was plotted to show variation over 3 years. We created mockups of potential reports displaying recipient characteristics alongside pretransplant and posttransplant outcomes and solicited feedback at patient and family interviews and focus groups, which included 39 individuals: 10 pilot interviews with candidates seeking liver transplant at the University of Minnesota-Fairview (UMNF) and 5 focus groups with 13 UMNF candidates, 6 UMNF family members, and 10 national recipients. Transcripts were analyzed using a thematic analysis. Several themes emerged: (1) Candidates experience gaps in existing education about center options; (2) patients requested information about how selection criteria might impact access to transplant; and (3) information tailored to a candidate's medical characteristics can inform decisions. Characteristics shown on mockups varied across centers (P < 0.01). Variation was widespread for small and large centers. In conclusion, variation exists in recipient and donor characteristics across centers. Liver transplant patients provide positive feedback upon viewing patient-specific search tools.

Project description:BackgroundExosomes are involved in cell-to-cell communication, neovascularization, cancer metastasis, and drug resistance, which all play an important role in the occurrence and progression of hepatocellular carcinoma (HCC). Because there are few mechanistic studies about the function of exosomes in HCC, the goals of this study were to identify exosome-related genes in HCC, to establish a reliable prognostic model for HCC, and to explore underlying mechanisms.MethodsThe exoRBase and The Cancer Genome Atlas (TCGA) databases were used to analyze differentially expressed genes (DEGs). Cox regression and least absolute shrinkage and selection operator analyses were used to identify DEGs closely related to the overall survival of patients with HCC. An exosome-related prognostic model was then constructed in TCGA and validated in the International Cancer Genome Consortium database. A nomogram was developed to predict survival. CIBERSORT was used to estimate the abundance of different types of immune cells. Immunotherapy-related DEGs were used to predict the effect of immunotherapy.ResultsForty-eight exosome-related DEGs were obtained; of them, five [exportin 1 (XPO1), lysosomal thiol reductase (IFI30), F-box protein 16 (FBXO16), calmodulin 1 (CALM1), MORC family CW-type zinc finger 3 (MORC3)] were selected to construct a predictive model. Patients with HCC were then divided into low- and high-risk groups using the best cut-off value, as determined by the X-tile software. Prognosis was significantly poorer in the high-risk than in the low-risk group (P=0.009; hazard ratio =2.65). Features related to exosomes were found to positively regulate immune response. Further analysis showed a higher risk score was associated with higher expression of immune checkpoint molecules, including programmed death ligand 1 (PD-L1), programmed death ligand 2 (PD-L2), T cell Ig and ITIM domain (TIGIT), and indoleamine-2,3-dioxygenase 1 (IDO1).ConclusionsThis study has identified a novel signature based on exosome-related genes that has potential as a prognostic biomarker for HCC. Our research provides an immunological perspective for the development of precision treatment for HCC.

Project description:BackgroundA common task in analyzing microarray data is to determine which genes are differentially expressed across two (or more) kind of tissue samples or samples submitted under experimental conditions. Several statistical methods have been proposed to accomplish this goal, generally based on measures of distance between classes. It is well known that biological samples are heterogeneous because of factors such as molecular subtypes or genetic background that are often unknown to the experimenter. For instance, in experiments which involve molecular classification of tumors it is important to identify significant subtypes of cancer. Bimodal or multimodal distributions often reflect the presence of subsamples mixtures. Consequently, there can be genes differentially expressed on sample subgroups which are missed if usual statistical approaches are used. In this paper we propose a new graphical tool which not only identifies genes with up and down regulations, but also genes with differential expression in different subclasses, that are usually missed if current statistical methods are used. This tool is based on two measures of distance between samples, namely the overlapping coefficient (OVL) between two densities and the area under the receiver operating characteristic (ROC) curve. The methodology proposed here was implemented in the open-source R software.ResultsThis method was applied to a publicly available dataset, as well as to a simulated dataset. We compared our results with the ones obtained using some of the standard methods for detecting differentially expressed genes, namely Welch t-statistic, fold change (FC), rank products (RP), average difference (AD), weighted average difference (WAD), moderated t-statistic (modT), intensity-based moderated t-statistic (ibmT), significance analysis of microarrays (samT) and area under the ROC curve (AUC). On both datasets all differentially expressed genes with bimodal or multimodal distributions were not selected by all standard selection procedures. We also compared our results with (i) area between ROC curve and rising area (ABCR) and (ii) the test for not proper ROC curves (TNRC). We found our methodology more comprehensive, because it detects both bimodal and multimodal distributions and different variances can be considered on both samples. Another advantage of our method is that we can analyze graphically the behavior of different kinds of differentially expressed genes.ConclusionOur results indicate that the arrow plot represents a new flexible and useful tool for the analysis of gene expression profiles from microarrays.

Dataset Information

Differentially expressed genes between early and advanced hepatocellular carcinoma (HCC) as a potential tool for selecting liver transplant recipients.

Publications

Differentially expressed genes between early and advanced hepatocellular carcinoma (HCC) as a potential tool for selecting liver transplant recipients.

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