Comprehensive evaluation of the relationship between biomarker profiles and neoadjuvant chemotherapy outcomes for breast cancer patients.

BACKGROUND: Accurately predicting the response to neoadjuvant chemotherapy (NAC) in breast cancer patients is crucial for guiding treatment strategies and enhancing clinical outcomes. Current studies have primarily focused on a limited set of biomarkers. More importantly, the results of many studies are in conflict. To address this, we conducted a comprehensive evaluation of the predictive value of a diverse range of clinically available molecular biomarkers in breast cancer, including HER2, ER, PR, TOPO II, EGFR, Ki67, CK5/6, AR, and p53. Additionally, we assessed changes in these biomarkers after NAC administration. METHODS: Our study involved 189 patients with invasive breast cancer who underwent NAC at our institute. We examined biomarker profiles in core-needle biopsies taken before NAC and in surgical specimens obtained after NAC. We examined the association between these biomarkers and NAC outcomes, focusing on two main aspects: the rate of pathological complete response (pCR) and the reduction in tumor size. We used Chi-square and Mann-Whitney U tests to compare biomarker status changes between pCR and non-pCR patients. Linear regression analysis was employed to evaluate the relationship between biomarker status and tumor shrinkage rate. Additionally, we compared the expression status of these biomarkers before and after NAC using Chi-square and Wilcoxon signed-rank tests. RESULTS AND CONCLUSIONS: Our results demonstrated significant differences in the expression levels of HER2, ER, PR, TOPO II, EGFR, and Ki67 between pCR and non-pCR patients, underscoring their potential as predictive markers for NAC outcomes. Importantly, our results have shed light on the contentious issue surrounding TOPO II in NAC outcome prediction. We have provided evidence that establishes a significantly positive association between TOPO II expression level and the pCR rate. Notably, tumor size was identified as a relevant predictive factor for achieving pCR. Regarding biomarker profiles, only Ki67 levels and TOPO II status exhibited changes following NAC, resolving previous controversies. While the ER and PR status remained unchanged, their expression values exhibited a slight but significant decrease post-NAC. Our results provide clarity and insights into the value and potential of using these biomarkers to predict NAC responses and prognosis in breast cancer patients.

A prognostic mathematical model based on tumor microenvironment-related genes expression for breast cancer patients.

Background: Tumor microenvironment (TME) status is closely related to breast cancer (BC) prognosis and systemic therapeutic effects. However, to date studies have not considered the interactions of immune and stromal cells at the gene expression level in BC as a whole. Herein, we constructed a predictive model, for adjuvant decision-making, by mining TME molecular expression information related to BC patient prognosis and drug treatment sensitivity. Methods: Clinical information and gene expression profiles were extracted from The Cancer Genome Atlas (TCGA), with patients divided into high- and low-score groups according to immune/stromal scores. TME-related prognostic genes were identified using Kaplan-Meier analysis, functional enrichment analysis, and protein-protein interaction (PPI) networks, and validated in the Gene Expression Omnibus (GEO) database. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was used to construct and verify a prognostic model based on TME-related genes. In addition, the patients' response to chemotherapy and immunotherapy was assessed by survival outcome and immunohistochemistry (IPS). Immunohistochemistry (IHC) staining laid a solid foundation for exploring the value of novel therapeutic target genes. Results: By dividing patients into low- and high-risk groups, a significant distinction in overall survival was found (p < 0.05). The risk model was independent of multiple clinicopathological parameters and accurately predicted prognosis in BC patients (p < 0.05). The nomogram-integrated risk score had high prediction accuracy and applicability, when compared with simple clinicopathological features. As predicted by the risk model, regardless of the chemotherapy regimen, the survival advantage of the low-risk group was evident in those patients receiving chemotherapy (p < 0.05). However, in patients receiving anthracycline (A) therapy, outcomes were not significantly different when compared with those receiving no-A therapy (p = 0.24), suggesting these patients may omit from A-containing adjuvant chemotherapy. Our risk model also effectively predicted tumor mutation burden (TMB) and immunotherapy efficacy in BC patients (p < 0.05). Conclusion: The prognostic score model based on TME-related genes effectively predicted prognosis and chemotherapy effects in BC patients. The model provides a theoretical basis for novel driver-gene discover in BC and guides the decision-making for the adjuvant treatment of early breast cancer (eBC).

Inula japonica ameliorated the inflammation and oxidative stress in LPS-induced acute lung injury through the MAPK/NF-κB and Keap1/Nrf2 signalling pathways.

OBJECTIVES: To investigate the protective effect and underlying mechanism of Inula japonica (TEIJ) in the treatment of acute lung injury (ALI). METHODS: Protective effects of TEIJ in the inflammation and oxidative stress were studied in lipopolysaccharide (LPS)-induced ALI mice. Meanwhile, Western blot and real-time qPCR were carried out to investigate the underlying mechanism of TEIJ for ALI as well as immunohistochemistry. KEY FINDINGS: TEIJ significantly alleviated the course of ALI via suppressing the interstitial infiltrated inflammatory cells, the increase of inflammatory factors and the decrease of anti-oxidative factors. TEIJ inactivated the MAPK/NF-κB signalling pathway to suppress the transcription of its downstream target genes, such as TNF-α, IL-6, etc. Meanwhile, TEIJ activated the Keap1/Nrf2 signalling pathway to regulate expression levels of Nrf2 and its target proteins. The results of LC-QTOF-MS/MS indicated potential active constituents of I. japonica, terpenoids and flavonoids. Additionally, terpenoids and flavonoids synergistically alleviated LPS-induced ALI depending on MAPK/NF-κB and Keap1/Nrf2 signalling pathways. CONCLUSION: I. japonica could be considered a potential agent to treat ALI via regulating the MAPK/NF-κB and Keap1/Nrf2 signalling pathways.

Prediction of axillary lymph node pathological complete response to neoadjuvant therapy using nomogram and machine learning methods.

Purpose: To determine the feasibility of predicting the rate of an axillary lymph node pathological complete response (apCR) using nomogram and machine learning methods. Methods: A total of 247 patients with early breast cancer (eBC), who underwent neoadjuvant therapy (NAT) were included retrospectively. We compared pre- and post-NAT ultrasound information and calculated the maximum diameter change of the primary lesion (MDCPL): [(pre-NAT maximum diameter of primary lesion - post-NAT maximum diameter of preoperative primary lesion)/pre-NAT maximum diameter of primary lesion] and described the lymph node score (LNS) (1): unclear border (2), irregular morphology (3), absence of hilum (4), visible vascularity (5), cortical thickness, and (6) aspect ratio <2. Each description counted as 1 point. Logistic regression analyses were used to assess apCR independent predictors to create nomogram. The area under the curve (AUC) of the receiver operating characteristic curve as well as calibration curves were employed to assess the nomogram's performance. In machine learning, data were trained and validated by random forest (RF) following Pycharm software and five-fold cross-validation analysis. Results: The mean age of enrolled patients was 50.4 ± 10.2 years. MDCPL (odds ratio [OR], 1.013; 95% confidence interval [CI], 1.002-1.024; p=0.018), LNS changes (pre-NAT LNS - post-NAT LNS; OR, 2.790; 95% CI, 1.190-6.544; p=0.018), N stage (OR, 0.496; 95% CI, 0.269-0.915; p=0.025), and HER2 status (OR, 2.244; 95% CI, 1.147-4.392; p=0.018) were independent predictors of apCR. The AUCs of the nomogram were 0.74 (95% CI, 0.68-0.81) and 0.76 (95% CI, 0.63-0.90) for training and validation sets, respectively. In RF model, the maximum diameter of the primary lesion, axillary lymph node, and LNS in each cycle, estrogen receptor status, progesterone receptor status, HER2, Ki67, and T and N stages were included in the training set. The final validation set had an AUC value of 0.85 (95% CI, 0.74-0.87). Conclusion: Both nomogram and machine learning methods can predict apCR well. Nomogram is simple and practical, and shows high operability. Machine learning makes better use of a patient's clinicopathological information. These prediction models can assist surgeons in deciding on a reasonable strategy for axillary surgery.

Organophosphate esters in human serum in Bohai Bay, North China.

Organophosphate esters (OPEs), as a class of emerging flame retardant and plasticizers, have attracted particular attention due to their ubiquitous existence in the environment and potential effects on human health. Here, we investigated the levels of OPEs in human serum and examined the role of demographic variables on the body burden of such compounds. Of 11 OPEs screened, 8 were detected in human serum samples collected from a population (n = 89) in Bohai Bay, North China. The ∑OPE concentrations ranged from 4.7 to 948 ng/g lipid weight (lw), with a median concentration of 243 ng/g lw. Tris(2-chloroethyl)phosphate (TCEP) was identified as the most abundant OPEs with a median concentration of 214 ng/g lw. The concentrations of the triphenyl phosphate (TPhP) in older adults were higher than those in young adults (p < 0.05), and lower concentrations of tri-iso-butyl phosphate (TIBP) were observed in female samples compared to males. Furthermore, significant differences were observed in tri-n-propyl phosphate (TPrP) concentrations between urban and rural residence groups (p < 0.05). This study provides important information on the accumulation potential of OPEs in human bodies and suggests the need for further investigation to understand the potential human health risk.

microRNA-98 inhibits the proliferation, invasion, migration and promotes apoptosis of breast cancer cells by binding to HMGA2.

Breast cancer is a major contributor leading to cancer death in females worldwide. The aim of the present study was to investigate the effects of microRNA-98 (miR-98) on the processes of cell proliferation, invasion, migration and apoptosis by binding to high-mobility group AT-hook 2 (HMGA2) in breast cancer. Breast cancer tissues and adjacent normal tissues were collected from 112 patients suffering from breast cancer. The target relationship between miR-98 and HMGA2 was verified by in connection with the bioinformatics website as well as a dual-luciferase reporter assay, both of which provided evidence indicating that HMGA2 was a target gene of miR-98. Human breast cancer MDA-MB-231 cells were treated with miR-98 mimics, miR-98 inhibitors, siRNA-HMGA2 or miR-98 inhibitors + siRNA-HMGA2. 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay and flow cytometry methods were performed to determine cell proliferation, cell cycle and apoptosis, respectively, while a Transwell assay was employed to detect cell migration and invasion. Breast cancer tissues exhibited decreased miR-98 expression, while increased expression levels of HMGA2 were recorded. The mRNA and protein expressions of HMGA2, cell proliferation, cells at the S phase, cell migration, invasion, expressions of matrix metalloproteinase (MMP)2 as well as MMP9 were all reduced in response to miR-98 mimics or siRNA-HMGA2, while a contradictory trend was observed in the miR-98 inhibitors group. In conclusion, the results of the study demonstrate that miR-98 inhibits cell proliferation, migration and invasion, while acting to promote apoptosis by negatively regulating HMGA2 in breast cancer.