Background: Inflammation plays an important role in tumor proliferation, metastasis, and resistance to chemotherapy. The systemic inflammation response index (SIRI), has been reported to be closely related to prognosis in many tumors, such as breast and gastric cancers. However, the predictive value of pretreatment SIRI on pathological complete response (pCR) rates in patients with breast cancer treated with neoadjuvant chemotherapy (NAC) is unknown. This study examined the correlation between SIRI and pCR in patients with breast cancer receiving NAC and identified convenient and accurate predictive indicators for pCR. Methods: We retrospectively analyzed the clinicopathological parameters and pretreatment peripheral blood characteristics of the 241 patients with breast cancer who received NAC between June 2015 and June 2020. Receiver operating characteristic (ROC) curves were used to determine the optimal cutoff of SIRI. ROC curves were also plotted to verify the accuracy of inflammatory markers for pCR prediction. The chi-squared test was used to explore the relationships of SIRI with pCR and other clinicopathological parameters. Multivariate analyses were performed using a logistic regression model. Results: Among the 241 patients, 48 (19.92%) achieved pCR. pCR was significantly related to SIRI, the neutrophil-lymphocyte ratio (NLR), the lymphocyte-monocyte ratio (LMR), molecular subtypes and other clinicopathological parameters, such as BMI, clinical T and N staging, and histological grade. Multivariate analyses indicated that the clinical T and N staging, SIRI, and NLR were independent prognostic factors for pCR in patients with breast cancer. The area under the ROC curve for SIRI was larger than that for NLR. Compared to patients with SIRI ≥0.72, patients with SIRI < 0.72 had a nearly 5-fold higher chance of obtaining pCR (odds ratio = 4.999, 95% confidence interval = 1.510–16.551, p = 0.000). Conclusions: Pretreatment SIRI is predictive of pCR in patients with breast cancer receiving NAC, and the index can assist physicians in formulating personalized treatment strategies.

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.

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.