Exploring the Molecular Mechanism and Biomakers of Liver Cancer Based on Gene Expression Microarray

Cysteine cathepsins: From diagnosis to targeted therapy of cancer

Cysteine cathepsins are a fascinating group of proteolytic enzymes that play diverse and crucial roles in numerous biological processes, both in health and disease. Understanding these proteases is essential for uncovering novel insights into the underlying mechanisms of a wide range of disorders, such as cancer. Cysteine cathepsins influence cancer biology by participating in processes such as extracellular matrix degradation, angiogenesis, immune evasion, and apoptosis. In this comprehensive review, we explore foundational research that illuminates the diverse and intricate roles of cysteine cathepsins as diagnostic markers and therapeutic targets for cancer. This review aims to provide valuable insights into the clinical relevance of cysteine cathepsins and explore their capacity to advance personalised and targeted medical interventions in oncology.

Bioinformatics and network pharmacology analysis of drug targets and mechanisms related to the comorbidity of epilepsy and migraine

OBJECTIVE

The present study aimed to explore the mechanisms underlying the comorbidity of epilepsy and migraine, identify potential common targets for drug intervention, and provide insight into new avenues for disease prevention and treatment using an integrated bioinformatic and network pharmacology approach.

METHODS

Disease targets in epilepsy and migraine were screened using the DisGeNET database to identify intersecting gene targets. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEEG) enrichment analyses were then performed using the WebGestalt database. Furthermore, the STRING database was used to construct a protein-protein interaction (PPI) network, and Cytoscape software was used to analyze the protein molecular signals at the intersection of epilepsy and migraine. The Drugbank database was used to identify common targets for antiepileptic drugs in epilepsy and migraine to further analyze the disease-gene-target-drug interaction network. Finally, molecular docking simulations were performed to verify the hypothesis that migraine and epilepsy share common diseases and drug targets.

RESULTS

A total of 178 common targets for epilepsy and migraine were identified using the DisGeNET database, and the 24 genes most related to the diseases were screened using the Score_gda gene scoring system. GO enrichment analysis indicated that common targets were mainly enriched in biological processes and molecular functions, including membrane potential regulation, inorganic ion transmembrane transport, axonal signaling, and ion channel activity. KEGG pathway enrichment analysis indicated that the mechanism of action might be related to neuroactive ligand receptors, AGE-RAGE, cAMP, and VEGF signaling pathways. The PPI network construction and analysis results showed that the PPI grid had 23 central nodes and 24 connected edges, with an average node degree of 2.09 and an average clustering coefficient of 0.384. The 10 genes with potentially important roles in epilepsy and migraine were CACNA1A, KCNQ2, KCNA1, SCN1A, PRRT2, SCN8A, KCNQ3, SCN2A, GRIN2A, and GABRG2. Drugbank database results indicated that antiepileptic drugs, including lamotrigine, topiramate, valproic acid, carbamazepine, gabapentin, and perampanel, also had common targets with migraine. The three most important targets exhibited strong binding affinity with drugs in the molecular docking simulations.

CONCLUSION

Our systematic and comprehensive analyses of disease-gene-target-drug interaction networks identified several biological processes and molecular functions common to migraine and epilepsy, most of which were related to neuroactive ligand-receptor interactions. These data provide a new theoretical basis and reference for the clinical treatment of comorbid epilepsy and migraine and may aid in the development of novel pharmacological strategies.

Identifying Potential New Gene Expression-Based Biomarkers in the Peripheral Blood Mononuclear Cells of Hepatitis B-Related Hepatocellular Carcinoma

OBJECTIVE

The analysis of the gene expression of peripheral blood mononuclear cells (PBMCs) is important to clarify the pathogenesis of hepatocellular carcinoma (HCC) and the detection of suitable biomarkers. The purpose of this investigation was to use RNA-sequencing to screen the appropriate differentially expressed genes (DEGs) in the PBMCs for the HCC.

METHODS

The comprehensive transcriptome of extracted RNA of PBMC (n = 20) from patients with chronic hepatitis B (CHB), liver cirrhosis, and early stage of HCC (5 samples per group) was carried out using RNA-sequencing. All raw RNA-sequencing data analyses were performed using conventional RNA-sequencing analysis tools. Next, gene ontology (GO) analyses were carried out to elucidate the biological processes of DEGs. Finally, relative transcript abundance of selected DEGs was verified using qRT-PCR on additional validation groups.

RESULTS

Specifically, 13, 1262, and 1450 DEGs were identified for CHB, liver cirrhosis, and HCC, when compared with the healthy controls. GO enrichment analysis indicated that HCC is closely related to the immune response. Seven DEGs (TYMP, TYROBP, CD14, TGFBI, LILRA2, GNLY, and GZMB) were common to HCC, cirrhosis, and CHB when compared to healthy controls. The data revealed that the expressions of these 7 DEGs were consistent with those from the RNA-sequencing results. Also, the expressions of 7 representative genes that had higher sensitivity were obtained by receiver operating characteristic analysis, which indicated their important diagnostic accuracy for HBV-HCC.

CONCLUSION

This study provides us with new horizons into the biological process and potential prospective clinical diagnosis and prognosis of HCC in the near future.

Identification of a glycolysis-related gene signature for predicting pancreatic cancer survival

BACKGROUND

Pancreatic cancer (PC) is one of the most common malignant tumors of the digestive tract. Its clinical symptoms are obscure and atypical. It is difficult to diagnose and treat. Tumor cells mainly obtain energy through glycolysis to promote their growth. Inhibiting glycolysis can inhibit proliferation and kill tumor cells.

METHODS

Using bioinformatics method, we investigate the relationships between glycolysis-related genes and PC tumor samples' epidemiologic information comprehensively.

RESULTS

Different expression levels of 27 genes were identified. Using bioinformatics methods, we plotted two subgroup curves based on glycolysis-related gene expression level. Potential predictive genes were screened and their prognostic values were analyzed. Survival among high-risk group and low risk group had significant difference. Receiver operating characteristic (ROC) curve analysis indicated that area under curve (AUC) of 10 genes was greater than 0.8. These genes could be used for clinical diagnosis and prediction for PC. Two potential predictors [Kinesin Family Member 20A (KIF20A) and MET Proto-Oncogene, Receptor Tyrosine Kinase (MET)] that met the independent predictive value were selected. In univariate analysis, we screened out 3 regulators MET, protein kinase CAMP-activated catalytic subunit alpha (PRKACA) and KIF20A. According to the 3 regulatory factors, the prognostic signals of PC were constructed, by which the samples with good prognosis and poor prognosis can be clearly distinguished independently of potential confounding factors.

CONCLUSIONS

Our results indicate that for PC, glycolysis -related genes could be promising therapeutic targets or prognostic indicators.

Prognostic gene biomarker identification in liver cancer by data mining

BACKGROUND

Liver cancer is a common cancer that enormously threatens the health of people worldwide. With the continuous advances of high-throughput gene sequencing technology and computer data mining technology, researchers can understand liver cancer based on the current accumulation of gene expression data and clinical information.

METHODS

We downloaded the TCGA data of liver cancer on the cancer-related website (https://genome-cancer.ucsc.edu/proj/site/hgHeatmap/), comprising 438 patients and 20,530 genes. After removing some patients with missing survival data, we collected 397 patients' samples. Our data were collected from a public database without real patient participation. While matching the patient samples in the gene expression spectrum, we attained 330 samples with primary tumors and 50 samples with normal solid tissue.

RESULTS

After the 330 tumor tissue samples were randomized into two equal-numbered groups (one is a training set, and the other is a test set), we selected 26 gene biomarkers from the training set and validated them in the test set. Based on the selected 26 gene biomarkers, RBM14, ALG11, MAG, SETD3, HOXD10 and other 26 genes were considered independent risk factors for the prognosis of liver cancer, and genes such as GHR significantly affect human growth hormone for liver cancer. The findings discovered that low-risk patients survived remarkably better than the high-risk patients (P<0.001), and the area under the curve (AUC) of receiver operating characteristic curve (ROC) was greater than 0.5.

CONCLUSION

Our numerical results showed that these 26 gene biomarkers can be used to guide the effective prognostic therapy of patients with liver cancer.

Three-gene prognostic biomarkers for seminoma identified by weighted gene co-expression network analysis

Testicular germ cell tumors (TGCTs) are common in young males, and seminoma accounts for a large proportion of TGCTs. However, there are limited records on the exploration of novel biomarkers for seminoma. Hence, we aimed to identify new biomarkers associated with overall survival in seminoma. mRNA-seq and clinical traits of TGCTs were downloaded from UCSC XENA and analyzed by weighted gene co-expression network analysis. After intersection with differentially expressed genes in GSE8607, common genes were subjected to protein-protein interaction (PPI) network construction and enrichment analyses. Then, the top 10 common genes were investigated by Kaplan–Meier (KM) survival analyses and univariate Cox regression analyses. Ultimately, TYROBP, CD68, and ITGAM were considered three prognostic biomarkers in seminoma. Based on correlation analysis between these genes and immune infiltrates, we suggest that the three biomarkers influence the survival of seminoma patients, possibly through regulating the infiltration of immune cells. In conclusion, our study demonstrated that TYROBP, CD68, and ITGAM could be regarded as prognostic biomarkers and therapeutic targets for seminoma patients.

Leading the invasion: The role of Cathepsin S in the tumour microenvironment

Elevated expression of the cysteine protease Cathepsin S has been correlated with a number of different cancer types in recent years. As tools have been developed to enable more accurate examination of individual cathepsin species, our knowledge and appreciation of the role that this protease plays in facilitating cancer has increased exponentially. This review focuses on our current understanding of the role of Cathepsin S within tumours and the surrounding microenvironment. While various publications have shown that Cathepsin S can be derived from tumour cells themselves, a plethora of more recent studies have identified that Cathepsin S can also be derived from other cell types within the tumour microenvironment including endothelial cells, macrophages and T cells. Furthermore, specific proteolytic substrates cleaved by Cathepsin S have also been identified which have reinforced our hypothesis that this protease facilitates key steps within tumours leading to their invasion, angiogenesis and metastasis.

MKP-4 suppresses hepatocarcinogenesis by targeting ERK1/2 pathway

BACKGROUND

Mitogen-activated protein kinase phosphatases-4 (MKP-4) is reported to exert a prognostic merit in hepatocarcinogenesis. However, the underlying molecular mechanisms have not been clearly defined.

METHODS

Immunoprecipitation-mass spectrometry (IP-MS) approach was used to identify interactive proteins with MKP-4. Western blot and immunohistochemistry were employed to detect proteins in HCC tissues. Cell counting kit-8, colony formation, Edu incorporation and sphere formation assays were performed to investigate functions of MKP-4/ERK1/2 interaction. Tumor xenografts in nude mice were used to determine effects in vivo.

RESULTS

Extracellular signal-regulated kinase 1 and 2 (ERK1/2) were identified as binding partners of MKP-4. Knockdown of MKP-4 increased cell proliferation and cancer stem cell (CSC) traits while upregulation of MKP-4 or pre-incubation with ERK1/2 inhibition reversed these effects. Mechanistically MKP-4 negatively regulated phosphorylation of ERK1/2 and reduced expressions of CyclinD1 and c-Myc. Both xenograft tumor models and clinical analysis of HCC patients indicated that lower expression of MKP-4 and higher expressions of ERK1/2 were associated with worse prognosis.

CONCLUSIONS

MKP-4-mediated dephosphorylation of ERK1/2 might serve as a novel tumor-suppressive mechanism and provide a potential therapy for HCC.

High expression of long non‑coding HOTAIR correlated with hepatocarcinogenesis and metastasis

HOX transcript antisense RNA (HOTAIR), a newly discovered long noncoding RNA (lncRNA), has been reported to be a poor prognostic marker in many types of cancers. The current study attempted to investigate the biological roles and clinicopathlogical implications of HOTAIR in hepatocellular carcinoma (HCC), as well as understand the molecular mechanisms of HOTAIR in HCC progression. HOTAIR expression in 95 HCC patients with paired HCC tissues and adjacent non‑cancer tissues were investigated using quantitative reverse transcription‑polymerase chain reaction. The association between HOTAIR expression and clinicopathological features was assessed. The effects of HOTAIR were examined in vitro assays by silencing the lncRNA. Pathway analyses were performed to illustrate the biological functions of the HOTAIR and coexpression genes. The expression level of HOTAIR was observed significantly higher in the HCC tissue than the adjacent non‑tumor tissue. HOTAIR expression levels were significantly higher in tumor samples from patients with distant metastasis, advanced stage, portal vein tumor embolus, vasoinvasion, tumor capsular infiltration or positive nm23 expression than those from patients without these conditions, correspondingly. The silencing of HOTAIR in liver cancer cells induced the inhibition of cell proliferation and promotion of apoptosis. Several pathways such as extracellular matrix‑receptor interaction, focal adhesion, pathways in cancer were annotated with the HOTAIR and coexpression genes. In summary, the present analysis indicates that HOTAIR might be an oncogene in HCC. It functions though promoting tumor cell growth and inhibiting apoptosis. HOTAIR may potentially be involved in HCC metastatic progression by several pathways correlated to cell adhesion, and may be a therapeutic target in future.

Identification of key genes and molecular mechanisms associated with dedifferentiated liposarcoma based on bioinformatic methods

Background

Dedifferentiated liposarcoma (DDLPS) is one of the most deadly types of soft tissue sarcoma. To date, there have been few studies dedicated to elucidating the molecular mechanisms behind the disease; therefore, the molecular mechanisms behind this malignancy remain largely unknown.

Materials and methods

Microarray profiles of 46 DDLPS samples and nine normal fat controls were extracted from Gene Expression Omnibus (GEO). Quality control for these microarray profiles was performed before analysis. Hierarchical clustering and principal component analysis were used to distinguish the general differences in gene expression between DDLPS samples and the normal fat controls. Differentially expressed genes (DEGs) were identified using the Limma package in R. Next, the enriched Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were obtained using the online tool DAVID (http://david.abcc.ncifcrf.gov/). A protein–protein interaction (PPI) network was constructed using the STRING database and Cytoscape software. Furthermore, the hub genes within the PPI network were identified.

Results

All 55 microarray profiles were confirmed to be of high quality. The gene expression pattern of DDLPS samples was significantly different from that of normal fat controls. In total, 700 DEGs were identified, and 83 enriched GO terms and three KEGG pathways were obtained. Specifically, within the DEGs of DDLPS samples, several pathways were identified as being significantly enriched, including the PPAR signaling pathway, cell cycle pathway, and pyruvate metabolism pathway. Furthermore, the dysregulated PPI network of DDLPS was constructed, and 14 hub genes were identified. Characteristic of DDLPS, the genes CDK4 and MDM2 were universally found to be up-regulated and amplified in gene copy number.

Conclusion

This study used bioinformatics to comprehensively mine DDLPS microarray data in order to obtain a deeper understanding of the molecular mechanism of DDLPS.

Aberrant promoter methylation profiles and association with survival in patients with hepatocellular carcinoma

The aim of this study was to investigate the prognostic and diagnostic value of genes with promoter methylation in hepatocellular carcinoma (HCC) patients. On the basis of The Cancer Genome Atlas data, we identified genes with differentially methylated promoters in HCC tissues and adjacent non-tumor tissues, using the linear models for microarray data approach. Cox proportional hazard regression analysis was applied to access the prognostic value of identified differentially methylated genes. The diagnostic value of the genes was evaluated through receiver operating characteristic. Pathway analyses were performed to illustrate biological functions of the identified genes. Compared to adjacent tissues, 77 genes with hypermethylated promoters and 2,412 genes with hypomethylated promoters were identified in HCC. The promoter hypomethylations of RNA5SP38, IL21, SDC4P, and MIR4439 were found to be associated with HCC patient survival (P=0.035, 0.040, 0.004, and 0.024, respectively). Hypomethylated SDC4P was associated with a better prognosis (hazard ratio, 0.482; 95% confidence interval [CI], −0.147–1.110; P=0.007). The combination of the promoter hypomethylations with RNA5SP38, IL21, and SDC4P showed an area under receiver operating characteristic curves of 0.975 (95% CI, 0.962–0.989; P=4.811E-25). Several pathways, including olfactory transduction, cytokine–cytokine receptor interaction, natural killer cell–mediated cytotoxicity, as well as inflammation mediated by chemokine and cytokine signaling pathway, were annotated with the hypomethylated promoter genes. SDC4P promoter hypomethylation may be a potential prognosis biomarker. A panel of promoter methylations in RNA5SP38, IL21, and SDC4P was proven a novel approach to diagnosis HCC. The pathway analysis defined the extensive functional role of DNA hypomethylation in cancer.