Plasma cell signatures predict prognosis and treatment efficacy for lung adenocarcinoma

Long Shu; Jun Tang; Shuang Liu; Yongguang Tao

doi:10.1007/s13402-023-00883-w

Plasma cell signatures predict prognosis and treatment efficacy for lung adenocarcinoma

Cell Oncol (Dordr). 2023 Oct 9. doi: 10.1007/s13402-023-00883-w. Online ahead of print.

Authors

Long Shu^{1

2}, Jun Tang², Shuang Liu³, Yongguang Tao^{4

5

6

7}

Affiliations

¹ Hunan Key Laboratory of Cancer Metabolism, The Affiliated Cancer Hospital of Xiangya School of Medicine, Hunan Cancer Hospital, Central South University, Changsha, Hunan, China.
² NHC Key Laboratory of Carcinogenesis (Central South University), Cancer Research Institute, School of Basic Medicine, Central South University, Changsha, 410078, Hunan, China.
³ Department of Oncology, Institute of Medical Sciences, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China. shuangliu2016@csu.edu.cn.
⁴ Hunan Key Laboratory of Cancer Metabolism, The Affiliated Cancer Hospital of Xiangya School of Medicine, Hunan Cancer Hospital, Central South University, Changsha, Hunan, China. taoyong@csu.edu.cn.
⁵ Department of Oncology, Institute of Medical Sciences, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China. taoyong@csu.edu.cn.
⁶ Key Laboratory of Carcinogenesis and Cancer Invasion, Department of Pathology, Xiangya Hospital, School of Basic Medicine, Ministry of Education, Central South University, Changsha, 410078, Hunan, China. taoyong@csu.edu.cn.
⁷ Hunan Key Laboratory of Early Diagnosis and Precision Therapy in Lung Cancer, Department of Thoracic Surgery, Second Xiangya Hospital, Central South University, Changsha, 410011, China. taoyong@csu.edu.cn.

PMID: 37814076
DOI: 10.1007/s13402-023-00883-w

Abstract

Purpose: This study aims to identify key genes regulating tumor infiltrating plasma cells (PC) and provide new insights for innovative immunotherapy.

Methods: Key genes related to PC were identified using machine learning in lung adenocarcinoma (LUAD) patients. A prognostic model called PC scores was developed using TCGA data and validated with GEO cohorts. We assessed the molecular background, immune features, and drug sensitivity of the high PC scores group. Real-time PCR was utilized to assess the expression of hub genes in both localized LUAD patients and LUAD cell lines.

Results: We constructed PC scores based on seventeen PC-related hub genes (ELOVL6, MFI2, FURIN, DOK1, ERO1LB, CLEC7A, ZNF431, KIAA1324, NUCB2, TXNDC11, ICAM3, CR2, CLIC6, CARNS1, P2RY13, KLF15, and SLC24A4). Higher age, TNM stage, and PC scores independently predicted shorter overall survival. The AUC value of PC scores for one year, three years, and five years of overall survival were 0.713, 0.716, and 0.690, separately. The nomogram model that integrated age, stage, and PC scores showed significantly higher predictive value than stage alone (P < 0.01). High PC scores group exhibited an immune suppressing microenvironment with lower B, CD8 + T, CD4 + T, and dendritic cell infiltration. Docetaxel, gefitinib, and erlotinib had lower IC50 in high PC groups (P < 0.001). After validation through the local cohort and in vitro experiments, we ultimately confirmed three key potential targets: MFI2, KLF15, and CLEC7A.

Conclusion: We proposed a prediction mode which can effectively identify high-risk LUAD patients and found three novel genes closely correlated with PC tumor infiltration.

Keywords: Lung adenocarcinoma; Machine learning; Overall survival; Plasma cells.

Abstract

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