Background: Cancer patients had been profoundly affected by the outbreak of COVID-19 especially after quarantine restrictions in China. We aimed to explore the treatment changes and delays of early breast cancer (EBC) during the first quarter of 2020. Methods: We did this retrospective, multicentre, cohort study at 97 cancer centres in China. EBC patients who received treatment regardless of preoperative therapy, surgery or postoperative therapy during first quarter of 2020 were included. Findings: 8397 patients were eligible with a median age of 50 (IQR 43À56). 0¢2% (15/8397) of EBC patients were confirmed as COVID-19 infection. Only 5¢2% of breast cancer diagnosis occurred after quarantine in Hubei compared with 15¢3% in other provinces (OR= 0¢30, 95%CI 0¢24À0¢38). postoperative endocrine

Background: A surge in newly diagnosed breast cancer has overwhelmed the public health system worldwide. Joint effort had beed made to discover the genetic mechanism of these disease globally. Accumulated research has revealed autophagy may act as a vital part in the pathogenesis of breast cancer.Objective: Aim to construct a prognostic model based on autophagy-related lncRNAs and investigate their potential mechanisms in breast cancer.Methods: The transcriptome data and clinical information of patients with breast cancer were obtained from The Cancer Genome Atlas (TCGA) database. Autophagy-related genes were obtained from the Human Autophagy Database (HADb). Long non-coding RNAs (lncRNAs) related to autophagy were acquired through the Pearson correlation analysis. Univariate Cox regression analysis as well as the least absolute shrinkage and selection operator (LASSO) regression analysis were used to identify autophagy-related lncRNAs with prognostic value. We constructed a risk scoring model to assess the prognostic significance of the autophagy-related lncRNAs signatures. The nomogram was then established based on the risk score and clinical indicators. Through the calibration curve, the concordance index (C-index) and receiver operating characteristic (ROC) curve analysis were evaluated to obtain the model's predictive performance. Subgroup analysis was performed to evaluate the differential ability of the model. Subsequently, gene set enrichment analysis was conducted to investigate the potential functions of these lncRNAs.Results: We attained 1,164 breast cancer samples from the TCGA database and 231 autophagy-related genes from the HAD database. Through correlation analysis, 179 autophagy-related lncRNAs were finally identified. Univariate Cox regression analysis and LASSO regression analysis further screened 18 prognosis-associated lncRNAs. The risk scoring model was constructed to divide patients into high-risk and low-risk groups. It was found that the low-risk group had better overall survival (OS) than those of the high-risk group. Then, the nomogram model including age, tumor stage, TNM stage and risk score was established. The evaluation index (C-index: 0.78, 3-year OS AUC: 0.813 and 5-year OS AUC: 0.785) showed that the nomogram had excellent predictive power. Subgroup analysis showed there were difference in OS between high-risk and low-risk patients in different subgroups (stage I-II, ER positive, Her-2 negative and non-TNBC subgroups; all P < 0.05). According to the results of gene set enrichment analysis, these lncRNAs were involved in the regulation of multicellular organismal macromolecule metabolic process in multicellular organisms, nucleotide excision repair, oxidative phosphorylation, and TGF-β signaling pathway.Conclusions: We identified 18 autophagy-related lncRNAs with prognostic value in breast cancer, which may regulate tumor growth and progression in multiple ways.

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Background Invasive ductal carcinoma (IDC) is a common pathological type of breast cancer that is characterized by high malignancy and rapid progression. Upregulation of glycolysis is a hallmark of tumor growth, and correlates with the progression of breast cancer. We aimed to establish a model to predict the prognosis of patients with breast IDC based on differentially expressed glycolysis-related genes (DEGRGs). Methods Transcriptome data and clinical data of patients with breast IDC were from The Cancer Genome Atlas (TCGA). Glycolysis-related gene sets and pathways were from the Molecular Signatures Database (MSigDB). DEGRGs were identified by comparison of tumor tissues and adjacent normal tissues. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression were used to screen for DEGRGs with prognostic value. A risk-scoring model based on DEGRGs related to prognosis was constructed. Receiver operating characteristic (ROC) analysis and calculation of the area under the curve (AUC) were used to evaluate the performance of the model. The model was verified in different clinical subgroups using an external dataset (GSE131769). A nomogram that included clinical indicators and risk scores was established. Gene function enrichment analysis was performed, and a protein-protein interaction network was developed. Results We analyzed data from 772 tumors and 88 adjacent normal tissues from the TCGA database and identified 286 glycolysis-related genes from the MSigDB. There were 185 DEGRGs. Univariate Cox regression and LASSO regression indicated that 13 of these genes were related to prognosis. A risk-scoring model based on these 13 DEGRGs allowed classification of patients as high-risk or low-risk according to median score. The duration of overall survival (OS) was longer in the low-risk group (P < 0.001), and the AUC was 0.755 for 3-year OS and 0.726 for 5-year OS. The results were similar when using the GEO data set for external validation (AUC for 3-year OS: 0.731, AUC for 5-year OS: 0.728). Subgroup analysis showed there were significant differences in OS among high-risk and low-risk patients in different subgroups (T1-2, T3-4, N0, N1-3, M0, TNBC, non-TNBC; all P < 0.01). The C-index was 0.824, and the AUC was 0.842 for 3-year OS and 0.808 for 5-year OS from the nomogram. Functional enrichment analysis demonstrated the DEGRGs were mainly involved in regulating biological functions. Conclusions Our prognostic model, based on 13 DEGRGs, had excellent performance in predicting the survival of patients with IDC of the breast. These DEGRGs appear to have important biological functions in the progression of this cancer.

Background The omission of axillary lymph node dissection (ALND) in patients with breast cancer who have metastatic sentinel lymph nodes (SLNs) undergoing mastectomy remains controversial. This meta-analysis explored the clinicopathological factors affecting the selection of ALND and the influences of ALND on survival outcomes in patients receiving mastectomy with positive SLNs. Methods Eligible studies published prior to 31 December 2022 were selected by searching the Embase, Web of Science and PubMed databases. Pooled analyses were performed using the number of events for clinicopathological parameters and HRs with 95% CIs for survival outcomes including disease-free survival (DFS), overall survival (OS), distant recurrence-free survival (DRFS) and locoregional recurrence-free survival (LRFS). Results A total of 10 retrospective studies enrolling only breast cancer patients with limited SLN metastases (no more than 3 positive SLNs or micrometastatic SLNs) undergoing mastectomy were included. Performing ALND in mastectomy patients who had limited SLN metastases was significantly correlated with invasive ductal carcinomas, larger tumors, lymphovascular invasion, higher tumor grade, macrometastatic SLNs, more positive SLNs, extranodal extension, positive surgical margins, negative ER, administration of adjuvant chemotherapy and nonwhite race (P \ 0.05). However, performing ALND did not result in significantly longer OS, DFS, LRFS or DRFS (P [ 0.05) in these patients. Conclusion The present meta-analysis indicated that ALND may be safely avoided in patients with breast cancer who had limited SLN metastases undergoing mastectomy. Further well-designed randomized clinical trials are warranted to validate our results.