Open Access

ARTICLE

Polo-like kinase 1 as a biomarker predicts the prognosis and immunotherapy of breast invasive carcinoma patients

JUAN SHEN^1,#, WEIYU ZHANG^2,3,#, QINQIN JIN^2,3,#, FUYU GONG^4,#, HEPING ZHANG⁵, HONGLIANG XU⁵, JIEJIE LI^2,3, HUI YAO^2,3, XIYA JIANG^2,3, YINTING YANG^2,3, LIN HONG^2,3, JIE MEI^2,3, YANG SONG^6,*, SHUGUANG ZHOU^2,3,7,*

1 School of Big Data and Artificial Intelligence, Anhui Xinhua University, Hefei, 230088, China
2 Department of Gynecology and Obstetrics, Maternity and Child Healthcare Hospital Affiliated to Anhui Medical University, Anhui Province Maternity and Child Healthcare Hospital, Hefei, 230001, China
3 Department of Gynecology and Obstetrics, The Fifth Clinical College of Anhui Medical University, Hefei, 230032, China
4 Departments of Breast Surgery, Fuyang Women and Children’s Hospital, Fuyang, 236000, China
5 Departments of Pathology, Anhui Province Maternity and Child Health Hospital, Hefei, 230001, China
6 Department of Pain, The First Affiliated Hospital of Anhui Medical University, Hefei, 230032, China
7 Department of Gynecology and Obstetrics, Linquan Maternity and Child Healthcare Hospital, Fuyang, 236400, China

* Corresponding Authors: YANG SONG. Email: ; SHUGUANG ZHOU. Email:
# These authors contributed equally

Oncology Research 2024, 32(2), 339-351. https://doi.org/10.32604/or.2023.030887

Received 30 April 2023; Accepted 03 August 2023; Issue published 28 December 2023

Abstract

Background: Invasive breast carcinoma (BRCA) is associated with poor prognosis and high risk of mortality. Therefore, it is critical to identify novel biomarkers for the prognostic assessment of BRCA. Methods: The expression data of polo-like kinase 1 (PLK1) in BRCA and the corresponding clinical information were extracted from TCGA and GEO databases. PLK1 expression was validated in diverse breast cancer cell lines by quantitative real-time polymerase chain reaction (qRT-PCR) and western blotting. Single sample gene set enrichment analysis (ssGSEA) was performed to evaluate immune infiltration in the BRCA microenvironment, and the random forest (RF) and support vector machine (SVM) algorithms were used to screen for the hub infiltrating cells and calculate the immunophenoscore (IPS). The RF algorithm and COX regression model were applied to calculate survival risk scores based on the PLK1 expression and immune cell infiltration. Finally, a prognostic nomogram was constructed with the risk score and pathological stage, and its clinical potential was evaluated by plotting calibration charts and DCA curves. The application of the nomogram was further validated in an immunotherapy cohort. Results: PLK1 expression was significantly higher in the tumor samples in TCGA-BRCA cohort. Furthermore, PLK1 expression level, age and stage were identified as independent prognostic factors of BRCA. While the IPS was unaffected by PLK1 expression, the TMB and MATH scores were higher in the PLK1-high group, and the TIDE scores were higher for the PLK1-low patients. We also identified 6 immune cell types with high infiltration, along with 11 immune cell types with low infiltration in the PLK1-high tumors. A risk score was devised using PLK1 expression and hub immune cells, which predicted the prognosis of BRCA patients. In addition, a nomogram was constructed based on the risk score and pathological staging, and showed good predictive performance. Conclusions: PLK1 expression and immune cell infiltration can predict post-immunotherapy prognosis of BRCA patients.

---

Keywords

---

Supplementary Material

Supplementary Material File

---