RNA biotype-associated molecular classification in hepatitis B-related hepatocellular carcinoma
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ABSTRACT:
PROVIDER: PRJNA453401 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR7061310_1.fastq.gz | Fastqsanger.gz | |||
| SRR7061310_2.fastq.gz | Fastqsanger.gz | |||
| SRR7061320_1.fastq.gz | Fastqsanger.gz | |||
| SRR7061320_2.fastq.gz | Fastqsanger.gz | |||
| SRR7061330_1.fastq.gz | Fastqsanger.gz |
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Project description:Recent advance of RNA-seq technology enabled us to profile the diverse variations of RNA transcripts precisely, including the variants from alternative splicing as well as non-coding transcripts. Here, by performing transcriptome profiling including coding and noncoding transcripts, we identify four molecular subtypes of hepatitis B-related hepatocellular carcinoma (HCC) patients that are characterized by enriched expression of the RNA biotypes of noncoding (NC) and immune-related transcripts (IM) (i.e., IM+NC+, IM-NC+, IM+NC-, IM-NC-). The subtype IM+NC+ shows better prognostic outcome, while the subtype IM+NC- shows the worst prognostic outcome, respectively. Further interrogation of the subtypes identifies long noncoding transcripts (i.e., LINC00844, C3P1, and TRPG1-AS1) as well as an alternatively spliced event of USO1 that play pivotal roles in HCC progression. In addition, we report an oncogenic fusion transcript SLC39A14-PIWIL2 that promotes an aggressive phenotype of HCC. Our comprehensive and systematic analysis of HCC transcriptome identify RNA biotype-based molecular classification, revealing novel driver transcriptome variants that can be potential biomarkers and/or therapeutic targets for precision medicine.
2022-04-01 | GSE113617 | GEO
Project description:Gene expression profiling of hepatocellular carcinoma (HCC) and background liver has been studied extensively; however, the relationship between the gene expression profiles of different lesions has not been assessed. We examined the expression profiles of 34 HCC specimens (17 hepatitis B virus [HBV]-related and 17 hepatitis C virus [HCV]-related) and 71 non-tumor liver specimens (36 chronic hepatitis B [CH-B] and 35 chronic hepatitis C [CH-C]) using an in-house cDNA microarray consisting of liver-predominant genes. Graphical Gaussian modeling (GGM) was applied to elucidate the interactions of gene clusters among the HCC and non-tumor lesions. Gene expression profiling of HCC and non-tumor lesions revealed the predisposing changes of gene expression in HCC. This approach has potential for the early diagnosis and possible prevention of HCC. We examined the expression profiles of 34 HCC specimens (17 hepatitis B virus [HBV]-related and 17 hepatitis C virus [HCV]-related) and 71 non-tumor liver specimens (36 chronic hepatitis B [CH-B] and 35 chronic hepatitis C [CH-C]) using an in-house cDNA microarray consisting of liver-predominant genes. Graphical Gaussian modeling (GGM) was applied to elucidate the interactions of gene clusters among the HCC and non-tumor lesions.
2013-11-14 | E-GEOD-44074 | biostudies-arrayexpress
Project description:Gene expression profiling of hepatocellular carcinoma (HCC) and background liver has been studied extensively; however, the relationship between the gene expression profiles of different lesions has not been assessed. We examined the expression profiles of 34 HCC specimens (17 hepatitis B virus [HBV]-related and 17 hepatitis C virus [HCV]-related) and 71 non-tumor liver specimens (36 chronic hepatitis B [CH-B] and 35 chronic hepatitis C [CH-C]) using an in-house cDNA microarray consisting of liver-predominant genes. Graphical Gaussian modeling (GGM) was applied to elucidate the interactions of gene clusters among the HCC and non-tumor lesions. Gene expression profiling of HCC and non-tumor lesions revealed the predisposing changes of gene expression in HCC. This approach has potential for the early diagnosis and possible prevention of HCC.
2013-11-14 | GSE44074 | GEO
Project description:Saliva is rich in proteins, DNA, RNA and microorganisms, and can be regarded as a biomarker library. In order to explore a noninvasive and simple means of early screening for liver cancer, proteomics was used to screen salivary markers of hepatitis B associated liver cancer. We used mass spectrometry coupled isobaric tags for relative and absolute quantitation (iTRAQ)-technology to identify differentially expressed proteins (DEPs). Western blot, immunohistochemistry and enzyme linked immunosorbent assay were used to detect marker expression of in tissues and saliva. Statistical analysis was used to analyze the diagnostic efficacy of the markers was analyzed through statistical analyses. By comparing the hepatocellular carcinoma (HCC) group with non-HCC groups, we screened out 152 salivary DEPs. We found orosomucoid 1(ORM1) had significantly higher expression in saliva of HCC patients compared with non-HCC groups (p<0.001) and the expression of ORM1 in liver cancer tissues was significantly higher than that in adjacent normal tissues(p<0.001). The combination of salivary ORM1 and alpha-fetoprotein (AFP) showed reasonable specificities and sensitivities for detecting HCC. In a word, salivary ORM1 as a new biomarker of hepatitis B associated hepatocellular carcinoma, combination of salivary ORM1 and AFP as an improved diagnostic tool for hepatocellular carcinoma.
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Project description:Background: The pathogenesis of hepatitis B virus (HBV)-caused hepatocellular carcinoma (HCC) is complex and not fully understood. In clinical, the effective prevention and treatment of HCC rely on the accurate diagnosis. We developed a biology network approach to investigate the potential mechanisms and biomarkers of each stages from HBV infection to HCC. Methods Global gene profiling of healthy individuals (HC), HBV carriers (HBVC), chronic hepatitis B patients (CHB), liver cirrhosis (LC) and HCC was analyzed by gene array. Differentially expressed genes (DEG) were found by RVM (Random variance model) corrective ANOVA and STC (Series Test of Cluster) analysis.
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Project description:Chronic infections by hepatitis B virus (HBV) and hepatitis C virus (HCV) appear to be the most significant causes of hepatocellular carcinoma (HCC). Aberrant promoter methylation is known to be deeply involved in cancer, including HCC. In this study, we analyzed aberrant promoter methylation on genome-wide scale in 6 HCCs including 3 HBV-related and 3 HCV-related HCCs, 6 matched noncancerous liver tissues and 3 normal liver tissues by methylated DNA immunoprecipitation-on-chip analysis. Candidate genes with promoter methylation were detected more frequently in HCV-related HCC. Candidate genes methylated preferentially to HBV-related or HCV-related HCCs were detected and selected, and methylation levels of the selected genes were validated using 125 liver tissue samples, including 61 HCCs (28 HBV-related HCCs and 33 HCV-related HCCs) and matched 59 matched noncancerous livers, and 5 normal livers, by quantitative methylation analysis using MALDI-TOF mass spectrometry. Among analyzed genes, preferential methylation in HBV-related HCC was validated in 1 gene only. However, 15 genes were found methylated preferentially in HCV-related HCC, which was independent from age. Hierarchical clustering of HCC using these 15 genes stratified HCV-related HCC as a cluster of frequently methylated samples. The 15 genes included genes inhibitory to cancer-related signaling such as RAS/RAF/ERK and Wnt/b-catenin pathways. It was indicated that genes methylated preferentially in HCV-related HCC exist, and it was suggested that DNA methylation might play an important role in HCV-related HCC by silencing cancer-related pathway inhibitors. we analyzed aberrant promoter methylation in 6 HCC clinical samples (including 3 HBV-related HCCs and 3 HCV-related HCCs) and their matched noncancerous tissues on genome-wide scale by the method. Candidate regions of promoter methylation preferentially to HBV-related HCC and HCV-related HCC were selected, and the methylation levels of these genes were measured quantitatively using MALDI-TOF mass spectrometry. Expression levels of these 6 pairs of HCC and 4 more pairs of HCCs and surrounding noncancerous tissues were analyzed by expression array and are reported in this Series. <br><br>This experiment was reloaded in November 2010 after additional curation. this dataset is part of the TransQST collection.
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