Screening effective differential expression genes for hepatic carcinoma with metastasis in the peripheral blood mononuclear cells by RNA-seq

Shen, Yanting; Bu, Lu; Li, Rui; Chen, Zhenzhu; Tian, Fei; Lü, Na; Ge, Qinyu; Bai, Ya Mei; Zhang, Lu

doi:10.18632/oncotarget.15855

Cited by 15 publications

(21 citation statements)

References 37 publications

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“…Byeno et al [ 5 ] reported that based on long-term survival data, the serum OPN and DKK1 levels in patients with liver cancer can be used as novel biomarkers that predict prognosis. Other serum markers, such as alpha-fetoprotein (AFP) and alkaline phosphatase (ALP or AKP), have also been reported in clinical practice, however, these markers lack sufficient sensitivity and specificity [ 6 ]. Therefore, it is necessary to find effective biomarkers essential for diagnosis and treatment for HCC.…”

Section: Introductionmentioning

confidence: 99%

Identification of a protein signature for predicting overall survival of hepatocellular carcinoma: a study based on data mining

Yang

2020

BMC Cancer

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Background: Hepatocellular carcinoma (HCC), is the fifth most common cancer in the world and the second most common cause of cancer-related deaths. Over 500,000 new HCC cases are diagnosed each year. Combining advanced genomic analysis with proteomic characterization not only has great potential in the discovery of useful biomarkers but also drives the development of new diagnostic methods. Methods: This study obtained proteomic data from Clinical Proteomic Tumor Analysis Consortium (CPTAC) and validated in The Cancer Proteome Atlas (TCPA) and TCGA dataset to identify HCC biomarkers and the dysfunctional of proteogenomics. Results: The CPTAC database contained data for 159 patients diagnosed with Hepatitis-B related HCC and 422 differentially expressed proteins (112 upregulated and 310 downregulated proteins). Restricting our analysis to the intersection in survival-related proteins between CPTAC and TCPA database revealed four coverage survival-related proteins including PCNA, MSH6, CDK1, and ASNS. Conclusion: This study established a novel protein signature for HCC prognosis prediction using data retrieved from online databases. However, the signatures need to be verified using independent cohorts and functional experiments.

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Section: Introductionmentioning

confidence: 99%

Identification of a protein signature for predicting overall survival of hepatocellular carcinoma: a study based on data mining

Yang

2020

BMC Cancer

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“…While nonsynonymous SNVs (nsSNVs), those SNVs that result in altered amino acid sequences, can directly change protein structure and therefore function, gene and microRNA (miRNA) dysregulation can alter normal expression and can also contribute to disease ( 6 , 7 ). Differential expression analysis of RNA-seq data can quantify the expression levels of genes or miRNAs across multiple samples and multiple conditions to identify important markers in disease diagnosis, progression, and treatment ( 8 , 9 ). Because of the potential wealth of clinically relevant information to be gleaned from these data, substantial efforts and resources have been dedicated to host, maintain, and make available to various research communities both normal and suspected disease-associated variation and expression data ( 2 , 10 – 13 ).…”

Section: Introductionmentioning

confidence: 99%

BioMuta and BioXpress: mutation and expression knowledgebases for cancer biomarker discovery

Dingerdissen

Torcivia-Rodriguez

et al. 2017

Nucleic Acids Research

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Single-nucleotide variation and gene expression of disease samples represent important resources for biomarker discovery. Many databases have been built to host and make available such data to the community, but these databases are frequently limited in scope and/or content. BioMuta, a database of cancer-associated single-nucleotide variations, and BioXpress, a database of cancer-associated differentially expressed genes and microRNAs, differ from other disease-associated variation and expression databases primarily through the aggregation of data across many studies into a single source with a unified representation and annotation of functional attributes. Early versions of these resources were initiated by pilot funding for specific research applications, but newly awarded funds have enabled hardening of these databases to production-level quality and will allow for sustained development of these resources for the next few years. Because both resources were developed using a similar methodology of integration, curation, unification, and annotation, we present BioMuta and BioXpress as allied databases that will facilitate a more comprehensive view of gene associations in cancer. BioMuta and BioXpress are hosted on the High-performance Integrated Virtual Environment (HIVE) server at the George Washington University at https://hive.biochemistry.gwu.edu/biomuta and https://hive.biochemistry.gwu.edu/bioxpress, respectively.

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“…Furthermore, in Table 6 the mean and standard deviation of the agreement matrices at cell-level and gene-level are listed for the different methods compared to RainDrop. To further emphasize that identified gene counts are biologically meaningful we have performed another test where we aggregated the genes of all cells and compared this distribution to an abundance estimation of bulk data from the same category (Accession numbers SRR1303990, SRR1373442, SRR1644186, SRR5074291 [17][18][19][20] for human and SRR327047, SRR3532922, SRR6753775 [21][22][23] for mouse). To process the bulk data we used RSEM (rsem-calculate-expression) which applies Bowtie2 for read mapping.…”

Section: Mapping Qualitymentioning

confidence: 99%

RainDrop: Rapid activation matrix computation for droplet-based single-cell RNA-seq reads

et al. 2020

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Background: Obtaining data from single-cell transcriptomic sequencing allows for the investigation of cell-specific gene expression patterns, which could not be addressed a few years ago. With the advancement of droplet-based protocols the number of studied cells continues to increase rapidly. This establishes the need for software tools for efficient processing of the produced large-scale datasets. We address this need by presenting RainDrop for fast gene-cell count matrix computation from single-cell RNA-seq data produced by 10x Genomics Chromium technology. Results: RainDrop can process single-cell transcriptomic datasets consisting of 784 million reads sequenced from around 8.000 cells in less than 40 minutes on a standard workstation. It significantly outperforms the established Cell Ranger pipeline and the recently introduced Alevin tool in terms of runtime by a maximal (average) speedup of 30.4 (22.6) and 3.5 (2.4), respectively, while keeping high agreements of the generated results. Conclusions: RainDrop is a software tool for highly efficient processing of large-scale droplet-based single-cell RNA-seq datasets on standard workstations written in C++. It is available at https://gitlab.rlp.net/stnieble/raindrop.

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Screening effective differential expression genes for hepatic carcinoma with metastasis in the peripheral blood mononuclear cells by RNA-seq

Cited by 15 publications

References 37 publications

Identification of a protein signature for predicting overall survival of hepatocellular carcinoma: a study based on data mining

Identification of a protein signature for predicting overall survival of hepatocellular carcinoma: a study based on data mining

BioMuta and BioXpress: mutation and expression knowledgebases for cancer biomarker discovery

RainDrop: Rapid activation matrix computation for droplet-based single-cell RNA-seq reads

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