Background: The aim of the present study was to identify diagnostic and prognostic values of minichromosome maintenance (MCM) gene expression in patients with hepatocellular carcinoma (HCC).Methods: The biological function of the MCM genes were investigated by bioinformatics analysis. The diagnostic and prognostic values of the MCM genes were investigated by using the data of HCC patients from the GSE14520 and The Cancer Genome Atlas (TCGA) databases.Results: Bioinformatics analysis of the MCM genes substantiated that MCM2-7 genes were significantly enriched in DNA replication and cell cycle, and co-expressed with each other. These genes also co-expressed in HCC tumor tissue in both the GSE14520 and TCGA cohort. We also observed that the expression of the MCM2-7 genes was increased in tumor tissue, and diagnostic receiver operating characteristic analysis of MCM2-7 indicated that these genes could serve as sensitive diagnostic markers in HCC. Survival analysis in the GSE14520 cohort suggested that expression of MCM2, MCM4, MCM5, and MCM6 were significantly associated with hepatitis B virus-related HCC overall survival (OS). However, none of the MCM genes were associated with recurrence-free survival in the GSE14520 cohort. The validation cohort of TCGA suggested that the expression of MCM2, MCM6, and MCM7 were significantly correlated with HCC OS.Conclusion: Our study indicated that MCM2-7 genes may be potential diagnostic biomarkers in patients with HCC. Among them, MCM2 and MCM6 may serve as potential prognostic biomarkers for HCC.

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Background/Aims: The aim of the current study was to identify potential prognostic long non-coding RNA (lncRNA) biomarkers for predicting survival in patients with hepatocellular carcinoma (HCC) using The Cancer Genome Atlas (TCGA) dataset and bioinformatics analysis. Methods: RNA sequencing and clinical data of HCC patients from TCGA were used for prognostic association assessment by univariate Cox analysis. A prognostic signature was built using stepwise multivariable Cox analysis, and a comprehensive analysis was performed to evaluate its prognostic value. The prognostic signature was further evaluated by functional assessment and bioinformatics analysis. Results: Thirteen differentially expressed lncRNAs (DELs) were identified and used to construct a single prognostic signature. Patients with high risk scores showed a significantly increased risk of death (adjusted P < 0.0001, adjusted hazard ratio = 3.522, 95% confidence interval = 2.307–5.376). In the time-dependent receiver operating characteristic analysis, the prognostic signature performed well for HCC survival prediction with an area under curve of 0.809, 0.782 and 0.79 for 1-, 3- and 5-year survival, respectively. Comprehensive survival analysis of the 13-DEL prognostic signature suggested that it serves as an independent factor in HCC, showing a better performance for prognosis prediction than traditional clinical indicators. Functional assessment and bioinformatics analysis suggested that the prognostic signature was associated with the cell cycle and peroxisome proliferator-activated receptor signaling pathway. Conclusions: The novel lncRNA expression signature identified in the present study may be a potential biomarker for predicting the prognosis of HCC patients.

Objective : The goal of our study is to identify a competing endogenous RNA (ceRNA) network using dysregulated RNAs between HCC tumors and the adjacent normal liver tissues from The Cancer Genome Atlas (TCGA) datasets, and to investigate underlying prognostic indicators in hepatocellular carcinoma (HCC) patients. Methods : All of the RNA- and miRNA-sequencing datasets of HCC were obtained from TCGA, and dysregulated RNAs between HCC tumors and the adjacent normal liver tissues were investigated by DESeq and edgeR algorithm. Survival analysis was used to confirm underlying prognostic indicators. Results : In the present study, we constructed a ceRNA network based on 16 differentially expressed genes (DEGs), 7 differentially expressed microRNAs and 34 differentially expressed long non-coding RNAs (DELs). Among these dysregulated RNAs, three DELs (AP002478.1, HTR2A-AS1, and ERVMER61-1) and six DEGs (enhancer of zeste homolog 2 [ EZH2 ], kinesin family member 23 [ KIF23 ], chromobox 2 [ CBX2 ], centrosomal protein 55 [ CEP55 ], cell division cycle 25A [ CDC25A ], and claspin [ CLSPN ]) were used for construct a prognostic signature for HCC overall survival (OS), and performed well in HCC OS (adjusted P <0.0001, adjusted hazard ratio = 2.761, 95% confidence interval = 1.838-4.147). Comprehensive survival analysis demonstrated that this prognostic signature may be act as an independent prognostic indicator of HCC OS. Functional assessment of these dysregulated DEGs in the ceRNA network and gene set enrichment of this prognostic signature suggest that both were enriched in the biological processes and pathways of the cell cycle, cell division and cell proliferation. Conclusions : Our current study constructed a ceRNA network for HCC, and developed a prognostic signature that may act as an independent indicator for HCC OS.

Background Hidden blood loss (HBL) is still not well known or used in the setting of spine surgery. Elucidating absolute and relative amount of HBL is of great importance in order to avoid potential complications. Therefore, we evaluated HBL and its possible risk factors among patients undergoing minimally invasive transforaminal lumbar interbody fusion (MIS-TLIF) for lumbar degenerative diseases. Methods Between June 2018 and March 2019, 137 consecutive patients with lumbar degenerative disease, who underwent operation with MIS-TLIF technique were enrolled in this study. The patient’s demographic characteristics and blood loss related parameters were collected respectively. Pearson or Spearman correlations analysis were used to investigate an association between patient’s characteristics and HBL. Multivariate linear regression analysis was used to confirmed independent risk factors of HBL. Results A total of 137 patients (86 males and 51 females, age range 19-78 years) were reviewed in our hospital. A substantial amount of HBL (488.4±294.0 ml, 52.5% of TBL) occurred after MIS-TLIF. Multivariate linear regression showed that the age, muscle thickness, The Patients’ Society of Anesthesiologists (ASA) classification, patient’s blood volume (PBV), total blood loss (TBL), postoperative(i.e., day 2 or 3) hematocrit (Hct), Hct loss, and fibrinogen level were independent risk factors for HBL (P1=0.000, P2=0.002, P3=0.006, P4=0.002, P5=0.003, P6=0.048, P7=0.004, P8=0.070). Conclusion A large amount of HBL was incurred in patients undergoing MIS-TLIF. More importantly, the age, muscle thickness, ASA classification, PBV, TBL, postoperative Hct, Hct loss, and fibrinogen level were independent risk factors for HBL in MIS-TLIF. HBL and its risk factors should be paid more attention to during perioperative period.

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Objective: Our study is aim to explore potential key biomarkers and pathways in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) using genome-wide expression profile dataset and methods.Methods: Dataset from the GSE14520 is used as the training cohort and The Cancer Genome Atlas dataset as the validation cohort. Differentially expressed genes (DEGs) screening were performed by the limma package. Gene set enrichment analysis (GSEA), weighted gene co-expression network analysis (WGCNA), gene ontology, the Kyoto Encyclopedia of Genes and Genomes, and risk score model were used for pathway and genes identification.Results: GSEA revealed that several pathways and biological processes are associated with hepatocarcinogenesis, such as the cell cycle, DNA repair, and p53 pathway. A total of 160 DEGs were identified. The enriched functions and pathways of the DEGs included toxic substance decomposition and metabolism processes, and the P450 and p53 pathways. Eleven of the DEGs were identified as hub DEGs in the WGCNA. In survival analysis of hub DEGs, high expression of PRC1 and TOP2A were significantly associated with poor clinical outcome of HBV-related HCC, and shown a good performance in HBV-related HCC diagnosis. The prognostic signature consisting of PRC1 and TOP2A also doing well in the prediction of HBV-related HCC prognosis. The diagnostic and prognostic values of PRC1 and TOP2A was confirmed in TCGA HCC patients.Conclusions: Key biomarkers and pathways identified in the present study may enhance the comprehend of the molecular mechanisms underlying hepatocarcinogenesis. Additionally, mRNA expression of PRC1 and TOP2A may serve as potential diagnostic and prognostic biomarkers for HBV-related HCC.