Skip Navigation
Skip to contents

Cancer Res Treat : Cancer Research and Treatment

OPEN ACCESS

Search

Page Path
HOME > Search
6 "Tumor-infiltrating lymphocytes"
Filter
Filter
Article category
Keywords
Publication year
Authors
Funded articles
Original Articles
Breast cancer
Changes in Invasive Breast Carcinomas after Neoadjuvant Chemotherapy Can Influence Adjuvant Therapeutic Decisions
Bárbara Jaime dos Santos, Débora Balabram, Virginia Mara Reis Gomes, Carolina Costa Café de Castro, Paulo Henrique Costa Diniz, Marcelo Araújo Buzelin, Cristiana Buzelin Nunes
Cancer Res Treat. 2024;56(1):178-190.   Published online August 1, 2023
DOI: https://doi.org/10.4143/crt.2023.386
AbstractAbstract PDFPubReaderePub
Purpose
Neoadjuvant chemotherapy (NACT) can change invasive breast carcinomas (IBC) and influence the patients’ overall survival time (OS). We aimed to identify IBC changes after NACT and their association with OS.
Materials and Methods
IBC data in pre- and post-NACT samples of 86 patients were evaluated and associated with OS.
Results
Post-NACT tumors changed nuclear pleomorphism score (p=0.025); mitotic count (p=0.002); % of tumor-infiltrating inflammatory cells (p=0.016); presence of in situ carcinoma (p=0.001) and lymphovascular invasion (LVI; p=0.002); expression of estrogen (p=0.003), progesterone receptors (PR; p=0.019), and Ki67 (p=0.003). Immunohistochemical (IHC) profile changed in 26 tumors (30.2%, p=0.050). Higher risk of death was significatively associated with initial tumor histological grade III (hazard ratio [HR], 2.94), high nuclear pleomorphism (HR, 2.53), high Ki67 index (HR, 2.47), post-NACT presence of LVI (HR, 1.90), luminal B–like profile (HR, 2.58), pre- (HR, 2.26) and post-NACT intermediate mitotic count (HR, 2.12), pre- (HR, 4.45) and post-NACT triple-negative IHC profile (HR, 4.52). On the other hand, lower risk of death was significative associated with pre- (HR, 0.35) and post-NACT (HR, 0.39) estrogen receptor–positive, and pre- (HR, 0.37) and post-NACT (HR, 0.57) PR-positive. Changes in IHC profile were associated with longer OS (p=0.050). In multivariate analysis, pre-NACT grade III tumors and pre-NACT and post-NACT triple negative IHC profile proved to be independent factors for shorter OS.
Conclusion
NACT can change tumor characteristics and biomarkers and impact on OS; therefore, they should be reassessed on residual samples to improve therapeutic decisions.
  • 3,171 View
  • 212 Download
Close layer
The Association of Estrogen Receptor Activity, Interferon Signaling, and MHC Class I Expression in Breast Cancer
In Hye Song, Young-Ae Kim, Sun-Hee Heo, Won Seon Bang, Hye Seon Park, Yeon ho Choi, Heejae Lee, Jeong-Han Seo, Youngjin Cho, Sung Wook Jung, Hee Jeong Kim, Sei Hyun Ahn, Hee Jin Lee, Gyungyub Gong
Cancer Res Treat. 2022;54(4):1111-1120.   Published online December 21, 2021
DOI: https://doi.org/10.4143/crt.2021.1017
AbstractAbstract PDFSupplementary MaterialPubReaderePub
Purpose
The expression of major histocompatibility complex class I (MHC I) has previously been reported to be negatively associated with estrogen receptor (ER) expression. Furthermore, MHC I expression, level of tumor-infiltrating lymphocytes (TILs), and expression of interferon (IFN) mediator MxA are positively associated with one another in human breast cancers. This study aimed to investigate the mechanisms of association of MHC I with ER and IFN signaling.
Materials and Methods
The human leukocyte antigen (HLA)-ABC protein expression was analyzed in breast cancer cell lines. The expressions of HLA-A and MxA mRNAs were analyzed in MCF-7 cells in Gene Expression Omnibus (GEO) data. ER and HLA-ABC expressions, Ki-67 labeling index and TIL levels in tumor tissue were also analyzed in ER+/ human epidermal growth factor receptor 2 (HER2)- breast cancer patients who randomly received either neoadjuvant chemotherapy or estrogen modulator treatment followed by resection.
Results
HLA-ABC protein expression was decreased after β-estradiol treatment or hESR-GFP transfection and increased after fulvestrant or IFN-γ treatment in cell lines. In GEO data, HLA-A and MxA expression was increased after ESR1 shRNA transfection. In patients, ER Allred score was significantly lower and the HLA-ABC expression, TIL levels, and Ki-67 were significantly higher in the estrogen modulator treated group than the chemotherapy treated group.
Conclusion
MHC I expression and TIL levels might be affected by ER pathway modulation and IFN treatment. Further studies elucidating the mechanism of MHC I regulation could suggest a way to boost TIL influx in cancer in a clinical setting.

Citations

Citations to this article as recorded by  
  • Estrogen receptor regulation of the immune microenvironment in breast cancer
    Conor McGuinness, Kara L. Britt
    The Journal of Steroid Biochemistry and Molecular Biology.2024; 240: 106517.     CrossRef
  • Bioinformatic-Experimental Screening Uncovers Multiple Targets for Increase of MHC-I Expression through Activating the Interferon Response in Breast Cancer
    Xin Li, Zilun Ruan, Shuzhen Yang, Qing Yang, Jinpeng Li, Mingming Hu
    International Journal of Molecular Sciences.2024; 25(19): 10546.     CrossRef
  • Hormone Receptor Signaling and Breast Cancer Resistance to Anti-Tumor Immunity
    Alexandra Moisand, Mathilde Madéry, Thomas Boyer, Charlotte Domblides, Céline Blaye, Nicolas Larmonier
    International Journal of Molecular Sciences.2023; 24(20): 15048.     CrossRef
  • 6,466 View
  • 173 Download
  • 3 Web of Science
  • 3 Crossref
Close layer
Gastrointestinal Cancer
LASSO-Based Machine Learning Algorithm for Prediction of Lymph Node Metastasis in T1 Colorectal Cancer
Jeonghyun Kang, Yoon Jung Choi, Im-kyung Kim, Hye Sun Lee, Hogeun Kim, Seung Hyuk Baik, Nam Kyu Kim, Kang Young Lee
Cancer Res Treat. 2021;53(3):773-783.   Published online December 29, 2020
DOI: https://doi.org/10.4143/crt.2020.974
AbstractAbstract PDFSupplementary MaterialPubReaderePub
Purpose
The role of tumor-infiltrating lymphocytes (TILs) in predicting lymph node metastasis (LNM) in patients with T1 colorectal cancer (CRC) remains unclear. Furthermore, clinical utility of a machine learning–based approach has not been widely studied.
Materials and Methods
Immunohistochemistry for TILs against CD3, CD8, and forkhead box P3 in both center and invasive margin of the tumor were performed using surgically resected T1 CRC slides. Three hundred and sixteen patients were enrolled and categorized into training (n=221) and validation (n=95) sets via random sampling. Using clinicopathologic variables including TILs, the least absolute shrinkage and selection operator (LASSO) regression model was applied for variable selection and predictive signature building in the training set. The predictive accuracy of our model and the Japanese criteria were compared using area under the receiver operating characteristic (AUROC), net reclassification improvement (NRI)/integrated discrimination improvement (IDI), and decision curve analysis (DCA) in the validation set.
Results
LNM was detected in 29 (13.1%) and 12 (12.6%) patients in training and validation sets, respectively. Nine variables were selected and used to generate the LASSO model. Its performance was similar in training and validation sets (AUROC, 0.795 vs. 0.765; p=0.747). In the validation set, the LASSO model showed better outcomes in predicting LNM than Japanese criteria, as measured by AUROC (0.765 vs. 0.518, p=0.003) and NRI (0.447, p=0.039)/IDI (0.121, p=0.034). DCA showed positive net benefits in using our model.
Conclusion
Our LASSO model incorporating histopathologic parameters and TILs showed superior performance compared to conventional Japanese criteria in predicting LNM in patients with T1 CRC.

Citations

Citations to this article as recorded by  
  • A web-based tool for cancer risk prediction for middle-aged and elderly adults using machine learning algorithms and self-reported questions
    Xingjian Xiao, Xiaohan Yi, Nyi Nyi Soe, Phyu Mon Latt, Luotao Lin, Xuefen Chen, Hualing Song, Bo Sun, Hailei Zhao, Xianglong Xu
    Annals of Epidemiology.2025; 101: 27.     CrossRef
  • Risk stratification scores for lymph node metastases in T1 colorectal cancer—A systematic review
    Rakesh Quinn, Giuleta Jamsari, Ewan MacDermid
    Colorectal Disease.2025;[Epub]     CrossRef
  • Additional staining for lymphovascular invasion is associated with increased estimation of lymph node metastasis in patients with T1 colorectal cancer: Systematic review and meta‐analysis
    Jun Watanabe, Katsuro Ichimasa, Yuki Kataoka, Atsushi Miki, Hidehiro Someko, Munenori Honda, Makiko Tahara, Takeshi Yamashina, Khay Guan Yeoh, Shigeo Kawai, Kazuhiko Kotani, Naohiro Sata
    Digestive Endoscopy.2024; 36(5): 533.     CrossRef
  • Risk assessment in pT1 colorectal cancer
    Emma Jane Norton, Adrian C Bateman
    Journal of Clinical Pathology.2024; 77(4): 225.     CrossRef
  • Identification and Analysis of Immune Microenvironment-Related Genes for Keloid Risk Prediction and Their Effects on Keloid Proliferation and Migration
    Yongyan Pei, Yikai Wu, Mengqi Zhang, Xuemin Su, Hua Cao, Jiaji Zhao
    Biochemical Genetics.2024; 62(4): 3174.     CrossRef
  • Multiple machine-learning tools identifying prognostic biomarkers for acute Myeloid Leukemia
    Yujing Cheng, Xin Yang, Ying Wang, Qi Li, Wanlu Chen, Run Dai, Chan Zhang
    BMC Medical Informatics and Decision Making.2024;[Epub]     CrossRef
  • Artificial Intelligence Applications in the Treatment of Colorectal Cancer: A Narrative Review
    Jiaqing Yang, Jing Huang, Deqian Han, Xuelei Ma
    Clinical Medicine Insights: Oncology.2024;[Epub]     CrossRef
  • Transcriptomic and machine learning analyses identify hub genes of metabolism and host immune response that are associated with the progression of breast capsular contracture
    Yukun Mao, Xueying Hou, Su Fu, Jie Luan
    Genes & Diseases.2024; 11(3): 101087.     CrossRef
  • Bioinformatics and Machine Learning Methods Identified MGST1 and QPCT as Novel Biomarkers for Severe Acute Pancreatitis
    Yang Sun, Jingjun Xie, Jun Zhu, Yadong Yuan
    Molecular Biotechnology.2024; 66(5): 1246.     CrossRef
  • Multi-omics reveals the role of ENO1 in bladder cancer and constructs an epithelial-related prognostic model to predict prognosis and efficacy
    Zhixiong Su, Lijie You, Yufang He, Jingbo Chen, Guifeng Zhang, Zhenhua Liu
    Scientific Reports.2024;[Epub]     CrossRef
  • Use of artificial intelligence in the management of T1 colorectal cancer: a new tool in the arsenal or is deep learning out of its depth?
    James Weiquan Li, Lai Mun Wang, Katsuro Ichimasa, Kenneth Weicong Lin, James Chi-Yong Ngu, Tiing Leong Ang
    Clinical Endoscopy.2024; 57(1): 24.     CrossRef
  • SLCO4A1, as a novel prognostic biomarker of non‑small cell lung cancer, promotes cell proliferation and migration
    Shihao Li, Zihao Li, Lan Huang, Zhenyang Geng, Feng Li, Bin Wu, Yinliang Sheng, Yifan Xu, Bowen Li, Yiming Xu, Zhuoyu Gu, Yu Qi
    International Journal of Oncology.2024;[Epub]     CrossRef
  • Overexpression of circulating CD38+ NK cells in colorectal cancer was associated with lymph node metastasis and poor prognosis
    Xueling Wang, Haoran Li, Huixian Chen, Kehua Fang, Xiaotian Chang
    Frontiers in Oncology.2024;[Epub]     CrossRef
  • Management after non-curative endoscopic resection of T1 rectal cancer
    Hao Dang, Daan A. Verhoeven, Jurjen J. Boonstra, Monique E. van Leerdam
    Best Practice & Research Clinical Gastroenterology.2024; 68: 101895.     CrossRef
  • Prediction of Lymph Node Metastasis in T1 Colorectal Cancer Using Artificial Intelligence with Hematoxylin and Eosin-Stained Whole-Slide-Images of Endoscopic and Surgical Resection Specimens
    Joo Hye Song, Eun Ran Kim, Yiyu Hong, Insuk Sohn, Soomin Ahn, Seok-Hyung Kim, Kee-Taek Jang
    Cancers.2024; 16(10): 1900.     CrossRef
  • Identification of immune characteristic biomarkers and therapeutic targets in cuproptosis for sepsis by integrated bioinformatics analysis and single-cell RNA sequencing analysis
    Tianfeng Wang, Xiaowei Fang, Ximei Sheng, Meng Li, Yulin Mei, Qing Mei, Aijun Pan
    Heliyon.2024; 10(5): e27379.     CrossRef
  • Possibilities and prospects of artificial intelligence in the treatment of colorectal cancer (review)
    A. Yu. Kravchenko, E. V. Semina, V. V. Kakotkin, M. A. Agapov
    Koloproktologia.2024; 23(2): 184.     CrossRef
  • A risk prediction nomogram for resistant hypertension in patients with obstructive sleep apnea
    Hongze Lin, Chen Zhou, Jiaying Li, Xiuqin Ma, Yan Yang, Taofeng Zhu
    Scientific Reports.2024;[Epub]     CrossRef
  • Unveiling the best predictive models for early‑onset metastatic cancer: Insights and innovations (Review)
    Liqing Yu, Zhenjun Huang, Ziqi Xiao, Xiaofu Tang, Ziqiang Zeng, Xiaoli Tang, Wenhao Ouyang
    Oncology Reports.2024;[Epub]     CrossRef
  • Exploration of the molecular biological mechanisms and review of postoperative radiotherapy cases in tenosynovial giant cell tumors
    Tianwei Zhang, Bin Zeng, Ke Liu, Qin Zeng, Na Wang, Ling Peng, Hongbo Qiu, Xiaomei Chen, Lin Wang
    Frontiers in Oncology.2024;[Epub]     CrossRef
  • Radiomics-based machine learning in the differentiation of benign and malignant bowel wall thickening
    Hande Melike Bülbül, Gülen Burakgazi, Uğur Kesimal, Esat Kaba
    Japanese Journal of Radiology.2024; 42(8): 872.     CrossRef
  • Potential Mechanism of Tibetan Medicine Liuwei Muxiang Pills against Colorectal Cancer: Network Pharmacology and Bioinformatics Analyses
    Shaochong Qi, Xinyu Liang, Zijing Wang, Haoran Jin, Liqun Zou, Jinlin Yang
    Pharmaceuticals.2024; 17(4): 429.     CrossRef
  • A new clinical model for predicting lymph node metastasis in T1 colorectal cancer
    Kai Wang, Hui He, Yanyun Lin, Yanhong Zhang, Junguo Chen, Jiancong Hu, Xiaosheng He
    International Journal of Colorectal Disease.2024;[Epub]     CrossRef
  • Comprehensive analysis of the interaction of antigen presentation during anti‐tumour immunity and establishment of AIDPS systems in ovarian cancer
    Wenhuizi Sun, Ping Xu, Kefei Gao, Wenqin Lian, Xiang Sun
    Journal of Cellular and Molecular Medicine.2024;[Epub]     CrossRef
  • Use of artificial intelligence for the prediction of lymph node metastases in early-stage colorectal cancer: systematic review
    Nasya Thompson, Arthur Morley-Bunker, Jared McLauchlan, Tamara Glyn, Tim Eglinton
    BJS Open.2024;[Epub]     CrossRef
  • Machine learning for predicting colon cancer recurrence
    Erkan Kayikcioglu, Arif Hakan Onder, Burcu Bacak, Tekin Ahmet Serel
    Surgical Oncology.2024; 54: 102079.     CrossRef
  • A machine learning screening model for identifying the risk of high-frequency hearing impairment in a general population
    Yi Wang, Xinmeng Yao, Dahui Wang, Chengyin Ye, Liangwen Xu
    BMC Public Health.2024;[Epub]     CrossRef
  • Potential clinical value of fibrinogen-like protein 1 as a serum biomarker for the identification of diabetic cardiomyopathy
    Yao Liu, Min Wang, Jia-Bao Su, Xiao Fu, Guan-Li Zheng, Shan Guo, Li-Juan Zhang, Qing-Bo Lu
    Scientific Reports.2024;[Epub]     CrossRef
  • CXCR4-mediated neutrophil dynamics in periodontitis
    Xuanwen Xu, Tiange Li, Jingqi Tang, Danlei Wang, Yi Zhou, Huiqing Gou, Lu Li, Yan Xu
    Cellular Signalling.2024; 120: 111212.     CrossRef
  • Hspb1 and Lgals3 in spinal neurons are closely associated with autophagy following excitotoxicity based on machine learning algorithms
    Lei Yan, Zihao Li, Chuanbo Li, Jingyu Chen, Xun Zhou, Jiaming Cui, Peng Liu, Chong Shen, Chu Chen, Hongxiang Hong, Guanhua Xu, Zhiming Cui, Suyan Tian
    PLOS ONE.2024; 19(5): e0303235.     CrossRef
  • Impact of artificial intelligence in the management of esophageal, gastric and colorectal malignancies
    Ayrton Bangolo, Nikita Wadhwani, Vignesh K Nagesh, Shraboni Dey, Hadrian Hoang-Vu Tran, Izage Kianifar Aguilar, Auda Auda, Aman Sidiqui, Aiswarya Menon, Deborah Daoud, James Liu, Sai Priyanka Pulipaka, Blessy George, Flor Furman, Nareeman Khan, Adewale Pl
    Artificial Intelligence in Gastrointestinal Endoscopy.2024;[Epub]     CrossRef
  • Identification and Validation of Nicotinamide Metabolism-Related Gene Signatures as a Novel Prognostic Model for Hepatocellular Carcinoma
    Sijia Yang, Ang Li, Lihong Lv, Jinxin Duan, Zhihua Zheng, Wenfeng Zhuo, Jun Min, Jinxing Wei
    OncoTargets and Therapy.2024; Volume 17: 423.     CrossRef
  • Machine learning algorithms integrate bulk and single-cell RNA data to unveil oxidative stress following intracerebral hemorrhage
    Chaonan Du, Cong Wang, Zhiwei Liu, Wenxuan Xin, Qizhe Zhang, Alleyar Ali, Xinrui Zeng, Zhenxing Li, Chiyuan Ma
    International Immunopharmacology.2024; 137: 112449.     CrossRef
  • Single-cell analysis and machine learning identify psoriasis-associated CD8+ T cells serve as biomarker for psoriasis
    Sijia He, Lyuye Liu, Xiaoyan Long, Man Ge, Menghan Cai, Junling Zhang
    Frontiers in Genetics.2024;[Epub]     CrossRef
  • DcR3-associated risk score: correlating better prognosis and enhanced predictive power in colorectal cancer
    Ying Duan, Hangrong Fang, Juanhong Wang, Banlai Ruan, Juan Yang, Jie Liu, Siqi Gou, Yijie Li, Zhengyi Cheng
    Discover Oncology.2024;[Epub]     CrossRef
  • Lymph node metastasis detection using artificial intelligence in T1 colorectal cancer: A comprehensive systematic review
    Xiaoyan Yao, Zhiyong Zhou, Shengxun Mao, Jiaqing Cao, Huizi Li
    Journal of Surgical Oncology.2024; 130(3): 637.     CrossRef
  • Development and Validation of an Interpretable Machine Learning Model for Early Prognosis Prediction in ICU Patients with Malignant Tumors and Hyperkalemia
    Zhi-Jun Bu, Nan Jiang, Ke-Cheng Li, Zhi-Lin Lu, Nan Zhang, Shao-Shuai Yan, Zhi-Lin Chen, Yu-Han Hao, Yu-Huan Zhang, Run-Bing Xu, Han-Wei Chi, Zu-Yi Chen, Jian-Ping Liu, Dan Wang, Feng Xu, Zhao-Lan Liu
    Medicine.2024; 103(30): e38747.     CrossRef
  • Machine learning-based model to predict composite thromboembolic events among Chinese elderly patients with atrial fibrillation
    Jiefeng Ren, Haijun Wang, Song Lai, Yi Shao, Hebin Che, Zaiyao Xue, Xinlian Qi, Sha Zhang, Jinkun Dai, Sai Wang, Kunlian Li, Wei Gan, Quanjin Si
    BMC Cardiovascular Disorders.2024;[Epub]     CrossRef
  • Identifying and validating necroptosis‐associated features to predict clinical outcome and immunotherapy response in patients with glioblastoma
    Qinghua Yuan, Weida Gao, Mian Guo, Bo Liu
    Environmental Toxicology.2024; 39(10): 4729.     CrossRef
  • CuPCA: a web server for pan-cancer association analysis of large-scale cuproptosis-related genes
    Yishu Xu, Zhenshu Ma, Yajie Wang, Long Zhang, Jiaming Ye, Yuan Chen, Zhengrong Yuan
    Database.2024;[Epub]     CrossRef
  • Transcriptomic analysis reveals oxidative stress-related signature and molecular subtypes in cholangio carcinoma
    Zichao Wu
    Molecular Genetics and Genomics.2024;[Epub]     CrossRef
  • The application value of support vector machine model based on multimodal MRI in predicting IDH-1mutation and Ki-67 expression in glioma
    He-Xin Liang, Zong-Ying Wang, Yao Li, An-Ning Ren, Zhi-Feng Chen, Xi-Zhen Wang, Xi-Ming Wang, Zhen-Guo Yuan
    BMC Medical Imaging.2024;[Epub]     CrossRef
  • The AC010247.2/miR-125b-5p axis triggers the malignant progression of acute myelocytic leukemia by IL-6R
    Fang Xie, Jialu Xu, Lina Yan, Xia Xiao, Liang Liu
    Heliyon.2024; 10(18): e37715.     CrossRef
  • Application of machine learning for predicting lymph node metastasis in T1 colorectal cancer: a systematic review and meta-analysis
    Chinock Cheong, Na Won Kim, Hye Sun Lee, Jeonghyun Kang
    Langenbeck's Archives of Surgery.2024;[Epub]     CrossRef
  • Short-term PV energy yield predictions within city neighborhoods for optimum grid management
    Stefani Peratikou, Alexandros G. Charalambides
    Energy and Buildings.2024; 323: 114773.     CrossRef
  • Integrating microarray-based spatial transcriptomics and RNA-seq reveals tissue architecture in colorectal cancer
    Zheng Li, Xiaojie Zhang, Chongyuan Sun, Zefeng Li, He Fei, Dongbing Zhao
    Journal of Big Data.2024;[Epub]     CrossRef
  • Construction and validation of a nomogram prediction model for the catheter-related thrombosis risk of central venous access devices in patients with cancer: a prospective machine learning study
    Guiyuan Ma, Shujie Chen, Sha Peng, Nian Yao, Jiaji Hu, Letian Xu, Tingyin Chen, Jiaan Wang, Xin Huang, Jinghui Zhang
    Journal of Thrombosis and Thrombolysis.2024;[Epub]     CrossRef
  • Association of coexposure to perfluoroalkyl and polyfluoroalkyl compounds and heavy metals with pregnancy loss and reproductive lifespan: The mediating role of cholesterol
    Hua Fang, Dai Lin, Ziqi Zhang, Haoting Chen, Zixin Zheng, Dongdong Jiang, Wenxiang Wang
    Ecotoxicology and Environmental Safety.2024; 286: 117160.     CrossRef
  • Identification of novel diagnostic biomarkers associated with liver metastasis in colon adenocarcinoma by machine learning
    Long Yang, Ye Tian, Xiaofei Cao, Jiawei Wang, Baoyang Luo
    Discover Oncology.2024;[Epub]     CrossRef
  • Assessing prospective molecular biomarkers and functional pathways in severe asthma based on a machine learning method and bioinformatics analyses
    Ya-Da Zhang, Yi-Ren Chen, Wei Zhang, Bin-Qing Tang
    Journal of Asthma.2024; : 1.     CrossRef
  • Identification of a disulfidptosis-related genes signature for diagnostic and immune infiltration characteristics in endometriosis
    Xiangyu Chang, Jinwei Miao
    Scientific Reports.2024;[Epub]     CrossRef
  • Machine learning model based on SERPING1, C1QB, and C1QC: A novel diagnostic approach for latent tuberculosis infection
    Linsheng Li, Li Zhuang, Ling Yang, Zhaoyang Ye, Ruizi Ni, Yajing An, Weiguo Zhao, Wenping Gong
    iLABMED.2024; 2(4): 248.     CrossRef
  • Development of machine learning models for predicting depressive symptoms in knee osteoarthritis patients
    Dan Li, Han Lu, Junhui Wu, Hongbo Chen, Meidi Shen, Beibei Tong, Wen Zeng, Weixuan Wang, Shaomei Shang
    Scientific Reports.2024;[Epub]     CrossRef
  • Integrated analysis of single-cell, spatial and bulk RNA-sequencing identifies a cell-death signature for predicting the outcomes of head and neck cancer
    Yue Pan, Lei Fei, Shihua Wang, Hua Chen, Changqing Jiang, Hong Li, Changsong Wang, Yao Yang, Qinggao Zhang, Yongwen Chen
    Frontiers in Immunology.2024;[Epub]     CrossRef
  • Screening the Best Risk Model and Susceptibility SNPs for Chronic Obstructive Pulmonary Disease (COPD) Based on Machine Learning Algorithms
    Zehua Yang, Yamei Zheng, Lei Zhang, Jie Zhao, Wenya Xu, Haihong Wu, Tian Xie, Yipeng Ding
    International Journal of Chronic Obstructive Pulmonary Disease.2024; Volume 19: 2397.     CrossRef
  • Identification of biomarkers related to Escherichia coli infection for the diagnosis of gastrointestinal tumors applying machine learning methods
    Tingting Ge, Wei Wang, Dandan Zhang, Xubo Le, Lumei Shi
    Heliyon.2024; 10(23): e40491.     CrossRef
  • Immune Microenvironment Alterations and Identification of Key Diagnostic Biomarkers in Chronic Kidney Disease Using Integrated Bioinformatics and Machine Learning
    Jinbao Shi, Aliang Xu, Liuying Huang, Shaojie Liu, Binxuan Wu, Zuhong Zhang
    Pharmacogenomics and Personalized Medicine.2024; Volume 17: 497.     CrossRef
  • Identification of alternative lengthening of telomeres-related genes prognosis model in hepatocellular carcinoma
    FanLin Zeng, YuLiang Chen, Jie Lin
    BMC Cancer.2024;[Epub]     CrossRef
  • Bioinformatics Analysis Identifies PLA2G7 as a Key Antigen-Presenting Prognostic Related Gene Promoting Hepatocellular Carcinoma through the STAT1/PD-L1 Axis
    Sihang Guo, Qinhe Yang
    Frontiers in Bioscience-Landmark.2024;[Epub]     CrossRef
  • Risk of intraoperative hemorrhage during cesarean scar ectopic pregnancy surgery: development and validation of an interpretable machine learning prediction model
    Xinli Chen, Huan Zhang, Dongxia Guo, Siyuan Yang, Bao Liu, Yiping Hao, Qingqing Liu, Teng Zhang, Fanrong Meng, Longyun Sun, Xinlin Jiao, Wenjing Zhang, Yanli Ban, Yugang Chi, Guowei Tao, Baoxia Cui
    eClinicalMedicine.2024; 78: 102969.     CrossRef
  • TRIM16 and PRC1 Are Involved in Pancreatic Cancer Progression and Targeted by Delphinidin
    Donghua Wang, Long Lv, Jinghu Du, Kui Tian, Yu Chen, Manyu Chen
    Chemical Biology & Drug Design.2024;[Epub]     CrossRef
  • Identification of Ferroptosis‐Related Gene in Age‐Related Macular Degeneration Using Machine Learning
    Meijiang Zhu, Jing Yu
    Immunity, Inflammation and Disease.2024;[Epub]     CrossRef
  • Integration of bioinformatics analysis reveals ZNF248 as a potential prognostic and immunotherapeutic biomarker for LIHC: machine learning and experimental evidence
    Lifang Weng
    American Journal of Cancer Research.2024; 14(11): 5230.     CrossRef
  • Development and Validation of a Nomogram for Predicting Suicidal Ideation Among Rural Adolescents in China
    Yunjiao Luo, Yuhao Wang, Yingxue Wang, Yihan Wang, Na Yan, Blen Shiferaw, Louisa Mackay, Ziyang Zhang, Caiyi Zhang, Wei Wang
    Psychology Research and Behavior Management.2024; Volume 17: 4413.     CrossRef
  • High serum mannose in colorectal cancer: a novel biomarker of lymph node metastasis and poor prognosis
    Xueling Wang, Haoran Li, Xiaotian Chang, Zibin Tian
    Frontiers in Oncology.2023;[Epub]     CrossRef
  • Development of a nomogram for predicting nosocomial infections among patients after cardiac valve replacement surgery
    Xue Yao, Na Li, Ranran Lu, Xujing Wang, Yujun Zhang, Shuhui Wang
    Journal of Clinical Nursing.2023; 32(7-8): 1466.     CrossRef
  • Machine learning-based warning model for chronic kidney disease in individuals over 40 years old in underprivileged areas, Shanxi Province
    Wenzhu Song, Yanfeng Liu, Lixia Qiu, Jianbo Qing, Aizhong Li, Yan Zhao, Yafeng Li, Rongshan Li, Xiaoshuang Zhou
    Frontiers in Medicine.2023;[Epub]     CrossRef
  • Artificial intelligence in colorectal surgery: an AI-powered systematic review
    A. Spinelli, F. M. Carrano, M. E. Laino, M. Andreozzi, G. Koleth, C. Hassan, A. Repici, M. Chand, V. Savevski, G. Pellino
    Techniques in Coloproctology.2023; 27(8): 615.     CrossRef
  • Prognostic significance of neutrophil count on in-hospital mortality in patients with acute type A aortic dissection
    Weiqi Feng, Huili Li, Qiuji Wang, Chenxi Li, Jinlin Wu, Jue Yang, Ruixin Fan
    Frontiers in Cardiovascular Medicine.2023;[Epub]     CrossRef
  • Machine learning algorithms assisted identification of post-stroke depression associated biological features
    Xintong Zhang, Xiangyu Wang, Shuwei Wang, Yingjie Zhang, Zeyu Wang, Qingyan Yang, Song Wang, Risheng Cao, Binbin Yu, Yu Zheng, Yini Dang
    Frontiers in Neuroscience.2023;[Epub]     CrossRef
  • Risk prediction of bronchopulmonary dysplasia in preterm infants by the nomogram model
    Yang Gao, Dongyun Liu, Yingmeng Guo, Menghan Cao
    Frontiers in Pediatrics.2023;[Epub]     CrossRef
  • LncRNA model predicts liver cancer drug resistance and validate in vitro experiments
    Qiushi Yin, Xiaolong Huang, Qiuxi Yang, Shibu Lin, Qifeng Song, Weiqiang Fan, Wang Li, Zhongyi Li, Lianghui Gao
    Frontiers in Cell and Developmental Biology.2023;[Epub]     CrossRef
  • Artificial intelligence–assisted treatment strategy for T1 colorectal cancer after endoscopic resection
    Katsuro Ichimasa, Shin-ei Kudo, Jonathan Wei Jie Lee, Tetsuo Nemoto, Khay Guan Yeoh
    Gastrointestinal Endoscopy.2023; 97(6): 1148.     CrossRef
  • A risk prediction model for type 2 diabetes mellitus complicated with retinopathy based on machine learning and its application in health management
    Hong Pan, Jijia Sun, Xin Luo, Heling Ai, Jing Zeng, Rong Shi, An Zhang
    Frontiers in Medicine.2023;[Epub]     CrossRef
  • LASSO-based machine learning algorithm to predict the incidence of diabetes in different stages
    Qianying Ou, Wei Jin, Leweihua Lin, Danhong Lin, Kaining Chen, Huibiao Quan
    The Aging Male.2023;[Epub]     CrossRef
  • Prediction model based on radiomics and clinical features for preoperative lymphovascular invasion in gastric cancer patients
    Ping Wang, Kaige Chen, Ying Han, Min Zhao, Nanding Abiyasi, Haiyong Peng, Shaolei Yan, Jiming Shang, Naijian Shang, Wei Meng
    Future Oncology.2023; 19(23): 1613.     CrossRef
  • Liang-Ge-San: a classic traditional Chinese medicine formula, attenuates acute inflammation via targeting GSK3β
    Liling Yang, Lijun Yan, Weifu Tan, Xiangjun Zhou, Guangli Yang, Jingtao Yu, Zibin Lu, Yong Liu, Liyi Zou, Wei Li, Linzhong Yu
    Frontiers in Pharmacology.2023;[Epub]     CrossRef
  • Synergistic inhibition of NUDT21 by secretory S100A11 and exosomal miR‐487a‐5p promotes melanoma oligo‐ to poly‐metastatic progression
    Bin Zeng, Yuting Chen, Hao Chen, Qiting Zhao, Zhiwei Sun, Doudou Liu, Xiaoshuang Li, Yuhan Zhang, Jianyu Wang, H. Rosie Xing
    Molecular Oncology.2023; 17(12): 2743.     CrossRef
  • Identification of immune-related lncRNA in sepsis by construction of ceRNA network and integrating bioinformatic analysis
    Tianfeng Wang, Si Xu, Lei Zhang, Tianjun Yang, Xiaoqin Fan, Chunyan Zhu, Yinzhong Wang, Fei Tong, Qing Mei, Aijun Pan
    BMC Genomics.2023;[Epub]     CrossRef
  • Applying machine learning algorithms to develop a survival prediction model for lung adenocarcinoma based on genes related to fatty acid metabolism
    Dan Cong, Yanan Zhao, Wenlong Zhang, Jun Li, Yuansong Bai
    Frontiers in Pharmacology.2023;[Epub]     CrossRef
  • Identification of raffinose family oligosaccharides in processed Rehmannia glutinosa Libosch using matrix‐assisted laser desorption/ionization mass spectrometry image combined with machine learning
    Huizhi Li, Shishan Zhang, Yanfang Zhao, Jixiang He, Xiangfeng Chen
    Rapid Communications in Mass Spectrometry.2023;[Epub]     CrossRef
  • Development and validation of a LASSO-based prediction model for immunosuppressive medication nonadherence in kidney transplant recipients
    Lei Dong, Xiao Zhu, Hongyu Zhao, Qin Zhao, Shan Liu, Jia Liu, Lina Gong
    Renal Failure.2023;[Epub]     CrossRef
  • An artificial intelligence prediction model outperforms conventional guidelines in predicting lymph node metastasis of T1 colorectal cancer
    Zheng Hua Piao, Rong Ge, Lu Lu
    Frontiers in Oncology.2023;[Epub]     CrossRef
  • Silicon versus Superbug: Assessing Machine Learning’s Role in the Fight against Antimicrobial Resistance
    Tallon Coxe, Rajeev K. Azad
    Antibiotics.2023; 12(11): 1604.     CrossRef
  • Predictive model for early death risk in pediatric hemophagocytic lymphohistiocytosis patients based on machine learning
    Li Xiao, Yang Zhang, Ximing Xu, Ying Dou, Xianmin Guan, Yuxia Guo, Xianhao Wen, Yan Meng, Meiling Liao, Qinshi Hu, Jie Yu
    Heliyon.2023; 9(11): e22202.     CrossRef
  • Multimodal and multi-omics-based deep learning model for screening of optic neuropathy
    Ye-ting Lin, Qiong Zhou, Jian Tan, Yulin Tao
    Heliyon.2023; 9(12): e22244.     CrossRef
  • Exploration and validation of key genes associated with early lymph node metastasis in thyroid carcinoma using weighted gene co-expression network analysis and machine learning
    Yanyan Liu, Zhenglang Yin, Yao Wang, Haohao Chen
    Frontiers in Endocrinology.2023;[Epub]     CrossRef
  • Current problems and perspectives of pathological risk factors for lymph node metastasis in T1 colorectal cancer: Systematic review
    Katsuro Ichimasa, Shin‐ei Kudo, Hideyuki Miyachi, Yuta Kouyama, Kenichi Mochizuki, Yuki Takashina, Yasuharu Maeda, Yuichi Mori, Toyoki Kudo, Yuki Miyata, Yoshika Akimoto, Yuki Kataoka, Takafumi Kubota, Tetsuo Nemoto, Fumio Ishida, Masashi Misawa
    Digestive Endoscopy.2022; 34(5): 901.     CrossRef
  • Tumor Location as a Prognostic Factor in T1 Colorectal Cancer
    Katsuro Ichimasa, Shin-ei Kudo, Yuta Kouyama, Kenichi Mochizuki, Yuki Takashina, Masashi Misawa, Yuichi Mori, Takemasa Hayashi, Kunihiko Wakamura, Hideyuki Miyachi
    Journal of the Anus, Rectum and Colon.2022; 6(1): 9.     CrossRef
  • The Importance of Being “That” Colorectal pT1: A Combined Clinico-Pathological Predictive Score to Improve Nodal Risk Stratification
    Alessandro Gambella, Enrico Costantino Falco, Giacomo Benazzo, Simona Osella-Abate, Rebecca Senetta, Isabella Castellano, Luca Bertero, Paola Cassoni
    Frontiers in Medicine.2022;[Epub]     CrossRef
  • A Predictive Model for Qualitative Evaluation of PG-SGA in Tumor Patients Through Machine Learning
    Xiangliang Liu, Yuguang Li, Wei Ji, Kaiwen Zheng, Jin Lu, Yixin Zhao, Wenxin Zhang, Mingyang Liu, Jiuwei Cui, Wei Li
    Cancer Management and Research.2022; Volume 14: 1431.     CrossRef
  • Deep Submucosal Invasion Is Not an Independent Risk Factor for Lymph Node Metastasis in T1 Colorectal Cancer: A Meta-Analysis
    Liselotte W. Zwager, Barbara A.J. Bastiaansen, Nahid S.M. Montazeri, Roel Hompes, Valeria Barresi, Katsuro Ichimasa, Hiroshi Kawachi, Isidro Machado, Tadahiko Masaki, Weiqi Sheng, Shinji Tanaka, Kazutomo Togashi, Chihiro Yasue, Paul Fockens, Leon M.G. Moo
    Gastroenterology.2022; 163(1): 174.     CrossRef
  • LASSO ‐derived nomogram predicting new‐onset diabetes mellitus in patients with kidney disease receiving immunosuppressive drugs
    Lina Shao, Chuxuan Luo, Chaoyun Yuan, Xiaolan Ye, Yuqun Zeng, Yan Ren, Binxian Ye, Yiwen Li, Juan Jin, Qiang He, Xiaogang Shen
    Journal of Clinical Pharmacy and Therapeutics.2022; 47(10): 1627.     CrossRef
  • Using random forest algorithm for glomerular and tubular injury diagnosis
    Wenzhu Song, Xiaoshuang Zhou, Qi Duan, Qian Wang, Yaheng Li, Aizhong Li, Wenjing Zhou, Lin Sun, Lixia Qiu, Rongshan Li, Yafeng Li
    Frontiers in Medicine.2022;[Epub]     CrossRef
  • Identification of diagnostic signatures associated with immune infiltration in Alzheimer’s disease by integrating bioinformatic analysis and machine-learning strategies
    Yu Tian, Yaoheng Lu, Yuze Cao, Chun Dang, Na Wang, Kuo Tian, Qiqi Luo, Erliang Guo, Shanshun Luo, Lihua Wang, Qian Li
    Frontiers in Aging Neuroscience.2022;[Epub]     CrossRef
  • Development and validation of a predictive model combining clinical, radiomics, and deep transfer learning features for lymph node metastasis in early gastric cancer
    Qingwen Zeng, Hong Li, Yanyan Zhu, Zongfeng Feng, Xufeng Shu, Ahao Wu, Lianghua Luo, Yi Cao, Yi Tu, Jianbo Xiong, Fuqing Zhou, Zhengrong Li
    Frontiers in Medicine.2022;[Epub]     CrossRef
  • Identification of Drug-Induced Liver Injury Biomarkers from Multiple Microarrays Based on Machine Learning and Bioinformatics Analysis
    Kaiyue Wang, Lin Zhang, Lixia Li, Yi Wang, Xinqin Zhong, Chunyu Hou, Yuqi Zhang, Congying Sun, Qian Zhou, Xiaoying Wang
    International Journal of Molecular Sciences.2022; 23(19): 11945.     CrossRef
  • Development and validation of a machine learning model to predict the risk of lymph node metastasis in renal carcinoma
    Xiaowei Feng, Tao Hong, Wencai Liu, Chan Xu, Wanying Li, Bing Yang, Yang Song, Ting Li, Wenle Li, Hui Zhou, Chengliang Yin
    Frontiers in Endocrinology.2022;[Epub]     CrossRef
  • Preoperative prediction of lymph node status in patients with colorectal cancer. Developing a predictive model using machine learning
    Morten Hartwig, Karoline Bendix Bräuner, Rasmus Vogelsang, Ismail Gögenur
    International Journal of Colorectal Disease.2022; 37(12): 2517.     CrossRef
  • A retrospective analysis based on multiple machine learning models to predict lymph node metastasis in early gastric cancer
    Tao Yang, Javier Martinez-Useros, JingWen Liu, Isaias Alarcón, Chao Li, WeiYao Li, Yuanxun Xiao, Xiang Ji, YanDong Zhao, Lei Wang, Salvador Morales-Conde, Zuli Yang
    Frontiers in Oncology.2022;[Epub]     CrossRef
  • Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology
    Chrysanthos D Christou, Georgios Tsoulfas
    World Journal of Gastroenterology.2021; 27(37): 6191.     CrossRef
  • 12,122 View
  • 356 Download
  • 100 Web of Science
  • 101 Crossref
Close layer
Prognostic Role and Clinical Association of Tumor-Infiltrating Lymphocyte, Programmed Death Ligand-1 Expression with Neutrophil-Lymphocyte Ratio in Locally Advanced Triple-Negative Breast Cancer
Jieun Lee, Dong-Min Kim, Ahwon Lee
Cancer Res Treat. 2019;51(2):649-663.   Published online July 31, 2018
DOI: https://doi.org/10.4143/crt.2018.270
AbstractAbstract PDFSupplementary MaterialPubReaderePub
Purpose
Tumor-infiltrating lymphocyte (TIL), programmed death-ligand 1 (PD-L1) expression and neutrophil-to-lymphocyte ratio (NLR) is associated to immunogenicity and prognosis of breast cancer. We analyzed baseline NLR, changes of NLR, TIL, and PD-L1 during neoadjuvant chemotherapy (NAC) and their clinical implication in triple-negative breast cancer (TNBC).
Materials and Methods
Between January 2008 to December 2015, 358 TNBC patients were analyzed. Baseline NLR, 50 paired NLR (initial diagnosis, after completion of NAC) and 34 paired tissues (initial diagnosis, surgical specimen after completion of NAC) were collected. Changes of TIL, CD4, CD8, forkhead box P3 (FOXP3), and PD-L1 expression were assessed with immunohistochemical stain.
Results
Low NLR (≤ 3.16) was associated to superior survival (overall survival: 41.83 months vs. 36.5 months, p=0.002; disease-free survival [DFS]: 37.85 months vs. 32.14 months, p=0.032). Modest NLR change after NAC (–30% < NLR change < 100%) showed prolonged DFS (38.37 months vs. 22.37 months, p=0.015). During NAC, negative or negative conversion of tumor PD-L1 expression was associated to poor DFS (34.77 months vs. 16.03 months, p=0.037), and same or increased TIL showed trends for superior DFS, but without statistical significance. Positive tumor PD-L1 expression (H-score ≥ 5) in baseline or post- NAC tissue was associated to superior DFS (57.6 months vs. 12.5 months, p=0.001 and 53.3 months vs. 18.9 months, p=0.040). Positive stromal PD-L1 expression in baseline was also associated to superior DFS (50.2 months vs. 20.4 months, p=0.002).
Conclusion
In locally advanced TNBC, baseline NLR, changes of NLR during NAC was associated to survival. Baseline PD-L1 expression and changes of PD-L1 expression in tumor tissue during NAC also showed association to prognosis.

Citations

Citations to this article as recorded by  
  • Neutrophil–lymphocyte ratio reflects tumour‐infiltrating lymphocytes and tumour‐associated macrophages and independently predicts poor outcome in breast cancers with neoadjuvant chemotherapy
    Joshua J X Li, Shelly Y B Ni, Julia Y S Tsang, Wai Yin Chan, Ray K W Hung, Joshua W H Lui, Sally W Y Ng, Leong Kwong Shum, Ying Fei Tang, Gary M Tse
    Histopathology.2024; 84(5): 810.     CrossRef
  • Circulating blood biomarkers correlated with the prognosis of advanced triple negative breast cancer
    Xingyu Li, Yanyan Zhang, Cheng Zhu, Wentao Xu, Xiaolei Hu, Domingo Antonio Sánchez Martínez, José Luis Alonso Romero, Ming Yan, Ying Dai, Hua Wang
    BMC Women's Health.2024;[Epub]     CrossRef
  • Recent advancements in nanoconstructs for the theranostics applications for triple negative breast cancer
    Ashutosh Gupta, Kumar Nishchaya, Moumita Saha, Gaurisha Alias Resha Ramnath Naik, Sarika Yadav, Shreya Srivastava, Amrita Arup Roy, Sudheer Moorkoth, Srinivas Mutalik, Namdev Dhas
    Journal of Drug Delivery Science and Technology.2024; 93: 105401.     CrossRef
  • Predictive value of stromal tumor-infiltrating lymphocytes in patients with breast cancer treated with neoadjuvant chemotherapy: A meta-analysis
    Guangfa Xia, Ziran Zhang, Qin Jiang, Huan Wang, Jie Wang
    Medicine.2024; 103(6): e36810.     CrossRef
  • The prognostic value of preoperative neoindices consisting of lymphocytes, neutrophils and albumin (LANR) in operable breast cancer: a retrospective study
    Yuan Wang, Jiaru Zhuang, Shan Wang, Yibo Wu, Ling Chen
    PeerJ.2024; 12: e17382.     CrossRef
  • A review concerning the breast cancer-related tumour microenvironment
    Oscar Hernán Rodríguez-Bejarano, Carlos Parra-López, Manuel Alfonso Patarroyo
    Critical Reviews in Oncology/Hematology.2024; 199: 104389.     CrossRef
  • Alteration of PD-L1 (SP142) status after neoadjuvant chemotherapy and its clinical significance in triple-negative breast cancer
    Ji Won Woo, Eun Kyung Han, Koung Jin Suh, Se Hyun Kim, Jee Hyun Kim, So Yeon Park
    Breast Cancer Research and Treatment.2024; 207(2): 301.     CrossRef
  • Predictive value of pretreatment circulating inflammatory response markers in the neoadjuvant treatment of breast cancer: meta-analysis
    Gavin P Dowling, Gordon R Daly, Aisling Hegarty, Sandra Hembrecht, Aisling Bracken, Sinead Toomey, Bryan T Hennessy, Arnold D K Hill
    British Journal of Surgery.2024;[Epub]     CrossRef
  • Pathology of neoadjuvant therapy and immunotherapy testing for breast cancer
    Elena Provenzano, Abeer M Shaaban
    Histopathology.2023; 82(1): 170.     CrossRef
  • Recent Advances in Targeted Nanocarriers for the Management of Triple Negative Breast Cancer
    Rajesh Pradhan, Anuradha Dey, Rajeev Taliyan, Anu Puri, Sanskruti Kharavtekar, Sunil Kumar Dubey
    Pharmaceutics.2023; 15(1): 246.     CrossRef
  • The prognostic value of tumour-infiltrating lymphocytes, programmed cell death protein-1 and programmed cell death ligand-1 in Stage I–III triple-negative breast cancer
    Guang-Yi Sun, Jing Zhang, Bing-Zhi Wang, Hao Jing, Hui Fang, Yu Tang, Yong-Wen Song, Jing Jin, Yue-Ping Liu, Yuan Tang, Shu-Nan Qi, Bo Chen, Ning-Ning Lu, Ning Li, Ye-Xiong Li, Jian-Ming Ying, Shu-Lian Wang
    British Journal of Cancer.2023; 128(11): 2044.     CrossRef
  • Effect of neoadjuvant chemotherapy on tumor immune infiltration in breast cancer patients: Systematic review and meta-analysis
    Manuela Llano-León, Laura Camila Martínez-Enriquez, Oscar Mauricio Rodríguez-Bohórquez, Esteban Alejandro Velandia-Vargas, Nicolás Lalinde-Ruíz, María Alejandra Villota-Álava, Ivon Johanna Rodríguez-Rodríguez, María del Pilar Montilla-Velásquez, Carlos Al
    PLOS ONE.2023; 18(4): e0277714.     CrossRef
  • Association of neutrophil-to-lymphocyte ratio with clinical, pathological, radiological, laboratory features and disease outcomes of invasive breast cancer patients: A retrospective observational cohort study
    Sarosh Khan Jadoon, Rufina Soomro, Muhammad Nadeem Ahsan, Raja Muhammad Ijaz Khan, Sadia Iqbal, Farah Yasmin, Hala Najeeb, Nida Saleem, Namiya Cho, Resham, Taha Gul Shaikh, Syeda Fatima Saba Hasan, Muhammad Zain Khalid, Sarosh Alvi, Ahsan Mujtaba Rizvi,
    Medicine.2023; 102(20): e33811.     CrossRef
  • A new prognostic model including immune biomarkers, genomic proliferation tumor markers (AURKA and MYBL2) and clinical-pathological features optimizes prognosis in neoadjuvant breast cancer patients
    Esmeralda García-Torralba, Esther Navarro Manzano, Gines Luengo-Gil, Pilar De la Morena Barrio, Asunción Chaves Benito, Miguel Pérez-Ramos, Beatriz Álvarez-Abril, Alejandra Ivars Rubio, Elisa García-Garre, Francisco Ayala de la Peña, Elena García-Martínez
    Frontiers in Oncology.2023;[Epub]     CrossRef
  • Tumor infiltrating lymphocytes and neutrophil-to-lymphocyte ratio in relation to pathological complete remission to neoadjuvant therapy and prognosis in triple negative breast cancer
    Meng Zhao, Hui Xing, Jiankun He, Xinran Wang, Yueping Liu
    Pathology - Research and Practice.2023; 248: 154687.     CrossRef
  • Minimal residual disease in advanced or metastatic solid cancers: The G0-G1 state and immunotherapy are key to unwinding cancer complexity
    Andrea Nicolini, Giuseppe Rossi, Paola Ferrari, Angelo Carpi
    Seminars in Cancer Biology.2022; 79: 68.     CrossRef
  • The advanced lung cancer inflammation index is a novel independent prognosticator in colorectal cancer patients after curative resection
    Taichi Horino, Ryuma Tokunaga, Yuji Miyamoto, Yukiharu Hiyoshi, Takahiko Akiyama, Nobuya Daitoku, Yuki Sakamoto, Naoya Yoshida, Hideo Baba
    Annals of Gastroenterological Surgery.2022; 6(1): 83.     CrossRef
  • The Characteristics of Tumor Microenvironment in Triple Negative Breast Cancer
    Yiqi Fan, Shuai He
    Cancer Management and Research.2022; Volume 14: 1.     CrossRef
  • Predictive and Prognostic Role of Peripheral Blood T-Cell Subsets in Triple-Negative Breast Cancer
    Meng Li, Junnan Xu, Cui Jiang, Jingyan Zhang, Tao Sun
    Frontiers in Oncology.2022;[Epub]     CrossRef
  • PD-L1 expression in breast invasive ductal carcinoma with incomplete pathological response to neoadjuvant chemotherapy
    Ahmad Alhesa, Heyam Awad, Sarah Bloukh, Mahmoud Al-Balas, Mohammed El-Sadoni, Duaa Qattan, Bilal Azab, Tareq Saleh
    International Journal of Immunopathology and Pharmacology.2022;[Epub]     CrossRef
  • Circulating proteins as predictive and prognostic biomarkers in breast cancer
    Hugo Veyssière, Yannick Bidet, Frederique Penault-Llorca, Nina Radosevic-Robin, Xavier Durando
    Clinical Proteomics.2022;[Epub]     CrossRef
  • Targeting the antigen processing and presentation pathway to overcome resistance to immune checkpoint therapy
    Silvia D’Amico, Patrizia Tempora, Ombretta Melaiu, Valeria Lucarini, Loredana Cifaldi, Franco Locatelli, Doriana Fruci
    Frontiers in Immunology.2022;[Epub]     CrossRef
  • Programmed death-ligand 1 (PD-L1) expression predicts response to neoadjuvant chemotherapy in triple-negative breast cancer: a systematic review and meta-analysis
    Hamdy A. Azim, Kyrillus S. Shohdy, Hagar Elghazawy, Monica M. Salib, Doaa Almeldin, Loay Kassem
    Biomarkers.2022; 27(8): 764.     CrossRef
  • Determining Factors in the Therapeutic Success of Checkpoint Immunotherapies against PD-L1 in Breast Cancer: A Focus on Epithelial-Mesenchymal Transition Activation
    Mariana Segovia-Mendoza, Susana Romero-Garcia, Cristina Lemini, Heriberto Prado-Garcia, francesca bianchi
    Journal of Immunology Research.2021; 2021: 1.     CrossRef
  • Kaiso (ZBTB33) subcellular partitioning functionally links LC3A/B, the tumor microenvironment, and breast cancer survival
    Sandeep K. Singhal, Jung S. Byun, Samson Park, Tingfen Yan, Ryan Yancey, Ambar Caban, Sara Gil Hernandez, Stephen M. Hewitt, Heike Boisvert, Stephanie Hennek, Mark Bobrow, Md Shakir Uddin Ahmed, Jason White, Clayton Yates, Andrew Aukerman, Rami Vanguri, R
    Communications Biology.2021;[Epub]     CrossRef
  • Relationship Between the Neutrophil to Lymphocyte Ratio, Stromal Tumor-infiltrating Lymphocytes, and the Prognosis and Response to Neoadjuvant Chemotherapy in Triple-negative Breast Cancer
    Jian Pang, Haiyan Zhou, Xue Dong, Shouman Wang, Zhi Xiao
    Clinical Breast Cancer.2021; 21(6): e681.     CrossRef
  • Immune cell composition and functional marker dynamics from multiplexed immunohistochemistry to predict response to neoadjuvant chemotherapy in the WSG-ADAPT-TN trial
    Monika Graeser, Friedrich Feuerhake, Oleg Gluz, Valery Volk, Michael Hauptmann, Katarzyna Jozwiak, Matthias Christgen, Sherko Kuemmel, Eva-Maria Grischke, Helmut Forstbauer, Michael Braun, Mathias Warm, John Hackmann, Christoph Uleer, Bahriye Aktas, Claud
    Journal for ImmunoTherapy of Cancer.2021; 9(5): e002198.     CrossRef
  • Clinicopathological and prognostic significance of programmed cell death ligand 1 expression in patients diagnosed with breast cancer: meta-analysis
    M G Davey, É J Ryan, M S Davey, A J Lowery, N Miller, M J Kerin
    British Journal of Surgery.2021; 108(6): 622.     CrossRef
  • INSTIGO Trial: Evaluation of a Plasma Protein Profile as a Predictive Biomarker for Metastatic Relapse of Triple Negative Breast Cancer
    Hugo Veyssière, Sejdi Lusho, Ioana Molnar, Myriam Kossai, Maureen Bernadach, Catherine Abrial, Yannick Bidet, Nina Radosevic-Robin, Xavier Durando
    Frontiers in Oncology.2021;[Epub]     CrossRef
  • Inhibitors of PD-1/PD-L1 and ERK1/2 impede the proliferation of receptor positive and triple-negative breast cancer cell lines
    Karen Bräutigam, Elodie Kabore-Wolff, Ahmad Fawzi Hussain, Stephan Polack, Achim Rody, Lars Hanker, Frank Köster
    Journal of Cancer Research and Clinical Oncology.2021; 147(10): 2923.     CrossRef
  • Prognostic Relevance of Neutrophil to Lymphocyte Ratio (NLR) in Luminal Breast Cancer: A Retrospective Analysis in the Neoadjuvant Setting
    Antonino Grassadonia, Vincenzo Graziano, Laura Iezzi, Patrizia Vici, Maddalena Barba, Laura Pizzuti, Giuseppe Cicero, Eriseld Krasniqi, Marco Mazzotta, Daniele Marinelli, Antonella Amodio, Clara Natoli, Nicola Tinari
    Cells.2021; 10(7): 1685.     CrossRef
  • Time-Sequencing of the Neutrophil-to-Lymphocyte Ratio to Predict Prognosis of Triple-Negative Breast Cancer
    Joo-Heung Kim, Nak-Hoon Son, Jun-Sang Lee, Ji-Eun Mun, Jee-Ye Kim, Hyung-Seok Park, Seho Park, Seung-Il Kim, Byeong-Woo Park
    Cancers.2021; 13(14): 3472.     CrossRef
  • Platelet-to-Lymphocyte Ratio Is Associated With Favorable Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer: A Study on 120 Patients
    Sejdi Lusho, Xavier Durando, Marie-Ange Mouret-Reynier, Myriam Kossai, Nathalie Lacrampe, Ioana Molnar, Frederique Penault-Llorca, Nina Radosevic-Robin, Catherine Abrial
    Frontiers in Oncology.2021;[Epub]     CrossRef
  • Role of neutrophil-to-lymphocyte ratio as a prognostic biomarker in patients with breast cancer receiving neoadjuvant chemotherapy: a meta-analysis
    Qiong Zhou, Jie Dong, Qingqing Sun, Nannan Lu, Yueyin Pan, Xinghua Han
    BMJ Open.2021; 11(9): e047957.     CrossRef
  • Neutrophil to Lymphocyte Ratio after Treatment Completion as a Potential Predictor of Survival in Patients with Triple-Negative Breast Cancer
    Kwang-Min Kim, Hyang Suk Choi, Hany Noh, In-Jeong Cho, Seung Taek Lim, Jong-In Lee, Airi Han
    Journal of Breast Cancer.2021; 24(5): 443.     CrossRef
  • Tumor Microenvironment Characterization in Breast Cancer Identifies Prognostic and Neoadjuvant Chemotherapy Relevant Signatures
    Fei Ji, Jiao-Mei Yuan, Hong-Fei Gao, Ai-Qi Xu, Zheng Yang, Ci-Qiu Yang, Liu-Lu Zhang, Mei Yang, Jie-Qing Li, Teng Zhu, Min-Yi Cheng, Si-Yan Wu, Kun Wang
    Frontiers in Molecular Biosciences.2021;[Epub]     CrossRef
  • Neutrophil to Lymphocyte Ratio as Prognostic and Predictive Factor in Breast Cancer Patients: A Systematic Review
    Iléana Corbeau, William Jacot, Séverine Guiu
    Cancers.2020; 12(4): 958.     CrossRef
  • A Rosetta Stone for Breast Cancer: Prognostic Value and Dynamic Regulation of Neutrophil in Tumor Microenvironment
    Wei Zhang, Yimin Shen, Huanhuan Huang, Sheng Pan, Jingxin Jiang, Wuzhen Chen, Ting Zhang, Chao Zhang, Chao Ni
    Frontiers in Immunology.2020;[Epub]     CrossRef
  • Perspectives on Triple-Negative Breast Cancer: Current Treatment Strategies, Unmet Needs, and Potential Targets for Future Therapies
    Gagan K. Gupta, Amber L. Collier, Dasom Lee, Richard A. Hoefer, Vasilena Zheleva, Lauren L. Siewertsz van Reesema, Angela M. Tang-Tan, Mary L. Guye, David Z. Chang, Janet S. Winston, Billur Samli, Rick J. Jansen, Emanuel F. Petricoin, Matthew P. Goetz, Ha
    Cancers.2020; 12(9): 2392.     CrossRef
  • Prognostic Role and Clinical Significance of Tumor-Infiltrating Lymphocyte (TIL) and Programmed Death Ligand 1 (PD-L1) Expression in Triple-Negative Breast Cancer (TNBC): A Systematic Review and Meta-Analysis Study
    Parisa Lotfinejad, Mohammad Asghari Jafarabadi, Mahdi Abdoli Shadbad, Tohid Kazemi, Fariba Pashazadeh, Siamak Sandoghchian Shotorbani, Farhad Jadidi Niaragh, Amir Baghbanzadeh, Nafiseh Vahed, Nicola Silvestris, Behzad Baradaran
    Diagnostics.2020; 10(9): 704.     CrossRef
  • Prediction of Late Recurrence and Distant Metastasis in Early-stage Breast Cancer: Overview of Current and Emerging Biomarkers
    A. Gouri, B. Benarba, A. Dekaken, H. Aoures, S. Benharkat
    Current Drug Targets.2020; 21(10): 1008.     CrossRef
  • Prognostic value of neutrophil‐to‐lymphocyte ratio and platelet‐to‐lymphocyte ratio for breast cancer patients: An updated meta‐analysis of 17079 individuals
    Wanying Guo, Xin Lu, Qipeng Liu, Ting Zhang, Peng Li, Weiqiang Qiao, Miao Deng
    Cancer Medicine.2019; 8(9): 4135.     CrossRef
  • Prognostic and clinicopathological value of PD-L1 expression in primary breast cancer: a meta-analysis
    Wenfa Huang, Ran Ran, Bin Shao, Huiping Li
    Breast Cancer Research and Treatment.2019; 178(1): 17.     CrossRef
  • Comparison of Preoperative Neutrophil-Lymphocyte and Platelet-Lymphocyte Ratios in Bladder Cancer Patients Undergoing Radical Cystectomy
    Ruiliang Wang, Yang Yan, Shenghua Liu, Xudong Yao
    BioMed Research International.2019; 2019: 1.     CrossRef
  • Immunotherapy in HER2-positive breast cancer: state of the art and future perspectives
    E. Krasniqi, G. Barchiesi, L. Pizzuti, M. Mazzotta, A. Venuti, M. Maugeri-Saccà, G. Sanguineti, G. Massimiani, D. Sergi, S. Carpano, P. Marchetti, S. Tomao, T. Gamucci, R. De Maria, F. Tomao, C. Natoli, N. Tinari, G. Ciliberto, M. Barba, P. Vici
    Journal of Hematology & Oncology.2019;[Epub]     CrossRef
  • Prognostic Implications of PD-L1 Expression in Breast Cancer: Systematic Review and Meta-analysis of Immunohistochemistry and Pooled Analysis of Transcriptomic Data
    Alexios Matikas, Ioannis Zerdes, John Lövrot, François Richard, Christos Sotiriou, Jonas Bergh, Antonios Valachis, Theodoros Foukakis
    Clinical Cancer Research.2019; 25(18): 5717.     CrossRef
  • 11,516 View
  • 571 Download
  • 56 Web of Science
  • 46 Crossref
Close layer
Expression of Immunoproteasome Subunit LMP7 in Breast Cancer and Its Association with Immune-Related Markers
Miseon Lee, In Hye Song, Sun-Hee Heo, Young-Ae Kim, In Ah Park, Won Seon Bang, Hye Seon Park, Gyungyub Gong, Hee Jin Lee
Cancer Res Treat. 2019;51(1):80-89.   Published online February 26, 2018
DOI: https://doi.org/10.4143/crt.2017.500
AbstractAbstract PDFPubReaderePub
Purpose
In the presence of interferon, proteasome subunits are replaced by their inducible counterparts to form an immunoproteasome (IP) plays a key role in generation of antigenic peptides presented by MHC class I molecules, leading to elicitation of a T cell‒mediated immune response. Although the roles of IP in other cancers, and inflammatory diseases have been extensively studied, its significance in breast cancer is unclear.
Materials and Methods
We investigated the expression of LMP7, an IP subunit, and its relationship with immune system components in two breast cancer cohorts.
Results
In 668 consecutive breast cancer cohort, 40% of tumors showed high level of LMP7 expression, and tumors with high expression of LMP7 had more tumor-infiltrating lymphocytes (TILs) in each subtype of breast cancer. In another cohort of 681 triple-negative breast cancer patients cohort, the expression of LMP7 in tumor cells was significantly correlated with the amount of TILs and the expression of interferon-associated molecules (MxA [p < 0.001] and PKR [p < 0.001]), endoplasmic reticulum stress-associated molecules (PERK [p=0.012], p-eIF2a [p=0.001], and XBP1 [p < 0.001]), and damage-associated molecular patterns (HMGN1 [p < 0.001] and HMGB1 [p < 0.001]). Patients with higher LMP7 expression had better disease-free survival outcomes than those with no or low expression in the positive lymph node metastasis group (p=0.041).
Conclusion
Close association between the TIL levels and LMP7 expression in breast cancer indicates that better antigen presentation through greater LMP7 expression might be associated with more TILs.

Citations

Citations to this article as recorded by  
  • Association of Proteasome Activity and Pool Heterogeneity with Markers Determining the Molecular Subtypes of Breast Cancer
    Irina Kondakova, Elena Sereda, Evgeniya Sidenko, Sergey Vtorushin, Valeria Vedernikova, Alexander Burov, Pavel Spirin, Vladimir Prassolov, Timofey Lebedev, Alexey Morozov, Vadim Karpov
    Cancers.2025; 17(1): 159.     CrossRef
  • Multikinase inhibitors modulate non-constitutive proteasome expression in colorectal cancer cells
    Alexander Burov, Ekaterina Grigorieva, Timofey Lebedev, Valeria Vedernikova, Vladimir Popenko, Tatiana Astakhova, Olga Leonova, Pavel Spirin, Vladimir Prassolov, Vadim Karpov, Alexey Morozov
    Frontiers in Molecular Biosciences.2024;[Epub]     CrossRef
  • Upregulation of MHC I antigen processing machinery gene expression in breast cancer cells by Trichostatin A
    A. H. Murtadha, N. A. Sharudin, I. I.M. Azahar, A. T. Che Has, N. F. Mokhtar
    Молекулярная биология.2024; 58(1): 121.     CrossRef
  • Immunoproteasome acted as immunotherapy ‘coffee companion’ in advanced carcinoma therapy
    Shaoyan Shi, Xuehai Ou, Chao Liu, Hao Wen, Ke Jiang
    Frontiers in Immunology.2024;[Epub]     CrossRef
  • Immunoediting in acute myeloid leukemia: Reappraising T cell exhaustion and the aberrant antigen processing machinery in leukemogenesis
    Ching-Yun Wang, Shiuan-Chen Lin, Kao-Jung Chang, Han-Ping Cheong, Sin-Rong Wu, Cheng-Hao Lee, Ming-Wei Chuang, Shih-Hwa Chiou, Chih-Hung Hsu, Po-Shen Ko
    Heliyon.2024; 10(21): e39731.     CrossRef
  • ONX0914 inhibition of immunoproteasome subunit LMP7 ameliorates diabetic cardiomyopathy via restraining endothelial–mesenchymal transition
    Mengwen Wang, Yujian Liu, Lei Dai, Xiaodan Zhong, Wenjun Zhang, Yang Xie, Hesong Zeng, Hongjie Wang
    Clinical Science.2023; 137(16): 1297.     CrossRef
  • The dichotomous role of immunoproteasome in cancer: Friend or foe?
    Boya Chen, Haiying Zhu, Bo Yang, Ji Cao
    Acta Pharmaceutica Sinica B.2023; 13(5): 1976.     CrossRef
  • Cancer Immunology: Immune Escape of Tumors—Expression and Regulation of HLA Class I Molecules and Its Role in Immunotherapies
    Yuan Wang, Simon Jasinski-Bergner, Claudia Wickenhauser, Barbara Seliger
    Advances in Anatomic Pathology.2023; 30(3): 148.     CrossRef
  • Increased expression of the immunoproteasome subunits PSMB8 and PSMB9 by cancer cells correlate with better outcomes for triple-negative breast cancers
    Karen Geoffroy, Bruna Araripe Saraiva, Melissa Viens, Delphine Béland, Marie-Claude Bourgeois-Daigneault
    Scientific Reports.2023;[Epub]     CrossRef
  • Adaptation of the Tumor Antigen Presentation Machinery to Ionizing Radiation
    Mi-Heon Lee, Duang Ratanachan, Zitian Wang, Jacob Hack, Lobna Adbulrahman, Nicholas P. Shamlin, Mirna Kalayjian, Jean Philippe Nesseler, Ekambaram Ganapathy, Christine Nguyen, Josephine A. Ratikan, Nicolas A. Cacalano, David Austin, Robert Damoiseaux, Ben
    The Journal of Immunology.2023; 211(4): 693.     CrossRef
  • Upregulation of MHC I Antigen Processing Machinery Gene Expression in Breast Cancer Cells by Trichostatin A
    A. H. Murtadha, N. A. Sharudin, I. I. M. Azahar, A. T. Che Has, N. F. Mokhtar
    Molecular Biology.2023; 57(6): 1212.     CrossRef
  • Emodin alleviates high glucose-induced oxidative stress, inflammation and extracellular matrix accumulation of mesangial cells by the circ_0000064/miR-30c-5p/Lmp7 axis
    Li Sun, Yanquan Han, Chuqiao Shen, Huan Luo, Zhuo Wang
    Journal of Receptors and Signal Transduction.2022; 42(3): 302.     CrossRef
  • Methylation of Immune-Related Genes in Peripheral Blood Leukocytes and Breast Cancer
    Tian Tian, JinMing Fu, DaPeng Li, YuPeng Liu, HongRu Sun, Xuan Wang, XianYu Zhang, Ding Zhang, Ting Zheng, Yashuang Zhao, Da Pang
    Frontiers in Oncology.2022;[Epub]     CrossRef
  • Analysis on methylation and expression of PSMB8 and its correlation with immunity and immunotherapy in lung adenocarcinoma
    Tongji Xie, Guangyu Fan, Liling Huang, Ning Lou, Xiaohong Han, Puyuan Xing, Yuankai Shi
    Epigenomics.2022; 14(22): 1427.     CrossRef
  • A few good peptides: MHC class I-based cancer immunosurveillance and immunoevasion
    Devin Dersh, Jaroslav Hollý, Jonathan W. Yewdell
    Nature Reviews Immunology.2021; 21(2): 116.     CrossRef
  • Expression of the immunoproteasome subunit β5i in non-small cell lung carcinomas
    Takayuki Kiuchi, Utano Tomaru, Akihiro Ishizu, Makoto Imagawa, Sari Iwasaki, Akira Suzuki, Noriyuki Otsuka, Yoshihito Ohhara, Ichiro Kinoshita, Yoshihiro Matsuno, Hirotoshi Dosaka-Akita, Masanori Kasahara
    Journal of Clinical Pathology.2021; 74(5): 300.     CrossRef
  • High immunoproteasome concentration in the plasma of patients with newly diagnosed multiple myeloma treated with bortezomib is predictive of longer OS
    Wioletta Breczko, Dorota Lemancewicz, Janusz Dzięcioł, Janusz Kłoczko, Łukasz Bołkun
    Advances in Medical Sciences.2021; 66(1): 21.     CrossRef
  • Methods for the Discovery of Small Molecules to Monitor and Perturb the Activity of the Human Proteasome
    Marianne E Maresh, Andres F Salazar-Chaparro, Darci J Trader
    Future Medicinal Chemistry.2021; 13(2): 99.     CrossRef
  • Immunoproteasome Function in Normal and Malignant Hematopoiesis
    Nuria Tubío-Santamaría, Frédéric Ebstein, Florian H. Heidel, Elke Krüger
    Cells.2021; 10(7): 1577.     CrossRef
  • Integrated genomic analysis of proteasome alterations across 11,057 patients with 33 cancer types: clinically relevant outcomes in framework of 3P medicine
    Na Li, Xianquan Zhan
    EPMA Journal.2021; 12(4): 605.     CrossRef
  • The Functional and Mechanistic Roles of Immunoproteasome Subunits in Cancer
    Satyendra Chandra Tripathi, Disha Vedpathak, Edwin Justin Ostrin
    Cells.2021; 10(12): 3587.     CrossRef
  • The Immunoproteasome: An Emerging Target in Cancer and Autoimmune and Neurological Disorders
    Breanna L. Zerfas, Marianne E. Maresh, Darci J. Trader
    Journal of Medicinal Chemistry.2020; 63(5): 1841.     CrossRef
  • Differential prognostic impact of CD8+ T cells based on human leucocyte antigen I and PD-L1 expression in microsatellite-unstable gastric cancer
    Yoonjin Kwak, Jiwon Koh, Yujun Park, Yun Ji Hong, Kyoung Un Park, Hyung-Ho Kim, Do Joong Park, Sang-Hoon Ahn, Woo Ho Kim, Hye Seung Lee
    British Journal of Cancer.2020; 122(9): 1399.     CrossRef
  • Tradeoff between metabolic i-proteasome addiction and immune evasion in triple-negative breast cancer
    Alaknanda Adwal, Priyakshi Kalita-de Croft, Reshma Shakya, Malcolm Lim, Emarene Kalaw, Lucinda D Taege, Amy E McCart Reed, Sunil R Lakhani, David F Callen, Jodi M Saunus
    Life Science Alliance.2020; 3(7): e201900562.     CrossRef
  • Targeting purine synthesis in ASS1-expressing tumors enhances the response to immune checkpoint inhibitors
    Rom Keshet, Joo Sang Lee, Lital Adler, Muhammed Iraqi, Yarden Ariav, Lisha Qiu Jin Lim, Shaul Lerner, Shiran Rabinovich, Roni Oren, Rotem Katzir, Hila Weiss Tishler, Noa Stettner, Omer Goldman, Hadas Landesman, Sivan Galai, Yael Kuperman, Yuri Kuznetsov,
    Nature Cancer.2020; 1(9): 894.     CrossRef
  • Proteasomes and Several Aspects of Their Heterogeneity Relevant to Cancer
    Alexey V. Morozov, Vadim L. Karpov
    Frontiers in Oncology.2019;[Epub]     CrossRef
  • Protein Barcodes Enable High-Dimensional Single-Cell CRISPR Screens
    Aleksandra Wroblewska, Maxime Dhainaut, Benjamin Ben-Zvi, Samuel A. Rose, Eun Sook Park, El-Ad David Amir, Anela Bektesevic, Alessia Baccarini, Miriam Merad, Adeeb H. Rahman, Brian D. Brown
    Cell.2018; 175(4): 1141.     CrossRef
  • 9,332 View
  • 315 Download
  • 24 Web of Science
  • 27 Crossref
Close layer
Expression of Myxovirus Resistance A (MxA) Is Associated with Tumor-Infiltrating Lymphocytes in Human Epidermal Growth Factor Receptor 2 (HER2)–Positive Breast Cancers
So Jeong Lee, Cheong-Soo Hwang, Young-Keum Kim, Hyun Jung Lee, Sang-Jeong Ahn, Nari Shin, Jung Hee Lee, Dong Hoon Shin, Kyung Un Choi, Do Youn Park, Chang Hun Lee, Gi Young Huh, Mi Young Sol, Hee Jin Lee, Gyungyub Gong, Jee Yeon Kim, Ahrong Kim
Cancer Res Treat. 2017;49(2):313-321.   Published online July 7, 2016
DOI: https://doi.org/10.4143/crt.2016.098
AbstractAbstract PDFPubReaderePub
Purpose
The prognostic significance of tumor-infiltrating lymphocytes (TILs) has been determined in breast cancers. Interferons can affect T-cell activity through direct and indirect mechanisms. Myxovirus resistance A (MxA) is an excellent marker of interferon activity. Here,we evaluated TILs and MxA expression in human epidermal growth factor receptor 2 (HER2)–positive breast cancers.
Materials and Methods
Ninety cases of hormone receptor (HR)+/HER2+ tumors and 78 cases of HR–/HER2+ tumors were included. The TILs level was assessed using hematoxylin and eosin–stained full face sections, and MxA expressionwas evaluated by immunohistochemistrywith a tissue microarray.
Results
MxA protein expression was significantly higher in tumors with high histologic grade (p=0.023) and high levels of TILs (p=0.002). High levels of TILs were correlated with high histological grade (p=0.001), negative lymphovascular invasion (p=0.007), negative lymph node metastasis (p=0.007), absence of HR expression (p < 0.001), abundant tertiary lymphoid structures (TLSs) around ductal carcinoma in situ (p=0.018), and abundant TLSs around the invasive component (p < 0.001). High levels of TILs were also associated with improved disease-free survival, particularly in HR–/HER2+ breast cancers. However, MxA was not a prognostic factor.
Conclusion
High expression of MxA in tumor cells was associated with high levels of TILs in HER2-positive breast cancers. Additionally, a high level of TILs was a prognostic factor for breast cancer, whereas the level of MxA expression had no prognostic value.

Citations

Citations to this article as recorded by  
  • Multi-resolution deep learning characterizes tertiary lymphoid structures and their prognostic relevance in solid tumors
    Mart van Rijthoven, Simon Obahor, Fabio Pagliarulo, Maries van den Broek, Peter Schraml, Holger Moch, Jeroen van der Laak, Francesco Ciompi, Karina Silina
    Communications Medicine.2024;[Epub]     CrossRef
  • The roles of tertiary lymphoid structures in chronic diseases
    Yuki Sato, Karina Silina, Maries van den Broek, Kiyoshi Hirahara, Motoko Yanagita
    Nature Reviews Nephrology.2023; 19(8): 525.     CrossRef
  • NFIC1 suppresses migration and invasion of breast cancer cells through interferon-mediated Jak-STAT pathway
    Jing Zhang, Mingyue Fan, Chanjuan Jin, Zhaoying Wang, Yutong Yao, Yueru Shi, Xin Hu, Youzhong Wan
    Archives of Biochemistry and Biophysics.2022; 727: 109346.     CrossRef
  • Low MxA Expression Predicts Better Immunotherapeutic Outcomes in Glioblastoma Patients Receiving Heat Shock Protein Peptide Complex 96 Vaccination
    Yi Wang, Chunzhao Li, Xiaohan Chi, Xijian Huang, Hua Gao, Nan Ji, Yang Zhang
    Frontiers in Oncology.2022;[Epub]     CrossRef
  • Myxovirus resistance 1 (MX1) is an independent predictor of poor outcome in invasive breast cancer
    Abrar I. Aljohani, Chitra Joseph, Sasagu Kurozumi, Omar J. Mohammed, Islam M. Miligy, Andrew R. Green, Emad A. Rakha
    Breast Cancer Research and Treatment.2020; 181(3): 541.     CrossRef
  • Expression of Immunoproteasome Subunit LMP7 in Breast Cancer and Its Association with Immune-Related Markers
    Miseon Lee, In Hye Song, Sun-Hee Heo, Young-Ae Kim, In Ah Park, Won Seon Bang, Hye Seon Park, Gyungyub Gong, Hee Jin Lee
    Cancer Research and Treatment.2019; 51(1): 80.     CrossRef
  • Grade II/III Glioma Microenvironment Mining and Its Prognostic Merit
    Jiawei Chen, Chongxian Hou, Peng Wang, Yong Yang, Dong Zhou
    World Neurosurgery.2019; 132: e76.     CrossRef
  • Programmed death-ligand 1 (PD-L1) expression in tumour cell and tumour infiltrating lymphocytes of HER2-positive breast cancer and its prognostic value
    Ahrong Kim, So Jeong Lee, Young Keum Kim, Won Young Park, Do Youn Park, Jee Yeon Kim, Chang Hun Lee, Gyungyub Gong, Gi Yeong Huh, Kyung Un Choi
    Scientific Reports.2017;[Epub]     CrossRef
  • 11,539 View
  • 273 Download
  • 8 Web of Science
  • 8 Crossref
Close layer

Cancer Res Treat : Cancer Research and Treatment
Close layer
TOP