bioRxivCancer BiologyPreprint
Identification and functional analysis of eRNA markers for hepatocellular carcinoma based on high-throughput data
Z. Chen, J. Chen, R. Zhang, Y. Zhu, D. Feng, H. Han, T. Li, X. Liu, X. Wang, Z. WangiD, H. Wang, L. Wang, B. Li, J. Li
Abstract
Hepatocellular carcinoma (HCC) is a common type of liver cancer with a high mortality rate. enhancer RNA (eRNA) has been proved to play an important role in cancer progress and development. However, the eRNA studies in HCC are still limited. In this study, we attempted to identify some eRNA biomarkers for HCC diagnosis and analyzed their biological function. First, we identified three eRNA biomarkers (CAP2e, COLEC10e, and MARCOe), which were significant differentially expressed between tumor and normal tissues in 115 HCC patients across three datasets. CAP2e demonstrated upregulation in tumors while COLEC10e and MARCOe were downregulated. These results could be validated in TCGA-LIHC data. There were significant positive correlations between the expression of these eRNAs and their host genes. Then, functional enrichment analysis of protein-coding genes associated with the eRNA biomarkers revealed their involvement in cancer-related pathways. MARCOe was suggested to be a potential target for therapeutic drugs in HCC by a drug related study. The next, survival analysis demonstrated significant prognostic values of these eRNAs in prediction of overall survival. Immune infiltration analysis revealed a positive correlation between MARCOe expression and immune cell infiltration level. Finally, we found similar expression patterns of these eRNA biomarkers in other cancers, such as cholangiocarcinoma, through a pan-cancer comparison. CAP2e and COLEC10e in HCC were validated by other studies. However, the studies about MARCOe in HCC were limited. In conclusion, as best as our knowledge, it is the first time to identify three eRNA biomarkers for HCC diagnosis. These biomarkers are proved to be involved in HCC progress and development, have prognosis prediction values, and are potential to be therapeutic targets.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: protein folding & dynamics
- approach: statistical model
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