The clinicopathological characteristics and genetic alterations between younger and older gastric cancer patients with curative surgery

Introduction

Gastric cancer (GC) is the sixth most common cancer and the second most common cause of cancer-related deaths worldwide [1]. Surgical intervention remains the only curative treatment for GC. Individuals between 50 and 70 years old show the most frequent GC prevalence.

A large series comparing the clinicopathological and molecular features between younger (<45 years) and older (≥45 years) GC patients [2] demonstrated that younger patients were associated with a female predominance and a more advanced stage, less frequent TP53 and HER-2 overexpression, fewer microsatellite instability-high (MSI-H) tumors, a higher level of Helicobacter pylori (HP) infection, and worse cancer-specific survival than older patients. Younger patients had higher cancer-related mortality than older patients.

To the best of our knowledge, few reports have compared the genetic mutations between younger and older GC patients. The incidence of TP53 mutations was reported to be different between younger and older GC patients [3]. In addition to GC, there is a lack of comprehensive and economic genetic analysis method for cancer-related genes that have been reported to differ between younger and older cancer patients in cancers related or non-related to the gastrointestinal tract. In colorectal cancer, loss of ARID1A expression was associated with a younger age [4]. In papillary thyroid carcinoma, patients with BRAF expression were associated with older age and higher tumor recurrence rates than patients without BRAF expression [5]. In endometrial carcinoma, PIK3CA amplifications, but not PIK3CA mutation, were associated with older age [6]. In breast cancer, PTEN mutations were associated with younger age [7].

According to the World Health Organization (WHO), most developed countries use a chronological age of 65 years or older to categorize elder or older persons. Therefore, in the present study, we separated patients into younger age (<65 years) and older age (≥65 years) groups. Since whole genome sequencing is expensive, we used mass spectrometric single nucleotide polymorphism genotyping technology for multiplex analysis, which included 68 validated mutations within eight genes (TP53, ARID1A, BRAF, and the PI3K/AKT pathway) [8] in accordance with previous data that mutations in these genes were shown for cancers related or non-related to the gastrointestinal tract. The aim of the present study was to compare the clinicopathological features, recurrence patterns, prognoses, and genetic alterations between younger and older GC patients with curative surgery.

Results

Survival analysis

As shown in Figure 1, the 5-year overall survival (OS) (68.0% vs. 54.6%, P<0.001) and disease-free survival (DFS) (65.4% vs. 53.0%, P<0.001) rates were higher in younger patients than in older patients.

Figure 1. The 5-year OS (68.0% vs. 54.6%, P<0.001) and DFS (65.4% vs. 53.0%, P<0.001) rates were significantly higher in younger patients (<65 years) than in older patients (≥65 years). The survival curves are shown as follows: (A) OS curves of GC patients. (B) DFS curves of GC patients.

As shown in Figure 2, the OS and DFS rates were significantly higher in younger patients than in older patients for stage I GC (5-year OS, 94.9% vs. 78.2%, P<0.001, Figure 2A and DFS, 94.1% vs. 77.8%, P<0.001, Figure 2B); stage II GC (5-year OS, 75.9% vs. 60.6%, P<0.001, Figure 2C and DFS, 72.7% vs. 57.5%, P<0.001, Figure 2D); and stage III GC (5-year OS, 33.3% vs. 23.6%, P<0.001, Figure 2E and DFS, 29.1% vs. 21.4%, P=0.001, Figure 2F).

Figure 2. For stage I GC, the 5-year OS (94.9% vs. 78.2%, P<0.001) and DFS (94.1% vs. 77.8%, P<0.001) rates were significantly higher in younger patients (< 65 years) than older patients (≥65 years), and similar results were observed for stage II GC, the 5-year OS, 75.9% vs. 60.6%, P<0.001 and DFS, 72.7% vs. 57.5%, P<0.001) and stage III GC, 5-year OS, 33.3% vs. 23.6%, P<0.001 and DFS, 29.1% vs. 21.4%, P=0.001. The survival curves are shown as follows: (A) OS curves of stage I GC patients. (B) DFS curves of stage I GC patients. (C) OS curves of stage II GC patients. (D) DFS curves of stage II GC patients. (E) OS curves of stage III GC patients. (F) DFS curves of stage III GC patients.

The univariate analysis demonstrated that the following seven factors were associated with OS and DFS: age, gender, tumor size, cell differentiation, lymphovascular invasion, Lauren’s classification, and pathological Tumor, Node, Metastasis (TNM) stage (Table 3). The aforementioned seven variables were included in a multivariate Cox proportional hazards model to adjust for the effects of covariates. The multivariate analysis demonstrated that age, tumor size, and pathological TNM stage were independent prognostic factors affecting OS and DFS (Table 3).

Table 3. Univariate and multivariate analysis of factors affecting OS and DFS of GC patients after curative surgery.

Prognostic factors	Univariate analysis
	OS			DFS
	HR	95%CI	P value	HR	95%CI	P value
Age	1.46	1.135-1.878	0.003	1.40	1.095-1.795	0.007
Gender	0.72	0.546-0.948	0.019	0.71	0.543-0.938	0.016
Tumor size	2.44	1.850-3.227	<0.001	2.41	1.835-3.168	<0.001
Cell differentiation	0.76	0.600-0.963	0.023	0.75	0.596-0.951	0.017
Lymphovascular invasion	2.69	1.964-3.680	<0.001	2.57	1.890-3.485	<0.001
Lauren’s classification	1.33	1.044-1.689	0.021	1.32	1.044-1.680	0.021
Adjuvant chemotherapy	0.86	0.609-1.199	0.364	0.89	0.643-1.239	0.496
Pathological TNM stage	2.52	2.125-2.981	<0.001	2.33	1.985-2.735	<0.001
Prognostic factors	Multivariate analysis
	OS			DFS
	HR	95%CI	P value	HR	95%CI	P value
Age	1.47	1.126-1.920	0.005	1.39	1.071-1.808	0.013
Gender	1.11	0.827-1.491	0.485	1.07	0.799-1.436	0.646
Tumor size	1.43	1.066-1.929	0.017	1.46	1.086-1.951	0.012
Cell differentiation	0.89	0.614-1.300	0.556	0.87	0.604-1.255	0.458
Lymphovascular invasion	1.14	0.798-1.620	0.478	1.13	0.799-1.603	0.485
Lauren’s classification	1.08	0.740-1.586	0.680	1.06	0.728-1.540	0.765
Pathological TNM stage	2.29	1.868-2.809	<0.001	2.10	1.729-2.552	<0.001
CI: confidence interval; HR: hazard ratio; OS: overall survival; DFS: disease-free survival; TNM: Tumor, Node, Metastasis; Bold: statistically significant;

Discussion

For GC patients with curative surgery, our results showed that older patients had worse survival outcomes than younger patients; similar results were also obtained at each TNM stage. Age itself is an independent prognostic factor. Older patients had more liver metastasis than younger patients. Regarding the molecular analysis, older patients had more MSI-H tumors and ARID1A mutations than younger patients. Our novel findings demonstrated that the modular differences varied between younger and older patients.

A large series demonstrated that young-onset GC patients had more advanced disease than older-onset GC patients, and age was not an independent prognostic factor [2]. However, in the present study, tumor stage was not significantly different between younger and older patients for all enrolled patients. Younger patients might have had better survival outcomes than older patients because they had relatively better health conditions and were more willing to undergo and could better tolerate adjuvant chemotherapy. The percentage of GC patients receiving adjuvant chemotherapy was significantly higher among younger patients (18.9%) than older patients (8.4%). The low percentage of adjuvant chemotherapy in our patient population occurred because adjuvant chemotherapy was not routinely performed before 2008, after which time it was shown to have a survival benefit [10]. Although adjuvant chemotherapy was not associated with patient survival in the present study, the increase in the initial recurrence pattern in older patients may be associated with less use of chemotherapy than in younger patients. In addition, younger patients might have received more aggressive chemotherapy than older patients; however, this hypothesis was not tested because the detailed chemotherapy regimens for each patient were not available in our database. In the present study, older patients had a significantly higher non-cancer-related death rate than younger patients (36.2% vs. 12.3%, P<0.001), which was similar to the findings of Seo et al [2]. Consequently, another reason why older patients had a worse survival rate than younger patients may be due to the increased amount of non-cancer-related deaths among older patients.

For clinical use and economic concerns, we used mass spectrometric SNP genotyping technology for mutational analysis in the present study. However, integrating multiplatform genomic features is currently quite popular for cancer studies. Vertical integration of multidimensional omics data is indispensable to determine a subset of important prognostic features, such as cancer phenotype, cancer status, and patient survival [11]. Our future study will focus on integrating multiplatform genomic analysis, and we believe that integration studies on multi-omics data will provide insights into investigating novel biomarkers for cancer treatment.

Our results showed no significant difference in TP53 mutations between younger and older patients, which was inconsistent with the findings of Rahman et al [3], who demonstrated more TP53 mutations in younger patients than older patients. Rahman et al reported a relatively higher incidence of HP infection (51/71, 71.8%) and TP53 mutation (52/71, 73.2%) in their patient group than the present study, and a significant correlation between HP infection and TP53 mutation was observed. In their study, 63.4% of patients were smokers, and 75% of these patients had TP53 mutations. In addition, some patients had a routine habit of consuming excess salt in their daily meals. A high salt diet and HP infection have been associated with the development of GC [12, 13]. Furthermore, smoking was reported to be associated with TP53 mutation [14]. The incidence of TP53 mutation in patients younger than 40 years was 94.4% [3], while no patients with TP53 mutation were observed among patients in our study who were younger than 40 years. The discrepancy in TP53 mutations between the present study and the study by Rahman et al [3] might be due to differences in the incidence of HP infection and the smoking and eating habits, which are considered to be the environmental factors.

The novel finding of the present study is that older patients had more ARID1A mutations than younger patients, which has not previously been reported. Even though we divided the patients into three age groups, the frequency of ARID1A mutations increased significantly with age. In colorectal cancer, loss of ARID1A expression was associated with younger age [4]. ARID1A is identified as a tumor suppressor gene in various cancers, especially gynecologic cancers, and an inverse correlation between ARID1A expression and tumor stage has been reported [15–17]. In the present study, patients with ARID1A mutations had significantly more MSI-H tumors than patients without ARID1A mutations (25% vs. 7.8%, P=0.001), which was similar to the results of other studies [18]. As a result, our older patients had more MSI-H tumors than younger patients, which may lead to a higher frequency of ARID1A mutations in older patients.

Environmental factors such as HP infection and dietary and smoking habits are known to impact the histologic type and genetic alterations during gastric carcinogenesis. Patients with HP infection were more likely to have KRAS mutation than patients without HP infection [19]. Another environmental factor, such as EBV infection, was reported to be associated with genetic mutations (AKT2, CCNA1, MAP3K4, and TGFBR1) and hypermethylation (ACSS1, FAM3B, IHH, and TRABD) in GC tissues; AKT2 mutation was associated with a poor survival in GC patients with EBV infection [20]. Age might be treated as an environmental factor that is correlated with the gene-environment interactions, resulting in an association between age and clinicopathological and molecular features.

There are some limitations in the present study. First, this is a retrospective study and selection bias exists. Second, not all enrolled patients had available tumor samples for the analysis of genetic alterations. Third, younger patients with more advanced disease and older patients with poor physical conditions are all not candidates for surgery, excluding these patients may severely biased the assessment of effect of age on GC patients. Although the authors only want to study on patients with curative GC, these patients were firstly selected based on conditions suitable for surgery, age is less important in these patients than in all GC patients, which can help to guide prevention, prediction, prognosis and treatment. Forth, this study included patients hospitalized from 1998-2013, in which period the treatments of GC, such as adjuvant therapy, perioperative therapy, D2 gastrectomy, minimally invasive surgery, endoscopic therapy and targeted therapy, all are inconsistent between the patients and may affect prognosis. Fifth, according to The Cancer Genome Atlas (TCGA) dataset [21], the mutation frequencies of AKT1 (0.9%), AKT2 (2.3%), AKT3 (1.8%) and BRAF (3.0%) are very low. Although we don't know which of them are driver genes and which of them are passenger genes, the test power is too weak to make a meaningful outcome when to compare their differences.

In conclusion, age was associated with clinicopathological features, recurrence patterns, patient prognoses and genetic alterations in GC with curative surgery. Our findings might be advanced by investigating gene-environment interactions while treating age as an environmental factor.

Materials and Methods

Supplementary Materials

Supplementary Figures

Supplementary Tables