Identification and Validation of Key Genes in Hepatocellular Carcinoma by Bioinformatics Analysis

Statistical and machine learning based platform-independent key genes identification for hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most prevalent and deadly form of liver cancer, and its mortality rate is gradually increasing worldwide. Existing studies used genetic datasets, taken from various platforms, but focused only on common differentially expressed genes (DEGs) across platforms. Consequently, these studies may missed some important genes in the investigation of HCC. To solve these problems, we have taken datasets from multiple platforms and designed a statistical and machine learning-based system to determine platform-independent key genes (KGs) for HCC patients. DEGs were determined from each dataset using limma. Individual combined DEGs (icDEGs) were identified from each platform and then determined grand combined DEGs (gcDEGs) from icDEGs of all platforms. Differentially expressed discriminative genes (DEDGs) was determined based on the classification accuracy using Support vector machine. We constructed PPI network on DEDGs and identified hub genes using MCC. This study determined the optimal modules using the MCODE scores of the PPI network and selected their gene combinations. We combined all genes, obtained from previous studies to form metadata, known as meta-hub genes. Finally, six KGs (CDC20, TOP2A, CENPF, DLGAP5, UBE2C, and RACGAP1) were selected by intersecting the overlapping hub genes, meta-hub genes, and hub module genes. The discriminative power of six KGs and their prognostic potentiality were evaluated using AUC and survival analysis.

Challenging Conventional Perceptions of Oncogenes and Tumor Suppressor Genes: A Comprehensive Analysis of Gene Expression Patterns in Cancer

Identifying genes involved in cancer is crucial for understanding the underlying molecular mechanisms of the disease and developing effective treatment strategies. Differential expression analysis (DEA) is the predominant method used to identify cancer-related genes. This approach involves comparing gene expression levels between different samples, such as cancerous and non-cancerous tissues, to identify genes that are significantly upregulated or downregulated in cancer. DEA is based on the commonly believed assumption that genes upregulated in cancerous tissues have the potential to function as oncogenes. Their expression levels often correlate with cancer advancement and unfavorable prognosis, whereas downregulated genes display the opposite correlation. However, contrary to the prevailing belief, our analysis utilizing The Cancer Genome Atlas (TCGA) databases revealed that the upregulated or downregulated genes in cancer do not always align with cancer progression or prognosis. These findings emphasize the need for alternative approaches for identifying cancer-related genes that may be more accurate and effective. To address this need, we compared the effectiveness of machine learning (ML) methods with that of traditional DEA in the identification of cancer-related genes. ML algorithms have the advantage of being able to analyze large-scale genomic data and identify complex patterns that may go unnoticed by traditional methods. Our results demonstrated that ML methods significantly outperformed DEA in the screening of cancer-related genes.

Detection of reproducible liver cancer specific ligand-receptor signaling in blood

Cell-cell communication mediated by ligand-receptor interactions (LRI) is critical to coordinating diverse biological processes in homeostasis and disease. Lately, our understanding of these processes has greatly expanded through the inference of cellular communication, utilizing RNA extracted from bulk tissue or individual cells. Considering the challenge of obtaining tissue biopsies for these approaches, we considered the potential of studying cell-free RNA obtained from blood. To test the feasibility of this approach, we used the BulkSignalR algorithm across 295 cell-free RNA samples and compared the LRI profiles across multiple cancer types and healthy donors. Interestingly, we detected specific and reproducible LRIs particularly in the blood of liver cancer patients compared to healthy donors. We found an increase in the magnitude of hepatocyte interactions, notably hepatocyte autocrine interactions in liver cancer patients. Additionally, a robust panel of 30 liver cancer-specific LRIs presents a bridge linking liver cancer pathogenesis to discernible blood markers. In summary, our approach shows the plausibility of detecting liver LRIs in blood and builds upon the biological understanding of cell-free transcriptomes.

Retracted: Identification and Validation of Key Genes in Hepatocellular Carcinoma by Bioinformatics Analysis

[This retracts the article DOI: 10.1155/2021/6662114.].

TRIM55 Promotes Proliferation of Hepatocellular Carcinoma Through Stabilizing TRIP6 to Activate Wnt/β-Catenin Signaling

PURPOSE

Tripartite motif containing 55 (TRIM55) is a member of the TRIM family and functions as an E3 ubiquitin ligase. It acts as a cancer promoter or suppressor in the malignant processes of multiple cancers. However, its proliferative function in hepatocellular carcinoma (HCC) has been poorly studied, and its underlying molecular mechanism remains unclear. In the present study, we investigated the role of TRIM55 in HCC and its mechanism of promoting HCC proliferation.

MATERIALS AND METHODS

Protein expression levels of TRIM55 were measured in paired HCC and normal tissue samples using immunohistochemical (IHC) staining. The correlation between TRIM55 and clinical features was evaluated by statistical analysis. At the same time, overexpression and knockdown experiments, cycloheximide (CHX) interference experiments, ubiquitination, co-immunoprecipitation and immunofluorescence staining experiments, as well as animal experiments were used to evaluate the potential mechanism that TRIM55 promotes proliferation of hepatocellular carcinoma in vitro and in vivo.

RESULTS

TRIM55 expression in HCC specimens was higher compared with the corresponding non-tumor tissues. The overall survival and disease-free survival time of patients with high TRIM55 expression were shorter than those with low expression of TRIM55. Functionally, TRIM55 promoted the proliferation of HCC cells and accelerated the growth of HCC xenografts. Mechanistically, TRIM55 interacted with thyroid receptor interacting protein 6 (TRIP6) and regulate its stability by influencing the ubiquitination process, thereby affecting the Wnt signaling pathway.

CONCLUSION

Our results indicate that TRIM55 promotes HCC proliferation by activating Wnt signaling pathways by stabilizing TRIP6. Therefore, targeting TRIM55 may be an effective therapeutic strategy to inhibit HCC growth.

Differentially expressed discriminative genes and significant meta-hub genes based key genes identification for hepatocellular carcinoma using statistical machine learning

Hepatocellular carcinoma (HCC) is the most common lethal malignancy of the liver worldwide. Thus, it is important to dig the key genes for uncovering the molecular mechanisms and to improve diagnostic and therapeutic options for HCC. This study aimed to encompass a set of statistical and machine learning computational approaches for identifying the key candidate genes for HCC. Three microarray datasets were used in this work, which were downloaded from the Gene Expression Omnibus Database. At first, normalization and differentially expressed genes (DEGs) identification were performed using limma for each dataset. Then, support vector machine (SVM) was implemented to determine the differentially expressed discriminative genes (DEDGs) from DEGs of each dataset and select overlapping DEDGs genes among identified three sets of DEDGs. Enrichment analysis was performed on common DEDGs using DAVID. A protein-protein interaction (PPI) network was constructed using STRING and the central hub genes were identified depending on the degree, maximum neighborhood component (MNC), maximal clique centrality (MCC), centralities of closeness, and betweenness criteria using CytoHubba. Simultaneously, significant modules were selected using MCODE scores and identified their associated genes from the PPI networks. Moreover, metadata were created by listing all hub genes from previous studies and identified significant meta-hub genes whose occurrence frequency was greater than 3 among previous studies. Finally, six key candidate genes (TOP2A, CDC20, ASPM, PRC1, NUSAP1, and UBE2C) were determined by intersecting shared genes among central hub genes, hub module genes, and significant meta-hub genes. Two independent test datasets (GSE76427 and TCGA-LIHC) were utilized to validate these key candidate genes using the area under the curve. Moreover, the prognostic potential of these six key candidate genes was also evaluated on the TCGA-LIHC cohort using survival analysis.

Pituitary Tumor-Transforming Gene 1/Delta like Non-Canonical Notch Ligand 1 Signaling in Chronic Liver Diseases

The management of chronic liver diseases (CLDs) remains a challenge, and identifying effective treatments is a major unmet medical need. In the current review we focus on the pituitary tumor transforming gene (PTTG1)/delta like non-canonical notch ligand 1 (DLK1) axis as a potential therapeutic target to attenuate the progression of these pathological conditions. PTTG1 is a proto-oncogene involved in proliferation and metabolism. PTTG1 expression has been related to inflammation, angiogenesis, and fibrogenesis in cancer and experimental fibrosis. On the other hand, DLK1 has been identified as one of the most abundantly expressed PTTG1 targets in adipose tissue and has shown to contribute to hepatic fibrosis by promoting the activation of hepatic stellate cells. Here, we extensively analyze the increasing amount of information pointing to the PTTG1/DLK1 signaling pathway as an important player in the regulation of these disturbances. These data prompted us to hypothesize that activation of the PTTG1/DLK1 axis is a key factor upregulating the tissue remodeling mechanisms characteristic of CLDs. Therefore, disruption of this signaling pathway could be useful in the therapeutic management of CLDs.

Identification of the biological function of miR-9 in spinal cord ischemia-reperfusion injury in rats

Spinal cord ischemia–reperfusion injury (SCII) is still a serious problem, and the mechanism is not fully elaborated. In the rat SCII model, qRT-PCR was applied to explore the altered expression of miR-9 (miR-9a-5p) after SCII. The biological function of miR-9 and its potential target genes based on bioinformatics analysis and experiment validation in SCII were explored next. Before the surgical procedure of SCII, miR-9 mimic and inhibitor were intrathecally infused. miR-9 mimic improved neurological function. In addition, miR-9 mimic reduced blood-spinal cord barrier (BSCB) disruption, inhibited apoptosis and decreased the expression of IL-6 and IL-1β after SCII. Gene Ontology (GO) analysis demonstrated that the potential target genes of miR-9 were notably enriched in several biological processes, such as “central nervous system development”, “regulation of growth” and “response to cytokine”. The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that the potential target genes of miR-9 were significantly enriched in several signaling pathways, including “Notch signaling pathway”, “MAPK signaling pathway”, “Focal adhesion” and “Prolactin signaling pathway”. We further found that the protein expression of MAP2K3 and Notch2 were upregulated after SCII while miR-9 mimic reduced the increase of MAP2K3 and Notch2 protein. miR-9 mimic or MAP2K3 inhibitor reduced the release of IL-6 and IL-1β. miR-9 mimic or si-Notch2 reduced the increase of cleaved-caspase3. Moreover, MAP2K3 inhibitor and si-Notch2 reversed the effects of miR-9 inhibitor. In conclusion, overexpression of miR-9 improves neurological outcomes after SCII and might inhibit BSCB disruption, neuroinflammation, and apoptosis through MAP2K3-, or Notch2-mediated signaling pathway in SCII.