Project description:A total of 180 Hepatocellular carcinoma (HCC) and 125 adjacent normal samples were examined. Genome-wide DNA methylation profiling was done with Illumina HumanMethylation850 Beadchip of approximately 850,000 CpG sites. Aims: To identify HCC-specific methylation based biomarkers which are suitable for liquid biopsy.
Project description:A serum miRNA combination could be a powerful classifier for the detection of hepatocellular carcinoma. Keywords: Non-coding RNA profiling by array
Project description:This SuperSeries is composed of the following subset Series: GSE9843: Gene expression profiling of 91 hepatocellular carcinomas with hepatitis C virus etiology, Samples with "vascular invasion: Yes/No" were included in the study. GSE20017: Gene Signature to Identify Vascular Invasion in Hepatocellular Carcinoma Refer to individual Series
Project description:Hepatocellular carcinoma (HCC) accounts for approximately 90% of primary liver cancers, the second leading cause of cancer mortality worldwide. Although HCC surgical treatment may be curative in the early stages, its five-year overall survival is only 50-70%. Advances in proteomic technologies have expanded the breadth and depth of cancer proteome characterization. Here, we present the largest characterization effort on proteomic profiling of 222 tumor and paired non-tumor tissues in clinically early HCC (Barcelona Clinic Liver Cancer (BCLC) stage 0 and A). Quantitative proteomic data identified three more-refined subtypes in the early- stage cohort of HCC (termed S-I, S-II and S-III) with different clinical outcomes. S-I retained hepatic detoxification and metabolic functions with the best prognosis, S-II increased molecular expression related to proliferation, and S-III showed distinct enrichment of tumor metastasis and immune response pathways and the poorest prognosis. The subtype specific signatures targeted by known FDA approved drugs or inhibitors under clinical investigations for HCC provide a novel resource for HCC therapeutic targets. A new mechanism of disrupted cholesterol homeostasis with aberrant accumulation of cholesteryl esters was also highlighted in S-III. Thus, this study represents the first proteomic stratification of early-stage HCC, providing insights into tumor biology and personalized targeted therapy.
Project description:Despite the diversity of liquid biopsy transcriptomic repertoire, numerous studies often 30 exploit only a single RNA type signature for diagnostic biomarker potential. This frequently results 31 in insufficient sensitivity and specificity necessary to reach diagnostic utility. Combinatorial biomarker approaches may offer a more reliable diagnosis. Here we investigated the synergistic contributions of circRNA and mRNA signatures derived from blood platelets as biomarkers for lung cancer detection. We developed a comprehensive bioinformatics pipeline permitting analysis of platelet-circRNA and mRNA derived from non-cancer individuals and lung cancer patients. An optimal selected signature is then used to generate the predictive classification model using machine learning algorithm. Using an individual signature of 21 circRNA and 28 mRNA, the predictive models reached an Area Under the Curve (AUC) of 0.88 and 0.81, respectively. Importantly, combinatorial analysis including both types of RNAs resulted in an 8-target signature (6 mRNA and 2 40 circRNA) enhancing the differentiation of lung cancer from controls (AUC of 0.92). Additionally, we identified five biomarkers potentially specific for early-stage detection of lung cancer. Our proof-of-concept study presents the first multi-analyte-based approach for the analysis of platelets-derived biomarkers, providing a potential combinatorial diagnostic signature for lung cancer detection.
Project description:Hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide, and is the one of the few cancers in which a continued increase in incidence has been observed over several years. HCC associated with chronic liver disease evolves from precancerous lesion and early HCC to overt cancer, and identifying key molecules contributing to early stage HCC is an urgent need. α-Fetoprotein (AFP) is the best serum biomarker for diagnosis of HCC, but sensitivity is low, particularly in detection of early-stage HCC. Therefore, novel and reliable diagnostic biomarkers to complement AFP are needed to improve HCC diagnosis. We aim to determine transcriptome-based molecular signature of multistep hepatocarcinogenesis, and to identify novel serum biomarkers to diagnose early stage HCC patient.
Project description:Recently, long noncoding rnas (lncRNAs) have been shown to have key roles in the development and prgression of hepatocellular carcinoma (HCC). However, the mechanism that contributes to the HCC biology is unknown, especially at the early and middle stages. In this study, we comprehensively investigaged lnRNAs and mRNAs expression in HCC. We found three significant lnRNAs for diagostic markers in HCC at early stage.