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Long non-coding RNA HOTTIP promotes hepatocellular carcinoma tumorigenesis and development: A comprehensive investigation based on bioinformatics, qRT-PCR and meta-analysis of 393 cases

Yu Zhang, Jia-Cheng Huang, Kai-Teng Cai, Xi-Bing Yu, You-Rong Chen, Wen-ya Pan, Zeliang He, Jun Lv et al.

International Journal of Oncology, 2017

38 citations

Abstract

HOTTIP functions as an independent biomarker in multiple cancers. However, the role of HOTTIP in hepatocellular carcinoma (HCC) remains unclear. In this study, we sought to investigate the HOTTIP expression in HCC and normal liver. We combined quantitative reverse transcription-polymerase chain reactions (qRT-PCR), Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA), Multi Experiment Matrix (MEM) and Oncomine database to assess the clinical role and the potential molecular mechanism of HOTTIP in HCC. Furthermore, a meta-analysis was performed to evaluate the relationship between HOTTIP and HCC tumorigenesis and development. Additionally, bioinformatics analysis, which contained Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and network analysis, were applied to investigate the underlying functions, pathways and networks of the potential genes. HOTTIP was obviously upregulated in HCC. A statistically significant higher expression of HOTTIP was found in TNM (III +IV), age (≥60), sex (male), race (white) and cirrhosis (no) compared to the control groups (P<0.05). Furthermore, the meta-analysis of 393 cases from multiple centers indicated that HOTTIP had high diagnostic value in HCC. Additionally, according to GO and KEGG analyses, we found that the most strongly enriched functional terms were gland development, transcription factor activity and extrinsic to membrane. Also, the HOTTIP co-expressed genes were significantly related to PPAR…

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Evidence Hub reads this as a meta-analysis, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.

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