Open Access
ARTICLE
A Metabolism-Related Gene Signature Predicts the Prognosis of Breast Cancer Patients: Combined Analysis of High-Throughput Sequencing and Gene Chip Data Sets
Lei Hu1,2,#, Meng Chen2,3,#, Haiming Dai2,3,4, Hongzhi Wang2,3,4,*, Wulin Yang2,3,4,*
1 School of Basic Medical Sciences, Wannan Medical College, Wuhu, 241001, China
2 Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China
3 Science Island Branch, Graduate School of University of Science and Technology of China, Hefei, 230026, China
4 Department of Pathology, Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, 230031, China
* Corresponding Authors: Hongzhi Wang. Email: ; Wulin Yang. Email:
(This article belongs to the Special Issue: Biomarkers for Breast Cancer Diagnosis and Treatment Selection: from Basic Research to Practice)
Oncologie 2022, 24(4), 803-822. https://doi.org/10.32604/oncologie.2022.026419
Received 04 September 2022; Accepted 25 November 2022; Issue published 31 December 2022
Abstract
Background and Aim: Hundreds of consistently altered metabolic genes have been identified in breast cancer
(BC), but their prognostic value remains to be explored. Therefore, we aimed to build a prediction model based
on metabolism-related genes (MRGs) to guide BC prognosis.
Methods: Current work focuses on constructing a
novel MRGs signature to predict the prognosis of BC patients using MRGs derived from the Virtual Metabolic
Human (VMH) database, and expression profiles and clinicopathological data from The Cancer Genome Atlas
(TCGA) and Gene Expression Omnibus (GEO) databases.
Results: The 3-MRGs-signature constructed by SERPINA1, QPRT and PXDNL was found to be an independent prognostic factor for the survival of patients, and
based on the model, the overall survival (OS) of the high-risk group was significantly lower. Furthermore, a
nomogram was developed based on risk score and independent prognostic clinical indicators, and its validity
of survival prediction was confirmed by the calibration curve, the concordance index, decision curve analysis
and receiver operating characteristic curve. The ssGSEA analysis showed a negative correlation between immune
cell infiltration and risk score, which is consistent with the GSEA result showing that low-risk score group was
associated with activated immune processes. Half-maximal inhibitory concentration of chemotherapeutic drugs
was estimated by pRRophetic algorithm to guide clinical medication.
Conclusion: We constructed and validated
an effective 3-MRGs (SERPINA1, QPRT and PXDNL)-based prognostic model, and demonstrated that lower-risk
patients were associated with higher immune infiltrations, underscoring the importance of immune ecosystems in
determining the prognosis of BC patients.
Keywords
Cite This Article
Hu, L., Chen, M., Dai, H., Wang, H., Yang, W. (2022). A Metabolism-Related Gene Signature Predicts the Prognosis of Breast Cancer Patients: Combined Analysis of High-Throughput Sequencing and Gene Chip Data Sets.
Oncologie, 24(4), 803–822. https://doi.org/10.32604/oncologie.2022.026419