Ipsilateral breast tumor recurrence (IBTR) after surgical resection in early breast cancer (EBC) patients is categorized into local recurrence following breast-conserving surgery and recurrence on the chest wall after mastectomy. IBTR may represent a true local recurrence (TLR), from growth of residual malignancy (in situ or invasive) or a new primary (NP) tumor, arising in the residual breast tissue. The underlying pathology of IBTR may involve the presence of subclinical residual lesions and tumor cell dormancy induced by resistance to previous adjuvant therapies. Currently, there is a lack of standardized criteria to distinguish between TLR and NP tumors, which poses significant challenges in clinical management. We reviewed the current clinical pathological understanding and discriminant criteria for IBTR, explored the treatment principles for isolated IBTR after EBC, and make the following conclusions. (1) Recurrent and metastatic breast cancer should be regarded as advanced disease states. (2) The likelihood of subclinical lesions and circulating tumor cells being present in the recurrent disease state is much higher than during the adjuvant therapy stage at initial diagnosis. (3) Systemic therapy options after IBTR are often more limited, with reduced efficacy and increased unpredictability. Thus, drug sensitivity testing becomes particularly important. For IBTR, systemic therapy should be considered first. Based on evidence-based medicine, a multidisciplinary team (MDT) should distinguish between TLR from a NP tumor, and formulate an individualized treatment plan.

To screen for differentially expressed RNA-binding protein genes (RBPs), construct a prognostic prediction model combined with risk score and clinicopathological characteristics of patients, validate it, and analyze the immunophenoscore (IPS) and drug sensitivity in different risk groups.

Transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) breast cancer cohort (1 106 breast cancer tumor samples and 137 adjacent normal samples) were collected as the training set, and the GSE86166 dataset (containing 330 breast cancer samples) was used as the validation set. Differentially expressed RBPs between tumor samples and adjacent normal samples were screened in the training set. Univariate Cox proportional hazards regression and least absolute shrinkage and selection operator (LASSO) regression analyses were performed to select core RBPs and construct a prognostic risk score model. Breast cancer patients were divided into high-risk group (649 cases) and low-risk group (457 cases) based on the risk score cut-off value. Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curves were used to evaluate model performance. External validation was conducted in the validation set samples (high-risk group 161 cases and low-risk group 169 cases) using the same risk score formula and cut-off value. In the TCGA training set, univariate and multivariate Cox proportional hazards regression analyses combined with patients clinicopathological characteristics were used to evaluate the independent prognostic value of the risk score. A prognostic model was constructed based on clinicopathological characteristics and the risk score, with calibration curves used to assess its accuracy and decision curve analysis (DCA) used to evaluate its clinical utility. IPS was used to assess the tumor immunophenotype characteristics of the high and low risk groups. The half maximal inhibitory concentration (IC₅₀) was used to evaluate the drug sensitivity of 296 commonly used clinical chemotherapeutic and targeted therapeutic drugs in the high and low risk groups. Using convenience sampling, 10 pairs of breast cancer tissue samples and corresponding adjacent normal tissue samples from Harbin Medical University Cancer Hospital collected between January 2023 and December 2025 were used to validate the expression differences of the 5 core genes at the protein level using histochemistry score(H-score).

A total of 126 differentially expressed RBPs were identified from 1 106 breast cancer tumor samples and 137 adjacent normal samples. Univariate Cox proportional hazards regression analysis and LASSO regression analysis ultimately identified 5 core RBPs (NUAK2, ACSL1, MAP1LC3C, WT1, and MYOCD), based on which a prognostic risk score model was established. Kaplan-Meier survival analysis showed that the median overall survival (OS) of patients in the high-risk group and low-risk group in the training set was 97.5 months (95%CI: 90.2-104.8) and 216.6 months (95%CI: 198.3-234.9), indicating a statistically significant difference (χ²=13.20, P<0.001) ; The median OS of patients in the high-risk group and low-risk group in the validation set was 76.8 months (95%CI: 70.5-83.1) and 182.4 months (95%CI: 165.7-199.2), indicating a statistically significant difference (χ²=4.14, P=0.042). ROC curve analysis showed that the area under the curve at 3, 5, and 7 years OS for the training and validation sets were 0.60 (95% CI: 0.54-0.66), 0.60 (95%CI: 0.53-0.67), 0.65 (95%CI: 0.59-0.71), and 0.64 (95%CI: 0.58-0.70), 0.60 (95%CI: 0.54-0.66), 0.62 (95%CI: 0.56-0.68), respectively, indicating that the model has prognostic predictive value in both the training and external validation sets. Multivariate Cox proportional hazards regression analysis showed that the risk score was an independent factor predicting overall survival (HR=6.807, 95%CI: 3.940-11.715, P<0.001). Calibration curves showed that the concordance index (c-index) of predicting prognostic model at 3, 5, and 7 years OS in breast cancer patients were 0.782, 0.765, and 0.748, respectively (χ²=8.62, 9.15, 7.89, all P>0.05), confirming the stable predictive performance of the model. DCA results showed that, within the clinical decision threshold interval of 0.153-0.604, the prognostic model provided a better net clinical benefit than both the treat-all and treat-none strategies.Tumor immunogenicity and immunotherapy response analysis showed that the IPS of the low-risk group was significantly higher than that of the high-risk group (all P<0.05). Drug sensitivity analysis showed that 146 drugs had lower IC₅₀ values in the low-risk group than in the high-risk group (all P<0.05), while 20 drugs had lower IC₅₀ values in the high-risk group than in the low-risk group (all P<0.05). The IC₅₀ values of seven classical chemotherapeutic drugs (paclitaxel, doxorubicin, carboplatin, oxaliplatin, cyclophosphamide, docetaxel and topotecan) were significantly lower in the low-risk group than in the high-risk group (all P<0.001). Protein validation results showed that the expression of NUAK2 (152.00±17.51 vs 16.00±13.08, t=16.60, P<0.001) and WT1 [35.00 (15.00, 72.50) vs 7.50 (1.75, 30.00), Z=−2.80, P=0.005] were higher in tumor tissues than in adjacent normal tissues, whereas the expression of MAP1LC3C (49.20±44.90 vs 128.00±37.06, t=-4.61, P=0.001), ACSL1 [145.00 (75.00, 187.50) vs 270.00 (247.50, 273.75), Z=−2.81, P=0.005], and MYOCD [100.00 (47.50, 140.00) vs 160.00 (150.00, 165.00), Z=−2.82, P=0.005] were lower in tumor tissues than in adjacent normal tissues.

In this study, the prognostic prediction model for breast cancer constructed based on 5 core RBPs has good predictive efficacy, and accordingly different risk groups show significant difference in IPS and drug sensitivity.

To systematically identify different subtypes of tandem duplication phenotype (TDP) in breast cancer and analyze their molecular characteristics and prognostic relevance.

A total of 1 098 breast cancer samples from the Cancer Genome Atlas (TCGA) and 60 breast cancer cell lines from the Cancer Cell Line Encyclopedia (CCLE) were collected. Tandem duplication events were identified based on copy number variation (CNV) data, and TDP scores were calculated. Samples were classified into the TDP group (TDP score > −0.710 and number of TD events ≥20) and the non-TDP group (TDP score < −0.835 or number of TD events < 20). TDP samples were classified into six subtypes using a Gaussian mixture model: short-segment unimodal type (group 1), intermediate-segment unimodal type (group 2), long-segment unimodal type (group 3), and three bimodal mixed types composed of unimodal patterns (group 1/2, group 1/3, and group 2/3). Group 1, group 1/2, and group 1/3 were further categorized as the short-segment tandem duplication group (SSG), whereas group 2, group 3, and group 2/3 were categorized as the large-segment tandem duplication group (LSG). The CNV complexity, clinical characteristics, and prognosis of different TDP subtypes were analyzed, along with GO and KEGG functional enrichment analyses and drug sensitivity analysis. Survival analysis was performed using the Kaplan–Meier method, and differences between groups were compared using the Log-rank test.

A total of 147 TDP patients were identified in the TCGA database, including 25 cases in the SSG group (2, 1 and 22 cases in groups 1, 1/2, and 1/3, respectively) and 122 cases in the LSG group (2, 50 and 70 cases in groups 2, 3, and 2/3, respectively). The CNV complexity values in the non-TDP (409 cases), SSG, and LSG groups were 7.55 (7.20, 8.03), 8.47 (8.29, 8.78), and 8.37 (7.98, 8.65), respectively, with a statistically significant difference among the three groups (H=135.12, P<0.001). Functional enrichment analysis showed that the SSG was more likely to involve tumor suppressor genes and pathways such as DNA damage repair, whereas group 2/3 was mainly characterized by oncogene amplification and enrichment in tumor-related signaling pathways. In the CCLE cohort, 47 TDP strains were identified, including 40 strains in the SSG group (2, 37 and 1 strain in groups 1, 1/2, and 1/3, respectively) and 7 strains in the LSG group (0, 0 and 7 strains in groups 2, 3, and 2/3, respectively). The CNV complexity values in the non-TDP (12 strains), SSG, and LSG groups were 8.91 (8.76，9.07), 9.95 (9.78，10.28), and 9.82 (9.72，9.91), respectively, with a statistically significant difference among the three groups (H=28.86, P<0.001). Exploratory drug sensitivity analysis showed that the median IC₅₀ values of LSG cell lines treated with 17-AAG and paclitaxel were higher than those of SSG cell lines. Survival analysis showed that the 5-year OS was 100.0% in the SSG and 80.5% in the LSG (95%CI: 71.3%–90.9%), with a statistically significant difference between 2 groups (χ²=4.90, P=0.027).

This study identified TDP in breast cancer based on CNV data and classified it into six subtypes. Different TDP subtypes showed differences in CNV burden, driver gene amplification, functional pathways, drug sensitivity, and prognosis. LSG was characterized by oncogene amplification and poorer prognosis, suggesting that TDP classification may provide a reference for analyzing molecular heterogeneity and prognostic stratification in breast cancer.

To explore the molecular mechanism by which mitochondrial matrix import factor 23 (MIX23) affects triple-negative breast cancer (TNBC).

The RNA-Seq expression profile and corresponding clinical data of breast cancer patients were downloaded from The Cancer Genome Atlas (TCGA) database, including 1 097 primary tumor tissue samples and 114 adjacent normal tissue samples. Immunohistochemistry staining images of MIX23 in breast cancer tissues were obtained from the Human Protein Atlas (HPA) database for quantitative analysis of protein expression levels. Expression profile data of the METABRIC breast cancer cohort were downloaded as an independent validation set. Based on the mean expression level of MIX23 in breast cancer patients, the TCGA and METABRIC datasets were each stratified into high- and low-expression groups (TCGA: 480 cases in high-expression group, 534 in low-expression group; METABRIC: 969 in high-expression group, 1,011 in low-expression group). Kaplan-Meier method was used to plot survival curves, and log-rank test was used to evaluate the difference in survival rate between the two groups. RT-qPCR and Western blot were used to detect the mRNA and protein expression of MIX23 in TNBC cell line. Lentiviral vector was used to construct MDA-MB-231 cells with stable MIX23 knockdown (sh-MIX23 group), with the cells transfected with lentiviral vectors carrying non-targeted scramble shRNA as control group. Cell clone formation assay, CCK-8 assay and cell scratch assay were adopted to detect cell proliferation and migration abilities. Differential gene analysis was performed based on TCGA database to identify downstream target genes.

In the TCGA cohort, the mRNA expression level of MIX23 was significantly higher in breast cancer tissues than in adjacent normal breast tissues (7.756±0.605 vs 7.145±0.341, t=16.613, P<0.001). HPA cohort analysis also showed that MIX23 expression was significantly higher in tumor tissues than in normal tissues (43.978±4.158 vs 55.211±7.339, t=-2.980, P=0.018). The expression levels of MIX23 in TNBC, HER-2 positive, Luminal A and Luminal B subtypes were 8.154±0.743, 7.858±0.628, 7.613±0.535 and 7.744±0.477, respectively, with significant difference (F=28.260, P<0.0001). Kaplan-Meier survival analysis revealed that in the TCGA cohort, high MIX23 expression was significantly associated with shorter disease-free interval (HR=1.667, 95%CI: 1.087–2.555, P=0.018). After MIX23 knockdown, compared with control group, the colony formation ability of cells in the sh-MIX23 group was significantly decreased (184.5±9.576 vs 110.2±6.976, t=12.578, P<0.001), the cell proliferation was markedly inhibited at 24, 48 and 72 h (0 h: 0.185±0.009 vs 0.182±0.007, t=0.515, P=0.626; 24 h: 0.399±0.057 vs 0.246±0.004, t=4.333, P=0.006; 48 h: 0.704±0.058 vs 0.403±0.021, t=9.642, P<0.001; 72 h: 0.999±0.0416 vs 0.452±0.031, t=21.110, P<0.0001). The relative migration area of cells in the sh-MIX23 group was significantly smaller than that in the control group at 24 h after scratching (62.861±12.252 vs 35.971±8.024, t=5.193, P<0.01). Compared with the control group, MIX23 could up-regulate the expression of osteoglycin (1.195±0.254 vs 1.958±0.223, t=-3.945, P=0.018) and inhibit the expression of Akt, mTOR and EGFR (1.000±0.023 vs 0.054±0.006, t=68.352, P<0.0001; 1.000±0.052 vs 0.058±0.007, t=31.092, P<0.0001; 1.000±0.094 vs 0.036±0.004, t=17.785, P<0.0001).

MIX23 is highly expressed in breast cancer and correlated with poor prognosis. Knockdown of MIX23 affects the growth and metastasis of breast cancer by up-regulating osteoglycin and inhibiting the downstream EGFR/Akt/mTOR signaling pathway.

To compare the comprehensive efficacy of vacuum-assisted breast biopsy (VABB) and core needle biopsy (CNB) in the diagnostic evaluation of breast cancer.

A retrospective multicenter design was adopted. Clinical data of 241 breast cancer patients admitted to the First People's Hospital of Aksu Prefecture and Zhejiang Cancer Hospital from January 2023 to August 2024 were enrolled. According to the preoperative biopsy method, patients were divided into VABB group (n=119) and CNB group (n=122). Taking the postoperative pathological examination results as the gold standard, the Kappa test was used to compare the consistency of the two methods with the pathological diagnosis. Receiver operating characteristic (ROC) curve analysis and the Delong test were employed to evaluate the difference in diagnostic performance between the two methods. Continuous data with normal distribution were compared by independent sample t test;unpaired categorical data were compared by χ² test or Fisher's exact test;paired categorical data were compared by McNemar test.

No significant statistical differences were found between the diagnostic outcomes of VABB/CNB and pathological results (gold standard) for triple negative subtype (VABB: P=0.754; CNB: P=0.804). The corresponding Kappa values were 0.711 and 0.674 (both P<0.001), demonstrating good consistency with pathological diagnosis. For HER-2-positive subtype , no statistically significant differences were observed as well (VABB: P=0.774; CNB: P=0.424). The Kappa values were 0.749 and 0.698 (both P<0.001), reflecting favorable consistency. Statistically significant differences existed in Luminal A and Luminal B subtypes between the diagnostic outcomes of VABB/CNB and pathological results (both P<0.05). The Kappa values of VABB were 0.232 and 0.162, while those of CNB were 0.425 and 0.374, presenting poor consistency. In the four molecular subtypes of breast cancer, there was no statistically significant difference in AUC between CNB and VABB (Delong test, all P>0.05) . The AUC values for Luminal B and triple negative subtypes were relatively high (Luminal B: CNB group AUC=0.836, VABB group AUC=0.882; triple negative: CNB group AUC=0.831, VABB group AUC=0.843), indicating good diagnostic value. The AUC values for HER-2 positive and Luminal A subtypes suggested limited diagnostic accuracy (HER-2 positive: CNB group AUC=0.684, VABB group AUC=0.604; Luminal A: CNB group AUC=0.757, VABB group AUC=0.624) . Sample integrity in the VABB group was consistently good. The sample volume in the VABB group was significantly larger than that in the CNB group [(1.04±0.30) cm³vs (0.10±0.02) cm³, t=-34.532, P<0.001]. The biopsy success rate of 100% and no repeated biopsies. The positive detection rate of tumor spatial heterogeneity in the VABB group was higher than that in the CNB group (30.3% vs 18.9%, χ²=4.234, P=0.040). Intraoperative blood loss in the VABB group was significantly higher than that in the CNB group [(2.24±0.72) ml vs (1.84±0.89) ml, t=-3.749, P<0.001] , but the incidence of postoperative hematoma was lower (0.8% vs 9.9%, χ²=9.553, P=0.002) , and the patient satisfaction score was higher (8.59±0.56 vs 6.16±0.80, t=–27.352, P<0.001) .

VABB is superior to CNB in obtaining high-quality tissue samples, improving the positive detection rate of tumor spatial heterogeneity and patient satisfaction, with good overall safety.