We are using global genomic approaches to profile clinical specimens that are associated with different stages of liver diseases. We have identified a unique diagnostic signature for patients with early onset of liver cancer and have also developed a unique molecular signature based on the mRNA gene expression of metastatic primary hepatocellular carcinoma (HCC) specimens to predict prognosis and metastasis of HCC patients. We found that this molecular signature could identify those patients who were most at risk for recurrence even in patients with early stage disease. More recently, we integrated genomic and transcriptomic profiles to search for metastasis driver genes. We found that primary tumor lesions and their match distant metastasis were similar, however significant differences could be identified between primary tumors with or without accompanying metastasis. Moreover, metastasis genes were principally tumor type and organ-site-specific, further solidifying that metastatic propensity is inherent to the primary tumor. We have also developed a unique molecular prognostic signature based on mRNA gene expression of the liver microenvironment of HCC patients. We found that a predominant humoral cytokine profile occurs in the metastatic liver microenvironment and that a shift toward anti-inflammatory/immune-suppressive responses may promote HCC metastases. Interestingly, the tumor signature is principally different from that of liver microenvironment. We have recently explored whether activated hepatic stellate cells (A-HSCs) contribute directly to HCC recurrence. We identified and validated an A-HSC-specific gene expression signature among nontumor tissues of HCC patients that was associated with HCC recurrence and survival. Further studies showed that A-HSCs preferentially alter monocyte populations to induce protumorigenic and progressive features by shifting their gene expression from an inflammatory to an immune suppressive signature. These findings indicate that disruption of the interactions and signaling events between inflammatory cells and components of the microenvironment may be useful therapeutic strategies for preventing HCC relapse. We have also found that small non-coding RNAs, termed microRNAs are associated with metastasis and could significantly predict patient survival and relapse even in early stage disease, while certain microRNAs (e.g. microRNA-26) are gender-related. Patients with low microRNA-26 expression had poor survival and were better responders to interferon therapy than those with normal expression. We developed a qRT-PCR-based matrix template and scoring algorithm (MIR26-DX) to assign patients into either low or high microRNA-26 groups. Patients with low microRNA-26 levels selected by the template were those that responded favorably to interferon-alpha therapy. We have now initiated a multi-center randomized control clinical trial in China based on these findings (NCT01681446). We also found that miR-29 family members were significantly down-regulated in AFP+ tumors with significant inverse correlation of DNMT3A gene expression and an increase in DNA methylation. AFP also inhibited transcription of the miR-29a/b-1 locus via c-MYC and promotes tumor growth of AFP- HCC cells in nude mice. Thus, tumor biology differs considerably between AFP+ HCC and AFP- HCC and that AFP is a functional antagonist of miR-29, which may contribute to global epigenetic alterations and poor prognosis in HCC. We have also used integrative approaches to identify HCC driver genes. We have combined high-resolution, array-based comparative genomic hybridization and transcriptome analysis of HCC samples to identify and validate a 10-gene signature associated with chromosome 8p loss and poor outcome. Functional studies demonstrated that three gene products have tumor suppressive properties and two of these genes, SORBS3 and SH2D4A, are linked and inhibit STAT3-mediated IL-6 signaling in HCC cells. We have also integrated metabolite and mRNA profiles to define key signaling events of HCC cancer stem cells. Our analysis revealed that stearoyl CoA desaturase (SCD), a key enzyme involved in fatty acid biosynthesis, and its related metabolites were highly elevated in stem cell-like HCC and are associated with HCC survival and aggressiveness. In a comparison of global metabolic profiles between liver, breast and pancreatic cancer tissues, we found that metabolites are principally unique to each tissue and cancer type. Thus, metabolic profiling could be applied as cancer classification tools to differentiate tumors based on tissue of origin. We have also recently analyzed metabolomic and transcriptomic profiles jointly collected from breast cancer and HCC patients to explore the associations between and build predictors of the expression of metabolic enzymes and the levels of the metabolites participating in the reactions they catalyze. A wide range of metabolites can be successfully predicted from the transcriptome. We also developed an integrative subgraph mining approach, iSubgraph, to discover patterns of miRNA-gene networks to stratify HCC patients. This algorithm could detect cooperative regulation of miRNAs and genes with highly stable class predictions. Thus, our methods can integrate various omics data derived from different platforms and with different dynamic scales to better define molecular tumor subtypes. Through recent analyses of HCC, we revealed that a large number of RNA binding proteins (RBPs) are dysregulated and that RBP dysregulation is associated with poor prognosis. We further identified that oncogenic activation of a top candidate RBP, negative elongation factor E (NELFE), via somatic copy-number alterations enhanced MYC signaling and promoted HCC progression. Interestingly, NELFE induces a unique tumor transcriptome by selectively regulating MYC-associated genes. Thus, our results revealed NELFE as an oncogenic protein that may contribute to transcriptome imbalance in HCC through the regulation of MYC signaling. The incidence of cholangiocarcinoma (CCA), a bile-duct-related cancer and second most frequent primary liver cancer (PLC), is prevalent, especially in the north-east area of Thailand. We initiated the Thailand Initiative for Genomics and Expression Research in Liver Cancer (TIGER-LC) to provide a comprehensive global analysis of genomic alterations related to the primary liver cancer types in Thai liver cancer patients. Recently, we have identified common molecular subtypes with key drivers linked to similar prognosis among 199 Thai CCA and HCC patients through systems integration of genomics, transcriptomics, and metabolomics. Our results indicate that ICC and HCC, while clinically treated as separate entities, share common molecular determinants, suggesting that a unified molecular landscape of liver cancer is required to improve diagnosis and therapy. Intratumor molecular heterogeneity of hepatocellular carcinoma is partly attributed to the presence of hepatic cancer stem cells (CSCs). Different CSC populations defined by various cell surface markers may contain different oncogenic drivers, posing a challenge in defining molecularly targeted therapeutics. We combined transcriptomic and functional analyses of hepatocellular carcinoma cells at the single-cell level to assess the degree of CSC heterogeneity. We found that hepatic CSCs at the single-cell level are phenotypically, functionally, and transcriptomically heterogeneous. CSC subpopulations contain distinct molecular signatures. Interestingly, distinct genes within different CSC subpopulations are independently associated with hepatocellular carcinoma prognosis, suggesting that a diverse hepatic CSC transcriptome affects intratumor heterogeneity and tumor progression.
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