Highlights d We build the genomic and transcriptomic landscape of 133 cHCC-ICCs d Integrative genomic analysis reveals distinct cHCC-ICC subtypes d Both mono-and multiclonal origins of cHCC-ICC are identified d Nestin expression can serve as a biomarker for the diagnosis and prognosis of cHCC-ICC

BackgroundMolecular analysis is a promising source of clinically useful prognostic biomarkers. The aim of this investigation was to identify prognostic biomarkers for patients with early-stage pancreatic ductal adenocarcinoma (PDAC) after pancreaticoduodenectomy.MethodsAn RNA sequencing dataset of PDAC was obtained from The Cancer Genome Atlas. Survival analysis and weighted gene co-expression network analysis were used to investigate the prognostic markers of early-stage PDAC after pancreaticoduodenectomy.ResultsUsing whole genome expression level screening, we identified 1,238 markers that were related to the prognosis of PDAC after pancreaticoduodenectomy, and identified 9 hub genes (ARHGAP30, HCLS1, CD96, FAM78A, ARHGAP15, SLA2, CD247, GVINP1, and IL16) using the weighted gene co-expression network analysis approach. We also constructed a signature comprising the 9 hub genes and weighted by the regression coefficient derived from a multivariate Cox proportional hazards regression model to divide patients into a high-risk group, with increased risk of death, and a low-risk group, with significantly improved overall survival (adjusted P=0.026, adjusted HR =0.513, 95% CI =0.285–0.924). The prognostic signature of the 9 genes demonstrated good performance for predicting 1-year overall survival (area under the respective receiver operating characteristic curves =0.641).ConclusionOur results have provided a new prospect for prognostic biomarkers of PDAC after pancreaticoduodenectomy, and may have a value in clinical application.

BackgroundMolecular analysis is a promising source of clinically useful prognostic biomarkers. The aim of this investigation was to identify prognostic biomarkers for patients with acute myeloid leukemia (AML) by using the gene expression profile dataset from public database.MethodsThe gene expression profile dataset and corresponding overall survival (OS) information of three cohorts of AML patients from GSE12417 and The Cancer Genome Atlas AML project (TCGA-LAML) were included in the present study. Prognostic gene screening was performed by using a survival package, whereas time-dependent receiver operating characteristic (ROC) curve analysis was performed using the survivalROC package.ResultsIn the three cohorts, 11 genes were identified that were significantly associated with AML OS. A linear prognostic model of the 11 genes was constructed and weighted by regression coefficient (β) from the multivariate Cox regression analyses of GSE12417 HG-U133A cohort to divide patients into high- and low-risk groups. GSE12417 HG-U133 plus 2.0 and TCGA-LAML were validation cohorts. Patients assigned to the high-risk group exhibited poor OS compared to patients in the low-risk group. The 11-gene signature is a prognostic marker of AML and demonstrates good performance for predicting 1-, 3-, and 5-year OS as evaluated by survivalROC in the three cohorts.ConclusionOur study has identified an mRNA signature including 11 genes, which may serve as a potential prognostic marker of AML.

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.

The purpose of the present study was to assess if guanylate-binding protein (GBP) mRNAs could be prognostic biomarkers for patients with skin cutaneous melanoma (SKCM). The prognostic value of GBP mRNA expression in patients with SKCM was investigated by analyzing gene expression data in 459 SKCM patients. The data were extracted from the OncoLnc database of The Cancer Genome Atlas. A high expression of GBP1, GBP2, GBP3, GBP4 and GBP5 were correlated with favorable overall survival (OS) in the SKCM patients followed for over 30 years. In addition, a high expression of GBP6 mRNA was not correlated with OS in the SKCM patients. A joint effects analysis showed that the co-incidence of the high expression of GBP1-5 was correlated with favorable overall survival in SKCM patients. Our findings suggest that GBP1-5 mRNAs in SKCM are associated with favorable prognosis and may be potential prognostic biomarkers. The combination of GBP1-5 could improve the sensitivity for predicting OS in SKCM patients.

BackgroundThe aim of the present study was to identify potential prognostic microRNA (miRNA) biomarkers for hepatocellular carcinoma (HCC) prognosis prediction based on a dataset from The Cancer Genome Atlas (TCGA).Materials and methodsA miRNA sequencing dataset and corresponding clinical parameters of HCC were obtained from TCGA. Genome-wide univariate Cox regression analysis was used to screen prognostic differentially expressed miRNAs (DEMs), and multivariable Cox regression analysis was used for prognostic signature construction. Comprehensive survival analysis was performed to evaluate the prognostic value of the prognostic signature.ResultsFive miRNAs were regarded as prognostic DEMs and used for prognostic signature construction. The five-DEM prognostic signature performed well in prognosis prediction (adjusted P < 0.0001, adjusted hazard ratio = 2.249, 95% confidence interval =1.491–3.394), and time-dependent receiver–operating characteristic (ROC) analysis showed an area under the curve (AUC) of 0.765, 0.745, 0.725, and 0.687 for 1-, 2-, 3-, and 5-year HCC overall survival (OS) prediction, respectively. Comprehensive survival analysis of the prognostic signature suggests that the risk score model could serve as an independent factor of HCC and perform better in prognosis prediction than other traditional clinical indicators. Functional assessment of the target genes of hsa-mir-139 and hsa-mir-5003 indicates that they were significantly enriched in multiple biological processes and pathways, including cell proliferation and cell migration regulation, pathways in cancer, and the cyclic adenosine monophosphate (cAMP) signaling pathway.ConclusionOur study indicates that the novel miRNA expression signature may be a potential prognostic biomarker for HCC patients.

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.

Hepatocellular carcinoma (HCC) is one of the most prevalent and life-threatening malignancies worldwide. There are few diagnostic and prognostic biomarkers and druggable targets for HCC. Aldehyde dehydrogenase 1 (ALDH1) is a marker of stem cells in a variety of cancers, but the mRNA levels and prognostic value of ALDH1 isoforms in HCC patients remain unknown. In the present study, gene ontology annotation of the ALDH1 family was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID), and the gene pathway analsis was performed using GeneMANIA software. The initial prognostic value of ALDH1 expression in 360 HCC patients was assessed using the OncoLnc database. The expression levels of ALDH1 isoforms in normal liver tissues and clinical specimens of cancer vs. normal control datasets were determined using the GTEx and Oncomine databases, respectively. We then analyzed the prognostic value of ALDH1 expression in 212 hepatitis B virus (HBV)–related HCC patients using the GEO database. We found that the ALDH1 isoform showed high aldehyde dehydrogenase activity. The ALDH1A1, ALDH1B1, and ALDH1L1 genes encoded for the ALDH1 enzyme. High ALDH1B1 expression had protective qualities in HCC patients. Moreover, HBV–related HCC patients who showed high ALDH1L1 gene expression had a better clinical outcomes. In addition, high ALDH1A1 expression was associated with a 57–month recurrence–free survival in HBV-related HCC patients. High ALDH1B1 expression was protective for HCCs with multiple nodules and high serum alpha–fetoprotein (AFP) level. Furthermore, high serum AFP levels contributed to lower ALDH1L1. ALDH1A1, ALDH1B1, and ALDH1L1, all of which were considered promising diagnostic and prognostic markers as well as potential drug targets.