Identification of a tumor immune-inflammation signature predicting prognosis and immune status in breast cancer

Yajing Liu; Wenhao Ouyang; Hong Huang; Yujie Tan; Zebang Zhang; Yunfang Yu; Herui Yao

doi:10.3389/fonc.2022.960579

Identification of a tumor immune-inflammation signature predicting prognosis and immune status in breast cancer

Front Oncol. 2023 Jan 12:12:960579. doi: 10.3389/fonc.2022.960579. eCollection 2022.

Authors

Yajing Liu¹, Wenhao Ouyang¹, Hong Huang², Yujie Tan¹, Zebang Zhang¹, Yunfang Yu^{1

3}, Herui Yao¹

Affiliations

¹ Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Department of Medical Oncology, Breast Tumor Center, Phase I Clinical Trial Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
² School of Medicine, Guilin Medical College, Guilin, China.
³ Faculty of Medicine, Macau University of Science and Technology, Taipa, Macao SAR, China.

Abstract

Background: Breast cancer has become the malignancy with the highest mortality rate in female patients worldwide. The limited efficacy of immunotherapy as a breast cancer treatment has fueled the development of research on the tumor immune microenvironment.

Methods: In this study, data on breast cancer patients were collected from The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) cohorts. Differential gene expression analysis, univariate Cox regression analysis, and least absolute shrinkage and selection operator (LASSO) Cox regression analysis were performed to select overall survival (OS)-related, tumor tissue highly expressed, and immune- and inflammation-related genes. A tumor immune-inflammation signature (TIIS) consisting of 18 genes was finally screened out in the LASSO Cox regression model. Model performance was assessed by time-dependent receiver operating characteristic (ROC) curves. In addition, the CIBERSORT algorithm and abundant expression of immune checkpoints were utilized to clarify the correlation between the risk signature and immune landscape in breast cancer. Furthermore, the association of IL27 with the immune signature was analyzed in pan-cancer and the effect of IL27 on the migration of breast cancer cells was investigated since the regression coefficient of IL27 was the highest.

Results: A TIIS based on 18 genes was constructed via LASSO Cox regression analysis. In the TCGA-BRCA training cohort, 10-year AUC reached 0.89, and prediction performance of this signature was also validated in the METABRIC set. The high-risk group was significantly correlated with less infiltration of tumor-killing immune cells and the lower expression level of the immune checkpoint. Furthermore, we recommended some small-molecule drugs as novel targeted drugs for new breast cancer types. Finally, the relationship between IL27, a significant prognostic immune and inflammation cytokine, and immune status was analyzed in pan-cancer. Expression of IL27 was significantly correlated with immune regulatory gene expression and immune cell infiltration in pan-cancer. Furthermore, IL27 treatment improved breast cancer cell migration.

Conclusion: The TIIS represents a promising prognostic tool for estimating OS in patients with breast cancer and is correlated with immune status.

Keywords: IL27; breast cancer; prognostic prediction; tumor immune microenvironment; tumor immune-inflammation signature.

Grants and funding

This study was supported by grants 81972471 and 82073408 from the National Natural Science Foundation of China, grants 202206010078 and 202201020574 from the Guangzhou Science and Technology Project, grant 2018007 from the Sun Yat-Sen University Clinical Research 5010 Program, grant SYS-C-201801 from the Sun Yat-Sen Clinical Research Cultivating Program, grant A2020558 from the Guangdong Medical Science and Technology Program, grant 7670020025 from the Tencent Charity Foundation, and grant YXQH202209 from the Scientific Research Launch Project of Sun Yat-Sen Memorial Hospital.