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pmid:37768502

Unveiling Potential Targeted Therapeutic Opportunities for Co-Overexpressed Targeting Protein for Xklp2 and Aurora-A Kinase in Lung Adenocarcinoma.

Arnab Mukherjee · Preeti Harigovind Yadav · K S Mukunthan

Molecular biotechnology · 2024 doi:10.1007/s12033-023-00879-9

Paper

complete

Experiment

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Data

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Analysis

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title
Unveiling Potential Targeted Therapeutic Opportunities for Co-Overexpressed Targeting Protein for Xklp2 and Aurora-A Kinase in Lung Adenocarcinoma.
doi
10.1007/s12033-023-00879-9
pmid
37768502
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Arnab Mukherjee
[1]
Preeti Harigovind Yadav
[2]
K S Mukunthan
journal
Molecular biotechnology
year
2024
abstract
Lung adenocarcinoma (LUAD) is one of the most prevalent and leading causes of cancer deaths globally, with limited diagnostic and clinically significant therapeutic targets. Identifying the genes and processes involved in developing and progressing LUAD is crucial for developing effective targeted therapeutics and improving patient outcomes. Therefore, the study aimed to explore the RNA sequencing data of LUAD from The Cancer Genome Atlas (TCGA) and gene expression profile datasets involving GSE10072, GSE31210, and GSE32863 from the Gene Expression Omnibus (GEO) databases. The differential gene expression and the downstream analysis determined clinically significant biomarkers using a network-based approach. These therapeutic targets predominantly enriched the dysregulation of mitotic cell cycle regulation and revealed the co-overexpression of Aurora-A Kinase (AURKA) and Targeting Protein for Xklp2 (TPX2) with high survival risk in LUAD patients. The hydrophobic residues of the AURKA-TPX2 interaction were considered as the target site to block the autophosphorylation of AURKA during the mitotic cell cycle. The tyrosine kinase inhibitor (TKI) dacomitinib demonstrated the strong binding potential to hinder TPX2, shielding the AURKA destabilization. This in silico study lays the foundation for repurposing targeted therapeutic options to impede the Protein-Protein Interactions (PPIs) in LUAD progression and aid in future translational investigations.
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All authors ensure that data and materials are available and transparent.
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[115]
GSM813526
sample_count
116
group_sizes
null
group_sizes_basis
null
platform
6884
taxon
Homo sapiens
title
Gene expression analysis of lung adenocarcinoma and matched adjacent non-tumor lung tissue
resolution_checked_at
2026-11-01T15:39:54Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
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licence_read
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gaps array · 6 items
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asset:data:geo:GSE10072 not downloaded
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[2]
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[3]
asset:data:geo:GSE31210 licence not read
[4]
asset:data:geo:GSE32863 not downloaded
[5]
asset:data:geo:GSE32863 licence not read
analysis object · 4 fields
state
partial
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id
method-1
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differential_expression
specific
edger
label
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normalise_counts
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differential_expression_fit
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fdr_bh
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count_matrix_with_condition
evidence object · 3 fields
score
1
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quote
The edgeR (V3.36.0) and limma (V3.50.3) packages of R (V4.1.2) were used to identify the differentially expressed genes (DEGs) in tumor vs. normal samples.
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method-2
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survival_analysis
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kaplan_meier
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Survival analysis
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[0]
survival_km
missing_operations array · 0 items
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[0]
time_to_event_with_censoring
evidence object · 3 fields
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0.8
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survival_analysis
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Survival analysis
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[0]
logrank_test
missing_operations array · 0 items
parameters object · 2 fields
median_survival
he log-rank P value and median survival were determined [41, 42
p_value
each gene, the log-rank P value and median survival wer
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[0]
time_to_event_with_censoring
evidence object · 3 fields
score
0.8
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high
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log-rank
[3] object · 10 fields
id
method-4
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differential_expression
specific
limma_voom
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Differential expression analysis
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[0]
normalise_counts
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differential_expression_fit
[2]
fdr_bh
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[0]
count_matrix_with_condition
evidence object · 3 fields
score
0.8
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high
quote
limma
[4] object · 10 fields
id
method-5
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network_analysis
specific
ppi_network
label
Network / pathway analysis
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[0]
build_graph
[1]
centrality_scores
[2]
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evidence object · 3 fields
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0.7
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medium
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PPI
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[0]
method.reader_differs_from_extractor
capability object · 3 fields
state
complete
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[0] object · 6 fields
capability_id
capability:computational_analysis
label
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]
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[7]
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[8]
gwas
candidate
Automated Assays Laravel application and simulated adapters
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readiness object · 8 fields
state
complete
tier
R1
blocked_on
nothing:replicable_now
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method object · 5 fields
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normalise_counts
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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: edger.
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]
GSE10072
[1]
GSE31210
[2]
GSE32863
resolved_accessions array · 3 items
[0]
GSE10072
[1]
GSE31210
[2]
GSE32863
statements array · 1 item
[0]
All authors ensure that data and materials are available and transparent.
basis
The paper names GSE10072, GSE31210, GSE32863 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
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normalise_counts
[1]
differential_expression_fit
[2]
fdr_bh
assets array · 2 items
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[1]
data:geo:GSE10072
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run_analysis
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data:geo:GSE10072
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review object · 5 fields
state
partial
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kind
extraction_review
reader object · 5 fields
model
MiniMax-M3.1-Flash
provider
minimax
provider_family
minimax
read_at
2026-11-01T18:54:39Z
independence
single_reader
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method
verbatim
availability
verbatim
calls
5
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13225
completion_tokens
1517
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264
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id
extraction
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provenance object · 7 fields
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pmid:37768502
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paper
label
pmid:37768502
[1] object · 3 fields
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source:pmid:37768502
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manuscript
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PMID 37768502 full text
[2] object · 3 fields
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data:geo:GSE10072
type
dataset
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GEO GSE10072
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activity-reading-sci-2026-11-01
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classification
used array · 3 items
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paper
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pmid:37768502
[1] object · 3 fields
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source:pmid:37768502
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manuscript
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PMID 37768502 full text
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GEO GSE10072
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agent:corpus-assay-engine
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lineage object · 4 fields
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s. Therefore, the study aimed to explore the RNA sequencing data of LUAD from The Cancer Genome Atlas (T
[1]
odology The study focuses on the analysis of transcriptome data from publicly accessible archives that
[2]
xpression The study involved the analysis of transcriptomic profiling data from TCGA-LUAD with primary t
order_basis
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mples [33]. The analysis also included three microarray datasets, GSE10072, GSE31210, and GSE32863 (
[1]
63 (https://www.ncbi.nlm.nih.gov/geo/). Each microarray dataset had different sample sizes and platf
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The edgeR (V3.36.0) and limma (V3.50.3) packages of R (V4.1.2) were used to identify the differentially expressed genes (DEGs) in tumor vs. normal samples.
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37768502", "kind": "science-record", "revision": 4, "content_hash": "sha256:501649566a795e6729dcb36a1913c427b0ba15f147add833618b6f0facd34d7e", "created_at": "2026-11-01T19:02:36Z", "updated_at": "2026-11-01T22:52:48Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Unveiling Potential Targeted Therapeutic Opportunities for Co-Overexpressed Targeting Protein for Xklp2 and Aurora-A Kinase in Lung Adenocarcinoma.", "doi": "10.1007/s12033-023-00879-9", "pmid": "37768502", "authors": ["Arnab Mukherjee", "Preeti Harigovind Yadav", "K S Mukunthan"], "journal": "Molecular biotechnology", "year": 2024, "abstract": "Lung adenocarcinoma (LUAD) is one of the most prevalent and leading causes of cancer deaths globally, with limited diagnostic and clinically significant therapeutic targets. Identifying the genes and processes involved in developing and progressing LUAD is crucial for developing effective targeted therapeutics and improving patient outcomes. Therefore, the study aimed to explore the RNA sequencing data of LUAD from The Cancer Genome Atlas (TCGA) and gene expression profile datasets involving GSE10072, GSE31210, and GSE32863 from the Gene Expression Omnibus (GEO) databases. The differential gene expression and the downstream analysis determined clinically significant biomarkers using a network-based approach. These therapeutic targets predominantly enriched the dysregulation of mitotic cell cycle regulation and revealed the co-overexpression of Aurora-A Kinase (AURKA) and Targeting Protein for Xklp2 (TPX2) with high survival risk in LUAD patients. The hydrophobic residues of the AURKA-TPX2 interaction were considered as the target site to block the autophosphorylation of AURKA during the mitotic cell cycle. The tyrosine kinase inhibitor (TKI) dacomitinib demonstrated the strong binding potential to hinder TPX2, shielding the AURKA destabilization. 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