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pmid:33680905

Identifying Apoptosis-Related Transcriptomic Aberrations and Revealing Clinical Relevance as Diagnostic and Prognostic Biomarker in Hepatocellular Carcinoma.

Jinyu Zhu · Bufu Tang · Xiuling Lv · Miaomiao Meng · Qiaoyou Weng · Nannan Zhang · Jie Li · Kai Fan · Liyun Zheng · Shiji Fang · Min Xu · Jiansong Ji

Frontiers in oncology · 2020 doi:10.3389/fonc.2020.519180

Paper

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Experiment

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Data

partial

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pmid:33680905
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title
Identifying Apoptosis-Related Transcriptomic Aberrations and Revealing Clinical Relevance as Diagnostic and Prognostic Biomarker in Hepatocellular Carcinoma.
doi
10.3389/fonc.2020.519180
pmid
33680905
authors array · 12 items
[0]
Jinyu Zhu
[1]
Bufu Tang
[2]
Xiuling Lv
[3]
Miaomiao Meng
[4]
Qiaoyou Weng
[5]
Nannan Zhang
[6]
Jie Li
[7]
Kai Fan
[8]
Liyun Zheng
[9]
Shiji Fang
[10]
Min Xu
[11]
Jiansong Ji
journal
Frontiers in oncology
year
2020
abstract
In view of the unsatisfactory treatment outcome of liver cancer under current treatment, where the mortality rate is high and the survival rate is poor, in this study we aimed to use RNA sequencing data to explore potential molecular markers that can be more effective in predicting diagnosis and prognosis of hepatocellular carcinoma. RNA sequencing data and corresponding clinical information were obtained from multiple databases. After matching with the apoptotic genes from the Deathbase database, 14 differentially expressed human apoptosis genes were obtained. Using univariate and multivariate Cox regression analyses, two apoptosis genes (BAK1 and CSE1L) were determined to be closely associated with overall survival (OS) in HCC patients. And subsequently experiments also validated that knockdown of BAK1 and CSE1L significantly inhibited cell proliferation and promoted apoptosis in the HCC. Then the two genes were used to construct a prognostic signature and diagnostic models. The high-risk group showed lower OS time compared to low-risk group in the TCGA cohort (P < 0.001, HR = 2.11), GSE14520 cohort (P = 0.003, HR = 1.85), and ICGC cohort (P < 0.001, HR = 4). And the advanced HCC patients showed higher risk score and worse prognosis compared to early-stage HCC patients. Moreover, the prognostic signature was validated to be an independent prognostic factor. The diagnostic models accurately predicted HCC from normal tissues and dysplastic nodules in the training and validation cohort. These results indicated that the two apoptosis-related signature effectively predicted diagnosis and prognosis of HCC and may serve as a potential biomarker and therapeutic target for HCC.
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[0]
group_summary
[1]
welch_t
[2]
bootstrap_mean_difference
[3]
group_comparison_anova
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
two_or_more_groups_continuous
evidence object · 3 fields
score
0.8
band
high
quote
analysis of variance
[1] object · 10 fields
id
method-2
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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
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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
0.7
band
medium
quote
Cox regression
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id
method-4
family
diagnostic_evaluation
specific
roc
label
Diagnostic test evaluation
dsl_operations array · 6 items
[0]
confusion_counts
[1]
roc_curve
[2]
sensitivity_specificity
[3]
solve_confusion_matrix
[4]
classification_metrics
[5]
published_precision_envelope
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
reference_standard_and_test
evidence object · 3 fields
score
0.8
band
high
quote
ROC curve
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
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true
required_operations array · 4 items
[0]
group_summary
[1]
welch_t
[2]
bootstrap_mean_difference
[3]
group_comparison_anova
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: anova.
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 · 1 item
[0]
GSE14520
resolved_accessions array · 1 item
[0]
GSE14520
statements array · 1 item
[0]
Publicly available datasets were analyzed in this study; these can be found in The Cancer Genome Atlas (https://www.tcga.org/) and Deathbase (http://www.deathbase.org/).
basis
The paper names GSE14520 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 · 4 items
[0]
group_summary
[1]
welch_t
[2]
bootstrap_mean_difference
[3]
group_comparison_anova
assets array · 2 items
[0]
source:pmid:33680905
[1]
data:geo:GSE14520
steps array · 1 item
[0]
run_analysis
must_build object · 5 fields
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readers array · 0 items
capabilities array · 0 items
data_gaps array · 1 item
[0]
data:geo:GSE14520
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
2026-10-31T19:26:57Z
independence
single_reader
evidence_check object · 2 fields
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absent
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verbatim
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3
usage object · 3 fields
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42009
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2492
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776
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[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
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absent
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not_run
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null
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[0]
execution.not_run
provenance object · 7 fields
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[0] object · 3 fields
id
pmid:33680905
type
paper
label
pmid:33680905
[1] object · 3 fields
id
source:pmid:33680905
type
manuscript
label
PMID 33680905 full text
[2] object · 3 fields
id
data:geo:GSE14520
type
dataset
label
GEO GSE14520
activities array · 3 items
[0] object · 4 fields
id
activity-reading-sci-2026-11-01
type
classification
used array · 3 items
[0] object · 3 fields
id
pmid:33680905
type
paper
label
pmid:33680905
[1] object · 3 fields
id
source:pmid:33680905
type
manuscript
label
PMID 33680905 full text
[2] object · 3 fields
id
data:geo:GSE14520
type
dataset
label
GEO GSE14520
agent
agent:corpus-assay-engine
[1] object · 4 fields
id
activity-read-33680905
type
extraction
used array · 1 item
[0]
source:pmid:33680905
agent
agent:minimax-reader
[2] object · 4 fields
id
activity-resolve-GSE14520
type
accession_resolution
used array · 1 item
[0]
data:geo:GSE14520
agent
agent:corpus-assay-engine
agents array · 2 items
[0] object · 3 fields
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agent:corpus-assay-engine
type
software
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Corpus assay engine
[1] object · 3 fields
id
agent:minimax-reader
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software
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MiniMax literature reader
source_run_id
reading-sci-2026-11-01
dsl_catalogue_fingerprint
343fb30756ba6ec3
capability_table_fingerprint
2cb3e42f047d2ecf
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ba34b49490d9bfaf
lineage object · 4 fields
parent_id
pmid:33680905
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sha256:a43bcf10ad6e9ea5a57122e99578882efa5bb51c6d2f92f004ea518848a6d0e9
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null
gaps array · 6 items
[0]
asset:data:geo:GSE14520 licence not read
[1]
asset:data:geo:GSE14520 not downloaded
[2]
execution.not_run
[3]
review.human_critique
[4]
review.independent_reader
[5]
review.machine_critique
assays array · 6 items
[0] object · 11 fields
role
data_generation
definition_id
apoptosis_assay
definition_version
99e1195c7d6f
required_operations array · 0 items
support_state
partial
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assay_lexicon
99e1195c7d6f
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0.99
evidence array · 4 items
[0]
Identifying Apoptosis-Related Transcriptomic Aberrations and Revea
[1]
database, 14 differentially expressed human apoptosis genes were obtained. Using univariate and mu
[2]
nd multivariate Cox regression analyses, two apoptosis genes (BAK1 and CSE1L) were determined to be
[3]
ly inhibited cell proliferation and promoted apoptosis in the HCC. Then the two genes were used to
order_basis
paper_text_order:0
id
assay:1
[1] object · 11 fields
role
data_generation
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rna_seq
definition_version
99e1195c7d6f
required_operations array · 0 items
support_state
partial
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assay_lexicon
99e1195c7d6f
confidence
0.99
evidence array · 3 items
[0]
Identifying Apoptosis-Related Transcriptomic Aberrations and Revealing Clinical Relevance
[1]
rate is poor, in this study we aimed to use RNA sequencing data to explore potential molecular markers
[2]
s and prognosis of hepatocellular carcinoma. RNA sequencing data and corresponding clinical information
order_basis
paper_text_order:0
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assay:2
[2] object · 11 fields
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data_generation
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transfection
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99e1195c7d6f
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assay_lexicon
99e1195c7d6f
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0.99
evidence array · 4 items
[0]
subsequently experiments also validated that knockdown of BAK1 and CSE1L significantly inhibited ce
[1]
erature incubator. SK-HEP1 and SMMC-7721 was transfected 50 nM BAK1 and CSE1L siRNA in basic DMEM med
[2]
Materials and Methods Cell Culture and siRNA Treatment The human hepatocellular carcinoma
[3]
MC-7721 was transfected 50 nM BAK1 and CSE1L siRNA in basic DMEM media for 6–8 h before change
order_basis
paper_text_order:911
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assay:3
[3] object · 11 fields
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data_generation
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cell_culture
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99e1195c7d6f
required_operations array · 0 items
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partial
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assay_lexicon
99e1195c7d6f
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0.77
evidence array · 1 item
[0]
Collection (ATCC) (Manassas, VA, USA). Cells cultured in DMEM media supplemented with 2 mM L-glutamin
order_basis
paper_text_order:2029
id
assay:4
[4] object · 11 fields
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data_generation
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99e1195c7d6f
required_operations array · 0 items
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partial
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assay_lexicon
99e1195c7d6f
confidence
0.93
evidence array · 1 item
[0]
lore the role of two apoptotic genes in HCC, qPCR assay was used to measure the mRNA of BAK1 a
order_basis
paper_text_order:5828
id
assay:5
[5] object · 11 fields
role
analysis
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group_comparison
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f2132e96ae6c7a4e
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group_summary
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f2132e96ae6c7a4e
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33680905", "kind": "science-record", "revision": 7, "content_hash": "sha256:c7bfba05c3dd6a4ad9abbeed169ecc187c9002773a62c90e8f11432561adb6d7", "created_at": "2026-11-01T15:41:27Z", "updated_at": "2026-11-01T22:03:15Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identifying Apoptosis-Related Transcriptomic Aberrations and Revealing Clinical Relevance as Diagnostic and Prognostic Biomarker in Hepatocellular Carcinoma.", "doi": "10.3389/fonc.2020.519180", "pmid": "33680905", "authors": ["Jinyu Zhu", "Bufu Tang", "Xiuling Lv", "Miaomiao Meng", "Qiaoyou Weng", "Nannan Zhang", "Jie Li", "Kai Fan", "Liyun Zheng", "Shiji Fang", "Min Xu", "Jiansong Ji"], "journal": "Frontiers in oncology", "year": 2020, "abstract": "In view of the unsatisfactory treatment outcome of liver cancer under current treatment, where the mortality rate is high and the survival rate is poor, in this study we aimed to use RNA sequencing data to explore potential molecular markers that can be more effective in predicting diagnosis and prognosis of hepatocellular carcinoma. RNA sequencing data and corresponding clinical information were obtained from multiple databases. After matching with the apoptotic genes from the Deathbase database, 14 differentially expressed human apoptosis genes were obtained. Using univariate and multivariate Cox regression analyses, two apoptosis genes (BAK1 and CSE1L) were determined to be closely associated with overall survival (OS) in HCC patients. And subsequently experiments also validated that knockdown of BAK1 and CSE1L significantly inhibited cell proliferation and promoted apoptosis in the HCC. Then the two genes were used to construct a prognostic signature and diagnostic models. The high-risk group showed lower OS time compared to low-risk group in the TCGA cohort (P < 0.001, HR = 2.11), GSE14520 cohort (P = 0.003, HR = 1.85), and ICGC cohort (P < 0.001, HR = 4). And the advanced HCC patients showed higher risk score and worse prognosis compared to early-stage HCC patients. Moreover, the prognostic signature was validated to be an independent prognostic factor. The diagnostic models accurately predicted HCC from normal tissues and dysplastic nodules in the training and validation cohort. 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