Knowledge mapping of AURKA in Oncology：An advanced Bibliometric analysis (1998-2023).

AURKA, also known as Aurora kinase A, is a key molecule involved in the occurrence and progression of cancer. It plays crucial roles in various cellular processes, including cell cycle regulation, mitosis, and chromosome segregation. Dysregulation of AURKA has been implicated in tumorigenesis, promoting cell proliferation, genomic instability, and resistance to apoptosis. In this study, we conducted an extensive bibliometric analysis of research focusing on Aurora-A in the context of cancer by utilizing the Web of Science literature database. Various sophisticated computational tools, such as VOSviewer, Citespace, Biblioshiny R, and Cytoscape, were employed for comprehensive literature analysis and big data mining from January 1998 to September 2023.The primary objectives of our study were multi-fold. Firstly, we aimed to explore the chronological development of AURKA research, uncovering the evolution of scientific understanding over time. Secondly, we investigated shifting trends in research topics, elucidating areas of increasing interest and emerging frontiers. Thirdly, we delved into intricate signaling pathways and protein interaction networks associated with AURKA, providing insights into its complex molecular mechanisms. To further enhance the value of our bibliometric analysis, we conducted a meta-analysis on the prognostic value of AURKA in terms of patient survival. The results were visually presented, offering a comprehensive overview and future perspectives on Aurora-A research in the field of oncology. This study not only contributes to the existing body of knowledge but also provides valuable guidance for researchers, clinicians, and pharmaceutical professionals. By harnessing the power of bibliometrics, our findings offer a deeper understanding of the role of AURKA in cancer and pave the way for innovative research directions and clinical applications.

A novel single-cell model reveals ferroptosis-associated biomarkers for individualized therapy and prognostic prediction in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is a prevalent malignancy with a pressing need for improved therapeutic response and prognosis prediction. This study delves into a novel predictive model related to ferroptosis, a regulated cell death mechanism disrupting metabolic processes. RESULTS: Single-cell sequencing data analysis identified subpopulations of HCC cells exhibiting activated ferroptosis and distinct gene expression patterns compared to normal tissues. Utilizing the LASSO-Cox algorithm, we constructed a model with 10 single-cell biomarkers associated with ferroptosis, namely STMN1, S100A10, FABP5, CAPG, RGCC, ENO1, ANXA5, UTRN, CXCR3, and ITM2A. Comprehensive analyses using these biomarkers revealed variations in immune infiltration, tumor mutation burden, drug sensitivity, and biological functional profiles between risk groups. Specific associations were established between particular immune cell subtypes and certain gene expression patterns. Treatment response analyses indicated potential benefits from anti-tumor immune therapy for the low-risk group and chemotherapy advantages for the high-risk group. CONCLUSIONS: The integration of this single-cell level model with clinicopathological features enabled accurate overall survival prediction and effective risk stratification in HCC patients. Our findings illuminate the potential of ferroptosis-related genes in tailoring therapy and prognosis prediction for HCC, offering novel insights into the intricate interplay among ferroptosis, immune response, and HCC progression.

Ferroptosis, a subtle talk between immune system and cancer cells: To be or not to be?

Ferroptosis, an established form of programmed cell death discovered in 2012, is characterized by an imbalance in iron metabolism, lipid metabolism, and antioxidant metabolism. Activated CD8 + T cells can trigger ferroptosis in tumor cells by releasing interferon-γ, which initiates the ferroptosis program. Despite the remarkable progress made in treating various tumors with immunotherapy, such as anti-PD1/PDL1, there are still significant challenges to overcome, including limited treatment options and drug resistance. In this review, we exam the potential biological significance of the ferroptosis phenotype using bioinformatics and review the latest advancements in understanding the mechanism of ferroptosis-mediated anti-tumor immunotherapy. Furthermore, we revisit the host immune system, immune microenvironment, ferroptotic defense system, metabolic reprogramming, and key genes that regulate the occurrence and resistance of ferroptosis of tumor cell. Additionally, several immune-combined ferroptosis treatment strategies were put forward to improve immunotherapy efficacy and to provide new insights into reversing anti-tumor immune drug resistance.

Ferroptosis-Related Hub Genes in Hepatocellular Carcinoma: Prognostic Signature, Immune-Related, and Drug Resistance Analysis.

Background: Hepatocellular carcinoma (HCC) is the most prevalent type of primary liver cancer with a high fatality rate and dismal prognosis because of frequent recurrence and lack of efficient therapies. Ferroptosis is a recently recognized iron-dependent cell death distinct from necroptosis and apoptosis. The relationship between ferroptosis-related hub gene expression and prognosis in HCC remains to be further elucidated. Methods: Ferroptosis-related genes from the FerrDb database and the mRNA sequencing data and clinical information of HCC patients were obtained from The Cancer Genome Atlas (TCGA) database. The least absolute shrinkage and selection operator (LASSO) Cox regression was applied to identify a prognostic signature consisting of five ferroptosis-related hub genes in the TCGA cohort. The International Cancer Genome Consortium (ICGC) database was utilized to validate the reliability of the signature. Functional enrichment and immune-related analysis, including single-sample gene set enrichment analysis (ssGSEA), immune checkpoints, TIP-related genes, tumor stemness, and m6A-related genes, were performed to analyze the underlying mechanism. Additionally, the correlations between ferroptosis and drug resistance were evaluated using the NCI-60 database. Results: A 5-hub-gene signature associated with ferroptosis was constructed by multivariate Cox regression analysis to stratify patients into two risk groups. Patients with high risk had worse prognosis than those with low risk. Multivariate Cox regression analysis uncovered that the risk score was an independent prognostic indicator. We also proved the signature's predictive capacity using the Kaplan-Meier method and receiver operating characteristic (ROC) curve analysis. Functional analysis showed that nuclear division and the cell cycle were enriched. Immune-related analysis revealed that the signature was enriched in immune-related pathways. Moreover, the risk signature was significantly associated with immune cell infiltration, immune checkpoints, TIP-related genes, tumor stem cells, as well as m6A-related genes. Furthermore, these genes were crucial regulators of drug resistance. Conclusion: We identified and validated a novel hub gene signature that is closely associated with ferroptosis as a new and efficient biomarker with favorable potential for predicting the prognosis of HCC patients. In addition, it also offers new insights into the molecular mechanisms of HCC and provides an effective approach for the treatment of HCC. Further studies are necessary to validate the results of our study.

Burkitt lymphoma-associated network construction and important network motif analysis.

Biological and medical researchers have discovered numerous transcription factors (TFs), microRNAs (miRNAs) and genes associated with Burkitt lymphoma (BL) through individual experiments; however, their regulatory mechanisms remain unclear. In the present study, BL-dysregulated and BL-associated networks were constructed to investigate these mechanisms. All data and regulatory associations were from known data resources and literature. The dysregulated network consisted of dysregulated TFs, miRNAs and genes, and partially determined the pathogenesis mechanisms underlying BL. The BL-associated network consisted of BL-associated TFs, miRNAs and genes. It has been indicated that the network motif consisted of TFs, miRNAs and genes serve potential functions in numerous biological processes within cancer. Two of the most studied network motifs are feedback loop (FBL) and feed-forward loop (FFL). The important network motifs were extracted, including the FBL motif, 3-nodes FFL motif and 4-nodes motif, from BL-dysregulated and BL-associated networks, and 10 types of motifs were identified from BL-associated network. Finally, 26/31 FBL motifs, 45/75 3-nodes FFL motifs and 54/94 4-nodes motifs were obtained from the dysregulated/associated networks. A total of four TFs (E2F1, NFKB1, E2F4 and TCF3) exhibit complicated regulation associations in BL-associated networks. The biological network does not demonstrate the dysregulated status for healthy people. When the individual becomes unwell, their biological network exhibits a dysregulated status. If the dysregulated status is regulated to a normal status by a number of medical methods, the diseases may be treated successfully. BL-dysregulated networks serve important roles in pathogenesis mechanisms underlying BL regulation of the dysregulated network, which may be an effective strategy that contributes to gene therapy for BL.