Clinicopathological Characteristics, Survival Outcomes, and Genetic Alterations of Younger Patient With Gastric Cancer: Results From China National Cancer Center and cBioPortal

doi:10.21203/rs.3.rs-862005/v1

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Research Article

Clinicopathological Characteristics, Survival Outcomes, and Genetic Alterations of Younger Patient With Gastric Cancer: Results From China National Cancer Center and cBioPortal

https://doi.org/10.21203/rs.3.rs-862005/v1

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published 22 Mar, 2022

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Background

Survival outcomes of younger patients with gastric cancer (GC) remains controversial. The present study sought to explore clinicopathological characteristics, survival outcomes, and genetic alterations in the younger and older patients with GC.

Methods

Patients with GC were identified from the China National Cancer Center Gastric Cancer Database (NCCGCDB) during 1998–2018. Survival analysis was conducted via Kaplan-Meier estimates and Cox proportional hazards models. Sequencing data were enrolled from the China National Cancer Center, TCGA, and MSKCC databases.

Results

A total of 1146 younger and 16988 older cases were included. Patients in the younger group were predominant in poor differentiation (53.7% versus 33.8%, P<0.0001), and pTNM stage IV (19.5% versus 11.8%, P<0.001). The 5-year overall survival (OS) of patients in NCCGCDB has noticeable increased from 1998 to 2018. Younger age was an independent prognostic factor for GC patients in pTNM stage III (P=0.014), while other stages showed no difference. Compared to the younger, older patients had a higher mutation frequency in LRP1B, GNAS, APC, KMT2D genes (all P< 0.05). In addition, although no significant difference, results of the China National Cancer Center, TCGA, and MSKCC cohorts indicated that younger patients dominated in CDH1, RHOA, and CTNNB1 mutations.

Conclusions

Stable proportion of younger cases and noticeable survival improvements were reported in the China National Cancer Center. Younger patients with pTNM stage III had a worse survival than older, while other tumor stages showed no difference. Furthermore, distinctive molecular characteristics were identified in younger GC patients, which might partly explain histopathological behaviors and prognosis of in this subpopulation.

Translational Medicine

Gastric cancer

Younger patient

Clinicopathological characteristics

Survival outcomes

Genetic alterations

Gastric cancer (GC) remained one of the most frequent digestive cancers and represented the fourth leading cause of cancer-related death worldwide [1]. More than 40 % of GC cases and deaths occurred in China, with an age-standardized 5-year survival rate of 35.1% [2, 3, 4]. It was generally considered that GC mostly onset in patients aged 50–70 years, and younger patients only account for 2.0-6.2% [5, 6]. Although the incidence of GC revealed a decline in the general population, concern has been raised about the stable or increased trends of younger patients in the last decades [7, 8, 9].

Younger patients with GC were more likely to exhibit distinctive biological behaviors, such as female predominance, poor differentiation, and advanced stage [10–13]. To date, results for the survival outcomes in younger patients with GC have been inconsistent [10–15]. Some studies reported that younger patients own a longer survival time than older [10, 11], whereas others showed no difference [12, 13]. Several studies even revealed poor survival of GC in younger adults [14, 15]. From the molecular perspective, the increased incidence of younger patients might owe to the distinctive pathogenesis, and certain genetic alterations have been demonstrated to play a critical role in the progression and prognosis of GC among age groups [16, 17]. However, recent knowledge regarding the mutation profiles in younger GC patients was limited, and a comprehensive molecular profile target younger patients should be defined to enhance understanding of molecular pathology in this subpopulation.

In this study, we sought to explore trends of clinicopathological characteristics and survival outcomes in younger and older patients from the China National Cancer Center over the past 20 years, which might serve as a reference for a large population-based study in China. Moreover, genetic alterations of younger patients were comprehensively investigated based on the China National Cancer Center and the cBioPortal for Cancer Genomics database, thus providing novel insights for the genomics and precision medicine of this subpopulation.

Data source and patient selection

The study population was described in detail in the previous research [18, 19]. Briefly, all histologically confirmed incident cases of GC diagnosed between 1998 and 2018 in the cancer center were identified through the China National Cancer Center Gastric Cancer Database (NCCGCDB). Enrollment criteria included age between 18 and 85 years, residence in China, and without a previous cancer diagnosis, except for GC. All GC cases that met the inclusion criteria were divided into two age groups: the younger group (< 40 years of age) and older group (≥ 40 years of age). The rationale for younger group was to remain consistent with the majority of prior studies [11, 20, 21]. Clinical data abstracted from the NCCGCDB included patients’ demographics, clinicopathological characteristics, and survival variables. Stage of GC was assessed according to the 8th edition of the American Joint Committee on Cancer (AJCC) TNM staging system. The primary endpoints of the study were OS and progression-free survival (PFS). According to the time of histological diagnosis, changing trends of the younger and older patients with GC were investigated in four consecutive periods: from 1998 to 2003 (period 1), from 2003 to 2008 (period 2), from 2008 to 2013 (period 3), and from 2013 to 2018 (period 4).

Of 29 patients with GC included in the further molecular analysis had their tumor specimens sequenced at our institution. All tumor samples were evaluated by pathologists prior to DNA extraction for sequencing. Moreover, we searched the online cBioPortal for Cancer Genomics (http://www.cbioportal.org/) to obtain high-volume genetic resources [19–22]. Finally, 8 gastric adenocarcinoma datasets from TCGA and MSKCC databases met our inclusion criteria, and a total of 1098 GC cases with clear clinical data and mutation detection by next-generation sequencing (NGS) were included in the comparative analysis.

DNA extraction and sequencing

DNA from tumor samples and peripheral blood was extracted via the QIAamp DNA Mini Kit (QIAGEN, Valencia, CA) according to the manufacturer’s instructions. QubitdsDNA assay was performed to measure the DNA concentration. A minimum of 50 ng of DNA is required for construction of an NGS library. For GC patients with available DNA, targeted DNA sequencing was performed. DNA was profiled by using a capture-based targeted sequencing panel (Burning Rock Biotech, Guangzhou, People’s Republic of China), targeting 520 genes and spanning 1.67M of Human genomic regions.

Sequencing data analysis

Sequencing data were mapped to the human genome (hg19) using BWA aligner 0.7.10. Local alignment optimization and variant calling were performed using GATK v3.2-2, while DNA translocation analysis was performed using both Tophat2 and Factera 1.4.3. Different mutation calling thresholds were applied on samples with different DNA qualities to avoid false-positive mutation calls due to DNA damage. Single nucleotide variants (SNVs) and insertion-deletions (INDELs) indels identified were annotated using the dbNSFP(v30a), COSMIC (v69), and dbSNP (snp138) database. Variants with a global minor allele frequency greater than 1.0% in 1000Genome Project (Phase3, http://www.1000genomes. org/data) were considered as common SNPs and removed. Tumor mutational burden (TMB) was defined as the number of somatic, coding, base substation, and indels per megabase of genome examined. Synonymous mutations were counted to reduce sampling noise. White blood cells were used to filter germline mutations.

Statistical Analysis

The bar chart was plotted to evaluate the variation in tumor stage for younger and older patients from 1998 to 2018. The Chi-squared test was performed to compare categorical variables between the two groups, while continuous variables were evaluated using Student’s t-test. OS and PFS for the younger and older group were calculated with the Kaplan-Meier method, while the log-rank test estimated the relevant survival discrepancy. Associations between risk factors and OS were investigated by univariate and multivariate Cox proportional hazard regression analysis, while the corresponding hazard ratio (HR) and 95% CI were generated. The covariates included in the final models were determined by using stepwise selection with minimized AIC. The significant level for adding variables was 0.05, and the significance level for removing variables was 0.10. Statistical significance was set at two-sided P values less than 0.05, and all analyses in the study were conducted using SAS software v9.4 (SAS Institute, Inc., Cary, NC).

Demographic and Clinicopathological features

From 1998 to 2018, a total of 1146 younger cases (6.3%) and 16988 older cases (93.7%) were finally included. Among all GC patients, 2035 were diagnosed in period 1, 3859 in period 2, 6054 in period 3, and 6150 in period 4 (As shown in Table 1). There were significant differences in the distribution of gender, smoking, alcohol history, BMI, primary tumor location, differentiation, pTNM stage between the younger group and older group (all P < 0.01). Distinctive demographic disparities among age groups were founded in the China National Cancer Center. Younger patients were predominant in females (50.1% versus 21.6%, P < 0.0001). Conversely, relatively higher percentages of smokers (21.1% versus 42.4%, P < 0.0001), alcohol drinkers (20.9% versus 34.5%, P < 0.0001), and overweight/obesity (BMI ≥ 23) (36.1% versus 53.4%, P < 0.0001) were revealed in older.

Table 1

Demographic and clinicopathological characteristics between younger group and older group in 4 consecutive periods (bidirectional cohort 1998-2018).
		Period 1 (1998-2003)				Period 2 (2004-2008)			Period 3 (2009-2013)			Period 4 (2014-2018)
		Younger group		Older group	P Value	Younger group	Older group	P Value	Younger group	Older group	P Value	Younger group	Older group	P Value
		n (%)		n (%)	P Value	n (%)	n (%)	P Value	n (%)	n (%)	P Value	n (%)	n (%)	P Value
Gender
	Male		83 (50.0%)	1,367 (73.14%)		112 (46.1%)	2,744 (75.1%)		209 (52.6%)	4,321 (76.4%)		168 (49.4%)	4,384 (75.5%)
	Female		83 (50.0%)	502 (26.86%)	<0.0001	131 (53.9%)	908 (24.9%)	<0.0001	188 (47.4%)	1,336 (23.6%)	<0.0001	172 (50.6%)	1,426 (24.5%)	<0.0001
Smoking history
	No		132 (79.5%)	1,180 (63.1%)		195 (80.3%)	2,242 (61.4%)		302 (76.1%)	3,110 (55.0%)		241 (70.9%)	2,813 (48.4%)
	Yes		33 (19.9%)	670 (35.9%)		39 (16.1%)	1,247 (34.2%)		84 (21.2%)	2,393 (42.3%)		86 (25.3%)	2,896 (49.9%)
	Unknown		1 (0.6%)	19 (1.0%)	0.0001	9 (3.7%)	163 (4.5%)	<0.0001	11 (2.8%)	154 (2.7%)	<0.0001	13 (3.8%)	101 (1.7%)	<0.0001
Drinking history
	No		131 (78.9%)	1,328 (71.1%)		206 (84.8%)	2,562 (70.2%)		304 (76.6%)	3,594 (63.5%)		230 (67.7%)	3,190 (54.9%)
	Yes		34 (20.5%)	520 (27.8%)		27 (11.1%)	919 (25.2%)		81 (20.4%)	1,908 (33.7%)		97 (28.5%)	2,519 (43.4%)
	Unknown		1 (0.6%)	21 (1.1%)	0.095	10 (4.1%)	171 (4.7%)	<0.0001	12 (3.0%)	155 (2.7%)	<0.0001	13 (3.8%)	101 (1.7%)	<0.0001
BMI (kg/m²) at diagnosis
	<18.5		29 (17.5%)	124 (6.6%)		50 (20.6%)	264 (7.2%)		52 (13.1%)	336 (5.9%)		54 (15.9%)	283 (4.9%)
	18.5-22.9		73 (44.0%)	733 (39.2%)		109 (44.9%)	1,346 (36.9%)		182 (45.8%)	2,162 (38.2%)		143 (42.1%)	2,101 (36.2%)
	23-27.4		48 (28.9%)	755 (40.4%)		53 (21.8%)	1,388 (38.0%)		110 (27.7%)	2,262 (40.0%)		98 (28.8%)	2,574 (44.3%)
	≥27.5		9 (5.4%)	196 (10.5%)		17 (7.0%)	423 (11.6%)		39 (9.8%)	701 (12.4%)		40 (11.8%)	766 (13.2%)
	Unknown		7 (4.2%)	61 (3.3%)	<0.0001	14 (5.8%)	231 (6.3%)	<0.0001	14 (3.5%)	196 (3.5%)	<0.0001	5 (1.5%)	86 (1.5%)	<0.0001
Weight loss
	None		61 (36.8%)	615 (32.9%)		104 (42.8%)	1,427 (39.1%)		241 (60.7%)	3,238 (57.2%)		209 (61.5%)	3,504 (60.3%)
	<10%		44 (26.5%)	461 (24.7%)		50 (20.6%)	842 (23.1%)		78 (19.7%)	1,419 (25.1%)		74 (21.8%)	1,531 (26.4%)
	≥10%		25 (15.1%)	191 (10.2%)		23 (9.5%)	339 (9.3%)		46 (11.6%)	547 (9.7%)		37 (10.9%)	557 (9.6%)
	Unknown		36 (22.7%)	602 (32.2%)	0.022	66 (27.2%)	1,044 (28.6%)	0.66	32 (8.1%)	453 (8.0%)	0.086	20 (5.9%)	218 (3.8%)	0.072
*H. pylori* infection
	Negative		0 (0.0%)	3 (0.2%)		1 (0.4%)	51 (1.4%)		23 (5.8%)	397 (7.0%)		59 (17.4%)	1,004 (17.3%)
	Positive		0 (0.0%)	11 (0.6%)		2 (0.8%)	42 (1.2%)		38 (9.6%)	522 (9.2%)		27 (7.9%)	524 (9.0%)
	Unknown		166 (100.0%)	1,855 (99.3%)	1.00	240 (98.8%)	3,559 (97.5%)	0.50	336 (84.6%)	4,738 (83.8%)	0.64	254 (74.7%)	4,282 (73.7%)	0.79
Primary tumor location
	Proximal		28 (16.9%)	836 (44.7%)		32 (13.2%)	1,403 (38.4%)		65 (16.4%)	2,164 (38.3%)		49 (14.4%)	2,115 (36.4%)
	Distal		126 (75.9%)	898 (48.1%)		198 (81.5%)	1,996 (54.7%)		296 (74.6%)	3,082 (54.5%)		265 (77.9%)	3,105 (53.4%)
	Overlapping lesions		6 (3.6%)	85 (4.6%)		3 (1.2%)	94 (2.6%)		19 (4.8%)	250 (4.4%)		15 (4.4%)	422 (7.3%)
	Unknown		6 (3.6%)	50 (2.7%)	<0.0001	10 (4.1%)	159 (4.4%)	<0.0001	17 (4.3%)	161 (2.9%)	<0.0001	11 (3.2%)	168 (2.9%)	<0.0001
Lauren classification
	Intestinal		0 (0.0%)	0 (0.0%)		0 (0.0%)	1 (0.0%)		16 (4.0%)	1,073 (19.0%)		23 (6.8%)	1,724 (29.7%)
	Diffuse		0 (0.0%)	1 (0.1%)		0 (0.0%)	1 (0.0%)		111 (28.0%)	942 (16.7%)		173 (50.9%)	1,380 (23.8%)
	Mixed		0 (0.0%)	1 (0.1%)		0 (0.0%)	4 (0.1%)		25 (6.3%)	607 (10.7%)		47 (13.8%)	1,115 (19.2%)
	Unknown		166 (100.0%)	1,867 (99.9%)	1.00	243 (100.0%)	3,646 (99.8%)	1.00	245 (61.7%)	3,035 (53.7%)	<0.0001	97 (28.5%)	1,591 (27.4%)	<0.0001
Borrmann classification
	Borrmann I		13 (7.8%)	210 (11.2%)		6 (2.5%)	316 (8.7%)		30 (7.6%)	537 (9.5%)		29 (8.5%)	587 (10.1%)
	Borrmann II		45 (27.1%)	750 (40.1%)		96 (39.5%)	1,327 (36.3%)		134 (33.8%)	2,115 (37.4%)		107 (31.5%)	2,085 (35.9%)
	Borrmann III		52 (31.3%)	400 (21.4%)		52 (21.4%)	859 (23.5%)		87 (21.9%)	1,304 (23.1%)		93 (27.4%)	1,666 (28.7%)
	Borrmann IV		16 (9.6%)	116 (6.2%)		23 (9.5%)	306 (8.4%)		37 (9.3%)	316 (5.6%)		20 (5.9%)	250 (4.3%)
	Mixed		2 (1.2%)	7 (0.4%)		0 (0.0%)	9 (0.3%)		1 (0.3%)	12 (0.2%)		11 (3.2%)	150 (2.6%)
	Unknown		38 (22.9%)	386 (20.7%)	0.0010	66 (27.2%)	835 (22.9%)	0.014	108 (27.2%)	1,373 (24.3%)	0.022	80 (23.5%)	1,072 (18.5%)	0.090
Linitis plastica
	No		162 (97.6%)	1,851 (99.0%)		232 (95.5%)	3,474 (95.1%)		359 (90.4%)	5,312 (93.9%)		321 (94.4%)	5,565 (95.8%)
	Yes		4 (2.4%)	9 (0.5%)		1 (0.4%)	13 (0.4%)		13 (3.3%)	57 (1.0%)		11 (3.2%)	72 (1.2%)
	Unknown		0 (0.0%)	9 (0.5%)	0.028	10 (4.1%)	165 (4.5%)	0.95	25 (6.3%)	288 (5.1%)	0.0001	8 (2.4%)	173 (3.0%)	0.0068
Differentiation
	Well		1 (0.6%)	44 (2.4%)		0 (0.0%)	60 (1.6%)		4 (1.0%)	126 (2.2%)		1 (0.3%)	130 (2.2%)
	Moderate		21 (12.7%)	658 (35.2%)		26 (10.7%)	1,162 (31.8%)		34 (8.6%)	1,882 (33.3%)		42 (12.4%)	2,167 (37.3%)
	Poor		87 (52.4%)	575 (30.8%)		116 (47.7%)	1,179 (32.3%)		216 (54.4%)	1,931 (34.1%)		196 (57.7%)	2,054 (35.4%)
	Undifferentiated		0 (0.0%)	1 (0.1%)		0 (0.0%)	0 (0.0%)		0 (0.0%)	2 (0.0%)		0 (0.0%)	1 (0.0%)
	Unknown		57 (34.3%)	591 (31.6%)	<0.0001	101 (41.6%)	1,251 (34.3%)	<0.0001	143 (36.0%)	1,716 (30.3%)	<0.0001	101 (29.7%)	1,458 (25.1%)	<0.0001
HER2 score
	0 (-)		1 (0.6%)	0 (0.0%)		3 (1.2%)	19 (0.5%)		90 (22.7%)	1,231 (21.8%)		120 (35.3%)	2,020 (34.8%)
	1 (+)		0 (0.0%)	1 (0.1%)		0 (0.0%)	3 (0.1%)		96 (24.2%)	1,291 (22.8%)		107 (31.5%)	1,523 (26.2%)
	2 (++)		0 (0.0%)	1 (0.1%)		0 (0.0%)	3 (0.1%)		27 (6.8%)	531 (9.4%)		19 (5.6%)	673 (11.6%)
	3 (+++)		0 (0.0%)	0 (0.0%)		0 (0.0%)	1 (0.0%)		12 (3.0%)	293 (5.2%)		7 (2.1%)	288 (5.0%)
	Unknown		165 (99.4%)	1,867 (99.9%)	0.23	240 (98.8%)	3,626 (99.3%)	0.46	172 (43.3%)	2,311 (40.9%)	0.13	87 (25.6%)	1,306 (22.5%)	0.0004
Pathologic T-stage
	T0+Tis		0 (0.0%)	1 (0.1%)		2 (0.8%)	13 (0.4%)		1 (0.3%)	20 (0.4%)		0 (0.0%)	24 (0.4%)
	T1		17 (10.2%)	159 (8.5%)		37 (15.2%)	357 (9.8%)		59 (14.9%)	796 (14.1%)		81 (23.8%)	1,269 (21.8%)
	T2		13 (7.8%)	135 (7.2%)		15 (6.2%)	248 (6.8%)		30 (7.6%)	491 (8.7%)		36 (10.6%)	554 (9.5%)
	T3		2 (1.2%)	13 (0.7%)		22 (9.1%)	311 (8.5%)		80 (20.2%)	1,712 (30.3%)		39 (11.5%)	1,159 (20.0%)
	T4		98 (59.0%)	1,156 (61.9%)		110 (45.3%)	1,903 (52.1%)		116 (29.2%)	1,374 (24.3%)		114 (33.5%)	1,654 (28.5%)
	TX		36 (21.7%)	405 (21.7%)	0.74	57 (23.5%)	820 (22.5%)	0.066	111 (28.0%)	1,264 (22.3%)	0.0004	70 (20.6%)	1,150 (19.8%)	0.0042
Pathologic N-stage
	N0		27 (16.3%)	418 (22.4%)		62 (25.5%)	830 (22.7%)		97 (24.4%)	1,518 (26.8%)		110 (32.4%)	1,893 (32.6%)
	N1		21 (12.7%)	273 (14.6%)		31 (12.8%)	520 (14.2%)		41 (10.3%)	775 (13.7%)		31 (9.1%)	729 (12.6%)
	N2		29 (17.5%)	310 (16.6%)		24 (9.9%)	510 (14.0%)		52 (13.1%)	797 (14.1%)		55 (16.2%)	766 (13.2%)
	N3		50 (30.1%)	459 (24.6%)		64 (25.3%)	888 (24.3%)		91 (22.9%)	1,222 (21.6%)		70 (20.6%)	1,195 (20.6%)
	NX		39 (23.5%)	409 (21.9%)	0.27	62 (25.5%)	904 (24.8%)	0.36	116 (29.2%)	1,345 (23.8%)	0.060	74 (21.8%)	1,227 (21.1%)	0.26
Pathologic M-stage
	M0		122 (73.5%)	1,563 (83.6%)		178 (73.3%)	2,939 (80.5%)		284 (71.5%)	4,517 (79.9%)		266 (78.2%)	4,882 (84.0%)
	M1		40 (24.1%)	261 (14.0%)		48 (19.8%)	430 (11.8%)		81 (20.4%)	714 (12.6%)		54 (15.9%)	601 (10.3%)
	MX		4 (2.4%)	45 (2.4%)	0.0020	17 (7.0%)	283 (7.8%)	0.0012	32 (8.1%)	426 (7.5%)	<0.0001	20 (5.9%)	327 (5.6%)	0.0051
pTNM stage
	0		0 (0.0%)	1 (0.1%)		2 (0.8%)	12 (0.3%)		1 (0.3%)	20 (0.4%)		0 (0.0%)	19 (0.3%)
	I		17 (10.2%)	210 (11.2%)		42 (17.3%)	470 (12.9%)		71 (17.9%)	1,010 (17.9%)		94 (27.7%)	1,422 (24.5%)
	II		10 (6.0%)	63 (3.4%)		24 (9.9%)	279 (7.6%)		40 (10.1%)	948 (16.8%)		34 (10.0%)	935 (16.1%)
	III		85 (51.2%)	1,103 (59.0%)		99 (40.7%)	1,895 (51.9%)		153 (38.5%)	2,182 (38.6%)		128 (37.7%)	2,048 (35.3%)
	IV		40 (24.1%)	261 (14.0%)		48 (19.8%)	430 (11.8%)		81 (20.4%)	714 (12.6%)		54 (15.9%)	601 (10.3%)
	Unknown		14 (8.4%)	231 (12.4%)	0.0039	28 (11.5%)	566 (15.5%)	<0.0001	51 (12.9%)	783 (13.8%)	<0.0001	30 (8.8%)	785 (13.5%)	0.0001
Lymphatic invasion
	No		76 (45.8%)	986 (52.8%)		123 (50.6%)	1,777 (48.7%)		183 (46.1%)	2,830 (50.0%)		152 (44.7%)	2,384 (41.0%)
	Yes		36 (21.7%)	357 (19.1%)		34 (14.0%)	749 (20.5%)		75 (18.9%)	1,182 (20.9%)		82 (24.1%)	1,959 (33.7%)
	Unknown		54 (32.5%)	526 (28.1%)	0.23	86 (35.4%)	1,126 (30.8%)	0.039	139 (35.0%)	1,645 (29.1%)	0.043	106 (31.2%)	1,467 (25.3%)	0.0008
Vascular invasion
	No		76 (45.8%)	988 (52.9%)		124 (51.0%)	1,776 (48.6%)		183 (46.1%)	2,828 (50.0%)		152 (44.7%)	2,375 (40.9%)
	Yes		36 (21.7%)	354 (18.9%)		34 (14.0%)	742 (20.3%)		74 (18.6%)	1,185 (21.0%)		82 (24.1%)	1,973 (34.0%)
	Unknown		54 (32.5%)	527 (28.2%)	0.22	85 (35.0%)	1,134 (31.1%)	0.051	140 (35.3%)	1,644 (29.1%)	0.031	106 (31.2%)	1,462 (25.2%)	0.0006
Nerve invasion
	No		110 (66.3%)	1,330 (71.2%)		153 (63.0%)	2,427 (66.5%)		186 (46.9%)	3,094 (54.7%)		117 (34.4%)	2,120 (36.5%)
	Yes		2 (1.2%)	13 (0.7%)		5 (2.1%)	100 (2.7%)		69 (17.4%)	917 (16.2%)		122 (35.9%)	2,321 (40.0%)
	Unknown		54 (32.5%)	526 (28.1%)	0.35	85 (35.0%)	1,125 (30.8%)	0.35	142 (35.8%)	1,646 (29.1%)	0.0064	101 (29.7%)	1,369 (23.6%)	0.034
Therapeutic regimen
	Surgery only		3 (1.8%)	99 (5.3%)		12 (4.9%)	149 (4.1%)		19 (4.8%)	347 (6.1%)		55 (16.2%)	871 (15.0%)
	Multimodality treatment		54 (32.5%)	452 (24.2%)		72 (29.6%)	908 (24.9%)		166 (41.8%)	1,800 (31.8%)		149 (43.8%)	2,052 (35.3%)
	Unknown		109 (65.7%)	1,318 (70.5%)	0.014	159 (65.4%)	2,595 (71.1%)	0.18	212 (53.4%)	3,510 (62.1%)	0.0002	136 (40.0%)	2,887 (49.7%)	0.0016
Surgical margin
	Negative		120 (72.3%)	1,355 (72.5%)		173 (71.2%)	2,650 (72.6%)		265 (66.8%)	4,110 (72.7%)		251 (73.8%)	4,417 (76.0%)
	Positive		11 (6.6%)	110 (5.9%)	0.92	4 (1.7%)	103 (2.8%)	0.41	8 (2.0%)	106 (1.9%)	0.037	7 (2.1%)	92 (1.6%)	0.58

As for tumor, younger patients were more common in distal location (77.2% versus 53.5%, P < 0.0001), Borrmann IV (8.4% versus 5.8%, P < 0.05), poor differentiation (53.7% versus 33.8%, P < 0.0001), and pTNM stage IV (19.5% versus 11.8%, P < 0.001). The percentage of negative surgical margin in the patients aged ≥ 40 was higher than that in younger patients, but there were no significant statistical differences in all four periods (all P ≥ 0.05). Among patients in period 3 and period 4, GC in the younger group was more likely to present with diffuse classification (P < 0.0001), and also had more lymphatic invasion and vascular invasion (P = 0.043, P = 0.0008, P = 0.031, P = 0.0006, resp.). In period 1, period 3 and period 4, younger patients with GC also demonstrated a better acceptance of multimodality treatment (32.5% versus 24.2%, P = 0.014; 41.8% versus 31.8%, P < 0.001; 43.8% versus 35.3%, P = 0.0016).

Trends of clinicopathological features of younger patients with GC over the past 20 years were investigated. The median proportion of patients in the younger group over time was 6.2% (range 5.5–8.2%). No trend for the proportion of young patients from period 2 to period 4 was noted on linear regression (P = 0.053). The proportion of HER2 score of 0(-) and 1(+) had increased from period 3 to period 4 (22.7–35.3%, 24.2–31.5%, resp.), while the score of 2(++) and 3(+++) showed downward trends (6.8–5.6%, 3.0–2.1%, resp.). As shown in Fig. 1, the percentages of pTNM tumor stage I and pTNM stage II in the younger group had increased gradually with time (from 10.2% in period 1 to 22.7% in period 4, from 6.0% in period 1 to 10.0% in period 4, resp.), whereas GC in pTNM stage III and pTNM stage IV showed a decrease (from 51.2% in period 1 to 37.7% in period 4, from 24.1% in period 1 to 15.9% in period 4, resp.). In the therapeutic regimen, both the percentages of younger patients undergoing surgery and the patients after multimodality treatment had increased from period 1 to period 4 (1.8–16.2%, 32.5–42.8%, resp.). As for surgical margin, a significant decrease was observed in positive margin for the younger group during 20 years. (from 6.6% in period 1 to 2.1% in period 4).

OS and PFS for younger patients with GC

Changing trends of OS and PFS between younger and older groups were shown in Table 2 and Fig. 2. In general, a noticeable survival increment of GC was demonstrated in Cancer Center during 20 years. The 5-year OS in younger and older group has increased from 44.4% (95%CI: 28.2%-60.7%) to 86.0% (95%CI: 81.7% -90.4%) and from 40.4% (95%CI: 36.0%-44.8%) to 86.3% (95%CI: 85.2%-87.4%), respectively. After stratification by pTNM stage, the increased survival of younger cases was pronounced in stages III and IV (31.3% in period 1 to 83.6% in period 4, 0% in period 1 to 55.5% in period 4, resp.). Moreover, the 5-year PFS of younger patients has increased from 17.1% (95%CI: 8.3% -26.0%) in period 1 to 65.5 % (95%CI: 58.4%-72.6%) in period 4, with an obvious dominance in pTNM stage III (8.1–62%).

Table 2

The 5-year overall and progression-free survival rates by cancer stages (bidirectional cohort 1998-2018).
Survival analysis		Total, % (95% Cl)	pTNM stage I, % (95% Cl)	pTNM stage II, % (95% Cl)	pTNM stage III, % (95% Cl)	pTNM stage IV, % (95% Cl)
Survival analysis		Total, % (95% Cl)	pTNM stage I, % (95% Cl)	pTNM stage II, % (95% Cl)	pTNM stage III, % (95% Cl)	pTNM stage IV, % (95% Cl)
Total
Overall survival
	Total (n = 13,533)	68.1 (67.2-68.9)	96.6 (95.8-97.3)	85.1 (83.3-86.8)	61.8 (60.4-63.2)	19.0 (16.3-21.8)
	Younger (n = 837)	61.8 (58.2-65.4)	99.5 (98.5-100.0)	80.6 (71.6-89.5)	55.1 (49.0-61.2)	16.2 (8.4-24.0)
	Older (n = 12,696)	68.5 (67.6-69.4)	96.3 (95.5-97.1)	85.3 (83.5-87.0)	62.3 (60.8-63.7)	19.4 (16.5-22.4)
Progression-free survival
	Total (n = 12,728)	53.4 (52.4-54.3)	90.4 (89.1-91.7)	75.4 (73.2-77.5)	46.8 (45.4-48.2)	8.5 (6.6-10.4)
	Younger (n = 856)	46.0 (42.4-49.6)	95.6 (92.4-98.8)	63.1 (52.2-74.1)	36.3 (30.7-42.0)	8.7 (3.6-13.8)
	Older (n = 11,872)	53.9 (53.0-54.9)	89.9 (88.6-91.3)	76.0 (73.9-78.2)	47.5 (46.0-49.0)	8.4 (6.3-10.4)
Period 1
Overall survival
	Total (n = 511)	40.7 (36.4-45.0)	94.0 (88.9-99.1)	78.3 (61.4-95.1)	32.4 (26.9-37.8)	4.8 (0.0-10.2)
	Younger (n = 36)	44.4 (28.2-60.7)	100.0 (100.0-100.0)	100.0 (100.0-100.0)	31.3 (8.5-54.0)	0.0 (0.0-0.0)
	Older (n = 475)	40.4 (36.0-44.8)	93.3 (87.7-99.0)	75.0 (56.0-94.0)	32.5 (26.8-38.1)	5.4 (0.0-11.3)
Progression-free survival
	Total (n = 794)	26.5 (23.4-29.5)	79.8 (71.9-87.7)	42.4 (25.6-59.3)	21.7 (17.9-25.4)	2.2 (0.0-5.1)
	Younger (n = 70)	17.1 (8.3-26.0)	88.9 (68.3-100.0)	16.7 (0.0-46.5)	8.1 (0.0-16.9)	0.0 (0.0-0.0)
	Older (n =724)	27.4 (24.1-30.6)	78.9 (70.5-87.3)	48.2 (29.3-67.0)	22.8 (18.8-26.8)	2.4 (0.0-5.7)
Period 2
Overall survival
	Total (n = 2,801)	60.7 (58.9-62.5)	93.9 (91.7-96.2)	77.2 (72.0-82.4)	57.5 (54.9-60.1)	12.2 (8.0-16.3)
	Younger (n = 178)	56.7 (49.5-64.0)	100.0 (100.0-100.0)	66.7 (46.5-86.8)	45.7 (34.0-5.74)	16.7 (3.3-30.0)
	Older (n = 2,623)	60.9 (59.1-62.8)	93.4 (90.9-95.8)	78.2 (72.8-83.5)	58.2 (55.5-60.8)	11.5 (7.2-15.9)
Progression-free survival
	Total (n = 2,561)	47.3 (45.4-49.2)	87.8 (84.5-91.0)	67.9 (62.0-73.7)	45.4 (42.7-48.0)	9.8 (6.1-13.4)
	Younger (n = 176)	46.0 (38.7-53.4)	94.3 (86.6-100.0)	52.2 (31.7-72.6)	37.3 (26.4-48.3)	16.1 (3.2-29.1)
	Older (n = 2,385)	47.4 (45.4-49.4)	87.1 (83.6-90.6)	69.5 (63.5-75.5)	45.9 (43.1-48.6)	8.9 (5.2-12.6)
Period 3
Overall survival
	Total (n = 5,211)	63.8 (62.5-65.1)	96.6 (95.5-97.7)	83.3 (80.8-85.7)	58.3 (56.2-60.5)	9.4 (6.9-11.9)
	Younger (n = 331)	52.9 (47.5-58.2)	98.6 (95.7-100.0)	80.0 (66.8-93.2)	49.2 (40.2-58.1)	6.7 (0.4-13.0)
	Older (n = 4,880)	64.6 (63.2-65.9)	96.4 (95.3-97.6)	83.4 (80.9-85.8)	58.9 (56.7-61.1)	9.8 (7.0-12.5)
Progression-free survival
	Total (n = 4,635)	50.1 (48.6-51.5)	91.3 (89.4-93.2)	74.4 (71.4-77.5)	43.7 (41.4-45.9)	4.3 (2.6-6.0)
	Younger (n = 318)	39.6 (34.2-45.0)	96.4 (91.6-100.0)	71.9 (56.3-87.5)	31.7 (23.5-39.9)	5.9 (0.3-11.5)
	Older (n = 4,317)	50.9 (49.4-52.3)	90.9 (88.9-92.9)	74.5 (71.5-77.6)	44.5 (42.2-46.9)	4.1 (2.4-5.9)
Period 4
Overall survival
	Total (n = 5,010)	86.3 (85.2-87.3)	98.7 (98.0-99.4)	94.1 (92.3-95.9)	83.5 (81.5-85.5)	53.0 (47.0-59.0)
	Younger (n = 292)	86.0 (81.7-90.4)	100.0 (100.0-100.0)	96.9 (90.8-100.0)	83.6 (75.8-91.4)	55.5 (37.5-73.5)
	Older (n = 4,718)	86.3 (85.2-87.4)	98.6 (97.9-99.3)	94.0 (92.1-95.8)	83.5 (81.4-85.6)	52.8 (46.5-59.1)
Progression-free survival
	Total (n = 4,738)	69.8 (67.9-71.7)	92.2 (89.4-94.9)	84.8 (81.9-87.7)	66.5 (63.3-69.6)	6.8 (0.3-13.4)
	Younger (n = 292)	65.5 (58.4-72.6)	95.3 (88.6-100.0)	80.3 (60.7-99.9)	62.0 (50.9-73.1)	0.0 (0.0-0.0)
	Older (n = 4,446)	70.1 (68.1-72.0)	91.9 (89.0-94.9)	85.0 (82.1-87.9)	66.8 (63.5-70.0)	7.7 (0.4-15.1)

Figure 3 revealed the Kaplan-Meier curves for OS between the younger and older groups (Fig. 3). The analysis showed younger patients had a worse survival outcome in stage III (P = 0.0095) but a better prognosis in stage I (P = 0.03). However, no significant difference was found in stage II and stage IV (P = 0.60, P = 0.37, resp.).

Prognostic Factors in Univariate and Multivariate Analyses

To investigate the significant factors impacting survival outcomes in young patients with GC, univariate and multivariate analyses were performed. The results revealed significantly different survival of GC based on the following parameters (Table 3): the period of diagnosis, drinking consumption, overweight/obesity (BMI ≥ 23, < 27.4), Weight loss ≥ 10%, H. pylori infection, distal tumor location, Borrmann IV, Linitis plastic, poor differentiation, pTNM stage II, III, and IV, Vascular invasion, and surgical margin (all P < 0.05). For the younger adults, prognosis factors only involved diagnosis period 4 (P < 0.0001), distal tumor location (P = 0.0007), Linitis plastica (P = 0.024), pTNM stage III (P < 0.0001), pTNM stage IV (P < 0.0001), and surgical margin (P = 0.011) (Supplementary Table 1).

Table 3

Univariate and multivariate survival analysis of predictors associated with overall survival for gastric cancer (bidirectional cohort 1998–2018).
Prognostic Factors		Univariate analysis			Multivariate anlaysis
Prognostic Factors		Hazard ratio	95% CI	P Value	Hazard ratio	95% CI	P Value
Period of diagnosis
	Period 1 (1998–2003)	1.00			1.00
	Period 2 (2004–2008)	0.54	0.48–0.61	< 0.0001	0.55	0.49–0.62	< 0.0001
	Period 3 (2009–2013)	0.42	0.42–0.53	< 0.0001	0.60	0.53–0.69	< 0.0001
	Period 4 (2014–2018)	0.18	0.16–0.21	< 0.0001	0.24	0.20–0.28	< 0.0001
Gender
	Male	1.00
	Female	1.06	0.99–1.14	0.091
Smoking history
	No	1.00
	Yes	0.90	0.84–0.96	0.0009
	Unknown	1.06	0.89–1.25	0.54
Drinking history
	No	1.00			1.00
	Yes	0.88	0.82–0.94	0.0001	0.93	0.87-1.00	0.045
	Unknown	1.06	0.89–1.25	0.52	0.86	0.70–1.07	0.18
BMI (kg/m²) at diagnosis
	< 18.5	1.17	1.04–1.32	0.0080	1.05	0.93–1.19	0.42
	18.5–22.9	1.00			1.00
	23-27.4	0.80	0.74–0.85	< 0.0001	0.93	0.86-1.00	0.036
	≥ 27.5	0.75	0.68–0.83	< 0.0001	0.94	0.85–1.05	0.25
	Unknown	1.51	1.31–1.74	< 0.0001	1.14	0.96–1.35	0.14
Weight loss
	None	1.00			1.00
	< 10%	1.38	1.28–1.48	< 0.0001	1.05	0.97–1.13	0.22
	≥ 10%	2.14	1.95–2.35	< 0.0001	1.22	1.11–1.35	< 0.0001
	Unknown	1.82	1.67–1.99	< 0.0001	1.07	0.97–1.19	0.16
H. pylori infection
	Negative	1.00			1.00
	Positive	1.64	1.32–2.05	< 0.0001	1.29	1.03–1.61	0.024
	Unknown	3.09	2.62–3.66	< 0.0001	1.54	1.30–1.83	< 0.0001
Primary tumor location
	Proximal	1.00			1.00
	Distal	0.87	0.81–0.92	< 0.0001	0.93	0.87-1.00	0.038
	Overlapping lesions	1.56	1.36–1.78	< 0.0001	1.10	0.96–1.27	0.18
	Unknown	1.20	1.02–1.40	0.026	0.65	0.53–0.80	< 0.0001
Lauren classification
	Intestinal	1.00			1.00
	Diffuse	1.83	1.58–2.12	< 0.0001	1.17	0.99–1.38	0.069
	Mixed	1.36	1.14–1.61	0.0005	0.97	0.81–1.15	0.70
	Unknown	3.75	3.33–4.23	< 0.0001	1.09	0.93–1.27	0.30
Borrmann classification
	Borrmann I	1.00			1.00
	Borrmann II	0.94	0.83–1.07	0.37	0.90	0.79–1.02	0.089
	Borrmann III	1.17	1.03–1.33	0.019	0.97	0.85–1.11	0.68
	Borrmann IV	2.48	2.13–2.88	< 0.0001	1.40	1.19–1.65	< 0.0001
	Mixed	0.42	0.25–0.70	0.0009	0.97	0.57–1.63	0.90
	Unknown	2.50	2.21–2.84	< 0.0001	1.06	0.93–1.21	0.42
Linitis plastica
	No	1.00			1.00
	Yes	2.33	1.84–2.94	< 0.0001	1.29	1.01–1.64	0.044
	Unknown	1.51	1.33–1.71	< 0.0001	0.85	0.72–0.99	0.042
Differentiation
	Well	1.00			1.00
	Moderate	3.10	2.03–4.74	< 0.0001	1.45	0.94–2.22	0.090
	Poor	4.48	2.94–6.83	< 0.0001	1.56	1.02–2.40	0.041
	Undifferentiated	0.001	0.001-999.999	0.91	0.000	0.000-999.999	0.90
	Unknown	9.73	6.39–14.81	< 0.0001	1.59	1.02–2.48	0.042
HER2 score
	0 (-)	1.00			1.00
	1 (+)	0.84	0.74–0.96	0.0082	0.96	0.85–1.09	0.54
	2 (++)	0.71	0.59–0.84	0.0001	0.90	0.75–1.08	0.27
	3 (+++)	1.05	0.85–1.29	0.68	1.08	0.87–1.33	0.51
	Unknown	2.41	2.20–2.64	< 0.0001	1.21	1.06–1.37	0.0042
Pathologic T-stage
	T0 + Tis	2.67	1.17–6.08	0.020	1.38	0.19–10.14	0.75
	T1	1.00			1.00
	T2	2.72	2.10–3.51	< 0.0001	1.44	1.07–1.95	0.017
	T3	7.01	5.70–8.62	< 0.0001	2.10	1.51–2.92	< 0.0001
	T4	11.29	9.24–13.80	< 0.0001	2.55	1.82–3.57	< 0.0001
	TX	19.07	15.58–23.33	< 0.0001	2.50	1.72–3.62	< 0.0001
Pathologic N-stage
	N0	1.00			1.00
	N1	2.28	1.98-2,63	< 0.0001	1.27	1.09–1.49	0.0020
	N2	3.52	3.09-4.00	< 0.0001	1.74	1.48–2.04	< 0.0001
	N3	6.85	6.11–7.67	< 0.0001	2.82	2.41–3.29	< 0.0001
	NX	8.72	7.79–9.75	< 0.0001	1.74	1.35–2.26	< 0.0001
Pathologic M-stage
	M0	1.00
	M1	5.76	5.34–6.22	< 0.0001
	MX	1.98	1.78–2.20	< 0.0001
pTNM stage
	0	2.53	1.04–6.19	0.042	1.76	0.20-15.49	0.61
	I	1.00			1.00
	II	3.70	2.99–4.57	< 0.0001	1.69	1.23–2.32	0.0012
	III	10.42	8.66–12.54	< 0.0001	1.85	1.29–2.66	0.0009
	IV	39.04	32.23–47.29	< 0.0001	5.04	3.42–7.42	< 0.0001
	Unknown	13.19	10.88–15.99	< 0.0001	1.80	1.21–2.68	0.0036
Lymphatic invasion
	No	1.00
	Yes	1.76	1.62–1.92	< 0.0001
	Unknown	3.73	3.47-4.00	< 0.0001
Vascular invasion
	No	1.00			1.00
	Yes	1.77	1.63–1.92	< 0.0001	1.17	1.07–1.28	0.0006
	Unknown	3.74	3.48–4.01	< 0.0001	1.17	1.00-1.38	0.054
Nerve invasion
	No	1.00
	Yes	1.06	0.96–1.16	0.23
	Unknown	3.15	2.95–3.37	< 0.0001
Therapeutic regimen
	Surgery only	1.00			1.00
	Multimodality treatment	3.73	3.04–4.58	< 0.0001	1.09	0.88–1.34	0.44
	Unknown	5.67	4.64–6.93	< 0.0001	1.28	1.04–1.57	0.022
Surgical margin
	Negative	1.00			1.00
	Positive	2.47	2.08–2.94	< 0.0001	1.24	1.04–1.48	0.015
	Unknown	3.10	2.91–3.30	< 0.0001	1.28	1.10–1.50	0.0020
Age group
	Younger group (< 40 years)	1.00			1.00
	Older group (≥ 40 years)	0.80	0.71–0.90	0.0001	0.92	0.82–1.04	0.20
The final model was builded by using stepwise selection with minimized AIC,and the covariates included in the final models were selected by the stepwise selection method, with a signifcant level for adding variables of 0.05 and a significant level of removing variables of 0.10.

In the univariate analysis, older patients with GC had better survival outcomes than the younger (HR = 0.80, 95% CI: 0.71–0.90, P = 0.0001). After stratification by pTNM stage, compared to the older, younger group showed a better prognosis in stage I (P = 0.04) but worse in stage III (P < 0.01) (Supplementary Table 2). However, the multivariate analysis demonstrated that younger age was not an independent factor for poor survival outcomes (P = 0.20). In subdivided pTNM stages, a comparison presented that younger group was associated with worse survival outcomes in tumor stage III (P = 0.014), but no statistical significance in stages I, II, and IV (P = 0.074, 0.59 and 0.76, resp.).

Genetic alterations of younger patients with GC

The genetic alterations of GC patients from the China National Cancer Center were shown in Fig. 4A and Supplementary Fig. 1. Of 29 eligible patients with cancer genomics data, 20 were older (68.9%), and the rest of 9 were younger (31.1%). Although no statistical disparity was found in the mutational profile, younger patients have a higher mutation frequency in RHOA (33% versus 15%), CDH1(22% versus 5%), KMT2D(22% versus 10%), SMAD4(11% versus 0%), ARID1A(11% versus 5%), ATM (11% versus 5%), CTNNB1(11% versus 5%), and PIK3CA(11% versus 5%), while older patients had dominated in TP53(70% versus 44%), LRP1B(30% versus 11%), FGFR2 (20% versus 0%), ARID2 (15% versus 0%), EP300 (15% versus 0%), GNAS(15% versus 11%), IRS2 (15% versus 11%),BRCA2 (10% versus 0%), and APC (5% versus 0%). Moreover, a high-volume analysis of the relationship between genetic alterations and age groups was further performed via the TCGA and MSKCC databases (Fig. 4B and Supplementary Fig. 2). GC tissues from older patients had higher likelihoods of LRP1B (30% versus 9%, P < 0.05), KMT2D (13% versus 0%, P < 0.05), APC (13% versus 2%, P < 0.05), GNAS mutations (11% versus 1%, P < 0.05) than the younger. Similar to the China National Cancer Center, although no significant differences were found, younger patients had mutation predominance in CDH1 (17% versus 9%), RHOA (7% versus 5%), and CTNNB1 mutations (9% versus 6%). In addition, of the pooled 8 gastric adenocarcinoma datasets, high-TMB was closely associated with older age at diagnosis (P < 0.0001) (Supplementary Fig. 3).

Our study provided a comprehensive analysis of younger GC patients at clinical and molecular levels. A primary finding was the stable proportion and significant survival improvements of younger cases in the China National Cancer Center from 1988 to 2018. Younger age was proven to be an independent prognostic factor for GC patients in pTNM stage III, while patients in stage I, stage II, and stage IV had similar survival among different age groups. Compare to the younger, a higher mutation frequency of LRP1B, KMT2D, APC, and GNAS genes in older patients was demonstrated in the current sequencing analysis, which might partly explain the histopathological behaviors and prognosis between the young and old groups.

Consistent with the previous study [10–15, 23, 24], our findings revealed unique clinical features in younger patients, including female dominance, higher proportions of poor differentiation, and advanced tumor stage. Although the mechanisms for female predominance in younger patients were not clear, some studies indicated that hormonal factors might account for the sex difference [25, 26]. Apart from H. pylori infection [27] and some gene distinctions [28], the vague symptoms and delayed diagnosis were also considerable reasons for the advanced stage of younger patients [29, 30].

With an increase of 41.6% for the younger group and 45.9% for the older group, a significant 5-year survival improvement of GC was revealed in our center, which was in line with the increasing trend of pTNM stages I and II, surgery, and multimodality treatment. In China, survival trends of GC could be attributed to an improvement of quality of clinical services, including the improved access to primary healthcare, greater availability of diagnostic facilities, as well as enhanced effectiveness of the treatment [4]. Until 2015, the screening and early detection programs including GC have expanded to 31 provinces [31]. Encouragingly, the detection rate of GC has increased steadily benefit from the large-scale application of upper gastrointestinal endoscopy [32]. In addition, the widespread decision-making by the multi-disciplinary team (MDT) [33] and the emergence of individually multimodal therapies [34] were also closely related to a better prognosis for GC.

To date, survival outcomes of younger patients were still controversial. Previous studies suggested younger patients had a worse survival [14, 15], while several analyses showed on difference [12, 13]. Current data demonstrated a worse prognosis for younger patients in pTNM stage III, whereas other stages showed similar survival. Therefore, variations of prognosis between the younger and older group might be related to stage distributions. Compare to older patients, the higher proportion of multimodality treatment in younger reflected a better tolerance for GC, while more advanced stage and aggressive behaviors might be related to a worse survival in this subpopulation [14]. Besides, the reduced organ functions and rising risk of comorbidities also potentially effect GC prognosis that occurred with age [35]. In addition, although genomic profiles of younger patients were not yet fully ascertained, several studies demonstrated a strong association between certain gene mutations and the prognosis of GC among age groups [16, 17].

The present study observed the differences in the prevalence of several gene mutations between younger and older patients, indicating a specific mutational spectrum in younger patients with GC. Significantly, a higher prevalence of LRP1B mutations were identified in old patients. LRP1B encoded a unique low-density lipoprotein receptor (LDLR) that functioned in the binding and internalization of ligands [36]. Until now, the concrete functions of LRP1B in carcinogenesis were controversial [37–40]. Considered as a tumor suppressor, several findings indicated the impaired LRP1B expression could promote the proliferation, migration, and invasion of carcinoma cells [38, 39]. However, the latest update of the Network of Cancer Genes (NCG 6.0) listed LRP1B as a potentially false-positive tumor suppressor [39]. Zhou et al. [40] also demonstrated LRP1B was one of the recurrently mutated driver genes, which was strongly associated with the development of GC. Further analysis was required to investigate the pathobiological property of LRP1B in GC.

We also observed that older patients had a higher likelihood of GNAS mutations than younger. GNAS was situated on chromosome 20q13.3 and encoded alpha subunit of the stimulatory G-protein [41]. It was suggested that mutations of GNAS caused the activation of Wnt and ERK1/2 MAPK pathways via autonomous synthesis of cyclic adenosine monophosphate (cAMP), thus promoting intestinal tumorigenesis [42]. After that, several studies demonstrated the frequent GNAS mutations in gastric adenocarcinoma [43, 44]. Consistent to current study, Esser et al.[41] also indicated the expression of GNAS was more prevalent in well- and moderately-differentiated GC, which was potentially correlated with older patients. Although the underlying mechanism was currently unclear, GNAS mutation might potentially affect the progression of GC via the protein kinase A (PKA), MAPK, and Wnt signaling pathways [45].

As in the past reports [46, 47], our analysis showed older patients had a higher prevalence of APC mutation than younger. Long considered a canonical tumor suppressor, APC could inhibit the excessive proliferation of tumor cells via regulating Wnt signaling pathway [48]. Mutation of APC was common in GC, particularly in well-differentiated adenocarcinoma [49, 50]. Zhan et al. [51] proposed a strong synergy between APC alterations and MEK inhibitors in enhancing the signaling output of the Wnt cascades. Gerner et al. [52] also observed mutant APC was associated with the elevation of nitric oxide synthase 2 and the dysregulation of polyamine metabolism. These data suggested that APC mutation might contribute to the oncogenesis of GC. In addition, a recent study also demonstrated a significant association target high expression of APC and adverse prognosis of GC patients [53]. The observed prognostic alteration could be the result of the disruption of Wnt cascades, metabolic dysregulation, or some other unknown mechanisms.

KMT2D was among the most frequently mutated genes in some types of cancer including GC [54–57]. Through encoding histone methyltransferase, KMT2D was critically involved in the epigenetic and transcriptional regulation of certain tumor-associated genes [56–60]. Lv et al. [56] suggested that KMT2D potentially prompt prostate carcinogenesis and metastasis via activating LIFR and KLF4 gene. Later, Xiong et al. [57] confirmed that KMT2D might play a role in cell proliferation and apoptosis of GC via the down-regulation of PTEN and up-regulation of LIFR and KLF4. However, recent studies also found the inhibiting effects of KMT2D in medulloblastoma [58], lung tumors [59], and lymphoma [60], as KMT2D could activate some specific pro-apoptotic genes or repress certain genes related to cell growth and survival pathways. The above results revealed that the KMT2D gene had different functions and biological effects depending on the type of cancer. Furthermore, several studies indicated that mutations of KMT2D were substantially higher in older patients than in younger [61, 62], and the age disparity was also demonstrated in our analysis. More experimental studies were necessary to elucidate the underlying functions of KMT2D in GC among age groups.

Our study has several strengths. First, clinicopathological characteristics and survival trends of the younger patients from 1998 to 2018 were comprehensively described based on NCCGCDB, which might serve as a reference for a large population-based study in China. Second, our study, for the first time, suggested distinctive mutation disparities including LRP1B, GNAS, APC, and KMT2D genes in younger and older patients, which might partly explain the progression and survival of GC among age groups in molecular levels. The major limitation of our study was the insufficient sequencing sample size, particularly for pTNM subtype analysis. As such, possibility might not be ruled out for some of the significant findings. Based on the TCGA and MSKCC, we further analyze a larger-volume sequencing sample to compare genetic alterations among age groups. In addition, regional and racial disparity of younger patients from China and western counties might also exist, which could affect the clinicopathological and molecular property of GC potentially. Therefore, parts of findings from TCGA and MSKCC might not be strongly generalizable to Chinese patients. Finally, some variables including tumor markers, neoadjuvant therapy, and adjuvant therapy were also needed to compare between the younger and older patients.

Our study found a stable proportion of young cases and significant survival improvements of younger patients in the China National Cancer Center from 1998 to 2018. Multivariate analysis suggested that younger patients with pTNM stage III had a worse survival than older, while other tumor stages showed no difference. Distinctive genetic alterations were further identified in younger patients, thus improving precision and personalized medicine in this subpopulation. Further large-scale studies are warranted to investigate more molecular characteristics and related mechanisms for GC patients at younger age.

GC: gastric cancer; NCCGCDB: China National Cancer Center Gastric Cancer Database; OS: overall survival; AJCC: the American Joint Committee on Cancer; PFS: progression-free survival; NGS: next-generation sequencing; SNVs: single nucleotide variants; INDELs: insertion-deletions; TMB: tumor mutational burden; HR: hazard ratio; LDLR: low-density lipoprotein receptor; cAMP: cyclic adenosine monophosphate; PKA: the protein kinase A

Ethics approval and consent to participate

The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was approved by the ethics committee of National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (No. 17-156/1412).

Consent for publication

Informed consent was obtained from all participants for publication.

Availability of data and materials

All data generated during this study are included in this published article and its supplementary files.

Competing interests

No potential conflicts of interest were disclosed.

Funding

This work was supported in part by the National Key R&D Program of China (No. 2017YFC0908300).

Authors' contributions

YC and DZ considered and designed the study. PN, HH and LZ performed experiments. TW, XZ and WW did data analyses. TW, XZ and WW collected tumor samples, tissue specimens and clinical data. PN, HH and LZ wrote the manuscript. YZ and CG contributed to the revision of manuscript. All authors read and approved the final manuscript.

Acknowledgements

We thank anonymous reviewers and editor for reading and commenting on the manuscript.

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FigureS1.jpg
Figure S1. The mutation spectrum of gastric cancer between younger and older groups based on the China National Cancer Center.
FigureS2.jpg
Figure S2. The mutation spectrum of younger patients with gastric cancer based on the TCGA and MSKCC databases.
FigureS3.png
Figure S3. Comparison of tumor mutation burden (TMB) between younger and older group. (A) TMB in the China National Cancer Center. (B) TMB in TCGA and MSKCC databases.
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Document S1. Funding Agency Copy of any Approval Document.
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Document S2. Institutional Review Board Approval Document.
SupplementalTables.docx
Table S1. Multivariate survival analysis of predictors associated with overall survival by age group (bidirectional cohort 1998–2018). Table S2. Multivariate survival analysis between younger group and older group. 2a: total patients; 2b: pTNM I patients; 2c: pTNM II patients; 2d: pTNM III patients; 2e: pTNM IV patients.

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Clinicopathological Characteristics, Survival Outcomes, and Genetic Alterations of Younger Patient With Gastric Cancer: Results From China National Cancer Center and cBioPortal

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