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The issue of employee turnover is always critical for companies, and accurate predictions can help them prepare in time. Most past studies on employee turnover have focused on analyzing impact factors or using simple network centrality measures. In this paper, we study the problem from a completely new perspective by modeling users' historical job records as a dynamic bipartite graph. Specifically, we propose a bipartite graph embedding method with temporal information called dynamic bipartite graph embedding (DBGE) to learn the vector representation of employees and companies. Our approach not only considers the relations between employees and companies but also incorporates temporal information embedded in consecutive work records. We first define the Horary Random Walk on a bipartite graph to generate a sequence for each vertex in chronological order. Then, we employ the skip-gram model to obtain a temporal low-dimensional vector representation for each vertex and apply machine learning methods to predict employee turnover behavior by combining embedded features with employees' basic information. Experiments on a real-world dataset collected from one of China's largest online professional social networks show that the features learned through DBGE can significantly improve turnover prediction performance. Moreover, experiments on public Amazon and Taobao datasets show that our approach achieves better performance in the link prediction and visualization task than other graph embedding methods that do not consider temporal information. INDEX TERMS Graph embedding, employee turnover prediction, dynamic bipartite graph, horary random walk.

Acquired chemotherapy resistance is one of the main culprits in the relapse of breast cancer. But the underlying mechanism of chemotherapy resistance remains elusive. Here, we demonstrate that a small adaptor protein, SH3BGRL, is not only elevated in the majority of breast cancer patients but also has relevance with the relapse and poor prognosis of breast cancer patients. Functionally, SH3BGRL upregulation enhances the chemoresistance of breast cancer cells to the first-line doxorubicin treatment through macroautophagic/autophagic protection. Mechanistically, SH3BGRL can unexpectedly bind to ribosomal subunits to enhance PIK3C3 translation efficiency and sustain ATG12 stability. Therefore, inhibition of autophagy or silence of PIK3C3 or ATG12 can effectively block the driving effect of SH3BGRL on doxorubicin resistance of breast cancer cells in vitro and in vivo. We also validate that SH3BGRL expression is positively correlated with that of PIK3C3 or ATG12, as well as the constitutive occurrence of autophagy in clinical breast cancer tissues. Taken together, our data reveal that SH3BGRL upregulation would be a key driver to the acquired chemotherapy resistance through autophagy enhancement in breast cancer while targeting SH3BGRL could be a potential therapeutic strategy against breast cancer. Abbreviations: ABCs: ATP-binding cassette transporters; Act D: actinomycin D; ACTB/β-actin: actin beta; ATG: autophagy-related; Baf A 1 : bafilomycin A 1 ; CASP3: caspase 3; CHX: cycloheximide; CQ: chloroquine; Dox: doxorubicin; FBS: fetal bovine serum; GAPDH: glyceraldehyde-3-phosphate dehydrogenase; GEO: gene expression omnibus; GFP: green fluorescent protein; G6PD: glucose-6-phosphate dehydrogenase; GSEA: gene set enrichment analysis; IHC: immunochemistry; KEGG: Kyoto Encyclopedia of Genes and Genomes; MAP1LC3B/LC3B: microtubule-associated protein 1 light chain 3 beta; 3-MA: 3-methyladenine; mRNA: messenger RNA; PIK3C3: phosphatidylinositol 3-kinase catalytic subunit type 3; SH3BGRL: SH3 domain binding glutamate-rich protein-like; SQSTM1/p62: sequestosome 1; ULK1: unc-51 like autophagy activating kinase 1

Background: The cumulative risk of distant recurrence of hormone receptor-positive (HR+) breast cancer in the past 20 years has ranged from 22% to 52% after 5 years of endo-therapy. The TNM stage, histological grade, and age are important clinical factors related to recurrence, however the exact mechanism of tamoxifen resistance is still unclear.Methods: Differentially expressed genes (DEGs) were identified in 10 pairs of patients who had relapsed and non-relapsed after tamoxifen treatment based on matching their clinicopathological factors. After analysis of the Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, 10 hub genes were identified using Cytoscape software. Next, real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR) and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) database were used to verify the expression and overall survival (OS) of the 10 hub genes respectively, and GSE96058 and Kaplan-Meier Plotter website were used to further verify the OS of C3, CX3CL1, CXCL2, and SAA1. Finally, Immune Cell Abundance Identifier (ImmuCellAI) and the TIMER database were used to estimate immune cell infiltration and the expression of prognostic genes.Results: The DEGs were mainly enriched in the inflammatory response and cytokine-receptor interaction.The expression and the survival analysis identified CX3CL1, CXCL2, and SAA1 as prognostic factors, whose overexpression in HR+/human epidermal growth factor receptor 2 (HER-2) negative breast cancer possibly predicted a longer disease-free survival. The expression levels of these 3 genes are positively correlated with immune cell infiltration. Their high expression levels may predict longer disease-free survival in breast cancer after tamoxifen treatment and may be biomarkers for tamoxifen-resistant therapy. Conclusions:In conclusion, the high expression of CX3CL1, CXCL2, and SAA1 may predict longer disease-free survival in breast cancer after tamoxifen treatment and may be a biomarker for tamoxifen therapy.

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