Background: Breast cancer (BC) is the most common malignant tumor around the world. Timely detection of the tumor progression after treatment could improve the survival outcome of patients. This study aimed to develop machine learning models to predict events (defined as either (1) the first tumor relapse locally, regionally, or distantly; (2) a diagnosis of secondary malignant tumor; or (3) death because of any reason.) in BC patients post-treatment. Methods: The patients with the response of stable disease (SD) and progressive disease (PD) after neoadjuvant chemotherapy (NAC) were selected. The clinicopathological features and the survival data were recorded in 1 year and 5 years, respectively. Patients were randomly divided into the training set and test set in the ratio of 8:2. A random forest (RF) and a logistic regression were established in both of 1-year cohort and the 5-year cohort. The performance was compared between the two models. The models were validated using data from the Surveillance, Epidemiology, and End Results (SEER) database. Results: A total of 315 patients were included. In the 1-year cohort, 197 patients were divided into a training set while 87 were into a test set. The specificity, sensitivity, and AUC were 0.800, 0.833, and 0.810 in the RF model. And 0.520, 0.833, and 0.653 of the logistic regression. In the 5-year cohort, 132 patients were divided into the training set while 33 were into the test set. The specificity, sensitivity, and AUC were 0.882, 0.750, and 0.829 in the RF model. And 0.882, 0.688, and 0.752 of the logistic regression. In the external validation set, of the RF model, the specificity, sensitivity, and AUC were 0.765, 0.812, and 0.779. Of the logistics regression model, the specificity, sensitivity, and AUC were 0.833, 0.376, and 0.619. Conclusion: The RF model has a good performance in predicting events among BC patients with SD and PD post-NAC. It may be beneficial to BC patients, assisting in detecting tumor recurrence.

In previous decades, chromogranin A (CgA) has been demonstrated to be the most promising biomarker for the diagnosis of neuroendocrine tumors (NETs), but its diagnostic value is still controversial. This meta-analysis aimed to estimate the potential diagnostic value of circulating CgA for NETs. We collected relevant studies from several electronic databases as well as from reference lists. Diagnostic indices of CgA were pooled with random effects models. Pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR) and summary receiver operating characteristic (SROC) curves for the diagnosis of NETs were used to estimate the overall diagnostic efficiency. Through a search strategy, 13 studies met the inclusion criteria and were included. These studies contained 1260 patients with NETs and 967 healthy controls in the total sample. As a result, the overall sensitivity, specificity and diagnostic odds ratio (DOR) were 0.73 (95% CI: 0.71 to 0.76), 0.95 (95% CI: 0.93 to 0.96) and 56.29 (95% CI: 25.27 to 125.38), respectively, while the summary positive likelihood ratio (PLR) and negative likelihood ratio (NLR) were 14.56 (95% CI: 6.62 to 32.02) and 0.26 (95% CI: 0.18 to 0.38), respectively. In addition, the area under the curve (AUC) of the circulating CgA in the diagnosis of NETs was 0.8962. These data demonstrate that circulating CgA is an efficient biomarker for the diagnosis of NETs with high sensitivity and specificity, which indicates that it may be helpful for the clinical management of NETs. However, further studies are needed to clarify this issue.

Triple-negative breast cancer (TNBC) represents a collection of malignant breast tumors that are often aggressive and have an increased risk of metastasis and relapse. Long non-coding RNAs are generally defined as RNA transcripts measuring 200 nucleotides or longer that do not encode for any protein. During the past decade, increasing evidence has shown that lncRNAs play important roles in oncogenesis and tumor suppression; however, the roles of lncRNAs in TNBC are poorly understood. To address this issue, we used Agilent human lncRNA microarray chips and bioinformatics tools, including Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG), to assess lncRNA expression in 3 pairs of TNBC tissues. A dysregulated lncRNA expression profile was identified by microarray and verified by qRT-PCR in 48 pairs of breast cancer subtype tissues. Metastasis is the major cause of cancer-related deaths, including those in TNBC, and the presence of dormant residual disseminated tumor cells (DTC) may be a key factor leading to metastasis. ANKRD30A, a potential target for breast cancer immunotherapy, is currently one of the most used DTC markers. Notably, we found the expression levels of the novel intergenic lncRNA LINC00993 to be associated with the expression levels of ANKRD30A. Furthermore, our qRT-PCR data indicated that the expression of LINC00993 was also associated with the expression of the estrogen receptor. In conclusion, our study identified a set of lncRNAs that were consistently aberrantly expressed in TNBC, and these dysregulated lncRNAs may be involved in the development and/or progression of TNBC.

Background: Recent studies have indicated that serpin peptidase inhibitor, clade A, member 3 (SERPINA3) is a potential marker associated with tumor progression, which connoted that SERPINA3 is related to malignant phenotypes in cancer. However, the biological function of SERPINA3 in breast cancer (BC) remains unclear. Methods: Bioinformatics data were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Immunohistochemical staining (IHC) was conducted to determine SERPINA3 expression. With strong aggressive abilities, triple-negative breast cancer (TNBC) cell lines (MDA-MB-231, BT549 and MDA-MB-436) were obtained to examine SERPINA3 expression and functions. Wound healing and Transwell assays were performed to measure cell migration and invasion. Cell Counting Kit-8 (CCK-8) assay was conducted to detect cell proliferation abilities and cell viabilities. Results: SERPINA3 was upregulated in BC tissues. Functional assays suggested that overexpression of SERPINA3 significantly promoted cell proliferation, where migration and invasion of TNBC cells were accelerated. Knockdown of SERPINA3 had the opposite effects. These results causing by overexpression of SERPINA3 were also confirmed in non-TNBC cell lines. Overexpression of SERPINA3 remarkably enhanced the epithelial–mesenchymal transition (EMT) by upregulating the EMT markers and EZH2. In addition, the overexpression of SERPINA3 reduced the sensitivity of TNBC cells to cisplatin. Conclusion: SERPINA3 can regulate the migration, invasion and EMT of TNBC cells and increased expression of SERPINA3 confers resistance to cisplatin in TNBC cells. We discern it is required for the regulation of BC progression and is a critical target for the clinical treatment of BC.

Chemokine (C‑X‑C motif) ligand 1 (CXCL1), a member of the CXC chemokine family, has been reported to be a critical factor in inflammatory diseases and tumor progression; however, its functions and molecular mechanisms in estrogen receptor α (ER)‑negative breast cancer (BC) remain largely unknown. The present study demonstrated that CXCL1 was upregulated in ER‑negative BC tissues and cell lines compared with ER‑positive tissues and cell lines. Treatment with recombinant human CXCL1 protein promoted ER‑negative BC cell migration and invasion in a dose‑dependent manner, and stimulated the activation of phosphorylated (p)‑ extracellular signal‑regulated kinase (ERK)1/2, but not p‑STAT3 or p‑AKT. Conversely, knockdown of CXCL1 in BC cells attenuated these effects. Additionally, CXCL1 increased the expression of matrix metalloproteinase (MMP)2/9 via the ERK1/2 pathway. Inhibition of MEK1/2 by its antagonist U0126 reversed the effects of CXCL1 on MMP2/9 expression. Furthermore, immunohistochemical analysis revealed a strong positive association between CXCL1 and p‑ERK1/2 expression levels in BC tissues. In conclusion, the present study demonstrated that CXCL1 is highly expressed in ER‑negative BC, and stimulates BC cell migration and invasion via the ERK/MMP2/9 pathway. Therefore, CXCL1 may serve as a potential therapeutic target in ER‑negative BC.

Neoadjuvant chemotherapy has been widely used in locally advanced and inflammatory breast cancer. Generally, complete pathological response after neoadjuvant chemotherapy treatment predicts surviva...