Identification of novel prognostic biomarkers in the TF-enhancer-target regulatory network in hepatocellular carcinoma and immune infiltration analysis

Abstract

Background: Hepatocellular carcinoma (HCC) remains notorious for its high malignancy, poor prognosis and high mortality. The exploration of novel therapeutic agents for HCC has remained challenging due to its complex aetiology. Therefore, it is necessary to elucidate the pathogenesis and mechanism of HCC for clinical intervention.Methods: We collected data from several public data portals and systematically analysed the association between transcription factors (TFs), eRNA-associated enhancers and downstream targets. We next filtered the prognostic genes and established a novel prognosis-related nomogram model. Moreover, we explored the potential mechanisms of the identified prognostic genes. The expression level was validated by several ways.Results: We first constructed a significant TF-enhancer-target regulatory network and identified DAPK1 as a coregulatory differentially expressed prognosis-related gene. We combined common clinicopathological factors and built a prognostic nomogram model for HCC. We found that our regulatory network was correlated with the processes of synthesizing various substances. Moreover, we explored the role of DAPK1 in HCC and found that it was associated with immune cell infiltration and DNA methylation. Several immunostimulators and targeting drugs could be promising immune therapy targets. The tumor immune microenvironment was analyzed. Finally, the lower DAPK1 expression in HCC was validated via the GEO database, UALCAN cohort, and qRT-PCR.Conclusion: In conclusion, we established a significant TF-enhancer-target regulatory network and identified downregulated DAPK1 as an important prognostic and diagnostic gene in HCC. Its potential biological functions and mechanisms were annotated using bioinformatics tools.

Machine learning to establish three sphingolipid metabolism genes signature to characterize the immune landscape and prognosis of patients with gastric cancer

“…All of the RNA was extracted from cells and tissues using TRIzol reagents (Ambion, Carlsbad, CA, USA) based on instructions provided by the kit manual. Total RNA was used as a template and reverse transcribed to cDNA with a GoScript Reverse Transcription (RT) System (Promega, Madison, WI, USA) according to the manufacturer’s instructions [ 19 ]. Then, qRT-PCR detection was performed via GoTaq qPCR Master Mix (Promega) whose conditions were as follows: 95°C for 5 min, followed by 40 cycles of 94°C for 15 s, 52°C for 30 s, and 72°C for 30 s. GAPDH mRNA was chosen to normalize and the primer sequences were as follows: GLA: forward, 5’-GCCCCTGAGGTTAATCTTAA-3’, reverse, 5’- AACTGTTCCCGTTGAGACTC-3’; LAMC1: forward, 5’- GCCTTCCTGACCGACTACAACAAC-3’, reverse, 5’- GCGGCTGGTGTGGAACTTGAG-3’; TRAF2: forward, 5’- GATGGGGGTCTTCATCTG-3’, reverse, 5’-CGTAGGTGGATGCCTCC-3’; GAPDH: forward, 5’-ACCCACTCCTCCACCTTTGAC-3’, reverse, 5’-TGTTGCTGTAGCCAAATTCGTT-3’.…”