Research Paper Volume 15, Issue 22 pp 12890—12906

TJP3 promotes T cell immunity escape and chemoresistance in breast cancer: a comprehensive analysis of anoikis-based prognosis prediction and drug sensitivity stratification

Liu Chaojun1, *, , Li Pengping3, *, , Li Yanjun2, , Zhu Fangyuan1, , He Yaning1, , Shao Yingbo1, , Chen Qi1, , Liu Hui1, ,

  • 1 Department of Breast Surgery, Henan Provincial People’s Hospital; People’s Hospital of Zhengzhou University, People’s Hospital of Henan University, Zhengzhou, Henan 450003, China
  • 2 Center for Clinical Single-Cell Biomedicine, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, School of Clinical Medicine, Henan University, Zhengzhou, Henan 450003, China
  • 3 Breast Surgery, The First People’s Hospital of Xiaoshan District, Zhejiang, Hangzhou 311000, China
* Equal contribution

Received: July 4, 2023       Accepted: October 12, 2023       Published: November 10, 2023
How to Cite

Copyright: © 2023 Chaojun et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Background: Overcoming anoikis is a necessity during the metastasis and invasion of tumors. Recently, anoikis has been reported to be involved in tumor immunity and has been used to construct prognosis prediction models. However, the roles of anoikis in regulating tumor immunity and drug sensitivity in breast cancer are still not clear and therefore worth uncovering.

Methods: TCGA and GEO data are the source of gene expression profiles, which are used to identify anoikis-related-gene (ARG)-based subtypes. R4.2 is used for data analysis.

Results: Breast cancer is divided into three subgroups, amongst which shows prognosis differences in pan-cancer cohort, ACC, BLCA, BRCA, LUAD, MESO, PAAD, and SKCM. In breast cancer, it shows significant differences in clinical features, immune cell infiltration and drug sensitivity. Machine learning constructs prognosis prediction model, which is useful to perform chemotherapy sensitivity stratification. Following, TJP3 is identified and verified as the key ARG, up-regulation of which increases tolerance of paclitaxel-induced cell toxicity, accompanied with increased expression of caspas3 and cleaved-caspase3. In addition, Down-regulation of TJP3 weakens the cell migration, which accompanied with increased expression of E-cad and decreased expression of vimentin, twist1, zeb1, and MMP7. Furthermore, the expression level of PD-L1 is negative correlated with TJP3.

Conclusion: ARGs-based subgroup stratification is useful to recognize chemotherapy sensitive cohort, and also is useful to predict clinical outcome. TJP3 promotes chemoresistance, tumor metastasis and potential immunotherapy escape in breast cancer.


ACC: Adrenocortical Carcinoma; BLCA: Bladder Urothelial Carcinoma; BRCA: Breast Invasive Carcinoma; CESC: Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma; CHOL: Cholangiocarcinoma; COAD: Colon Adenocarcinoma; DLBC: Lymphoid Neoplasm Diffuse Large B-cell Lymphoma; ESCA: Esophageal Carcinoma; GBM: Glioblastoma Multiforme; HNSC: Head and Neck Squamous Cell Carcinoma; KICH: Kidney Chromophobe; KIRC: Kidney Renal Clear Cell Carcinoma; KIRP: Kidney Renal Papillary Cell Carcinoma; LAML: Acute Myeloid Leukemia; LGG: Brain Lower Grade Glioma; LIHC: Liver Hepatocellular Carcinoma; LUAD: Lung Adenocarcinoma; LUSC: Lung Squamous Cell Carcinoma; MESO: Mesothelioma; OV: Ovarian Serous Cystadenocarcinoma; PAAD: Pancreatic Adenocarcinoma; PCPG: Pheochromocytoma and Paraganglioma; PRAD: Prostate Adenocarcinoma; READ: Rectum Adenocarcinoma; SARC: Sarcoma; SKCM: Skin Cutaneous Melanoma; STAD: Stomach Adenocarcinoma; TGCT: Testicular Germ Cell Tumors; THCA: Thyroid Carcinoma; THYM: Thymoma; UCEC: Uterine Corpus Endometrial Carcinoma; UCS: Uterine Carcinosarcoma; UVM: Uveal Melanoma; ARGS: Anoikis-related gene; AI: Artificial intelligence; LASSO: Least Absolute Shrinkage and Selection Operator; ROC: Receptor operation curve; TCGA: The Cancer Genome Atlas; GEO: Gene Expression Omnibus; UALCAN: The University Alabama at Birmingham Cancer data analysis Portal; THPA: The Human Protein Atlas; PCAWG: Pan-Cancer Analysis of Whole Genomes; IHC: Immunohistochemical staining; MMP7: Matrix metalloproteinase 7; TJP3: Tight junction protein 3; E-cad: E-cadherin; SVM: Support Vector Machine; XGboost: Extreme Gradient Boosting; DL: Deep learning; ssGSEA: Simple sample Gene Set Enrichment Analysis.