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pmid:34979994

A2M is a potential core gene in intrahepatic cholangiocarcinoma.

Guanran Zhang · Xuyue Liu · Zhengyang Sun · Xiaoning Feng · Haiyan Wang · Jing Hao · Xiaoli Zhang

BMC cancer · 2022 doi:10.1186/s12885-021-09070-2

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Experiment

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Data

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title
A2M is a potential core gene in intrahepatic cholangiocarcinoma.
doi
10.1186/s12885-021-09070-2
pmid
34979994
authors array · 7 items
[0]
Guanran Zhang
[1]
Xuyue Liu
[2]
Zhengyang Sun
[3]
Xiaoning Feng
[4]
Haiyan Wang
[5]
Jing Hao
[6]
Xiaoli Zhang
journal
BMC cancer
year
2022
abstract
BACKGROUND: Intrahepatic cholangiocarcinoma (ICC) is a type of malignant tumor ranking the second in the incidence of primary liver cancer following hepatocellular carcinoma. Both the morbidity and mortality have been increasing in recent years. Small duct type of ICC has potential therapeutic targets. But overall, the prognosis of patients with ICC is usually very poor. METHODS: To search latent therapeutic targets for ICC, we programmatically selected the five most suitable microarray datasets. Then, we made an analysis of these microarray datasets (GSE26566, GSE31370, GSE32958, GSE45001 and GSE76311) collected from the Gene Expression Omnibus (GEO) database. The GEO2R tool was effective to find out differentially expressed genes (DEGs) between ICC and normal tissue. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were executed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) v 6.8. The Search Tool for the Retrieval of Interacting Genes (STRING) database was used to analyze protein-protein interaction of these DEGs and protein-protein interaction of these DEGs was modified by Cytoscape3.8.2. Survival analysis was performed using Gene Expression Profiling Interactive Analysis (GEPIA) online analysis tool. RESULTS: A total of 28 upregulated DEGs and 118 downregulated DEGs were screened out. Then twenty hub genes were selected according to the connectivity degree. The survival analysis results showed that A2M was closely related to the pathogenesis and prognosis of ICC and was a potential therapeutic target for ICC. CONCLUSIONS: According to our study, low A2M expression in ICC compared to normal bile duct tissue was an adverse prognostic factor in ICC patients. The value of A2M in the treatment of ICC needs to be further studied.
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We used the GEO2R analysis tool (https://www.ncbi.nlm.nih.gov/geo/geo2r/) to discover the DEGs between ICC and healthy samples and screen out the DEGs based on the value of adjusted P-value (Padj) and fold change (|logFC|).
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sections[7] "Data processing of DEGs" paragraph 2
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We used the GEO2R analysis tool (https://www.ncbi.nlm.nih.gov/geo/geo2r/) to discover the DEGs between ICC and healthy samples and screen out the DEGs based on the value of adjusted P-value (Padj) and fold change (|logFC|).
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sections[23] "Availability of data and materials" paragraph 2
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The raw data of this study are freely available from the website https://www.ncbi.nlm.nih.gov/geo, and all the analyzed data are included in this manuscript.
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Low A2M expression in ICC compared to normal bile duct tissue was an adverse prognostic factor in ICC patients.
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According to our study, low A2M expression in ICC compared to normal bile duct tissue was an adverse prognostic factor in ICC patients.
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Low A2M expression in ICC compared to normal bile duct tissue was an adverse prognostic factor in ICC patients.
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[70]
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[78]
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[110]
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[134]
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[135]
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[136]
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[137]
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[138]
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[139]
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[140]
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[141]
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[142]
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[143]
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[144]
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[145]
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[146]
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[147]
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[148]
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[150]
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[154]
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[155]
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[156]
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[157]
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[158]
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[159]
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[160]
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[161]
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[162]
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[163]
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[164]
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[165]
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[166]
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[167]
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[168]
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[169]
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[170]
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[171]
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[172]
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[173]
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[174]
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[175]
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[176]
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[177]
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[178]
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[179]
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[180]
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[181]
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[182]
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[183]
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[184]
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[185]
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[186]
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[187]
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[188]
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[189]
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[190]
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[191]
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[192]
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[193]
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[194]
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[195]
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[196]
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[197]
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[198]
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[199]
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[200]
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[201]
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[202]
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[203]
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[204]
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[205]
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[206]
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[207]
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[208]
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[209]
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[210]
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[211]
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[212]
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[213]
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[214]
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[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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[225]
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[226]
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[227]
GSM1976821
[228]
GSM1976822
[229]
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[230]
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[231]
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[232]
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[233]
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[234]
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[235]
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[236]
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[237]
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[238]
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[239]
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[240]
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[241]
GSM1976835
[242]
GSM1976836
[243]
GSM1976837
[244]
GSM1976838
[245]
GSM1976839
[246]
GSM1976840
[247]
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[248]
GSM1976842
[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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[254]
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[255]
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[256]
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[257]
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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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[264]
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[265]
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[266]
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[267]
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[268]
GSM1976862
[269]
GSM1976863
[270]
GSM1976864
[271]
GSM1976865
[272]
GSM1976866
[273]
GSM1976867
[274]
GSM1976868
[275]
GSM1976869
[276]
GSM1976870
[277]
GSM1976871
[278]
GSM1976872
[279]
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[280]
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[281]
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[282]
GSM1976876
[283]
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[284]
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[285]
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[286]
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[287]
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[288]
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[289]
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[290]
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[291]
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[292]
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[293]
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[294]
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[295]
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[296]
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[297]
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[298]
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[299]
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[300]
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[301]
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[302]
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[303]
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[304]
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[305]
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[306]
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[307]
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[308]
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[309]
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[310]
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[311]
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[312]
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[313]
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[314]
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[315]
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[316]
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[317]
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[318]
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[319]
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[320]
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[321]
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[322]
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[323]
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[324]
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[325]
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[326]
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[327]
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[328]
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[329]
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[330]
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[331]
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[332]
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[333]
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[334]
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[335]
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[336]
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[337]
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[338]
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[339]
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[340]
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[341]
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[342]
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[343]
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[344]
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[345]
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[346]
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[347]
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[348]
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[349]
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[350]
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[351]
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[352]
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[353]
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[354]
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[355]
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[356]
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[357]
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[358]
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[359]
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[360]
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[361]
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[362]
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[363]
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[364]
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[365]
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[366]
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[367]
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[368]
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[369]
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[370]
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[371]
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[372]
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[373]
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[374]
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[375]
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[376]
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[377]
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[378]
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[379]
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[380]
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[381]
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[382]
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[383]
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[384]
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[385]
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[386]
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[387]
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[388]
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[389]
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[390]
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[391]
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[392]
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[393]
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[394]
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[395]
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[396]
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[397]
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[398]
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[399]
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[400]
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[401]
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[402]
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[403]
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[404]
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[405]
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[406]
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[407]
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[408]
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[409]
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[410]
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[411]
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[412]
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[413]
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[414]
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[415]
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[416]
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[417]
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[418]
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[419]
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[420]
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[421]
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[422]
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[423]
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[424]
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[425]
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[426]
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[427]
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[428]
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[429]
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[430]
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[431]
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[432]
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[433]
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[434]
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[435]
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[436]
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[437]
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[438]
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[439]
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[440]
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[441]
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[442]
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[443]
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[444]
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[445]
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[446]
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[447]
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[448]
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[449]
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[450]
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[451]
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[452]
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[453]
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[454]
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[455]
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[456]
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[457]
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[458]
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[459]
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[460]
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[461]
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[462]
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[463]
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[464]
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[465]
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[466]
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[467]
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[468]
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[469]
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[470]
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[471]
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[472]
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[473]
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[474]
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[475]
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[476]
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[477]
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[478]
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[479]
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[480]
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[481]
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[482]
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[483]
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[484]
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[485]
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[486]
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[487]
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[488]
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[489]
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[490]
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[491]
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[492]
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[493]
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[494]
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[495]
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[496]
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[497]
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[498]
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[499]
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[500]
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[501]
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[502]
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[503]
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[504]
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[505]
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[506]
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[507]
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[508]
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[509]
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[510]
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[511]
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[512]
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[513]
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[514]
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[515]
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[516]
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[517]
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[518]
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[519]
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[520]
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[521]
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[522]
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[523]
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[524]
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[525]
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[526]
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[527]
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[528]
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[529]
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[530]
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[531]
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[532]
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[533]
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[534]
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[535]
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[536]
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[537]
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[538]
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[539]
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[540]
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[541]
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[542]
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[543]
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[544]
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[545]
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[546]
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[547]
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[548]
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[549]
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[550]
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[551]
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[552]
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[553]
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[554]
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[555]
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[556]
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[557]
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[558]
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[559]
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[560]
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[561]
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[562]
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[563]
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[564]
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[565]
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[566]
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[567]
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[568]
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[569]
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[570]
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[571]
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[572]
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[573]
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[574]
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[575]
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[576]
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[577]
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[578]
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[579]
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[580]
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[581]
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[582]
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[583]
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[584]
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[590]
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[591]
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[592]
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[593]
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[594]
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[595]
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[596]
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[597]
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[598]
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[600]
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[602]
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[603]
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[604]
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[605]
GSM1979668
[606]
GSM1979669
[607]
GSM1979670
sample_count
608
group_sizes
null
group_sizes_basis
null
platform
17586;6801
taxon
Homo sapiens
title
Human hepatocellular carcinoma (HCC) and Cholangiocarcinoma (CCA) from Thailand Initiative in Genomics and Expression Research for Liver Cancer (TIGER-LC)
resolution_checked_at
2026-11-01T17:30:42Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
gaps array · 10 items
[0]
asset:data:geo:GSE26566 not downloaded
[1]
asset:data:geo:GSE26566 licence not read
[2]
asset:data:geo:GSE31370 not downloaded
[3]
asset:data:geo:GSE31370 licence not read
[4]
asset:data:geo:GSE32958 not downloaded
[5]
asset:data:geo:GSE32958 licence not read
[6]
asset:data:geo:GSE45001 not downloaded
[7]
asset:data:geo:GSE45001 licence not read
[8]
asset:data:geo:GSE76311 not downloaded
[9]
asset:data:geo:GSE76311 licence not read
analysis object · 4 fields
state
complete
methods array · 3 items
[0] object · 10 fields
id
method-1
family
differential_expression
specific
limma_voom
label
Differential expression analysis
dsl_operations array · 3 items
[0]
normalise_counts
[1]
differential_expression_fit
[2]
fdr_bh
missing_operations array · 0 items
parameters object · 1 field
fold_change
-value and absolute log fold change
data_shapes array · 1 item
[0]
count_matrix_with_condition
evidence object · 3 fields
score
1
band
verbatim
quote
We used the GEO2R analysis tool (https://www.ncbi.nlm.nih.gov/geo/geo2r/) to discover the DEGs between ICC and healthy samples and screen out the DEGs based on the value of adjusted P-value (Padj) and fold change (|logFC|).
[1] object · 10 fields
id
method-2
family
network_analysis
specific
ppi_network
label
Network / pathway analysis
dsl_operations array · 3 items
[0]
build_graph
[1]
centrality_scores
[2]
pathway_enrich
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
edge_list
evidence object · 3 fields
score
0.7
band
medium
quote
protein-protein interaction
[2] object · 10 fields
id
method-3
family
network_analysis
specific
pathway_enrichment
label
Network / pathway analysis
dsl_operations array · 1 item
[0]
pathway_enrich
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
edge_list
evidence object · 3 fields
score
0.7
band
medium
quote
pathway enrichment
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 · 3 items
[0]
normalise_counts
[1]
differential_expression_fit
[2]
fdr_bh
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: limma_voom.
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 · 5 items
[0]
GSE26566
[1]
GSE31370
[2]
GSE32958
[3]
GSE45001
[4]
GSE76311
resolved_accessions array · 5 items
[0]
GSE26566
[1]
GSE31370
[2]
GSE32958
[3]
GSE45001
[4]
GSE76311
statements array · 1 item
[0]
The raw data of this study are freely available from the website https://www.ncbi.nlm.nih.gov/geo, and all the analyzed data are included in this manuscript.
basis
The paper names GSE26566, GSE31370, GSE32958, GSE45001, GSE76311 in Gene Expression Omnibus (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 · 3 items
[0]
normalise_counts
[1]
differential_expression_fit
[2]
fdr_bh
assets array · 2 items
[0]
source:pmid:34979994
[1]
data:geo:GSE26566
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:GSE26566
gaps array · 0 items
review object · 5 fields
state
partial
machine_critique object · 5 fields
kind
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rammatically selected the five most suitable microarray datasets. Then, we made an analysis of these
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datasets. Then, we made an analysis of these microarray datasets (GSE26566, GSE31370, GSE32958, GSE4
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We used the GEO2R analysis tool (https://www.ncbi.nlm.nih.gov/geo/geo2r/) to discover the DEGs between ICC and healthy samples and screen out the DEGs based on the value of adjusted P-value (Padj) and fold change (|logFC|).
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34979994", "kind": "science-record", "revision": 5, "content_hash": "sha256:79b2c5fa3ee086b016bb78cce2058ed1dad11f470c84977eb673703fded17cd7", "created_at": "2026-11-01T17:36:41Z", "updated_at": "2026-11-01T22:48:09Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A2M is a potential core gene in intrahepatic cholangiocarcinoma.", "doi": "10.1186/s12885-021-09070-2", "pmid": "34979994", "authors": ["Guanran Zhang", "Xuyue Liu", "Zhengyang Sun", "Xiaoning Feng", "Haiyan Wang", "Jing Hao", "Xiaoli Zhang"], "journal": "BMC cancer", "year": 2022, "abstract": "BACKGROUND: Intrahepatic cholangiocarcinoma (ICC) is a type of malignant tumor ranking the second in the incidence of primary liver cancer following hepatocellular carcinoma. Both the morbidity and mortality have been increasing in recent years. Small duct type of ICC has potential therapeutic targets. But overall, the prognosis of patients with ICC is usually very poor.\nMETHODS: To search latent therapeutic targets for ICC, we programmatically selected the five most suitable microarray datasets. Then, we made an analysis of these microarray datasets (GSE26566, GSE31370, GSE32958, GSE45001 and GSE76311) collected from the Gene Expression Omnibus (GEO) database. The GEO2R tool was effective to find out differentially expressed genes (DEGs) between ICC and normal tissue. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were executed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) v 6.8. The Search Tool for the Retrieval of Interacting Genes (STRING) database was used to analyze protein-protein interaction of these DEGs and protein-protein interaction of these DEGs was modified by Cytoscape3.8.2. Survival analysis was performed using Gene Expression Profiling Interactive Analysis (GEPIA) online analysis tool.\nRESULTS: A total of 28 upregulated DEGs and 118 downregulated DEGs were screened out. Then twenty hub genes were selected according to the connectivity degree. The survival analysis results showed that A2M was closely related to the pathogenesis and prognosis of ICC and was a potential therapeutic target for ICC.\nCONCLUSIONS: According to our study, low A2M expression in ICC compared to normal bile duct tissue was an adverse prognostic factor in ICC patients. The value of A2M in the treatment of ICC needs to be further studied.", "claims": [{"id": "claim-method", "statement": "We used the GEO2R analysis tool (https://www.ncbi.nlm.nih.gov/geo/geo2r/) to discover the DEGs between ICC and healthy samples and screen out the DEGs based on the value of adjusted P-value (Padj) and fold change (|logFC|).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34979994", "locator": "sections[7] \"Data processing of DEGs\" paragraph 2", "quote": "We used the GEO2R analysis tool (https://www.ncbi.nlm.nih.gov/geo/geo2r/) to discover the DEGs between ICC and healthy samples and screen out the DEGs based on the value of adjusted P-value (Padj) and fold change (|logFC|)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The raw data of this study are freely available from the website https://www.ncbi.nlm.nih.gov/geo, and all the analyzed data are included in this manuscript.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34979994", "locator": "sections[23] \"Availability of data and materials\" paragraph 2", "quote": "The raw data of this study are freely available from the website https://www.ncbi.nlm.nih.gov/geo, and all the analyzed data are included in this manuscript."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Low A2M expression in ICC compared to normal bile duct tissue was an adverse prognostic factor in ICC patients.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:34979994", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "According to our study, low A2M expression in ICC compared to normal bile duct tissue was an adverse prognostic factor in ICC patients."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Low A2M expression in ICC compared to normal bile duct tissue was an adverse prognostic factor in ICC patients.", "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:34979994"], "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:GSE26566"], "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": {"fold_change": "-value and absolute log fold change"}, "inputs": ["data:geo:GSE26566"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:34979994", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12885-021-09070-2", 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carcinoma (HCC) and Cholangiocarcinoma (CCA) from Thailand Initiative in Genomics and Expression Research for Liver Cancer (TIGER-LC)", "resolution_checked_at": "2026-11-01T17:30:42Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE26566 not downloaded", "asset:data:geo:GSE26566 licence not read", "asset:data:geo:GSE31370 not downloaded", "asset:data:geo:GSE31370 licence not read", "asset:data:geo:GSE32958 not downloaded", "asset:data:geo:GSE32958 licence not read", "asset:data:geo:GSE45001 not downloaded", "asset:data:geo:GSE45001 licence not read", "asset:data:geo:GSE76311 not downloaded", "asset:data:geo:GSE76311 licence not read"]}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "family": "differential_expression", "specific": "limma_voom", "label": "Differential expression analysis", "dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "expressible": true, "parameters": {"fold_change": "-value and absolute log fold change"}, "data_shapes": ["count_matrix_with_condition"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "We used the GEO2R analysis tool (https://www.ncbi.nlm.nih.gov/geo/geo2r/) to discover the DEGs between ICC and healthy samples and screen out the DEGs based on the value of adjusted P-value (Padj) and fold change (|logFC|)."}}, {"id": "method-2", "family": "network_analysis", "specific": "ppi_network", "label": "Network / pathway analysis", "dsl_operations": ["build_graph", "centrality_scores", "pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.7, "band": "medium", "quote": "protein-protein interaction"}}, {"id": "method-3", "family": "network_analysis", "specific": "pathway_enrichment", "label": "Network / pathway analysis", "dsl_operations": ["pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.7, "band": "medium", "quote": "pathway enrichment"}}], "targets": [], "gaps": []}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: limma_voom."}, "capability": {"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": {"tier": "R1", "reachable": true, "accessions": ["GSE26566", "GSE31370", "GSE32958", "GSE45001", "GSE76311"], "resolved_accessions": ["GSE26566", "GSE31370", "GSE32958", "GSE45001", "GSE76311"], "statements": ["The raw data of this study are freely available from the website https://www.ncbi.nlm.nih.gov/geo, and all the analyzed data are included in this manuscript."], "basis": "The paper names GSE26566, GSE31370, GSE32958, GSE45001, GSE76311 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "assets": ["source:pmid:34979994", "data:geo:GSE26566"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE26566"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T17:13:53Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 12348, "completion_tokens": 1475, "cached_tokens": 256}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:34979994", "type": "paper", "label": "pmid:34979994"}, {"id": "source:pmid:34979994", "type": "manuscript", "label": "PMID 34979994 full text"}, {"id": "data:geo:GSE26566", "type": "dataset", "label": "GEO GSE26566"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:34979994", "type": "paper", "label": "pmid:34979994"}, {"id": "source:pmid:34979994", "type": "manuscript", "label": "PMID 34979994 full text"}, {"id": "data:geo:GSE26566", "type": "dataset", "label": "GEO GSE26566"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-34979994", "type": "extraction", "used": ["source:pmid:34979994"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE26566", "type": "accession_resolution", "used": ["data:geo:GSE26566"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:34979994", "parent_hash": "sha256:a9042b4b12806ef3cd4d9132a54144a71147a67d179841d141dcbe6cabebacc3", "fork_of": null, "generation": 4}, "gaps": ["asset:data:geo:GSE26566 licence not read", "asset:data:geo:GSE26566 not downloaded", "asset:data:geo:GSE31370 licence not read", "asset:data:geo:GSE31370 not downloaded", "asset:data:geo:GSE32958 licence not read", "asset:data:geo:GSE32958 not downloaded", "asset:data:geo:GSE45001 licence not read", "asset:data:geo:GSE45001 not downloaded", "asset:data:geo:GSE76311 licence not read", "asset:data:geo:GSE76311 not downloaded", "execution.not_run", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "data_generation", "definition_id": "microarray", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["rammatically selected the five most suitable microarray datasets. Then, we made an analysis of these", "datasets. Then, we made an analysis of these microarray datasets (GSE26566, GSE31370, GSE32958, GSE4"], "order_basis": "paper_text_order:501", "id": "assay:1"}, {"role": "analysis", "definition_id": "differential_expression", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["We used the GEO2R analysis tool (https://www.ncbi.nlm.nih.gov/geo/geo2r/) to discover the DEGs between ICC and healthy samples and screen out the DEGs based on the value of adjusted P-value (Padj) and fold change (|logFC|)."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}