Identification of novel biomarkers for hepatocellular carcinoma using transcriptome analysis

Xia, Qianlin; Li, Zehuan; Zheng, Jianghua; Zhang, Xu; Yang, Di; Ding, Jin; Yu, Die; Li, Yan; Shen, Longqiang; Yan, Dong; Jia, Ning; Chen, Weiping; Feng, Yinchang; Wang, Jin

doi:10.1002/jcp.27283

Cited by 36 publications

(27 citation statements)

References 71 publications

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“…The unprecedented proliferation of recent large-scale and multiomics databases of cancers provides numerous new insights into genomic and epigenomic dysregulation in cancer discovery (Rappoport and Shamir, 2018). Publicly available databases like TCGA, METABRIC, or GEO, which are common in the cancer research community, help better understand tumor heterogeneity, detect biomarker genes, and define hidden molecular mechanisms in multi-omics research (Xia Q. et al, 2019). Moreover, lines of previous evidence indicate notable relationships between CNA and mRNA, such as there is a high association of CNA with the development and progression of cancers by regulating gene expression level (Huang et al, 2017;Samulin Erdem et al, 2017;Zhou et al, 2017;Gut et al, 2018), as well as the similar regulatory associations between MET and mRNA (Herman and Baylin, 2003;Shen and Laird, 2013).…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Multi-Omics Analysis Detects Novel Prognostic Subgroups of Breast Cancer

Nguyen¹,

Nguyen

et al. 2020

Front. Genet.

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The unprecedented proliferation of recent large-scale and multi-omics databases of cancers has given us many new insights into genomic and epigenomic deregulation in cancer discovery in general. However, we wonder whether or not there exists a systematic connection between copy number aberrations (CNA) and methylation (MET)? If so, what is the role of this connection in breast cancer (BRCA) tumorigenesis and progression? At the same time, the PAM50 intrinsic subtypes of BRCA have gained the most attention from BRCA experts. However, this classification system manifests its weaknesses including low accuracy as well as a possible lack of association with biological phenotypes, and even further investigations on their clinical utility were still needed. In this study, we performed an integrative analysis of three-omics profiles, CNA, MET, and mRNA expression, in two BRCA patient cohorts (one for discovery and another for validation)-to elucidate those complicated relationships. To this purpose, we first established a set of CNAcor and METcor genes, which had CNA and MET levels significantly correlated (and anti-correlated) with their corresponding expression levels, respectively. Next, to revisit the current classification of BRCA, we performed single and integrated clustering analyses using our clustering method PINSPlus. We then discovered two biologically distinct subgroups that could be an improved and refined classification system for breast cancer patients, which can be validated by a third-party data. Further studies were then performed and realized each-subgroup-specific genes and different interactions between each of the two identified subgroups with the age factor. These findings can show promise as diagnostic and prognostic values in BRCA, and a potential alternative to the PAM50 intrinsic subtypes in the future.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Multi-Omics Analysis Detects Novel Prognostic Subgroups of Breast Cancer

Nguyen¹,

Nguyen

et al. 2020

Front. Genet.

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“…24,25 Therefore, multi-omics studies can help determine tumor heterogeneity and screen therapeutic targets and tumor biomarkers, which have greater advantages. 26 The study screens and identifies 9-gene signature associated with COAD prognosis by analyzing multi-omics data, including transcriptome data, copying number variation data and mutation data. The 9-gene signature established by screening has strong robustness and stable prediction performance in both internal verification set and external verification set.…”

Section: Discussionmentioning

confidence: 99%

<p>Gene Expression Along with Genomic Copy Number Variation and Mutational Analysis Were Used to Develop a 9-Gene Signature for Estimating Prognosis of COAD</p>

Lu¹,

Wu²,

Cui³

et al. 2020

OTT

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This study aims to systematically analyze multi-omics data to explore new prognosis biomarkers in colon adenocarcinoma (COAD). Materials and Methods: Multi-omics data of COAD and clinical information were obtained from The Cancer Genome Atlas (TCGA). Univariate Cox analysis was used to select genes which significantly related to the overall survival. GISTIC 2.0 software was used to identify significant amplification or deletion. Mutsig 2.0 software was used to identify significant mutation genes. The 9-gene signature was screened by random forest algorithm and Cox regression analysis. GSE17538 dataset was used as an external dataset to verify the predictive ability of 9-gene signature. qPCR was used to detect the expression of 9 genes in clinical specimens. Results: A total of 71 candidate genes are obtained by integrating genomic variation, mutation and prognostic data. Then, 9-gene signature was established, which includes HOXD12, RNF25, CBLN3, DOCK3, DNAJB13, PYGO2, CTNNA1, PTPRK, and NAT1. The 9-gene signature is an independent prognostic risk factor for COAD patients. In addition, the signature shows good predicting performance and clinical practicality in training set, testing set and external verification set. The results of qPCR based on clinical samples showed that the expression of HOXD12, RNF25, CBLN3, DOCK3, DNAJB13, and PYGO2 was increased in colon cancer tissues and the expression of CTNNA1, PTPRK, NAT1 was decreased in colon cancer tissues. Conclusion: In this study, 9-gene signature is constructed as a new prognostic marker to predict the survival of COAD patients.

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“…In recent years, high-throughput sequencing provides a better understanding of HCC-related lncRNAs. A large number of dysregulated lncRNAs have been identified, and they are associated with a variety of biological processes in HCC, such as cell proliferation, apoptosis, migration, invasion, and angiogenesis 19 – 21 . Emerging researches have revealed that SNHG family plays important roles in tumorigenesis and the immune escape of cancer via sponging miRNA 22 – 24 .…”

Section: Discussionmentioning

confidence: 99%

LncRNA SNHG14 promotes hepatocellular carcinoma progression via H3K27 acetylation activated PABPC1 by PTEN signaling

Zhang

Kurban

et al. 2020

Cell Death Dis

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Hepatocellular carcinoma (HCC) is the fifth most common cancer worldwide. LncRNA small nucleolar RNA host gene 14 (SNHG14) functions as an oncogene in a variety of cancers. However, the role of SNHG14 in HCC remains elusive. The aim of this study is to unravel the functional role and regulatory mechanism of SNHG14 in HCC. A cohort of 40 HCC tumor tissues and paired adjacent normal tissues were collected. Histopathological changes were analyzed by hematoxylin and eosin and immunohistochemistry. qRT-PCR and western blotting were performed to determine the levels of SNHG14, PABPC1, and PTEN signaling molecules. CCK-8, immunofluorescence, and colony formation assays were conducted to monitor cell proliferation. Wound healing and tube formation assays were employed to determine cell migration and angiogenesis. ChIP assay was performed to investigate the enrichment of H3K27 acetylation in PABPC1 promoter. Xenograft mice model was constructed to further verify the SNHG14/PABPC1 axis in vivo. SNHG14 was highly expressed in HCC tissues and cells, which promoted cell proliferation, migration, and angiogenesis in Hep3B and HepG2 cells. PABPC1 functioned as a downstream effector of SNHG14. SNHG14 dramatically induced upregulation of PABPC1 via H3K27 acetylation. In addition, SNHG14/PABPC1 promoted cell proliferation and angiogenesis via PTEN signaling pathway in vitro and in vivo. SNHG14 promoted cell proliferation and angiogenesis via upregulating PABPC1 through H3K27 acetylation and modulating PTEN signaling in the tumorigenesis of HCC.

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Identification of novel biomarkers for hepatocellular carcinoma using transcriptome analysis

Cited by 36 publications

References 71 publications

Multi-Omics Analysis Detects Novel Prognostic Subgroups of Breast Cancer

Multi-Omics Analysis Detects Novel Prognostic Subgroups of Breast Cancer

<p>Gene Expression Along with Genomic Copy Number Variation and Mutational Analysis Were Used to Develop a 9-Gene Signature for Estimating Prognosis of COAD</p>

LncRNA SNHG14 promotes hepatocellular carcinoma progression via H3K27 acetylation activated PABPC1 by PTEN signaling

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