MASAN: a novel staging system for prognosis of patients with
oesophageal squamous cell carcinoma
, Jian-zhong He
, Shao-hong Wang
, De-kai Liu
, Xue-feng Bai
, Xiu-e Xu
, Jian-yi Wu
, Yong Jiang
, Chun-quan Li
, En-min Li
and Li-yan Xu
BACKGROUND: Oesophageal squamous cell carcinoma (ESCC) is one of the most malignant cancers worldwide. Treatment of ESCC
is in progress through accurate staging and risk assessment of patients. The emergence of potential molecular markers inspired us
to construct novel staging systems with better accuracy by incorporating molecular markers.
METHODS: We measured H scores of 23 protein markers and analysed eight clinical factors of 77 ESCC patients in a training set,
from which we identiﬁed an optimal MASAN (MYC, ANO1, SLC52A3, Age and N-stage) signature. We constructed MASAN models
using Cox PH models, and created MASAN-staging systems based on k-means clustering and minimum-distance classiﬁer. MASAN
was validated in a test set (n = 77) and an independent validation set (n = 150).
RESULTS: MASAN possessed high predictive accuracies and stratiﬁed ESCC patients into three prognostic groups that were more
accurate than the current pTNM-staging system for both overall survival and disease-free survival. To facilitate clinical utilisation, we
also constructed MASAN-SI staging systems based on staining indices (SI) of protein markers, which possessed similar prognostic
performance as MASAN.
CONCLUSION: MASAN provides a good alternative staging system for ESCC prognosis with a high precision using a simple model.
British Journal of Cancer (2018) 118:1476–1484; https://doi.org/10.1038/s41416-018-0094-x
Oesophageal squamous cell carcinoma (ESCC) is the fourth
leading cause of cancer-related mortality, and approximately half
of the world’s 500,000 new ESCC cases occur annually in China.
The survival for ESCC is poor, with a 5-year overall survival (OS) of
Treatment of ESCC remains a challenging problem.
However, treatment outcomes are being improved through
accurate staging and risk assessment of patients.
staging techniques, including molecular staging, allow us to
understand prognosis and to tailor therapy to individuals to
achieve the best outcomes.
Currently, the most commonly used staging systems for ESCC is
the pTNM (pathological tumour-node metastasis) staging system
(the 7th edition) proposed by the American Joint Committee on
The AJCC pTNM system has become a standar-
dised staging system for evaluating cancer at a population level.
However, the development of molecular biology and discovery of
molecular factors that predict cancer outcome and response to
treatment with better accuracy has led cancer experts to question
the utility of the pTNM-staging system at the individual level.
Molecular factors, such as protein markers, are attracting more
and more attention and have been demonstrated to beneﬁt the
diagnosis and prognosis of ESCC. Incorporating molecular factors
into predictive models may further improve the accuracy of the
Over the past few decades, hundreds of dysregulated proteins
have been detected in ESCC patients.
Many of them were
identiﬁed to be independent prognostic factors, such as MYC,
On the other hand, some clinical character-
istics, such as N-stage, have always been predominant prognostic
factors for ESCC.
Thus, Tan et al. proposed to combine protein
markers and clinical characteristics, and built a FENSAM-staging
system, which possessed high-classiﬁcation precision similar to
the pTNM-staging system, but was much simpler for clinical use.
However, the protein markers used to build FENSAM were still
limited. The predictive power of combinations of additional newly
found protein markers needs further investigation. In addition,
with more and more variables available for building predictive
models, the anticipated predictive performance may not increase
linearly with the number of variables due to complex interactions
Received: 23 January 2018 Revised: 26 March 2018 Accepted: 3 April 2018
Published online: 16 May 2018
The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou 515041, China;
Mathematics, Heilongjiang Institute of Technology, Harbin 150050, China;
Institute of Oncologic Pathology, Shantou University Medical College, Shantou 515041, China;
Department of Pathology, Shantou Central Hospital, Afﬁliated Shantou Hospital of Sun Yat-Sen University, Shantou 515041, China;
Department of Biochemistry and Molecular
Biology, Shantou University Medical College, Shantou 515041, China;
Department of Medical Informatics, Harbin Medical University-Daqing, Daqing 163319, China and
Department of Thoracic Surgery, West China Hospital of Sichuan University, Chengdu 610041, China
Correspondence: En-min Li (firstname.lastname@example.org) or Li-yan Xu (email@example.com)
These authors contributed equally: Wei Liu, Jian-zhong He.
© Cancer Research UK 2018