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LncRNA PVT1 as an effective biomarker for cancer diagnosis and detection based on transcriptome data and meta-analysis.

ABSTRACT: PURPOSE:Long noncoding RNA (lncRNA) PVT1 was detected all types of cancer from Cancer Genome Atlas (TCGA) project; however, the role of PVT1 in cancer is not clear. This study aimed to reanalyze and determine the effect of PVT1 on cancer diagnosis, especially detection in serum. MATERIALS AND METHODS:Differential expression of PVT1 between cancers and corresponding normal tissues and receiver operating characteristic (ROC) curve were analyzed for all types of cancers in TCGA database. RevMan5.3, Meta-DiSc1.4 and STATA14.0 were used to estimate pooled diagnostic effects of PVT1 in tissue as well as serum. RESULTS:Compared to corresponding normal tissues, PVT1 expression was significantly upregulated in 18 types of cancer and further being an effective diagnosis biomarker in 16 of them. For the 23 diagnosis tests performed in tissue, the pooled AUC and diagnostic odd ratio (DOR) were estimated to be 0.81 (95% CI: 0.76-0.86) and 17.25 (95% CI: 8.43-35.27), when the pooled AUC and DOR were 0.83 (95%CI: 0.75-0.91) and 13.86 (95% CI: 4.70-40.66) for serum tests. Furthermore, the pooled sensitivity and specificity were 0.83 (95% CI: 0.76-0.89) and 0.74 (95% CI:0.70-0.84) for tissue as well as 0.81 (95% CI: 0.76-0.86) and 0.76 (95% CI:0.70-0.81) for serum. CONCLUSIONS:PVT1, especially in serum, might be a usable biomarker for cancer diagnosis / detection.

SUBMITTER: Zeng Y

PROVIDER: S-EPMC5650436 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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LncRNA PVT1 as an effective biomarker for cancer diagnosis and detection based on transcriptome data and meta-analysis.

Zeng Yunhong Y Wang Tieqiang T Liu Yi Y Su Zhan Z Lu Pingtao P Chen Xiaoliang X Hu Dongsheng D

Oncotarget 20170904 43

<h4>Purpose</h4>Long noncoding RNA (lncRNA) PVT1 was detected all types of cancer from Cancer Genome Atlas (TCGA) project; however, the role of PVT1 in cancer is not clear. This study aimed to reanalyze and determine the effect of PVT1 on cancer diagnosis, especially detection in serum.<h4>Materials and methods</h4>Differential expression of PVT1 between cancers and corresponding normal tissues and receiver operating characteristic (ROC) curve were analyzed for all types of cancers in TCGA datab ...[more]

PMID: 29088881

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Project description:Similar to other droplet-based single cell assays, single nucleus ATAC-seq (snATAC-seq) data harbor multiplets that confound downstream analyses. Detecting multiplets in snATAC-seq data is particularly challenging due to data sparsity and limited dynamic range (0 reads: closed chromatin, 1: open on in one parental chromosome allele, 2: open on in both alleles chromosomes). Yet, these unique data features offer an opportunity to identify multiplets. ATAC-DoubletDetector (https://ucarlab.github.io/ATAC-DoubletDetector/) AMULET (Atac MULtiplet Estimation Tool) exploits these unique features to detect multiplets by studying enumerates the number of regions with >2 uniquely aligned reads across the genome to effectively detect multiplets - an effective alternative to methods based on artificially-generated multiplets. We evaluated the method by generating snATAC-seq data (e.g., state-of-the-art ArchR). For benchmarking we generated snATAC-seq data and generated data fromeasured the efficacy of AMULET inm in two primary human tissues: peripheral human blood mononuclear cells (PBMCs) and pancreatic islet samples. AMULET detects had high multiplets with an estimated precision (estimated via donor-based multiplexing) and high recall (estimated via simulated doublets) compared to alternatives 0.57 precision and achieves 0.85 recall. When and was the most effective when a certain read depth is achieved (a certain read depth per nucleus is achieved samples are sequenced deeply (e.g., median read count per nucleus >20K25K) reads per nucleus in PBMCs), ATAC-DoubletDetector captured 85% of simulated doublets (i.e., recall), significantly outperforming ArchR (24%). For lower read depth, ATAC-DoubletDetector and ArchR produced complementary results. Moreover, ATAC-DoubletDetector was equally effective in identifying homotypic multiplets (i.e., multiplets from the same cell type), which are missed by simulation-based methods. Cell-specific marker peaks enabled accurate (85%) tracing of cellular origins of snATAC-seq multiplets. Accordingly, more abundant cells within a tissue are more likely to form multiplets and the majority of multiplets are homotypic. ATAC-DoubletDetector is a fast and effective multiplet detection/annotation tool for improved single cell epigenomic data analyses across diverse biological systems and conditions.

Dataset Information

LncRNA PVT1 as an effective biomarker for cancer diagnosis and detection based on transcriptome data and meta-analysis.

Publications

LncRNA PVT1 as an effective biomarker for cancer diagnosis and detection based on transcriptome data and meta-analysis.

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