Towards a potential pan-cancer prognostic signature for gene expression based on probesets and ensemble machine learning

Chicco, Davide; Alameer, Abbas; Rahmati, Sara; Jurman, Giuseppe

doi:10.1186/s13040-022-00312-y

BioData Mining

Table 1 List of gene expression datasets employed in our analysis, sorted by cancer type

From: Towards a potential pan-cancer prognostic signature for gene expression based on probesets and ensemble machine learning

	dataset name	GEO code	cancer type	neg#	pos#	samples#	neg%	pos%
1	dataHeaton2011	GSE33371	adrenocortical cancer	16	7	23	69.57	30.43
2	dataReister2012	GSE31684	bladder cancer	38	27	65	58.46	41.54
3	dataDedeurwaerder2011	GSE20711	breast cancer	63	25	88	71.59	28.41
4	dataDesmedt2007	GSE7390	breast cancer	141	56	197	71.57	28.43
5	dataHatzis2009	GSE25066	breast cancer	152	45	197	77.16	22.84
6	dataHuang2014	GSE48390	breast cancer	11	69	80	13.75	86.25
7	dataIvshina2006	GSE4922	breast cancer	160	89	249	64.26	35.74
8	dataJezequel2015	GSE58812	breast cancer	29	77	106	27.36	72.64
9	dataKarn2011	GSE31519	breast cancer	22	41	63	34.92	65.08
10	dataKim2020	GSE135565	breast cancer	7	76	83	8.43	91.57
11	dataLin2009	GSE19697	breast cancer	6	17	23	26.09	73.91
12	dataLoi2008	GSE9195	breast cancer	63	13	76	82.89	17.11
13	dataMetzgerFilho2016	GSE88770	breast cancer	19	97	116	16.38	83.62
14	dataMiller2013	GSE45255	breast cancer	116	18	134	86.57	13.43
15	dataSabatier2010	GSE21653	breast cancer	168	83	251	66.93	33.07
16	dataSchmidt2008	GSE11121	breast cancer	154	45	199	77.39	22.61
17	dataSinn2019	GSE124647	breast cancer	43	96	139	30.94	69.06
18	dataWang2010	GSE19615	breast cancer	14	100	114	12.28	87.72
19	dataYenamandra2015	GSE61304	breast cancer	38	20	58	65.52	34.48
20	dataBeauchamp2014	GSE38832	colorectal cancer	28	93	121	23.14	76.86
21	dataChen2020	GSE161158	colorectal cancer	145	59	204	71.08	28.92
22	dataDelRoi2017	GSE72970	colorectal cancer	32	91	123	26.02	73.98
23	dataGotoh2018	GSE92921	colorectal cancer	53	5	58	91.38	8.62
24	dataMarisa2013	GSE39582	colorectal cancer	384	194	578	66.44	33.56
25	dataShinto2020	GSE143985	colorectal cancer	75	15	90	83.33	16.67
26	dataSieber2010	GSE14333	colorectal cancer	50	176	226	22.12	77.88
27	dataSmith2009a	GSE17536	colorectal cancer	73	103	176	41.48	58.52
28	dataSmith2009b	GSE17537	colorectal cancer	20	34	54	37.04	62.96
29	dataStaub2009	GSE12945	colorectal cancer	12	49	61	19.67	80.33
30	dataHerold2011	GSE22762	leukemia	26	17	43	60.47	39.53
31	dataHerold2013	GSE37642	leukemia	307	109	416	73.80	26.20
32	dataMetzeler2018	GSE12417	leukemia	103	59	162	63.58	36.42
33	dataSpivak2014	GSE47018	leukemia	7	13	20	35.00	65.00
34	dataBild2005	GSE3141	lung cancer	57	53	110	51.82	48.18
35	dataBotling2012	GSE37745	lung cancer	144	51	195	73.85	26.15
36	dataHeiskanen2015	GSE68465	lung cancer	236	207	443	53.27	46.73
37	dataKohno2011	GSE31210	lung cancer	35	191	226	15.49	84.51
38	dataMicke2011	GSE28571	lung cancer	52	47	99	52.53	47.47
39	dataPhilipsen2010	GSE19188	lung cancer	49	32	81	60.49	39.51
40	dataPintilie2013	GSE50081	lung cancer	75	105	180	41.67	58.33
41	dataPotti2006	GSE3593	lung cancer	54	143	197	27.41	72.59
42	dataRousseaux2013	GSE30219	lung cancer	199	93	292	68.15	31.85
43	dataSon2007	GSE8894	lung cancer	68	69	137	49.64	50.36
44	dataTsao2010	GSE14814	lung cancer	60	72	132	45.45	54.55
45	dataXie2011	GSE29013	lung cancer	18	36	54	33.33	66.67
46	dataZChen2020	GSE157011	lung cancer	219	264	483	45.34	54.66
47	dataIqbal2015	GSE58445	lymphoma	76	50	126	60.32	39.68
48	dataKawaguchi2012	GSE34771	lymphoma	23	10	33	69.70	30.30
49	dataLeich2009	GSE16131	lymphoma	91	88	179	50.84	49.16
50	dataLenz2008	GSE10846	lymphoma	165	249	414	39.86	60.14
51	dataVanLoo2009	GSE7788	lymphoma	6	9	15	40.00	60.00
52	dataMulligan2007	GSE9782	multiple myeloma	103	160	263	39.16	60.84
53	dataShi2010	GSE24080	multiple myeloma	78	480	558	13.98	86.02
54	dataHiyama2009	GSE16237	neuroblastoma	11	39	50	22.00	78.00
55	dataUehara2015	GSE65986	ovarian cancer	6	48	54	11.11	88.89
56	dataBogunovic2009	GSE19234	skin cancer	20	23	43	46.51	53.49
57	dataPasini2021	GSE38749	stomach cancer	9	5	14	64.29	35.71
	average			77.70	79.68	157.39	48.29	51.71
	median			53	56	121	49.64	50.36
	minimum			6	5	14	8.43	8.62
	maximum			384	480	578	91.38	91.57

All these datasets are based on the GPL96, GPL97, or GPL570 Affymetrix platforms and were downloaded from Gene Expression Omnibus (GEO) in April and May 2021.Positive sample: survived patient diagnosed with cancer. Negative sample deceased patient diagnosed with cancer. pos# number of positive samples in the dataset. neg# number of negative samples in the dataset. pos% percentage of positive samples in the dataset. neg% percentage of negative samples in the dataset. These prognostic datasets refer to 12 different cancer types: 17 breast cancer datasets, 13 lung cancer datasets, 10 colorectal cancer datasets, 5 lymphoma datasets, 4 leukemia datasets, 2 multiple myeloma datasets, 1 dataset for adrenocortical cancer, bladder cancer, neuroblastoma, ovarian cancer, skin cancer, and stomach cancer

Back to article page

ISSN: 1756-0381

Contact us

General enquiries: journalsubmissions@springernature.com