Impact of Socioeconomic Factors on Prognosis and Clinical

667

ORIGINAL ARTICLE

Novel Staging System Showed Better Prediction Capacity

Su et al.

*These authors contributed equally to this work

Corresponding author: Guoqing Jiang, e-mail: jgqing2003@hotmail.com

Received: July 31, 2020 Accepted: January 13, 2021 Available Online Date: September 8, 2021

DOI: 10.5152/tjg.2021.20617

LIVER

Impact of Socioeconomic Factors on Prognosis and Clinical

Management in Patients with Hepatocellular Carcinoma

Bing-Bing Su* , Bao-Huan Zhou* , Dou-Sheng Bai , Jian-Jun Qian , Chi Zhang , Sheng-Jie Jin , Guo-Qing Jiang

Department of Hepatobiliary Surgery, Clinical Medical College, Yangzhou University, Yangzhou, China

ABSTRACT

Background: The prognosis for patient survival using the tumor–node–metastasis (TNM) staging system may be imperfect, as it based

only on biological factors and does not include the socioeconomic factors (SEFs). We integrated the SEFs into the TNM system (TNM-

SEF), and evaluated whether the novel TNM-SEF staging system showed better prediction capacity and improved clinical guidance in

hepatocellular carcinoma (HCC).

Methods: We selected data of 12 514 cases with HCC between 2010 and 2015 from the SEER database. The Kaplan–Meier survival

curves and Cox proportional hazards regression were used to analyze cancer-specific survival (CSS) among the TNM-SEF stages.

Results: Multivariate Cox analyses showed that insurance status, marital status, year of diagnosis, and income were prominent prog-

nostic SEFs (all P < .05). When compared with the SEF0 stage, the SEF1 stage was significantly associated with a 36.1% increased risk

of cancer-specific mortality in HCC overall, a 22.2% increased risk of metastatic HCC, and a 41.8% increased risk of non-metastatic

HCC (all P < .001). The concordance index of the TNM-SEF stage (0.768) was better than that of the TNM stage (0.764). Furthermore,

patients with SEF0 stage showed higher 5-year CSS than those with SEF1 stage (I: 48.7% vs. 28.1%; II: 41.0% vs. 25.1%; IIIA: 12.8% vs.

5.0%; IIIB: 7.8% vs. 6.0%; IIIC: 6.4% vs. 6.7%; IVA: 8.4% vs. 2.5%; IVB: 2.1% vs. 0.8%; all P < .05).

Conclusion: We have proved that the SEF stage is an independent predictor for HCC. The combined SEF stage with TNM staging war-

rants more clinical attention, for improved prognostic prediction and clinical guidance.

Keywords: Socioeconomic factors, hepatocellular carcinoma, TNM staging system, SEER, prognostication

INTRODUCTION

Hepatocellular carcinoma (HCC), the sixth most com-

mon malignant tumor, is the fourth leading cause of

cancer mortality worldwide.

Disease factors and patient

factors, such as the biological factors and socioeconomic

factors (SEFs) affect the prognosis of HCC. The influ-

ence of different biological factors on survival in HCC

patients has been investigated, including the factors like

tumor–node–metastasis (TNM) staging and tumor size.

Some studies have shown that SEFs, including marital

status, socioeconomic status, insurance, employment,

and education are associated with the survival of HCC

patients.

5-9

However, as far as we know, SEFs have not

yet been researched in the prognostic prediction of

HCC. Besides, prognostication using the TNM staging

system is only based on the extent of invasion of the

primary tumor, status of lymph node metastasis, and

distant spread. The TNM system is not optimal for clini-

cal prognostic prediction and treatment,

therefore, a

more accurate prognostic prediction system with a

combination of the TNM staging system or other prog-

nostic factors is necessary. However, the knowledge

regarding the combination of the TNM stage and SEFs

for prediction in HCC remains extremely limited.

We conducted a population-based study to explore the

impact of different SEFs, such as income, level of educa-

tion, year of diagnosis, employment status, insurance sta-

tus, and marital status, on survival in HCC. We then chose

those factors that were independent prognostic factors

for further study. The purpose of our study was to pro-

pose and evaluate the novel combination of TNM stage

and SEF stage (TNM-SEF stage) in terms of the clinical

prognostication and management of HCC.

MATERIALS AND METHODS

Data Source and Patients

The Surveillance, Epidemiology, and End Results (SEER)

database is an almost universally accepted source of

information about cancer in the United States. Moreover,

32 8

Cite this article as: Su B, Zhou B, Bai D, et al. Impact of socioeconomic factors on prognosis and clinical management in patients

with hepatocellular carcinoma. Turk J Gastroenterol. 2021; 32(8): 667-677.

Turk J Gastroenterol 2021; 32(8): 667-677 Su et al. Novel Staging System Showed Better Prediction Capacity

668

it is a general database, including almost all newly diag-

nosed cancers occurring where individuals reside in

SEER-participating areas, representing about 28% of the

United States population. All data of demographic and

tumor variables were extracted from the SEER database.

In a previous study, researchers have discussed the char-

acteristics and representativeness of this population-

based database.

We extracted the following data: gender, race, age, year

of diagnosis, pathological grade, TNM stage, tumor size,

insurance status, marital status, county percentage with

a bachelor’s degree, county percentage unemployed,

county-level median household income, surgical status,

SEER cause-specific death classification, SEER other-

cause-of-death classification, survival months, and vital

statistics.

The data of patients in our study, diagnosed with HCC

between January 1, 2010 and December 31, 2015, were

selected using SEER-Stat software (SEER*Stat 8.3.5,

https://seer.cancer.gov/seerstat/software/). Those

patients with a diagnosis of HCC (Histology codes

8170 to 8175) and only 1 primary tumor were selected

for this study. We excluded patients with unknown

race, diagnosis confirmation, insurance status, income,

tumor size, marital status, and TNM stage. We also

excluded patients in whom it was unknown whether

surgery was performed. We also excluded patients who

were 65 years or older, because these patients are gen-

erally enrolled in or qualify for medical insurance ben-

efits. Additionally, we excluded patients younger than

19 years, as most people in that age group are unmar-

ried (Figure 1).

SEF Stage and Statistical Analysis

We performed multivariate Cox regression analysis for

all prognostic predictors with a value of P < .05 in the

univariate analysis of SEF (marital status, insurance sta-

tus, median household income, and year of diagnosis).

Hazard ratios (HRs) were used with 95% CI. The analysis

results showed that insurance status, median household

income, marital status, and year of diagnosis were sig-

nificant prognostic SEFs of HCC cause-specific survival

(HCSS).

We stratified patients based on the prognostic score

incorporating the 4 SEFs, as shown in Figure 2. Firstly, the

point in each group of SEF equivalents was regarded as

the HR value. We then calculated the summation of the

points (HRs) in the 4 SEFs as the total prognostic score

for each patient. For instance, in a married and uninsured

patient with HCC whose income and year of diagnosis

were $43.83-$53.16 K, and 2010, respectively, the point

is calculated as the summation of 1.000, 1.406, 1.041, and

1.223, which equals 4.670. The total scores ranged from

3.919 to 4.885, with a full-scale prognostic score based on

the 4 SEFs, which was 3.919 for the best prognosis; patients

with a score of 4.885 had the worst prognosis. Then we

divided the prognostic score into 2 groups, and the median

value of the prognostic score was regarded as the cutoff

point. Lower scores were assigned to the SEF0 stage and

higher scores were assigned to the SEF1 stage.

Statistical Analysis

We used the chi-square test to compare baseline patient

demographics and tumor characteristics. We used multi-

variate Cox analysis to determine the prognosis of the SEF

stage as well as the combined TNM stage and SEF stage

(TNM-SEF stage). The primary endpoint of this study was

HCSS, a specified time from the date of diagnosis to the

date of death owing to HCC. We used Kaplan–Meier sur-

vival curves to assess the prognostic prediction of each

TNM-SEF stage. Additionally, we used the concordance

index (C-index) to evaluate the discriminative abilities of

the TNM-SEF staging system. A value of P < .05 was con-

sidered to indicate a significant difference. All statistical

analyses were conducted using the IBM SPSS Version 25

(IBM Corp., Armonk, NY, USA).

RESULTS

Using the selection criteria, we identified 12 514 patients

with HCC diagnosed between January 1, 2010 and

December 31, 2015. The baseline characteristics of

patients with HCC included in our study are shown in

MAIN POINTS

• The socioeconomic factors (SEF) were independent predic-

tors for HCC.

• At each TNM stage, all of the HRs of each tumor-node-

metastasis-socioeconomic factors (TNM-SEF) stage

showed that patients with TNM-SEF0 stage had lower HRs

than those with TNM-SEF1 stage.

• Some HRs of patients with TNM-SEF1 stage even exceeded

the HRs of those with TNM-SEF0 stage who had higher

TNM stages.

• The C-index of the TNM-SEF stage was larger than that of

the only TNM stage.

• The novel TNM-SEF staging system could make the pre-

cision of prognostic prediction and clinical guidance more

accurate in HCC.

Su et al. Novel Staging System Showed Better Prediction Capacity Turk J Gastroenterol 2021; 32(8): 667-677

669

Table 1. Compared with the general population, patients

with HCC were more likely to be male (82.6%). Most

patients (86.6%) were aged from 51 to 64 years, White

(68.0%), and insured (57.4%).

Association of SEFs With HCSS

The univariate analysis showed that race, sex, tumor size,

surgery, grade, TNM stage, insurance status, marital sta-

tus, county percentage with bachelor’s degree, household

Figure 1. Flow diagram of patient population selected from the Surveillance, Epidemiology, and End Results (SEER) database.

Figure 2. Patient prognostic score in hepatocellular carcinoma (HCC): risk-stratifications.

Turk J Gastroenterol 2021; 32(8): 667-677 Su et al. Novel Staging System Showed Better Prediction Capacity

670

income, and percentage of unemployed were all indepen-

dently associated with HCSS (all P < .05). We analyzed

these factors in the multivariate Cox analysis. The results

demonstrated that SEFs including insurance status, year

of diagnosis, household income, and marital status, were

all independent predictors for survival (Table 2).

Table 1. Baseline Characteristics of Patients With Hepatocellular Carcinoma Included in Our Study

Variable n%

Race

White 8507 (68.0%)

Black 1985 (15.9%)

Other

2022 (16.1%)

Sex

Male 10340 (82.6%)

Female 2174 (17.4%)

Tumor grade

Well differentiated 1176 (9.4%)

Moderately differentiated 1849 (14.8%)

Poorly differentiated 929 (7.4%)

Undifferentiated 67 (0.5%)

Unknown 8493 (67.9%)

TNM stage

I 4880 (39.0%)

II 2693 (21.5%)

IIIA 1130 (9.0%)

IIIB 941 (7.5%)

IIIC 229 (1.8%)

IVA 566 (4.5%)

IVB 2075 (16.6%)

Surgery

Performed 3459 (27.6%)

Not performed 9055 (72.4%)

County % with bachelor

degree

5.43-17.55% 3181 (25.4%)

17.56-24.86% 3538 (28.3%)

24.87-30.81% 2911 (23.3%)

30.82-51.31% 2884 (23.0%)

County-level median household

income

16.27-40.44 K 3129 (25.0%)

40.45-43.82 K 3203 (25.6%)

43.83-53.16 K 3125 (25.0%)

53.17-79.89 K 3057 (24.4%)

Variable n%

County % who were

unemployed

1.83-4.76% 3128 (25.0%)

4.77-5.93% 3152 (25.2%)

5.94-8.23% 3721 (29.7%)

8.24-17.17% 2513 (20.1%)

Year of diagnosis

2010 1893 (15.1%)

2011 2028 (16.2%)

2012 2125 (17.0%)

2013 2088 (16.7%)

2014 2187 (17.5%)

2015 2193 (17.5%)

Tumor size

<3 cm 3800 (30.4%)

3-5 cm 3047 (24.3%)

>5 cm 4403 (35.2%)

Unknown 1264 (10.1%)

Age at diagnosis (years)

19-50 1676 (13.4%)

51-55 2739 (21.9%)

56-60 4555 (36.4%)

61-64 3544 (28.3%)

Insurance status

Insured 7187 (57.4%)

Medicaid 4428 (35.4%)

Uninsured 899 (7.2%)

Marital status

Married 6255 (50.0%)

Single 3914 (31.3%)

Divorced 1890 (15.1%)

Widowed 455 (3.6%)

*Other includes American Indian/Alaska Native, Asian/Pacific Islander, and

unknown.

County-level median household incomeshown in US dollars.

TNM, tumor, node, metastasis.

Su et al. Novel Staging System Showed Better Prediction Capacity Turk J Gastroenterol 2021; 32(8): 667-677

671

Table 2. Univariate Survival Analysis for Evaluating the Influence on HCSS Using Data from the SEER Database

Variable Reference Characteristic

Univariate Analysis Multivariate Analysis

HR (95% CI) SE P HR (95% CI) SE P

Race Black White 0.814 (0.796-0.862) 0.029 <.001 0.974 (0.918-1.033) 0.030 .376

Other

0.687(0.637-0.742) 0.039 <.001 0.868 (0.802-0.940) 0.041 <.001

Age 19-50 51-55 1.105 (1.026-1.191) 0.038 .009 1.163 (1.079-1.254) 0.038 <.001

56-60 1.062 (0.991-1.139) 0.035 .088 1.144 (1.066-1.228) 0.036 <.001

61-64 1.033 (0.961-1.111) 0.037 .378 1.160 (1.077-1.249) 0.038 <.001

Sex Male Female 0.768 (0.724-0.815) 0.030 <.001 0.881 (0.829-0.936) 0.031 <.001

County% with

bachelor degree

30.82–51.31% 24.87–30.81% 1.143 (1.072-1.218) 0.052 <.001 1.011 (0.934-1.096) 0.041 .780

17.56–24.86% 1.202 (1.131-1.278) 0.047 <.001 1.041 (0.950-1.414) 0.047 .388

5.43-17.55% 1.321 (1.241-1.405) 0.048 <.001 1.089 (0.987-1.210) 0.050 .088

County % who were

unemployed

1.83–4.76% 4.77-5.93% 1.079 (1.015-1.147) 0.031 .015 1.011 (0.945-1.082) 0.034 .749

5.94-8.23% 1.135 (1.070-1.203) 0.030 <.001 1.078 (0.997-1.166) 0.040 .059

8.24-17.17% 1.282 (1.203-1.366) 0.032 <.001 1.035 (0.954-1.124) 0.042 .408

Grade Well differentiated Moderately

differentiated

1.103 (0.996-1.221) 0.052 .059 1.218 (1.099-1.349) 0.052 <.001

Poorly differentiated 2.142 (1.920-2.390) 0.056 <.001 1.794 (1.605-2.004) 0.057 <.001

Undifferentiated 3.349 (2.573-4.360) 0.135 <.001 2.299 (1.764-2.997) 0.135 <.001

Unknown 1.928 (1.772-2.097) 0.043 <.001 1.356 (1.244-1.477) 0.044 <.001

Tumor size <3 cm 3-5 cm 1.640 (1.537-1.751) 0.033 <.001 1.384 (1.296-1.479) 0.034 <.001

>5 cm 3.328 (3.141-3.526) 0.030 <.001 1.990 (1.857-2.133) 0.035 <.001

Unknown 5.654 (5.241-6.100) 0.039 <.001 2.513 (2.308-2.737) 0.043 <.001

Surgery Performed Not performed 4.158 (3.910-4.421) 0.031 <.001 2.765 (2.581-2.961) 0.035 <.001

TNM stage I II 1.150 (1.079-1.226) 0.033 <.001 1.189 (1.114-1.269) 0.033 <.001

IIIA 2.729 (2.530-2.943) 0.039 <.001 1.529 (1.403-1.666) 0.044 <.001

IIIB 3.940 (3.639-4.266) 0.041 <.001 2.305 (2.161-2.555) 0.043 <.001

IIIC 4.068 (3.526-4.694) 0.073 <.001 2.656 (2.295-3.073) 0.074 <.001

IVA 3.756 (4.411-4.137) 0.049 <.001 2.215 (2.004-2.447) 0.051 <.001

IVB 5.898 (5.542-6.276) 0.032 <.001 2.986 (2.786-3.199) 0.035 <.001

Insurance status Insured Medicaid 1.503 (1.437-1.573) 0.023 <.001 1.269 (1.210-1.332) 0.025 <.001

Uninsured 2.105 (1.946-2.277) 0.040 <.001 1.406 (1.296-1.527) 0.042 <.001

County-level

household median

income

53.17-79.89 K 43.83-53.16 K 1.104 (1.038-1.174) 0.032 .002 1.041 (0.967-1.121) 0.038 .288

40.45-43.82 K 1.141 (1.073-1.214) 0.031 <.001 0.919 (0.833-1.014) 0.050 .091

16.27-40.44 K 1.355 (1.275-1.439) 0.031 <.001 1.062 (0.968-1.165) 0.072 .205

Turk J Gastroenterol 2021; 32(8): 667-677 Su et al. Novel Staging System Showed Better Prediction Capacity

672

Association of SEF Stage With HCSS

The SEF0 stage was attributed to 6300 patients (50.3%)

and SEF1 stage was attributed to 6214 patients (49.7%).

The multivariate analysis suggested that the SEF stage

was an independent predictor of survival. When com-

pared with the SEF0 stage, the SEF1 stage was inde-

pendently associated with a 36.1% increased risk of

cancer-specific mortality (HR: 1.361, 95% CI: 1.303-

1.422, P < .001; Table 3). We also performed multivari-

able Cox analysis in patients with non-metastatic (TNM

stage I-III) HCC (n = 9873) and metastatic (TNM stage

IV) HCC (n = 2641). The 2 outcomes proved that the SEF

stage was independently associated with cancer-specific

mortality. In patients with metastatic HCC, we observed

a 22.2% increased risk of cancer-specific mortality in the

SEF1 stage as compared with the SEF0 stage (HR: 1.222,

95% CI: 1.126-1.326, P < .001; see Supplementary Table

1). However, in non-metastatic HCC, a 41.8% increased

risk of cancer-specific mortality was observed in the

SEF1 stage as compared with the SEF0 stage (HR: 1.418,

95% CI: 1.345-1.494, P < .001; see Supplementary Table

2); this result was slightly higher than that in the over-

all cohort, suggesting that the efficacy of the prognostic

prediction of SEF stage was improved in the TNM stage

I-III HCC patients.

Prognostic Prediction of TNM-SEF Stage

The C-index of the TNM-SEF stage (0.768, 95% CI:

0.774-0.762) was larger than that of the TNM stage

(0.764, 95% CI: 0.770-0.758). We used the Kaplan–Meier

survival analysis of SEF-TNM stages (the TNM staging

system including I, IIA, IIB, IIC, IIIA, IIIB, IIIC, IVA, and IVB,

combined with SEF0 stage or SEF1 stage) to assess the

prognostic prediction ability of the SEF-TNM stages, as

seen in Figure 3. The figure also shows an increased HCSS

in patients with stage SEF0-TNM as compared with

those who had stage SEF1-TNM, at each TNM stage. For

instance, we found an increased HCSS in IIA-SEF0 stage

as compared with IIA-SEF1 stage (5-year HCSS: 41.0%

vs. 25.1%, χ

= 92.24; P < .001; Figure 4). Notably, we also

found a decreased HCSS in I-SEF1 stage as compared

with IIA-SEF0 stage (5-year HCSS: 28.1% vs. 41.0%, χ

63.94; P < .001; Figure 4) and in IIIC-SEF1 stage as com-

pared with IVA-SEF0 stage (5-year HCSS: 1.7% vs. 8.4%,

= 12.51; P < .001; Figure 4).

Multivariate Cox analysis to compare the HRs of each TNM-

SEF stage showed that patients with TNM-SEF0 stage

had lower HRs than those with TNM-SEF1 stage, at

each TNM stage (Figure 4). Interestingly, some HRs of

patients with TNM-SEF1 stage even exceeded the HRs

Variable Reference Characteristic

Univariate Analysis Multivariate Analysis

HR (95% CI) SE P HR (95% CI) SE P

Year of diagnosis 2015 2014 1.068 (0.985-1.158) 0.041 .112 1.115 (1.028-1.209) 0.041 .009

2013 1.161 (1.072-1.257) 0.041 <.001 1.196 (1.103-1.296) 0.041 <.001

2012 1.172 (1.083-1.268) 0.040 <.001 1.195 (1.104-1.294) 0.040 <.001

2011 1.122 (1.036-1.216) 0.041 <.001 1.168 (1.078-1.265) 0.041 <.001

2010 1.230 (1.135-1.332) 0.041 <.001 1.223 (1.128-1.326) 0.041 <.001

Marital status Married Single 1.411 (1.345-1.481) 0.025 <.001 1.131 (1.073-1.191) 0.027 <.001

Divorced 1.305 (1.227-1.388) 0.031 <.001 1.130 (1.016-1.204) 0.032 <.001

Widowed 1.200 (1.069-1.346) 0.059 .002 1.194 (1.062-1.343) 0.060 .003

*Other includes American Indian/Alaska Native, Asian/Pacific Islander, and unknown.

Shown in US dollars.

TNM, tumor, node, metastasis; HR, hazard ratio; CI, confidence interval; SE, standard error; HCSS, hepatocellular carcinoma cancer-specific survival; SEER, Surveillance, Epidemiology, and End

Results.

Table 2. Univariate Survival Analysis for Evaluating the Influence on HCSS Using Data from the SEER Database (Continued)

Su et al. Novel Staging System Showed Better Prediction Capacity Turk J Gastroenterol 2021; 32(8): 667-677

673

of those with TNM-SEF0 stage who had higher TNM

stages. For example, as shown in Figure 4, when taking

stage I-SEF0 as a reference, the HR was higher in patients

with I-SEF1 stage (HR: 1.741, 95% CI: 1.607-1.886) than

in those with II-SEF0 stage (HR: 1.206, 95% CI: 1.095-

1.328); in patients with IIIA-SEF1 stage (HR: 4.470, 95%

CI: 4.018-4.973) or IIIB-SEF1 stage (HR: 5.941, 95%

CI: 5.309-6.649), as compared with patients who had

IIIC-SEF0 stage (HR: 4.368, 95% CI: 3.505-5.444); and

in patients with IIIB-SEF1 stage (HR: 5.941, 95% CI:

5.309-6.649) or IIIC-SEF1 stage (HR: 6.547, 95% CI:

5.412-7.919) as compared with patients who had IVA-

SEF0 stage (HR: 4.480, 95% CI: 3.904-5.141).

DISCUSSION

Great progress has been made in the research on HCC at

the levels of cellular and molecular biology.

12,13

However,

only some studies have focused on prognostic SEFs

such as marital status, socioeconomic status, insurance,

employment, and education.

5-9

Furthermore, no research

has studied more than 3 SEFs together in 1 study, and

no studies have incorporated SEFs into the TNM staging

Table 3. Multivariable Cox Regression Analyses of Independent Prognostic Factors in Hepatocellular Carcinoma

Variable Reference Characteristic

Cancer-Specific Survival

HR (95% CI) SE P

Race Black White 0.958 (0.903-1.015) 0.030 .147

Other

0.837 (0.774-0.905) 0.040 <.001

Age 19-50 51-55 1.161 (1.077-1.252) 0.038 <.001

56-60 1.130 (1.053-1.212) 0.036 .001

61-64 1.134 (1.054-1.220) 0.037 .001

Sex Male Female 0.876 (0.825-0.930) 0.030 <.001

County % with bachelor degree 30.82-51.31% 24.87-30.81% 1.035 (0.964-1.111) 0.036 .341

17.56-24.86% 1.039 (0.962-1.122) 0.039 .329

5.43-17.55% 1.096 (1.013-1.185) 0.040 .022

County % who were unemployed 1.83-4.76% 4.77–5.93% 1.020 (0.954-1.090) 0.034 .569

5.94–8.23% 1.064 (0.990-1.145) 0.037 .094

8.24–17.17% 1.034 (0.955-1.119) 0.040 .407

Grade Well Moderately 1.231 (1.111-1.364) 0.052 <.001

Poorly 1.806 (1.616-2.017) 0.056 <.001

Undifferentiated 2.204 (1.691-2.872) 0.135 <.001

Unknown 1.367 (1.254-1.489) 0.044 <.001

Tumor size < 3 cm 3-5 cm 1.391 (1.303-1.486) 0.034 <.001

> 5 cm 2.000 (1.866-2.144) 0.035 <.001

Unknown 2.521 (2.316-2.745) 0.043 <.001

Surgery Performed Not performed 2.763 (2.580-2.959) 0.035 <.001

TNM stage I II 1.192 (1.116-1.272) 0.033 <.001

III A 1.533 (1.407-1.670) 0.044 <.001

III B 2.357 (2.168-2.563) 0.043 <.001

III C 2.655 (2.296-3.072) 0.074 <.001

IV A 2.223 (2.012-2.456) 0.051 <.001

IV B 3.007 (2.807-3.222) 0.035 <.001

SEF stage Stage 0 Stage 1 1.361 (1.303-1.422) 0.022 <.001

*Other includes American Indian/Alaska Native, Asian/Pacific Islander, and unknown.

TNM, tumor, node, metastasis; HR, hazard ratio; CI, confidence interval; SE, standard error; SEF, socioeconomic factor.

Turk J Gastroenterol 2021; 32(8): 667-677 Su et al. Novel Staging System Showed Better Prediction Capacity

674

system to improve the prognostic prediction and clinical

guidelines in HCC.

In 2016, a population-based study demonstrated that

married patients had higher survival rates than unmar-

ried patients.

A similar conclusion has been reached for

nearly all cancers including pancreatic, gastric, colon, and

rectal cancers,

14-17

among others. Some underlying rea-

sons may be that marriage could improve cardiovascular,

endocrine, and immune functions

and married patients

are more likely to accept effective treatment, leading to

longer survival.

Figure 3. Kaplan–Meier survival curves of the tumor-node-metastasis-socioeconomic factor (TNM-SEF) staging system. (A) Cancer-

specific survival (CSS) of the I-S0 stage, I-S1 stage, II-S0 stage, and II-S1 stage. (B) CSS of the IIIA-S0 stage, IIIA-S1 stage, IIIB-S0 stage,

and IIIB-S1 stage, IIIC-S0 stage, and IIIC-S1 stage. (C) CSS of IVA-S0 stage, IVA-S1 stage, IVB-S0 stage, and IVB-S1 stage.

Su et al. Novel Staging System Showed Better Prediction Capacity Turk J Gastroenterol 2021; 32(8): 667-677

675

In other studies, Medicaid status or not having insur-

ance is related with adverse survival compared with hav-

ing insurance.

19,20

We considered that the poor prognosis

of Medicaid status and lack of insurance might result in

patients having a more advanced tumor stage at diagno-

sis and late or inadequate treatment after diagnosis .

The diagnosis and treatment of diseases in medical insti-

tutions can be expected to gradually and substantially

improve with time. This was proven in a previous study

showing the year of diagnosis as an independent predic-

tor in HCC.

Similar results were obtained in the present

research.

We also found that a higher household income among

patients was associated with relatively longer survival.

The possible reasons may include early patient diagnosis

and adequate treatment. Our results are consistent with

prior research.

Although the TNM staging system is widely used clini-

cally in countries worldwide, it only considers certain

biological factors, such as the extent of invasion of the

primary tumor, the number of lymph nodes, and distant

spread.

Although the TNM system has been modified

many times, it is not yet optimal for prognostic predic-

tion. TNM staging neither takes into account the SEF, nor

the other biological factors that affect the prognosis of

HCC. Hence, the need for a more comprehensive staging

system that includes other biological factors or SEFs is a

concern.

SEFs have not yet been systematically studied in the

prognosis of HCC. Our study is the first to combine SEFs

with the TNM staging system. In this research, the novel

SEF stage (based on the combination of marital sta-

tus, insurance status, year of diagnosis, and household

income) was indicated to be an independent prognos-

tic factor, and patients with SEF0 stage showed signifi-

cantly increased HCSS as compared with those who had

SEF1 stage at each TNM stage, especially TNM stage I-III.

Additionally, our studies indicated that the SEF1 stage

showed a 36.1% decreased risk of cancer-specific mor-

tality in HCC overall when compared with the SEF0 stage,

a 41.8% decreased risk in non-metastatic HCC, and

a 22.2% decreased risk in metastatic HCC. This phe-

nomenon indicated that the SEF stage plays a relatively

important role in survival among patients with early-stage

cancer; patients with SEF0 stage could receive a greater

survival benefit in TNM stages I-III than in TNM stage IV.

Figure 4. Prognosis of tumor-node-metastasis-socioeconomic factor (TNM-SEF) stage in hepatocellular carcinoma (HCC).

Turk J Gastroenterol 2021; 32(8): 667-677 Su et al. Novel Staging System Showed Better Prediction Capacity

676

Besides, the improved C-index of TNM-SEF also proved

that the TNM-SEF staging system offers greater advan-

tages concerning prognostic ability than the TNM staging

system alone. Based on the above findings, the TNM-SEF

staging system is more helpful in the accurate progno-

sis of survival in HCC and in more comprehensive clinical

treatment and management in HCC patients.

Commonly, the more advanced the TNM staging of

HCC at diagnosis, the worse the prognosis, that is, the

poorer the prognosis expected in TNM stage II than

stage I, in TNM stage III than stage II, and in TNM stage

IV than stage III.

However, the present analysis mani-

fested that the cancer-specific mortality of patients

with HCC in several TNM-SEF1 stages exceeded that

of patients with TNM-SEF0 stage who had higher TNM

stages. For instance, the cancer-specific mortality was

lower in patients with IIA-SEF0 stage than in those with

stage I-SEF1, in patients with IIIC-SEF0 stage than in

those with IIIB-SEF1 stage, and in patients with IVA-

SEF0 stage than in those with IIIC-SEF1 stage. The

phenomenon of these 3 subgroups indicates that the

TNM-SEF stage may better reflect survival than the

TNM stage, and SEF0 stage is associated with a better

survival benefit than SEF1 stage.

Several potential limitations exist in our research. First,

the overall cohort comprised 12 514 patients from the

SEER database, but samples from some subgroups (e.g.,

IIIC-SEF0, IIIC-SEF1, IVA-SEF0, IVA-SEF1) were relatively

small. Second, the applicability of our result is limited to

America; the results may differ in other areas with differ-

ent health care systems. Finally, because our data were

retrospectively reviewed, future prospective studies are

needed to validate our findings.

CONCLUSION

We proved that marital status, insurance status, house-

hold income, and year of diagnosis were all independent

prognostic factors in HCC. Importantly, the SEF stage

was a strongly independent prognostic factor, which

warrants greater attention among healthcare profes-

sionals and institutions taking care of HCC patients.

Greater attention is especially needed in patients with

poor SEFs who may benefit from additional resources

and support during therapy for HCC. The new stag-

ing system could therefore improve the accuracy of

prognostic prediction and the clinical guidance in HCC,

strongly supporting the combination of the SEF stage

with the TNM staging system.

Ethics Committee Approval: All procedures performed in studies

involving human participants were in accordance with the ethical

standards of the institutional and/or national research committee

and with the 1964 Helsinki declaration and its later amendments or

comparable ethical standards. This article does not contain any

studies with human participants or animals performed by any of the

authors. It has been permitted to obtain the data from SEER data-

base (Reference Number 10778-Nov2018).

Informed Consent: As this study is based on a publicly available

database without identifying patient information, informed consent

was not needed.

Peer-review: Externally peer-reviewed.

Author Contributions: Concept – B.B.S., B.H.Z.,G.Q.J.; Design - B.B.S.,

D.S.B., J.J.Q.; Supervision - B.H.Z., C.Z., S.J.J; Resource - B.B.S,D.S.B,

G.Q.J; Materials - B.B.S., B.H.Z., C.Z.; Data Collection and/or Processing

- D.S.B., J.J.Q., S.J.J.; Analysis and/or Interpretation - D.S.B., C.Z.,

B.B.S.; Literature Search - B.B.S., G.Q.J., D.S.B.; Writing - B.B.S., B.H.Z.,

G.Q.J.; Critical Reviews -B.B.S., G.Q.J., D.S.B.

Acknowledgements: We would be grateful to the SEER database for

its open access. And we thank Analisa Avila. ELS, of Liwen Bianji,

Edanz Group China (www.liwenbianji.cn/ac), for editing the English

text of a draft of this manuscript.

Conflict of Interest: The authors have no conflict of interests to

declare.

Financial Disclosure: This work was supported by the Project of

Invigorating Health Care through Science, Technology and

Education: Jiangsu Provincial Medical Youth Talent (QNRC2016331).

REFERENCES

1. Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics

2018: GLOBOCAN estimates of incidence and mortality worldwide

for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68(6):394-

424. [CrossRef]

2. Zhao J, Mao J, Li W. Association of tumor grade With long-term

survival in patients With hepatocellular carcinoma After liver trans-

plantation. Transplant Proc. 2019;51(3):813-819. [CrossRef]

3. Liu H, Cen D, Yu Y, et al. Does fibrosis have an impact on survival

of patients with hepatocellular carcinoma: evidence from the SEER

database? BMC Cancer. 2018;18(1):1125. [CrossRef]

4. Yang A, Xiao W, Chen D, et al. The power of tumor sizes in predict-

ing the survival of solitary hepatocellular carcinoma patients. Cancer

Med. 2018;7(12):6040-6050. [CrossRef]

5. Wu C, Chen P, Qian JJ, et al. Effect of marital status on the survival

of patients with hepatocellular carcinoma treated with surgical

resection: an analysis of 13,408 patients in the surveillance, epide-

miology, and end results (SEER) database. Oncotarget.

2016;7(48):79442-79452. [CrossRef]

6. Shebl FM, Capo-Ramos DE, Graubard BI, McGlynn KA, Altekruse SF.

Socioeconomic status and hepatocellular carcinoma in the United

States. Cancer Epidemiol Biomarkers Prev. 2012;21(8):1330-1335.

[CrossRef]

7. Kokabi N, Xing M, Duszak R, Jr, et al. Sociodemographic impact on

survival in unresectable hepatocellular carcinoma: a survival

Su et al. Novel Staging System Showed Better Prediction Capacity Turk J Gastroenterol 2021; 32(8): 667-677

677

epidemiology and end results study. Future Oncol. 2016;12(2):183-

198. [CrossRef]

8. Artinyan A, Mailey B, Sanchez-Luege N, et al. Race, ethnicity, and

socioeconomic status influence the survival of patients with hepa-

tocellular carcinoma in the United States. Cancer. 2010;116(5):1367-

1377. [CrossRef]

9. Wang J, Ha J, Lopez A, et al. Medicaid and uninsured hepatocel-

lular carcinoma patients have more advanced tumor stage and are

less likely to receive treatment. J Clin Gastroenterol. 2018;52(5):437-

443. [CrossRef]

10. Kamarajah SK, Frankel TL, Sonnenday C, et al. Critical evaluation

of the American joint commission on cancer (ajcc). 8th ed staging

system for patients with hepatocellular carcinoma (hcc): a surveil-

lance, epidemiology, end results (seer) analysis. J Surg Oncol.

2018;117:644-650. [CrossRef]

11. Nathan H, Pawlik TM. Limitations of claims and registry data in

surgical oncology research. Ann Surg Oncol. 2008;15(2):415-423.

[CrossRef]

12. Zhang L, Niu H, Ma J, et al. The molecular mechanism of LncR-

NA34a-mediated regulation of bone metastasis in hepatocellular

carcinoma. Mol Cancer. 2019;18(1):120. [CrossRef]

13. Nault JC, Galle PR, Marquardt JU. The role of molecular enrich-

ment on future therapies in hepatocellular carcinoma. J Hepatol.

2018;69(1):237-247. [CrossRef]

14. Wang XD, Qian JJ, Bai DS, et al. Marital status independently

predicts pancreatic cancer survival in patients treated with surgical

resection: an analysis of the SEER database. Oncotarget.

2016;7(17):24880-24887. [CrossRef]

15. Shi RL, Chen Q, Yang Z, et al. Marital status independently pre-

dicts gastric cancer survival after surgical resection--an analysis of

the SEER database. Oncotarget. 2016;7(11):13228-13235. [CrossRef]

16. Yang CC, Cheng LC, Lin YW, et al. The impact of marital status

on survival in patients with surgically treated colon cancer. Med (Bal-

tim). 2019;98(11):e14856. [CrossRef]

17. Li Z, Wang K, Zhang X, Wen J. Marital status and survival in

patients with rectal cancer: A population-based STROBE cohort

study. Med (Baltim). 2018;97(18):e0637. [CrossRef]

18. Gallo LC, Troxel WM, Matthews KA, Kuller LH. Marital status and

quality in middle-aged women: associations with levels and trajec-

tories of cardiovascular risk factors. Health Psychol. 2003;22(5):453-

463. [CrossRef]

19. Du XL, Lin CC, Johnson NJ, Altekruse S. Effects of individual-level

socioeconomic factors on racial disparities in cancer treatment and

survival: findings from the National Longitudinal Mortality Study,

1979-2003. Cancer. 2011;117(14):3242-3251. [CrossRef]

20. Koroukian SM, Bakaki PM, Raghavan D. Survival disparities by

Medicaid status: an analysis of 8 cancers. Cancer. 2012;118(17):4271-

4279. [CrossRef]

21. Shen Y, Guo H, Wu T, et al. Lower education and household

income contribute to advanced disease, less treatment received and

poorer prognosis in patients with hepatocellular carcinoma. J Can-

cer. 2017;8(15):3070-3077. [CrossRef]

22. Yarbro JW, Page DL, Fielding LP, Partridge EE, Murphy GP.

American Joint Committee on Cancer Prognostic Factors Consen-

sus. American Joint Committee on Cancer prognostic factors con-

sensus conference. Cancer Conference. 1999;86(11):2436-2446.

[CrossRef]

23. Chun YH, Kim SU, Park JY, et al. Prognostic value of the 7th edi-

tion of the AJCC staging system as a clinical staging system in

patients with hepatocellular carcinoma. Eur J Cancer.

2011;47(17):2568-2575. [CrossRef]