Identification of hub genes involved in the development of hepatocellular carcinoma by transcriptome sequencing

A novel focal adhesion related gene signature for prognostic prediction in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a highly heterogeneous disease. Reduced expression of focal adhesion is considered as an important prerequisite for tumor cell invasion and metastasis. However, the prognostic value of focal adhesion related genes in HCC remains to be further determined. In this study, RNA expression profiles were downloaded from public databases. A five focal adhesion related gene signature model was established by the least absolute shrinkage and selection operator Cox regression analysis, which categorized patients into high- and low-risk groups. Multivariate Cox regression analysis showed that the risk score was an independent predictor for overall survival. Single-sample gene set enrichment analysis revealed that immune status was different between the two risk groups, and tumor-related pathways were enriched in high-risk group. The risk score was significantly associated with tumor grade, tumor stage, immune scores, and immune infiltrate types. Pearson correlation showed that the expression level of prognostic genes was associated with anti-tumor drug sensitivity. Besides, the mRNA and protein expression of prognostic genes was significantly different between HCC tissues and adjacent non-tumorous tissues in our separate cohort. Taken together, a novel focal adhesion related gene signature can be used for prognostic prediction in HCC, which may be a therapeutic alternative.

System-Wide Pollution of Biomedical Data: Consequence of the Search for Hub Genes of Hepatocellular Carcinoma Without Spatiotemporal Consideration

Biomedical institutions rely on data evaluation and are turning into data factories. Big-data storage centers, supercomputing systems, and increased algorithmic efficiency allow us to analyze the ever-increasing amount of data generated every day in biomedical research centers. In network science, the principal intrinsic problem is how to integrate the data and information from different experiments on genes or proteins. Data curation is an essential process in annotating new functional data to known genes or proteins, undertaken by a biobank curator, which is then reflected in the calculated networks. We provide an example of how protein-protein networks today have space-time limits. The next step is the integration of data and information from different biobanks. Omics data and networks are essential parts of this step but also have flawed protocols and errors. Consider data from patients with cancer: from biopsy procedures to experimental tests, to archiving methods and computational algorithms, these are continuously handled so require critical and continuous "updates" to obtain reproducible, reliable, and correct results. We show, as a second example, how all this distorts studies in cellular hepatocellular carcinoma. It is not unlikely that these flawed data have been polluting biobanks for some time before stringent conditions for the veracity of data were implemented in Big data. Therefore, all this could contribute to errors in future medical decisions.

Spatial proteome profiling by immunohistochemistry-based laser capture microdissection and data-independent acquisition proteomics

Understanding the tumor heterogeneity through spatially resolved proteome profiling is important for biomedical research and clinical application. Laser capture microdissection (LCM) is a powerful technology for exploring local cell populations without losing spatial information. Conventionally, tissue sections are stained with hematoxylin and eosin (H&E) for cell-type identification before LCM. However, it generally requires experienced pathologists to distinguish different cell types, which limits the application of LCM to broad cancer research field. Here, we designed an immunohistochemistry (IHC)-based workflow for cell type-resolved proteome analysis of tissue samples. Firstly, targeted cell type was marked by IHC using antibody targeting cell-type specific marker to improve accuracy and efficiency of LCM. Secondly, to increase protein recovery from chemically crosslinked IHC tissues, we optimized a decrosslinking procedure to seamlessly combine with the integrated spintip-based sample preparation technology SISPROT. This newly developed approach, termed IHC-SISPROT, has comparable performance as H&E staining-based proteomic analysis. High sensitivity and reproducibility of IHC-SISPROT were achieved by combining with data independent acquisition proteomics. More than 3500 proteins were identified from only 0.2 mm² and 12 μm thickness of hepatocellular carcinoma (HCC) tissue section. Furthermore, using 5 mm² and 12 μm thickness of HCC tissue section, 6660 and 6052 protein groups were quantified from cancer cells and cancer-associated fibroblasts (CAFs) by the IHC-SISPROT workflow. Bioinformatic analysis revealed the enrichment of cell type-specific ligands and receptors and potentially new communications between cancer cells and CAFs by these signaling proteins. Therefore, IHC-SISPROT is a sensitive and accurate proteomic approach for spatial profiling of cell type-specific proteome from tissues.

DNA methylation-driven genes for constructing diagnostic, prognostic, and recurrence models for hepatocellular carcinoma

In this study, we performed a comprehensively analysis of gene expression and DNA methylation data to establish diagnostic, prognostic, and recurrence models for hepatocellular carcinoma (HCC).

Methods: We collected gene expression and DNA methylation datasets for over 1,200 clinical samples. Integrated analyses of RNA-sequencing and DNA methylation data were performed to identify DNA methylation-driven genes. These genes were utilized in univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses to build a prognostic model. Recurrence and diagnostic models for HCC were also constructed using the same genes.

Results: A total of 123 DNA methylation-driven genes were identified. Two of these genes (SPP1 and LCAT) were chosen to construct the prognostic model. The high-risk group showed a markedly unfavorable prognosis compared to the low-risk group in both training (HR = 2.81; P < 0.001) and validation (HR = 3.06; P < 0.001) datasets. Multivariate Cox regression analysis indicated the prognostic model to be an independent predictor of prognosis (P < 0.05). Also, the recurrence model successfully distinguished the HCC recurrence rate between the high-risk and low-risk groups in both training (HR = 2.22; P < 0.001) and validation (HR = 2; P < 0.01) datasets. The two diagnostic models provided high accuracy for distinguishing HCC from normal samples and dysplastic nodules in the training and validation datasets, respectively.

Conclusions: We identified and validated prognostic, recurrence, and diagnostic models that were constructed using two DNA methylation-driven genes in HCC. The results obtained by integrating multidimensional genomic data offer novel research directions for HCC biomarkers and new possibilities for individualized treatment of patients with HCC.

Transcriptomic analysis and identification of prognostic biomarkers in cholangiocarcinoma

Cholangiocarcinoma (CCA) is acknowledged as the second most commonly diagnosed primary liver tumor and is associated with a poor patient prognosis. The present study aimed to explore the biological functions, signaling pathways and potential prognostic biomarkers involved in CCA through transcriptomic analysis. Based on the transcriptomic dataset of CCA from The Cancer Genome Atlas (TCGA), differentially expressed protein-coding genes (DEGs) were identified. Biological function enrichment analysis, including Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, was applied. Through protein-protein interaction (PPI) network analysis, hub genes were identified and further verified using open-access datasets and qRT-PCR. Finally, a survival analysis was conducted. A total of 1,463 DEGs were distinguished, including 267 upregulated genes and 1,196 downregulated genes. For the GO analysis, the upregulated DEGs were enriched in ‘cadherin binding in cell-cell adhesion’, ‘extracellular matrix (ECM) organization’ and ‘cell-cell adherens junctions’. Correspondingly, the downregulated DEGs were enriched in the ‘oxidation-reduction process’, ‘extracellular exosomes’ and ‘blood microparticles’. In regards to the KEGG pathway analysis, the upregulated DEGs were enriched in ‘ECM-receptor interactions’, ‘focal adhesions’ and ‘small cell lung cancer’. The downregulated DEGs were enriched in ‘metabolic pathways’, ‘complement and coagulation cascades’ and ‘biosynthesis of antibiotics’. The PPI network suggested that CDK1 and another 20 genes were hub genes. Furthermore, survival analysis suggested that CDK1, MKI67, TOP2A and PRC1 were significantly associated with patient prognosis. These results enhance the current understanding of CCA development and provide new insight into distinguishing candidate biomarkers for predicting the prognosis of CCA.

Identification of cancer/testis antigen 2 gene as a potential hepatocellular carcinoma therapeutic target by hub gene screening with topological analysis

The 5-year survival rate of hepatocellular carcinoma (HCC) is <20%; thus, identifying new potential therapeutic targets or novel biomarkers for prognosis prediction is crucial. The present study aimed to screen hub genes by constructing protein-protein interaction (PPI) subnetworks using topological analysis methods, as well as reveal their clinical significance through big data analytics and their association with the clinicopathological features. Firstly, the PPI subnetworks were constructed using four topological analysis methods, including the MCC, DMNC, MNC and degree methods, to obtain 6 hub genes. Subsequently, the hub gene cancer/testis antigen 2 (CTAG2), which affects the prognosis of HCC (overall survival, P=0.035), was acquired by analysing clinical data in The Cancer Genome Atlas database. Meanwhile, CTAG2 expression was significantly associated with the age at diagnosis (P=0.003), T stage (P=0.028), TNM stage (P=0.028) and α-fetoprotein (AFP) expression (P=0.045). Further immunohistochemical analysis of samples collected in our hospital revealed that the expression level of CTAG2 in 46 HCC tissues was significantly higher in comparison with that in paired adjacent tissues. The clinical data indicated that the expression of CTAG2 was significantly correlated with the hepatitis B virus status (P=0.010) and AFP expression (P=0.004). These results were then found to be consistent with the results of big data analytics. Furthermore, Gene Set Enrichment Analysis demonstrated that the function of CTAG2 in HCC may be associated with the cell cycle. Taken together, these findings suggest that CTAG2 may serve as a new potential therapeutic target for HCC patients.

Bioinformatics analysis of aberrantly methylated-differentially expressed genes and pathways in hepatocellular carcinoma

AIM

To discover methylated-differentially expressed genes (MDEGs) in hepatocellular carcinoma (HCC) and to explore relevant hub genes and potential pathways.

METHODS

The data of expression profiling GSE25097 and methylation profiling GSE57956 were gained from GEO Datasets. We analyzed the differentially methylated genes and differentially expressed genes online using GEO2R. Functional and enrichment analyses of MDEGs were conducted using the DAVID database. A protein-protein interaction (PPI) network was performed by STRING and then visualized in Cytoscape. Hub genes were ranked by cytoHubba, and a module analysis of the PPI network was conducted by MCODE in Cytoscape software.

RESULTS

In total, we categorized 266 genes as hypermethylated, lowly expressed genes (Hyper-LGs) referring to endogenous and hormone stimulus, cell surface receptor linked signal transduction and behavior. In addition, 161 genes were labelled as hypomethylated, highly expressed genes (Hypo-HGs) referring to DNA replication and metabolic process, cell cycle and division. Pathway analysis illustrated that Hyper-LGs were enriched in cancer, Wnt, and chemokine signalling pathways, while Hypo-HGs were related to cell cycle and steroid hormone biosynthesis pathways. Based on PPI networks, PTGS2, PIK3CD, CXCL1, ESR1, and MMP2 were identified as hub genes for Hyper-LGs, and CDC45, DTL, AURKB, CDKN3, MCM2, and MCM10 were hub genes for Hypo-HGs by combining six ranked methods of cytoHubba.

CONCLUSION

In the study, we disclose numerous novel genetic and epigenetic regulations and offer a vital molecular groundwork to understand the pathogenesis of HCC. Hub genes, including PTGS2, PIK3CD, CXCL1, ESR1, MMP2, CDC45, DTL, AURKB, CDKN3, MCM2, and MCM10, can be used as biomarkers based on aberrant methylation for the accurate diagnosis and treatment of HCC.