Blood-based machine learning classifiers for early diagnosis of gastric cancer via multiple miRNAs

Table 1 Basic information of the included plasma samples from three clinical centres, n (%)

Characteristics	Control (n = 107)	Early GC (n = 71)	Advanced GC (n = 97)	1P value	2P value
Gender
Male	37 (34.6)	38 (53.5)	67 (69.1)	0.012	< 0.001
Female	70 (65.4)	33 (46.5)	30 (30.9)
Age (year)
< 60	101 (94.4)	52 (73.2)	86 (88.7)	< 0.001	0.139
≥ 60	6 (5.6)	19 (26.8)	11 (11.3)
AJCC stage
I	0	71 (100)	37 (38.1)
II	0	0	5 (5.2)
III	0	0	17 (17.5)
IV	0	0	38 (39.2)
T
1	0	71 (100)	0
2	0	0	42 (43.3)
3	0	0	6 (6.2)
4	0	0	49 (50.5)
N
0	0	71 (100)	42 (43.3)
1	0	0	12 (12.4)
2	0	0	22 (22.7)
3	0	0	21 (21.6)
M
0	0	71 (100)	66 (68.0)
1	0	0	31 (32.0)

¹Early gastric cancer patients vs healthy controls.

²Advanced gastric cancer patients vs healthy controls.

GC: Gastric cancer; AJCC: American Joint Committee on Cancer.

Table 2 Detailed information regarding the included public serum samples

Dataset	Platform	Year	Country	GC (n)	Control (n)
Cohort-3: GSE211692	GPL21263	2022	Japan	1418	5643
Cohort-4: GSE112264	GPL21263	2019	Japan	50	41
Cohort-5: GSE106817	GPL21263	2018	Japan	115	2759

GC: Gastric cancer.