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pmid:33832071

Genomic analysis of pancreatic cancer reveals 3 molecular subtypes with different clinical outcomes.

Ji Woong Hwang · Soo Kyung Jang · Dong Jin Lee

Medicine · 2021 doi:10.1097/MD.0000000000024969

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complete

Experiment

complete

Data

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Analysis

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title
Genomic analysis of pancreatic cancer reveals 3 molecular subtypes with different clinical outcomes.
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10.1097/MD.0000000000024969
pmid
33832071
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[0]
Ji Woong Hwang
[1]
Soo Kyung Jang
[2]
Dong Jin Lee
journal
Medicine
year
2021
abstract
ABSTRACT: Pancreatic cancer has a very high mortality with a 5-year survival of <5%. The purpose of this study was to classify specific molecular subtypes associated with prognosis of pancreatic cancer using The Cancer Genome Atlas (TCGA) multiplatform genomic data.Multiplatform genomic data (N = 178), including gene expression, copy number alteration, and somatic mutation data, were obtained from cancer browser (https://genome-cancer.ucsc.edu, cohort: TCGA Pancreatic Cancer). Clinical data including survival results were analyzed. We also used validation cohort (GSE50827) to confirm the robustness of these molecular subtypes in pancreatic cancer.When we performed unsupervised clustering using TCGA gene expression data, we found three distinct molecular subtypes associated with different survival results. Copy number alteration and somatic mutation data showed different genomic patterns for these three subtypes. Ingenuity pathway analysis revealed that each subtype showed differentially altered pathways. Using each subtype-specific genes (200 were selected), we could predict molecular subtype in another cohort, confirming the robustness of these molecular subtypes of pancreatic cancer. Cox regression analysis revealed that molecular subtype is the only significant prognostic factor for pancreatic cancer (P = .042, 95% confidence interval 0.523-0.98).Genomic analysis of pancreatic cancer revealed 3 distinct molecular subtypes associated with different survival results. Using these subtype-specific genes and prediction model, we could predict molecular subtype associated with prognosis of pancreatic cancer.
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Association of each subtype with overall survival was evaluated using Kaplan-Meier plots and log rank test.
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The datasets generated during and/or analyzed during the current study are publicly available.
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Analysis of TCGA genomic data showed novel molecular subtypes of pancreatic cancer associated with clinical behavior
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asset:data:geo:GSE50827 licence not read
analysis object · 4 fields
state
complete
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[0] object · 10 fields
id
method-1
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survival_analysis
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kaplan_meier
label
Survival analysis
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survival_km
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data_shapes array · 1 item
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evidence object · 3 fields
score
1
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verbatim
quote
Association of each subtype with overall survival was evaluated using Kaplan-Meier plots and log rank test.
[1] object · 10 fields
id
method-2
family
survival_analysis
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cox_proportional_hazards
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Survival analysis
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data_shapes array · 1 item
[0]
time_to_event_with_censoring
evidence object · 3 fields
score
0.7
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Cox regression
[2] object · 10 fields
id
method-3
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survival_analysis
specific
log_rank_test
label
Survival analysis
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[0]
logrank_test
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parameters object · 0 fields
data_shapes array · 1 item
[0]
time_to_event_with_censoring
evidence object · 3 fields
score
0.8
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log rank
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id
method-4
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clustering
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Clustering
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[0]
hierarchical_cluster
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[0]
feature_matrix
evidence object · 3 fields
score
0.8
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high
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hierarchical clustering
[4] object · 10 fields
id
method-5
family
contingency_analysis
specific
chi_square
label
Categorical (contingency table) analysis
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[0]
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evidence object · 3 fields
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0.8
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high
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χ2
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capability object · 3 fields
state
complete
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capability_id
capability:computational_analysis
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Computational analysis platform
assay_families array · 9 items
[0]
meta_analysis
[1]
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[2]
survival_analysis
[3]
statistical_regression
[4]
pca
[5]
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[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
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blocked_on
nothing:replicable_now
axes object · 3 fields
method object · 5 fields
tier
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required_operations array · 1 item
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survival_km
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basis
The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: kaplan_meier.
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
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accessions array · 1 item
[0]
GSE50827
resolved_accessions array · 1 item
[0]
GSE50827
statements array · 1 item
[0]
The datasets generated during and/or analyzed during the current study are publicly available.
basis
The paper names GSE50827 in Gene Expression Omnibus 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
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survival_km
assets array · 6 items
[0]
source:pmid:33832071
[1]
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[4]
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[5]
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steps array · 1 item
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must_build object · 5 fields
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data:geo:GSE50827
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review object · 5 fields
state
partial
machine_critique object · 5 fields
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extraction_review
reader object · 5 fields
model
MiniMax-M3.1-Flash
provider
minimax
provider_family
minimax
read_at
2026-10-31T18:28:56Z
independence
single_reader
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method
verbatim
availability
verbatim
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usage object · 3 fields
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provenance object · 7 fields
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paper
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pmid:33832071
[1] object · 3 fields
id
source:pmid:33832071
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manuscript
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PMID 33832071 full text
[2] object · 3 fields
id
data:geo:GSE50827
type
dataset
label
GEO GSE50827
activities array · 3 items
[0] object · 4 fields
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activity-reading-sci-2026-11-01
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classification
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paper
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pmid:33832071
[1] object · 3 fields
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source:pmid:33832071
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PMID 33832071 full text
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data:geo:GSE50827
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dataset
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GEO GSE50827
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agent:corpus-assay-engine
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agent
agent:minimax-reader
[2] object · 4 fields
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activity-resolve-GSE50827
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accession_resolution
used array · 1 item
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agent:corpus-assay-engine
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343fb30756ba6ec3
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asset:data:geo:GSE50827 licence not read
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[2]
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Association of each subtype with overall survival was evaluated using Kaplan-Meier plots and log rank test.
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Complete file
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The purpose of this study was to classify specific molecular subtypes associated with prognosis of pancreatic cancer using The Cancer Genome Atlas (TCGA) multiplatform genomic data.Multiplatform genomic data (N = 178), including gene expression, copy number alteration, and somatic mutation data, were obtained from cancer browser (https://genome-cancer.ucsc.edu, cohort: TCGA Pancreatic Cancer). Clinical data including survival results were analyzed. We also used validation cohort (GSE50827) to confirm the robustness of these molecular subtypes in pancreatic cancer.When we performed unsupervised clustering using TCGA gene expression data, we found three distinct molecular subtypes associated with different survival results. Copy number alteration and somatic mutation data showed different genomic patterns for these three subtypes. Ingenuity pathway analysis revealed that each subtype showed differentially altered pathways. Using each subtype-specific genes (200 were selected), we could predict molecular subtype in another cohort, confirming the robustness of these molecular subtypes of pancreatic cancer. Cox regression analysis revealed that molecular subtype is the only significant prognostic factor for pancreatic cancer (P = .042, 95% confidence interval 0.523-0.98).Genomic analysis of pancreatic cancer revealed 3 distinct molecular subtypes associated with different survival results. 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