Identification of Ten Core Hub Genes as Potential Biomarkers and Treatment Target for Hepatoblastoma

Decreased Expression of GLYATL1 Predicts Poor Prognosis in Patients with Clear Cell Renal Cell Carcinoma

Background

GLYATL1 is a member of the glycine-N-acyltransferase family, which catalyses acyl group transfer. The role of GLYATL1 in cancer is largely unknown; therefore, the potential value of GLYATL1 in clear cell renal cell carcinoma (ccRCC) was explored.

Methods

The ccRCC gene expression profiles and clinical data were obtained from the University of California Santa Cruz Xena platform. Differential expression and survival analysis were performed using R software. Samples from the TIMER public database and real-world were used for validation. The potential molecular mechanism of GLYATL1 in ccRCC was explored using gene set enrichment analysis (GSEA).

Results

GLYATL1 was downregulated, indicating a poor prognosis in ccRCC. Low expression of GLYATL1 was significantly associated with advanced stage and higher histological grade ccRCC. The differential expression of GLYATL1 was validated at the protein level using clinical samples and tissue microarray. The results of GSEA showed that multiple crucial signalling pathways including fatty acid metabolism, adipogenesis, oxidative phosphorylation and epithelial-mesenchymal transition were enriched.

Conclusion

This study demonstrated that GLYATL1 downregulation has an unfavourable impact on the survival of patients with ccRCC. The resulting data indicated that GLYATL1 could be a potential new target for ccRCC therapy, which may be helpful for the personalized treatment of such individuals.

UBE2C expression is elevated in hepatoblastoma and correlates with inferior patient survival

Hepatoblastoma (HB) is the most common malignant liver tumor among children. To gain insight into the pathobiology of HB, we performed RNA sequence analysis on 5 patient-derived xenograft lines (HB-243, HB-279, HB-282, HB-284, HB-295) and 1 immortalized cell line (HUH6). Using cultured hepatocytes as a control, we found 2,868 genes that were differentially expressed in all of the HB lines on mRNA level. The most upregulated genes were ODAM, TRIM71, and IGDCC3, and the most downregulated were SAA1, SAA2, and NNMT. Protein-protein interaction analysis identified ubiquitination as a key pathway dysregulated in HB. UBE2C, encoding an E2 ubiquitin ligase often overexpressed in cancer cells, was markedly upregulated in 5 of the 6 HB cell lines. Validation studies confirmed UBE2C immunostaining in 20 of 25 HB tumor specimens versus 1 of 6 normal liver samples. The silencing of UBE2C in two HB cell models resulted in decreased cell viability. RNA sequencing analysis showed alterations in cell cycle regulation after UBE2C knockdown. UBE2C expression in HB correlated with inferior patient survival. We conclude that UBE2C may hold prognostic utility in HB and that the ubiquitin pathway is a potential therapeutic target in this tumor.

Exploration of Potential Biomarkers and Immune Landscape for Hepatoblastoma: Evidence from Machine Learning Algorithm

This study aimed to investigate the immune landscape in hepatoblastoma (HB) based on deconvolution methods and identify a biomarkers panel for diagnosis based on a machine learning algorithm. Firstly, we identified 277 differentially expressed genes (DEGs) and differentiated and functionally identified the modules in DEGs. The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and GO (gene ontology) were used to annotate these DEGs, and the results suggested that the occurrence of HB was related to DNA adducts, bile secretion, and metabolism of xenobiotics by cytochrome P450. We selected the top 10 genes for our final diagnostic panel based on the random forest tree method. Interestingly, TNFRSF19 and TOP2A were significantly down-regulated in normal samples, while other genes (TRIB1, MAT1A, SAA2-SAA4, NAT2, HABP2, CYP2CB, APOF, and CFHR3) were significantly down-regulated in HB samples. Finally, we constructed a neural network model based on the above hub genes for diagnosis. After cross-validation, the area under the ROC curve was close to 1 (AUC = 0.972), and the AUC of the validation set was 0.870. In addition, the results of single-sample gene-set enrichment analysis (ssGSEA) and deconvolution methods revealed a more active immune responses in the HB tissue. In conclusion, we have developed a robust biomarkers panel for HB patients.

Screening of Potential Biomarkers in the Peripheral Serum for Steroid-Induced Osteonecrosis of the Femoral Head Based on WGCNA and Machine Learning Algorithms

Background. Steroid-induced osteonecrosis of the femoral head (SONFH) has produced a substantial burden of medical and social experience. However, the current diagnosis is still limited. Thus, this study is aimed at identifying potential biomarkers in the peripheral serum of patients with SONFH. Methods. The expression profile data of SONFH (number: GSE123568) was acquired from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in SONFH were identified and used for weighted gene coexpression network analysis (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to investigate the biological functions. The protein-protein interaction (PPI) network and machine learning algorithms were employed to screen for potential biomarkers. Gene set enrichment analysis (GSEA), transcription factor (TF) enrichment analysis, and competing endogenous RNA (ceRNA) network were used to determine the biological functions and regulatory mechanisms of the potential biomarkers. Results. A total of 562 DEGs, including 318 upregulated and 244 downregulated genes, were identified between SONFH and control samples, and 94 target genes were screened based on WGCNA. Moreover, biological function analysis suggested that target genes were mainly involved in erythrocyte differentiation, homeostasis and development, and myeloid cell homeostasis and development. Furthermore, GYPA, TMCC2, and BPGM were identified as potential biomarkers in the peripheral serum of patients with SONFH based on machine learning algorithms and showed good diagnostic values. GSEA revealed that GYPA, TMCC2, and BPGM were mainly involved in immune-related biological processes (BPs) and signaling pathways. Finally, we found that GYPA might be regulated by hsa-miR-3137 and that BPGM might be regulated by hsa-miR-340-3p. Conclusion. GYPA, TMCC2, and BPGM are potential biomarkers in the peripheral serum of patients with SONFH and might affect SONFH by regulating erythrocytes and immunity.

Identification of an Innate Immune-Related Prognostic Signature in Early-Stage Lung Squamous Cell Carcinoma

Background

Early-stage lung squamous cell carcinoma (LUSC) progression is accompanied by changes in immune microenvironments and the expression of immune-related genes (IRGs). Identifying innate IRGs associated with prognosis may improve treatment and reveal new immunotherapeutic targets.

Methods

Gene expression profiles and clinical data of early-stage LUSC patients were obtained from the Gene Expression Omnibus and The Cancer Genome Atlas databases and IRGs from the InnateDB database. Univariate and multivariate Cox regression and LASSO regression analyses were performed to identify an innate IRG signature model prognostic in patients with early-stage LUSC. The predictive ability of this model was assessed by time-dependent receiver operator characteristic curve analysis, with the independence of the model-determined risk score assessed by univariate and multivariate Cox regression analyses. Overall survival (OS) in early-stage LUSC patients was assessed using a nomogram and decision curve analysis (DCA). Functional and biological pathways were determined by gene set enrichment analysis, and differences in biological functions and immune microenvironments between the high- and low-risk groups were assessed by ESTIMATE and the CIBERSORT algorithm.

Results

A signature involving six IRGs (SREBF2, GP2, BMX, NR1H4, DDX41, and GOPC) was prognostic of OS. Samples were divided into high- and low-risk groups based on median risk scores. OS was significantly shorter in the high-risk than in the low-risk group in the training (P < 0.001), GEO validation (P = 0.00021) and TCGA validation (P = 0.034) cohorts. Multivariate Cox regression analysis showed that risk score was an independent risk factor for OS, with the combination of risk score and T stage being optimally predictive of clinical benefit. GSEA, ESTIMATE, and the CIBERSORT algorithm showed that immune cell infiltration was higher and immune-related pathways were more strongly expressed in the low-risk group.

Conclusion

A signature that includes these six innate IRGs may predict prognosis in patients with early-stage LUSC.

miR-126 in Extracellular Vesicles Derived from Hepatoblastoma Cells Promotes the Tumorigenesis of Hepatoblastoma through Inducing the Differentiation of BMSCs into Cancer Stem Cells

Background

Extracellular vesicles (EVs) can deliver miRNAs between cells and play a crucial role in hepatoblastoma progression. In this study, we explored the differentially expressed miRNAs related to tumor cell-derived EVs and the mechanism by which EVs regulate hepatoblastoma progression.

Methods

Bioinformatics analysis was performed to explore the differentially expressed miRNAs between the hepatoblastoma and adjacent normal tissues. TEM, NTA, and western blotting were conducted to identify EVs. The expression of miR-126-3p, miR-126-5p, miR-30b-3p, miR-30b-3p, SRY, IL-1α, IL-6, and TGF-β was detected by RT-qPCR. Immunofluorescence (IF) was used to analyze the expression of PKH67, and flow cytometry was applied to assess the ratio of CD44+ CD90+ CD133+ cells. ELISA was used to evaluate the levels of IL-6 and TGF-β. A xenograft mouse model was constructed to detect the function of EVs with downregulated miR-126. IHC was performed to calculate β-catenin levels in tumor tissues.

Results

miR-126 was upregulated in hepatoblastoma. EVs derived from hepatoblastoma cells significantly increased the ratio of CD44+ CD90+ CD133+ cells and increased the expression of IL-6, Oct4, SRY, and TGF-β in bone marrow mesenchymal stem cells (BMSCs), while EVs with downregulated miR-126 reversed these phenomena. miR-126 downregulation notably attenuated hepatoblastoma tumor growth and decreased the ratio of CD44+ CD90+ CD133+ cells and increased the expression of IL-6, Oct4, SRY, TGF-β, and β-catenin in tumor tissues of mice. Furthermore, EVs with downregulated miR-126 inhibited the differentiation of BMSCs into cancer stem cells.

Conclusions

Exosomal miR-126 derived from hepatoblastoma cells promoted the tumorigenesis of liver cancer through inducing the differentiation of BMSCs into cancer stem cells.

Human cell transformation by combined lineage conversion and oncogene expression

Cancer is the most complex genetic disease known, with mutations implicated in more than 250 genes. However, it is still elusive which specific mutations found in human patients lead to tumorigenesis. Here we show that a combination of oncogenes that is characteristic of liver cancer (CTNNB1, TERT, MYC) induces senescence in human fibroblasts and primary hepatocytes. However, reprogramming fibroblasts to a liver progenitor fate, induced hepatocytes (iHeps), makes them sensitive to transformation by the same oncogenes. The transformed iHeps are highly proliferative, tumorigenic in nude mice, and bear gene expression signatures of liver cancer. These results show that tumorigenesis is triggered by a combination of three elements: the set of driver mutations, the cellular lineage, and the state of differentiation of the cells along the lineage. Our results provide direct support for the role of cell identity as a key determinant in transformation and establish a paradigm for studying the dynamic role of oncogenic drivers in human tumorigenesis.