Identification and Functional Enrichment Analysis of Potential Diagnostic and Therapeutic Targets in Adamantinomatous Craniopharyngioma

Ganoderma lucidum (Curtis) P. Karst. Immunomodulatory Protein Has the Potential to Improve the Prognosis of Breast Cancer Through the Regulation of Key Prognosis-Related Genes

Background/Objectives: Breast cancer in women is the most commonly diagnosed and most malignant tumor. Although luminal A breast cancer (LumA) has a relatively better prognosis, it still has a persistent pattern of recurrence. Ganoderma lucidum (Curtis) P. Karst. is a kind of traditional Chinese medicine and has antitumor effects. In this study, we aimed to identify the genes relevant to prognosis, find novel targets, and investigate the function of the bioactive protein from G. lucidum, called FIP-glu, in improving prognosis. Methods: Gene expression data and clinical information of LumA breast cancer patients were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Using bioinformatics methods, a predictive risk model was constructed to predict the prognosis for each patient. The cell counting kit-8 (CCK8) and clone formation assays were used to validate gene function. The ability of FIP-glu to regulate RNA levels of risk genes was validated. Results: Six risk genes (slit-roundabout GTPase-activating protein 2 (SRGAP2), solute carrier family 35 member 2 (SLC35A2), sequence similarity 114 member A1 (FAM114A1), tumor protein P53-inducible protein 11 (TP53I11), transmembrane protein 63C (TMEM63C), and polymeric immunoglobulin receptor (PIGR)) were identified, and a prognostic model was constructed. The prognosis was worse in the high-risk group and better in the low-risk group. The receiver operating characteristic (ROC) curve confirmed the model's accuracy. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses showed that the differentially expressed genes (DEGs) between the high- and low-risk groups were significantly enriched in the immune responses. TMEM63C could promote tumor viability, growth, and proliferation in vitro. FIP-glu significantly regulated these risk genes, and attenuated the promoting effect of TMEM63C in breast cancer cells. Conclusions: SRGAP2, SLC35A2, FAM114A1, TP53I11, TMEM63C, and PIGR were identified as the potential risk genes for predicting the prognosis of patients. TMEM63C could be a potential novel therapeutic target. Moreover, FIP-glu was a promising drug for improving the prognosis of LumA breast cancer.

Identification of a Potential PGK1 Inhibitor with the Suppression of Breast Cancer Cells Using Virtual Screening and Molecular Docking

BACKGROUND/OBJECTIVES

Breast cancer is the second most common malignancy worldwide and poses a significant threat to women's health. However, the prognostic biomarkers and therapeutic targets of breast cancer are unclear. A prognostic model can help in identifying biomarkers and targets for breast cancer. In this study, a novel prognostic model was developed to optimize treatment, improve clinical prognosis, and screen potential phosphoglycerate kinase 1 (PGK1) inhibitors for breast cancer treatment.

METHODS

Using data from the Gene Expression Omnibus (GEO) database, differentially expressed genes (DEGs) were identified in normal individuals and breast cancer patients. The biological functions of the DEGs were examined using bioinformatics analysis. A novel prognostic model was then constructed using the DEGs through LASSO and multivariate Cox regression analyses. The relationship between the prognostic model, survival, and immunity was also evaluated. In addition, virtual screening was conducted based on the risk genes to identify novel small molecule inhibitors of PGK1 from Chemdiv and Targetmol libraries. The effects of the potential inhibitors were confirmed through cell experiments.

RESULTS

A total of 230 up- and 325 down-regulated DEGs were identified in HER2, LumA, LumB, and TN breast cancer subtypes. A new prognostic model was constructed using ten risk genes. The analysis from The Cancer Genome Atlas (TCGA) indicated that the prognosis was poorer in the high-risk group compared to the low-risk group. The accuracy of the model was confirmed using the ROC curve. Furthermore, functional enrichment analyses indicated that the DEGs between low- and high-risk groups were linked to the immune response. The risk score was also correlated with tumor immune infiltrates. Moreover, four compounds with the highest score and the lowest affinity energy were identified. Notably, D231-0058 showed better inhibitory activity against breast cancer cells.

CONCLUSIONS

Ten genes (ACSS2, C2CD2, CXCL9, KRT15, MRPL13, NR3C2, PGK1, PIGR, RBP4, and SORBS1) were identified as prognostic signatures for breast cancer. Additionally, results showed that D231-0058 (2-((((4-(2-methyl-1H-indol-3-yl)-1,3-thiazol-2-yl)carbamoyl)methyl)sulfanyl)acetic acid) may be a novel candidate for treating breast cancer.

Bioinformatics analysis and validation of the critical genes associated with adamantinomatous craniopharyngioma

Adamantinomatous craniopharyngioma (ACP) is an epithelial tumor that arises when Rathke’s pouch remains during embryonic development. The pathogenesis of ACP remains unclear, and treatment options are limited. Here, we reveal the critical genes expressed in ACP and provide a basis for further research and treatment. The raw dataset GSE94349 was downloaded from the GEO database. We selected 24 ACP and 27 matched samples from individuals with no documented tumor complications (control group). Then, we screened for differentially expressed genes (DEGs) to identify key signaling pathways and associated DEGs. A total of 470 DEGs were identified (251 upregulated and 219 downregulated). Hierarchical clustering showed that the DEGs could precisely distinguish the ACP group from the control group (CG). Gene Ontology (GO) enrichment analysis indicated that the upregulated DEGs were mainly involved in cell adhesion, inflammatory responses, and extracellular matrix management. The downregulated DEGs were primarily involved in cell junction and nervous system development. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis indicated that the critical pathway was pathways in cancer. In the PPI network, CDH1, SHH, and WNT5A had the highest degrees of interaction and were associated with the formation of ACP. CDH1 was verified as a critical gene by quantitative reverse transcription–polymerase chain reaction (qRT-PCR) in ACP and CG samples. We found that CDH1 may play an important role in the pathways in cancer signaling pathway that regulates ACP development. The CDH1 gene may be a target for future research and treatment of ACP.

SPP1 and FN1 are significant gene biomarkers of tongue squamous cell carcinoma

Tongue squamous cell carcinoma (TSCC) is one of the most common malignant tumor types in the oral and maxillofacial region. The etiology and pathogenesis behind TSCC is complicated. In the present study, three gene expression profiles, namely GSE31056, GSE13601 and GSE78060, were downloaded from the Gene Expression Omnibus (GEO). The GEO2R online tool was utilized to identify differentially expressed genes (DEGs) between TSCC and normal tissue samples. Furthermore, a protein-protein interaction (PPI) network was constructed and hub genes were validated and analyzed. A total of 83 common DEGs were obtained in three datasets, including 48 upregulated and 35 downregulated genes. Pathway enrichment analysis indicated that DEGs were primarily enriched in cell adhesion, extracellular matrix (ECM) organization, and proteolysis. A total of 63 nodes and 218 edges were included in the PPI network. The top 11 candidate hub genes were acquired, namely plasminogen activator urokinase (PLAU), signal transducer and activator of transcription 1, C-X-C motif chemokine ligand 12, matrix metallopeptidase (MMP) 13, secreted phosphoprotein 1 (SPP1), periostin, MMP1, MMP3, fibronectin 1 (FN1), serpin family E member 1 and snail family transcriptional repressor 2. Overall, 83 DEGs and 11 hub genes were screened from TSCC and normal individuals using bioinformatics and microarray technology. These genes may be used as diagnostic and therapeutic biomarkers for TSCC. In addition, SPP1 and FNl were identified as potential biomarkers for the progression of TSCC.

Screening of Hub Gene Targets for Lung Cancer via Microarray Data

BACKGROUND

Lung cancer is one of the malignancies exhibiting the fastest increase in morbidity and mortality, but the cause is not clearly understood. The goal of this investigation was to screen and identify relevant biomarkers of lung cancer.

METHODS

Publicly available lung cancer data sets, including GSE40275 and GSE134381, were obtained from the GEO database. The repeatability test for data was done by principal component analysis (PCA), and a GEO2R was performed to screen differentially expressed genes (DEGs), which were all subjected to enrichment analysis. Protein-protein interactions (PPIs), and the significant module and hub genes were identified via Cytoscape. Expression and correlation analysis of hub genes was done, and an overall survival analysis of lung cancer was performed. A receiver operating characteristic (ROC) curve analysis was performed to test the sensitivity and specificity of the identified hub genes for diagnosing lung cancer.

RESULTS

The repeatability of the two datasets was good and 115 DEGs and 10 hub genes were identified. Functional analysis revealed that these DEGs were associated with cell adhesion, the extracellular matrix, and calcium ion binding. The DEGs were mainly involved with ECM-receptor interaction, ABC transporters, cell-adhesion molecules, and the p53 signaling pathway. Ten genes including COL1A2, POSTN, DSG2, CDKN2A, COL1A1, KRT19, SLC2A1, SERPINB5, DSC3, and SPP1 were identified as hub genes through module analysis in the PPI network. Lung cancer patients with high expression of COL1A2, POSTN, DSG2, CDKN2A, COL1A1, SLC2A1, SERPINB5, and SPP1 had poorer overall survival times than those with low expression (p <0.05). The CTD database showed that 10 hub genes were closely related to lung cancer. Expression of POSTN, DSG2, CDKN2A, COL1A1, SLC2A1, SERPINB5, and SPP1 was also associated with a diagnosis of lung cancer (p<0.05). ROC analysis showed that SPP1 (AUC = 0.940, p = 0.000*, 95%CI = 0.930-0.973, ODT = 7.004), SLC2A1 (AUC = 0.889, p = 0.000*, 95%CI = 0.791-0.865, ODT = 7.123), CDKN2A (AUC = 0.730, p = 0.000*, 95%CI = 0.465-1.000, ODT = 6.071) were suitable biomarkers.

CONCLUSION

Microarray technology represents an effective method for exploring genetic targets and molecular mechanisms of lung cancer. In addition, the identification of hub genes of lung cancer provides novel research insights for the diagnosis and treatment of lung cancer.

Insights regarding novel biomarkers and the pathogenesis of primary colorectal carcinoma based on bioinformatic analysis

BACKGROUND

Biomarkers are important in the study of tumor processes for early detection and precise treatment. The biomarkers that have been previously detected are not useful for clinical application for primary colorectal carcinoma (PCRC). The aim of this study was to explore clinically valuable biomarkers of PCRC based on integrated bioinformatic analysis.

MATERIAL AND METHODS

Gene expression data were acquired from the GSE41258 dataset, and the differentially expressed genes were determined between PCRC and normal colorectal samples. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were implemented via Gene Set Enrichment Analysis. A protein-protein interaction (PPI) network was constructed. The significant modules and hub genes were screened and identified in the PPI network.

RESULTS

A total of 202 DEGs were identified, including 58 upregulated and 144 downregulated genes in PCRC samples compared to those in normal colorectal samples. Enrichment analysis demonstrated that the gene sets enriched in PCRC were significantly related to bicarbonate transport, regulation of sodium ion transport, potassium ion homeostasis, regulation of telomere maintenance, and other processes. A total of 10 hub genes was identified by cytoHubba: PYY, CXCL3, CXCL11, CXCL8, CXCL12, CCL20, MMP3, P2RY14, NPY1R, and CXCL1.

CONCLUSION

The hub genes, such as NPY1R, P2RY14, and CXCL12, and the electrolyte disequilibrium resulting from the differential expression of genes, especially bicarbonate imbalance, may provide novel insights and evidence for the future diagnosis and targeted therapy of PCRC.