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pmid:33490259

Identification of 5-Gene Signature Improves Lung Adenocarcinoma Prognostic Stratification Based on Differential Expression Invasion Genes of Molecular Subtypes.

Zhimin Zheng · Weijie Deng · Jiansheng Yang

BioMed research international · 2020 doi:10.1155/2020/8832739

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Data

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title
Identification of 5-Gene Signature Improves Lung Adenocarcinoma Prognostic Stratification Based on Differential Expression Invasion Genes of Molecular Subtypes.
doi
10.1155/2020/8832739
pmid
33490259
authors array · 3 items
[0]
Zhimin Zheng
[1]
Weijie Deng
[2]
Jiansheng Yang
journal
BioMed research international
year
2020
abstract
BACKGROUND: The acquisition of invasive tumor cell behavior is considered to be the cornerstone of the metastasis cascade. Thus, genetic markers associated with invasiveness can be stratified according to patient prognosis. In this study, we aimed to identify an invasive genetic trait and study its biological relevance in lung adenocarcinoma. METHODS: 250 TCGA patients with lung adenocarcinoma were used as the training set, and the remaining 250 TCGA patients, 500 ALL TCGA patients, 226 patients with GSE31210, 83 patients with GSE30219, and 127 patients with GSE50081 were used as the verification data sets. Subtype classification of all TCGA lung adenocarcinoma samples was based on invasion-associated genes using the R package ConsensusClusterPlus. Kaplan-Meier curves, LASSO (least absolute contraction and selection operator) method, and univariate and multivariate Cox analysis were used to develop a molecular model for predicting survival. RESULTS: As a consequence, two molecular subtypes for LUAD were first identified from all TCGA all data sets which were significant on survival time. C1 subtype with poor prognosis has higher clinical characteristics of malignancy, higher mutation frequency of KRAS and TP53, and a lower expression of immune regulatory molecules. 2463 differentially expressed invasion genes between C1 and C2 subtypes were obtained, including 580 upregulation genes and 1883 downregulation genes. Functional enrichment analysis found that upregulated genes were associated with the development of tumor pathways, while downregulated genes were more associated with immunity. Furthermore, 5-invasion gene signature was constructed based on 2463 genes, which was validated in four data sets. This signature divided patients into high-risk and low-risk groups, and the LUDA survival rate of the high-risk group is significantly lower than that of the low-risk group. Multivariate Cox analysis revealed that this gene signature was an independent prognostic factor for LUDA. Compared with other existing models, our model has a higher AUC. CONCLUSION: In this study, two subtypes were identified. In addition, we developed a 5-gene signature prognostic risk model, which has a good AUC in the training set and independent validation set and is a model with independent clinical characteristics. Therefore, we recommend using this classifier as a molecular diagnostic test to assess the prognostic risk of patients with LUDA.
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Univariate and multivariate Cox survival analyses of clinical characteristics and RiskScore were used to demonstrate the independence of the RiskScore model.
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[155]
GSM773695
[156]
GSM773696
[157]
GSM773697
[158]
GSM773698
[159]
GSM773699
[160]
GSM773700
[161]
GSM773701
[162]
GSM773702
[163]
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GSM773704
[165]
GSM773705
[166]
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[167]
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[168]
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[169]
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[171]
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[172]
GSM773712
[173]
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[174]
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[175]
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[176]
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[178]
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[179]
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[180]
GSM773720
[181]
GSM773721
[182]
GSM773722
[183]
GSM773723
[184]
GSM773724
[185]
GSM773725
[186]
GSM773726
[187]
GSM773727
[188]
GSM773728
[189]
GSM773729
[190]
GSM773730
[191]
GSM773731
[192]
GSM773732
[193]
GSM773733
[194]
GSM773734
[195]
GSM773735
[196]
GSM773736
[197]
GSM773737
[198]
GSM773738
[199]
GSM773739
[200]
GSM773740
[201]
GSM773741
[202]
GSM773742
[203]
GSM773743
[204]
GSM773744
[205]
GSM773745
[206]
GSM773746
[207]
GSM773747
[208]
GSM773748
[209]
GSM773749
[210]
GSM773750
[211]
GSM773751
[212]
GSM773752
[213]
GSM773753
[214]
GSM773754
[215]
GSM773755
[216]
GSM773756
[217]
GSM773757
[218]
GSM773758
[219]
GSM773759
[220]
GSM773760
[221]
GSM773761
[222]
GSM773762
[223]
GSM773763
[224]
GSM773764
[225]
GSM773765
[226]
GSM773766
[227]
GSM773767
[228]
GSM773768
[229]
GSM773769
[230]
GSM773770
[231]
GSM773771
[232]
GSM773772
[233]
GSM773773
[234]
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[235]
GSM773775
[236]
GSM773776
[237]
GSM773777
[238]
GSM773778
[239]
GSM773779
[240]
GSM773780
[241]
GSM773781
[242]
GSM773782
[243]
GSM773783
[244]
GSM773784
[245]
GSM773785
sample_count
246
group_sizes
null
group_sizes_basis
null
platform
570
taxon
Homo sapiens
title
Gene expression data for pathological stage I-II lung adenocarcinomas
resolution_checked_at
2026-11-01T15:39:54Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
[3] object · 15 fields
id
data:geo:GSE50081
role
raw
format
geo-series
media_type
application/vnd.ncbi.geo-series+json
uri
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE50081
sha256
null
bytes
null
access
open
state
resolved
reader
null
reader_status
reader_required
identifier
GSE50081
version
null
note
The analyzed data sets generated during the study are available from the corresponding author on reasonable request.
structure object · 15 fields
repository
GEO
access_route
GEO accession URL
access_statement_verbatim
The analyzed data sets generated during the study are available from the corresponding author on reasonable request.
access_statement_kind
data_availability_statement
sample_accessions array · 181 items
[0]
GSM1213669
[1]
GSM1213670
[2]
GSM1213671
[3]
GSM1213672
[4]
GSM1213673
[5]
GSM1213674
[6]
GSM1213675
[7]
GSM1213676
[8]
GSM1213677
[9]
GSM1213678
[10]
GSM1213679
[11]
GSM1213680
[12]
GSM1213681
[13]
GSM1213682
[14]
GSM1213683
[15]
GSM1213684
[16]
GSM1213685
[17]
GSM1213686
[18]
GSM1213687
[19]
GSM1213688
[20]
GSM1213689
[21]
GSM1213690
[22]
GSM1213691
[23]
GSM1213692
[24]
GSM1213693
[25]
GSM1213694
[26]
GSM1213695
[27]
GSM1213696
[28]
GSM1213697
[29]
GSM1213698
[30]
GSM1213699
[31]
GSM1213700
[32]
GSM1213701
[33]
GSM1213702
[34]
GSM1213703
[35]
GSM1213704
[36]
GSM1213705
[37]
GSM1213706
[38]
GSM1213707
[39]
GSM1213708
[40]
GSM1213709
[41]
GSM1213710
[42]
GSM1213711
[43]
GSM1213712
[44]
GSM1213713
[45]
GSM1213714
[46]
GSM1213715
[47]
GSM1213716
[48]
GSM1213717
[49]
GSM1213718
[50]
GSM1213719
[51]
GSM1213720
[52]
GSM1213721
[53]
GSM1213722
[54]
GSM1213723
[55]
GSM1213724
[56]
GSM1213725
[57]
GSM1213726
[58]
GSM1213727
[59]
GSM1213728
[60]
GSM1213729
[61]
GSM1213730
[62]
GSM1213731
[63]
GSM1213732
[64]
GSM1213733
[65]
GSM1213734
[66]
GSM1213735
[67]
GSM1213736
[68]
GSM1213737
[69]
GSM1213738
[70]
GSM1213739
[71]
GSM1213740
[72]
GSM1213741
[73]
GSM1213742
[74]
GSM1213743
[75]
GSM1213744
[76]
GSM1213745
[77]
GSM1213746
[78]
GSM1213747
[79]
GSM1213748
[80]
GSM1213749
[81]
GSM1213750
[82]
GSM1213751
[83]
GSM1213752
[84]
GSM1213753
[85]
GSM1213754
[86]
GSM1213755
[87]
GSM1213756
[88]
GSM1213757
[89]
GSM1213758
[90]
GSM1213759
[91]
GSM1213760
[92]
GSM1213761
[93]
GSM1213762
[94]
GSM1213763
[95]
GSM1213764
[96]
GSM1213765
[97]
GSM1213766
[98]
GSM1213767
[99]
GSM1213768
[100]
GSM1213769
[101]
GSM1213770
[102]
GSM1213771
[103]
GSM1213772
[104]
GSM1213773
[105]
GSM1213774
[106]
GSM1213775
[107]
GSM1213776
[108]
GSM1213777
[109]
GSM1213778
[110]
GSM1213779
[111]
GSM1213780
[112]
GSM1213781
[113]
GSM1213782
[114]
GSM1213783
[115]
GSM1213784
[116]
GSM1213785
[117]
GSM1213786
[118]
GSM1213787
[119]
GSM1213788
[120]
GSM1213789
[121]
GSM1213790
[122]
GSM1213791
[123]
GSM1213792
[124]
GSM1213793
[125]
GSM1213794
[126]
GSM1213795
[127]
GSM1213796
[128]
GSM1213797
[129]
GSM1213798
[130]
GSM1213799
[131]
GSM1213800
[132]
GSM1213801
[133]
GSM1213802
[134]
GSM1213803
[135]
GSM1213804
[136]
GSM1213805
[137]
GSM1213806
[138]
GSM1213807
[139]
GSM1213808
[140]
GSM1213809
[141]
GSM1213810
[142]
GSM1213811
[143]
GSM1213812
[144]
GSM1213813
[145]
GSM1213814
[146]
GSM1213815
[147]
GSM1213816
[148]
GSM1213817
[149]
GSM1213818
[150]
GSM1213819
[151]
GSM1213820
[152]
GSM1213821
[153]
GSM1213822
[154]
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[155]
GSM1213824
[156]
GSM1213825
[157]
GSM1213826
[158]
GSM1213827
[159]
GSM1213828
[160]
GSM1213829
[161]
GSM1213830
[162]
GSM1213831
[163]
GSM1213832
[164]
GSM1213833
[165]
GSM1213834
[166]
GSM1213835
[167]
GSM1213836
[168]
GSM1213837
[169]
GSM1213838
[170]
GSM1213839
[171]
GSM1213840
[172]
GSM1213841
[173]
GSM1213842
[174]
GSM1213843
[175]
GSM1213844
[176]
GSM1213845
[177]
GSM1213846
[178]
GSM1213847
[179]
GSM1213848
[180]
GSM1213849
sample_count
181
group_sizes
null
group_sizes_basis
null
platform
570
taxon
Homo sapiens
title
Validation of a histology-independent prognostic gene signature for early stage, non-small cell lung cancer including stage IA patients
resolution_checked_at
2026-11-01T15:39:54Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
[4] object · 16 fields
id
asset:table:pmid:33490259:1
role
table
format
jats-table
media_type
application/vnd.jats.table+json
uri
null
sha256
sha256:01497f7e8e7ff4041dbce27ac7241069fae86d758e1b9f0a6d291754987c878c
bytes
589
access
open
state
verified
reader
read_table
reader_status
available
identifier
section[10]/table-wrap[1]#tab1
version
null
note
The detailed information of five prognostic mRNAs significantly associated with overall survival in patients with LUAD.
structure object · 5 fields
pmcid
PMC7790577
section_index
10
section_title
3.4. Construction and Verification of Prognostic Models Based on Differential Genes of Molecular Subtypes
row_count
6
column_count
6
table object · 8 fields
label
Table 1
caption
The detailed information of five prognostic mRNAs significantly associated with overall survival in patients with LUAD.
rows array · 6 items
[0] array · 6 items
[0]
Gene
[1]
Coef
[2]
HR
[3]
HR (lower, 0.95)
[4]
HR (upper, 0.95)
[5]
p
[1] array · 6 items
[0]
KRT8
[1]
0.258
[2]
1.295
[3]
1.000
[4]
1.675
[5]
4.97E-02
[2] array · 6 items
[0]
MAFK
[1]
0.365
[2]
1.441
[3]
1.182
[4]
1.756
[5]
3.01E-04
[3] array · 6 items
[0]
PTTG1
[1]
0.355
[2]
1.426
[3]
1.136
[4]
1.792
[5]
0.002
[4] array · 6 items
[0]
ENPP5
[1]
-0.235
[2]
0.791
[3]
0.649
[4]
0.964
[5]
0.020
[5] array · 6 items
[0]
INPP5J
[1]
-0.434
[2]
0.648
[3]
0.496
[4]
0.846
[5]
0.001
locator
section[10]/table-wrap[1]#tab1
[5] object · 16 fields
id
asset:table:pmid:33490259:2
role
table
format
jats-table
media_type
application/vnd.jats.table+json
uri
null
sha256
sha256:96c5db9822e1f9b67b415d1e74efe9f0800511d56fd06b4127e64a0cb9ba328c
bytes
1224
access
open
state
verified
reader
read_table
reader_status
available
identifier
section[13]/table-wrap[1]#tab2
version
null
note
Univariate and multivariate Cox survival analysis.
structure object · 5 fields
pmcid
PMC7790577
section_index
13
section_title
3.7. Univariate and Multivariate Survival Analysis
row_count
27
column_count
9
table object · 8 fields
label
Table 2
caption
Univariate and multivariate Cox survival analysis.
rows array · 27 items
[0] array · 3 items
[0]
Feature
[1]
Univariable analysis
[2]
Multivariable analysis
[1] array · 6 items
[0]
HR
[1]
95% CI of HR
[2]
p
[3]
HR
[4]
95% CI of HR
[5]
p
[2] array · 4 items
[0]
Lower
[1]
Upper
[2]
Lower
[3]
Upper
[3] array · 1 item
[0]
Age
[4] array · 9 items
[0]
≤65
[1]
[2]
[3]
[4]
0.192
[5]
[6]
[7]
[8]
0.068
[5] array · 7 items
[0]
>65
[1]
1.217
[2]
0.906
[3]
1.635
[4]
1.406
[5]
0.976
[6]
2.025
[6] array · 1 item
[0]
Gender
[7] array · 9 items
[0]
Female
[1]
[2]
[3]
[4]
0.747
[5]
[6]
[7]
[8]
0.813
[8] array · 7 items
[0]
Male
[1]
1.049
[2]
0.784
[3]
1.405
[4]
0.957
[5]
0.667
[6]
1.375
[9] array · 1 item
[0]
T stage
[10] array · 9 items
[0]
T1-T2
[1]
[2]
[3]
[4]
<1e-5
[5]
[6]
[7]
[8]
0.008
[11] array · 7 items
[0]
T3-T4
[1]
2.298
[2]
1.568
[3]
3.366
[4]
1.934
[5]
1.19
[6]
3.143
[12] array · 1 item
[0]
N stage
[13] array · 9 items
[0]
N0
[1]
[2]
[3]
[4]
<1e-5
[5]
[6]
[7]
[8]
0.002
[14] array · 7 items
[0]
N1-N3
[1]
2.58
[2]
1.918
[3]
3.47
[4]
1.986
[5]
1.292
[6]
3.054
[15] array · 1 item
[0]
M stage
[16] array · 9 items
[0]
M0
[1]
[2]
[3]
[4]
0.006
[5]
[6]
[7]
[8]
0.226
[17] array · 7 items
[0]
M1
[1]
2.133
[2]
1.245
[3]
3.654
[4]
1.515
[5]
0.773
[6]
2.967
[18] array · 1 item
[0]
Smoking
[19] array · 9 items
[0]
1
[1]
[2]
[3]
[4]
0.536
[5]
[6]
[7]
[8]
0.541
[20] array · 7 items
[0]
2-4
[1]
0.878
[2]
0.581
[3]
1.326
[4]
0.852
[5]
0.51
[6]
1.423
[21] array · 1 item
[0]
Stage
[22] array · 9 items
[0]
I+II
[1]
[2]
[3]
[4]
<1e-5
[5]
[6]
[7]
[8]
0.678
[23] array · 7 items
[0]
III+IV
[1]
2.584
[2]
1.893
[3]
3.527
[4]
1.118
[5]
0.66
[6]
1.892
[24] array · 1 item
[0]
RiskType
[25] array · 9 items
[0]
Low
[1]
[2]
[3]
[4]
<1e-5
[5]
[6]
[7]
[8]
<1e-5
[26] array · 7 items
[0]
High
[1]
2.497
[2]
1.834
[3]
3.4
[4]
2.165
[5]
1.461
[6]
3.209
locator
section[13]/table-wrap[1]#tab2
gaps array · 6 items
[0]
asset:data:geo:GSE30219 not downloaded
[1]
asset:data:geo:GSE30219 licence not read
[2]
asset:data:geo:GSE31210 not downloaded
[3]
asset:data:geo:GSE31210 licence not read
[4]
asset:data:geo:GSE50081 not downloaded
[5]
asset:data:geo:GSE50081 licence not read
analysis object · 4 fields
state
partial
methods array · 3 items
[0] object · 10 fields
id
method-1
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
0.7
band
medium
quote
Kaplan-Meier
[1] object · 10 fields
id
method-2
family
correlation_analysis
specific
pearson
label
Correlation analysis
dsl_operations array · 1 item
[0]
pearson_r
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
paired_numeric_columns
evidence object · 3 fields
score
0.8
band
high
quote
Pearson correlation
[2] object · 10 fields
id
method-3
family
null
specific
null
label
Univariate and multivariate Cox survival analysis
dsl_operations array · 0 items
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 0 items
evidence object · 3 fields
score
1
band
verbatim
quote
Univariate and multivariate Cox survival analyses of clinical characteristics and RiskScore were used to demonstrate the independence of the RiskScore model.
targets array · 0 items
gaps array · 1 item
[0]
method.reader_named_unmapped
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]
survival_km
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: 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
reachable
true
accessions array · 3 items
[0]
GSE31210
[1]
GSE30219
[2]
GSE50081
resolved_accessions array · 3 items
[0]
GSE31210
[1]
GSE30219
[2]
GSE50081
statements array · 1 item
[0]
The analyzed data sets generated during the study are available from the corresponding author on reasonable request.
basis
The paper names GSE31210, GSE30219, GSE50081 in GEO database 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]
survival_km
assets array · 4 items
[0]
source:pmid:33490259
[1]
data:geo:GSE30219
[2]
asset:table:pmid:33490259:1
[3]
asset:table:pmid:33490259:2
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:GSE30219
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
2026-10-31T18:26:28Z
independence
single_reader
evidence_check object · 2 fields
method
verbatim
availability
verbatim
calls
3
usage object · 3 fields
prompt_tokens
31932
completion_tokens
2311
cached_tokens
784
stages array · 1 item
[0] object · 4 fields
id
extraction
state
complete
reader
MiniMax-M3.1-Flash
provider
minimax
gaps array · 3 items
[0]
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:33490259
type
paper
label
pmid:33490259
[1] object · 3 fields
id
source:pmid:33490259
type
manuscript
label
PMID 33490259 full text
[2] object · 3 fields
id
data:geo:GSE30219
type
dataset
label
GEO GSE30219
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:33490259
type
paper
label
pmid:33490259
[1] object · 3 fields
id
source:pmid:33490259
type
manuscript
label
PMID 33490259 full text
[2] object · 3 fields
id
data:geo:GSE30219
type
dataset
label
GEO GSE30219
agent
agent:corpus-assay-engine
[1] object · 4 fields
id
activity-read-33490259
type
extraction
used array · 1 item
[0]
source:pmid:33490259
agent
agent:minimax-reader
[2] object · 4 fields
id
activity-resolve-GSE30219
type
accession_resolution
used array · 1 item
[0]
data:geo:GSE30219
agent
agent:corpus-assay-engine
agents array · 2 items
[0] object · 3 fields
id
agent:corpus-assay-engine
type
software
label
Corpus assay engine
[1] object · 3 fields
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 object · 4 fields
parent_id
pmid:33490259
parent_hash
sha256:8c611ae9cba97bf2c99a49dfa74c7b92d64555be3c7546e040a4382cee2014ac
fork_of
null
gaps array · 11 items
[0]
asset:data:geo:GSE30219 licence not read
[1]
asset:data:geo:GSE30219 not downloaded
[2]
asset:data:geo:GSE31210 licence not read
[3]
asset:data:geo:GSE31210 not downloaded
[4]
asset:data:geo:GSE50081 licence not read
[5]
asset:data:geo:GSE50081 not downloaded
[6]
execution.not_run
[7]
method.reader_named_unmapped
[8]
review.human_critique
[9]
review.independent_reader
[10]
review.machine_critique
assays array · 1 item
[0] object · 11 fields
role
analysis
definition_id
survival_analysis
definition_version
f2132e96ae6c7a4e
required_operations array · 1 item
[0]
survival_km
support_state
supported
source_hashes object · 1 field
dsl_catalogue
f2132e96ae6c7a4e
evidence array · 1 item
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Kaplan-Meier
order_basis
analysis_only
id
assay:1
Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33490259", "kind": "science-record", "revision": 6, "content_hash": "sha256:a5561ce56872429d1e3d25a0c7fb3b918451aaddfb60b34c484d7ad74d66164a", "created_at": "2026-11-01T15:41:23Z", "updated_at": "2026-11-01T21:24:34Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of 5-Gene Signature Improves Lung Adenocarcinoma Prognostic Stratification Based on Differential Expression Invasion Genes of Molecular Subtypes.", "doi": "10.1155/2020/8832739", "pmid": "33490259", "authors": ["Zhimin Zheng", "Weijie Deng", "Jiansheng Yang"], "journal": "BioMed research international", "year": 2020, "abstract": "BACKGROUND: The acquisition of invasive tumor cell behavior is considered to be the cornerstone of the metastasis cascade. Thus, genetic markers associated with invasiveness can be stratified according to patient prognosis. In this study, we aimed to identify an invasive genetic trait and study its biological relevance in lung adenocarcinoma.\nMETHODS: 250 TCGA patients with lung adenocarcinoma were used as the training set, and the remaining 250 TCGA patients, 500 ALL TCGA patients, 226 patients with GSE31210, 83 patients with GSE30219, and 127 patients with GSE50081 were used as the verification data sets. Subtype classification of all TCGA lung adenocarcinoma samples was based on invasion-associated genes using the R package ConsensusClusterPlus. Kaplan-Meier curves, LASSO (least absolute contraction and selection operator) method, and univariate and multivariate Cox analysis were used to develop a molecular model for predicting survival.\nRESULTS: As a consequence, two molecular subtypes for LUAD were first identified from all TCGA all data sets which were significant on survival time. C1 subtype with poor prognosis has higher clinical characteristics of malignancy, higher mutation frequency of KRAS and TP53, and a lower expression of immune regulatory molecules. 2463 differentially expressed invasion genes between C1 and C2 subtypes were obtained, including 580 upregulation genes and 1883 downregulation genes. Functional enrichment analysis found that upregulated genes were associated with the development of tumor pathways, while downregulated genes were more associated with immunity. Furthermore, 5-invasion gene signature was constructed based on 2463 genes, which was validated in four data sets. This signature divided patients into high-risk and low-risk groups, and the LUDA survival rate of the high-risk group is significantly lower than that of the low-risk group. Multivariate Cox analysis revealed that this gene signature was an independent prognostic factor for LUDA. Compared with other existing models, our model has a higher AUC.\nCONCLUSION: In this study, two subtypes were identified. In addition, we developed a 5-gene signature prognostic risk model, which has a good AUC in the training set and independent validation set and is a model with independent clinical characteristics. Therefore, we recommend using this classifier as a molecular diagnostic test to assess the prognostic risk of patients with LUDA.", "claims": [{"id": "claim-method", "statement": "Univariate and multivariate Cox survival analyses of clinical characteristics and RiskScore were used to demonstrate the independence of the RiskScore model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33490259", "locator": "sections[5] \"2.3. 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This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE30219"], "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:GSE30219"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:33490259", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1155/2020/8832739", "sha256": 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