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pmid:30719133

High Expression Levels of AGGF1 and MFAP4 Predict Primary Platinum-Based Chemoresistance and are Associated with Adverse Prognosis in Patients with Serous Ovarian Cancer.

Haiyue Zhao · Qian Sun · Lisong Li · Jinhua Zhou · Cong Zhang · Ting Hu · Xuemei Zhou · Long Zhang · Baiyu Wang · Bo Li · Tao Zhu · Hong Li

Journal of Cancer · 2019 doi:10.7150/jca.28127

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title
High Expression Levels of AGGF1 and MFAP4 Predict Primary Platinum-Based Chemoresistance and are Associated with Adverse Prognosis in Patients with Serous Ovarian Cancer.
doi
10.7150/jca.28127
pmid
30719133
authors array · 12 items
[0]
Haiyue Zhao
[1]
Qian Sun
[2]
Lisong Li
[3]
Jinhua Zhou
[4]
Cong Zhang
[5]
Ting Hu
[6]
Xuemei Zhou
[7]
Long Zhang
[8]
Baiyu Wang
[9]
Bo Li
[10]
Tao Zhu
[11]
Hong Li
journal
Journal of Cancer
year
2019
abstract
Primary platinum-based chemoresistance occurs in approximately one-third of patients with serous ovarian cancer (SOC); however, traditional clinical indicators are poor predictors of chemoresistance. So we aimed to identify novel genes as predictors of primary platinum-based chemoresistance. Gene expression microarray analyses were performed to identify the genes related to primary platinum resistance in SOC on two discovery datasets (GSE51373, GSE63885) and one validation dataset (TCGA). Univariate and multivariate analyses with logistic regression were performed to evaluate the predictive values of the genes for platinum resistance. Machine learning algorithms (linear kernel support vector machine and artificial neural network) were applied to build prediction models. Univariate and multivariate analyses with Cox proportional hazards regression and log-rank tests were used to assess the effects of these gene signatures for platinum resistance on prognosis in two independent datasets (GSE9891, GSE32062). AGGF1 and MFAP4 were found highly expressed in patients with platinum-resistant SOC and independently predicted platinum resistance. Platinum resistance prediction models based on these targets had robust predictive power (highest AUC: 0.8056, 95% CI: 0.6338-0.9773; lowest AUC: 0.7245, 95% CI: 0.6052-0.8438). An AGGF1- and MFAP4-centered protein interaction network was built, and hypothetical regulatory pathways were identified. Enrichment analysis indicated that aberrations of extracellular matrix may play important roles in platinum resistance in SOC. High AGGF1 and MFAP4 expression levels were also related to shorter recurrence-free and overall survival in patients with SOC after adjustment for other clinical variables. Therefore, AGGF1 and MFAP4 are potential predictive biomarkers for response to platinum-based chemotherapy and survival outcomes in SOC.
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Univariate analysis with Cox proportional hazards regression model was used to evaluate the correlations of AGGF1 and MFAP4 expression statuses with RFS and OS, and multivariate analysis with Cox proportional hazards regression model was applied to test the independent predictive ability of these two gene signatures for RFS and OS after adjusting for various clinical characteristics.
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sections[9] "Survival analysis of AGGF1 and MFAP4" paragraph 2
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Univariate analysis with Cox proportional hazards regression model was used to evaluate the correlations of AGGF1 and MFAP4 expression statuses with RFS and OS, and multivariate analysis with Cox proportional hazards regression model was applied to test the independent predictive ability of these two gene signatures for RFS and OS after adjusting for various clinical characteristics.
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GSE63885 and GSE51373 were defined as the discovery sets to explore gene signatures with high efficiency to distinguish platinum-resistant and platinum-sensitive patients, and TCGA SOC dataset (hereinafter inferred as TCGA) was analyzed as the validation dataset.
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sections[5] "Screening and validation of platinum resistance-related genes" paragraph 2
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GSE63885 and GSE51373 were defined as the discovery sets to explore gene signatures with high efficiency to distinguish platinum-resistant and platinum-sensitive patients, and TCGA SOC dataset (hereinafter inferred as TCGA) was analyzed as the validation dataset.
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sample_count
101
group_sizes
null
group_sizes_basis
null
platform
570
taxon
Homo sapiens
title
Gene expression profiling in ovarian cancer
resolution_checked_at
2026-11-01T15:39:55Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
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[4] object · 15 fields
id
data:geo:GSE9891
role
raw
format
geo-series
media_type
application/vnd.ncbi.geo-series+json
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https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE9891
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null
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null
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reader_status
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GSE9891
version
null
note
GSE63885 and GSE51373 were defined as the discovery sets to explore gene signatures with high efficiency to distinguish platinum-resistant and platinum-sensitive patients, and TCGA SOC dataset (hereinafter inferred as TCGA) was analyzed as the validation dataset.
structure object · 15 fields
repository
GEO
access_route
GEO accession URL
access_statement_verbatim
GSE63885 and GSE51373 were defined as the discovery sets to explore gene signatures with high efficiency to distinguish platinum-resistant and platinum-sensitive patients, and TCGA SOC dataset (hereinafter inferred as TCGA) was analyzed as the validation dataset.
access_statement_kind
repository_route_statement
sample_accessions array · 285 items
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[43]
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[48]
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[49]
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[51]
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[52]
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[58]
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[59]
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[60]
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[61]
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[62]
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[63]
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[64]
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[65]
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[66]
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[67]
GSM249782
[68]
GSM249783
[69]
GSM249784
[70]
GSM249785
[71]
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[72]
GSM249788
[73]
GSM249789
[74]
GSM249790
[75]
GSM249791
[76]
GSM249792
[77]
GSM249793
[78]
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[79]
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[80]
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[81]
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[82]
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[83]
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[84]
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[85]
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[86]
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[87]
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[88]
GSM249804
[89]
GSM249805
[90]
GSM249806
[91]
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[92]
GSM249808
[93]
GSM249809
[94]
GSM249810
[95]
GSM249811
[96]
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[97]
GSM249813
[98]
GSM249814
[99]
GSM249815
[100]
GSM249816
[101]
GSM249817
[102]
GSM249818
[103]
GSM249819
[104]
GSM249820
[105]
GSM249821
[106]
GSM249822
[107]
GSM249823
[108]
GSM249824
[109]
GSM249825
[110]
GSM249826
[111]
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[112]
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[113]
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[114]
GSM249830
[115]
GSM249831
[116]
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[117]
GSM249833
[118]
GSM249834
[119]
GSM249835
[120]
GSM249836
[121]
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[122]
GSM249838
[123]
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[124]
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[125]
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[126]
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[127]
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[128]
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[129]
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[130]
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[131]
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[132]
GSM249848
[133]
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[134]
GSM249850
[135]
GSM249851
[136]
GSM249852
[137]
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[138]
GSM249854
[139]
GSM249855
[140]
GSM249856
[141]
GSM249857
[142]
GSM249858
[143]
GSM249859
[144]
GSM249860
[145]
GSM249861
[146]
GSM249862
[147]
GSM249863
[148]
GSM249864
[149]
GSM249865
[150]
GSM249866
[151]
GSM249867
[152]
GSM249868
[153]
GSM249869
[154]
GSM249870
[155]
GSM249871
[156]
GSM249872
[157]
GSM249873
[158]
GSM249874
[159]
GSM249875
[160]
GSM249876
[161]
GSM249877
[162]
GSM249878
[163]
GSM249879
[164]
GSM249880
[165]
GSM249881
[166]
GSM249882
[167]
GSM249883
[168]
GSM249884
[169]
GSM249885
[170]
GSM249886
[171]
GSM249887
[172]
GSM249888
[173]
GSM249889
[174]
GSM249890
[175]
GSM249891
[176]
GSM249892
[177]
GSM249893
[178]
GSM249894
[179]
GSM249895
[180]
GSM249896
[181]
GSM249897
[182]
GSM249898
[183]
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[184]
GSM249900
[185]
GSM249901
[186]
GSM249902
[187]
GSM249903
[188]
GSM249904
[189]
GSM249905
[190]
GSM249906
[191]
GSM249907
[192]
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[193]
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[194]
GSM249910
[195]
GSM249911
[196]
GSM249912
[197]
GSM249913
[198]
GSM249914
[199]
GSM249915
[200]
GSM249916
[201]
GSM249917
[202]
GSM249918
[203]
GSM249919
[204]
GSM249920
[205]
GSM249921
[206]
GSM249922
[207]
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[208]
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[209]
GSM249925
[210]
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[211]
GSM249927
[212]
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[213]
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[214]
GSM249930
[215]
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[216]
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[217]
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[218]
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[219]
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[220]
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[221]
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[222]
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[223]
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[224]
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[232]
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[237]
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[239]
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[241]
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[242]
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[243]
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[244]
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[245]
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[246]
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[247]
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[248]
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[249]
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[250]
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[251]
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[252]
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[253]
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[255]
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[258]
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[259]
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[260]
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[261]
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[262]
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[263]
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[266]
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[267]
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[268]
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[269]
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[270]
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[271]
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[272]
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[273]
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[274]
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[275]
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[276]
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[277]
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[278]
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[279]
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[280]
GSM249997
[281]
GSM249998
[282]
GSM249999
[283]
GSM250000
[284]
GSM250001
sample_count
285
group_sizes
null
group_sizes_basis
null
platform
570
taxon
Homo sapiens
title
Expression profile of 285 ovarian tumour samples
resolution_checked_at
2026-11-01T15:39:55Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
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[5] object · 16 fields
id
asset:table:pmid:30719133:1
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section[3]/table-wrap[1]#T1
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note
General information of involved 3 datasets for developing gene signatures of predicting platinum resistance.
structure object · 5 fields
pmcid
PMC6360311
section_index
3
section_title
Preparation of SOC datasets
row_count
17
column_count
4
table object · 8 fields
label
Table 1
caption
General information of involved 3 datasets for developing gene signatures of predicting platinum resistance.
rows array · 17 items
[0] array · 4 items
[0]
Characteristic
[1]
GSE63885
[2]
GSE51373
[3]
TCGA
[1] array · 4 items
[0]
Platform
[1]
HG-U133_Plus_2
[2]
HG-U133_Plus_2
[3]
HT_HG-U133A
[2] array · 4 items
[0]
Total sample number
[1]
101
[2]
28
[3]
578
[3] array · 4 items
[0]
Enrolled samples*
[1]
70
[2]
28
[3]
454
[4] array · 4 items
[0]
Age
[1]
[2]
[3]
[5] array · 4 items
[0]
median (range)
[1]
/
[2]
54 (47 - 79)
[3]
58 (26 - 89)
[6] array · 3 items
[0]
Chemotherapeutic response
[1]
[2]
[7] array · 4 items
[0]
sensitive
[1]
38
[2]
18
[3]
318
[8] array · 4 items
[0]
resistance
[1]
32
[2]
10
[3]
136
[9] array · 4 items
[0]
Tumor grade
[1]
[2]
[3]
[10] array · 4 items
[0]
1/2/3/4/unknown
[1]
0/9/46/15/0
[2]
high grade
[3]
5/55/383/1/10
[11] array · 4 items
[0]
Tumor stage
[1]
[2]
[3]
[12] array · 4 items
[0]
I/II/III/IV/unknown
[1]
0/2/59/9/0
[2]
0/5/19/3/1
[3]
13/21/350/68/2
[13] array · 4 items
[0]
Debulking
[1]
[2]
[3]
[14] array · 4 items
[0]
optimal
[1]
14
[2]
/
[3]
301
[15] array · 4 items
[0]
suboptimal
[1]
56
[2]
/
[3]
111
[16] array · 4 items
[0]
unknown
[1]
0
[2]
/
[3]
42
locator
section[3]/table-wrap[1]#T1
[6] object · 16 fields
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asset:table:pmid:30719133:2
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jats-table
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sha256:aca598dbcbf9fdfd1c7aeeff4d59036ed4e6e9d29a3f23fc884815fedb856b77
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section[12]/table-wrap[1]#T2
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null
note
Univariable and multivariable logistic regression models for platinum resistance in the discovery and validation datasets.
structure object · 5 fields
pmcid
PMC6360311
section_index
12
section_title
AGGF and MFAP4 were independent predictors of primary platinum resistance
row_count
31
column_count
8
table object · 8 fields
label
Table 2
caption
Univariable and multivariable logistic regression models for platinum resistance in the discovery and validation datasets.
rows array · 31 items
[0] array · 3 items
[0]
Datasets and clinical factors
[1]
Univariate analysis
[2]
Multivariate analysis
[1] array · 6 items
[0]
OR
[1]
95%CI
[2]
p value
[3]
OR
[4]
95%CI
[5]
p value
[2] array · 1 item
[0]
GSE63885
[3] array · 4 items
[0]
Tumor stage
[1]
II
[2]
1 (reference)
[3]
1 (reference)
[4] array · 7 items
[0]
III
[1]
13205836.02
[2]
0 - Inf
[3]
0.99
[4]
3537708.16
[5]
0 - Inf
[6]
0.99
[5] array · 7 items
[0]
IV
[1]
19564201.51
[2]
0 - Inf
[3]
0.99
[4]
4665494.34
[5]
0 - Inf
[6]
0.99
[6] array · 4 items
[0]
Tumor grade
[1]
G2
[2]
1 (reference)
[3]
1 (reference)
[7] array · 7 items
[0]
G3
[1]
2.94
[2]
0.55 - 15.7
[3]
0.20
[4]
1.87
[5]
0.27 - 12.9
[6]
0.52
[8] array · 7 items
[0]
G4
[1]
5.25
[2]
0.8 - 34.43
[3]
0.08
[4]
3.05
[5]
0.36 - 26.11
[6]
0.30
[9] array · 8 items
[0]
AGGF1
[1]
high vs. low
[2]
3.71
[3]
1.33 - 10.32
[4]
0.01
[5]
3.32
[6]
1.07 - 10.28
[7]
0.03
[10] array · 8 items
[0]
MFAP4
[1]
high vs. low
[2]
4.67
[3]
1.69 - 12.9
[4]
0.003
[5]
3.64
[6]
1.18 - 11.22
[7]
0.02
[11] array · 1 item
[0]
GSE51373
[12] array · 4 items
[0]
Tumor stage
[1]
II
[2]
1 (reference)
[3]
1 (reference)
[13] array · 7 items
[0]
III
[1]
2.33
[2]
0.22 - 25.24
[3]
0.48
[4]
0.64
[5]
0 - 139.25
[6]
0.87
[14] array · 7 items
[0]
IV
[1]
8
[2]
0.31 - 206.37
[3]
0.21
[4]
0.52
[5]
0 - 279.37
[6]
0.83
[15] array · 8 items
[0]
Age
[1]
>=60 vs. <60
[2]
0.13
[3]
0.01 - 1.22
[4]
0.07
[5]
0.11
[6]
0 - 3.02
[7]
0.19
[16] array · 8 items
[0]
AGGF1
[1]
high vs. low
[2]
14.14
[3]
1.46 - 137.3
[4]
0.02
[5]
18.51
[6]
0.93 - 369.88
[7]
0.05
[17] array · 8 items
[0]
MFAP4
[1]
high vs. low
[2]
10.4
[3]
1.62 - 66.9
[4]
0.01
[5]
15.22
[6]
1.11 - 207.9
[7]
0.04
[18] array · 1 item
[0]
TCGA
[19] array · 4 items
[0]
Tumor stage
[1]
Ⅰ
[2]
1 (reference)
[3]
1 (reference)
[20] array · 7 items
[0]
II
[1]
0.28
[2]
0.02 - 3.39
[3]
0.31
[4]
0
[5]
0 - Inf
[6]
0.98
[21] array · 7 items
[0]
III
[1]
2.39
[2]
0.52 - 10.97
[3]
0.26
[4]
1.02
[5]
0.2 - 5.06
[6]
0.98
[22] array · 7 items
[0]
IV
[1]
3.2
[2]
0.66 -15.61
[3]
0.15
[4]
1.64
[5]
0.31 - 8.72
[6]
0.56
[23] array · 4 items
[0]
Tumor grade
[1]
G1
[2]
1 (reference)
[3]
1 (reference)
[24] array · 7 items
[0]
G2
[1]
1.95
[2]
0.2 - 18.7
[3]
0.56
[4]
1.5
[5]
0.13 - 16.7
[6]
0.74
[25] array · 7 items
[0]
G3
[1]
1.72
[2]
0.19 - 15.52
[3]
0.63
[4]
1.15
[5]
0.11 - 11.81
[6]
0.90
[26] array · 7 items
[0]
G4
[1]
0
[2]
0 - Inf
[3]
0.98
[4]
0
[5]
0 - Inf
[6]
0.99
[27] array · 8 items
[0]
Age
[1]
>=60 vs. <60
[2]
1.08
[3]
0.72 - 1.62
[4]
0.71
[5]
0.99
[6]
0.62 - 1.59
[7]
0.98
[28] array · 8 items
[0]
Surgery
[1]
Suboptimal vs. Optimal
[2]
3.36
[3]
2.13 - 5.3
[4]
< 0.001
[5]
3.16
[6]
1.93 - 5.16
[7]
< 0.001
[29] array · 8 items
[0]
AGGF1
[1]
high vs. low
[2]
2.24
[3]
1.47 - 3.41
[4]
< 0.001
[5]
2.54
[6]
1.58 - 4.08
[7]
< 0.001
[30] array · 8 items
[0]
MFAP4
[1]
high vs low
[2]
1.81
[3]
1.21 - 2.72
[4]
0.004
[5]
1.64
[6]
1.03 - 2.62
[7]
0.03
locator
section[12]/table-wrap[1]#T2
[7] object · 16 fields
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asset:table:pmid:30719133:3
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table
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jats-table
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sha256:9a0a38a3d4aacc11a61c3b4353cf879248d710a39fa274e7d85b103bff9d6aca
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section[19]/table-wrap[1]#T3A
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null
note
Univariate and multivariate Cox regression analysis for recurrence free survival in GSE9891 and GSE32062.
structure object · 5 fields
pmcid
PMC6360311
section_index
19
section_title
(untitled)
row_count
22
column_count
8
table object · 8 fields
label
Table 3A
caption
Univariate and multivariate Cox regression analysis for recurrence free survival in GSE9891 and GSE32062.
rows array · 22 items
[0] array · 3 items
[0]
Datasets and clinical factors
[1]
Univariate analysis
[2]
Multivariate analysis
[1] array · 6 items
[0]
HR
[1]
95%CI
[2]
p value
[3]
HR
[4]
95%CI
[5]
p value
[2] array · 1 item
[0]
GSE9891
[3] array · 8 items
[0]
Age
[1]
≥60y vs. <60y
[2]
1.282
[3]
0.954 -1.724
[4]
0.10
[5]
1.479
[6]
1.056 - 2.071
[7]
0.023
[4] array · 4 items
[0]
Tumor stage
[1]
I
[2]
1 (reference)
[3]
1 (reference)
[5] array · 7 items
[0]
II
[1]
2.603
[2]
0.477 - 14.22
[3]
0.269
[4]
2.972
[5]
0.540 - 16.377
[6]
0.211
[6] array · 7 items
[0]
III
[1]
9.774
[2]
2.418 - 39.51
[3]
0.001
[4]
7.966
[5]
1.941 - 32.692
[6]
0.004
[7] array · 7 items
[0]
IV
[1]
14.809
[2]
3.452 - 63.52
[3]
2.86e-04
[4]
10.498
[5]
2.314 - 47.623
[6]
0.002
[8] array · 4 items
[0]
Tumor grade
[1]
G1
[2]
1 (reference)
[3]
1 (reference)
[9] array · 7 items
[0]
G2
[1]
3.116
[2]
1.252 - 7.755
[3]
0.015
[4]
1.711
[5]
0.604 - 4.845
[6]
0.312
[10] array · 7 items
[0]
G3
[1]
2.723
[2]
1.109 - 6.684
[3]
0.029
[4]
1.406
[5]
0.502 - 3.936
[6]
0.517
[11] array · 8 items
[0]
Surgery
[1]
Suboptimal vs. optimal
[2]
2.058
[3]
1.489 - 2.844
[4]
1.218e-05
[5]
1.704
[6]
1.203 - 2.413
[7]
0.003
[12] array · 8 items
[0]
AGGF1
[1]
high vs. low
[2]
1.532
[3]
1.071 - 2.192
[4]
0.020
[5]
1.578
[6]
1.069 - 2.330
[7]
0.022
[13] array · 8 items
[0]
MFAP4
[1]
high vs. low
[2]
1.708
[3]
1.260 - 2.315
[4]
5.595e-04
[5]
1.418
[6]
1.003 - 2.006
[7]
0.048
[14] array · 1 item
[0]
GSE32062
[15] array · 4 items
[0]
Tumor stage
[1]
III
[2]
1 (reference)
[3]
1 (reference)
[16] array · 7 items
[0]
IV
[1]
1.515
[2]
1.088 - 2.109
[3]
0.014
[4]
1.327
[5]
0.948 - 1.858
[6]
0.099
[17] array · 4 items
[0]
Tumor grade
[1]
G2
[2]
1 (reference)
[3]
1 (reference)
[18] array · 7 items
[0]
G3
[1]
1.151
[2]
0.868 - 1.527
[3]
0.330
[4]
1.121
[5]
0.840 - 1.498
[6]
0.438
[19] array · 8 items
[0]
Surgery
[1]
Suboptimal vs. optimal
[2]
1.772
[3]
1.315 - 2.387
[4]
1.683e-04
[5]
1.667
[6]
1.234 - 2.250
[7]
0.001
[20] array · 8 items
[0]
AGGF1
[1]
high vs. low
[2]
1.777
[3]
1.263 - 2.5
[4]
9.666e-04
[5]
1.757
[6]
1.246 - 2.476
[7]
0.001
[21] array · 8 items
[0]
MFAP4
[1]
high vs. low
[2]
1.494
[3]
1.096 - 2.037
[4]
0.011
[5]
1.417
[6]
1.039 - 1.933
[7]
0.028
locator
section[19]/table-wrap[1]#T3A
[8] object · 16 fields
id
asset:table:pmid:30719133:4
role
table
format
jats-table
media_type
application/vnd.jats.table+json
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null
sha256
sha256:5048cc7fb2cac3326bf45c3e6fab519008c6c02352a84624c9668981ac299533
bytes
1880
access
open
state
verified
reader
read_table
reader_status
available
identifier
section[19]/table-wrap[2]#T3B
version
null
note
Univariate and multivariate Cox regression analysis for overall survival in GSE9891 and GSE32062.
structure object · 5 fields
pmcid
PMC6360311
section_index
19
section_title
(untitled)
row_count
22
column_count
8
table object · 8 fields
label
Table 3B
caption
Univariate and multivariate Cox regression analysis for overall survival in GSE9891 and GSE32062.
rows array · 22 items
[0] array · 4 items
[0]
Datasets and clinical factors
[1]
[2]
Univariate analysis
[3]
Multivariate analysis
[1] array · 7 items
[0]
[1]
HR
[2]
95%CI
[3]
p value
[4]
HR
[5]
95%CI
[6]
p value
[2] array · 8 items
[0]
GSE9891
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[3] array · 8 items
[0]
Age
[1]
≥60y vs <60y
[2]
1.458
[3]
1.001 - 2.122
[4]
0.049
[5]
1.729
[6]
1.125 - 2.655
[7]
0.012
[4] array · 8 items
[0]
Tumor stage
[1]
I
[2]
1 (reference)
[3]
[4]
[5]
1 (reference)
[6]
[7]
[5] array · 8 items
[0]
[1]
II
[2]
0.483
[3]
0.044 - 5.347
[4]
0.553
[5]
0.705
[6]
0.062 - 7.947
[7]
0.777
[6] array · 8 items
[0]
[1]
III
[2]
3.672
[3]
0.903 - 14.935
[4]
0.069
[5]
3.103
[6]
0.740 - 13.020
[7]
0.122
[7] array · 8 items
[0]
[1]
IV
[2]
5.739
[3]
1.301 - 25.319
[4]
0.021
[5]
4.929
[6]
1.035 - 23.483
[7]
0.045
[8] array · 8 items
[0]
Tumor grade
[1]
G1
[2]
1 (reference)
[3]
[4]
[5]
1 (reference)
[6]
[7]
[9] array · 8 items
[0]
[1]
G2
[2]
3.070
[3]
0.937 - 10.06
[4]
0.064
[5]
1.343
[6]
0.387 - 4.663
[7]
0.642
[10] array · 8 items
[0]
[1]
G3
[2]
3.181
[3]
0.999 - 10.13
[4]
0.0502
[5]
1.534
[6]
0.453 - 5.192
[7]
0.491
[11] array · 8 items
[0]
Surgery
[1]
Suboptimal vs optimal
[2]
1.662
[3]
1.117 - 2.471
[4]
0.012
[5]
1.309
[6]
0.850 - 2.014
[7]
0.221
[12] array · 8 items
[0]
AGGF1
[1]
high vs. low
[2]
1.662
[3]
1.081 - 2.555
[4]
0.021
[5]
2.014
[6]
1.240 - 3.270
[7]
0.005
[13] array · 8 items
[0]
MFAP4
[1]
high vs. low
[2]
1.98
[3]
1.33 - 2.947
[4]
0.001
[5]
1.660
[6]
1.065 - 2.586
[7]
0.025
[14] array · 8 items
[0]
GSE32062
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[15] array · 8 items
[0]
Tumor stage
[1]
III
[2]
1 (reference)
[3]
[4]
[5]
1 (reference)
[6]
[7]
[16] array · 8 items
[0]
[1]
IV
[2]
1.466
[3]
0.981 - 2.189
[4]
0.062
[5]
1.323
[6]
0.881 - 1.986
[7]
0.177
[17] array · 8 items
[0]
Tumor grade
[1]
G2
[2]
1 (reference)
[3]
[4]
[5]
1 (reference)
[6]
[7]
[18] array · 8 items
[0]
[1]
G3
[2]
0.978
[3]
0.684 - 1.399
[4]
0.904
[5]
0.926
[6]
0.645 - 1.328
[7]
0.675
[19] array · 8 items
[0]
Surgery
[1]
Suboptimal vs optimal
[2]
2.013
[3]
1.363 - 2.972
[4]
4.347e-04
[5]
1.909
[6]
1.291 - 2.822
[7]
0.001
[20] array · 8 items
[0]
AGGF1
[1]
high vs. low
[2]
1.678
[3]
1.091 - 2.579
[4]
0.018
[5]
1.543
[6]
1.001 - 2.376
[7]
0.049
[21] array · 8 items
[0]
MFAP4
[1]
high vs. low
[2]
1.622
[3]
1.081 - 2.436
[4]
0.020
[5]
1.581
[6]
1.053 - 2.374
[7]
0.027
locator
section[19]/table-wrap[2]#T3B
gaps array · 8 items
[0]
asset:data:geo:GSE32062 not downloaded
[1]
asset:data:geo:GSE32062 licence not read
[2]
asset:data:geo:GSE51373 not downloaded
[3]
asset:data:geo:GSE51373 licence not read
[4]
asset:data:geo:GSE63885 not downloaded
[5]
asset:data:geo:GSE63885 licence not read
[6]
asset:data:geo:GSE9891 not downloaded
[7]
asset:data:geo:GSE9891 licence not read
analysis object · 4 fields
state
complete
methods array · 7 items
[0] object · 10 fields
id
method-1
family
survival_analysis
specific
cox_proportional_hazards
label
Survival analysis
dsl_operations array · 1 item
[0]
cox_ph
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
time_to_event_with_censoring
evidence object · 3 fields
score
1
band
verbatim
quote
Univariate analysis with Cox proportional hazards regression model was used to evaluate the correlations of AGGF1 and MFAP4 expression statuses with RFS and OS, and multivariate analysis with Cox proportional hazards regression model was applied to test the independent predictive ability of these two gene signatures for RFS and OS after adjusting for various clinical characteristics.
[1] object · 10 fields
id
method-2
family
survival_analysis
specific
kaplan_meier
label
Survival analysis
dsl_operations array · 1 item
[0]
survival_km
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
time_to_event_with_censoring
evidence object · 3 fields
score
0.8
band
high
quote
Kaplan-Meier
[2] object · 10 fields
id
method-3
family
survival_analysis
specific
log_rank_test
label
Survival analysis
dsl_operations array · 1 item
[0]
logrank_test
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
time_to_event_with_censoring
evidence object · 3 fields
score
0.7
band
medium
quote
log-rank
[3] object · 10 fields
id
method-4
family
machine_learning
specific
support_vector_machine
label
Machine learning
dsl_operations array · 3 items
[0]
train_classifier
[1]
cross_validate
[2]
roc_auc
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
labelled_feature_matrix
evidence object · 3 fields
score
0.7
band
medium
quote
support vector machine
[4] object · 10 fields
id
method-5
family
regression_modelling
specific
ols
label
Regression modelling
dsl_operations array · 1 item
[0]
ols_fit
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
predictors_and_outcome
evidence object · 3 fields
score
0.8
band
high
quote
linear regression
[5] object · 10 fields
id
method-6
family
regression_modelling
specific
logistic
label
Regression modelling
dsl_operations array · 1 item
[0]
glm_logit_fit
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
predictors_and_outcome
evidence object · 3 fields
score
0.7
band
medium
quote
logistic regression
[6] object · 10 fields
id
method-7
family
contingency_analysis
specific
chi_square
label
Categorical (contingency table) analysis
dsl_operations array · 1 item
[0]
chi_square_test
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
categorical_cross_tabulation
evidence object · 3 fields
score
0.8
band
high
quote
chi-square
targets array · 0 items
gaps array · 0 items
capability object · 3 fields
state
complete
requirements array · 1 item
[0] object · 6 fields
capability_id
capability:computational_analysis
label
Computational analysis platform
assay_families array · 9 items
[0]
meta_analysis
[1]
machine_learning
[2]
survival_analysis
[3]
statistical_regression
[4]
pca
[5]
molecular_docking
[6]
network_analysis
[7]
differential_expression
[8]
gwas
candidate
Automated Assays Laravel application and simulated adapters
gaps array · 0 items
readiness object · 8 fields
state
complete
tier
R1
blocked_on
nothing:replicable_now
axes object · 3 fields
method object · 5 fields
tier
R1
expressible
true
required_operations array · 1 item
[0]
cox_ph
missing_operations array · 0 items
basis
The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: cox_proportional_hazards.
capability object · 4 fields
tier
R1
capability_id
capability:computational_analysis
availability_state
owned
basis
The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here.
data object · 6 fields
tier
R1
reachable
true
accessions array · 4 items
[0]
GSE63885
[1]
GSE51373
[2]
GSE9891
[3]
GSE32062
resolved_accessions array · 4 items
[0]
GSE63885
[1]
GSE51373
[2]
GSE9891
[3]
GSE32062
statements array · 1 item
[0]
GSE63885 and GSE51373 were defined as the discovery sets to explore gene signatures with high efficiency to distinguish platinum-resistant and platinum-sensitive patients, and TCGA SOC dataset (hereinafter inferred as TCGA) was analyzed as the validation dataset.
basis
The paper names GSE63885, GSE51373, GSE9891, GSE32062 in National Center for Biotechnology Information Gene Expression Omnibus database (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read.
can_run object · 3 fields
dsl_operations array · 1 item
[0]
cox_ph
assets array · 6 items
[0]
source:pmid:30719133
[1]
data:geo:GSE32062
[2]
asset:table:pmid:30719133:1
[3]
asset:table:pmid:30719133:2
[4]
asset:table:pmid:30719133:3
[5]
asset:table:pmid:30719133:4
steps array · 1 item
[0]
run_analysis
must_build object · 5 fields
dsl_operations array · 0 items
converters array · 0 items
readers array · 0 items
capabilities array · 0 items
data_gaps array · 1 item
[0]
data:geo:GSE32062
gaps array · 0 items
review object · 5 fields
state
partial
machine_critique object · 5 fields
kind
extraction_review
reader object · 5 fields
model
MiniMax-M3.1-Flash
provider
minimax
provider_family
minimax
read_at
2026-10-31T18:13:47Z
independence
single_reader
evidence_check object · 2 fields
method
verbatim
availability
null
calls
3
usage object · 3 fields
prompt_tokens
32572
completion_tokens
3162
cached_tokens
656
stages array · 1 item
[0] object · 4 fields
id
extraction
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complete
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MiniMax-M3.1-Flash
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minimax
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review.independent_reader
[1]
review.machine_critique
[2]
review.human_critique
execution object · 5 fields
state
absent
status
not_run
mode
null
runs array · 0 items
gaps array · 1 item
[0]
execution.not_run
provenance object · 7 fields
entities array · 3 items
[0] object · 3 fields
id
pmid:30719133
type
paper
label
pmid:30719133
[1] object · 3 fields
id
source:pmid:30719133
type
manuscript
label
PMID 30719133 full text
[2] object · 3 fields
id
data:geo:GSE32062
type
dataset
label
GEO GSE32062
activities array · 3 items
[0] object · 4 fields
id
activity-reading-sci-2026-11-01
type
classification
used array · 3 items
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id
pmid:30719133
type
paper
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pmid:30719133
[1] object · 3 fields
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source:pmid:30719133
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manuscript
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PMID 30719133 full text
[2] object · 3 fields
id
data:geo:GSE32062
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dataset
label
GEO GSE32062
agent
agent:corpus-assay-engine
[1] object · 4 fields
id
activity-read-30719133
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used array · 1 item
[0]
source:pmid:30719133
agent
agent:minimax-reader
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id
activity-resolve-GSE32062
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accession_resolution
used array · 1 item
[0]
data:geo:GSE32062
agent
agent:corpus-assay-engine
agents array · 2 items
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agent:corpus-assay-engine
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Corpus assay engine
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agent:minimax-reader
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MiniMax literature reader
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dsl_catalogue_fingerprint
343fb30756ba6ec3
capability_table_fingerprint
2cb3e42f047d2ecf
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lineage object · 4 fields
parent_id
pmid:30719133
parent_hash
sha256:cce6039912458b2e0c39430a14217d87ad16c8ca504cf801a19e827ac2bf17f7
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null
gaps array · 12 items
[0]
asset:data:geo:GSE32062 licence not read
[1]
asset:data:geo:GSE32062 not downloaded
[2]
asset:data:geo:GSE51373 licence not read
[3]
asset:data:geo:GSE51373 not downloaded
[4]
asset:data:geo:GSE63885 licence not read
[5]
asset:data:geo:GSE63885 not downloaded
[6]
asset:data:geo:GSE9891 licence not read
[7]
asset:data:geo:GSE9891 not downloaded
[8]
execution.not_run
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data_generation
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99e1195c7d6f
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partial
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99e1195c7d6f
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evidence array · 1 item
[0]
tinum-based chemoresistance. Gene expression microarray analyses were performed to identify the gene
order_basis
paper_text_order:435
id
assay:1
[1] object · 11 fields
role
analysis
definition_id
survival_analysis
definition_version
f2132e96ae6c7a4e
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cox_ph
support_state
supported
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dsl_catalogue
f2132e96ae6c7a4e
evidence array · 1 item
[0]
Univariate analysis with Cox proportional hazards regression model was used to evaluate the correlations of AGGF1 and MFAP4 expression statuses with RFS and OS, and multivariate analysis with Cox proportional hazards regression model was applied to test the independent predictive ability of these two gene signatures for RFS and OS after adjusting for various clinical characteristics.
order_basis
consumes_earlier_output
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assay:2
Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:30719133", "kind": "science-record", "revision": 8, "content_hash": "sha256:ad7f942ddaab569b859eac2cbc06441c5d5a1c48d2afed59ec0f65c99c32677c", "created_at": "2026-11-01T15:40:41Z", "updated_at": "2026-11-01T22:00:20Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "High Expression Levels of AGGF1 and MFAP4 Predict Primary Platinum-Based Chemoresistance and are Associated with Adverse Prognosis in Patients with Serous Ovarian Cancer.", "doi": "10.7150/jca.28127", "pmid": "30719133", "authors": ["Haiyue Zhao", "Qian Sun", "Lisong Li", "Jinhua Zhou", "Cong Zhang", "Ting Hu", "Xuemei Zhou", "Long Zhang", "Baiyu Wang", "Bo Li", "Tao Zhu", "Hong Li"], "journal": "Journal of Cancer", "year": 2019, "abstract": "Primary platinum-based chemoresistance occurs in approximately one-third of patients with serous ovarian cancer (SOC); however, traditional clinical indicators are poor predictors of chemoresistance. So we aimed to identify novel genes as predictors of primary platinum-based chemoresistance. Gene expression microarray analyses were performed to identify the genes related to primary platinum resistance in SOC on two discovery datasets (GSE51373, GSE63885) and one validation dataset (TCGA). Univariate and multivariate analyses with logistic regression were performed to evaluate the predictive values of the genes for platinum resistance. Machine learning algorithms (linear kernel support vector machine and artificial neural network) were applied to build prediction models. Univariate and multivariate analyses with Cox proportional hazards regression and log-rank tests were used to assess the effects of these gene signatures for platinum resistance on prognosis in two independent datasets (GSE9891, GSE32062). AGGF1 and MFAP4 were found highly expressed in patients with platinum-resistant SOC and independently predicted platinum resistance. Platinum resistance prediction models based on these targets had robust predictive power (highest AUC: 0.8056, 95% CI: 0.6338-0.9773; lowest AUC: 0.7245, 95% CI: 0.6052-0.8438). An AGGF1- and MFAP4-centered protein interaction network was built, and hypothetical regulatory pathways were identified. Enrichment analysis indicated that aberrations of extracellular matrix may play important roles in platinum resistance in SOC. High AGGF1 and MFAP4 expression levels were also related to shorter recurrence-free and overall survival in patients with SOC after adjustment for other clinical variables. Therefore, AGGF1 and MFAP4 are potential predictive biomarkers for response to platinum-based chemotherapy and survival outcomes in SOC.", "claims": [{"id": "claim-method", "statement": "Univariate analysis with Cox proportional hazards regression model was used to evaluate the correlations of AGGF1 and MFAP4 expression statuses with RFS and OS, and multivariate analysis with Cox proportional hazards regression model was applied to test the independent predictive ability of these two gene signatures for RFS and OS after adjusting for various clinical characteristics.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:30719133", "locator": "sections[9] \"Survival analysis of AGGF1 and MFAP4\" paragraph 2", "quote": "Univariate analysis with Cox proportional hazards regression model was used to evaluate the correlations of AGGF1 and MFAP4 expression statuses with RFS and OS, and multivariate analysis with Cox proportional hazards regression model was applied to test the independent predictive ability of these two gene signatures for RFS and OS after adjusting for various clinical characteristics."}], "state": "complete"}, {"id": "claim-data-access", "statement": "GSE63885 and GSE51373 were defined as the discovery sets to explore gene signatures with high efficiency to distinguish platinum-resistant and platinum-sensitive patients, and TCGA SOC dataset (hereinafter inferred as TCGA) was analyzed as the validation dataset.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:30719133", "locator": "sections[5] \"Screening and validation of platinum resistance-related genes\" paragraph 2", "quote": "GSE63885 and GSE51373 were defined as the discovery sets to explore gene signatures with high efficiency to distinguish platinum-resistant and platinum-sensitive patients, and TCGA SOC dataset (hereinafter inferred as TCGA) was analyzed as the validation dataset."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:30719133"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE32062"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE32062"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:30719133", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.7150/jca.28127", "sha256": 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"role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:aca598dbcbf9fdfd1c7aeeff4d59036ed4e6e9d29a3f23fc884815fedb856b77", "bytes": 2203, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[12]/table-wrap[1]#T2", "version": null, "note": "Univariable and multivariable logistic regression models for platinum resistance in the discovery and validation datasets.", "structure": {"pmcid": "PMC6360311", "section_index": 12, "section_title": "AGGF and MFAP4 were independent predictors of primary platinum resistance", "row_count": 31, "column_count": 8}, "table": {"label": "Table 2", "caption": "Univariable and multivariable logistic regression models for platinum resistance in the discovery and validation datasets.", "rows": [["Datasets and clinical factors", "Univariate analysis", "Multivariate analysis"], ["OR", "95%CI", "p value", "OR", "95%CI", "p value"], ["GSE63885"], ["Tumor stage", "II", "1 (reference)", "1 (reference)"], ["III", "13205836.02", "0 - Inf", "0.99", "3537708.16", "0 - Inf", "0.99"], ["IV", "19564201.51", "0 - Inf", "0.99", "4665494.34", "0 - Inf", "0.99"], ["Tumor grade", "G2", "1 (reference)", "1 (reference)"], ["G3", "2.94", "0.55 - 15.7", "0.20", "1.87", "0.27 - 12.9", "0.52"], ["G4", "5.25", "0.8 - 34.43", "0.08", "3.05", "0.36 - 26.11", "0.30"], ["AGGF1", "high vs. low", "3.71", "1.33 - 10.32", "0.01", "3.32", "1.07 - 10.28", "0.03"], ["MFAP4", "high vs. low", "4.67", "1.69 - 12.9", "0.003", "3.64", "1.18 - 11.22", "0.02"], ["GSE51373"], ["Tumor stage", "II", "1 (reference)", "1 (reference)"], ["III", "2.33", "0.22 - 25.24", "0.48", "0.64", "0 - 139.25", "0.87"], ["IV", "8", "0.31 - 206.37", "0.21", "0.52", "0 - 279.37", "0.83"], ["Age", ">=60 vs. <60", "0.13", "0.01 - 1.22", "0.07", "0.11", "0 - 3.02", "0.19"], ["AGGF1", "high vs. low", "14.14", "1.46 - 137.3", "0.02", "18.51", "0.93 - 369.88", "0.05"], ["MFAP4", 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"reader_status": "available", "identifier": "section[19]/table-wrap[2]#T3B", "version": null, "note": "Univariate and multivariate Cox regression analysis for overall survival in GSE9891 and GSE32062.", "structure": {"pmcid": "PMC6360311", "section_index": 19, "section_title": "(untitled)", "row_count": 22, "column_count": 8}, "table": {"label": "Table 3B", "caption": "Univariate and multivariate Cox regression analysis for overall survival in GSE9891 and GSE32062.", "rows": [["Datasets and clinical factors", "", "Univariate analysis", "Multivariate analysis"], ["", "HR", "95%CI", "p value", "HR", "95%CI", "p value"], ["GSE9891", "", "", "", "", "", "", ""], ["Age", "≥60y vs <60y", "1.458", "1.001 - 2.122", "0.049", "1.729", "1.125 - 2.655", "0.012"], ["Tumor stage", "I", "1 (reference)", "", "", "1 (reference)", "", ""], ["", "II", "0.483", "0.044 - 5.347", "0.553", "0.705", "0.062 - 7.947", "0.777"], ["", "III", "3.672", "0.903 - 14.935", "0.069", "3.103", "0.740 - 13.020", "0.122"], 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