Integration of Chemoinformatics and Multi-Omics Analysis Defines ECT2 as a Potential Target for Cancer Drug Therapy

ECT2 promotes the occurrence and is a prognostic biomarker of head and neck squamous cell carcinoma

Background: Epithelial Cell Transforming Sequence 2 (ECT2) has been implicated in various tumorigenic processes, including proliferation, migration, and invasion. However, its specific role in head and neck squamous cell carcinoma (HNSCC) remains unclear. Methods: This study integrates transcriptomic and single-cell RNA sequencing (scRNA-seq) data to explore the potential role of ECT2 in HNSCC. Differential expression analysis, cell-based assays (including CCK-8 for proliferation, transwell for migration, invasion assays, and flow cytometry for apoptosis and cell cycle analysis), and enrichment analysis were employed to investigate ECT2 expression levels and its regulatory effects on cellular phenotypes. Additionally, Mendelian randomization analysis was utilized to identify genes causally related to HNSCC using publicly available Genome-Wide Association Study (GWAS) data. Results: ECT2 is highly expressed in HNSCC samples and its downregulation inhibits proliferation, migration, invasion, induces apoptosis, and affects the cell cycle transition in HSC-3 cells. Furthermore, differential analysis revealed significant differences in the immune microenvironment and drug sensitivity between high and low ECT2 expression groups. The pathways enriched in different groups include CCR and its related chemokines, as well as HLA in antigen presentation and immune response. There are also significant differences in the sensitivity to drugs such as bortezomib and dasatinib between the two groups. Prognostic models constructed from prognosis-related genes showed significant differences in prognosis between high and low-risk groups. Integration of scRNA-seq data identified Monocyte clusters as high-scoring cell clusters based on genes interacting with ECT2.Mendelian randomization analysis identified three genes (LGALS2, SLC11A1, and TKT) causally related to HNSCC within this cell cluster. Conclusion: The findings suggest that ECT2 overexpression is associated with the survival rate of HNSCC, indicating its potential as a prognostic biomarker for this malignancy.

Zaki M, Soltan MA, Noreldin AE. Oncogenic Potential of Replication Factor C Subunit 4: Correlations with Tumor Progression and Assessment of Potential Inhibitors

Replication Factor C Subunit 4 (RFC4), an oncogene implicated in many human cancers, has yet to be extensively studied in many cancer types to determine its expression patterns and tumor tissue function. Various bioinformatics tools were used to analyze RFC4 as a potential oncogene and therapeutic target across many cancers. We first examined RFC4 expression levels in several human tumor types to determine relationships with tumor grade, stage, metastasis, and patient survival. We also examined RFC4's genetic changes, epigenetic methylation, and effect on tumor microenvironment (TME) immune cell infiltration. We also analyzed RFC4's connections with immunological checkpoints to identify potential molecular pathways involved in carcinogenesis. Our findings show that RFC4 is upregulated in several tumor types and associated with poor prognoses in many human cancers. This study shows that RFC4 significantly affects the tumor immunological microenvironment, specifically immune cell populations. Finally, we screened for RFC4-inhibiting pharmacological compounds with anti-cancer potential. This study fully elucidates RFC4's carcinogenic activities, emphasizing its potential as a prognostic biomarker and a target for anti-cancer therapy.

Cyclin Dependent Kinase Inhibitor 2A Genetic and Epigenetic Alterations Interfere with Several Immune Components and Predict Poor Clinical Outcome

Cyclin dependent kinase inhibitor 2A (CDKN2A) is a well-known tumor suppressor gene as it functions as a cell cycle regulator. While several reports correlate the malfunction of CDKN2A with the initiation and progression of several types of human tumors, there is a lack of a comprehensive study that analyzes the potential effect of CDKN2A genetic alterations on the human immune components and the consequences of that effect on tumor progression and patient survival in a pan-cancer model. The first stage of the current study was the analysis of CDKN2A differential expression in tumor tissues and the corresponding normal ones and correlating that with tumor stage, grade, metastasis, and clinical outcome. Next, a detailed profile of CDKN2A genetic alteration under tumor conditions was described and assessed for its effect on the status of different human immune components. CDKN2A was found to be upregulated in cancerous tissues versus normal ones and that predicted the progression of tumor stage, grade, and metastasis in addition to poor prognosis under different forms of tumors. Additionally, CDKN2A experienced different forms of genetic alteration under tumor conditions, a characteristic that influenced the infiltration and the status of CD8, the chemokine CCL4, and the chemokine receptor CCR6. Collectively, the current study demonstrates the potential employment of CDKN2A genetic alteration as a prognostic and immunological biomarker under several types of human cancers.

Advances in the Applications of Bioinformatics and Chemoinformatics

Chemoinformatics involves integrating the principles of physical chemistry with computer-based and information science methodologies, commonly referred to as "in silico techniques", in order to address a wide range of descriptive and prescriptive chemistry issues, including applications to biology, drug discovery, and related molecular areas. On the other hand, the incorporation of machine learning has been considered of high importance in the field of drug design, enabling the extraction of chemical data from enormous compound databases to develop drugs endowed with significant biological features. The present review discusses the field of cheminformatics and proposes the use of virtual chemical libraries in virtual screening methods to increase the probability of discovering novel hit chemicals. The virtual libraries address the need to increase the quality of the compounds as well as discover promising ones. On the other hand, various applications of bioinformatics in disease classification, diagnosis, and identification of multidrug-resistant organisms were discussed. The use of ensemble models and brute-force feature selection methodology has resulted in high accuracy rates for heart disease and COVID-19 diagnosis, along with the role of special formulations for targeting meningitis and Alzheimer's disease. Additionally, the correlation between genomic variations and disease states such as obesity and chronic progressive external ophthalmoplegia, the investigation of the antibacterial activity of pyrazole and benzimidazole-based compounds against resistant microorganisms, and its applications in chemoinformatics for the prediction of drug properties and toxicity-all the previously mentioned-were presented in the current review.