Added value of systemic inflammation markers for monitoring response to neoadjuvant chemotherapy in breast cancer patients.

BACKGROUND: Complete response after neoadjuvant chemotherapy (rNACT) elevates the surgical outcomes of patients with breast cancer, however, non-rNACT have a higher risk of death and recurrence. AIM: To establish novel machine learning (ML)-based predictive models for predicting probability of rNACT in breast cancer patients who intends to receive NACT. METHODS: A retrospective analysis of 487 breast cancer patients who underwent mastectomy or breast-conserving surgery and axillary lymph node dissection following neoadjuvant chemotherapy at the Hubei Cancer Hospital between January 1, 2013, and October 1, 2021. The study cohort was divided into internal training and testing datasets in a 70:30 ratio for further analysis. A total of twenty-four variables were included to develop predictive models for rNACT by multiple ML-based algorithms. A feature selection approach was used to identify optimal predictive factors. These models were evaluated by the receiver operating characteristic (ROC) curve for predictive performance. RESULTS: Analysis identified several significant differences between the rNACT and non-rNACT groups, including total cholesterol, low-density lipoprotein, neutrophil-to-lymphocyte ratio, body mass index, platelet count, albumin-to-globulin ratio, platelet-to-lymphocyte ratio, and lymphocyte-to-monocyte ratio. The areas under the curve of the six models ranged from 0.81 to 0.96. Some ML-based models performed better than models using conventional statistical methods in both ROC curves. The support vector machine (SVM) model with twelve variables introduced was identified as the best predictive model. CONCLUSION: By incorporating pretreatment serum lipids and serum inflammation markers, it is feasible to develop ML-based models for the preoperative prediction of rNACT and therefore facilitate the choice of treatment, particularly the SVM, which can improve the prediction of rNACT in patients with breast cancer.

LncRNA-HAGLR motivates triple negative breast cancer progression by regulation of WNT2 via sponging miR-335-3p.

BACKGROUND: Triple negative breast cancer (TNBC) is a group of highly heterogeneous mixed breast cancer at the level of gene expression profile. Therefore, it is of great clinical significance to explore the molecular mechanism of TNBC and find a targeted therapeutic approach from the molecular level. METHODS: Long non-coding RNA (lncRNA) HAGLR expression level was measured by and qRT-PCR in TNBC tissues and cell lines. EdU, MTT, wound healing and Transwell assays were performed to explore the role of HAGLR on the malignancy of TNBC cells. Luciferase assay was used to clarify the binding between miR-335-3p with HAGLR and WNT2. The tumor formation experiment in nude mice was used to explore the function of HAGLR in vivo. RESULTS: HAGLR was increased in TNBC tissues and cell lines. Silencing of HAGLR inhibited viability, proliferation, migration, and invasion of BT549 cells. Furthermore, HAGLR acted as a sponge of miR-335-3p and inhibited its expression. And miR-335-3p directly targeted WNT2. Functionally, forced expression of miR-335-3p or knockdown of WNT2 removed the promoted effects of lncRNA HAGLR on TNBC development. In vivo tumorigenesis experiments indicated HAGLR accelerated tumor growth via miR-335-3p/WNT2 axis. CONCLUSION: Our study revealed that HAGLR promoted the growth of TNBC, which was mediated by miR-335-3p/WNT2 axis.

An analysis of Ki-67 expression in stage 1 invasive ductal breast carcinoma using apparent diffusion coefficient histograms.

BACKGROUND: To investigate the value of apparent diffusion coefficient (ADC) histograms in differentiating Ki-67 expression in T1 stage invasive ductal breast carcinoma (IDC). METHODS: The records of 111 patients with pathologically confirmed T1 stage IDC who underwent magnetic resonance imaging prior to surgery were retrospectively reviewed. The expression of Ki-67 in tumor tissue samples from the patients was assessed using immunohistochemical (IHC) staining, with a cut-off value of 25% for high Ki-67 labeling index (LI). ADC images of the maximum lay of tumors were selected, and the region of interest (ROI) of each lay was delineated using the MaZda software and analyzed by histogram. The correlations between the histogram characteristic parameters and the Ki-67 LI were investigated. Additionally, the histogram characteristic parameters of the high Ki-67 group (n=54) and the low Ki-67 group (n=57) were statistically analyzed to determine the characteristic parameters with significant difference. Receiver operator characteristic (ROC) analyses were further performed for the significant parameters. RESULTS: The mean value, and the 1st, 10th, 50th, 90th, and 99th percentiles were found to be negatively correlated with the expression of Ki-67 (all P values <0.001), with a correlation coefficient of -0.624, -0.749, -0.717, -0.621, -0.500, and -0.410, respectively. In the high Ki-67 group, the mean value, and the 1st, 10th, 50th, 90th, and 99th percentiles extracted by the histogram were significantly lower (all P values <0.05) than that of the low Ki-67 group, with areas under the ROC curves ranging from 0.717-0.856. However, the variance, skewness, and kurtosis did not differ between the two groups (all P values >0.05). CONCLUSIONS: Histogram-derived parameters for ADC images can serve as a reliable tool in the prediction of Ki-67 proliferation status in patients with T1 stage IDC. Among the significant ADC histogram values, the 1st and 10th percentiles showed the best predictive values.

Identification of potential core genes in triple negative breast cancer using bioinformatics analysis.

BACKGROUND: Triple-negative breast cancer (TNBC) is a subtype of breast cancer with poor clinical outcome and limited treatment options. Lacking molecular targets, chemotherapy is the main adjuvant treatment for TNBC patients. MATERIALS AND METHODS: To explore potential therapeutic targets for TNBC, we analyzed three microarray datasets (GSE38959, GSE45827, and GSE65194) derived from the Gene Expression Omnibus (GEO) database. The GEO2R tool was used to screen out differentially expressed genes (DEGs) between TNBC and normal tissue. Gene Ontology function and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were performed using the Database for Annotation, Visualization and Integrated Discovery to identify the pathways and functional annotation of DEGs. Protein-protein interaction of these DEGs was analyzed based on the Search Tool for the Retrieval of Interacting Genes database and visualized by Cytoscape software. In addition, we used the online Kaplan-Meier plotter survival analysis tool to evaluate the prognostic value of hub genes expression in breast cancer patients. RESULTS: A total of 278 upregulated DEGs and 173 downregulated DEGs were identified. Among them, ten hub genes with a high degree of connectivity were picked out. Overexpression of these hub genes was associated with unfavorable prognosis of breast cancer, especially, CCNB1 overexpression was observed and indicated poor outcome of TNBC. CONCLUSION: Our study suggests that CCNB1 was overexpressed in TNBC compared with normal breast tissue, and overexpression of CCNB1 was an unfavorable prognostic factor of TNBC patients. Further study is needed to explore the value of CCNB1 in the treatment of TNBC.

Incidence of BRCA1 somatic mutations and response to neoadjuvant chemotherapy in Chinese women with triple-negative breast cancer.

BACKGROUND: The prevalence of BRCA1 somatic mutations status in triple-negative breast cancer (TNBC) has not been well documented. The aims of this study were to determine the frequency of BRCA1 somatic mutations and to investigate the association between BRCA1 deleterious somatic mutation status and response to neoadjuvant chemotherapy in women with TNBC. METHODS: Two hundred and five TNBC patients without BRCA1 germline mutations were enrolled in this study. Fresh tumor tissues were available for this cohort of 205 patients, including 112 patients with fresh core needle biopsy tumor tissues before treatment and 93 patients with fresh tumor tissues procured after surgery. BRCA1 somatic mutations were determined in the tumor samples using PCR-direct sequencing assay. Among the 112 patients with core needle biopsy samples, 97 patients received neoadjuvant chemotherapy. RESULTS: Eight patients (3.9%) carried a BRCA1 pathogenic somatic mutation in this cohort of 205 TNBC patients. These eight BRCA1 deleterious somatic mutations included five frameshift or nonsense mutations (c.191_212del22, c.1664delA, c.4674_4675+17del, c.3671_3672insTTCC, c.1162A>T), one splicing site mutation (c.134+2T>G) and two missense mutations (c.5511G>C and c.286G>A). No significant differences in tumor characteristics between BRCA1 deleterious somatic mutation carriers and non-carriers were observed. The pCR (pathologic complete response) rate was 32.0% in the 97 patients who received neoadjuvant chemotherapy. BRCA1 deleterious somatic mutation carriers (n=5) had a higher pCR rate than did non-carriers (n=92) (BRCA1 carriers vs non-carriers, 60.0% vs 30.4%, P=0.32), although it did not reach a significance due to a small sample size. CONCLUSIONS: A small subset of TNBC patients carried a BRCA1 deleterious somatic mutation; BRCA1 somatic mutation carriers are likely to respond to neoadjuvant chemotherapy.