Integrative analysis identifies key mRNA biomarkers for diagnosis, prognosis, and therapeutic targets of HCV-associated hepatocellular carcinoma.
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ABSTRACT:
SUBMITTER: Zhang Y
PROVIDER: S-EPMC8148482 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Growing evidence has illustrated critical roles of competing endogenous RNA (ceRNA) regulatory network in human cancers including hepatocellular carcinoma. In this study, we aimed to find promising diagnostic and prognostic biomarkers for patients with hepatocellular carcinoma. Three novel unfavorable prognosis-associated genes (CELSR3, GPSM2, and CHEK1) was first identified. We also demonstrated that these genes were significantly upregulated in hepatocellular carcinoma cell lines and tissues. Next, 154 potential miRNAs of CELSR3, GPSM2, and CHEK1 were predicted. CHEK1-hsa-mir-195-5p/hsa-mir-497-5p and GPSM2-hsa-mir-122-5p axes were defined as two key pathways in carcinogenesis of hepatocellular carcinoma by combination of in silico analysis and experimental validation. Subsequently, lncRNAs binding to hsa-mir-195-5p, hsa-mir-497-5p, and hsa-mir-122-5p were predicted via starBase and miRNet databases. After performing expression analysis and survival analysis for these predicted lncRNAs, we showed that nine lncRNAs (SNHG1, SNHG12, LINC00511, HCG18, FGD5-AS1, CERS6-AS1, NUTM2A-AS1, SNHG16, and ASB16-AS1) were markedly increased in hepatocellular carcinoma and their upregulation indicated poor prognosis. Moreover, a similar mRNA-miRNA-lncRNA analysis for six "known" genes (CLEC3B, DNASE1L3, PTTG1, KIF2C, XPO5, and UBE2S) was performed. Subsequently, a comprehensive mRNA-miRNA-lncRNA triple ceRNA network linked to prognosis of patients with hepatocellular carcinoma was established. Moreover, all RNAs in this network exhibited significantly diagnostic values for patients with hepatocellular carcinoma. In summary, the current study constructed a mRNA-miRNA-lncRNA ceRNA network associated with diagnosis and prognosis of hepatocellular carcinoma.
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Project description:We performed DNA methylation (HELP array) and gene expression profiling in 215 samples of adult B-lineage acute lymphoblastic leukemia (ALL) and 12 normal preB samples. Adult B-lineage acute lymphoblastic leukemia (B-ALL) is an aggressive disease with <40% long-term survival. Genetic alterations such as BCR/ABL, E2A/PBX1 and MLL rearrangement (tMLL) define distinct B-ALL subtypes, which are associated with poor clinical outcome. It has been shown that these B-ALL subtypes have distinct expression profiles. However, the role of the epigenome in shaping these expression profiles and how the aberrant epigenetic gene regulation contributes to the biological and clinical features of those ALL subtypes is largely unknown. To address this question, we performed genome-wide DNA methylation and gene expression profiling on a large cohort of 215 well-characterized adult B-ALL specimens from the ECOG E2993 phase III clinical trial and a cohort of normal precursor B (preB) cells from 12 healthy bone marrows. The integrative analysis of these profiles led to the identification of key gene networks deregulated at the epigenetic and transcriptional levels within each subtype. In BCR/ABL, we identified a network centered on IL2RA(CD25), which is itself hypomethylated and overexpressed in most BCR/ABL B-ALL and confers poor clinical outcomes. In the tMLL subtype, we uncovered aberrant epigenetic and transcriptional activities that include hypomethylation and upregulation of FLT3 and BCL6. After showing that MLL/AF4 fusion protein binds to these genes as well as other hypomethylated and overexpressed genes in tMLL ALL cells, we showed that a specific BCL6 inhibitor, RI-BPI, kills tumor cells in both tMLL ALL cell lines and patient samples. BCL6 inhibition may therefore represent a novel therapeutic strategy for B-ALL patients with MLL translocations.
2012-11-27 | GSE34861 | GEO
Project description:Introduction: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths worldwide due to poor survival outcome. Thus, there is an urgent need to identify effective biomarkers for early diagnosis and prognosis prediction. Methods: A total of 389 differentially expressed genes (DEGs) between HCC samples and normal were selected based on the Robust Rank Aggregation (RRA) method. We combined DEGs expression and clinical traits to construct a gene co-expression network through WGCNA. Forty hub genes were selected from the key module. Among them, YWHAB, PPAT, NOL10 were eventually identified as prognostic biomarkers using multivariate Cox regression model. Biomarkers expression pattern was investigated by informatic analysis and verified by RNA-seq of 32 patients with HCC. DiseaseMeth 2.0, MEXPRESS, and Tumor Immune Estimation Resource (TIMER) were used to assess the methylation and immune status of biomarkers. GSVA, CCK8, colony formation assay, Edu imaging kit, wound-healing assay, and xenograft tumor model were utilized to investigate the effects of biomarkers on proliferation, metastasis of HCC cells in vitro, and in vivo. The Kaplan-Meier (KM) plotter and ROC curves were used to validate the prognostic and diagnostic value of biomarker expression. Results: All the selected biomarkers were upregulated in HCC samples and higher expression levels were associated with advanced tumor stages and T grades. The regulation of YWHAB, PPAT, NOL10 promoter methylation varied in tumors, and precancerous normal tissues. Immune infiltration analysis suggested that the abnormal regulations of these biomarkers were likely attributed to B cells and dendritic cells. GSVA for these biomarkers showed their great contributions to proliferation of HCC. Specific inhibition of their expression had strong effects on tumorigenesis in vitro and in vivo. ROC and KM curves confirmed their usefulness of diagnosis and prognosis of HCC. Conclusions: These findings identified YWHAB, PPAT, and NOL10 as novel biomarkers and validated their diagnostic and prognostic value for HCC.
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Project description:BackgroundClinicopathological studies suggest that Alzheimer's disease (AD) pathology begins ?10-15 years before the resulting cognitive impairment draws medical attention. Biomarkers that can detect AD pathology in its early stages and predict dementia onset would, therefore, be invaluable for patient care and efficient clinical trial design. We utilized a targeted proteomics approach to discover novel cerebrospinal fluid (CSF) biomarkers that can augment the diagnostic and prognostic accuracy of current leading CSF biomarkers (A?42, tau, p-tau181).Methods and findingsUsing a multiplexed Luminex platform, 190 analytes were measured in 333 CSF samples from cognitively normal (Clinical Dementia Rating [CDR] 0), very mildly demented (CDR 0.5), and mildly demented (CDR 1) individuals. Mean levels of 37 analytes (12 after Bonferroni correction) were found to differ between CDR 0 and CDR>0 groups. Receiver-operating characteristic curve analyses revealed that small combinations of a subset of these markers (cystatin C, VEGF, TRAIL-R3, PAI-1, PP, NT-proBNP, MMP-10, MIF, GRO-?, fibrinogen, FAS, eotaxin-3) enhanced the ability of the best-performing established CSF biomarker, the tau/A?42 ratio, to discriminate CDR>0 from CDR 0 individuals. Multiple machine learning algorithms likewise showed that the novel biomarker panels improved the diagnostic performance of the current leading biomarkers. Importantly, most of the markers that best discriminated CDR 0 from CDR>0 individuals in the more targeted ROC analyses were also identified as top predictors in the machine learning models, reconfirming their potential as biomarkers for early-stage AD. Cox proportional hazards models demonstrated that an optimal panel of markers for predicting risk of developing cognitive impairment (CDR 0 to CDR>0 conversion) consisted of calbindin, A?42, and age.Conclusions/significanceUsing a targeted proteomic screen, we identified novel candidate biomarkers that complement the best current CSF biomarkers for distinguishing very mildly/mildly demented from cognitively normal individuals. Additionally, we identified a novel biomarker (calbindin) with significant prognostic potential.
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Project description:Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death worldwide. Increasing studies showed that long non-coding RNAs (lncRNAs), a novel class of RNAs that are greater than 200 nucleotides in length but lack the ability to encode proteins, exert crucial roles in the occurrence and progression of HCC. LncRNAs promote the proliferation, migration, invasion, autophagy, and apoptosis of tumor cells by regulating downstream target gene expression and cancer-related signaling pathways. Meanwhile, lncRNA can be used as biomarkers to predict the efficacy of HCC treatment strategies, such as surgery, radiotherapy, chemotherapy, and immunotherapy, and as a potential individualized tool for HCC diagnosis and treatment. In this review, we overview up-to-date findings on lncRNAs as potential biomarkers for HCC surgery, radiotherapy, chemotherapy resistance, target therapy, and immunotherapy, and discuss the potential clinical application of lncRNA as tools for HCC diagnosis and treatment.
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